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Personal
Biography
I was
born in New Delhi. I have a
Bachelor's degree in Computer Science and Engineering from the Indian Institute of Technology, Delhi (1991) and a
Masters in Finance from Carnegie
Mellon University, Pittsburgh (1997)). In
between the two degrees I worked in Tokyo with Banque Indosuez, Credit Suisse
First Boston, Merrill Lynch and Deutsche Bank.
After
completing my masters degree, I went back to Deutsche Bank, Tokyo as a
Vice
President in the Alternative Investment Strategies group. While at Deutsche
Bank, I rubbed shoulders with superstars such as Craig Dibble, Richard Dunne, Jim Ortiz, Jeff Sams.
In
1999, I moved to New York city with Harborview
Trading Associates, which is a market neutral equities hedge fund. I worked
with Tom Reynolds, who was one of the youngest specialists in his younger
days. In 2000, two important things happened - I married Naina, and I joined Bdellium
Asset Management in a senior role.
In 2001, I moved to Singapore, within the
firm.
In
2004, I started trading for Victor Niederhoffer’s firm. I live and work
in Connecticut, and it is a
great privilege to be work for a legend like him. Six of his former employees
have gone on to achieve greatness, Monroe Trout, Toby
Crabel and Roy Niderhoffer among them.
My
main professional activity is using statistics to study the stock market. My
favourite strategies involve short term trading in the SP500
index futures.
Links
to home pages of some of my family
Stocks Market & Finance
Victor
Niedehoffer's Books and Website.
Victor
Niederhoffer is one of the giants of our time, a figure who has excelled in
many unrelated spheres. He was the US National Squash
chamopin for 5 years and was also the World Champion. He holds a Ph.D. from
the University of Chicago and was a
professor at Berkely. And he is one of the greatest traders in
the world. He is the author of the two best books on trading ever
written, The
Education of a Speculator and Practical
Speculation (with Laurel Kenner). This is a link to their website, dailyspeculations.com
Trading and the US Stock Market
Earnings
Surprise and Estimate Revision.
Zacks is
the most commonly used source for earnings surprise information.
Chicago Analytics have an earnings surprise
model, which is a little expensive. A newer more reasonably priced Earnings
Surprise/ Analyst Revision service is Starmine.com
.
Mathematical
Finance
These
days all finance and economic analyses use Mathematics, so the term
"Mathematical Finance"
is
heavily over used.
The
use of quantitative techniques in finance has led to the creation of
Computational Finance or Mathematical Finance. Much of the efforts have
focused on the applications of Stochastic Calculus to areas of
designing interest rate models and pricing and hedging financial
derivatives.
Robert Merton
and Myron Scholes are
famous names in Computational Finance and were awarded the Nobel Prize in
Economics in 1997. (Kenneth Arrow
and Gerard Debreau
are other famous mathematicians who made important contributions to
mathematical finance and who were awarded the Nobel Prize in Economics)
There
are many good scientists tackling this field, but the one who
taught me the little mathematical finance I know is Dr. Steve Shreve
of Carnegie Mellon University. His yellow book
is the Bible of Stochastic Calculus. He, and my father, are the two most thorough
teachers I have had the privilege of studying under. I have never encountered
a mathematical problem which has escaped solution at the hands of either of
them. Two recently launched journals are Journal of Computational Finance and
Mathematical
Finance.
Risk Magazine is a popular magazine
oriented towards the "practitioners". It has good articles, but
many people in the largest investment banks subscribe to it in order to read
the last page which describes who has changed jobs from which firm to which
other firm. As investment banks are always hiring and firing, this page is
never dull.
Here are
two more webpages of my former teachers at Carnegie Mellon that have
interesting and useful materials.
Fallaw Sowell's Home Page
Sanjay Srivastava's home page
I am
listing some links on Mathematical Finance thanks to my friend Hideki
Murakami, who works with Merrill Lynch Japan and trades
exotics options. In the mid 90's, when both Murakami san and I were
bachelors, we used to meet every Sunday in his office to study papers in term
structure modelling. I am very grateful to him for sharing his knowledge and
library with me.
Here
is a set of less well known, but very useful, links on Mathematical Finance
courtesy of Hideki Murakami
http://www.warwick.ac.uk/statsdept/Staff/JEK/
LCG Roger's page
http://www.math.nyu.edu/research/carrp/papers/pdf/index.html
http://www.finasto.uni-bonn.de/papers/
http://www.rebonato.com/
http://www.scicom.uwaterloo.ca/~paforsyt/
http://www.science-finace.fr/publications.html
http://www.business.uts.edu.au/finance/qfr/respapers.html
http://www.stern.nyu.edu/~aradhakr/
http://www.stats.ox.ac.uk/~sen/
http://www.fisc-ny.com/Research/finance.htm
http://www.risklab.ch/Papers
http://www.math.uni-mannheim.de/~schroder/
http://www.sam.sdu.dk/~krm/krm_pub.htm
http://www.wystup.com/papers.html
http://www.wystup.com/colloquium/index.html
http://www.mathfinance.de/unilinks.html#banks
http://www.rotman.utoronto.ca/finance/papers/index.htm
Stochastic
calculus for finance (Oxford University)
lhttp://www.stats.ox.ac.uk/~etheridg/finmath/2yprobn.pdf
http://www.eur.nl/few/people/pelsser
rhttp://www.wias-berlin.de/priate/schoenma
http://www.jaschke-net.de/papers/
http://www.phy.cuhk.edu.hk/~cflo/finance.html
http://www.socs.waseda.ac.jp/or-finance/old.html
http://www.csie.ntu.edu.tw/~lyuu
http://www.mit.edu/people/jnt/publ.htmlhttp://img.hkkk.fi/efa99/papers/index2.html
http://www.ifi.uio.no/~skavhaug/Options/http://binky.enpc.fr/~bl/publications.html
http://www.contrib.andrew.cmu.edu/~vecer/
http://www.iro.umontreal.ca/~lecuyer/papers.html
http://www.resarch.ibm.com/people/b/berger/papers.html
http://www.mysunrise.ch/users//marc.henrard/publications/publi.eng.html
http://web.tiscali.it/damianohome/
http://www.mathematik.uni-ulm.de/~rossberg/index_neu.html
http://www.ieor.columbia.edu/~kou/
http://hammer.prohosting.com/~hrb/stu_index.shtml
http://www.cwi.nl/~jiri/http://www.rondvari.com/
http://cba.162.bus.utk.edu/
http://www.maths.strath.ac.uk/~aas96106/
http://www.xplore-stat.de/ebooks/ebooks.html
http://www.lu.unisi.ch/assistenti_eco/trojanif/homepage/
http://math.bu.edu/people/murad/
http://phobos.ge.infm.it/~ecph/Index.html
http://www.math.unipd.it/~vargiolu/ricerca.html
http://mayet.som.yale.edu/~amj23/papers.htm
http://www.whu-koblenz.de/banking/sgf.htm
http://l3www.cern.ch/homepages/susinnog/finance/Welcome.html
http://jpmorgan.com/businesses/deres/index.html
http://www.math.duke.edu/education/ccp/index.html
http://pluto.mscc.huji.ac.il/~mswiener/research/research.htm
http://www.tinbergen.nl/home.html
http://www.few.eur.nl/few/people/pelsser/publications.htm
http://www.ires.ucl.ac.be/csssp/home_pa_pers/anderson/courses299.html
http://www.act.ku.dk/~schmidli/#publi
http://www.econ.upf.es/~kohatsu/
http://www.inria.fr/recherche/equipes/mathfi.fr.html
http:/www.ismacentre.co.uk/
http://www.ntu.edu.sg/home/ayxyan/index.htm
Behavioral
Finance
Much
of finance assumes that investors will act "rationally" - but the
way investors act is not the way financial economists expect "rational
investors" to act. Behavioral Finance deals with the influence of human
psychology on the behavior of financial practitioners.
