Big Ideas

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

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