RK's Musings

Talent Code : Summary

This book makes a case that talent is something else besides the nature and nurture stuff. Author based on his field research says that there are three ingredient's to talent which are

1. Deep Practice
2. Ignition
3. Master Coaching

   

Deep practice involves struggling in a targeted way so that the learning happens at an accelerated pace. It is not usual "practice, practice , practice" mantra BUT one needs to stretch to reach a specific target and keep learning in the process. Roger Federer,for example does not play 1000's of shots each day. He practices for a few hours and works on his timing. So, deep practice at the outset is a little different where one can describe it as error induced learning. It involves three stages as described in the book. Chunking the task + Repeating it + Learning to feel it.

The author gives a great blend of examples to support his case.

• Bronte's sisters little books
• Brazilian football Futsal
• Z Boys - Empty Swimming Pool
• Florentine artists - Apprenticeship
• Russian Tennis stars - Practicing with imaginary ball
• Dallas Music School - How the students learn stuff differently

Ignition , the second attribute of talent is basically a mix of long term commitment, deliberate practice, persistence etc. The beauty of this book is the range of examples quoted like KIPPS Schools in US, South Korean Woman Golfers, Anna Kournikova's role in Russia's tennis talent upsurge, Scrooge Principle etc.

The third attribute is Master coaching. This aspect is a great read for a wannabe teacher or for that matter anybody who wants to groom talent, be it in a school / organization / game.

Applying the above stuff : Let's take a programming language and let's say you want to be good at it. How can one deep practice it ?

Writing 1000 programs each having a random objective would be helpful but the learning pace would be slow. Let's say one wants to master data visualization skills. Using a specific library to carry out a task is ok but if you want to master it, you have got to create some specific mini projects and work towards them. These mini projects should be challenging enough to make you stretch , but at the same time, should be with in a reachable distance , else one might give up. So one of the challenges in mastering a language is to create mini projects which are with in one's reach, where there is a vague idea that you will commit errors in the process of completing the mini project. These errors are the key to learning. This mini project could span a few days, to few weeks to a month. If one has not made any mistake for more than a week or so, then it is clear that one has set an easy target and the whole purpose of working on a mini project becomes futile. Now this has to be on a regular basis. So, one needs to be pretty creative to come up a list of progressively tougher mini projects which will grow your Myelin .What's Myelin..well, that's the stuff mentioned in the book which actually gives some scientific reason behind author's arguments. Statements rooted in science are easily believable ...In that sense, this book has a very strong case for an approach towards talent development.

Takeaway from this book filled with beautiful examples : Stop + Struggle + Make Errors + Learn is the key to developing any talent

Well, if not for anything else, the examples in the book make it a worthy read.

Concise Intro to Econometrics : Summary

The only reason I chose to read this book on a weekend was to verify the null hypothesis of the author, " an intuitive guide" :) Well, intuition and econometrics are kind of animals that don't sleep together that well in the SAME book. Either econometrics books are very math/probability oriented OR they are "stats for dummies" types. I had tons of other things to do on this weekend, but took a peek in to this ~100 page double line spacing book,just out of curiosity.

Points that grabbed my attention, not becoz of the math , but becoz of the nice angrezi that was used :)

• Model building means moving from a unconditional expectation to conditional expectation.
• Expected value of a Unbiased estimator equals the parameter to be estimated, irrespective of the number of trials
• While unbiased is difficult to verify in most situations, Consistent estimator is what is generally needed
• Examining the asymptotic behavior of the estimate is a clue to its consistency
• OLS regression method essentially minimizes the variance of the error term by choosing the appropriate parameters for the linear model
• Don't ask whether random walk fits the data or not ? Instead ask , "How can I beat the random walk ?"
• "Are mid-sized cars equally expensive in Japan and in the US, after correction for the exchange rate between these two countries ?" IS FAR BETTER A QUESTION than "Does the purchasing power parity hold good for Japan and USA? "
• Economic theories deal with aggregated quantities..Empirical work needs fine grained data
• 7 case studies to illustrate various models
  1. convergence of countries attributes - a Convergence model with no stochastic behavior
  2. Direct mailing problem - probit model for selecting customers for direct mail (a truncated regression model )
  3. Does automated trading improve trading efficiency - use multiple equation regression model for futures and spot
  4. Unemployment and recession linkage -- auto regressive model with latent variable.... With all this jazz could anyone have predicted the 10% unemployment rate that the US is facing now !! Sometimes it makes me feel that all macroeconomic variable prediction is just some jazz and has no practical value beyond keeping a few economists/econometricians in a job!
  5. Brand loyalty of the customers -- Multinomial probit
  6. Do people make up their minds before elections -- artificial neural network problem ..Basically I look at it as a non-linear regression with fancy functions!
  7. Temperature forecast uncertainty -- Use GARCH to judge the correlation structure of the forecast errors

My takeaway : Considering my interest is modeling variables in finance, I think that it would be naive to depend on some linear parameters of an equation with gaussian assumptions in it. Even though a flavor of models are presented in this book, I don't think models of such type are going to be useful anymore. THE GAME OF GAUSSIAN ERROR TERM is over.

Super Freakonomics : Summary

Last week has been a pretty unproductive week. Fell ill on Tue and Wed. Barely managed to work for a few hours on Thu. But by Friday I was back to normalcy. Generally one of the ways I get over from an inertia state to an active state is to read something interesting. So, picked up this frequel( from the back cover : nice name for freakonomics sequel ). I knew there was a lot of controversy relating to this book. Some people argued that authors got it completely wrong as far as global warming is concerned. Some argued it was a hotch potch of some articles.

Whatever others have to say about this, I have found this book a real page turner. Even though some people criticized the book saying that it was a mish mash of random thoughts, the author clearly mention their intention in the very first note. They identify the theme as "role of incentives" and their hypothesis is that people respond to incentives which are not necessarily predictable. These stories are based on "economic approach" where the underlying premise is that behavior is driven by a rich set of values and preferences.

While presenting this opinion about incentives, authors introduce a number of interesting examples like the following

1.This campaign by US Dept of transportation urging people not to drive drunk. Authors estimate that walking while drunk is 8 times fatal than driving drunk. Simple alternative (a seemingly safe one) to driving drunk is actually more dangerous.

2. How Cable TV might have improved the status of women in India

There are 5 chapters in the book with each chapter filled with umpteen number of stories, where results are presented based on the economic approach, looking at micro data, trying to ask questions and teasing out some answers. In a sense, this kind of thing actually makes economics interesting I guess. Its the same with stock market . Long term predictions(read anything more than a couple of months) are very difficult. Near term predictions are also not great too. If one looks at stock market data, almost everyone is dealing with the same starting point, univariate time series of stock prices, option quotes. You can do a low frequency, medium frequency or high frequency analysis. More and more I get exposed to data, I have a feeling that there is money to be made only in high frequency trades ..

Coming back to the book,

How is a street prostitute similar to deparment store santa ?

The first chapter is about authors understanding demand curves of prostitutes. One of the challenges of asking questions about such topics is data. Everyone knows the more sensitive the topic is, the more difficult it is to procure data. Drugs, Violence, Sex are some of the things where getting data becomes a dangerous adventure. So, credit does go to Sudhir Venkatesh's hard work to actually get the data in the first place. Once you have some structured data , slicing and dicing is the fun part and here is where the authors excel is bringing out some interesting observations. In creating a narrative, the authors come out with a lot of aspects about women, women's lives, prewar prostitution etc. Here are some aspects mentioned in the book

• It is hard to be a woman , be it developing countries or developed countries, say the authors. Practices such as breast ironing , abandoning girl childs in China, pay scale mismatch in corporate America etc. make it difficult to lead a woman's life
• Prostitution has by far managed to be a woman's game( though things are changing pretty fast:) )
• Prewar prostitutes were earning far more than the current street prostitutes. It is argued that one of the reasons could be the rise of premarital sex.
• Whole lot of observations are based on the working paper, Empirical Analysis of Street-level Prostitution . It's abstract says the following :
  • Combining transaction-level data on street prostitutes with ethnographic observation and official police force data, we analyze the economics of prostitution in Chicago. Prostitution, because it is a market, is much more geographically concentrated than other
    criminal activity. Street prostitutes earn roughly $25-$30 per hour, roughly four times their hourly wage in other activities, but this higher wage represents relatively meager compensation for the significant risk they bear. Prostitution activities are organized very
    differently across neighborhoods. Where pimps are active, prostitutes appear to do better, with pimps both providing protection and paying efficiency wages. Condoms are used only one-fourth of the time and the price premium for unprotected sex is small. The
    supply of prostitutes is relatively elastic, as evidenced by the supply response to a 4th of July demand shock. Although technically illegal, punishments are minimal for prostitutes and johns. A prostitute is more likely to have sex with a police officer than to
    get officially arrested by one. We estimate that there are 4,400 street prostitutes active in Chicago in an average week.
• Reference to NYTimes article(The economy of desire) which makes a case(weak one though) that having a relative in the family with AIDS changes the sexual behavior as well as self-reported identity and desire of a person
• Article supporting Larry Summers Hypothesis - He, Once a She, Offers Own View On Science Spat
• Why legalizing sex work is dangerous for sex workers .. Taboo / Illegal things are priced at a premium. Once you remove restrictions, prices drop like crazy and thats bad news for escorts. From the freakonomics blog , here is an interview with a high profile prostitute.
  Link A Call Girl’s View of the Spitzer Affair

Authors finally after all these stories, anecdotes, empirical evidence suggest that street prostitute is similar to a department store santa, they both take advantage of short-term job opportunities bought about by holiday spikes in demand


Why should Suicide bombers buy Life Insurance?

As I began reading this note, I had a feeling that it was similar to "outliers" , Gladwell, story all over. Thankfully the authors mention that the point was already made by several other books and stop belabouring the same point. The whole chapter revolves around Craid Feied who manages to set up a database for ER and thus revolutionizes information management systems in a lot of hospitals. Some of the interesting things mentioned in this part are

• The effect of surname in academic profession :) Link : The benefits of being economic professor A ( and not Z)
• Deliberate Practice has 3 components. Setting specific goals, obtaining immediate feedbacks , and concentrating as much on technique as on outcome
• Why terrorism is so cheap and easy ?
• How the estate tax has an impact on the death rates of people who inherit tons of money ? very funny things mentioned in this context
• The most interesting part is the fact that hospital database is being used to track terrorists

The authors answer the question about suicide bombers . The database suggests that one of the defining characteristic of a suicide bomber is , he /she doesn't buy life insurance. So the quirky take on that fact is : Suicide bombers should buy life insurance to leave no trail behind them.

Unbelievable Stories about Apathy and Altruism

This part was a little dragging. Behavioral economics, apathy shown by 38 people towards a murder in a NY neighborhood, stories about altruism etc are the aspects touched upon

Fix is in - and It's cheap and Simple

I think this is the BEST chapter in the book

Premise under which the narrative is built around : Cheap and Simple fixes often address problems that seem impervious to any solution.

• Starts off with the story of Hungarian Scientist Ignatz Semmelweis who came up with a simple fix to solve perpetual fever, something witnessed immediately after child birth in many hospitals. Fix was : Disinfect your hands before operating on maternity patients. Simple but powerful fix which has saved innumerable lives till date.
• Polio is another vaccine which can be considered as a fix. Instead of dealing with the problem after the fact, see to it it doesn't arise in the first place.
• McNamara fix of seat belts in cars to prevent fatalities
• Nathan's solution to hurricanes is fascinating. A little dig in to Nathan's intellectual venture gives a superb account of what he and his team is up to.

What do Al Gore and Mount Pintabu have in common ?

Reader meet Nathan and his team again in this chapter where the solution to global warming is presented in a different way. There is a considerable debate about this chapter. I have no clue on what is right and what is wrong about global warming. The only thing I feel is the solution of actually spraying something in the air to global warming is lateral thinking.. Who knows, it might work.

To digress at this point, recently I came across an interesting example of lateral thinking. This was long time ago when prince of Turkey wanted to modernize the society. He did not want women to wear burkhas!. Well the usual boring way to stop women from wearing burkhas is to impose some law, penalty, etc... However he was a great lateral thinker. He passed a law where , "All prostitutes were ordered to wear burkha" .Behavior change among normal women was immediate !!. Terrific example of how you can fix things, change aspects of societal behavior by lateral thinking...... May be Nathan and his team need to be given a hearing and funding to see whether their lateral solutions works..Who knows, it might just work...

My takeaway from this book : Understanding data and teasing out answers to interesting questions is a great fun activity. If one spends enough time gathering data or visualizing data, one is bound to ask useful questions. Lateral thinking and open minded ness towards solutions usually result in interesting solutions.

Panic : Summary

Michael Lewis, needs no introduction . His previous works have been best-sellers. When he writes a book about subprime crisis, I was expecting a dot-com esque narration of subprime mess with all the ingredients of a thriller movie ride.It turned out to be exactly what I expected.This book is essentially a collection of articles, essays by various people before panics, be it the 1987 crash | asian currency crisis | dot-com bust | the recent sub-prime bust. Reading this book is like bringing old newspaper articles, magazines from your attic to go over what people said about various crisis situations.

The book talks about four panics

---

Oct 20, 1987 Panic - Black Monday

The more I read about Oct 1987 crash in various books, the more I think that nobody knew why it happened. Even though 6Billion USD of the total sales of 40 Billion USD (Stocks + Futures) on the day of crash was represented with portfolio insurance selling, it could be hypothesized that portfolio insurance was the effect rather than the cause. Michael Lewis in the first part of the book presents various articles appearing in NYTimes, WSJ ,research reports to take the reader back to 1987 environment. There are articles which blame Program trading in the form of index arb or portfolio insurance for the crisis. Some feel that synthetic option creation itself was a flawed concept. If you want to manufacture an option on the market, you cannot do that by selling and buying the market as the market will not allow you to do it.

Facts are : Index arb saw that the futures were going through a free fall and stock market was trading at a huge premium. Most of the trades knew that the stock index was calculated using Friday's closing prices but still chose to believe that they were updated. They shorted the index and waited for the futures to fall to close the other leg of the arb. Meanwhile large fund houses who could not short the stock, shorted futures instead. However when the stocks finally opened on Monday, the cash futures spread was not juicy and index arb ran amuck trying to close the other leg of the transaction thus buying futures for a brief period of time. Meanwhile stocks were dumped by everyone and thus futures continued the free fall.

After the fact analysis :

• There should have been circuit breakers
• Futures trading is dangerous
• Portfolio Insurance is crap
• Dynamic option manufacturing is a sham
• Coordination problems between Futures and Spot market regulators
• One thing to fear is "FEAR"
• Margin requirement should be drastically increased in futures market
• Uptick rule should be applied to futures market
• Shorting restriction in futures market

As any after the fact analysis appears, they appear correct in hindsight but nobody said anything before crash. ! Is it age-old willingness to suspend one's critical judgement when lots of money is made ?

A gem of a statement in this part of the book is :
Best animal behavioral experts don't pretend to know why antelopes panic precisely when they do.

Can we know why humans panic? Very unlikely

---

Asian Currency Panic

Well, the gory details of the currency crisis is known to all. Foreigners lend large amounts of money as short term capital and South east asian countries lap them up. However the capital was short term and it was naturally difficult to fund long term projects, irrespective of whether the countries intended. The currency panic is combined with LTCM failure. One thing I feel about reading these panics is that it gives some numbers about the sheer volume of mess that was created. Here are a few numbers

• 175 Billion of Short term debt was lapped by South east asian countries
• Russian Treasury Bills were offering 50% interest rate
• LTCM blew away 4 Billion in 6 weeks
• LTCM lost $120M in one day and lost $500 M on one trade, selling put options on index
• Partners put in $1.9B of the total $4.4 B fund

---

Dot.com Panic

A classic recount of dot com crisis. Again numbers are something which hits me , every time I read about dot com mania. Here are some numbers and quotes from the book

• Dumbest Dot-com , All Advantage raise 175 Million USD and spent 40 Million USD in the first quarter . Their model , pay users to surf. It was valued at 700 Million USD by the venture capitalist !
• Theglobe stock rose 600% on the first day of its IPO. Their model, replicate society on the net !
• Computer.com spent 60% of its initial funding on 90 second Superbowl Ad.
• In 2000, 371 Internet companies were value at $1.3 Trillion , 8% of the entire US Stock market
• NASDAQ peaked on March 10th 2000 and 10 days later, an article in Barrons, "Burning Up", changed the world of dotcom
• Dotcom era : Instead of stock price reflecting the fundamentals of the company, it started affecting the fundamentals of the company
• Investors doled out $78 Billion to the employees of 100 start up companies
• Bill Gross 1000 page article on GE Capital financing made the stock tank by 25%
• A boom with out crooks is like a dog with out fleas. It does not happen.
• When some one talks badly about a boon, turn back to him and ask him,"Where were you during the boom time" ?.

