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Detecting Insider Trading in the Indian Stock Market: An Optimized Deep Learning Approach

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  • Prashant Priyadarshi

    (National Institute of Technology)

  • Prabhat Kumar

    (National Institute of Technology)

Abstract

A novel approach is proposed in this study for identifying insider trading in the Indian stock market by classifying multiple multivariate time series financial data using deep learning. The model utilizes multi-channel convolutional neural network (MTC-CNN) and MTC-CNN with Optuna hyperparameter optimization. In order to test the method, insider trading samples from 2001 to 2020 are used, along with corresponding non-insider trading samples from the same period. As a result of our experiments, we found that under the following conditions of 30-, 60-, and 90-day time window lengths, the accuracy of the proposed method are 87.50%, 75.00%, and 62.50%, respectively. It has also been found that using OPTUNA hyperparameter optimization, the false positive rate was reduced by 20% for all the time windows. These accuracy rates surpass those of the benchmark models like logistic regression, random forest, and convolutional neural network, providing evidence that the proposed system is effective in identifying the activities of insider traders. The proposed deep learning model serves as a valuable tool for market regulators and investors in detecting and preventing illicit trading practices, ultimately fostering integrity and fairness in the Indian securities market.

Suggested Citation

  • Prashant Priyadarshi & Prabhat Kumar, 2025. "Detecting Insider Trading in the Indian Stock Market: An Optimized Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3923-3943, June.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:6:d:10.1007_s10614-024-10697-z
    DOI: 10.1007/s10614-024-10697-z
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    References listed on IDEAS

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