Big Ideas

Has the last generation of academic research clarified which factors/characteristics/indicators predict which stocks will outperform and which stocks will not? How can academia do better? In his August 2009 paper entitled “The Cross-Section of Expected Stock Returns: What Have We Learnt from the Past Twenty-Five Years of Research?”, Avanidhar Subrahmanyam reviews recent research on cross-sectional predictors of stock returns at monthly or longer horizons and offers observations on how to improve this research. Citing a large number of relevant studies, he concludes that: Keep Reading

Is it possible to measure the value of fundamental investment research? How does the degree of measurability affect the behaviors of investors and financial markets? In the June 2009 version of his paper entitled “Investment Research: How Much is Enough?”, Bradford Cornell speculates on answers to these questions. Citing a range of research on mutual fund research practices and performance, he concludes that: Keep Reading

In his 2009 book, Reading Minds and Markets: Minimizing Risk and Maximizing Returns in a Volatile Global Marketplace, author Jack Ablin, Chief Investment Officer for Harris Private Bank, seeks “to help individual investors gain a foothold in a fiercely competitive investment marketplace… My own approach to doing this is not a get-rich-quick scheme… To successfully outperform the market over the long term…, you need to learn how to read the market’s mind, to figure out where the risk and rewards are most acute at any given point in time… I use the model and the tools I describe in this book every day of the week. …I show you exactly how to do so…” The principal messages of the book are: Keep Reading

In his 1996 book, Against the Gods: The Remarkable Story of Risk, financial historian, economist and educator Peter Bernstein traces in narrative fashion the development of probability and statistics in the service of risk management. In the closing chapter, he offers a few overarching conclusions, as follows: Keep Reading

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