---

Subprime Panic

Any numbers reported for this panic will be incorrect as we are still living through that crisis. I remember reading a book titled " Trillion Dollar Meltdown". With in a few months, the same author came up with another book . "Two Trillion Dollar Meltdown". By Feb 08, there were numbers being quoted as high as 7 Trillion Dollars as the global impact. Is this really a panic ? When I hear the word panic, I have a mental image of an event happening very quickly. Subprime mess keeps telling us that there is more to come.

In any case, the good part of the book is that you don't have to read it sequentially. Since it has articles and independent views from various people, one can start reading from any page....So, I guess, the book can be a good subway read/ office commute read..

A Certain Ambiguity : Book Summary

For the last one month I was completely occupied with work . Weekdays and Weekends flew away before I realized that it has been quite sometime since I had read any book. This weekend , being a long weekend , decided to spend my time reading a book on ambiguity. I stumbled on to a reference to this book on some blog and was intending to read at some point in time. A nice holiday break and festive mood around was good enough to motivate me to read this book, which was touted as a mathematical novel.

There are 2 stories which are intertwined in this novel. One about Ravi Kapoor, who narrates his learnings from a class on Infinity at Stanford. The second story is about circumstances in the life of Ravi's grandpa, Vijay Sahni who gets imprisoned for a brief period in his life. The narration of these two stories can be visualized by the following image:

The upper box represents the story of Vijay Sahni. The lower box represents Ravi's story in the novel.

The conversations between Vijay Sahni and Judge Taylor brings out Euclidean and Non Euclidean Geometry

The five postulates of Euclid based on which the entire "ELEMENTS" is built are :
Postulates :
1. It is possible to draw a straight line from any point to any point
2. It is possible to produce a finite straight line continuously in a straight line
3. It is possible to describe any circle with center and radius
4. All right angles equal one another
5. If a straight line falling on two straight lines makes the interior angles on the same side less than two right angles, the two straight lines, if produced indefinitely, meet on that side on which are the angles less than the two right angles.

The fifth postulate controversial nature lead to the development of Non-Euclidean Geometry

The lower box is Ravi's story where his stanford professor Nico, uses a perfect blend of examples and historical narrative and leads to the Continuum problem

Continuum Problem: Is there a set whose cardinality is greater than the cardinality of natural numbers, but less than the cardinality of the real
numbers?

What is the connection between the two stories ? Why does the book develop these themes in the first place ? What set theory and geometry have in common ? A reader would definitely be delighted from the way connections are made in the book using two stories.

Here is a snippet from one of the conversations between Vijay Sahni and the Judge
This freedom has me in awe. It is unbounded, and every single one of us possesses it. We are free to believe or not to believe; we may create mathematics or build homes or write poetry or do nothing at all; we can marry and raise a family or stay in bachelorhood; we can quest for new adventure or find comfort in the familiar; we can seek meaning or we can doubt that it is possible to find meaning. Every path is there to be taken or ignored, and none is ordained. We are given no certainties, yet we are given the capacity to feel certainty. There is no absolute meaning to latch onto, yet transcendence is within our grasp. We are free to chart our course, free to pursue our passions, and free to create the axioms of our lives. And it is in this glorious freedom that I find grace. This freedom, then, is my proof of His existence.

Here is a list of mathematicians who figure in the book as the stories progress

Zeno popularly known for his Paradox

Italian thinker Giordano Bruno
Galileo

Bhaskara (1114-1185)
Pythagoras (570-490 bc)

Cantor
Baruch (Benedict) Spinoza (1632-1677)
Euclid

David Hilbert (1862-1943),

Girolamo Saccheri (1667-1733):

Farkas (Wolfgang) Bolyai (1775-1856)
Farkas Bolyai's son Janos (Johann) Bolyai

Nikolay Ivanovich Lobachevsky (1792-1856)

Johann Carl Friedrich Gauss (1777-1855)

Bernhard Riemann (1826-1866)

Overall this book reminds me of "The Lady Tasting Tea" which gives a good historical narrative of people contributing to the field of statistics.

I guess the take away from the book, besides getting a thrilling ride on the basis of mathematics is this gem of a statement:

It is true that absolute certainty may lie outside our reach, but we live for that magic moment of discovery when we are attuned to this sense of order and connectedness. And the existence of this order and connectedness is a leap of faith.

Efficient Asset Management : Summary

Everyone who goes through the BSchool /CFA rut knows about mean variance optimization . Some of them might know that it does not work in the practical world. So, what if noble prizes have been awarded!! It doesn't work in practice...Like all models, however, it does give a framework to think about and nothing more than that....So, What's the alternative ? If you are given a couple of assets and are asked to create an asset mgmt plan, how would you go about it ? Pick up any random Bschool student and ask this question and I am certain that only a few of them would be able to answer coherently..Why ? Finance education is stuck in gaussian world!! anyways, the point of this post is to summarize this book which suggests alternatives. I came across a reference to this book in a paper and was curious to know more about it, almost a year ago. Was intending to use Bayesian estimates if ever I had to solve a portfolio allocation framework. This weekend , I had decided to take a look at this book out for sheer curiosity. What I was found in this book was a fascinating critique of classic MV solution and more than that, a statistically solid approach to the MV optimization problem.

If one doesn't want to ponder on this, one can follow the usual procedure of computing covariance matrix of the returns , leading to efficient frontier which spews out specific portfolio compositions for a level of risk....Amidst all this jargon, one quickly realizes that this is good in paper, this is good for software vendors who make plugins for mean variance optimization, good for hiding the complexities that actually take place in the real world. But if you want to really manage assets using MV approach, one needs to think about its limitations and work around it.

First, something about this book. 2 methods mentioned in the book , Resampled Efficient Frontier and Portfolio Rebalancing, Analysis and Monitoring have been patented in US and are awaiting patents in other countries. Well, patenting is only one part of story. The view of the book is from a statistician perspective which makes a case for a healthy understanding , than taking a pure numerical approach / analytically efficient approach.

Molehill of garbage in and Mountain of garbage out , is the way the authors describe MV optimization. To begin with, the limitations of MV approach are the following:

• Returns are assumed gaussian
• Investor's Utility function is not considered
• How to use it in Multi period context ?

Typically one uses the mean and variance of the historical data to construct the frontier. However the estimation errors gives rise to instability in the portfolios. Also there is an ambiguity in testing the differences between a portfolio on efficient frontier and outside of it. Authors of this book urge the investment community to follow approaches based on statistical grounding to grapple with the instability and ambiguity issues of portfolio optimization.

The books starts off with a basic introduction to classic mean variance approach which comprises applying quadratic programming technique which comprises an objective function(portfolio variance) with a set of equalities and inequalities like expected return of the portfolio, sector allocation rules, budget constraint rules, sector allocation rules , non negative weights for portfolio weights etc. The basic technique is minimization search algo that finds the best allocation across the assets in the portfolio. Concept of parametric quadratic programming is also touched upon where corner portfolios and its importance are discussed in the Markowitz framework.

For a detailed understanding of corner portfolios, refer to RiskLatte and Sharpe . Sharpe's exposition is more leisurely.

Author then moves on to giving a gist of alternative approaches that have emerged :
1. Alternative measures of risk instead of second moment
2. Utility function Optimization
3. Multi period investment horizons
4. Monte carlo techniques for asset -liability flow simulations
5. Linear programming optimization

Each of the above though touted as alternatives to Mean variance approach has in itself another set of limitations!

The book then explores Unbounded MV frontier and Linear Constrained MV Frontier. The first approach limitation can be seen by simply bootstrapping a sample from the historical returns and seeing that sharpe ratio of the sampled data is very different from the actual data. Thus the unbounded method clearly is of no investment value for a portfolio manager. One needs to give a great importance to estimation errors as it will turn out a different MV portfolio. Thus it looks like Unbounded MV solution, though analytically elegant is basically useless for an investment manager.

In most of the cases, the portfolio manager puts some constraints on the unbounded mv problem like sector specificity or sum of weights = 1 etc and the bounded one seems to be doing ok for a few repeated sampling. The book points out the reason being that in the unbounded case, the higher returns are usually over represented, which means the higher estimation errors are over represented and vice versa. In the bounded, the role of constraints is just to minimize that aspect. Apart from that Linear Constrained MV is also not good. Here the author explores a intensive bootstrap experiment to introduce the concept of Efficient Frontier Variance

The above 2 graphs summarize the concept of frontier variance. Efficient frontier, for the same set of data, under bootstrapping gives rise to variability in the frontier. Also efficient portfolios for a set of risk level( figure on the right) also span a specific area. If an investment manager has to use MV , then the statistical nature of MV has to be taken in to consideration. The book mentions the following approaches , each of which taken individually improve the MV portfolios, but when taken together can substantially improve the investment management procedure.

1. Resampled Efficient Frontier Optimization
2. Resampled Efficiency rebalancing, monitoring, analysis
3. Stein Estimation
4. Bayesian Estimation
5. Avoiding Optimization design errors

Resampled Efficient Frontier (REF) Optimization

"If you are 100% confident of the estimates, use Classic MV ", is what the author states!, reason being that MV is extremely sensitive to estimation errors. Book goes on to make a strong case for REF by saying that REF are average portfolios and provide safe bets on assets under the realistic case of estimation errors in the input. There is also a simulation based proof of the usefulness of REF. What is the look-back period for resampling ? This gives the flexibility which is missing in the rigid classic MV. A value based manager can take a longer history while a growth based manager might be willing to look at short term horizons. Another related concept is the Maximum return point, which is not present in classic MV approach. This Maximum return point gives higher return for a lesser sigma as compared to some assets which give lower returns with higher sigma!! Paradoxical it might seem to a person who is only thinking in terms of Classic MV !! However, considering the variability of the input estimates , this is very much a situation where there are inferior assets present in the asset universe included for optimization. A quick illustration would help one see this point.

One of the best things in this book is the clear illustration of REF Vs Classic MV approach using a story of a person trying to find his pet

Let me summarize this story , for this is a beautiful analogy to look at REF vs Classic MV approach.
The story is about Robert who has a pet hedgehog . One day he notices that it has escaped from the cage and is somewhere in the fence. The fence is sturdy and hedgehog escape proof. Fortunately, Robert had fitted a GPS device. He gets signals about the location of his pet.
Now if one were living in unbounded MV world , depending upon what the signal shows, Robert would go and search the location. For example , if A were the location displayed on GPS, he would go to A. However he knows that there is a fence that his pet cannot go out. Thus he ignores A. This is like living in a bounded MV world. If he believes that GPS is 100% accurate in its estimates , then he would go to whichever location that GPS points out with in the boundary.

However he knows that GPS has always some estimation error with it. So, he accumulates a set of points and then depending on the cluster properties , ventures out to search for his pet. This is similar to REF where you resample the input so that you are aware of estimation error and the portfolio you construct is more appropriate. So, instead of going to M which the GPS currently points out, he would probably go out and venture location R.

Portfolio Rebalancing, Analysis and Monitoring
Once a portfolio has been selected, it needs to be obviously rebalanced according to market conditions. Most of the present rules are adhoc , to say the least. Rebalancing is done quarterly, monthly,annually....for god knows why ? if asset weights exceed a specific percentage level, they are rebalanced ? why ? Most adhoc rules like technical analysis that is prevalent in todays world is completely arbitrary. There is no statistical significance attached to the decisions. This chapter makes a case for brining in statistical mindset to this process. By formulating a distance measure between the current portfolio and the previous selected portfolio, the author makes a case for portfolio rebalancing.

At this stage of the book, the book introduces meta-sampling method....I could not understand this method well enough to write about. Hope to understand this concept in the days to come.

Input Estimation and Stein Estimators
This is an important chapter of the book where there is a discussion on the sample means and the way they are used every where for multivariate case. Charles Stein(1955) was the first person to prove that sample means are not an admissable statistic for a multivariate population mean. Since then there have been a lot of work in this area, though finance community has been wary of adopting them, for no reason i guess.
The basic funda behind these estimators are that it starts off with a prior and then based on the sample means and their variances, population mean is calculated. James Stein estimator, Frost-Savarino estimator, Stein covariance estimation and Ledoit Covariance estimation are discussed in the book. Preliminary results of applying these estimators are provided, though the author admits that it is in the interest of finance community , industry to be specific, to focus / prod academic research in this area.

The last couple of chapters talk about benchmark mean variance optimization, bayes fundas and general aspects that need to be kept in mind while optimization.

My take on this book : It is a terrific book which will make you think about a lot of aspects about mean variance optimization. One of the highlights of this book is that most of the stuff is explained intuitively and through diagrams. This is a must read for anybody who is connected to asset management work !.

Shop Class as Soulcraft : Summary

It has been more than one and half months since I have read any book. Was involving in developing some stuff @work, so was completely immersed in it. Decided to read this book on a beautiful Saturday.

Came across this book in an NY times article and was looking forward to read the book, as it was relating to an aspect which is close to my heart, hacking.Hacking in geek's jargon is ripping apart software, packages, libraries, things and understanding stuff at level 0. In some sense,I guess there are two types of people, first kind who do not want to get their hands dirty a lot and would rather assemble, manage a set of people to accomplish some task, second kind are those who want to hack things and build things from scratch. By the kind of education I have undergone, I should be doing the former but I have found joy in the latter activity. In that sense, I could relate to the author was a Chicago Phd , academic, whom one would expect to write papers, do research and be a professor. Instead he chooses to become a mechanic. Why ? This book answers that question and much more. Author has organized the book in such a way that it traces author's experiences from being a young apprentice in a repair shop , to being a PhD from Chicago, to working in a cubicle, to finally coming round the circle to repair shop again.

Separation of Thinking from Doing

The author traces the dichotomy that has been built in to the society, courtesy the industrial clock-work mentality. In the so called knowledge worker world, it seems to many of us that knowledge , thinking etc is crucial and implementation can be done easily..outsource the code, break down the task in to process and decouple the process in to a simple components which require less skilled labor. Interesting the word, "compensation", comes from the fact that work which required high skilled engagement of both physical and mental faculties is reduced to a few mundane repeatable steps and the worker has to be compensated for robbing of this kind of work and replacing it with some less engaging work.
This essay is a call towards picking up a trade and working on that trade for a long time to understand, engage and produce something of value. According to the author, nowadays everybody is called an Einstein, be it a best buy worker, or a management consultant. However it is easy to forget the fact that Einstein or Rembrandt are , what they are because of their commitment and submission to something in the initial stages and subsequently mastering it over a long period of time.

To be a master of one's own stuff

The ideology of freedom is at the heart of consumerist culture; a promise to disburden us of mental and bodily involvement with our own stuff so we can pursue ends we have freely chosen; For some of us, we don't even know what end we are pursuing, whether the end is a figment of imagination based on current circumstances, whether it is changing etc. Anyway coming back to this so called freedom from DOING and INVOLVEMENT, robs of the experience of having a direct responsibility of things.

A true gem of a statement in the book :

To be master of your own stuff entails also being mastered by it

There are umpteen number of examples quoted which go on to make this point that by subjecting oneself to being mastered by something else, one can experience joy. Be it music, where the rigid rules of keys and notes need to be submitted to, before novelty can be displayed. Learning a new language is also akin to submission. Let's say you are learning French and like any language it goes by a set of rules. Only by submitting to the rules, one can enjoy the experience of the language. If you have ever written a software library, developed a UI, developed a software, built a system, the initial days are usually where the language/syntax etc is your master. You have to obey it or get kicked( debug errors). But once you know the rules, then it's like heaven. You can play with the code, components, rules and create something innovative. The more low level language, the more joy it brings to your work. Imagine the difference between building a library using C/C++ vis-a-vis building something using point and click interfaces. There is a ton of difference in doing things at a lower level than merely working using the surface level pre-built things.

In today's world, autonomy , individualism, freedom are the buzz words where every individual strives to be an individual, in the literal sense of the word. He/she picks and chooses from an array of choices available and tries to establish an identity for oneself. Marketing, is hell bent on showing how customization is the new cool thing.. If one ponders a bit, one realizes that the marketing jargon is doing something else to us. It kind of equates picking and choosing relevant things to freedom. Nope, we are merely selecting. We are not tinkering with the details, which means we are still operating at the surface and are disconnecting ourselves with the true nature of the system that we are interacting.

The best thing mentioned in the essay is about Yamaha warrior 2007 ad:

Life is what you make it . Start making it your own

The picture shows a guy in his home focused intently on the warrior. He's not smiling for the camera. He's lost in his work. Underneath the caption is written in small font, You only get one shot at life- may as well make it mean something!.
This sort of joy obtained by hacking things is never obtained by mere selection of pre built things, like wearing a good helmet, having a good rear mud guard etc. Joy , as suggested by the ad is only obtained by close tinkering of the machine to suit one's likes and dislikes. and such a close tinkering provides JOY and not superficial selection is what the entire essay is about.