Two
pioneering economists, Daniel Kahneman
and the late Amos
Tversky put Behavioral Finance on a high pedestal. Before gaining fame as
financial economists, they worked in the Israeli airforce and were
investigating of what best motivated ace fighter pilots. From that work came
prospect theory. Their book "Judgement under Uncertainty: Heuristics and
Biases" is well known. Richard
Thaler is another well known figure in this area. His best known books
are Winner's
Curse , Quasi
Rational Economics; and Advances
in Behavioral Finance. I have included a Bibliography on Behavioral
Finance at the end of this section.
Folklore
has it that stock markets gyrate because of "fear and greed", it is
modern cliché. Behavioral Finance is not just about "Fear and
Greed" , it is about Hope and Greed. or a good
introduction, read Hersh Shefrin's book- Beyond
Greed and Fear. In his book, Shefrin breaks the field of Behavioral
Finance into three broad themes.
1. Heuristic Driven Bias. Finance
practitioners use rules of thumb or heuristics to process data. For example,
people use past performance as the best predictor for future performance and
often invest in the mutual funds with the best five year track records. These
rules are likely to be faulty and lead to poor decisions. Relying on such
heuristics is called Heuristic Bias. Some different types of heuristic driven
biases:
Representativeness. This type of
bias arises when people use stereotypes to help their decision making. This
bias is very evident in finance. First proposed by Kahneman and Tversky in
1972, it is analysed in detail in a series of papers in Judgement
Under Uncertainty: Heuristics and Biases edited by Daniel Kahneman,
Paul
Slovic and Amos Tversky. An example from the field of investments will be
illustrative. Werner
De Bondt and Richard Thaler found that stocks that have done extremely
poorly in the previous three years do better than the extreme best performers
in the same preceding three years. De Bondt also shows that long term
earnings forecasts made by security analysts are biased in the direction of
recent success - analysts over-react by being too optimistic about companies
doing well in the recent times and by being overly pessimistic about
companies that have disappointed recently.
The same
effect tends people to confuse the notion of "regression to the
mean". When a bull market has been running on for a long time, it
is common to hear financial gurus talking about next years returns
being very poor because "markets regress to the mean".
Regression to the mean does suggest that future returns might be closer to
their historical average than they have been recently, but it does not mean
future returns will tend to be lower than their historical average.
Gambler's
Fallacy or the "Law of Small Numbers"be If 8 tosses of
a fair coin all turn out to be heads, the probability that the nest toss will
be heads is still 0.50. (Provided the coin is fair.) But many people have a
notion that in coin tossings, there should be a roughly even number of heads
and tails; if there are heads in a row, then a tail is overdue. Again, this
is a case of representative bias. The law of large numbers when applied to a
small sample will produce such a bias.
Anchoring
and Over-confidence Security
analysts tend not to revise their estimates enough to reflect new
information. This is why performs companies that have positive earnings
surprise in one quarter tend to repeat the surprise in following
quarters.
Subjects
in an experiment were asked to guess the weight of an air
plane; they were also asked to guess a range between which they
felt 90 percent confident that the weight of the air plane would lie.
Sounds simple - guess a very wide range and you would hope to be right.
Yet the high estimate of most people in this experiment is too low. Even
though nothing stops anyone from giving a very high number for the high end -
say a billion trillion tonnes - most people are too overconfident and
therefore their high guess is too low
In
David Dreman's book "Contrarian Investment Strategies", there is a
section on how the difficulty of making accurate predictions, not ust in
finance but in all walks of life. There are a number of highly interesting
examples of famous people making predictions that turned out to be way off
the mark. I will reproduce some of those examples.
In
August 1941, Captain William T.Pulleston, the former chief of U.S. Naval
Intelligence, stated, “The Hawaiian Islands are
over-protected; the entire Japanese Fleet and Air Force could not seriously
threaten Oahu.” Said Secretary of the Navy Frank Knox on December 4, 1941, “No matter what
happens the U.S. Navy is not going
to be caught napping.” Three days later the officer in charge of radar
at Pearl Harbor was told by a
subordinate that a radar signal indicated at least 50 planes, possibly far
more, were approaching Oahu at almost 180
miles an hour. His reply, “Well don’t worry about it … it’s nothing.”
John
Foster Dulles, the Secretary of State who was influential in shaping American
postwar foreign policy, said in 1941, “Only hysteria entertains the idea that
… Japan contemplates war
upon us.”
The
London Critic wrote in
1855, “Walt Whitman is as unacquainted with art as a hog is with
mathematics.” But U.S. reviewers gave
the Brits as good as they received. “I’m sorry Mr.Kipling, but you just
don’t know how to use the English language,” wrote the editor of the San Francisco Examiner in 1889,
informing Kipling that he should not send in further articles. In
rejecting the thriller The Day of the Jackal in April 1970, a publisher wrote
author Frederick Forsyth, “ (Your) book has no reader interest.” By
1983, uninterested readers had bought 8 million copies. Another expert
observed, “Gone With the Wind is going to be the biggest flop in Hollywood history.
I’m glad it will be Clark Gable falling flat on his face and not Gary
Cooper.” The observer – Gary Cooper. Marilyn Monroe was told
early in her career, “You’d better learn secretarial work, or else get
married.” A Universal Studio executive dismissed two actors at the same
meeting, telling the first, “You have no talent,” and the second, “You
have a chip on your tooth, your Adam’s apple sticks out too far, and you talk
too slow.” The first actor was Burt Reynolds and the second was Clint
Eastwood, the movies’ two biggest box-office draws in the 1970s.
The
manager of the Grand Ole Opera told one young singer, “You ain’t going
nowhere … son. You ought to go back to drivin’ a truck.” The
singer was Elvis Presley. “We don’t like their sound. Groups of
guitars are on their way out,” said a Decca Recording Company executive
in 1962 in turning down the Beatles. “The biggest no-talent I ever
worked with,” said a senior executive in firing Buddy Holly from the
Decca label in 1956.
