Big Ideas

Can investors beat the market by iteratively finding and exploiting the current hot anomaly? In the September 2009 update of his paper entitled “Real-Time Profitability of Published Anomalies: An Out-of-Sample Test”, Zhijian Huang investigates whether a trader can realize excess returns by repeatedly picking the anomaly with the best return during a rolling historical window from an expanding universe of anomalies as published, with a specific objective of suppressing data snooping bias. The universe includes anomalies that: (1) have been published in at least one of five top-ranked finance journals; (2) relate to the calendar or to cross-sectional predictability; and, (3) can be re-evaluated annually. Using monthly return data associated with 11 anomalies published during 1972-2005 (Monday/weekend effect, January effect and cross-sectional effects related to size, book-to-market ratio, momentum, earnings-price ratio, cash flow-price ratio, dividend yield, debt-equity ratio, sales growth and trading volume/turnover) as available from 1926 through 2008, he concludes that: Keep Reading

Is diversification across equity anomalies beneficial? In his December 2009 preliminary paper entitled “Diversification Across Characteristics”, Erik Hjalmarsson combines long-short portfolios formed on seven stock anomalies:

1. Short-term (one-month) reversal (ST-R)
2. Medium-term (11 months plus skip-month) momentum (Mom)
3. Long-term (four years plus skip-year) reversal (LT-R)
4. Book-to-market value (B/M)
5. Cash flow-to-price ratio (C/P)
6. Earnings-to-price ratio (E/P)
7. Market capitalization (Size)

The portfolio for each anomaly is long (short) on an equally weighted basis the tenth of stocks expected to generate the most positive (negative) returns, reformed each month. Using monthly firm characteristics and return data for all NYSE, AMEX and NASDAQ stocks over the period July 1951 through December 2008, he finds that: Keep Reading

How scientific can economics and finance be? In the March 2010 draft of their paper entitled “WARNING: Physics Envy May Be Hazardous To Your Wealth!”, Andrew Lo and Mark Mueller present a framework to help investors, portfolio managers, regulators and policymakers understand the potential effectiveness and inherent limitations of economics and finance. Focusing on levels of uncertainty (fully reducible, partially reducible, and irreducible) to explain some of the key differences between finance and physics and on the role of quantitative models in theory and practice, they conclude that: Keep Reading

Given the sometimes high correlations in movements among local equity markets, how valuable is international diversification in a global era? In the February 2010 draft of their paper entitled “International Diversification Works (in the Long Run)”, Clifford Asness, Roni Israelov and John Liew examine the argument that global markets are undiversified (correlated) when you need diversification and diversified (uncorrelated) when you don’t. They use an equally weighted 22-country global portfolio for their investigation. Using monthly local currency-denominated total returns, exchange rates and inflation data for Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Japan, Netherlands, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, UK and U.S. for 1950-2008, they conclude that: Keep Reading

Can people reliably distinguish between actual financial markets time series and randomized data? In the February 2010 draft of their paper entitled “Is It Real, or Is It Randomized?: A Financial Turing Test”, Jasmina Hasanhodzic, Andrew Lo and Emanuele Viola report the results of a web-based experiment designed to test the ability of people to distinguish between time series of returns for eight commonly traded financial assets (including stock indexes, a bond index, currencies and commodities, all given names of animals) and randomized data. Using a sample of 8015 guesses from 78 participants over eight contests conducted during 2009, they conclude that: Keep Reading

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