Education of a Gearhead

The author narrates his experience as an apprentice in a repair shop in California. His interactions with Lance , Chas makes him work on things and allows him to ponder about the different worlds, one at home where his dad is a mathematical physicist who deals with beautiful abstractions, the other world is the mundane repair shop where utilitarian aspects triumph abstract things. Being in close proximity to repair personnel, he realizes the importance of attentiveness while repairing vehicles. The same set of data can be give different signals to different people based on their involvement levels. Spectator skills won't do. I get the same feeling when people merely sit around the fences and trying to guess the next hottest startup, next hottest trend....Why sit as a spectator? Shouldn't one not get involved in the game ? Well, as they say , different people get attracted to different types of dispositions.

Pure Mathematicians and Builders fall in one category. If you think about it, the builder and mathematician builds something out with a specific mental image of something. If the theory or building falls, it is entirely the responsibility of the creator. However motor repairing skills / doctor's skills are very different. They are inherently dealing with things they have not built. They are looking at fixing things which means the realizations of the problems are stochastic( This word was used by Aristotle to describe specific kind of arts which require attention and openness to things as they are). A specific write up in Robert Pirig's Zen and art of motorcycle maintenance is also mentioned to bring home the point that " At work, being good-natured , friendly , easy going and UNINVOLVED is useless" . In our lives we come across so many people such as described above who are good in nature but don't involve themselves completely in any specific activity and hence appear as spectators. It isn't fun to watch spectators as much as to watch players hitting the runs. Well, I believe it is better to play the sport and than watch either spectators or players :)

The point of this essay is that practical world needs a person to be involved, attentive , make the work personal. At the same time, develop a paradoxical feeling of being unimposing of their views / assumptions etc on the things they come across. A true statistician would say , form a null hypothesis and test it out. However that needs to be appended with another aspect, " develop a personal connection to the thing you are testing, only then there is fun, joy and in the end utilitarian.

Further Education of a Gearhead : Amateur to Professional

This essay recounts the tension between engaging in work completely Vis-a-vis client's budget constraints. There are instances when the author spend more than required time , reason being a obsessive drive towards perfection. Not always was the client intent on spending money for all the hours spent. There are cases when the client needs the motorcycle to just run and give him just enough service in his budget constraints. Working in abstractions is good in ivory towers but not in real world where different people have different agendas in your work.

The Contradictions of the Cubicle

This note contains author's experiences as a knowledge worker and sheer paradox between the quality of work and intellectual status the work offers. Author goes on to talk about the degree inflation, the so called corporate culture where most of us seek an identity, a place where in the name of team work, management creates more vague notions of work satisfaction. There is also a comparison between Site crew and a team at a office which shows the vast differences between the two. A clear objective measurement and sense of achievement is usually seen in a trades job, whereas in a big company, the contribution one makes is often difficult to measure and it could be result of a ton of random forces beyond your control.

Thinking as Doing

Author makes a strong case that tacit knowledge that is obtained from years of understanding, tinkering can never be replaced with algorithms, computer systems. I don't necessarily concur fully with the view of the author as I believe systems and raw computing power can be used as an effective aid for understanding. Lets say before the advent of statistical software, simulating and understanding various aspects probability distributions, stochastic processes would have been very difficult. High frequency trading which permeates the world of finance would have never flourished had there been no money produced by those systems.

However I do agree to the point that our society gives more importance to thinking rather than doing. However increasingly the lines are blurring. A feedback loop is the key and a person who is involved in both the steps is the person who is going to do something productive. Confining to only one part of the loop is not going to be effective, point in case are technical writers in IT companies. They write documentation with out ever writing a line of code, with out ever building a user interface. Result : Reams of useless English documents which serve as no purpose. Yes , indeed tacit knowledge which is gained by doing is somehow thought to be replaced by computers/ processes in a few fields,is a shaky ground which will collapse soon if not now.

Work , Leisure and Full Engagement

I felt that the last essay is the best essay of the book, because it probably echoes a similar feeling that I have about work and life. Examples quoted in this essay are numerous. Why does a mortgage banker work like mad and looks forward to Climbing Everest holiday? Why does corporate jetsetting consultant thirsts for a vacation in australia, Amazon's rain forests etc ? There are a class of people who live dual lives. In one part of their lives, they work and accumulate money. In the second part of their lives, for some it is weekends, for some it is end of year vacation, they release their pent up need for an experience where feelings and action are linked together. However there are a set of trades where one can live in one unified world where work and life intermingle and have a meaningful relationship between them. There are so many wonderful points made in this essay that my summary would do no justice to it. Overall takeaway from this essay is that finding work as a way of life is crucial to live a joyous life, else an individual is forever living in 2 worlds, one in which he/she makes money, and the other in which he connects with core need !

This become reminds me of Hackers and Painters by Paul Graham. Excellent in prose and to the point , where the central message of the book is to work on things which offer you to mix thinking and doing.

For those who are time strapped and are looking for a 2 minute video recap of the book, here is a youtube link :

for one more day

It has been almost 2 years since I have read any fiction book. Had to go to a nearby mall to get my weekly groceries, when I saw this book. Did not have any great inclination to pick up such a book but did an impulsive purchase .On Sunday evening, felt like taking a break and read this book in a few hrs time. Well, the book is a work of fiction where the protagonist meets his dead mother and has conversations with her , just when he decides to commit suicide. This book is a gentle reminder to readers that one has to take time out and be with their mothers,because not doing it consciously might result in a day , when they realize that its too late and they can never go back in time and make their mothers happy.

The Drunkard's Walk : Summary

It's Saturday morning and it's pouring in Mumbai. A perfect setting for a cup of hot tea and a book. Simple pleasures of life:) are sometimes all that one needs, I guess.

Was planning to read this book for quite sometime as it was touted as a book similar to Against the Gods and Lady Tasting Tea, books which I thoroughly enjoyed. After recovering from a brief illness this week, I thought I should spend my time peacefully with a book and managed to read this book in about 2 sittings. Let me summarize this book:

This book is a mix of historical narrative of statistics and author's own opinion about randomness. So, this book is NOT like the book mentioned above. It is more like Tableb's fooled by randomness, not as sarcastic as taleb though. It starts off with a view that randomness is not appreciated by most of us in our lives. We tend to make sense of patterns, situations, people using some notions which themselves could be based on some chance events. We do not carefully appraise our prior probabilities/ prior distributions/ prior notions based on the events we see.

Having made the point that lives, events, people, victories, defeats, success, love, emotions, relationships and pretty much everything is a stochastic process, the author goes on to give a brief historical narrative. Greeks , the pioneers of mathematics in terms of their contribution like axioms, proofs rarely ventured out to do anything on the probabilistic side of events. The same argument is presented here as that given in Against the Gods .They believed that the order/chaos is the hand of god. Romans, however were more flexible in their approach. They were interested more on the practical side of things and were the earliest people who started tinkering with the ideas of probability(BTW probabilis is a Greek word).

Cardano

Author writes at length about Gerolamo Cardano,an gambler by heart was the first to speak in terms of sample space. The concept of sample space is discussed in the context of Monty Hall problem.

Galileo too made a powerful observation that the chances of event depend on the number of ways in which it can occur. This is the frequentist view of things in the current world. This view had a tremendous influence on the developments in the field of probability.

Pascal Fermat

Pascal was hooked on the probability by thinking about the "balla problem", an example cited in almost every introductory session on probability. He also sought help of Fermat to vet his ideas. Pascals contribution among many others was the Pascals triangle, an easy way to know the number of ways in which a specific events can happen.

An interesting question which was not answered yet, at this point of time in history is ,
How to figure out the numbers in lets say 10,000th row / Millionth row in Pascal's triangle ?
Pause for a second and think about it

It was a problem during those times, but we all know that for large row number, the above pattern which was later called as binomial distribution approximates to a normal distribution..Anyways that's statistics 101..Now back to the book.

The author digresses at this point to talk about the importance of sampling and how we can be fooled by small numbers. Benford's law was a new learning for me(real-world measurements are often distributed logarithmically).

Jacob Bernoulli

The author then talks about the fusion of ideas from calculus and probability , thus culminating in to Jacob Bernoulli's Golden theorem( Law of large numbers). Until this point , the questions tackled were," What is the forecast for a set of fixed probabilities?" What are the number of trials to be fairly certain about an estimate of a distribution, given the population distribution ?"

But we all know that nobody knows prior distributions with certainty. Most of our lives we deal with data and we have to make inferences of the underlying probabilities. This is one the ways to distinguish PROBABILITY and STATISTICS. Statistics involves inferring about distributions. Probabilities that we learn in high school concerns pretty boring but important details about various things , once probabilities are known. Somehow this distinction was never made anywhere during my schooling/ college education. May be my instructors did make this point, but somehow I failed it appreciate it then. One needs to keep this distinction always in mind when dealing with real life problems. Given a poisson process with a lambda, you can do all the jazz in the world about various events and their likelihood of occurring..But unfortunately in real life, one has to face the questions like , " If I buy an option based on NIFTY, do I have use a GBM model for hedging or Poisson Jump Model ? How should I validate the hedging results and make estimates of a better underlying distribution ?. These are statistical questions!,..Anyways I am digressing from the book.

Bayes was the first person who used conditional probabilities in his work. He never publicized his work..Fortunately Laplace was also working on similar ideas and thus was instrumental in the development of this area. This is one field sadly which has still not been used widely , atleast in the finance discipline. People still use a lot of gaussian models and no wonder they fail...Life is not gaussian!. Gaussian Copula , one of the reasons of the subprime mess( Wired article here)

From here onwards the frequentist world dominated statistics. Gauss, DeMoivre, Fischer, Galton and a host of brilliant people pushed statistics in to solving real life problems. Einstein's usage of Brownian motion etc is one of the spectacular developments.

Here is where the book suddenly stops the narrative. Ideally there have been a lot many developments after this point in time, but the book kind of goes on to give author's view on randomness. These include the way we try to attribute patterns to randomness, randomness to patterns , the way we delude ourselves of being intelligent, when we are just being lucky etc. The last couple of chapters belabor this point, more than required I guess.

Overall, a nice little recount of people involved with randomness and author's view of randomness.

A trivia from the book which is kind of interesting. Much as we might think randomness is everywhere, it is pretty difficult to actually manufacture randomness. well, we all know there are host of methods to create random numbers, each with its own bias..The trivia which I found interesting is about Apple iPod Shuffle feature. Apparently there was a lot of customer feedback that shuffle was not really throwing up random numbers and listeners were under the impression that Shuffle was not developed with good randomization feature.....Here is what apple did. It DID NOT MAKE, the algo more random. it actually MADE IT LESS random and customers felt that songs were now more randomized!!!
Strange are the traits of randomness!

More than you know : Summary - Part 2

Continuing from my last post where I had left the juicy part of the book for this post, here I go :

The last topic contains ideas which I find fascinating. Markets are complex adaptive systems and one can understand it better if you have systems thinking. Peter Senge's Book "The Fifth discipline" had a great impact on my way of thinking and each essay in the last part of the book is a suggestion towards developing such a kind of thinking.

The author points out to the need for diversity in thinking. Reading about how stats is used in baseball to improve a team's performance can teach you a lot than trying to look for cases in your own domain. A healthy discussion with a ecologist can give you more insights about speculation in financial markets than trying to look at indicators from the same field. Google's search engine and the way it used eigen value decomposition can provide you with a far more insight in to the usual factor analysis of stock returns. Research evidence from study of ants, bee-hives can give clues about how markets aggregate information.

Decision markets, and readings like "Wisdom of Crowds", " Wikinomics" can give you insight in to taking positions on a stock or a derivative.
Study of fractals is more meaningful than studying and working on the same old academic finance theories which are based on normal and log normal distributions. Power laws and the ways Power laws are used in fields like economics, psephology and like can help one understand markets better.

The entire point of the essays under this section is to urge you to keep your mind active and open to things happening in fields which might not seem related at the outset. Markets as a system can be understood better by applying ideas and thoughts from other fields.

Take any branch of finance in today's date..It is crying for contributions from other fields..It is being flooded by sociologists, ecologists , psychologists, who know that finance as a discipline can grow only by fusion of ideas from various fields. Case in point , Mandelbrot's fractal finance. Finance literature has virtually nothing on fractal finance as compared to the other kinds of literature. I had a chance meeting with the Mr.Barone Adesi , the person behind closed form solution for american option. I asked him whether he would consider research in fractal finance a viable option for a budding researcher. He said, he had himself worked on it for 2 years and was hopeful that research will pick up in this area.

I remember another faculty giving an analogy of ants in research literature. If you place something that attracts ants and you see to it that there are only 2 possible ways, inevitable one of the ways gets used by ants and the whole path is jam packed, even though an alternate path is available. This is similar to academic literature where it is only by getting references and citations from similar research initiatives , can one publish papers. Coming back to the analogy of Ants, once in a while , a few ants move from the pack and explore new paths and that is how ants as a community survive. Finance has reached a stage where it needs such diverse thinking and one can only hope a few people from academia or a few practitioners will actively stray from the usual path and help us answer some of our questions on

• How do markets aggregate information ?
• What does aggregation mean from market efficiency ?
• Concept of risk remains incomplete
• We don't know how our brains work ?.........................and many more such questions that need to be looked at.
  
  This book reminds me of a set of essays I read a few years ago, "Essays on Derivatives" by Don Chance. Each of the essays revolved around a simple but powerful idea. I get the same feeling after reading this book.This book provides a lot of food for thought. If you want to think on the areas where finance concepts have to be worked on, read this book..Out of 30 ideas, I am certain that atleast a few will interest you.
  
  

More than you know : Summary - 1

This book contains about 30 essays , meaning 30 ideas which can be read, ruminated and possibly applied in real life situations wherever they are applicable. Though the book is about finance, it draws considerable amount of research evidence from Biology, Math, Sociology,etc . At a 10,000 ft view , actually each discipline is nothing but an effort to understand the world using a set of constructs.

I would term Finance as a soft science rather than hard science where the discipline needs to be understood from various eyes . For a number cruncher , it becomes all the more important to realize that numbers,statistics is after all ONLY one part of the story in markets. One must consider market as a complex adaptive animal where numbers is ONLY one part of understanding. The more the mental models one has, the better one is equipped to understand what's going on.

I will try to summarize the key idea from each of the 30 essays from the book.

Part I

1. Process is more important than Outcome in Investing
   Always consider success as a lucky realization unless you backtest it. Just because you make money using some trading rule does not mean that you are going to make it again. There is an undoubtedly a strong relation between good process used to make a decision and a good outcome. Think about it, this applies not only to investing but also to most of the things we do in life. Lets assume that you think that C# is the best language to create something that is needed, but somehow your skills are rusty and you would rather do it with VBA and build the same screens etc. Well, it might be probably good for short term, but can anyone use the tool apart from you? Is it Scalable ? Does it have a wow! element ? Is it extensible ?
   
2. Constant see-saw between Investing considered as Business or Profession
   This is the same point which Daniel Kahneman makes about the scale struggle. Money managers consider Investing as a business and work for the immediate goals and immediate returns (quarterly/yearly) but Business operates on a longer scale. I think instead of thinking of it as a Business or Profession, it is best if one can think of it as a Business and Profession. There will always be some assets which can be shorted at a professional scale and always some assets which can be longed at a business scale.
3. Babe Ruth Effect
   Well, this one is pretty well known. Focusing on frequencies and not on the Expected value can kill a risk manager. The same idea as Taleb's Pi Lambda. In Mediocristan, Frequencies are important because payoff is pretty much symmetrical. But in Extremistan, it is important to understand the payoffs are not symmetrical. Infact they are very skewed and following frequentist approach is equivalent to being a sucker.
4. Proper Categorization is the Key (Context based)
   Most often than not, we categorize things and do not check the stationarity of the categories. My best ever read on categorization is an essay by Clay Shirky (Ontology is Overrated). Categorization as a subject has always fascinated me. In this book however the author says theories based on static categorization are not good. Well, we all know it, but how many of us actually keep that in mind when we go about theorizing, categorizing world events.
5. Learn to translate uncertainty to Probability.
   Degrees of Disbelief, Propensities and Frequencies are three ways to get to probability . Academic world is orgasmic about Frequentist world.Successful traders love propensities and uninformed trades live in the world of degrees of disbelief. For a trader who wants to trade by numbers, it is very important that he follows all the three ways depending on the context. For a plain vanilla options, frequentist is not a bad path to take. Well, you can rise over the usual theories known , do your own analysis on whether to follow mixture models, poisson+ gamma , whatever. However propensity based path needs to be taken too based on the context
   
6. There is a Hot Hand in Investing, if not in baseball
   Long streaks of success typically indicate luck imposed on great skill
7. Equity Premium Puzzle
   Loss aversion and Myopia can explain the equity premium puzzle. Somehow I felt that is is the same time scale difference that is being brought out by the author
   
   
   
   Part II : Psychology of Investing
   
8. Link between Stress and Suboptimal portfolio Management
   Essay beats the same point. Money managers operate on Shortening horizons, leading to stress to leading to suboptimal portfolio performance
9. Persuasion
   Robert Caldini's 30 year work is wonderfully summarized in this essay. The construct that there are 6 tendencies that spur a behavioral change : Reciprocation, Consistency , Social Validation, Looking, Authority and Scarcity . Each of these 6 tendencies are important when you need to think about the trading behavior change that can be observed in the market or trading rooms
10. Emotion and Intuition in Decision making
    
11. Guppy Love : Role of Imitation in Markets
    Asymmetric information, Agency costs and Preference for conformity are three things which force traders to confirm. But this also leads to Herding.
12. Beware of Behavioral Finance
    This chapter was like a breath of fresh air in this book. Behavioral Finance follows the reasoning, Individuals are irrational and an aggregation of irrational individuals must lead to an irrational market. However the essay says that " Markets can still be rational when investors are individually irrational". Meaning there is a law of large numbers at play. The very fact that players operate at different time scales, investment styles and investment approaches means that diversity will always ensure collective rationality.. Which means whenever there are imbalances the market corrects itself. Well, Do I agree with this view ? I don't think I am knowledgeable enough to form a view. But as such as a basic premise,I do believe in collective rationality and individual irrationality. But again, it is just a belief with no evidence to back it up
    
13. Essay on Keynes Expectation theory
    Comparison of Markets to "Beauty Contest" and "El Farol Bar". The idea that is conveyed in this essay is about deductive Vs inductive reasoning. Inductive is how we operate and it brings itself with a variety of bias. Deductive is how economics tries to operate , applying general principles to specific. In the case of markets, it ought to have been deductive but looking at market players, it appears inductive.This debate of deductive and inductive reasoning is brought out clearly.
    
