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Stock market manipulation detection using feature modelling with hybrid recurrent neural networks

Author

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  • Sashank Sridhar
  • Siddartha Mootha

Abstract

A stock market is a potent platform which handles a large of number of transactions within a second. Keeping track of every single transaction is a daunting task for regulatory bodies. The objective of a regulatory body is to ensure a fair trading environment and to verify that the price of a stock is not being manipulated. This paper proposes a hybrid stacked artificial neural network and recurrent neural network to model the static and dynamic features of stock data. Based on the manipulated stocks, affidavits provided by the Securities and Exchange Board (SEBI) of India, a daily trading dataset is created by scraping the Bombay Stock Exchange (BSE) website. The system is capable of identifying three types of manipulation scenarios. The proposed hybrid system is compared to various supervised algorithms, and various ensemble models and the system outperforms all with an accuracy of 96.06%.

Suggested Citation

  • Sashank Sridhar & Siddartha Mootha, 2022. "Stock market manipulation detection using feature modelling with hybrid recurrent neural networks," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 26(1/2), pages 47-79.
    • Handle: RePEc:ids:ijnvor:v:26:y:2022:i:1/2:p:47-79
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