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Big data application and corporate investment decisions: Evidence from A-share listed companies in China

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  • Wang, Li
  • Wu, Yuhan
  • Huang, Zeyu
  • Wang, Yanan

Abstract

Based on a natural language processing (NLP) method and annual reports from A-share listed companies in China from 2011 to 2020, this paper constructs a big data index for the corporate level and studies the impact of big data on corporate investment decisions. The study finds that big data significantly improves corporate investment decisions. From the perspective of index decomposition, data mining and mobile service are found to significantly improve corporate investment efficiency, while algorithm development and intelligent manufacturing mainly enhance the corporate investment level. The study also notes that heterogeneity may exist due to differences in financial, innovation, and legal environments. In terms of mechanisms, big data can positively influence corporate investment decisions by improving R&D investment and reducing transaction costs.

Suggested Citation

  • Wang, Li & Wu, Yuhan & Huang, Zeyu & Wang, Yanan, 2024. "Big data application and corporate investment decisions: Evidence from A-share listed companies in China," International Review of Financial Analysis, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:finana:v:94:y:2024:i:c:s1057521924002631
    DOI: 10.1016/j.irfa.2024.103331
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    1. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2021. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 333-372, January.
    2. Holmstrom, Bengt, 1989. "Agency costs and innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 12(3), pages 305-327, December.
    3. Heitor Almeida & Murillo Campello, 2007. "Financial Constraints, Asset Tangibility, and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1429-1460, 2007 12.
    4. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    5. Holmström, Bengt, 1989. "Agency Costs and Innovation," Working Paper Series 214, Research Institute of Industrial Economics.
    6. Brandon Julio & Youngsuk Yook, 2012. "Political Uncertainty and Corporate Investment Cycles," Journal of Finance, American Finance Association, vol. 67(1), pages 45-84, February.
    7. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    8. Nicholas Bloom & Raffaella Sadun & John Van Reenen, 2012. "Americans Do IT Better: US Multinationals and the Productivity Miracle," American Economic Review, American Economic Association, vol. 102(1), pages 167-201, February.
    9. Steven M. Fazzari & R. Glenn Hubbard & Bruce C. Petersen, 1988. "Financing Constraints and Corporate Investment," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 19(1), pages 141-206.
    10. Thomas Niebel & Fabienne Rasel & Steffen Viete, 2019. "BIG data – BIG gains? Understanding the link between big data analytics and innovation," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(3), pages 296-316, April.
    11. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    12. Chen, Sheng-Syan & Chen, Yan-Shing & Liang, Woan-lih & Wang, Yanzhi, 2013. "R&D Spillover Effects and Firm Performance Following R&D Increases," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1607-1634, October.
    13. 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.
    14. Maryam Farboodi & Roxana Mihet & Thomas Philippon & Laura Veldkamp, 2019. "Big Data and Firm Dynamics," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 38-42, May.
    15. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    16. Kohtamäki, Marko & Parida, Vinit & Oghazi, Pejvak & Gebauer, Heiko & Baines, Tim, 2019. "Digital servitization business models in ecosystems: A theory of the firm," Journal of Business Research, Elsevier, vol. 104(C), pages 380-392.
    17. Christina Zhu, 2019. "Big Data as a Governance Mechanism," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2021-2061.
    18. Pikulina, E.S. & Renneboog, Luc & Tobler, P.N., 2017. "Overconfidence and investment : An experimental approach," Other publications TiSEM 940a1d28-f38f-4953-9790-5, Tilburg University, School of Economics and Management.
    19. Pikulina, Elena & Renneboog, Luc & Tobler, Philippe N., 2017. "Overconfidence and investment: An experimental approach," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 175-192.
    20. Steven N. Kaplan & Luigi Zingales, 1997. "Do Investment-Cash Flow Sensitivities Provide Useful Measures of Financing Constraints?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(1), pages 169-215.
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