    
    
    
    Part III: Innovation and Competitive Strategy
    
    
    
    
    I had a very strong tendency to skip over these essays for the simple reason that I have grown indifferent to the words strategy, competition, innovation etc. These are words, I remember , back in B school days, I was so fond of. It seemed as though using these words and thinking on these lines was leading to productive thinking. Over a period of time, I have realized that these are just fillers in conversations when you don't have clear thinking. In fact all the buzz words that I learnt at B school, I have consciously tried to avoid over the past few years. These words look good in case studies and HBR articles ... that's all there is to it. If you have to do anything productive, I think it is better to ignore these words and think in simple terms.
    With this kind of strong aversion towards such words, I was in no mood to go over these essays on strategy. But then I gave in, thinking that I might get at least some simple & meaningful thoughts from the next 7 essays. What did I find ?
14. blah-blah-blah-blah
15. blah-blah-blah-blah
16. blah-blah-blah-blah
17. blah-blah-blah-blah
18. blah-blah-blah-blah
19. blah-blah-blah-blah
20. blah-blah-blah-blah
21. blah-blah-blah-blah
    
    
    As expected the entire part on strategy was like reading some old Bschool case..It bored me to death!! The only thing that I found was somewhat educative was about fitness landscapes. Again you can term it as old wine in new bottle. The other possible idea which deserves a mention is the analogy of chess players and traders
    -- Don't look too far ahead
    -- Develop options and continuously revise them based on the changing conditions
    -- Know your competition
    -- Seek Small advantages
    Again the above points are cliched, but the essay is a little better as it brings about nice chess analogies using these points.
    
    
    Part IV: Complexity Theory
    
    
    
    This part of the book rocks!
    
    Will summarize the last part of the book in my next post
    

Sway : Summary

 

It is almost 2 months since I read a book. Finally after the bhag daud in Mumbai , managed to find time to peacefully sit and read a book. Thanks to Sanjiv Sir's awesome book collection, managed to get my hands on "SWAY". The first time I came across a mention about this book was when a trader recounted his experience about a Variance swap trade and related his behavior to that explained in the book "SWAY".

Well, tons of books have been written after the explosive growth of behavioral economics. "Does this book offer anything new to me", was my feeling when I began reading.

Loss aversion : KLM disaster mentioned is a case of loss aversion where dangerous decisions are made to avert losses !! . The possibility of a potential loss makes you avoid that loss at any cost...Sometimes it is too costly as it the case with KLM airline where the pilot had a potential loss of reputation at stake that lead to one of the most ghastly flight accidents in the airline history.

Commitment Sway : $1 auction at Harvard is an example of commitment where a strange type of auction brings about the irrational tendencies in perfectly rational people. This is similar to, well, I have already committed so much of time and effort towards this..Let me go about it doing the same thing....It happens in startups, relationships, companies, everywhere...Sometimes, it is difficult to say QUIT, even though it is an rational thing to do. Reminds me of "DIP" from Seth Godin...Its a wonderful book that makes your mind flexible and forces to take some tough decisions.

Value attribution : We assign value to things unknowingly and we make decisions based on the value attribution with out rechecking our assumptions that lead to ascribing the value.

Diagnosis Error :
The take away from this is : When we are in a position to make a diagnosis, we all become overly confident in our predictive abilities and overly optimistic about the future. For a number cruncher, this is probably the most important thing to keep in mind. If you see some pattern, back test it rigorously, have a healthy skepticism for whatever you are seeing.

Fair Value
Fair value is something most of the traders think day in day out. Is it priced at fair value ? Umpteen examples in this book show that pure objectivist fair value assessment is not the norm of the day. One of the interesting examples quoted is that of the popular TV show, "Who wants to be a millionaire". Audience poll is something which the contestant uses when he/she doesn't know the answer thinking that the audience will help him/her out. In some specific countries it has been found that audience deliberately misleads the person who is playing!! Fair value reasoning is used to explain this behavior

Compensation and Cocaine:
There is an interesting chapter on influence on incentives. Performance incentives and its effect on productivity is always something that is paradoxical to me. If you give me a differential equation and ask it to solve, i might work on it for hours and solve it to just to get kicked about the fact that I am able to crack it. However if you attach $X to it and ask me to solve, somehow I start reacting to the same situation differently. I become less motivated. I have read at innumerable places about a similar behavior , though in different contexts. An activity which is done to derive pure pleasure out of it, seems different where monetary incentives are attached to it. I remember a friend of mine who loved writing and he decided to join a media firm . Soon enough, he was demotivated and left the media firm. By attaching a $X to his writing, probably he felt less motivated to write any more and was forced to think about deadlines!.

This type of issue always makes me think of wallstreet people, traders etc who , atleast as the media portrays are motivated by monetary incentives. So, "Is beating the market that gives them thrill" OR the result of beating the market,money, is what drives them is something I do not know. At least the world portrays that year end bonus is all traders look for...If that is the case, they are not there to beat the market for the sake for it, but purely money driven. Incentive structure, especially monetary is wild animal. For some people, it motivates to do better, For some people , it actually demotivates. To this day, I have never really understood the reason.

Coming back to the contents of this chapter, the authors say that there are 2 distinct centers in our brain. One area is related to addictive substances like monetary incentives, cocaine, drugs and other area which is altruistically inclined. Book quotes research evidence that the pleasure center hijacks altruistic center and changes the behavior of the person completely. Well, I know one friend of mine who is working towards building a great firm and ultimate wants to make tons of money. However when asked the reason for doing the same, he says the drive is purely based out of altruistic reasons. The evidence in the book at least says that it is generally the addictive center which will have a major say in how one goes about in life. So, whenever you hear the words, " I want to make money so that I can donate them later ", you got to read this chapter from the book to convince yourself that it is actually the pleasure center that has hijacked the person's mind and altruistic center is somewhere in the back ground.

Subroto Bagchi, in his book, High performance entrepreneur , also echoes the same sentiment. An entrepreneur has to be financially driven and he invokes an Indian mythology argument saying that Money is Goddess Laxmi and she runs from you at the slightest instance of disrespect or apathy. Well, if you pitch this argument to any Indian, mostly they are going to buy it :) .Anyways the point is I guess, one needs to be clear on these 2 aspects and make decisions accordingly in various situations.

Book is extremely light read and will make you ponder atleast a few things , considering that there are tons of examples cited. For a time strapped reader, the synopsis at the end of the book should more than suffice. I have "Nudge" , a book echoing similar sentiments is far more engrossing.Will read it someday.

Outliers : Summary

 

Bought this book in India for $2 , less than the shipping cost of an item from Amazon!. Price difference of books sometimes amazes me. If one really wants to read a set a books, one can manage the stuff with a very lean budget in India.

This is a book about outliers. In some sense, everybody wants to be an outlier. Ask any person and he/she might say " I want to be different" . He/She wants to be different. However there are only few people in this world who manage to be very very different from the rest of us, as far as the eventual world-measured-success is concerned. This book emphasizes one single point through out. Outstanding achievement/Success is made by certain individuals because of the culture they belong to. There could be many variables that effect the success of an individual. Malcolm Gladwell takes one such variable, culture, of the individual and tries to weave experimental and anecdotal evidence to argue that , culture, probably could be one of the most significant variables that determines success of an individual.

The book starts off with an interesting finding at a place , some 100 miles away from Bancor, Pennsylvania. A surprisingly healthy community who all come from a place in Italy and are unusually healthy. This example brings out the "Outlier" effect. Is health related to the usual known causes or Does culture have a big role to play in it ?

Gladwell talks about the Canadian Hockey league and shows that date of birth , and specifically date and month of birth have a tremendous effect on the success of the person. The eligibility date of the Canadian Hockey league somehow seem to have a great effect on the type of successful players which the system churns out. Someone born near the cut off date has a far more likelihood to be grouped with a set of younger students and eventually displays far more superior skills in the group and thus gets to the top , thanks to the asymmetric age distribution in the group.

10,000 hours is the effort required to master anything , as per gladwell's research. Be it Bill Joy, Bill Gates, Beatles, Paul Allen, every one of the rock stars/ chess players/ entrepreneurs seems to have put 10,000 hours of passionate efforts to become excellent in their respective work. If you put in 12 hrs of effort, 7 days a week , which is 84 hours a week. In a year it translates to approx 4200 hours. If you can spend at least 3 years this schedule of 12hr days, then there is no doubt that you can become proficient, as per Gladwell.

Author runs a story about Joe Flom , a successful lawyer and glosses over the usual reasons that are given for the success of an individual. Instead he focuses on specific events that arise in Joe Flom's life , Being Jewish, Demographic luck, 3 elements of flow (autonomy+complexity+effort & reward ) coming together in the type of law practiced. These factors , gladwell, cogently argues are the main reasons behind Joe Flom's success.

Personally, I found the story on Rice paddies to be the most important part of the book. Gladwell explains the connection between the effort spent on rice fields and the way the same traits are carried in to the math abilities of Asians. Truly the most interesting narrative that I have read in the recent times

There is also an account of KIPP school in Bronx which illustrates the importance of "more schooling" rather "less schooling" being the reason behind the turn around of the school. Author presents the data which says that the only reason which the elite school kids perform better other kids is the way they spend their spare time. Well, the spare time is what decides the expertise of a person.

The book ends with the story about author's mother where a set of fortuitous events lead his mom from Jamaica to US .

The book is a great one-time read of a few stories which go on to hammer the point that , culture, where you come from has a significant bearing on your success. If you are a gladwell fan , you might not want to miss this book.

The Power of Less : Summary

 

This books falls under a specific category of books which includes titles like " In Praise of Slow", Zen books which focus on simplicity, Productivity books etc. As the title hints, there is one simple theme running through out the book - "Declutter your life, in all aspects of your life." Cutting down variables and living with fewer and lesser variables in life is a far richer experience than dealing with a lot of variables and trying to do justice to all the variables.

This book is a classic productivity book but is completely in odds with the "Getting things done" - David Allen talk. The author Leo Babautta says that simplifying things relating to various aspects like, information intake, email reading habits, internet browsing time, goals in life, people whom you deal with, working on the daily tasks, etc. makes a lot of sense. The book gives some practical tips to simplify things in life.

The book can be read in less than hour's time and at the end of it will make you feel that Life is a objective function where the lesser number variables makes it easier to find the maximum value of the function. In fact, it makes me feel that the local maxima could also be the global maxima!, if you choose the right kind of variables!

For those who feel overwhelmed by stuff in life, this book is going to be a welcome breather. However one can get the best out of this book only if one implements and experiments with at least a few of the stuff mentioned in the book.

Stumbling on Happiness : Summary

Stumbling on Happiness, is one of those books which I decided to read, mainly to take a break from the usual type of books that I read.

Last weekend I went to Boston to meet my friend Anoop as I was leaving US for good. As always, the 4.5 hr journey from NY to Boston gives any one a wonderful time to read. Generally, I tend to always remember distinctly some specific facts about books. About this book, I came across this book for the first time in 2006 while working at Fidelity. I remember it browsing through at South Station, Boston. My first impression was that it was some sort of a self-help book. After glancing through the contents a few pages, it did not smell of a self-help book. However I was too busy with my project and work at Fidelity that I did not find time to read this book. Anways , One fine day, I found myself strolling at Borders (near Penn Station, NY)when this book was screaming "Pick me up" from one of the aisles. So, made an impulse purchase knowing that the same book from amazon would cost me 40% cheaper.

After I purchased this book, somehow I did not find time for reading the book , probably thanks to my arb research. Boston visit provided me with one such opportunity to read the book.
Not to be over dramatic, but this book has made a definite impact in the way I think/ thought I think

Indeed, if you have even the slightest curiosity about human condition, you ought to read this book. Would it benefit you in your professional life? I don't know.
Would it give you "Ten tips to become happy", No.
Would it give you a recipe to live a happy life? No.
Would it tell you about the reasons you are unhappy? No
....... Well, I can go on and on. However this book does not belong to such category of books. The reason one should read this book is to not let your mind fool you in to believing something , especially believing stuff which you think might make you happy ?

Let me summarize this beautiful book in terms of broad themes it talks about.

This book is a scientific take on the errors we make when we try to imagine our futures. Book strongly argues that our errors are lawful, regular and systematic. A cognizance of nature of errors is very valuable and in that sense this book is an awesome read.

Subjectivity

In the initial part on "Happiness", the author delves on a scientific take on the subject and explores interesting aspects. There are 2 main arguments which the author makes on happiness. Happiness experienced by one person is dependent on specific context, life events, circumstances and can never be compared. Much to the chagrin of the scientific minds who want to compare happiness based on some scales, author says that such scales are meaningless. The second argument that the author makes is that , law of large numbers is the saving grace to deal scientifically with a subject like happiness, in spite of its extreme personal stampish trait attributed to happiness.

The author then goes to talk about three shortcomings of Imagination,which is used to think about future

Realism: Imagination works so quickly, quietly and effectively that we are insufficiently skeptical of its products

Mind remembers only parts of information of events. It tries to recreate the tapestry of events by filling in details and leaving out details. This process creates a strange narration of past by the mind, a narration which needs to be looked at with some skepticism. We expect the future to unfold with the details we have imagined and we fail to be critically aware of this mind trick.
It is very important to recognize that mind creates this synthetic past and future. Take away - Be aware of Synthetic happiness.

Presentism: Imagined futures often look so much like the actual present

This part says " Our imagination is heavily biased with the current state of thinking " . We find it difficult to disassociate our present feelings with our imagination of the future. This creates problems when we make decisions etc that we think bring happiness in our future lives.

Rationalization: We have hard time telling how we will think about the future when we actually get there

Context, Recency and Frequency play a big role in our future imagination. In most of the cases, we cook up facts and theories to make us believe things about future. But we have no clue whether those theories hold good, whether those theories make sense and ultimately whether our concept of happiness based on rationalization makes sense in the long run.

Corrigibility

• We remember endings and hence this effects the way in which we recollect the feelings
• We have theories about how we must have felt and that decides our ability to remember our feelings
• We remember specific things/ unusual events and base out experiences on it, rather than the complete event.

Takeaway is that the ability to recollect exact feelings is error prone and it is very very important to keep this in mind.

Is there a remedy to these shortcomings of our imagination ? Yes, there is one . However it is little difficult to put in to practice as we increasingly think we are unique, different from others, etc etc. We fail to comprehend or fail to choose to comprehend that it is easy to find surrogators who are facing our simulated situation currently. Since our imagination of the future is limited, we can at least find a surrogate who is currently facing a situation that we are imagining and look at how happy he/she is. This , according to the author, can cut down the bias we create when we imagine about the future.

This book makes one think about one's own imagination, biases that we carry on, reevaluate the way in which we have/are taking decisions. One takeaway for me is that I will keep in mind these bias while trading/speculating. I intend to keep a detailed log of trading results and my thinking behind those decisions. In that way, at least I can go back and retrace some of the thinking , with out these bias creeping in.

This book has so many important insights that it will delight any kind of reader!

For those who are time strapped / attention strapped, here are a few video links. ( These links might in turn motivate you to read the book)
Video Links
Dan Gilbert at Ted -1
Dan Gilbert at Ted -2
Dan Gilbert at Poptech - 1
Dan Gilbert at Poptech - 2

Where's the end ?

Via Bloomberg

The IMF report released yesterday , Jan 27, signaled that write downs and losses at banks totaling $1.1 TRILLION so far are only half of what’s to come. Losses on that scale would leave banks needing at least $500 billion in fresh capital to restore confidence in their balance sheets.

