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Reducing Capital Market Anomaly: The Role of Information Technology Using an Information Uncertainty Lens

Author

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  • Ning Jia

    (School of Economics and Management, Tsinghua University, 100084 Beijing, People’s Republic of China;)

  • Arun Rai

    (Center for Process Innovation and Department of Computer Information Systems, Robinson College of Business, Georgia State University, Atlanta, Georgia 30303;)

  • Sean Xin Xu

    (Research Center for Contemporary Management, Key Research Institute of Humanities and Social Sciences at Universities, School of Economics and Management, Tsinghua University, 100084 Beijing, People’s Republic of China)

Abstract

We investigate how firms use information technology (IT) implementation to mitigate an anomaly in capital markets: investors underreacting to new public information. The theory of information uncertainty (IU) suggests that the anomaly is amplified with IU; that is, with ambiguity in information about firm value. We theorize that a firm’s IT in general—and enterprise systems (ES) in particular—can mitigate IU, thus reducing the IU-induced underreaction anomaly. Based on a difference-in-differences analysis of a sample of 572 ES implementations, our main finding is that ES implementation does reduce IU-induced underreaction anomaly. This is achieved through a reduction in the firm’s fundamentals volatility and an improvement in information quality. We also find that firms with greater IT capability are better positioned to realize the anomaly-reducing benefits of ES implementation and that ES’s anomaly-reducing effect is most pronounced when high levels of both functional and operational ES modules are implemented. We obtain remarkably consistent results when using alternate empirical design, samples, and measures of news. Such IT impacts are economically highly consequential because they improve capital market efficiency.

Suggested Citation

  • Ning Jia & Arun Rai & Sean Xin Xu, 2020. "Reducing Capital Market Anomaly: The Role of Information Technology Using an Information Uncertainty Lens," Management Science, INFORMS, vol. 66(2), pages 979-1001, February.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:2:p:979-1001
    DOI: 10.1287/mnsc.2018.3235
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