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The application of neural networks to predict abnormal stock returns using insider trading data

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  • Alan M. Safer

Abstract

Until now, data mining statistical techniques have not been used to improve the prediction of abnormal stock returns using insider trading data. Consequently, an investigation using neural network analysis was initiated. The research covered 343 companies for a period of 4½ years. Study findings revealed that the prediction of abnormal returns could be enhanced in the following ways: (1) extending the time of the future forecast up to 1 year; (2) increasing the period of back aggregated data; (3) narrowing the assessment to certain industries such as electronic equipment and business services and (4) focusing on small and midsize rather than large companies. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Alan M. Safer, 2002. "The application of neural networks to predict abnormal stock returns using insider trading data," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(4), pages 381-389, October.
    • Handle: RePEc:wly:apsmbi:v:18:y:2002:i:4:p:381-389
    DOI: 10.1002/asmb.466
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    Cited by:

    1. M. Fevzi Esen & Emrah Bilgic & Ulkem Basdas, 2019. "How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(2), pages 60-70, April.

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