Here is how fast things are changing...A couple of months back, I read a book called "The Trillion Dollar Meltdown" by Charles R Morris. My review for the book is here . Now there is a new book by the same author titled " The Two Trillion Dollar Meltdown"

Going by the current trend , I think he is currently writing a third book and the title is easy to guess :)

Evidence Based Technical Analysis : Summary

 

This is a book which I have been planning to read for quite some time, may be since an an year . Finally found sometime to spend on this book.
However the book turned out be just about OK, not engaging, considering the amount of hype around it .Having said that, this book has to be read by anyone who is in to back testing trading rules / strategies and has only a vague idea of stats!

Anyways, Here are my brief takeaways :

Chapter 1 - Objective Rules and Their Evaluation

Takeaways

• One needs to formulate a basic idea of a rule. Rule is an objective statement that can be implemented by a computer program and that generates unambiguous long/short/neutral signals
• Position bias needs to be tested in a trading rule. Randomize + Equivalent position would serve as a good control group
• Detrending is very imp in back testing rules
• Avoiding look-ahead bias and accounting for trading costs are very important!

Chapter 2 - The Illusory Validity of Subjective Technical Analysis

Takeaways

• Important to understand the various bias that can creep in validating a claim
• Overconfidence,Optimism bias
• Hindsight bias
• Confirmation bias
• Illusory correlation
• Sample size neglect
• Representative bias
• Biased second hand knowledge
• Attribution bias
• Knowledge Illusion
• It is important to recognize it is easy to fit a pattern to a time series. However it could be only wishful thinking unless it is tested vigorously with scientific rigor.
• Good stories well told can make people misweight or ignore facts.
• People are not naturally rigorous logicians and statisticians. A need to simplify complexity and cope with uncertainty makes us prone to seeing and accepting unsound correlations. We tend to overweight vivid examples, recent data and inferences from small samples.
• Scientific method is a reliable path to validity, mitigating the misleading effects of our cognitive biases

Chapter 3 - The Scientific Method and Technical Analysis

Takeaways

This chapter summarizes the development of scientific method. Aristotle's syllogisms, Bacon's contradictory thinking, Popper's falsification method, Null Hypothesis-Alternate hypothesis development. This chapter leaves the reader with a powerful impression that advancement in any field needs the 5 stages of work

• Observations
• Hypothesis
• Predictions
• Verifications
• Conclusion

Unless these become a part of TA, practitioners of TA are nothing but astrologers, alchemists and folk healers

Chapter 4 - Statistical Analysis

Takeaways

Nothing much ...For a person who doesn't know abt stats, this chapter might be useful...Definitely skimmable for someone who uses stats for rozee roti

Chapter 5 - Confidence Intervals

Takeaways
Nothing much...Except that one can use resampling method Or Montecarlo method for testing hypothesis and establishing confidence intervals.
Most of the content in this chapter is Stats101. One takeaway for me,is that one has to zero-center the sample for conducting resampling method. This is done to align with the universal null hypothesis of a trading rule -Ho- Returns from the trading rule are 0.

Chapter 6 - Data mining Bias - Fool's gold of Objective TA

Takeaways
For me, this section is the meat of the book. I took quite some time to read slowly what this section talks about and here are my takeaways.

• After the fact probability tends to much higher before the fact. A monkey coming up with a master piece is very less, but given a history, the probability of a masterpiece being produced is very high
• Be aware of data mining bias - Random component + Genuine predictive power of rule , the random component dominates the data mining route. You are just lucky with the rule in the history and out of sample is bound to under perform.
• Data mining - Always the best performing rule is chosen...Well, the rule was just lucky in that period
• Five Factors one needs to think about Number of rules being tested, Data size, Correlation between rules, variation in the expected returns amongst the rules, Presence of outliers
• Three ways to cut DM Bias - Out of sample testing(walk-forward testing), Markowitz scaling down method, Resampling methods and Montecarlo methods

Chapter 7 - Theories of Non Random Price motion

Takeaways
This section looks at the shaky foundations of Efficient Market Hypothesis. All forms of EHM are analyzed. Arguments based on logic as well as empirical evidence are put forth in order to show that EMH is crap and there is enough scope for Arbitrage opps in a market

S&P Case study:
The last part of the book deals with testing about 60,000 binary signals to S&P data. If you can read through relatively dry chapters 1-7, you are bound to enjoy this part of the book. Once you start thinking about the ways in which trading rules can be built , this section is priceless as allows to zoom in to various aspects of back testing..

Personally, I think that the S&P case study at the end is far more valuable than the first 400 pages of dry read. However if you haven't used stats for a while, you might like the first 400 pages too.


My takeaway from the book is : There are a ton of insights from the S&P case study towards the end of the book!, the rest is jazz.

The Lady Tasting Tea : Visual Summary

This post is going to serve as a good visual narrative of the development of frequentist world. If you love stats and have worked on identifying patterns in data, it is but obvious that you would have met a host of tests, with different names from different fields. Statistics is one field where the contribution has been made from all kinds of fields ranging from agriculture, clinical psychology, math, finance etc.

WHY do you think so many people contributed to this field ? Just pause for a few seconds and think about it.

I had never paused and thought about it.... and the opening remarks of this book provided that insight,though very obvious in the hindsight. Statistics, a significant part of it deals with the way experiments need to be conducted ,and experiment/test/learn is the way of life for any scientist irrespective of the domain he/she is working. So ,the contributions to the field of stats are going to come from all possible fields.

Ok,let me attempt to do a visual summary of this book, for the simple reason that this book is a fantastic narrative of the history of statistics, the people who contributed, a peek in to their idiosyncrasies, their likes,their dislikes, is something that is a delight for the readers. I will try to cover most of the personalities mentioned in the book and their contributions.

Karl Pearson
Pearson was the first to look at the world from the eyes of a "distribution". What we are see are nothing but realizations of a distribution. If we collect more data, then we know more about the distribution. Under this assumption , he went on to create families of distributions based on mean, standard deviation , skewness and kurtosis. Experimental results are thus a distribution of numbers and distribution equations tell the probability of occurrence of these numbers. Measurements themselves have a probability than the errors in measurements which was the prevalent thought ( Galton , Abraham De Moivre, Carl Gauss).

Pearson also needed a tool to fit the measurements to distributions and he came up with a powerful tool called "Goodness of fit" which is used till date. (An example, if you have to select amongst a host of ARIMA models , for a given realization, one uses goodness of fit like AICC and chooses the model)

Gosset

Gosset was a classical empiricist. He felt Pearson theory is good but difficult to practice in reality as one needs to deal with small sample sizes. He worked on this problem after his work hours( classic success of "sex and cash" theory) . He said two estimates mean, standard deviation are enough to say something about the distribution. He published under a pseudonym - "Student t". This was very useful ..think about it. With pearson's case, you estimate 4 parameters, then you estimate the estimate of 4 parameters, ..it is an infinite loop. But with Gosset's insight, you stop at the first computation....wow!! It is a marvelous achievement.

Ronald Aylmer Fisher
Fisher, a personality, has had tremendous influence in the development and usage of statistics. His experiments, "Studies of crop variation", produced gem of results. ANOVA, MANOVA,ANCOVA, etc. Separating the main effects of the experiment was the underlying philosophy behind randomization. "Statistical Methods for Research Workers", a book stripped of complex math equations and made available for easy usage was instrumental in the adoption of Fischerian thought everywhere. Fischer was the person who first introduced the words "Degrees of freedom". may be becoz, he was always inclined to think geometrically.

The fundamental difference between Person's philosophy and Fischerian view is that : Pearson believed that if data represented distribution. Fischer believed that distribution is an abstract concept and he believed that all one can do is find a statistic describing the abstract concept. This statistic can be anything, mean, median, iqr etc. All these statistics will be random and hence one needs to study these estimates as such. He was also instrumental in coming up with "Maximum Likelihood estimates" , a way to iteratively figure the best values for the estimates give the data and a distribution .Today with the advent of computers, mle is a command away to this powerful and time consuming procedure which was very mathematical and laborious to do by hand.

Fischer also introduced p values, which went on to become the basis for hypothesis testing

Tippett Gumbel, Emil Julius

100 year flood prediction is difficult using Fischerian concepts . Hence Tippet and Gumbel studies this aspect and made a significant contribution. Today we know the work by the name Gumbel distribution.

Neyman

Neyman was the first person to ponder on the question, of connecting p values to hypothesis statements. He figured out that one needs to have a null hypothesis and alternate hypothesis for an experiment to use p values. One can only reject null hypothesis or fail to reject null hypothesis. No causality is being talked in here. This was his biggest contribution. He also developed and gave the interpretation for the word "confidence intervals"

Bayes

Bayes , a priest by profession, made an outstanding contribution to the world of stats. He dealt with the world of inverse probability. Look at the data and adjust the prior hypothesized distribution was the thought, a thought which was very very radical. For some reason, Bayesian stats is still not taught properly in MBA courses, Finance courses across the world, in spite of the fact that Google made tons of money using baye's fundas. There are umpteen disciplines that are crying for the application of bayesian principles . Risk management, for certain!

LEBESGUE, Henri Léon - Lebesgue measure

Lebesgue measure -- Key to the development of Real Analysis

Kolmogorov, Andrey Nikolayevich - MOZART of Mathematics Kolmogorov's contribution to probability and statistics is pivotal.

He was the first mathematician to use measure theory and lift probability from a step sister treatment in math, to the grand status of what it is today. With out Kolmogorov, it is very unlikely that development would have happened at this rate. He pondered on 2 questions and spent his entire life on them

• What are the mathematical foundations of probability ?
• If there is a set of data in a time interval, how does one interpret them ?
  
  He also coined the term "stochastic process" which is the bread and butter of quants

Florence Nightingale David

Lot of people think that she was founder of nursing profession. That is just one part of story.But for stats guys, her contribution to the theory of statistics is path breaking. Any paper you pick on stats, with in 2 -3 handshakes, you will find a reference to F.N. David's work

Wilcoxon, Mann of Mann-Whitney test

Why not do away with parameters, a revolutionary thought, in the hind-sight was first envisaged by Wilcoxon, Prof Mann and his student Donald Whitney. Their efforts gave rise to an entire branch on non parametric statistics.

Prasanta Chandra Mahalanobis
Mahalanobis is credited for coming up with "Randomized sampling", instead of "opportunity" Or "judgment" sampling. Under the then Indian PM, Nehru, he went to create economic indices which became crucial for tracking the performance of various five year plans.

Jerome Cornfield

The first example of inverting a 24*24 matrix and helping a noble prize winning economist are some of the few contributions of Jerome Cornfield. In a way one can say that he helped in bringing out the popular Input-Output economic analysis.

Many of Professor Cornfield's numerous contributions to both biostatistics and public health grew out of his research on the health effects of smoking. He became interested in the use of case-control studies after reading seminal papers by Doll and Hill (1950) and Wynder and Graham (1950), which used this methodology in early discoveries of the association between smoking and lung cancer. Professor Cornfield then demonstrated that case-control studies can be used to estimate the risk of disease as a function of smoking status so long as the rate of disease in the population is known. He also showed that the odds ratio, an approximation to the relative risk for rare diseases, can be estimated either prospectively or retrospectively. These results form the basis for much of the modern era's epidemiologic research.

Gertrude Cox

Cox is the first woman to be elected into the International Statistical Institute.In 1950 Cox and William G. Cochran wrote the book Experimental Design that became a classic in the design and analysis of replicated experiments.

Stella Cunliffe

Another towering woman in the statistical world

Samuel S Wilks

Samuel Stanley Wilks was an American mathematician and academic who played an important role in the development of mathematical statistics, especially in regard to practical applications.Wilks worked with the Educational Testing Service in developing the standardized tests like the SAT that have had a profound effect on American education. He also worked with Walter Shewhart on statistical applications in quality control in manufacturing.

J Tukey - The Picasso of Statistics

Fast Fourier Transforms, Exploratory data analysis, box plots, stem-and-leaf plots, rootgram instead of histogram are some of the stellar contributions of this versatile scientist.

George Box Gertrude Cox

Famously remembered for the Box-Cox transformation, a technique used to reduce data variation, make the data more normal distribution-like, improve the correlation between variables and for other data stabilization procedures.

Deming

Deming is credited to have brought quality movement. His contribution to the quality was from a statistical standpoint where he looked at variation in output as a combination of common cause variation and special cause variation. He championed for the reduction of common cause variation and that changed the face of Japan

LÉVY, Paul Pierre

Levy, was dissatisfied with counting methods in probability. He developed and applied Martingales to clinical trial studies and today Martingales are used in so many domains, finance, modeling, you name it...Martingale has entered the common vocabulary, thanks to Levy

David Dickey ( Dickey-Fuller unit root test - Stationarity test)

Stationarity of residuals , one of the methods to check is the dickey fuller test.

BAHADUR, Raghu Raj

India born mathematical statistician considered by peers to be "one of the architects of the modern theory of mathematical statistics". He is popularly known in the context of Anderson-Bahadur algorithm

Wald

Inverse Gaussian distribution, also called Wald Distribution , came from Abraham Wald.Also popularly known for Wald Chi square Test which is a statistical test, typically used to test whether an effect exists or not. In other words, it tests whether an independent variable has a statistically significant relationship with a dependent variable.

Brad Efron

Credited for discovering "Resampling method", one of the greatest breakthroughs in field of statistics

Finally, the author who has has put a fascinating account of the above personalities and many more, all in one book - Dr. David Salsburg

Fortune's Formula : Summary

I came across so many references to this book that I finally decided to take a peek in to it. At the heart of it, the book is about Kelly's criterion.A simple example made me motivate to read this book.

If you know that you gain 50% with prob = 0.6 and lose 50% with prob = 0.4...AND...if you have X dollars, how much do you bet ?A naive strategy of using expectation over one step is dangerous. Expected gain over 1 step = 0.6*0.5-0.4*0.5=0.1 Expected profit >0 . Should you bet your entire $X ? Common sense tells us that it cannot be the answer as there is a high probability that you would be out of the game before law of large numbers kicks in. Kelly was interested in knowing what fraction of the wealth should one bet, given the odds of gains. Well, the answer for the riddle is f=2p-1 where f is the fraction and p is probability of a gain. This is all the matters if you want to have a 10,000 ft view. However if you want to know the STORY behind the criterion, this book is a wonderful way to spend time and understand the story which William Poundstone has thoroughly documented. This book is pretty readable..It just tells the story behind the criterion and it tells it in a fantastic way.

PART 1 - ENTROPY :

The book starts like a crime thriller. It tells a story of John Payne who starts a wire service to communicate horse track results to the bookies. In a strange twist of events, AT&T rise and Payne's original network converge to the establishment of Bell Labs, a place which is credited to have been the place of innumerable famous personalities. The first part of the book introduces some famous personalities .

(Shannon) (Thorpe) (John Kelly)

Firstly, about Claude Shannon, one of the few brilliant scientists who was single handedly responsible for bringing "Information theory" to the world. His contribution was immediately applied to a wide range of fields. Edward Thorpe , a brilliant empiricist who sets Shannon in to a direction where they team up to build a tool to estimate probabilities at a roulette wheel. In their adventure, they realize that it is very important to have a betting strategy in place. The simple martingale strategy of betting twice on loss until you win is a risky proposition as the gambler might be bankrupt before he manages to win.

Shannon's key idea was the essence of a message is its unpredictability. John Kelly's , another brilliant scientist's key insight in to this idea was:
Greedy-though prudent better is faced a similar situation as a receiver of a noisy message in shannon's case. Kelly extrapolated the same shannon's ideas to the horse betting scheme. The key equation spelled out by Kelly is : Gmax = R where Gmax is the growth rate of gambler's money and R is the information transmission rate (Shannon's theory). It is also popularly known as edge/odds kelly criterion which is often quoted in the media.Ed Thorpe used Kellys ideas in BlackJack and made a killing in the vegas casinos.

Henry Latene idea : If in each period, the investor chooses the alternative with the largest geometric mean across possible states, the geometric mean strategy is going to dominate all the strategies. I am facing a paucity of time but I would love to read more on this. STREETWISE, a book having a collection of papers relating to portfolio management has one of the papers on Henry Latene. May be I will read it some day.

The first part of book titled entropy covers the people behind kelly criterion and gives a non-mathematical introduction to the same. After reading this part ,it is difficult not to go beyond this introduction and explore the ideas of risk management. Let's say I have a statarb strategy to pick stocks. How do you manage the risk of the portfolio becomes extremely important ? I should somehow find time to read about these issues and work on its implementation.

PART 2 - BLACK JACK :

This part is a journalistic account of Thorpe using Kelly Criterion in Las Vegas to make money. The story highlights one key idea that is recurrent in various forms through out the book.

