Big Ideas

Just how much do investors in U.S. equities pay for the hope of beating the market? In his April 2008 paper entitled “The Cost of Active Investing”, Kenneth French estimates the cost of active investing in the U.S. stock market as the difference between the total cost of investing and an estimate of the cost if everyone invested passively. He constructs the total cost of investing as the sum of four components: (1) fees/expenses investors pay for open-end, closed-end and exchange-traded funds; (2) investment management fees for institutional investors; (3) fees investors pay for hedge funds and funds of hedge funds; and, (4) costs all investors pay to trade. Using data for investing costs and market returns during 1980-2006 for NYSE, Amex and NASDAQ stocks, he concludes that: Keep Reading

Does the “overpricing” of out-of-the-money (OTM) stock index put options imply an investor estimate of the likelihood and size of economic disasters and stock market crashes? In his June 2008 paper entitled “How Bad Will the Potential Economic Disasters Be? Evidences From S&P 500 Index Options Data”, Du Du estimates the the frequency and magnitude of U.S. economic disasters as implied by S&P 500 index option data within a model involving rare sharp drops in consumption and consumption habit formation. In his model, consumption drops induce stock market crashes via: (1) commensurate declines in dividends, and (2) elevated investor risk aversion. Using S&P 500 index option data for the period 4/4/88-6/30/05 and contemporaneous economic data, he concludes that: Keep Reading

Which stock return anomalies are trustworthy, and which are not? In the June 2007 draft of their paper entitled “Dissecting Anomalies”, Eugene Fama and Kenneth French apply both sorts and regressions to examine the robustness of the momentum, net stock issuance, accruals, profitability and asset growth anomalies. They note that sorts on an anomaly variable offer a simple picture of how average returns vary, but microcaps (a few big stocks) can dominate the performance of a sort-based equal-weighted (value-weighted) hedge portfolio. In addition, sorts are ill-suited to determinations of: (1) the exact relationship between an anomaly variable and returns, and (2) relationships among anomalies. They note also that extreme behavior by microcaps and outliers generally can distort inference from regressions. Using a robust set of firm data for a broad set of U.S. stocks allocated to three size groups (microcap, small and big) over the period 1963-2005, they conclude that: Keep Reading

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