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Predicting Anomaly Premiums Across Asset Classes

Posted in Bonds, Currency Trading, Equity Premium

Are anomaly premiums (expected winners minus losers among assets within a class, based on some asset characteristic) more or less predictable than broad market returns? In their April 2017 paper entitled “Predicting Relative Returns”, Valentin Haddad, Serhiy Kozak and Shrihari Santosh apply principal component analysis to assess the predictability of premiums for published asset pricing anomalies spanning stocks, U.S. Treasuries and currencies. For tractability, they simplify asset classes by forming portfolios of assets within them, as follows:

  • For stocks, they consider the long and short legs of portfolios reformed monthly into tenths (deciles) based on each of the characteristics associated with 26 published stock return anomalies (monthly data for 1973 through 2015).
  • They sort zero-coupon U.S. Treasuries by maturity from one to 15 years to assess term premiums (yield data for 1985 through 2014).
  • They sort individual exchange rates into five portfolios reformed daily based on interest rate differentials with the U.S. to assess the carry trade premium (daily data as available for December 1975 through December 2016).

Using the specified data, they find that:

  • For equities, dominant principal components of anomaly long-short strategies are more predictable than the broad stock market.
  • For U.S Treasuries, the slope of the yield curve (term premium) is more predictable than level of the curve. A portfolio of maturities weighted according to their predicted returns produces gross annualized Sharpe ratio about 0.7 out-of-sample.
  • Carry portfolio return is more predictable than the return to a basket of all currencies against the U.S. dollar.

In summary, evidence indicates that anomaly premiums are generally more predictable than broad market returns across asset classes.

Cautions regarding findings include:

  • Predicted anomaly premiums are gross, not net. Accounting for anomaly portfolio reformation frictions and, as applicable, shorting costs and constraints would reduce these premiums. These frictions and costs vary by asset class and equity anomaly. Net findings may therefore differ from gross findings.
  • The perspective in the paper is largely academic, with translation to investment strategies and tactics requiring considerable further effort beyond the reach of most investors, who would bear fees for delegating the work to investment/fund managers.
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