Law of Large numbers is largely misunderstood by gamblers. The probabilities are realizations over the long run and you need to a criterion to bet , to avoid Gambler's ruin. Kelly's criterion is one such criterion, edge/odds tells you the way you should bet on successive outcomes. This part of the book compares 4 strategies , Bet it all, Martingale, fixed wager system, Kellys system. At a first glance , Martingale system looks good but there is a gambler's ruin in the pursuit of the strategy, meaning, there is a chance of ruin before the law of large numbers strikes. This is where kelly criterion , which is a geometric criterion does exceedingly well. Even though the trajectory path is jittery, the kelly criterion beats all the strategies.

The above reliance on the way to make law of large numbers is exactly what Shannon used in Information theory. Sometimes reading all these beautiful ways to look at things, makes me feel that I should never try to lose this habit of reading regularly. However I have seen that once I start working in a company / startup, i find it difficult to devote time to read in silence. I hope to change that pattern this time around!

One thing that will make any math inclined reader to ponder is , "How to use Kelly criterion in stock trading strategies ?" I am certain that there is a ton of literature out there. I should find some time to go over it sometime!. Also a few of the books mentioned in this part of the book are
"Beat the dealer", "Beat the market". I guess the ideas of delta hedging was put to use by Thorpe in Newport Ventures, a successful company, much before Black scholes used it in the famous Black scholes formula

Part 3 - Arbitrage

This is the part of the book I love the most because it is a topic close to my heart. It talks about Thorpe's attention to convertible bond arbitrage and the way he devised a risk neutral strategy for warrants. The delta hedging technique which Black Scholes popularized years later was being practically implemented years earlier by Thorpe. This book talks about the rise of random walk in finance literature and the popularization of efficient market hypothesis .Samuelson, Fama, Sharpe, all touted EMH. Shannon on the other hand believed that the only way to make money was through arbitrage. However he himself did not use the technique in the earlier years to make money. His success came from picking few stocks.

However Thorpe who had already used Kelly criterion, used warrants to make money, wanted to expand and get more money to manage. One way he did was to popularize his concepts in "Beat the market". The book's popularity brought him investors who were eager to put in money. Thorpe's Princeton-Newport ventures did a tremendous business which is still a sort of benchmark for all the money managers. A dollar invented in 1968 would have grown to $14.78 in 1988. Over 19 years this was a return of 15.1 %(S&P averaged 8.8%). The most interesting thing was about the standard deviation which was 4%, meaning a Sharpe ratio of 3.7, a bloody good Sharpe ratio by any standards. If you ever think of managing money, this ratio should always be in your mind to remind you that there was guy who beat the market hands down with a Sharpe ratio of 3.7. WOW!, till this day, money managers talk about this performance. If you read this part of the boo,k, you cannot help but start thinking about kelly criterion, delta hedging, information ratios. The meat of the book lies in this and the next part

On a side note, there is a new term that I came across in this part of the book:

Paul Samuelson coined the term , Performance Quotient. Like IQ, this measures a portfolio manager's ability to generate alpha.A PQ of 100 is average.Sameulson theorized that if such people existed, they would all be invisible. :You would not find them working for Ibanks. They have too high an IQ for that.They would operate by stealth, investing their own money or their friends money.They would keep the system to themselves."

Part 4 - St. Petersburg Wager

This is the famous wager problem where expected value of the wager is infinite. So, the question is that no one would pay infinite amount to play the game. whats the fair value of the game. This paradox created a lot of interest and lead to popularization of utility function ,Bernoulli's masterpiece where he mentions that risky ventures should be evaluated based on the geometric mean of the outcomes. This ultimately lead to the involvement of Henry Latane, Markowitz, Kelly and finally it was being accepted that mean-variance analysis does not talk about compounding of investments. But Kelly's world is about compounding of returns, the winnings are being reinvested continuously.Even though Samuelson and Merton were strict opponents of geometric criterion, other people like Henry Latane, Thorpe believed in Kelly criterion.

The world to this day is divided I guess between Kelly and Mean-Variance approach. However Thorpe, the man with one of the highest PQs believed in kelly(half-kelly to be conservative). Kelly has no log normal assumption or distribution assumption. It has no utility function behind it. It is a plain simple formula to avoid ruin. Will it work in specific markets ? Can some on use Kelly in India for stock selection? I do not know whether people are using it. But it will be fun to play with the money and see whether it works.

Part 5 - RICO

This part gives a historical recount of RICO, a tax evasion law that became attached to Princeton-Newport ventures. Most of the partners got indicted. Thorpe was one person who was found innocent. The whole issue with princeton-Newport ventures was that they were parking stocks at firms and buying back at a price so as to offset short term gains. Tax evasion!! was good in the short run until RICO hit them. However this did not happen during the stellar performance of the fund where it had a Sharpe ratio of 3.7. So , for all the aspiring fund managers, that metric can still be considered as a benchmark for your performance !

Part 5 - Blowing up

This details the blow up that took place at LTCM. Ironically , the fund failed to be long term!. The point in illustrating this case by the author is to bring the relevance of Kelly criterion and how the criterion would have saved LTCM from a blow up. Thorpe brilliant point about convergence trades made me think about the relevance of some trading strategies that I know about.

Part 6 - Signal Vs Noise

Well , the last part of the book is a short collection of diverse views on Kelly . Shannon, it turns out was more than a stock picker than arbitrage exploiter. Thorpe believed in relative value and made a killing in various markets. The book ends with the author not taking any side to the debate on Kelly. Left to the imagination of the reader and interpretation, I feel that somebody soon , the heavy weights(PhDs) from ivy/other schools with get behind the Kelly bandwagon and make it popular. Ideas about mean variance will die soon and may be then , people will start giving importance to Kelly criterion.

This book has made me inquisitive about the actual implementation of Kelly formula, how to use it in a long-short portfolio.

Lets say I manage to identify some long-short trades, is my current implementation of risk management sound ? May be I should simulate P&L with Kelly criterion and see how it behaves!. The following are some of the wonderful references which I hope to read some day:

• Beat the market - Thorpe
• Streetwise - Peter Bernstein
• Portfolio Choice and Kelly Criterion
• Henry Latane's paper on geometric mean
• Shannon's basic funda relating to Information theory
• Implementation of Kelly Function
• How to implement Gmax = R

Wow!! its a long way to go for me, before before understanding all the stuff clearly..

Plight of the Fortune Tellers : Summary

There are a couple of books which I think , every risk manager should read. After reading them I hypothesize that there will be 4 outcomes

• Risk manager ignores the view as just another view and moves along ( and moves the firm too , in a downward direction)
• Risk manager starts introspecting his/her methods and starts becoming skeptical about them and will probably do something about it
• Risk manager starts introspecting his/her methods and becomes extremely skeptical about them and will do something about it, hopefully
• Risk manager quits his job and does something else, far removed from the field of risk management. Who knows, he might just embrace risk with out giving any consideration to the frame of thinking at his/her previous job

"Plight of the Fortune Tellers" is one such book which should be on the must-read list for any risk management professional. It is a book which was written years ago , before this subprime mess hit the economy. Its insightful view of the risk management world from the world of frequentist and bayesian worlds is bound to shatter a lot of myths for an open-minded reader. I always intuitively knew that bayesian is the way to go. But this book reinforced my thoughts as the author brings a lot of facets and goes very deep in to the subject to show that Risk management's current practices are completely flawed. BTW, there is not a single equation in the book . It is a remarkable achievement for any author on risk management to avoid equations and explain his ideas in simple english!!. This book rocks!

"The Cardsharps", by Caravaggio ,

shows a handsome and probably very naive young man who is engaging in a financially rather risky activity:gambling. We become aware of how risky this game of cards really is when we note that a man is looking over the shoulders of the young player and communicating to his fellow cardsharp the cards in the young man’s hands: unfortunately, a lowly “three.”And, when our attention is drawn by the action of the playing cardsharp to the dagger concealed in the waist band, we begin to fear that what the young man is risking may be more than a few ducats.
The dire consequences in miscalculating the odds of a risky activity, is what the painting talks about!

Here are the main ideas from the book:

Author pitching to the reader to spend time and effort on the book

• Leibniz usage of statistics to forward the cause of Prince Fredrick of Prussia. Statistics is used to take decisions and not mere hypothesis about stuff and write papers about it
• Fundamental need to rethink about the role of probability, role of rare events, time homogeneity and forecast period!

Thinking about Risk

• There are 2 types of probabilistic assessment. System I and System II
• System II is analytical, tools, math based
• System I is heuristics based, where we overestimate odds of one group and underestimate odds of another group
• Risk management in the current environment is heavily skewed towards System II thinking
• What is needed is a good blend of System I and System II thinking

Thinking about Probabilities

• Frequentist Probabilities and Subjective Probabilities
• Subjective does not mean "anything goes". You should be able to bet with those odds
• Why cant we say that coin is 40% fair. Why should we always live in the world of accept/reject world ? Either coin is fair or biased ?
• Point estimate + confidence intervals == frequentist world...
• Risk management needs Bayesian Probability where there is a constant interaction of prior assumption and data
• With limited amount of data, in the frequentist world, there is always an external model which one uses!

Making Choices

• Prospect theory and Utility theory - What have they got to say on choices ?
• Problems associated with each of the above theories

What is Risk Management for ?

• In a bank or a firm, whats the purpose of the risk management division. Shouldn't investors themselves indulge in diversification and any form of risk management done by the firm will not suit all the investors ?
• The truth of the matter is that investors almost always do not have the complete information about what is happening in the firm
• All the investors see is stock price and earnings , their volatility. High earnings volatility is a signal to the investors that the firm might not do well
• The sole purpose of risk management is to create stable earnings estimate. - This is what the author thinks. If you ask Taleb, he would probably say, firm should get rid of risk management division completely :)

Var&Co - How it all started ?

• 3-6-3 World of a banker
• Firms started to undercut the bank by directly issuing bonds
• Banks started banking! on trading revenues
• Complex instruments --> More desks --> More variables
• VaR , a convenient metric but which is actually VariableAndwRong

Looking beneath the surface?

• There are approaches to Var , namely Historical simulation, Empirical fitting, Fundamental Fitting
• Historical simulation is dumb but least controversial as it involves no parametric calibration
• Empirical fitting is intellectual orgasm. Pick one of the distributions in the stats101 book , find the best fit and then you have all the freedom in the world to talk about 99.979999 percentile!!!(arggg). Even if you have data about 1000 days, you can still predict a 10000 day event. Does it scare you?
• Fundamental fitting - This is what happens when physicists try see finance as a science. Finance is social science and there are no universal structural equations!! alas!! its not pondered upon a lot
• Monte Carlo - More sin has been committed with Monte Carlo technique than any other, in the statistical world. WHY ? Montecarlo doesnt say a thing about distribution. Given a distribution , it will tell you how to proceed...But ...who gives you that initial distribution
• Central Limit theorem - It doesn't work in tails. It talks only about the initial moments!!! So, rare event is out of question using CLT
• Correlation is a metric for elliptical joint distributions. But why should joint distributions be elliptical for the observed real world variables ?
• If I want to assign meaningful probabilities to very rare events, I cannot escape the link between the frequency of data collection, the relevance of the data, and the rarity of the event in question. And if the event I am interested in depends on the co dependence among very many variables, the complexity of the problem literally explodes

Which probability matters in risk management ?

 

• Probability Square - Four vertices - Frequency of Data Collection + Time homogeneity + Rarity of the event + Forecast Horizon
• Frequentist world : High frequency of data is good as the event is time invariant.
• We need external model /view which says that the data observed in the past is relevant to the forecast horizon. But then how do we validate that external model ?? It is not math / sciences where one can take axioms as god given and move along
• If the phenomenon I want to investigate is very time homogeneous, if I can collect data very frequently, if what I am interested in happens not too rarely, and if the horizon of my statistical prediction is short, I am clearly in Frequentist Land
• Prediction of Short term horizons - Market Risk - Large data sets available ; Is it relevant data ? For valuing CDS how far backward is relevant data ?
• Prediction of Long term horizons - Does market to market make sense ? Important to model trend , but it is inherently more variable than estimating volatility. Long term nature of the issue at hand cuts down the data points, cuts down the confidence.
• Statistics 101 - Point estimate + confidence intervals is crap in the risk management world
• In Markowitz’s Portfolio Selection bible, for instance, the possibility that all the probabilities attaching to the different outcomes may not be perfectly known only makes its appearance on p. 257 of a 303-page book—by which time the assumption that the objective probabilities are perfectly known has become solidly embedded in the psyche of the reader
• The frequency with which we can update the value of a long-term portfolio (if we do not mark it to market), the length of the projection horizon of interest for these portfolios, the difficulty in estimating the trend and its high dependence on our models and prior beliefs, the importance of the trend itself in determining the overall portfolio performance—all these factors strongly suggest that a subjective-probability approach is the appropriate one.

The promise of economic capital ?

• Need for the regulatory capital should be linked in a beneficial manner to the risk management of the firm. A bank should not end up gaming the regulatory capital requirements

What can we do instead ?

• Subjective probability is the relevant type of probability for assessing returns, and judiciously employed frequentist analysis is useful to assess risk, it then follows that we cannot use the same statistical tools to evaluate the risk and expected reward from a project.
• Treat Risk and Return separately and use different tools for each of them
• For risk, look at the asymmetry of the distribution, dispersion and kurtosis
• Three decisional crutches - Best Possible Scenario Worst possible scenario , Break even analysis
• A call to arms for related disciplines such as bayesian analysis, decision theory and experimental psychology

This book cannot be speed read at all. It needs to be read slowly, allow the points to sink in and ultimately help you understand the nature of risks in a better way. Personally, after reading this book, I think there is tons of innovations that need to come in to Risk management field. WHY ? The current mess is a clear indication that things are not right and risk management as a discipline should strive harder than ever before to prove its worth.

A reread of Black Swan

It's close to 2 years since I read "Black Swan" for the first time. It had a definite effect on me for most of the arguments the author puts forth are very strong. One thing that propelled me in rereading this book was an article by one of my Prof's who said that the teaching system for MFE courses should radically transform itself for it to have any use in the times to come . I was immediately reminded of Ludic Fallacy which Taleb mentions in his book. So, managed to reread the book.

Let me list my takeaways from the book

• There are two kinds of worlds - Mediocristan and Extremistan . Dynamics are completely different in these two worlds.
• In Mediocristan, Law of Large Numbers works. In Extremistan, you cannot estimate the probabilities of rare events
• Many domains in our lives are increasingly falling under Extremistan
• Beautiful example of Turkey which is fed 1000 days before it is killed .For turkey , 1001 day is a Black swan.For a butcher it is not
• Do you want to be speculator or a prostitute in life ? This question is similar to what Mr. Nama had asked me years ago when I was doing my internship at GE ? He guided/suggested me to be a speculator rather than a prostitute. The principle is same as the cash flow quadrant idea brought out Roberto Kiyosaki
• Beware of the Ludic Fallacy
• Keep in mind the various types of bias - Confirmation bias, Narrative Fallacy, Silent Evidence, Tunneling in to Future
• Beware of EXPERTS in domains where things move.
• Melting Icecube analogy to show how complex the estimation of probability is, in real life
• Barbell Strategy - Invest 80-90% of money in risk free securities and 10% of money in extremely high risk securities. If one thinks of this strategy and applies to real life, I think it will have tremendous implications.
• An entire chapter which bashes Gaussian thinking.
• Introduction to Mandelbrot's fractal way of thinking . Such thinking makes black swans atleast gray swans , in taleb's words
• History Jumps, It doesn't crawl...Just forget the geometric brownian motion, arithmetic brownian motion, etc etc...It's all a bunch of crap as at its fundamental level, it believes in a gaussian world
• AVOID BEING A SUCKER , by exposing yourself to a lot of blackswans instead of avoiding blackswans
• Tinkering is the only way to know what works and what doesnt
• Missing a train is only painful if you run after it
• It is more difficult to be a loser in a game you set for yourself. - ( Brilliant point..Set up your own game and have fun , rather than living somebody else's life)

The reread was as entertaining as the first time I read it 2 years back...Books are indeed man's best friends!

Volatility and Correlation

For some reason , I have never come across anyone in my friends circle/my faculty , who have mentioned this book.I have read a lot of books on option pricing , volatility , but none , NONE, so far is any where close to this beautiful book by Riccardo Rebonato.

It is so gripping(well gripping might sound far fetched for a book on options, but that's what it is) right from the first page of the book.

I regret to have not read this book before. Better late than never. There are 4 parts in the book , first talks about black scholes world ,second talks about smiles, third talks about interest rate derivatives and final part is based on smiles relevant to Interest rate derivatives.

In the last 2 days , I have managed to go over the first part and it is nothing less than a brilliant exposition of various facets of option pricing. If ever , any one wants my suggestion on the book to read on options, immediately after books on Options101(Hull,Sheldon Ross, Neftci etc), then I would strongly recommend this book. Its a gem. If you intend to trade, I think this book is a must!. I am looking forward to understanding Vol Smiles from this book. I am certain that it will be as engaging as the first part of the book.

