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Beat the Market with Hot-Anomaly Switching?

March 22, 2010 • Posted in Big Ideas

Can investors beat the market by iteratively finding and exploiting the current hot anomaly? In the September 2009 update of his paper entitled “Real-Time Profitability of Published Anomalies: An Out-of-Sample Test”, Zhijian Huang investigates whether a trader can realize excess returns by repeatedly picking the anomaly with the best return during a rolling historical window from an expanding universe of anomalies as published, with a specific objective of suppressing data snooping bias. The universe includes anomalies that: (1) have been published in at least one of five top-ranked finance journals; (2) relate to the calendar or to cross-sectional predictability; and, (3) can be re-evaluated annually. Using monthly return data associated with 11 anomalies published during 1972-2005 (Monday/weekend effect, January effect and cross-sectional effects related to size, book-to-market ratio, momentum, earnings-price ratio, cash flow-price ratio, dividend yield, debt-equity ratio, sales growth and trading volume/turnover) as available from 1926 through 2008, he concludes that:

  • Assuming trading frictions of 1% for Treasury bills-stock turnover and 2% for stock-stock turnover (except 5% for the Monday effect), iteratively switching each year to the best past performer among published anomalies beats the market by an average 4.76% to 12.25% annually. (See the chart below.)
  • Excess returns are highest for past performance (training) periods of two to five years.
  • While the January effect and the momentum effect are drivers, similar results accrue from consideration of only a subgroup of anomalies. Increasing the number of anomalies considered steadily boosts average realized excess returns.
  • Outperformance of benchmark is robust to higher general and small capitalization-specific trading frictions, to an empirical model of trading frictions and to consideration of subgroups of anomalies rather than the entire group of 11.
  • Returns for nine of 11 anomalies decline after publication (see the table below). However, anomalies still generally retain value within the context of the hot anomaly selection strategy. “Old” anomalies published several years ago perform just as well as just-published “new” ones.

The following chart, taken from the paper, compares the cumulative wealth through 2008 from $1 initial investments in 1977 based on the best performing anomaly over past one, two, five, and ten years, retested/reselected annually. It shows that five-year and two-year past performance (training) periods generate the greatest excess returns, although there is no systematic relationship between training period length and cumulative return. All results include trading frictions as described above.

The following table, excerpted from the paper, compares the annual returns of the 11 anomalies before and after publication: Monday effect, January effect (JanEffect) and cross-sectional effects related to size, book-to-market ratio (B/M), momentum, earnings-price ratio (E/P), cash flow-price ratio (CF/P), dividend yield (Div/P), debt-equity ratio (D/E), sales growth (GS) and trading volume/turnover. Excess Before (Excess After) is the average annual return in excess of the the benchmark return before (after) the year of publication. Relative performance declines post-publication for 10 of 11 anomalies. Data for the Volume anomaly is limited.

In summary, evidence indicates that a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.

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