Big Ideas

In his 2007 book The Black Swan: The Impact of the Highly Improbable, Nassim Taleb addresses human inability to process natural randomness, particularly combinations of low predictability and large impact. “It is easy to see that life is the cumulative effect of a handful of [largely unpredictable] significant shocks.” This logic “makes what you don’t know far more relevant than what you do know.” Models that ignore this logic (such as those assuming Gaussian probability distributions for financial variables) inculcate mistakes that “can lead to severe consequences.” Focusing principally on the perspective of an investor, here is a chapter-by-chapter review of some of the insights in this book: Keep Reading

Can investors/traders outperform by exploiting (or avoiding) the black swans that populate daily equity market returns? In his November 2007 paper entitled “Black Swans and Market Timing: How Not To Generate Alpha”, Javier Estrada investigates the influence of the best and worst days on long-term equity returns and the likelihood that investors can predict when these outliers will occur. Using evidence from 15 international equity markets and over 160,000 daily returns, he concludes that: Keep Reading

Are factor models universal, or does each group of related stocks have a unique set of factors for predicting differences in future returns? In their September 2007 paper entitled “How Common Are Common Return Factors Across NYSE/AMEX and Nasdaq?”, Amit Goyal, Christophe Perignon and Christophe Villa propose a general procedure to identify pervasive risk factors and apply the methodology to identify similarities and differences between the return structures of the specialist-controlled NYSE/AMEX and the computer-driven Nasdaq. Using monthly return data for large samples of NYSE/AMEX and Nasdaq stocks over the period 1978-2002 (25 years), divided into five 60-month subperiods, they find that: Keep Reading

Which factors are most predictive of differences in future returns among individual stocks? In their September 2007 paper entitled “Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns”, Gregory Connor, Matthias Hagmann and Oliver Linton develop a new method for analyzing the influence of simple fundamental and technical factors on the returns of individual stocks. The method accommodates consideration of additional factors more readily than widely used alternative approaches. Using monthly return data and associated fundamentals for a broad sample of stocks over the period 1962-2005, they find that: Keep Reading

When times are good, the powers that be (central banks for interest rates and corporate boards for payouts to shareholders) have more latitude to buffer reactions to changes in the business environment. Does this latitude make widely used indicators of future stock returns less useful, or useless, during expansions? In their June 2007 draft paper entitled “Short-Horizon Predictability and Information Erosion”, Sam James Henkel, Spencer Martin and Federico Nardari investigate how the predictability of equity market returns varies across the business cycle. They focus on the dividend yield, the short-term interest rate, the slope of the term structure and the default premium as predictors of stock returns. Using monthly data spanning April 1953 to December 2005 for the U.S. (634 months) and comparable data as available for Canada, France, Germany, Italy, Japan and the UK, they find that: Keep Reading

Is there a way to deal with structural breaks more precisely than just expressing vague skepticism about the usefulness of old data? In the April 2007 draft of their paper entitled “How Useful Are Historical Data for Forecasting the Long-run Equity Return Distribution?”, John Maheu and Thomas McCurdy describe and test a methodology for identifying and calibrating structural breaks in long-term excess equity returns. Using monthly U.S. equity return and risk-free rate data for the period February 1885 through December 2003, they conclude that: Keep Reading

Both the academic community and practitioners generate large numbers of studies, formal and informal, analyzing and forecasting financial markets. In this blog, we offer an organization of financial markets research by topics such as The Value Premium and Buybacks and Secondaries. Are there other organizing principles that might convey a more fundamental understanding? Reflecting on the hundreds of studies we have reviewed and the limitations of this research with regard to practical application, here is another framework for thinking about financial markets research: Keep Reading

Financial econometrics gives empirical life (and death) to financial market models. Where has this rapidly growing branch of economics been, where is it now and where is it going? In the October 2006 revision of his article entitled “Financial Econometrics”, Andrew Lo provides an introduction to four decades of the field’s most influential academic papers. Some of his key points are: Keep Reading

Are investing results ultimately just good or bad luck? In conflict with intuition perhaps derived from an essential human sense of self-worth, the strictest form of the Efficient Markets Hypothesis (EMH) says yes. Where has the EMH been and where is it going? In his recent article for The New Palgrave: A Dictionary of Economics entitled “Efficient Markets Hypothesis”, Andrew Lo assesses the past and the future of the EMH. Some of his key points are: Keep Reading

Many equity market researchers assume conventional three-factor (market return, size, book-to-market) and four-factor (plus momentum) models as standards of comparison for discovery of new sources of abnormal returns. Are they the best standards? Could they be derivatives of more economically fundamental sources of differences among individual stock returns? In their March 2007 paper entitled “Too Many Factors! Do We Need Them All?”, Soosung Hwang and Chensheng Lu seek to identify the minimum number of economically fundamental factors needed to explain why different stocks generate different returns. They investigate 16 factors (12 firm characteristics and four macroeconomic measures) that others have found to explain such return differences. Their principal test is to measure returns from zero-cost portfolios that are long stocks with high (top third) values and short stocks with low (bottom third) values of evaluated factors. Using data for a large sample of non-financial stocks during 1963-2005 and contemporaneous macroeconomic data, they conclude that: Keep Reading