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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

A Few Notes on Outliers: The Story of Success

In his 2008 book, Outliers: The Story of Success, author Malcolm Gladwell argues for transforming outliers (extraordinary levels of individual success) from mysteries to rational outcomes by isolating explanatory factors and narrowing samples to instances exposed to those factors. There are aspects of the arguments presented in the book that are relevant for investors/traders, such as: Keep Reading

Classic Paper: Financial Instability Hypothesis

We occasionally select for retrospective review an all-time “best selling” research paper of the past from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize Hyman Minsky’s May 1992 paper entitled “The Financial Instability Hypothesis” (download count over 3,400), a theory of the impact of debt on economic system behavior. The Financial Instability Hypothesis (FIH) challenges the view that a capitalist economy with a sophisticated financial system constantly seeks equilibrium, instead proposing that some conditions are deviation-amplifying. Specifically, he proposes that: Keep Reading

Surviving by Staying Out of the Fourth Quadrant

Can one survive over the long run in the “wild” Fourth Quadrant, in which many investments appear to reside and for which normal (Gaussian) statistics mislead rather than guide? In his February 2009 draft paper entitled “Errors, Robustness, and The Fourth Quadrant”, Nassim Taleb investigates the (in)tractability of economic and financial series and characterizes approaches to accommodating such fundamental unpredictability. Based on a broad set of worldwide economic data that includes 38 tradable variables with daily price data, he concludes that: Keep Reading

Four Factors and Two Regimes

Do returns associated with the four famous factors (market, size, book-to-market, momentum) vary systematically with the state of the market (such as bull or bear)? In their January 2009 paper entitled “The Effect of Market Regimes on Style Allocation”, Manuel Ammann and Michael Verhofen investigate how returns for the four factors differ between market states as determined by a multivariate two-state model of the overall equity market. Using U.S. stock market and factor data spanning 1927-2004, they conclude that: Keep Reading

Predictable Pieces of the Market?

Are commonly used stock market indicators more predictive for some subsets of stocks than for the stock market overall? In the November 2008 update of their paper entitled “How Predictable are Components of the Aggregate Market Portfolio?”, Aiguo Kong, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou analyze return predictability for various subsets of the overall U.S. stock market, defined by portfolios sorted into 33 industry, 10 market capitalization and 10 book-to-market ratio segments. They consider 14 economic variables and lagged returns for 33 industries as predictors. Using economic indicator and industry/size/book-to-market return data from the end of 1945 through 2004, they conclude that: Keep Reading

Different Paths to the Same (Disconcerting) Destination?

The Efficient Market Hypothesis (EMH) and the “Black Swan” Hypothesis (BSH) take very different paths to the same destination, as follows: Keep Reading

Stock Returns for New Industries

Do new industries offer exceptionally good stock returns, whether through strong growth or investor exuberance? In their September 2008 paper entitled “Returns to Investors in Stocks in New Industries”, Gerald Dwyer Jr. and Cora Barnhart examine stock return distributions and summary statistics for the following major new industries in the U.S. over the periods of their initial development (15-23 years): personal computers, airlines, aircraft manufacturing, automobile manufacturing, railroads and telegraph. Using return data for the stocks of companies in the selected industries and contemporaneous market indexes, they conclude that: Keep Reading

Factor Fishing Expedition

Many equity market researchers assume conventional three-factor (excess market return or beta, size, book-to-market ratio) and four-factor (plus momentum) models as standards of comparison for discovery of new sources of abnormal returns. Are they the best standards? In their November 2008 paper entitled “Fishing with a Licence: an Empirical Search for Asset Pricing Factors”, Soosung Hwang and Alexandre Rubesam investigate the empirical power of 12 previously identified asset pricing factors using a Bayesian variable selection method called Stochastic Search Variable Selection (see the paper for a description). The factor candidates are: excess market return, liquidity, coskewness, cokurtosis, downside risk, size, book-to-market ratio, momentum, asset growth, idiosyncratic volatility, volume and long-term reversal. Using data for thousands of individual U.S. stocks and associated firm characteristics, 25 factor-based portfolios and 30 industry portfolios over the period 1967-2006, they conclude that: Keep Reading

The Fourth Quadrant: No Realm for the Normal

New sample points from the past two months are substantially shifting correlations in several our past analyses of relationships between indicators and future stock returns (published updates pending). Here are some recent relevant observations from Nassim Taleb’s September 2008 essay in Edge entitled “The Fourth Quadrant: A Map of the Limits of Statistics”. In the aftermath of the collapse of Fannie Mae, Bear Stearns and Lehman Brothers, he observes that: Keep Reading

Sensitivity of Stock Market Return Predictability to Predictor Measurement Interval

Does the predictability of stock market returns depend on exactly when and for how long one measures the predictive variable? In the October 2008 draft of their paper entitled “Return Predictability Revisited”, Ben Jacobsen, Ben Marshall and Nuttawat Visaltanachoti anticipate a substantial fraction of the variation in monthly stock market returns by judiciously refining the observation intervals for a set of predictive variables (prices for the 22 commodities with the largest world production during 2003-2008). The causality chain is, presumably, commodity price changes affect future corporate earnings and/or inflation, and investor expectations about earnings and inflation affect equity valuation. The authors test the predictive power of commodity price changes over a range of measurement intervals under assumptions of both near efficiency (rapid response of equity prices to commodity prices) and gradual information diffusion (delayed response of equity prices). Using daily commodity spot prices as available and monthly stock market returns for the U.S. and 18 other countries since 1970, they conclude that: Keep Reading

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