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Equilibrium Data Mining and Data Abundance

Author

Listed:
  • Jérôme Dugast

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Thierry Foucault

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

Abstract
We model of the search for predictors by speculators (active asset managers) and use it to analyze how the improvement in data processing power and the growth in available data ("data abundance") affect the diversity of trading signals used by speculators, the dispersion of their profits and the similarities of their holdings. Our central message is that data abundance and computing power do not have the same effects. In particular, an improvement in computing power always raises the bar for the quality of predictors that managers consider good enough to exploit while more data lower it when data becomes sufficiently abundant. When this happens, the diversity of speculators' signals and the dispersion of their trading profits increase in equilibrium while their holdings become less correlated.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jérôme Dugast & Thierry Foucault, 2023. "Equilibrium Data Mining and Data Abundance," Post-Print hal-04390474, HAL.
    • Handle: RePEc:hal:journl:hal-04390474
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    as
    1. Ewens, Michael & Nanda, Ramana & Rhodes-Kropf, Matthew, 2018. "Cost of experimentation and the evolution of venture capital," Journal of Financial Economics, Elsevier, vol. 128(3), pages 422-442.
    2. Shiyang Huang & Yan Xiong & Liyan Yang, 2022. "Skill Acquisition and Data Sales," Management Science, INFORMS, vol. 68(8), pages 6116-6144, August.
    3. Jules H. van Binsbergen & Xiao Han & Alejandro Lopez-Lira, 2020. "Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases," NBER Working Papers 27843, National Bureau of Economic Research, Inc.
    4. Li, Haitao & Zhang, Xiaoyan & Zhao, Rui, 2011. "Investing in Talents: Manager Characteristics and Hedge Fund Performances," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(1), pages 59-82, February.
    5. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    6. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.
    7. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    8. Bai, Jennie & Philippon, Thomas & Savov, Alexi, 2016. "Have financial markets become more informative?," Journal of Financial Economics, Elsevier, vol. 122(3), pages 625-654.
    9. Han, Jungsuk & Sangiorgi, Francesco, 2018. "Searching for information," Journal of Economic Theory, Elsevier, vol. 175(C), pages 342-373.
    10. Joseph Gerakos & Juhani T. Linnainmaa & Adair Morse, 2021. "Asset Managers: Institutional Performance and Factor Exposures," Journal of Finance, American Finance Association, vol. 76(4), pages 2035-2075, August.
    11. Marcin Kacperczyk & Amit Seru, 2007. "Fund Manager Use of Public Information: New Evidence on Managerial Skills," Journal of Finance, American Finance Association, vol. 62(2), pages 485-528, April.
    12. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1.
    13. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    14. Martin, Ian W.R. & Nagel, Stefan, 2022. "Market efficiency in the age of big data," Journal of Financial Economics, Elsevier, vol. 145(1), pages 154-177.
    15. Nicolae Gârleanu & Lasse Heje Pedersen, 2018. "Efficiently Inefficient Markets for Assets and Asset Management," Journal of Finance, American Finance Association, vol. 73(4), pages 1663-1712, August.
    16. Grennan, Jillian & Michaely, Roni, 2021. "FinTechs and the Market for Financial Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 56(6), pages 1877-1907, September.
    17. Judith Chevalier & Glenn Ellison, 1999. "Are Some Mutual Fund Managers Better Than Others? Cross‐Sectional Patterns in Behavior and Performance," Journal of Finance, American Finance Association, vol. 54(3), pages 875-899, June.
    18. Vives, Xavier, 1995. "Short-Term Investment and the Informational Efficiency of the Market," The Review of Financial Studies, Society for Financial Studies, vol. 8(1), pages 125-160.
    19. Laura L. Veldkamp, 2011. "Information Choice in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 9621.
    20. William D. Nordhaus, 2021. "Are We Approaching an Economic Singularity? Information Technology and the Future of Economic Growth," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 299-332, January.
    21. Campbell R. Harvey, 2017. "Presidential Address: The Scientific Outlook in Financial Economics," Journal of Finance, American Finance Association, vol. 72(4), pages 1399-1440, August.
    22. Xuemin (Sterling) Yan & Lingling Zheng, 2017. "Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1382-1423.
    23. Christina Zhu, 2019. "Big Data as a Governance Mechanism," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2021-2061.
    24. Verrecchia, Robert E, 1982. "Information Acquisition in a Noisy Rational Expectations Economy," Econometrica, Econometric Society, vol. 50(6), pages 1415-1430, November.
    25. Berk, Jonathan B. & van Binsbergen, Jules H., 2015. "Measuring skill in the mutual fund industry," Journal of Financial Economics, Elsevier, vol. 118(1), pages 1-20.
    26. Maryam Farboodi & Laura Veldkamp, 2020. "Long-Run Growth of Financial Data Technology," American Economic Review, American Economic Association, vol. 110(8), pages 2485-2523, August.
    27. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, September.
    28. Dugast, Jérôme & Foucault, Thierry, 2018. "Data abundance and asset price informativeness," Journal of Financial Economics, Elsevier, vol. 130(2), pages 367-391.
    29. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    30. Itay Goldstein & Shijie Yang & Luo Zuo, 2020. "The Real Effects of Modern Information Technologies: Evidence from the EDGAR Implementation," NBER Working Papers 27529, National Bureau of Economic Research, Inc.
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    1. Boot, Arnoud & Hoffmann, Peter & Laeven, Luc & Ratnovski, Lev, 2021. "Fintech: what’s old, what’s new?," Journal of Financial Stability, Elsevier, vol. 53(C).

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    More about this item

    Keywords

    Alternative Data; Data Abundance; Data Mining; Price Informativeness; Search for Information;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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