By |Published On: November 3rd, 2014|Categories: Research Insights, Value Investing Research|

Decomposing the Price-Earning Ratio

Abstract:

The price-earnings ratio is a widely used measure of the expected performance of companies, and it has almost invariably been calculated as the ratio of the current share price to the previous year’s earnings. However, the P/E of a particular stock is partly determined by outside influences such as the year in which it is measured, the size of the company, and the sector in which the company operates. Examining all UK companies since 1975, we propose a modified price-earnings ratio that decomposes these influences. We then use a regression to weight the factors according to their power in predicting returns. The decomposed price-earnings ratio is able to double the gap in annual returns between the value and glamour deciles, and thus constitutes a useful tool for value fund managers and hedge funds.

Core Idea:

The paper decomposes P/E ratio into 4 factors, and then uses a regression to find the optimal weights of the factors according to their power in predicting returns:

  1. Time effects (average market P/E varies year by year);
  2. Sector effects (sectors grow at different rates over time);
  3. Size effects (positive relationship between a company’s market cap and the P/E);
  4. Idiosyncratic effects (e.g., insider buying/selling, analysts recommendations).

Alpha Highlight:

  • Using the optimum weightings doubles the average annual difference in returns between glamour and value deciles from 5.25% to 10.5%.
  • The new value portfolio constructed based on weighted P/E bracket outperforms the old by 2.4% annually.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=739665

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.

An interesting idea, but anytime results are shown with “optimal” weights, we tend to proceed with caution, as the optimal weight today may not be the optimal weight tomorrow.

About the Author: Wesley Gray, PhD

Wesley Gray, PhD
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 dedicated to an impact mission of empowering investors through education. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes has published multiple academic papers and four books, including Embedded (Naval Institute Press, 2009), Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016). Dr. Gray currently resides in Palmas Del Mar Puerto Rico with his wife and three children. He recently finished the Leadville 100 ultramarathon race and promises to make better life decisions in the future.

Important Disclosures

For informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice. Certain information is deemed to be reliable, but its accuracy and completeness cannot be guaranteed. Third party information may become outdated or otherwise superseded without notice.  Neither the Securities and Exchange Commission (SEC) nor any other federal or state agency has approved, determined the accuracy, or confirmed the adequacy of this article.

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

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