Stock Market Manipulation Detection using Deep Learning approach

Abstract

"The world of financial markets changes rapidly, which makes it hard to identify stock market frauds. Even though traditional rule-based systems work, they don't always have the advanced detection tools needed to identify complex frauds. These schemes n hurt the fairness of the market, , also cost investors and companies considerable amounts of funds, which means that financial world require more effective methods to identify it. In order to address this problem, the author is proposed to create an novel method for finding anomalies which employs a selective learning transformer-based architecture The model uses selective learning in an attempt identify the complex patterns of market influence that other methods miss a majority of the time. The novel approach has the potential to be far more reliable than rule-based systems, also marks an important leap forward in using deep learning to find frauds in finance. The proposed model has achieved an overall better accuracy compared to other models, such as LSTM, in the market manipulation domain. Additionally, this research has received better feedback from evaluators. In conclusion, this project has managed to contribute significantly to the financial domain."