By |Published On: February 26th, 2014|Categories: Research Insights|

Geographic Diffusion of Information and Stock Returns

Abstract:

This study shows that value-relevant information about firms is geographically distributed across U.S. states and the market is slow in aggregating this information. The earnings and cash flow of firms can be predicted using the past performance of firms in economically relevant geographical regions, but sell-side equity analysts and institutional investors do not fully incorporate this information in their earnings forecasts and trades, respectively. Consequently, firms exhibit stronger post-earnings-announcement drift and stronger momentum in returns when geographic information is more dispersed and difficult to aggregate. A Long−Short trading strategy that exploits the slow diffusion of geographic information earns an annual, abnormal risk-adjusted return of about 9%.

Data Sources:

EDGAR, BLS, CRSP/COMPUSTAT 1995 to 2010.

Alpha Highlight:

geo

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.

~7-8%  alpha a year tracking delays in information? Not bad!

Strategy Summary:

  1. Paper first identifies economically-connected (EC) firms using the following methodology.
    • Count the number of times states are referenced in the 10-K filings. When a firm lists a state in the 10-K, all firms in this state are considered to be economically connected (EC).
      • Speficically count the number of state references in these four sections of the 10-K: “Item 1: Business”, “Item 2: Properties”, “Item 6: Consolidated Financial Data”, and “Item 7: Management’s Discussion and Analysis.”
    • EC Earnings (Cash-flow) is the citation-share weighted Earnings (Cash-flow) of firms located in EQ states, excluding the firms in the HQ state.

    2.  

  2. Using a Fama-MacBeth regression, find that past-quarter EC Earnings (Cash-flow) has predictive ability for a firm’s next-quarter Earnings (Cash-flow).
    • This has incremental predictive ability over past-quarter HQ Earnings (Cash-flow), which is the Earnings (Cash-flow) of firms in the same state as the firm’s HQ.
    • This predictive ability works at an annual-data frequency, using industry-adjusted earnings and cash-flow, in simple and conglomerate firms, and when excluding firms with strong economic links.
    • Find that analysts do not incorporate the EC information into their earnings forecast.

    3.  

  3. Construct a trading strategy:
    • A Long-short trading strategy where you long (short) firms with high (low) expected earnings surprise.
      • Forecast EPS using Fama-MacBeth regressions, and subtract analyst forecats to create expected earnings surprise variable (Pages 33-34).
      • This generates a monthly alpha of 0.75% or an annual premium of 9%.

Strategy Commentary:

  • Slow diffusion (a delay) of geographically dispersed information generates predictable patterns in stock returns.
  • Portfolio construction for trading strategy is complicated to construct.
    • A much simpler trading strategy may outperform this trading strategy.

Ready to build a textual analysis tool to identify the importance of a state to a firm?

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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.

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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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