Emile
Zola, one of the great French novelists of his day and a leading defender of
Impressionist artists, saidn in 1900 of Paul Cezanne, “Paul may have had the
genius of a great painter, but he never had the persistence to become
one.” Cezanne’s best works regularly fetch $10 million to $20
million. Edouard Manet, one of the earliest Impressionists, said to
Claude Monet of Pierre-Auguste Renoir, “He has no talent at all, that boy …
Tell him to please give u painting.” Renoir is one of the acknowledged masters
of Impressionism, whose masterpieces have sold above $60 million. A
well-known American art critic said of Picasso in 1934: “(Picasso’s)
prestige is rapidly waning and the custodians of his fame and his pictures
are fighting a losing battle to elevate him to a position among the
immortals.” Picasso painted many of his important works in the next
forty years.
A
parliamentary commission in Great Britain set up to investigate the value of
the incandescent lightbulb concluded in 1878 that “(Edison’s idea are) good
enough for our transatlantic friends … but unworthy of the attention of
practical or scientific men.”
Alexander
Graham Bell patented the telephone in 1876 and tried to sell it to Western
Union, but the company was not interested. Lord Kelvin, on eof the
preeminent British scientists of the nineteenth century said that “Radio has
no future.”
A
potential initial investor in the Ford Motor Company was told by his banker,
“The horse is here to stay, but the automobile is only a novelty – a fad.”
The investor bought $5,000 worth of Ford stock anyway and sold his shares
several years later for #12.5 million. The editor of the London Daily
Express, when told in 1922 that the inventor of television wanted to see him,
said, “For God’s sake go down to reception and get rid of the lunatic who’s
down there. He says he’s got a machine for seeing by wireless!
Watch him – he may have a razor on him.”
Newer
technology seems to have been greeted no better. Thomas J.Watson, the
founder of IBM, said in 1943, “I think there is a world market for about five
computers.” Ken Olson, the founder of Digital Equipment, stated in 1977
just before the PC revolution began, “There is no reason for any individual
to have a computer in their home.”
2. Frame Dependence When Nobel
Laureate Merton Miller was asked to describe in twenty five words or less,
his contributions to finance, he said - "If you transfer a dollar
from your left pocket to the right, you are no wealthier. I and France
(Modigliani) proved that rigorously."
The
manner in which a problem is stated or represented is called its frame. Frame
independence means that the manner in which a decision theoretic problem is
framed is irrelevant; traditional finance assumes framing is transparent. Or,
practitioners can see through all different ways cash flows might be
described. In reality some frames may be opaque.
Here are
the different ways Frame Dependence may be relevant. Here is a textbook
example which was created by Kevin McKean in an article on Kahneman and
Tversky in Discover magazine. It is reproduced in many books and articles,
one example being the book Why
Smart People Make Big Money Mistakes and How to Avoid Them by Gary Belsky
and Thomas Gillovich.
"Imagine
you are the commander in the army threatened by a superior force. Your staff
says your soldiers will be caught in an ambush in which six hundred of them
will die unless you lead them to safety by one of two available routes. If
you take route A, two hundred soldiers will be saved. If you take route B,
there is a one third chance that six hundred soldiers will be saved and a two
thirds chance that none will be saved. Which route should you take?
Imagine
that you are once again a commander in the army, threatened by a superior
force. Once again, your staff tells you that if you take route A, four
hundred soldiers will die. If you take route B, there is a one third chance
that no soldiers will die and a two thirds chance that six hundred soldiers
will perish. Which route do you choose?"
Research
by Kahneman and Tversky showed that most people would choose route A in
the first scenario because you would save two hundred lives, but the same
people end up choosing route B in scenario B because there is a one third
chance no lives are lost. The scenarios have the same end result in each
option - but the two scenarios are framed differently. In one, the emphasis on
how many lives are saved and the respondents want to be cautious and save as
many lives as possible. In the second case, the emphasis is on how many lives
are lost and most people try to gamble or be adventurous to avoid the certain
death of four hundred.
Older
investors specially retired people who might finance their living from their
investment portfolio often worry about spending their capital too quickly, or
outliving their wealth. They are afraid of having too little self
control and spending their money too quickly, and therefore sometimes will
not sell stocks but will happily spend dividends.
This is because stocks are framed as capital and dividends are framed.
Loss Aversion and Prospect Theory: Kahneman and
Tversky created a new field of study called Prospect Theory which a
description of the way people make decisions in the presence of uncertainty
and risk. If you are given a choice between (a) accepting a pure loss of $75
and (b) a gamble where there is a 25% chance of losing nothing but a 75%
chance of losing $100, what would you choose? The expected loss - $75 - is
the same in each case, but most people choose (b). Most people hate to lose,
and under option (b) there is a chance of escaping loss altogether, even
though there is a bigger chance of losing a bigger amount. Kahneman and
Tversky term this as loss Aversion- they found a loss has about two and a
half times the impact of a gain of similar amount.
Most
people stubbornly hold on to loss making stocks in the hope of exiting when they
break even. Companies that have invested a large amount of money in a project
which has not made any money and has poor prospects, will still continue to
throw more money at it, because they also suffer from the "get
even" disease - in reality they are throwing good money after bad. This
is also termed as the Sunk Cost Fallacy.
Mental
Accounting. People have a tendency to treat different cash flows
differently depending on the source of the cash flow. A lot of people would
not gamble with "hard earned money", but if they bet bet 5 dollars
and win 10,000 thousand dollars with it, they might be less averse to
gambling with all 10,000 dollars. Money is money, but many people would not
mind betting or losing money that was won this way. While traditional
finance suggests people should not distinguish between dollars in different
pockets, in reality people do make the distinction. Here is another
illustration.
Scenario
A. Imagine you have purchased a ticket to a theatre. On reaching the theatre
you find that the ticket is lost and that it costs a hundred dollars to buy
another ticket. Would you buy another ticket or go home?
Scenario
B. You arrive at the theatre and queue up to buy the ticket when you realize
you have lost 100 dollars somewhere. Would you still buy the ticket or go
home? (assuming of course that your wealth is much more than 100 dollars and
that you have cash or credit cards readily available)
It
turns out that several people would go home in scenario A but the same people
would pull out another 100 dollars in scenario B. In reality the outcomes are
identical - you have lost 100 dollars and if you want to see the theatre you
need to pay another 100 dollars. But people often have "mental
accounts" - in this case a mental account for entertainment, for
which they may be willing to spend 100 but not 200 dollars.
Similarly,
one could add a third scenario to the two above- you own a hundred
shares of Microsoft which is down 100 dollars today, and will your answer
change now?
Tolerance
for Risk ( material to be typed)
Regret
Avoidance. Regret is a negative emotion arising from taking a decision
which turned out to be not so good. People who sell a stock just before it
sky rockets often experience regret. Example.