Traders Guns and Money : Summary Part III

In my last post on this wonderful book , I will try to summarize the Credit products highlighted in the book.

Credit Risk is managed by Commercial Banks and Market Risk is managed by Ibanks. Ibanks are not comfortable holding stuff for a long time. However with the onset of credit wars, Ibanks Vs Commercial banks, it started the biggest party of the decade, the credit derivatives party.

What's the real use of credit derivatives ? It is not to transfer risk , it is an instrument to free up capital. Total return swap by Bankers trust was the first taste of slew of derivative products to hit the market. First to default basked was picked up eagerly by all the investors. Slowly CDS market started to bloom. Initially there were a ton of difficulties in kickstarting CDS market. The problems were , firstly, CDS is like an insurance on the credit characteristics of the loan. There was a quite a lot of time spent on details such as whether making this asset a legal asset in the first place. The other problems associated with CDS was WHO and HOW ? Who was the reference entity in a CDS and How much would the payment be ? As products become exotic WHO and HOW questions became very very complicated to answer.

Securitization and Synthetic Securitization further complicated the situations. SPV, basically unregulated banks started mushrooming everywhere. BISTRO deal created by JPMorgan started the synthetic securitization game and a game that created the biggest mess of all times. Terms such as mezzanine investor, monoline insurers, rating agencies , SPVs , Credit correlations all started becoming a common vocabulary of everyday people. and then the party stopped and we are in 2008 ..

:) Today Wachovia declared a loss of 27 Billion dollars for a quarter!!!! .Wow!! that's the magnitude of mess we are in!!

Anyways , this book has no doubt been one of the best books on Derivatives that I have read till date. The very fact that I have written 3 posts on the book means a lot to me!! I would treasure this book forever and read it once a while , not for anything else, but to laugh at the world of derivatives trading. Now, I need to get back to calibrate Heston stochastic volatility model !! After all I want to be a part of this entire troupe called quants...Will try to sit and calibrate stochastic vol model for now, but once in a while , will read this wonderful book to remind myself to take it easy and chill..After all its a drama out there!!

Link to my first 2 posts relating to this book :
Traders Guns & Money - Summary Part I
Traders Guns & Money - Summary Part II

Traders Guns & Money - Summary Part II

Continuing from the previous post on Traders Guns and Money, here is second part of the book summary!.

 

Show me the Money :

What's this chapter about ?It is about profit and loss and how dicey things can get.

Profits in derivatives can be compared to Heisenberg uncertainty principle where basic matter behaves as a form of particle and wave, the experiment result depends on what is your initial assumption. Profits in derivatives behave in a similar way. There is accounting profit, there is trading profit and then there is THE PROFIT, which is the bonus that the trader gets at the end of the year. There can be three different numbers for the same time period for a trader. Profit process is a stochastic process I guess :)

Losses too behave in the same way. You can pretty much write down any asset at any value, this is especially true for exotic products!!

Overall the P&L of derivative trades can be made to appear in whatever way one wants it to be.

Risk Management : Voodoo:

This is a topic which author freaks out as this area has been in the recent times come under tremendous criticism , be it from taleb, or other VAR critics...One can find a new meaning for VAR in this chapter..
Variable and wRong Yes , if one looks at the crisis that have hit in the past, one can say that the acronym is a very apt description . The following are the list of events which are supposed to happen once in 100 years...But they have happened in much lesser frequency :

• 1987 Stock market crash
• 1990 Junk bond crisis
• 1991 Gulf war
• 1994 Mexican crisis
• 1997 Asian crisis
• 1998 Russian Default
• 1998 LTCM Default
• 1999 Gold market
• 2000 TMT stocks
• 2001 Argentina default on debt
• 2001 US Corp bond crisis
• 2003/2004 Oil price increase
• 2005 - GM Ford downgraded to Junk
• 2007-2008 Credit crisis like never seen before!!
  
  Should one really believe in risk management !! after all this??

Super Models - Derivative Algorithms:

Like Super Models, Financial Models are idealized representations.
They are not real. They don't quite work the way real world works

Black Scholes Merton brought the glamor to the random world of trading. Introducing PDEs , GBMs , replicating arguments, diffusion principles, they glamorized finance. Even though BS never wanted to price an option in the first place, they were interested in pricing shares as a call option on asset value of the firm. However they ended up with a formula for a call option premium. However the biggest contribution from them is the replication argument. Greeks became the normal vocabulary of the traders. Models churn out greeks and traders hedge using the greeks and make money by either going short vol or long vol.

Math finance either in fixed income world or equities world has always found it difficult to prove its utility . Even though there were a lot of conveyor belts( MFE programs) , the supply far exceeded the real demand. The real demand is in valuing instruments fair value , but it is notoriously difficult. Learning GBM. PDEs , Finite difference does not mean that it would be useful in finance. All it says that if a model does not perform , one can speak in some math jargon what went wrong. There is reams of academic literature on option pricing, vol surface determination, American option pricing, etc etc...Think of it..with all these sophisticated techniques, we are in the worst of times ... Recession, stock markets are screwed beyond shape, Large write downs, Defaults....Steven Shreve says, we need better financial engineering..Is it ? I don't know...May be ..May be not...
Why have banks, hedge funds, prime brokers all become POW - Prisoners of Wall Street ? Why cant we just connect interest rate points with French curves rather than cubic spline, quartic spline all that math jazz ?

With all this math jazz, hedging is still extremely difficult. What happens to the underlying on the day of expiry ? Should you hedge or leave it ? Would you get gamma-ed ? No trader can provide consistent answers to this ? After spending one year in math fin , i feel there is more jazz than required to extract alpha..Or may be I am too naive to understand math fin contribution..

One thing is for sure, this book has made me more skeptical about modeling applied to finance!!

Inverse World of Structured Products:

This chapter talks about the big bad world of structured products, structured vehicles. It grew in to prominence as investors wanted better yield , dealers wanted better hedges, etc. However soon it became very commoditized when Triple Currency Bonds became the pool of assets in the structured vehicles. There have been umpteen blowups in the structured products business..Game continued till 2007-2008 when major blowups happened. and not Structured Product Vehicle is synonymous with bullshit , post credit crisis.

The last sections on the book talks about equity and credit derivatives. I have never written a three post summary for any book till date. This book deserves it as there are so many funny things mentioned in the book. For any financial engineer, this book is a reminder to take life easy :) ..Afterall , it is an opera out there and you can act out your role and chill in life :) Not to take one's work seriously is perhaps the single most learning from this book , atleast if you are working in the financial services business!!

Would try to summarize the last portion of the book in my next post.

Link to : Traders Guns & Money - Summary Part I

Traders Guns & Money : Summary Part I

This is a book which is guaranteed to make you laugh!!! Yes , it is a book on derivatives but it is written in a great humorous style that it WILL MAKE YOU LAUGH. I bet!

 

I picked up this book almost 10 months back. Speed read it . Had lent it to one of my friends and hence could not write a summary in time. Now that this book is back with me, Let me try to summarize a couple of interesting things from the book.

This book is mainly about the author's personal experiences as a derivatives trader. The tone of the book is witty and sarcastic from the word go. It gives a sneak peek in to the so called derivative trading world.

Financial WMD's

o

The very first chapter is about the author's experience with structured products world. Satyajit Das started his banking career in 1977 and those were times the times when emails to put across deals , be it currency swaps or interest rate swaps. The volume and demand for these swap markets lead to the creation of warehouses where firms started taking principal repayment risk too. From the days of 1970s where everyone was taking Chicago Univs word as THE word on finance, author gives a quick summary of events . CBOT , CME exchange formation , evolution of IR Swaps, Currency Swaps, derivative warehouses , 1987 crisis, 1998 LTCM crisis, Inverse floater which screwed Orange County, etc. The first chapter is a whirlwind tour of crisis, structured product market evolution .

Beautiful Lies - Sell Side

Sell side, bankers and dealers comprise this side. The constant turf battles between sales and trading personnel, support and front end teams is brought out in this chapter , again with a sarcastic tone :). It also mentions the ethnic cleansing that has happened over the sell side, with various ethnic groups carving a role for themselves in the entire structure. There are a lot of funny acronyms which are introduced to the reader. One set relates to Management Directors. Considering the innumerable MDs at firms, author comes up with acronyms to categorize them. MD/NR ( Managing Director / Not really ). MD/NVI(Managing Director Not Very Important) MD/PLN(Managing Director / Please leave now). FILTH(Failed in London Try Honkers), LBFM( pls read the book to find out what this acronym stands for :) ..or just take a guess as to what a trader posted in Asia would expect as a perk :)

I had a wonderful laugh when author ponders over his perspective on equity analysts and the excruciating detail to which they analyse. He wonders whether the equity analyst is analyzing companies or RUNNING companies :) I get the same feeling too sometimes when I see equity analysts who would have picked up some degree in chemistry or some discipline and they give out buy/sell/hold recommendations using extensive mind blowing analysis . God knows from where they motivate themselves to study a company dealing with live cattle/ pork bellies.. I have always always never ever understood the contribution of equity analysts!!! As the author rightly points out, which I agree fully , equity research is just mean to shove reams of pdfs on to clients side so that they just buy in to the document. After all if somebody does a worthwhile research, the firm will instead use it to trade for itself....so, all the beautiful documents you see out there are just good advertising material !! nothing more than that!!

ok, here are some more funny interpretation of org structures from the author

Diversification : Let's do several things that we dont know anything about badly
Focus : Lets get back to doing what we once did if anybody can remember what is is and how to do it
Decentralization : Massive duplication, confusion and creation of thousands of petty empires
Matrix Structures: Everybody reports to everybody, no one knows who they work for and there is no accountability
Flat organizations: Managers who can't manage now manage tens of direct reports
Business Process Reorganization: A process by which you cut everything that is essential, leaving only everything that you don't need.

:):)

True Lies - Buy Side

Who are the artists on this side :) ? Pension fund managers , Investors, Insurance agencies, banks etc are all the people on this side. This chapter was very very funny. Even though it narrates a couple of crisis that were the result of using derivatives like Proctor & Gamble using IR Swap which blew up, Ashanti gold mines using futures to lock in profits, Walt Disney's screwed up hedging experience, MG's fucked up stack trade etc. Overall , if you understand the products or have taken Derivatives 101 course somewhere, you will shudder at the dangerous world of derivatives!!

The best part of the chapter is the author's take on various investment philosophies and vision statements. Here is a good list that author provides

• Index Funds :The fund manager has given up trying to beat the market
• Active Management :Triumph of hope over experience
• Momentum investing: Shit must be good. Millions of flies cant be wrong :)
• Value Investing: Pure Luck
• Yield enhancement: You are taking on a whole lot of risk. You will be lucky to get your money back
• Portfolio Insurance: Hang on, wasnt a return of principal the least that you are entitled to?
• Structured investment: A con job to charge you more fees
• Alternative Investment Strategies : Fund manager has no clue on investing..he is merely throws darts!!
  
  There are a list of USPs and their real meaning that's provided: here is a sample
• There is currently a window in the market to execute this transaction : Meaning -It is almost a bonus time and we need to close this deal urgently to make sure we can pay for our new porsches
• We make markets in all our products at all times : Meaning - If you want to sell this back to us, good luck, sucker

There are many more listed in the book....It is a joy to read such things coming from a veteran derivatives trader!! Not many people are that honest and sarcastic i guess!!

More on this book in my next post....

Guesstimation

The day I read "Innumeracy", I began appreciating numbers. Even though I was dealing with numbers prior to that day, that book clearly showed me that I was innumerate .That coupled with my love of life( the book which I have fallen in love and my romance continues till this day) , "The Art of Profitability " , made me realize that I was total crap in numeracy skills. From that day, I have constantly strived to improved my numeracy skills.

It also meant that I picked up and read almost any book that I came across, which was relevant to numeracy. Last week I chanced upon a wonderful book which belongs to that category. It is titled "Guesstimation"

 

What's new in this book ?

It is the first book that I have come across where all the discussion is based on SI system. All my education happened in SI units and in that aspect, this book is a welcome relief where I need to concentrate on only one step , which is guesstimate. I don't have to keep track of metric system conversions which is a big relief. This book is not a one sitting read book. It has about 50 odd examples and One example a day would do. Like everything else in life ,that you want to master, numeracy skills need to be worked on each day. I haven't reached the state where I spend atleast a few minutes daily. But I working towards it though. i compare such activities to working out in the gym. The more regular one is, the better one feels about it.

Any ways, happy to have stumbled on to this book, mainly because it deals in SI units!

The Trillion Dollar Meltdown : Book Summary

 

This book traces the key events in the US political and economy history that have supposedly led to the current crisis. Books such as these attempt to try to make a good narrative of the events around us. As they say , connecting dots backwards is easy ..and so this book in that sense does a better job.

Usually crisis is associated with a specific asset class. Junk bonds, CMO's, Real estate, etc. However the current financial crisis is a different one . It is a credit crisis which permeates across all asset classes. It is like poisoning air and there is no respite for any living being in the environment. Credit crisis permeates across all assets as it is the lifeblood of finance and that makes this crisis VERY dangerous. The asset classes which are going to get impacted are not just the dodgy mortgages but Commercial Mortgages, Credit Card debts, High yield bonds, Leverage Loans, Complex bond structures, all in total, according to the author's estimate is ONE TRILLION DOLLARS!!! That's equivalent to the GDP of India!! scary to think of it.
LTCM was just 100 billion in positions and 10 million assets at risk. This time , the numbers are scary and bailout means unbelievable sum of money.

Liberalism to Monetarism

The author starts off discussing the era of 1970s , an era characterized by a change of philosophy at the center. Nixon was a liberal who believed in the top down approach. 1970s were bad times for US economy and Nixon tried using the top down approach of price controls, import tax, etc. But by 1980s US was struggling to compete. It has huge inflation , high unemployment .Enter the Keynesian Monetarism. Ronald Reagon was an out and out Monetarist who believed that inflation is always a monetary phenomenon. If the money supply is controlled with respect to economic growth, inflation is always in reins. With this philosophy in mind, US started seeing a new regime , the regime of Monetarism.

Paul Volcker and Inflation

Nixon appointed Paul Volcker as a fed chairman in 1979 and he was a thorough monetarist. With the help of money supply instruments, he successfully brought down the inflation levels . Even though the growth rate became negative, slowly over a period , growth was established and wall street was sold the idea that monetarism is an effective instrument to control the economy. However there were 2 crisis that were a part of 1980s. First being the LBO boom and bust, second being Savings and Loans bubble. Both these incidents illustrate the fragility of free markets.

1990's is called the Goldilocks economy , a new term which I learned today. An economy which is neither hot, i.e not plagued by inflation nor cold, i.e not plagued by unemployment is called Goldilocks economy. So 90s as such was a happy happy :) period for everyone. With monetarists in control , credit was pushed in to economy and US economy was on a roll.

Not all hunky-dory

 

It was not that there were no crisis in the markets. There were three significant ones . First was CMO bubble which is a bubble created by tranching a pool of loans and then selling off different pieces to different investors. It was a genuine instrument, until the time when greed took over . 125 tranches became a common place and that was the end of it. CMO market collapsed . Big firms like Kidder Peabody shut their operations. The next crisis worth mentioning are Portfolio insurance crash in 1987 and LTCM debacle in 1998. In all the three stories, there was a common thread. Financial markets were becoming more unregulated, agency problem was becoming more dangerous and the concept of everything can be mathematicized was taking prominence.

Think about it : CMO crisis should have taught us something and subprime crisis could have been averted. Well, there were other forces that were shaping US financial industry. HEDGE Funds and INVESTMENT Banks were transforming the financial markets.

Instruments Galore

From 2000 to mid 2005, US witnessed the biggest housing boom in its history. An increasing housing activity led banks to start issuing instruments to hedge default risk. What started as a hedging exercise resulted in something completely new. Commercial Mortgage securitization was a bit difficult. Hence banks started involving rating agencies. Once rating agencies gave some rating, investors were sold on it. With the rise of hedge funds, credit derivative swaps, the credit market saw a huge increase in notional as well as the type of instruments being traded. 125 tranches for the securitized assets was being done, synthetic CDOs, CDO squares, etc..you name it...all that was needed was a computational power, a few math geeks , greedy investors and the game was set..A bubble was on and it was only a matter of time that party would come to a stop and it did...Housing market slow down blew away a couple of hedge funds AND it was a sign of things to come.

Unwinding Effect

With the easy credit available, the CDO's made a comeback. Why ? Well there was always a problem to sell the bottom tranche of the structured security. However with hedge funds in play more SIVs were being formed as there were good enough hedge funds to buy the crap. Hedge funds , as of 2007 deployed 2T of capital , and economic capital 5 times the size in to US economy. Usually a credit hedge fund investor is leveraged 100:1 . How is that ? Well 5:1 is the leverage provided by prime broker, and the investment like a CDO typically provides another 20:1 leverage . Thus credit hedge funds are highly leverage plays which provide liquidity to the market. The other players are Ibanks. Thus with I banks and hedge funds providing 50% of credit to the market, there was a huge built of portfolio with dicey assets.

