Big Ideas

Is there a steady, zero or varying supply of stock market return predictability? In their January 2010 paper entitled “Stock Return Predictability and the Adaptive Markets Hypothesis: Evidence from Century Long U.S. Data”, Jae Kim, Kian-Ping Lim and Abul Shamsuddin employ a battery of tests to evaluate the evolution of U.S. stock market return predictability over the last century and determine whether this evolution is consistent with the Adaptive Markets Hypothesis. Using monthly Dow Jones Industrial Average (DJIA) return data, along with various indicators of market conditions and economic fundamentals, for 1900 through 2009, they conclude that: Keep Reading

Do the methods and assumptions used in studies of the power of variables to predict differences in future returns across stocks accurately represent implementable trading strategies? In his December 2009 paper entitled “Economic and Statistical Properties of Implementable Trading Strategies”, Andrew Christie assesses the realism of widely used portfolio-level tests for anomalous cross-sectional stock returns. Using analysis and (as an example) the results from some past portfolio studies on the predictive power of standardized unexpected earnings, he concludes that: Keep Reading

More than one reader has asked for a ranking of firm characteristics or factors by predictive power for future stock returns, or for a ranking of trading strategies by alpha. Keep Reading

A reader asked: “Do the equity markets still follow a random walk? Has CXO Advisory Group LLC completed an autocorrelation test of S&P 500 Index returns recently? It would be informative to discover if randomness still dominates daily, weekly, monthly, quarterly and annual equity returns.” Keep Reading

Is The Black Swan: The Impact of the Highly Improbable gimmicky or profound? In his October 2009 paper entitled “Common Errors in Interpreting the Ideas of The Black Swan and Associated Papers”, Nassim Taleb seeks to clarify the import of this book and related publications, with some key points as follows: Keep Reading

A reader asked: “Do you have any insights on protecting one’s portfolio from the bad losses in the fat left tails of return distributions? Is there any data about stop-losses that actually work?” Keep Reading

A reader suggested: “I know you’ve looked at Didier Sornette’s work in the past, but I think it would be worthwhile to look at his work again. His latest is ‘Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles’, with abstract as follows:” Keep Reading

A reader asked: “I came across an interesting article comparing the Nikkei 225 Index in the 1990s with the S&P 500 Index in the 2000s. Do you have any opinion on this study? Also I would love for you to post some data on how different asset classes performed in Japan from 1991 (the beginning of the ‘Lost Decade’) until now.” Keep Reading

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