Scenario
A. You own 1000 shares of microsoft and they tumble in price by 30%.
Scenario
B. You own 1000 shares of microsoft which you sell, and with the proceeds,
buy some shares of Intel, which tumble 30%.
In which
scenario are you more unhappy? Even though the net result is the same (loss
of 30%) more people would feel worse in scenario B, as they ended up taking
an active decision that lost them money - they feel responsible for the loss
in scenario B but less so in scenario A. Most people want to avoid the pain
of regret and also do not want to own up to the fact that they are
responsible for their own losses. Hence, they often avoid taking decisions
altogether which might cause regret.
Endowment Effect and Status Quo Bias
"A
Wine loving economist purchased some nice Bordeaux wines years ago at low
prices. The wines have greatly appreciated in value, so that a bottle that
cost less than $10 when purchased would now fetch $200 at an auction. This
economist now drinks some of this wine occasionally but would neither be
willing to sell the wine at the auction price nor buy an additional bottle at
this price."
-
Excerpted from Chapter 6 of The Winner's Curse by Richard Thaler, page 63.
This
pattern- the fact that people demand much more to give up an object than they
would be willing to pay to acquire it is called the endowment effect or
status quo bias.
Money Illusion a Even
though most people now now how to adjust for inflation when comparing
dollar amount across different time periods, most people still find it
natural to think in nominal rather than real money terms. This is termed as
Money Illusion
3.
Market Inefficiency. Traditional finance assumes that
markets are efficient, that except for brief periods of time, prices of
securities will reflect fundamental values, even if the practitioners suffer
from Heuristic Biases and Frame Dependence. Behavioral Finance does not
suffer from such unrealistic assumptions. In view of the interesting nature
of this subject, I will devote a separate section of this website to this
topic.
A Selected
Bibliography on Behavioral Finance.
1. Abarbanell,
Jeffrey & Victor Bernard 1992 "Tests of Analysis'
Overreaction/Underreaction to Earnings Information as an Explanation for
Anomalous Stock Price Behavior." Journal of Finance 47, no. 3: 1181-1208
2. Amir, Eli & Yoav
Ganzach 1998 "Overreaction and Underreaction in Analysts'
Forecasts." Journal of Economic Behavior & Organization 37: 333-347
3. Asquith, Paul 1983
"Merger Bids, Uncertainty, and Stockholder Returns." Journal of
Financial Economics 11: 51-83
4. Ball, Ray, and S.P.
Kothari 1989 "Non-Stationary Expected Returns: Implications for
Tests of Market Efficiency and Serial Correlation in Returns." Journal
of Financial Economics 25:51-74
5. Barber, Brad, Reuven
Lehavy, Maureen McNichols, and Brett Trueman 1998 "Can Investors Profit
from the Prophets? Consensus Analyst Recommendations and Stock
Returns." Working paper, University of California, Berkeley
6. Barber, Brad, and
Terrance Odean 1998a "Boys Will Be Boys: Gender, Over-confidence,
and Common Stock Investment." Working paper, University of
California, Davis
7. Barber, Brad, and
Terrance Odean 1998b "The Common Stock Investment Performance of
Individual Invesstors." Working paper, University of California,
Davis
8. Barberis, N.A.
Shleifer, and R. Vishny 1997 "A Model of Investor Sentiment."
Journal of Financial Economics 49, no 3: 307-344
9. Basu, S 1983
"The Relationship Between Earnings Yield, Market Value, and Return for
NYSE Common Stocks: Further Evidence:" Journal of Financial Economics
12: 129-156
10. Benartzi, Shlomo,
and Richard Thaler 1995 "Myopic Loss Aversion and the Equity Premium
Puzzle." Quarterly Journal of Economics 110, no: 1:73-92
11. Benartzi, Shlomo,
and Richard Thaler 1998 "Illusionary Diversification and Its
Implications for the U.S and Chilean Retirement Systems." Working paper,
University of California, Los Angeles
12. Benartzi, Shlomo,
and Richard Thaler 1999 "Risk Aversion or Myopia? Choices in
Repeated Gambles and Retirement Investments." Mangement
Science, forthcoming
13. Bernard, Victor
1993 "Stock Price Reactions to Earnings Announcements: A Summary of
Recent Anonalous Evidence and Possible Explanations. In Advances in
Behavioral Finance, edited by Richard H.Thaler, 303-340. New
York: Russell Sage Foundation.
14. Bernard, Victor,
and Jacob Thomas 1989 "Post-Earnings-Announcement Drift: Delayed
Price Response or Risk Premium?" Journal of Accounting Research 27:1-36
15. Bernard, Victor and
Jacob Thomas 1990 "Evidence That Stock Prices Do Not Fully Reflect the
Implications of Current Earnings for Future Earnings." Journal of
Accounting and Economics 13:305-340
16. Black, Fischer 1993
"Noise." In Advances in Behavioral Finance, edited by Richard H Thaler,
3-22. New York: Russell Sage Foundation
17. Black, Fischer, and
Myron Scholes 1973 "The Pricing of Options and Corporate
Liabilities." Journal of Political Economy 81 (May-June):637-659
18 Bodurtha, James N
Jr., Dong-Soon Kim, and Charles M.C.Lee 1995 "Closed End Country Funds
and U.S. Market Sentiment," Review of Financial Studies 8, no.3:879-918
19 Bowen, John J. Jr,
and Meir Statman 1997 "Performance Games." Financial Analysts
Journal 23, no.2:8-15
20. Campbell, John Y.,
Andrew W.Lo and A.Craig Mackinlay 1997 The Econometrics of Financial
Markets. Princeton, N.J.: Princeton University Press
21. Capmbell, J.Y., and
R.J.Shiller 1988 "Stock Prices, Earnings, and Expected Dividends."
Journal of Finance 43, no. 3:661-676
22. Canner, Niko,
N.Gregory Mankiw, and David N.Weil 1997 "An Asset Allocation
Puzzle." American Economic Review 87, no.1:181-191
23. Canina, Linda, and
Stephen Figlewski 1993 "The Informational Content of Implied
Volatility." Review of Financial Studies 6, no.3:659-681
24. Chopra, Navin,
Josef Lakonishok, and Jay Ritter 1993 "Measuring Abnormal
Performance: Do Stocks Overreact?" In Advances in Behavioral
Finance, edited by Richard H. Thaler, 265-302. New York: Russell
Sage Foundation.
