Big Ideas

The widely used Fama-French three-factor model explains stock returns based on aggregate market return, firm size (small versus large) and firm valuation (value versus growth). Since the Fama-French model does not explain the stock price momentum effect, researchers and investors often add momentum as a fourth factor to predict future stock returns. Might some other small set of factors (three) outperform the Fama-French model in explaining stock returns, obviating the need for a momentum factor and accounting for other stock return anomalies as well? In their June 2009 paper entitled “A Better Three-Factor Model That Explains More Anomalies”, Long Chen and Lu Zhang argue that a three-factor model based on aggregate market return, level of firm investment relative to assets (low versus high) and return on assets (high versus low) substantially outperforms the Fama-French model in explaining stock returns. Using a wide range of firm and stock data for a broad sample of stocks over the period 1972-2006 to test this model, they conclude that: Keep Reading

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

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

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

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

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

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

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

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

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