Stock Anomaly Smorgasbord–Wow!

/Stock Anomaly Smorgasbord–Wow!

Stock Anomaly Smorgasbord–Wow!

By | 2017-08-18T16:56:42+00:00 November 7th, 2014|Research Insights|6 Comments
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(Last Updated On: August 18, 2017)

Digesting Anomalies: An Investment Approach

Abstract:

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model’s performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies

Alpha Highlight:

The laundry list of items tested…

SSRN-id2508322.pdf_2014-11-07_08-00-41

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About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, 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 that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • sx

    Hi Wes, are you aware any literature survey that summarizes the relationship between companies performance and companies Qualitative factors?

  • Denys Glushkov

    Green, Hand and Zhang (2013, 2014) claim to test 100 anomalies and find that a remarkably large 24 of them are “multidimensionally” priced, Harvey, Liu, and Zhu (2014) conclude that only a handful of the factors among 315 tested are actually statistically significant. whereas Levi and Welch (2014) examined 600 factors and found that 49% of the factors produced zero to negative premia out-of-sample. So the dizzying factor zoo is growing.

    The real question is why investment and profitability in and out of themselves are risk factors. If profitability was a risk factor, it would make me believe that prices of profitable firms would be bid up, hence, lowering future expected returns of these stocks. In fact, it is the opposite. Behavioral stories also are not that intuitive. More importantly, only a handful of factors were found to be robust in international setting, namely, Value, Low Beta/Low Vol, and Momentum. The rest such as ROE, Gross Profitability and other Quality-like metrics do not work in international setting. So, as Hsu and Kalesnik (2014) put it, “we will gladly bet a simple blend of market, value, low beta and momentum exposures against anyone’s optimized N-factor portfolio”.

  • In general, research involving surveys is not considered reliable due to bias data samples–hence the reason you don’t see many studies like this in the academic finance journals. It would be wonderful to tie qualitative elements with quantitative elements, but the data challenges are too great. Here is an example of making it work: https://faculty.fuqua.duke.edu/~charvey/Research/Working_Papers/W105_Managerial_miscalibration.pdf

  • sx

    Would love to see some robust test based on qualitative elements!! I believe that lot of financial statement related quant factors are backward looking data, but factors like companies culture, companies’ executives, process and product , which are qualitative factors and are forward looking data, are more likely to drive companies performance.

  • hard to say without some data on the subject. Intuitively that makes sense, but intuition is often flawed

  • Bob Marlin

    Hello, I tried reading the paper “Implied Equity Duration: A New Way to measure equity risk”. I didn’t really understand it. Is there a way to make a stock screen using the results of the paper? I don’t really have a handle on the statistics to make sense of it.

    Thanks,
    Bob