25. Chopra, Navin,
Charles M.C.Lee, Andrei Shleifer, and Richard H. Thaler. 1993 "Yes,
Discounts on Closed-End Funds Are a Sentiment Index." Journal of Finance
48, no.2:801-808; and "Summing Up," 811-812
26. De Bondt, Werner
1989 "Stock Price Reversals and Overreaction to News Events: A
Survey of Theory and Evidence." In A Reappraisal of the Efficiency of
Financial Markets, edited by S.J.Taylor et al. New York: Springer
Verlag
27. De Bondt, Werner
1991 "What Do Economists Know About the Stock Market?" Journal of
Portfolio Management 17, no.2:84-91
28. De Bondt, Werner
1992 Earnings Forecasts and Share Price Reversals.
Charlottesville, Va: Research Foundation of the Institute of Chartered
Financial Analysis
29. De Bondt, Werner
1993 "Betting on Trendys: Intuitive Forecasts of Financial Risk
and Return." International Journal of Forecasting 9:355-371
30. De Bondt, Werner
1998 "A Portrait of the Individual Investor." European Economic
Review 42:831-844
31. De Bondt, Werner,
and Mary Bange 1992 "Inflation, Money Illusion, and Time Variation in
Term Premia." Journal of Financial and Quantitative Analysis 27, no.
4:479-496
32. De Bondt, Werner,
and Anil Makhija 1998 "Throwing Good Money After Bad? Nuclear
Power Plant Decisions and the Relevance of Sunk Costs. Journal of Economic
Behavior & Organization 10:173-199
33. De Bondt, Werner,
and Richard Thaler 1985 "Does the Stock Market Overreact?" Journal
of Finance 40:793-805
34. De Bondt, Werner,
and Richard Thaler 1987 "Further Evidence on Investor Overreaction and
Stock Market Seasonality." Journal of Finance 42:793-557-805
35. De Bondt, Werner,
and Richard Thaler 1989 "A Mean Reverting Walk Down Wall Street."
Journal of Economic Perspectives 3, no.1:189-202
36. De Bondt, Werner,
and Richard Thaler 1990 "Do Security Analysts Overreact?" American
Economic Review 80, no.252-57
37. De Bondt, Werner,
and Richard Thaler 1995 "Financial Decision Making in Markets and
Firms." In Finance, Series of Handbooks in Operations Research and
Management Science, edited by R.Jarrow, V. Maksimovic, and W.T. Ziemba,
Amsterdam: Elsevier-Science:385-410
38. Dreman, David
N. 1995 "Exploiting Behavioral Finance: Portfolio Strategy
and Construction." In Behavioral Finance and Decision Theory in
Investment Management, edited by Arnold S. Wood. Charlottesville, Va:
Association for Investsment Management and Research: 42-49
39. Dreman, David
N. 1998 Contrarian Investment Strategies: The Next
Generation: Beat the Market by Going Against the Crowd. New
York: Simon & Schuster
40. Dreman, David N.,
and Michael Berry 1995 "Analyst Forecasting Errors and Their
Implications for Security Analysts." Financial Analysts Journal 51,
no.3:30-41
41. Fama, Eugene 1970
"Efficient Capital Markets: A Review of Theory and Empirical
Work." Journal of Finance 25, no.2383-417
42. Fama, Eugene 1991
"Efficient Capital Markets: II." Journal of Finance 46,
no.5:1575-1618
43 Fama, Eugene 1998a
"Efficiency Survives the Attack of the Anomalies." GSB Chicago
(winter):14-16
44. Fama, Eugene 1998b
"Market-Efficiency, Long-Term Returns, and Behavioral Finance."
Journal of Financial Economics 49, no.3:283-306
45. Fama, Eugene 1992
"The Cross-Section of Expected Stock Returns." Journal of Finance
47:427-465
46. Fisher, Kenneth,
and Meir Statman 1997 "Investment Advice from Mutual Fund
Companies." Journal of Portfolio Management (fall): 9-25
47. Fisher, Kenneth,
and Meir Statman 1999a "A Behavioral Framework for Time
Diversification." Financial Analysts Journal, forthcoming
48. Gilovich, Thomas
R.and Victoria Husted-Medvec 1993 "The Experience of Regret: What, When,
and Why." Psychological Review 102, no.2:379-395
49. Gilovich, Thomas
R., Robert Vallone, and Amos Tversky 1995 "The Hot Hand in
Basketball: On the Misperception of Random Sequences." Cognitive
Psychology 17: 295-314
50. Goetzmann, William
N., and Roger G.Ibbotson 1994a "Do Winners Repeat?" Journal of
Portfolio Management (winter):9-18
51. Goetzmann, William
N., and Roger G.Ibbotson 1994b "Games Mutual Fund Companies Play:
Strategic Response to Investor Beliefs in the Mutual Fund Industry."
Working paper, Yale University, New Haven, Conn
52. Jegadeesh,
Narasimhan, and Sheridan Titman 1993 "Returns to Buying Winners and
Selling Losers: Implications for Stock Market Efficiency." Journal
of Finance 48:65-91
53. Kahneman, Daniel,
Jack Knetsch, and Richard Thaler 1991 "Fairness as a Constraint on
Profit Seeking: Entitlements in the Market." In Quasi-Rational
Economics, edited by Richard Thaler. New York: Russell Sage
Foundation: 199-219
54. Kahneman, Daniel,
and Mark W.Riepe 1998 "The Psychology of the Non-Professional
Investor." Journal of Portfolio Management 24, no.4:52-65
55. Kahneman, Daniel,
Paul Slovic, and Amos Tversky 1982 Judgement Under Uncertainty:
Heuristics and Biases. New York: Cambridge University Press
56. Kahneman, Daniel,
and Amos Tversky 1979 "Prospect Theory: An Analysis of Decision
Making Under Risk." Econometrica 47, no.2:263-291
57. Lakonishok, Josef,
Andrei Shleifer, Richard H.Thaler, and Robert Vishny 1991 "Window Dressing
by Pension Fund Managers." American Economic Review 81, no.2:227-231
58. Lakonishok, Josef,
Andrei Shleifer, and Robert Vishny 1992 "The Structure and Performance
of the Money Management Industry." Brookings Papers on Economic
Activity. Washington, D.C.:Brookings Instituion: 331-339
59. Lakonishok, Josef,
Andrei Shleifer, and Robert Vishny 1994 "Contrarian Investment,
Extrapolation, and Risk." Journal of Finance 49, no.5:1541-1578
60. Lakonishok, Josef,
and Seymour Smidt 1986a "Are Seasonal Anomalies Real? A Ninety-Year
Perspective." Review of Financial Studies 1, no. 4: 403-425
61. Lakonishok, Josef,
and Seymour Smidt 1986b "Capital Gain Taxation and Volume of
Trading." Journal of Finance 41:951-974
62. Odean, Terrace
1998a "Are Investors Reluctant to Realize Their Losses?" Journal of
Finance 53:1755-1798
63. Olsen, Robert 1998
"Behavioral Finance and Its Implications for Stock-Price
Volatility." Financial Analysts Journal 54, no 2: 10-18
64. O'Neill,
Barbara 1990 How Real People Handle Their Money. Newton,
N.J: Rutgers Cooperative Extension
65. Roll, Richard 1984
"Orange Juice and Weather." American Economic Review 74, no.