The highlight of this book is a table which the author provides. This table gives author's estimate of the total unwinding that is due in the US markets. Table is really scary as it says that the mess has just started. Whole lot of asset classes are going to take a hit like credit card industry , high yield corporate bonds, monoline insurers etc. Here is the table. It would be worthwhile to keep a watch on this table and see whether this this is a 1 Trillion mess. The numbers before the brackets denote the outstanding and numbers in brackets denote the estimated loss. There are some asset classes where these numbers are not provided. May be the crap is too exotic to put a number on

All numbers in $Billion

• Residential Mortgages
  • Subprime - 1500 (150)
  • CDO Bonds - 1200 (300)
• Corporate Debt
  • High Yield bond defaults - 1000(50)
  • High Yield bond writedowns - 900(180)
  • CLO Defaults - 500(25)
  • CLO writedowns - 450(90)
• CMBS Defaults - 800(40)
• CMBS writedowns - 720 (108)
• Credit Cards - 900(67)
• Monoline -- ??
• Credit Default Swap defaults -- ??
• CDS Writedowns - ??
• Leverage Loans - ??

The total mess is about 1Trillion, a conservative estimate.

WOW!! Is this the place to stay and make a career ? Well, looking at the leverage that has been built up, it would take atleast 2-3 years before things return to normalcy...what would quants do till then ? What will other players in the fin industry do ? The other day , one of my friend's friend, a trader from morgan stanley was asked not to trade for few hours as the situation was hopeless and nobody knew where the market is going. No one knew where is bottom ? US has become , as you would have seen a highly leveraged nation. Consumer spending will remain flat and go down atleast in the coming years. So, where are the opportunities ? Emerging countries are also going to take a hit. This is the best time to start a firm and put some time and effort in building a valuable asset. Any ways, there will not be any earth shattering work in the economy. Hence this is the time to start and work on building an asset. Recession / Depression always gives rise to some spectacular companies. We will see some of them in the years to come.Who are building them ? Well , atleast a few of the brilliant folks from wallstreet who have been hit by the crisis will take the brave step of getting out of the usual rut and work towards building a great asset / value in the coming years.

What's the prescription ?

The prescription offered by the author should not come as a surprise. Financial regulation and more direct control of the government rather than believing completely in the markets. 1970s and 80s flourished because of free markets . The current crisis is the demonish side of free markets and may be there is a case for going back to liberalism , more govt controlled measures etc. May be. The next president will have a tough job to do . Will the markets which have been given free rein be controlled. it is easy to give away control but very difficult to put controls.

This book certainly makes a very crucial point. US is leverage to hilt and this meltdown will hit all the asset classes and it will take atleast 2-3 years before things return to normal!!

Black Scholes and beyond : Summary

It is 4:00 am on saturday morning and it is raining outside. Wonderful setting to write something. So, here I go.

This book was referred by Prof Stefanica, the director of Baruch MFE program . I stacked this book in my inventory under the assumption that I will read it at some point of time soon. Few backs , I had to go to Boston to visit Anoop, my childhood friend. I love long journey for it gives me a chance to be away from comp and just read, reflect and think about various things....I managed to go over this book in the journey. Let me attempt to summarize this book :

My first take on this book : This is probably one of the best books where almost every equation is replaced by an intuitive argument. The analogy of hedging with that of bet maker which spans about 4-5 pages is so beautiful that those pages alone make this book worth buying !! Ok , I will summarize in a random order and not in the order the book is organized.

Volatility Smile:

Constant volatility input in BS model is only for discussion purpose. In market , one sees that Lower strikes have high implied vol than higher strikes. A smile , as it is advertised in the finance literature, is when the volatility Vs Strike shows a behavior where OTM puts are expensive than BS Put value and ITM calls are cheaper than BS Call value.
How to incorporate volatility model in to BS world ? Well, there are three types of vol- Historical, model and combination of the the two. It is widely accepted that historical vol is a mean reverting process , meaning, it reverts to a mean, with a specific turn around time, with a specific volatility (yup, this is volatility of volatility - call it meta volatility ). Now how does one go about defining this model and calibrating the model . One way is to look at models like heston model which are mean reverting vol models , the other options is look at the universe of GARCH models like GARCH/AGARCH/EGARCH/GJR-GARCH/TGARCH, well..there is no end to this GARCH life that you can live. So, once you get a hold on mean reversion process, then you can think of pricing , hedging, calibrating and trading on that volatility.

If the correlation between stock prices and vol is +, then returns distribution is generally fat right tailed
If the correlation between stock prices and vol is -, then returns distribution is generally fat left tailed

Another little takeaway from this chapter is the stoppage time for bisection and newton raphson methods. log(sigma/power(2,k))< tolerance , it is pretty self explanatory why the stoppage condition is set up in that manner.

Trees & Basic Option Pricing with Binomial Trees:

Binomial tree is best described in Joshi - Chap 4. Having read such nice explanation ,it will be difficult to find a book which gives such a clear explanation. It was not a surprise that Option pricing on trees was an okish explanation. What does one need to know in Trees

The whole idea of trees is to build u,d,p in such a way that these prices at each discretization step matches the local volatility Sigma. Well there are a couple of ways to do it, eitherbyassumingp=q=0.5 or by assuming ud=1. The former is robust as forward price always falls between up and down move. A math-fin student should have all the formulae of local vol, u, d on his finger tips !!

• Bond + Delta shares can replicate an option - Pricing by replication
• Pricing by Hedging
• Risk Neutral valuation
• One comes across Quantization error and Option Specification error while working on trees
• Best thing about trees is that american option can be easily priced by asking a simple question - Which is greater, the cost of hedging or cost of paying for immediate exercise?
• Early exercise barrier for American options
• Tree pricing with continous dividends
• Tree pricing with discrete lumpy dividends
• ARROW - DEBREU Price of a node is Value of a security that pays a dollar if the price reaches that node and 0 otherwise
• Connection between butterfly and arrow-debreu price

Black Scholes World:

Chapters 1 through 5 talks about BS world, their implications , assumptions, delta hedging , greeks. For a person familiar with the subject , this book is light a good bedtime read with nice intuitive explanations for delta hedging, greeks behavior etc.

In every book , there are things when one needs to carefully understand what the author is trying to say and get the best out of the book. In this book too, there are sections on Implied volatility trees that make an amateur math fin student stretch his thinking. Hence I would carefully summarize my learnings of implied volatility trees now.

What if I were to completely trust the markets and then want to hedge an option that is written. That's where implied vol trees come in to focus. Thinking is this :

For a given (option price ,strike) points , lets build a price tree , so that for every price point, I know the option price. What do I get from this ? I can calculate all the hedge parameters and hence I am safe after writing an option.

There are a lot of wrinkles that one need to work on , like false probabilities etc. I don't think I will have time to code up an implied vol tree considering my schedule. Implied Binomial trees are slightly different from implied vol trees where investor preferences are taken as input and all the options for a specific fixed expiration date is taken as input.

Even though I understand the basic concept behind implied vol trees and implied binomial trees, I will not be able to appreciate until I code these pricing techniques.!!! Hmm...Need to somehow find time.

Overall , my takeaway from this book :

A great book which explains intuitively a lot of things from BS world and extends the BS world by looking at its limitations and work arounds.

A Demon Of Our Own Design : Summary -Part II

This is the second post on this book which will try to summarize the chapters on Hedge funds from this book.

Brave new world of Hedge Funds

Hedge funds exploded in to the financial world in the recent years. There are various types of hedge funds, classic one being top down. A few nice take aways from this chapter is the discussion of tulip mania where the author reasons that the existence and flourishing of futures market was the reason for speculative mania. Also a discussion of the importance of float in the market is discussed in the context of Internet bubble.
The biggest takeaway from this chapter is the answer to the following : Why does the price move ? It moves because of liquidity. Unlike the MBA crap that information moves price, this explanation is nifty . Broker dealers provide liquidity to the clients and they trade in suck liquidity. Elegant view to look at intra day price movements.
The author talks about the rise of pairs trading technique, relative value trades, LTCM and the recent evolution of long/short portfolios . I never knew that Bamberger from Morgan Stanley was the actual brain child between Pairs trading and Tartaglia stole it and scaled it .

Cockroaches and Hedge Funds

Well, this item of the book gained a lot of popularity.This chapter rocks for the author discusses examples from evolutionary biology and discusses about its relation to financial markets. Cockroaches, over centuries of evolution have developed response mechanisms that carry responses from sensory hairs to its legs. They are not filtered by the brain. This is a coarse decision rules in action. In another example, crayfish, also has developed a stimuli triggers a set of stimuli and then one set of action is taken up amongst all the alternative actions.

Another contrasting example is Furu, which is a specific type of fish which has developed a very specialized kind of response behavior, however, it just could not sustain sudden changes in the environment. Thus the author says that a coarse ruled based system is better for evolutionary systems. In that sense, a better risk management need not mean, more reporting, more metrics ..In fact, the more specialized the risk management becomes, the more it carries a risk of being ineffective. This chapter resonates with black swan where the conclusion is more or less similar to black swan. Risk management needs one to be prepared to deal with black swans, the unobservable. Sometimes, the danger to the system is the system itself . A beautiful description and comparison between evolutionary behavior and financial markets. JUST THIS CHAPTER MAKES THE BOOK WORTH BUYING

Hedge Fund Existential

Talks about hedge funds in general and talks about the difficulties in classifying Hedge funds. The author manages to give his own classification with the attributes being Asset Class, Direction, Investment Type, Geographic region and Liquidity. Author ends the chapter with a prediction that ultimately hedge funds will become standardized investment vehicles. Reasoning: Hedge funds cannot be classified. As many investment strategies there are in the world, there can be that many hedge funds. Whats the advantage with a hedge fund ? An short term opportunity which is passed up established regulated funds will be lapped up by hedge funds..So, it is clear that over a period of time, hedge funds will dominate

To conclude

What's the answer to the paradox that this book seeks to answer ?

"Simpler financial instruments and less leverage make up a painfully obvious prescription for fixing the design of our markets. These modifications will lead to a financial marketplace that will be apparently less finely tuned and less responsive to investor needs. But, like the coarse response mechanism of the cockroach, when faced with the inevitable march of the events that we cant even contemplate, simpler financial instruments and less leverage will create a market that is more robust and survivable"

Overall fantastic book and a wonderfully spent 24 hours.

Link : A Demon Of Our Own Design : Summary -Part I

A Demon Of Our Own Design : Summary -Part I

If one looks around, there are a tons of innovations happening in the financial sector.

• From the instruments side, the world has graduated from simple plain vanilla options to exotic derivatives, CDO's on CDO , an alphabet soup of all mortgage derivative products. Ideally all these should help in better management in risk, right ?
• There are more players in the markets, different types of players, hedgers, arbitrageurs, speculators, market makers, hedge fund managers, prop desk traders etc. These players should be smart enough in making the market more stable ???
• Tons of legality around stock markets and asset trading, new rules to shorting etc,
• Infrastructure to support trading has grown by leaps and bounds. On your vacation to hawai, lying on a beach, you can execute a complex derivatives trade with a click ,
• Price transparency wise , again a big yes
• More crisis, More boom bust cycles.

All the above should have made the investors more wise and made the markets efficient, less volatile. However we are seeing the markets becoming more volatile, more manias , more structurally inefficient products being traded,..Does it sound like an irony ? Well, this book sets out to explain WHY ? The author has been a trader in MorganStanley , Smith Barney, LTCM, and has worked on a host of significant roles that his view can be understood not from a passive spectator narration of somebody but from a perspective of a player in the market. This means that it will be more - matter of fact - rather than color it from journalist flavor.

Today morning, for some reason I could not concentrate on my work at all . Spoke to Kiran for sometime and it helped me a bit to get over my mood, and then figured out that I need to get DRUNK to restore my sanity. In my case, getting drunk is equivalent to reading a superb book from my inventory . I had this book in my inventory for quite some time and felt that the best way to get over my bad mood was to read this wonderful account of " Demons of our own design". Let me try to give a summary of the book , chapter by chapter. Each chapter is a gem.

DEMONS OF 87:

This chapter took me through the 1987 oct 19 S&P Crash by 20%. There are some unique reasons which the author quotes for explaining the crash. One word that is synonymous with the crash is "Portfolio Insurance". This was a strategy to hedge against a market crash by shorting S&P Futures. All was rosy until the markets actually begins to take a beating. Ideally since the portfolio is hedged, people shouldn't have lost money. However this strategy is a lame strategy if every portfolio manager has the same game plan to bail out . Yes that's what happened .Market became ILLIQUID. Well, this part I was anyway aware. However new stuff that I leant from this chapter is the role of Cash Futures Arbitrageurs. When there was a huge sell of S&P Futures, and combined with a fall of stock market, there was a great spread between cash market and stock futures market. Thus a different breed of players started to take the market in a precipice. Traders longed stock futures and shorted the stocks thus aggravating the already free fall of S&P, free fall of S&P Futures and the immense amount of hedging that happens for portfolio insurance products at the money. Note that gamma for at the money option is max. This means the more market putters around the floor of the portfolio loss set, the more managers with delta hedge and it becomes more expensive to delta hedge than have a losing trade..Yes, that's what happened according to the book. It was expensive to delta hedge and cheaper to hold on to losing position...WOW!! What sort of market that would have been ?

Biggest take away from this chapter is the fact that - Time frame in which futures market operates is VERY different from time frame in which spot markets operate. Hence the crisis in the futures markets will take time to get communicated in the stock markets and this time delay aggravates the crisis even more as other arbitrageurs, buy side folks, market makers try to have their own strategies and at the end , manage to screw up the entire market.

New Sheriff in the Town

This chapter talks about the rise of fixed income trading and instruments in 1980's. Cites a few major disasters that occurred in the fixed income world. Orange Country, Citron Fund which went long on short term interest rates and long on long term interest rates:) . But inverted yield curve killed the fund and it was saddled with 1.7 Billion Loss .
The next account in this chapter is about Nick Leeson who single handedly brought Barings bank down , by entering in to a straddle on Nikkei 225. Straddle is great if the market stays put. However it is a loss making position if the market moves a lot. Essentially it has negative gamma. And yes Nikkei moved like crazy and Leeson who was showing abnormal profits from account 88888 went bust.
Another trader who brought Kidder (of Kidder Peabody) down was a harvard grad , Joe Jett who used an accounting loop hole in STRIPS sales and showed profit until Kidder couldn't digest the losses.
The author also mentions the rise of index-amortizing swaps, use of APL language in Morgan stanley and its limitations about not being able to perform fast Montecarlo valuations, make a good read. The chapter ends with the most important question -

If WallStreet is swarmed with so many risk managers, why is there a crisis ? The problem is risk managers look for what is already present and measure it like crazy..However risk management is about what one cannot see. It is about BlackSwans ...Alas!! World like to believe in " What I see is What I believe" philosophy .

Salomon Rolls Dice

This chapter takes the reader through the build up of bond trading infra in Salomon. The only takeaway from this chapter is the MCI/BT merger trade which Salomon lost. In any merger obviously prices converge. If I hold a long short portfolio then if the merger happens, I make money. The only thing that matters is the probability of the merger. In Salomon's case, it got this bet hugely wrong. In the end Traveler's group and Salomon merged in 1997 and the next chapter in this book talks about how Salomon was killed after the purchase

They bought Salomon, then they Killed it

The highlight of this chapter is the way it describes the tracking error on yield curve trades which killed the US arb division in the merged entity. Thus Salomon props teams operations were shut down and the traders were politely asked to leave. One of the great takeaways from this chapter is the mention of liquidity and its associated problems. The essential trait of investment portfolio- ability to liquidate is the very root of crisis most of the times. How do models take care of Liquidity risk ? I have no clue. By putting in a friction rate, is the liquidity risk captured ?

LTCM - Lesson in Leverage

Well, lot of folks know about LTCM. I know a little bit about it from my previous readings. What does this book offer me new ? That was approach I took before reading this chapter.Well, at the end of it, I didn't get anything new from this narration

Complexity, Tight Coupling and Normal Accidents

This chapter is about complexity and the way finance with all the myriad interactions, is made ineffective by more controls. More controls need not necessary make the markets less prone to disasters.2 classic examples from the engineering world are discussed, Three Mile Island case and ValueJet case which are tight coupled systems where myriad controls did not help avert the disaster. A great analogy in this chapter is that of postal system where the system is tightly coupled but not interactively complex. This means that a single failure in communication is not catastrophic. It is a fairly less interactive system, meaning , the failure or errors are not fed in to the system and they don't aggregate over time. University with department is another example of system with less interactive complexity.However , it is not the case with Financial markets. We are dealing with interactively complex , tight coupled systems where errors, crisis effects the markets in a non linear way. Extra controls are going to add more variables to the system and make it more error prone than less.

I will summarize the next part of the book which is based on Hedge funds in another post.