Detection of Market Manipulation using Ensemble Neural Networks

A stock market is a large trading environment, capable of handling millions of transactions. It is extremely difficult for regulatory bodies to manually detect whether a transaction was fraudulent or not. With the help of machine learning, it is possible to detect various scenarios of market manipulation. Market manipulation is when traders try to inflate or deflate the price of a stock to their advantage. This paper proposes to identify and detect market manipulation by implementing an Ensemble Neural Network. Our proposed system can identify three types of manipulation scenarios, i.e. Price manipulation, Volume Manipulation, and Trade Reversal. Based on the affidavit information provided by the Securities and Exchange Board of India (SEBI), a daily trading dataset was created from the Bombay Stock Exchange (BSE) website. The Ensemble Neural Network model with and without trainable sub-model layers was implemented on the daily trading dataset. The model with trainable sub-model layers achieved an accuracy of 91% and without trainable submodel layers achieved an accuracy of 96%