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On the Capital Market Consequences of Big Data: Evidence from Outer Space

Published online by Cambridge University Press:  05 April 2024

Zsolt Katona ,
Marcus O. Painter ,
Panos N. Patatoukas Open the ORCID record for Panos N. Patatoukas [Opens in a new window]  and
Jean Zeng
Zsolt Katona
Affiliation:
UC Berkeley [email protected]
Marcus O. Painter
Affiliation:
Saint Louis University [email protected]
Panos N. Patatoukas*
Affiliation:
UC Berkeley
Jean Zeng
Affiliation:
National University of Singapore [email protected]
*
[email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

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We use the introduction of satellite coverage of major retailers to study the capital market implications of unequal access to big data. Satellite data enabled sophisticated investors with access to such data to formulate profitable trading strategies, especially by targeting the upcoming reports of retailers with bad news for the quarter. The introduction of satellite data led to more informed short-selling activity, less informed individual buying activity, and lower stock liquidity around the reports of retailers with satellite coverage. We conclude that unequal access to big data can increase information asymmetry among market participants without immediately enhancing price discovery.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 29
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank RS Metrics and Orbital Insight for providing satellite imagery data of parking lot traffic, and Markit for providing securities lending market data. For helpful comments and discussions, we thank Hendrik Bessembinder (the editor), Chris Clifford, Zhi Da (the referee), Mike Farrell, Jill Fisch, Jillian Grennan, Kristine Hankins, Franz Hinzen (discussant), Russell Jame, Thomas Lee, Tamara Nefedova (discussant), Frank Partnoy, the PhD students at Berkeley Haas, former SEC Commissioner Robert Jackson, the DERA of the SEC, and the members of the University of Chicago Applied Math Club. We also thank seminar participants at the 2018 Miami Behavioral Finance Conference, the University of Florida, the University of Kentucky, the University of Missouri, the 2019 Future of Financial Information Conference at the Stockholm Business School, the 2019 Annual Meeting of the Financial Management Association, Harvard Business School, Kellogg School of Management, and Tulane University. We gratefully acknowledge financial support from the Fisher Center for Business Analytics at Berkeley Haas. This article is the product of merging two related papers, one of which had the current title and another that was titled “Un-Levelling the Playing Fiield: The Investment Value and Capital Market Consequences of Alternative Data.”

