Big Ideas

Can investors/traders outperform by exploiting (or avoiding) the black swans that populate daily emerging market equity returns? In his September 2008 paper entitled “Black Swans in Emerging Markets”, Javier Estrada investigates the influence of the best and worst days on long-term equity returns in emerging markets and the naive likelihood that investors can predict when these outliers will occur. Using evidence from 16 international equity markets and over 110,000 daily returns from start dates based on data availability through 2007, he concludes that: Keep Reading

Just what does it mean to be a quant? In his December 2002 article entitled “The Boy’s Guide to Pricing & Hedging “, Emanuel Derman offers an “abbreviated poor man’s guide” to quantitative finance. He observes that: Keep Reading

Why does the conventional wisdom on the predictability of stock market returns morph (no, yes, maybe, probably not) over time? In their July 2008 paper entitled “Time-Varying Short-Horizon Return Predictability”, Sam James Henkel, Spencer Martin and Federico Nardari apply a regime-switching vector autoregression (RSVAR) framework to explore and explain the degree to which the predictability of equity market returns at a one-month forecast horizon changes over time. They focus on the following four potential predictors: dividend yield, short-term interest rate, interest rate term spread and default spread between high-grade and low-grade corporate bonds. Using monthly stock market returns and contemporaneous economic data for the G7 countries (Canada, France, Germany, Italy, Japan, UK and U.S.) as available through 2007, they conclude that: Keep Reading

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