Big Ideas

Are commonly used stock market indicators more predictive for some subsets of stocks than for the stock market overall? In the November 2008 update of their paper entitled “How Predictable are Components of the Aggregate Market Portfolio?”, Aiguo Kong, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou analyze return predictability for various subsets of the overall U.S. stock market, defined by portfolios sorted into 33 industry, 10 market capitalization and 10 book-to-market ratio segments. They consider 14 economic variables and lagged returns for 33 industries as predictors. Using economic indicator and industry/size/book-to-market return data from the end of 1945 through 2004, they conclude that: Keep Reading

The Efficient Market Hypothesis (EMH) and the “Black Swan” Hypothesis (BSH) take very different paths to the same destination, as follows: Keep Reading

Do new industries offer exceptionally good stock returns, whether through strong growth or investor exuberance? In their September 2008 paper entitled “Returns to Investors in Stocks in New Industries”, Gerald Dwyer Jr. and Cora Barnhart examine stock return distributions and summary statistics for the following major new industries in the U.S. over the periods of their initial development (15-23 years): personal computers, airlines, aircraft manufacturing, automobile manufacturing, railroads and telegraph. Using return data for the stocks of companies in the selected industries and contemporaneous market indexes, they conclude that: Keep Reading

Many equity market researchers assume conventional three-factor (excess market return or beta, size, book-to-market ratio) and four-factor (plus momentum) models as standards of comparison for discovery of new sources of abnormal returns. Are they the best standards? In their November 2008 paper entitled “Fishing with a Licence: an Empirical Search for Asset Pricing Factors”, Soosung Hwang and Alexandre Rubesam investigate the empirical power of 12 previously identified asset pricing factors using a Bayesian variable selection method called Stochastic Search Variable Selection (see the paper for a description). The factor candidates are: excess market return, liquidity, coskewness, cokurtosis, downside risk, size, book-to-market ratio, momentum, asset growth, idiosyncratic volatility, volume and long-term reversal. Using data for thousands of individual U.S. stocks and associated firm characteristics, 25 factor-based portfolios and 30 industry portfolios over the period 1967-2006, they conclude that: Keep Reading

New sample points from the past two months are substantially shifting correlations in several our past analyses of relationships between indicators and future stock returns (published updates pending). Here are some recent relevant observations from Nassim Taleb’s September 2008 essay in Edge entitled “The Fourth Quadrant: A Map of the Limits of Statistics”. In the aftermath of the collapse of Fannie Mae, Bear Stearns and Lehman Brothers, he observes that: Keep Reading

Does the predictability of stock market returns depend on exactly when and for how long one measures the predictive variable? In the October 2008 draft of their paper entitled “Return Predictability Revisited”, Ben Jacobsen, Ben Marshall and Nuttawat Visaltanachoti anticipate a substantial fraction of the variation in monthly stock market returns by judiciously refining the observation intervals for a set of predictive variables (prices for the 22 commodities with the largest world production during 2003-2008). The causality chain is, presumably, commodity price changes affect future corporate earnings and/or inflation, and investor expectations about earnings and inflation affect equity valuation. The authors test the predictive power of commodity price changes over a range of measurement intervals under assumptions of both near efficiency (rapid response of equity prices to commodity prices) and gradual information diffusion (delayed response of equity prices). Using daily commodity spot prices as available and monthly stock market returns for the U.S. and 18 other countries since 1970, they conclude that: Keep Reading

Are the major known stock return anomalies as exploitable as they seem to investors looking back at historical returns? In their September 2008 paper entitled “Do Anomalies Exist Ex Ante?”, Ginger Wu and Lu Zhang examine a wide range of anomalies (book-to-market, composite issuance, net stock issues, abnormal investment, asset growth, price momentum, earnings surprises, total and discretionary accruals, net operating assets, and failure probability) from the perspective of a forward-looking investor. They employ in their analysis expected returns derived from growth rates of fundamentals (dividends, earnings, sales and equity), rather than backward-looking historical (realized) returns. Using monthly price and return data for a broad sample of stocks, along with contemporaneous firm fundamentals, over the period 1965-2007, they conclude that: Keep Reading

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