References

Ahn, B. H.; Bushman, R. M.; and Patatoukas, P. N.. “Under the Hood of Activist Fraud Campaigns: Private Information Quality, Disclosure Incentives and Stock Lending Dynamics.” Working Paper, U.C. Berkeley (2023).Google Scholar
Ahn, B. H., and Patatoukas, P. N.. “Identifying the Effect of Stock Indexing: Impetus or Impediment to Arbitrage and Price Discovery?” Journal of Financial and Quantitative Analysis, 57 (2022), 2022–2062.CrossRefGoogle Scholar
Banerjee, S.; Davis, J.; and Gondhi, N.. “When Transparency Improves, Must Prices Reflect Fundamentals Better?” Review of Financial Studies, 31 (2018), 2377–2414.CrossRefGoogle Scholar
Barardehi, Y. H.; Bernhardt, D.; Da, Z.; and Warachka, M.. “Uncovering the Liquidity Premium in Stock Returns Using Retail Liquidity Provision.” Available at SSRN 4057713 (2023).CrossRefGoogle Scholar
Barber, B. M.; Huang, X.; Jorion, P.; Odean, T.; and Schwarz, C.. “A (Sub) Penny for Your Thoughts: Tracking Retail Investor Activity in TAQ.” Available at SSRN 4202874 (2023).CrossRefGoogle Scholar
Battalio, R.; Jennings, R.; Saglam, M.; and Wu, J.. “Identifying Market Maker Trades As “Retail” From Taq: No Shortage of False Negatives and False Positives.” Working Paper, University of Notre Dame (2022).CrossRefGoogle Scholar
Boehmer, E.; Jones, C.; Zhang, X.; and Zhang, X.. “Tracking Retail Investor Activity.” Journal of Finance, 75 (2021), 2249–2305.CrossRefGoogle Scholar
Cao, S.; Jiang, W.; Wang, J. L.; and Yang, B.. “From Man vs. Machine to Man+Machine: The Art and AI of Stock Analyses.” NBER Working Paper No. w28800 (2021).CrossRefGoogle Scholar
Chang, R., and Da, Z.. “Nowcasting Firms’ Fundamentals: Evidence from the Cloud.” Working Paper, University of Notre Dame (2022).CrossRefGoogle Scholar
Copeland, T. E., and Galai, D.. “Information Effects on the Bid–Ask Spread.” Journal of Finance, 38 (1983), 1457–1469.Google Scholar
Dessaint, O.; Foucault, T.; and Frésard, L.. “Does Alternative Data Improve Financial Forecasting? The Horizon Effect.” Available at SSRN 3702411 (2021).CrossRefGoogle Scholar
Dugast, J., and Foucault, T.. “Data Abundance and Asset Price Informativeness.” Journal of Financial Economics, 130 (2018), 367–391.CrossRefGoogle Scholar
Dugast, J., and Foucault, T.. “Equilibrium Data Mining and Data Abundance.” HEC Paris Research Paper No. FIN-2020-1393, Université Paris-Dauphine Research Paper (3710495) (2021).CrossRefGoogle Scholar
Easley, D., and O’Hara, M.. “Price, Trade Size, and Information in Securities Markets.” Journal of Financial Economics, 19 (1987), 69–90.CrossRefGoogle Scholar
Froot, K.; Kang, N.; Ozik, G.; and Sadka, R.. “What Do Measures of Real-Time Corporate Sales Say about Earnings Surprises and Post-Announcement Returns?” Journal of Financial Economics 125 (2017), 143–162.CrossRefGoogle Scholar
Gerken, W. C., and Painter, M.. “The Value of Differing Points of View: Evidence from Financial Analysts’ Geographic Diversity.” Available at SSRN 3479352 (2019).CrossRefGoogle Scholar
Glosten, L. R., and Milgrom, P. R.. “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders.” Journal of Financial Economics, 14 (1985), 71–100.CrossRefGoogle Scholar
Gofman, M.; Segal, G.; and Wu, Y.. “Production Networks and Stock Returns: The Role of Vertical Creative Destruction.” Review of Financial Studies, 33 (2020), 5856–5905.CrossRefGoogle Scholar
Goodman-Bacon, A. “Difference-in-Differences with Variation in Treatment Timing.” Journal of Econometrics, 225 (2021), 254–277.CrossRefGoogle Scholar
Grennan, J., and Michaely, R.. “FinTechs and the Market for Financial Analysis.” Journal of Financial and Quantitative Analysis, 56 (2021), 1877–1907.CrossRefGoogle Scholar
Grossman, B. S. J., and Stiglitz, J. E.. “On the Impossibility of Informationally Efficient Markets.” American Economic Review, 70 (1980), 393–408.Google Scholar
Hayek, F. A. “The Use of Knowledge in Society.” American Economic Review, 35 (1945), 519–530.Google Scholar
Holden, C. W., and Jacobsen, S.. “Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions.” Journal of Finance, 69 (2014), 1747–1785.CrossRefGoogle Scholar
IHS Markit. “Demystifying Alternative Data: Can Alternative Data Really Enhance Your Investment Strategy?” Available at the IHS Markit Website (2019).Google Scholar
Jank, S.; Roling, C.; and Smajlbegovic, E.. “Flying Under the Radar: The Effects of Short-Sale Disclosure Rules on Investor Behavior and Stock Prices.” Journal of Financial Economics, 139 (2021), 209–233.CrossRefGoogle Scholar
Jones, C. M.; Reed, A. V., and Waller, W.. “Revealing Shorts: An Examination of Large Short Position Disclosures.” Review of Financial Studies 29 (12), 3278–3332 (2016).CrossRefGoogle Scholar
Kang, J. K.; Stice-Lawrence, L.; and Wong, Y. T. F.. “The Firm Next Door: Using Satellite Images to Study Local Information Advantage.” Journal of Accounting Research, 59 (2021), 713–750.CrossRefGoogle Scholar
Kelley, E. K., and Tetlock, P. C.. “How Wise Are Crowds? Insights from Retail Orders and Stock Returns.” Journal of Finance, 68 (2013), 1229–1265.CrossRefGoogle Scholar
Kim, O., and Verrecchia, R. E.. “Trading Volume and Price Reactions to Public Announcements.” Journal of Accounting Research, 29 (1991), 302–321.CrossRefGoogle Scholar
Kyle, A. S. “Continuous Auctions and Insider Trading.” Econometrica: Journal of the Econometric Society, 53 (1985), 1315–1335.CrossRefGoogle Scholar
Lee, C. M., and Ready, M. J. “Inferring trade direction from intraday data”. The Journal of Finance, 46 (2), 733–746 (1991).CrossRefGoogle Scholar
Lee, C. M.; Mucklow, B.; and Ready, M. J.. “Spreads, Depths, and the Impact of Earnings Information: An Intraday Analysis.” Review of Financial Studies, 6 (1993), 345–374.CrossRefGoogle Scholar
McLean, R. D., and Pontiff, J.. “Does Academic Research Destroy Stock Return Predictability?” Journal of Finance, 71 (2016), 5–32.CrossRefGoogle Scholar
Mihet, R. “Financial Technology and the Inequality Gap.” Working Paper. Available at SSRN 3474720 (2022).Google Scholar
Mukherjee, A.; Panayotov, G.; and Shon, J.. “Eye in the Sky: Private Satellites and Government Macro Data.” Journal of Financial Economics, 141 (2021), 234–254.CrossRefGoogle Scholar
Pedersen, L. H., Efficiently Inefficient. Princeton, NJ: Princeton University Press (2015).Google Scholar
Petersen, M. A., and Fialkowski, D.. “Posted Versus Effective Spreads: Good Prices or Bad Quotes?” Journal of Financial Economics, 35 (1994), 269–292.CrossRefGoogle Scholar
Roberts, M. R., and Whited, T. M.. “Endogeneity in Empirical Corporate Finance.” In Handbook of the Economics of Finance, Vol. 2, Constantinides, G., Harris, M., and Stulz, R., eds. Amsterdam, The Netherlands: Elsevier (2013), 493–572.CrossRefGoogle Scholar
Verrecchia, R. E. “Information Acquisition in a Noisy Rational Expectations Economy.” Econometrica 50 (1982), 1415–1430.CrossRefGoogle Scholar
Walton, S., and Huey, J., Made in America, Vol. 216. New York: Doubleday (1992).Google Scholar
Weller, B. M., “Does Algorithmic Trading Reduce Information Acquisition?” Review of Financial Studies, 31 (2018), 2184–2226.CrossRefGoogle Scholar
Zhu, C. “Big Data as a Governance Mechanism.” Review of Financial Studies, 32 (2019), 2021–2061.CrossRefGoogle Scholar
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