The Firm Next Door: Using Satellite Images to Study Local Information Advantage

  • Kang, Stice-Lawrence, Wong
  • Journal of Accounting Research, 2021
  • A version of this paper can be found here.
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

What are the Research Questions?

Prior academic research has shown(1) that “local” investors (i.e., investors are physically located near the company) may benefit from an information advantage. Researchers in this paper study use satellite imagery, which was not commercially available during the study sample, to identify the source of “local” investor edge.

The researchers ask the following question:

  1. Do institutional investors have an information advantage relating to local operations?

What are the Academic Insights?

By using satellite image data on the number of cars in nearby store parking lots as a measure of store-level performance (92,668 stores of 71 publicly listed U.S. retailers during the years 2009 to 2015(2)), the authors find that:

  1. Investors adjust their stock holdings in line with the car counts of local stores, but not with the concurrent performance of the overall firm. On average, a one standard deviation increase in local car counts is related to investors increasing their holdings by 9% to 14%.

  2. Trades that incorporate local car count information yield annualized abnormal returns of roughly 2%. These abnormal returns are comparable in economic magnitude to those documented in the previous literature(3) and suggest that information about local operations is one source of local information advantage.

  3. The response to local information is greater in regions with stronger social ties.

  4. Institutional investors who are industry specialists are more responsive to local information.

  5. Institutional investors who charge their clients performance fees respond more to local information, consistent with them having greater incentives to use local information when they stand to benefit personally.

  6. The response to local information is largest for non-indexer institutional investors, whose active investment strategies are more reliant on timely information signals.

  7. After the satellite data used in this study became commercially available, institutional investors who had access to the data began incorporating the car count information of all stores into their trades, with no incremental response to the performance of local stores. These results suggest that access to big data enables investors to overcome their non-local information disadvantage, providing additional evidence on the benefits of big data.

Why does it matter?

This paper adds to the emerging field which utilizes big data to examine economic questions. It is particularly timely as investors now routinely supplement the formal financial statements with less traditional information sources such as satellite imagery. At the same time, there are some caveats to keep in mind: the results of the study may have limited generalizability because it examines only institutional investors of retail firms, and examines only the local information that is correlated with car counts. Also, the study cannot observe investors’ information sets and must infer their information by observing their trades

The Most Important Chart from the Paper:

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.


We use novel satellite data that track the number of cars in the parking lots of 92,668 stores for 71 publicly listed U.S. retailers to study the local information advantage of institutional investors. We establish car counts as a timely measure of store-level performance and find that institutional investors adjust their holdings in response to the performance of local stores, and that these trades are profitable on average. These results suggest that local investors have an advantage when processing information about nearby operations. However, some institutional investors do not adjust for the quality of their local information and continue to rely on local signals even when they are poor predictors of firm performance and returns. This overreliance on poor local information is reduced for institutional investors with greater industry expertise and those with greater incentives to maximize short-term trading profits.

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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.

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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