5:861-880
66. Shafir, Eldan,
Peter Diamond, and Amos Tversky 1997 "Money Illusion." Quarterly
Journal of Economics 112, no: 2:341-374
67. Shefrin, Hersh 1984
"Inferior Forecasters, Cycles, and the Efficient-Markets Hypothesis: A
Comment." Journal of Political Economy 92: 156-161
68. Shefrin, Hersh,
Meir Statman. 1984 "Explaining Investor Preference for Cash
Dividends." Journal of Financial Economics 13, no.2: 253-282
69. Shefrin, Hersh,
Meir Statman. 1985 "The Disposition to Sell Winners Too Early and Ride
Losers Too Long: Theory and Evidence." Journal of Finance 40:777-790
70. Shefrin, Hersh,
Meir Statman. 1986 "How Not to Make Money in the Stock Market."
Psychological Today, February, 52-57
71. Shefrin, Hersh,
Meir Statman. 1993a "Behavioral Aspects of the Design and Marketing of
Financial Products." Financial Management 22, no. 2:123-134
72. Shefrin, Hersh,
Meir Statman. 1993B "Ethics, Fairness and Efficiency in Financial
Markets." Financial Analysts Journal 49, no. 6: 21-29
73. Shefrin, Hersh,
Meir Statman. 1994 "Behavioral Capital Asset Pricing Theory."
Journal of Financial and Quantitative Analysis 29, no.3: 323:349
74. Shefrin, Hersh,
Meir Statman. 1995 "Making Sense of Beta, Size, and
Book-to-Market." Journal of Port-folio Management 21, no.2:26-34
75. Shefrin, Hersh,
Meir Statman. 1998 "Comparing Return Expectations with Realized
Returns." Working paper, Santa Clara University, Santa Clara,
Calif
76. Shefrin, Hersh,
Meir Statman. 1999 "Behavioral Portfolio Theory." Working
paper, Santa Clara University, Santa Clara, Calif
77. Shefrin, Hersh,
Meir Statman. 1988 "The Behavioral Life Cycle Hypothesis." Economic
Inquiry 24: 609-643
78 Shiller, Robert
1993a "Do Stock Prices Move Too Much to Be Justified by Subsequent
Changes in Dividends." In Advances in Behavioral Finance, edited by
Richard H. Thaler, 107-132. New York: Russell Sage Foundation
79 Shiller, Robert
1993b "Speculative Prices and Popular Models." In Advances in
Behavioral Finance, edited by Richard H. Thaler, 493-506. New
York: Russell Sage Foundation.
80 Shleifer, Andrei,
and Robert Vishny 1997 "The Limits of Arbitrage." Journal of Finance
52: 35-56
81. Statman, Meir 1987
"How Many Stocks Make a Diversified Portfolio?" Journal of
Financial and Quantitative Analysis 22, no.3: 353-364
82. Statman, Meir 1995a
"A Behavioral Framework for Dollar Cost Averaging." Journal of
Portfolio Management (fall): 70-78
83. Statman, Meir 1995b
"Behavioral Finance Versus Standard Finance." In Behavioral Finance
and Decision Theory in Investment Management, edited by Arnold S. Wood.
Charlottesville, Va: Association for Investsment Management and Research:
42-49
84. Thaler, Richard
1985 "Mental Accounting and Consumer Choice." Marketing Science 4,
no.3: 199-214
85. Thaler, Richard
1991 "Toward a Positive Theory of Consumer Choice." In
Quasi-Rational Economics, edited by Richard H. Thaler, 3-24 New York: Russell
Sage Foundationi
86. Thaler, Richard
1993a The Winner's Curse. New York: Russell Sage Foundation
87. Thaler, Richard
1993b Advances in Behavioral Finance. New York Russell Sage
Foundation
88. Thaler, Richard and
Eric Johnson 1991 "Gambling with the House Money and Trying to
Break Even: The Effects of Prior Outsomes on Risky Choice." In
Quasi-Rational Economics, edited by Richard H.Thaler, 48-73. New York Russell Sage Foundation
89. Thaler, Richard and
Hersh Shefrin 1981 "An Economic Theory of Self Control."
Journal of Political Economy 89, no.2:392-406
90. Tversky, Amos, and
Daniel Kahneman 1971 "Belief in the Law of Small Numbers."
Psychological Bulletin, 105-110
91. Tversky, Amos, and
Daniel Kahneman 1974 "Judgment Under Uncertainty: Heuristics and
Biases." Science (185): 1124-1131
92. Tversky, Amos, and
Daniel Kahneman 1986 "Rational Choice and the Framing of
Decisions." Journal of Business 59, no. 2: 251-278
93. Tversky, Amos, and
Daniel Kahneman 1992 "Advances in Prospect Theory: Cumulative
Representation of Uncertainty." Journal of Risk and Uncertainty
5:297-323
94. Wiggins, James
B. 1991 "Do Misperceptions About the Earnings Process Contribute
to Post-Earnings-Announcement Drift?" Working paper, Cornell University, Ithaca, N.Y.
95. Womack, Kent 1996 "Do Brokerage
Analysts' Recommendations Have Investment Value?" Journal of Finance 51,
no.1:137-168
Is the Stock
Market "Efficient"?
Whether
financial markets are efficient or not is a matter of religious debate
amongst academics and practitioners alike.
First
of all, what is Market Efficiency? Simply put, an efficient financial market
is one which security prices fully reflect all available information. If
markets are efficient, then security prices should not diverge much from
their fundamental value for significant periods of time, and secondly, in the
absence of any news or change in fundamentals, prices should be stable.
If the Efficient Market Hypothesis holds then stock prices should follow a
"random walk"
Market Efficiency
comes in three forms. The Weak Form of Market Efficiency states that it is
impossible to earn superior risk adjusted returns in the long run based in
the knowledge of only past prices of securities.
The Semi-Strong Form
of Market Efficiency states that it is impossible to earn
superior risk-adjusted returns by using any publicly available
information. As soon information becomes public, it is immediately reflected
in the prices and therefore an investor cannot
hope to
earn supperior returns in the future by using his information. The
semi-strong form of market efficiency does not rule out the possibility that
an investor may may earn abnormal risk-adjusted profits by trading on
information that is not yet known to market particiapants. That is, it does
not rule out the possibility of earning risk-adjusted superior returns by the
use of insider information.
The Strong Form of
Market Efficiency states that it is impossible to eanr risk
adjusted superior profits even by the use of insider information becaue this
information quickly leaks out and gets reflected in the prices.. Most
supporters of the Efficent Market Hypothesis (henceforth EMH) have focused on
proving the Weak and the Semi-Strong forms of the EMH. The high priest of the
EMH is Eugene
Fama at the University of Chicago. Chief amongst
the hundreds of noted economists who uphold the EMH are Kenneth French,
Michael Jensen,
. The list of those arguing the other side has equally distinguished
names- Andrew Lo, Richard Thaler,
Daniel Kahneman and Amos Tversky, Robert Schiller, John Campbell,
Richard Roll,Andrei Schleifer, Shefrin and Werner De Bondt, Robert Vishny,
Josef Lakonishok, Narasimhan
Jegadeesh and Robert Haugen.
