Predicting anomaly returns with politics, weather, global warming, sunspots, and the stars

///Predicting anomaly returns with politics, weather, global warming, sunspots, and the stars

Predicting anomaly returns with politics, weather, global warming, sunspots, and the stars

By |2017-08-18T16:59:15+00:00May 18th, 2012|Research Insights|

 

Professor Novy-Marx does some really great research. We’ve highlighted his work on a few occasions. His specific work on the gross profits factor has influenced a lot of my own research.

Novy-Marx has a really interesting piece discussing “data-mining:”

Ferson, Sarkissian and Simin (2003) warn that persistence in expected returns generates spurious regression bias in predictive regressions of stock returns, even though stock returns are themselves only weakly auto correlated. Despite this fact a growing literature attempts to explain the performance of stock market anomalies with highly persistent investor sentiment. The data suggest, however, that the potential misspecification bias may be large. Predictive regressions of real returns on simulated regressors are too likely to reject the null of independence, and it is far too easy to find real variables that have “significant power” predicting returns. Standard OLS predictive regressions find that the party of the U.S. President, cold weather in Manhattan, global warming, the El Nino phenomenon, atmospheric pressure in the Arctic, the conjunctions of the planets, and sunspots, all have “significant power” predicting the performance of anomalies. These issues appear particularly acute for anomalies prominent in the sentiment literature, including those formed on the basis of size, distress, asset growth, investment, profitability, and idiosyncratic volatility.

 

Some of the more intriguing time series that apparently predict when anomalies will perform the best (I highlighted the titles for effect):

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Anyway, in the end Fama is probably going to end up being right and all of us will go down in flames trying to trade crazy quantitative strategies or value strategies that are supposed to make us 15-20% a year. But at least we’ll have fun along the way!

 


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About the Author:

Wesley Gray, PhD
Wes Gray has published multiple academic papers and four books, including Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016).After serving as a Captain in the United States Marine Corps, Dr. Gray earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel Prize Winner Eugene Fama. Next, Wes took an academic job in his wife’s hometown of Philadelphia and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management firm that delivers affordable active exposures for tax-sensitive investors. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes currently resides in the suburbs of Philadelphia with his wife and three children.
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