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Big Ideas

These blog entries offer some big ideas of lasting value relevant for investing and trading.

Factor Fishing Expedition

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

The Fourth Quadrant: No Realm for the Normal

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

Sensitivity of Stock Market Return Predictability to Predictor Measurement Interval

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

Anomalies Tested with Expected (Rather Than Historical) Returns

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

The Futility of Timing Emerging Equity Markets?

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

Quantitative Finance in a Nutshell

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 the Story on Predictability Keeps Changing

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

The Cost of Hope?

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

Extracting Disaster from Index Option Prices

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

Fama and French Dissect Anomalies

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

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