Read
Burton Malkiels's A
Random Walk Down Wall Street, and then read Andrew Lo and Archie C.
Mackinlay's book "A
Non Random Walk Down Wall Street" to gain an insights into both
sides of the debate. Robert Haugen has three books, The
New Finance : The Case Against Efficient Markets, Beast
on Wall Street and The
Inefficient Stock Market: What Pays Off and Why. David
Dreman is a well known and successful "Contrarian" money
manager whose books argue against market efficiency.
The
entire section on Behavioral
Finance in this website has references relevant to market
efficiency/inefficiency debate.
Here
are some illustrations, examples and anamolies that weaken the case of
Market Efficiency
Calendar
Effects
Many
sources have identified specific times of the year when the stock market
appears to have an upside bias. "The Santa Claus Rally" around the
Christmas holidays, and the "January Effect" at the beginning of
the year have received much attention in the financial press.
Inclusion
in Major Index: according to the EMH, inclusion in th s&P500 index
should not be accompanied by significant price increases - however 5 to 10%
jumps in Stock price following the announcement of inclusion in the S&P
500 are common place.
Small
Firm effect
Investor
Preference for Dividends Richard Thaler in a study co-authored
with Michaely and Womack studied the price action of stocks following a
dividend cut or increase in the period 1964 to 1988. In the year following
the dividend cut, on average a stock underperformed the stock market by 11%
and by 15.3% in the 3 year period. On the other hand for, stocks with the
dividend increases, the numbers were an outperformance of 7.5% and
outperformance of 24.8% respectively. The failure of markets to adjust
quickly to information dents the EMH.
Post Earnings
Announcement Drift. There have been several articles and
papers describing the phenomenon that the returns to holding companies which
had a positive earnings surprise tended to persist for the following three
quarters. Stocks were be grouped in deciles using their SUE (Standardized
Unexpected Earnings, or the Earnings Surprise for he current quarter
divided by the standard deviation of earnings surprise in the quarter.)
The
stocks in the decile with the highest SUE outperformed by 2% and the
ones in the lowest decile underperformed by 2.2%; over the next 60 days. For
small and medium sized companies these effects were even more pronounced.
Again the market is too slow to react to news, good or bad. Behavioral
finance will explain this in terms of overconfidence and anchoring.
Momentum
in the intermediate Term and Overreaction in the Long Term
Barberis,
Schleifer and Vishny in a paper suggested that
Specualtive
Bubbles. The fact that specualtive bubbles form once every generation
if not more often, that prices stay so much higher than their
"fundamental value" for long periods of time also seems to contradict
the EMH. I am including a link on specualtive bubbles; the internet and fibre
optic stock bubbles having burst, most of the stories about Tulip
mania, the South Sea Bubble, etc are to fresh in every one's mind. http://www.clarity.net/~jake/bubble.htm
Richard Roll's Study
on Orange Juice futures. Richard Roll examined the
influence of weather-related news on the price of orange
juice futures prices. Since the production of oranges in the US is geographically
cnocentrated and the patterns of demand and taste for orange juice are
stable, the weather-related news should account for most of variation price
of orange juice futures, if the EMH were to hold here. But Roll found that
weather explained a relatively small proportion of this variation.
Closed End Funds Puzzle. In a closed-end fund, the number of
shares is fixed after the initial offering. The only way for investors to buy
shares in the fund is to buy them from existing shareholders. On the other
hand, in an open-end fund the number of shares is not fixed. Investors can
purchase shares from the investment company running the fund who can simply
issue more shares as the demand justifies. A puzzling fact of closed-end
funds is that their price systematically diverges from their fundamental
value, or Net Asset Value (NAV). When the value of the shares is higher than
the NAV the shares are said to trade at a premium; if the value of the shares
is less than the NAV they are said to trade at a discount. The average
closed-end fund is initially priced at a premium of 10%. Within 120 days of
being brought out the average fund trades at a discount of 10%. The magnitude
of the discount is not stable it keeps changing. When the closed-end fund is changed
to open-end fund, the price of its shares tends to rise and the discount
tends to disappear.
No
part of this puzzle can be explained by traditional finance but using opaque
framing, heuristic driven biases and market inefficiency we can offer pretty
good explanations.
Fat Tailed
Distributions in Finance
What
distribution the returns of financial assets come from are of paramount
importance in option pricing, risk management, value at risk calculations and
portfolio management. Most financial models assume a Gaussian or normal
distribution of asset returns, but empirical studies show evidence for
lepto-kurtosis or fait tails in the distributions. This means that extreme
events are more likely to occur than would be suggested by a normal
distribution.
Benoit Mandelbrot
advocated the stable, or Pareto-Levy distributions for modelling financial
asset returns. Since then stochastic volatility and GARCH
models have been used to model asset price returns. The list is
far too exhaustive. Clive Granger, Nelson and Bollerslev are the most well
known researchers in the area. S-Plus (see under the section on software for quantitative
analysis) has GARCH toolbox which allows estimation/fitting of several
"flavours" of GARCH models to data.
For
general links to fat tailed distributions, a good web site is to visit JP Nolan's home page.
Currency Crisis
I
have been interested in the academic literature on models of speculative
attacks on currencies, contagion, and related areas. The first model was
developed by Paul Krugman, who
drew upon the work of Steve
Salant.Salant had used the Hotelling lemma (1931) to study speculative
attacks on the price of government controlled price of gold. Krugman realized
that a similar analysis could be applied to fixed exchange rates. Krugman's
model was simplified by Robert Flood and Peter Garber in 1984. Collectively,
their research came to be known as the First Generation models. First
generation models show how a fixed exchange rate policy combined with
excessively expansionary pre-crisis economic fundamentals can push the
economy into crisis, with speculators trying to benefit when the inconsistent
policies are dismantled.
The
newer, Second Generation models try and explain those attacks where the
economic fundamentals were not inconsistent with exchange rate
policies. Maurice
Obstfeld is an important researcher in this area. A good paper on
contagion is by Barry
Eichengreen, Andrew Rose andCharlesWyplosz,
Contagious
Currency Crises. Carmen Reinhart
is another important researcher on Contagion and Early Warning Indicators of
Currency Crisis. The IMF and NBER web sites are good places to download
papers by authors mentioned here.
Statistical Learning & Pattern Recognition
Support Vector
Machines
"Support
Vector Machines are learning machines that can perform binary classification
and regression estimation tasks. They perform the structural risk
minimization principle. SV machines create a classifier with minimized VC
dimension. If the VC dimension is low, the expected probability of error is
low as well, which means good generalization.
Support
Vector Machines non linearly map their n-dimensional input space into a high
dimensional feature space. In this high dimensional feature space a linear
classifier is constructed. Two results make this approach successful:
The
generalization ability of this learning machine depends on the VC dimension
of the set of functions that the machine implements rather than on the
dimensionality of the space. A function that describes the data well and
belongs to a set with low VC dimension will generalize well regardless of the
dimensionality of the space.
Construction
of the classifier only needs to evaluate an inner product between two vectors
of the training data. An explicit mapping into the high dimensional feature
space is not necessary. In Hilbert space inner products have simple kernel
representations an therefore can be easily evaluated."
-Excerpted
from the website of the Computer
Learning Research Centre at Royal Holloway, University of London.
The famous Vladmir
Vapnik as well as Alexey Chervonenkis
of the VC Dimension fame are both Fellows at Royal Holloway
Support
Vector MAchines are the hot new area in statistical learning. They are being
applied very successfully for both classification and prediction. In fact,
they are more successful than Neural Networks in many applications.
Most
recent books on statistical learning have a chapter on Support Vector
Machines, but the most popular book in this area is
AN INTRODUCTION TO SUPPORT VECTOR
MACHINES by N. Cristianini and J. Shawe-Taylor. Three more good books in
this area are:
Advances in Kernel Methods - Support
Vector Machines , edited by B. Schölkopf, C. Burges,
and A. Smola, MIT Press, MA;
Advances
in Large Margin Classifiers , edited by A. Smola, P. Bartlett, B. Schölkopf,
and D. Schuurmans, MIT Press, MA;
Learning with Kernels, by B.
Schölkopf and A. Smola, MIT Press, MA (in press);
Learning and Soft Computing by Vojislav
Kecman, The MIT Press. There is accompanying Matlab code available
from the author's website.
There
are many good websites with links to SVM's and other kernel based learning
methods, so I will just list the links here.
Support Vector Machines
at the National University of Singapore (S.Sathiya Keerthi)
Home Page of O.L. Mangasarian
Olvi
Mangasarian and his student Glenn
Fung presented a 'proximal SVM' engine at the
latest
KDD which is only
6 lines of Matlab code:
I
reproduce that snippet of Matlab code out of interest.
function
[w, gamma] = psvm(A,d,nu)
%
PSVM: linear and nonlinear classification
%
INPUT: A, d=diag(D), nu. OUTPUT: w, gamma
% [w,
gamma] = pvm(A,d,nu);
[m,n]=size(A);
e=ones(m,1);
H=[A
-e];
v=(d'*H)' %v=H'*D*e;
r=(speye(n+1)/nu+H'*H)\v
% solve (I/nu+H'*H)r=v
w=r(1:n);gamma=r(n+1);
% getting w,gamma from r
SVM
Applications List
Gavin Cawley's Matlab
Toolbox for SVM
Steve Gunn's Matlab
Toolbox for SVM's
Another Matlab
toolbox for SVM
Yet another Matlab
Toolbox for SVM (at the Ohio State University Website)
Kernel-Machines.org
Cluster Analysis
One of
the best texts on Cluster Analysis is ClusterAnalysis by Everitt, Landau and
Leese. Another good one is Finding Groups in Data by Kaufmann and
Rousseuw. Both should be available on amazon.com. Everitt et al's Chapter 6 (
Finite Mixture densities as Models for Cluster Analysis) is very interesting.
Chapter 7 discusses density search clustering techniques, Fuzzy Clustering.
Fuzzy
Clustering and Overlapping Clusters are also discussed in these books.
S-plus
has many clustering algorithms' implementations. The usual Partitionaing as
well as Agglomerative Clusterings are all available in S-Plus, and the fuzzy
clustering algorithms are also there. The popular k-means with many
types of linkage (single linkage, complete linkage, average distance,Centroid
linkage,
ward
Linkage is available in MATLAB as well. If you search the web, almost
everything is available as a MATLAB toolbox). The more recent and
sophisticated techniques for clustering like Self Organzing Maps or Kohonen
maps are also have MATLAB implementations (see below).
Self
Organizing Maps
"The
SOM is a new, effective software tool for the visualization of
high-dimensional data. It converts complex, nonlinear statistical
relationships between high-dimensional data items into simple geometric
relationships on a low-dimensional display. As it thereby compresses
information while preserving the most important topological and metric
relationships of the primary data items on the display, it may also be
thought to produce some kind of abstractions. These two aspects,
visualization and abstraction, can be utilized in a number of ways in complex
tasks such as process analysis, machine perception, control, and
communication. "
-Excerpt
from the website of T. Kohonen ,
who is the leading figure in SOM's and pattern recognition.
The best
place to learn about Self Organizing Maps is the website of the Laboratory of
Computer and Information Science (CIS) at the Helsinki University of Technology.
The also have a Matlab
Toolbox for Self Organizing Maps.
Neural Networks
There are
so many books and websites devoted to Neural Networks that not much mention is
needed here. I will therefore, merely list some Matlab toolboxes and other
software for Neural Networks.
Matlab
Toolbox for Neural Networks from Mathworks.
Netlab, a
feedforward neural networks package Matlab Toolbox. There is an accompanying
book, Netlab:
Algorithms for Pattern Recognition by Ian Nabney.
NNSYSID, a
(Neural Optimization Development Engine library Matlab toolbox for
neural network based identification of nonlinear dynamic systems.
PR Tools, a
Matlab Toolbox for statistical Pattern Recognition.
An RBF
network toolbox in Matlab
NODElib,
(Neural Optimization Development Engine library) : a C++ library for neural
networks.
Neuralware has a
commercial product called NeuralWorks with an Excel interface.
Bayesian Networks
or Belief Networks or Graphical Models
Graphical
Models are an attempt to combine Graph Theory and Probability Theory in
tackling machine learning problems as well as the classical Multivariate
Statistics. Graphical models are graphs in which nodes represent random
variables, and the lack of arcs represent conditional independence
assumptions.
Directed
graphical models (which cannot have directed cycles) are also called Bayesian
Networks or Belief Networks (BNs). Bayesian networks do not necessarily imply
a commitment to Bayesian methods; rather, they are so called because they use
Bayes' rule for inference.
Finite
mixture models, factor analysis, hidden Markov models and Kalman filters are
special cases of the general Graphical.
In
this section, my thrust is not to explain the uses and theory behind Belief
Nets, but rather, to list the Matlab toolboxes and some other
software products.
Bayes Net Toolbox
at Berkeley.
Hidden Markov
Model Toolbox in Matlab; yet another HMM toolbox in
Matlab
Visual Basic BN Toolkit.
Carnegie Mellon University's
JavaBayes
The BUGS (Bayesian
inference Using Gibbs Sampling ) software
BayesBuilder a
tool for constructing and testing Bayesian networks. The tool is particularly
suited for the design of medical diagnostic systems and data mining.
A
list of Bayesian
Networks software with comparisons.
Independent
Components analysis and Blind Source Separation
"Independent
component analysis (ICA) is a statistical
and computational technique for revealing hidden factors that underlie sets
of random variables, measurements, or signals.
ICA
defines a generative model for the observed |