Stock Price Manipulation Detection using Spiking Neural Networks

Stock market is an open marketplace for the creation, acquisition, and exchange of stocks that trade over the counter or on a stock exchange, this market accommodates billions of transactions. Stock market manipulation occurs when dealers attempt to fraudulently raise or lower a stock price to personal benefit. Pump and dump is a common manipulation technique that is frequently used to artificially boost and collapse stock prices and manually determining such activity has proven to be cumbersome. Machine learning and deep learning models have been utilized to recognize a range of stock manipulation scenarios; however, they lack the ability to model dynamically in continuous real time and trend identification gets hindered due to significant noise-to-signal ratio. Hence, this work focused on the viability of Spiking Neural Networks (SNNs) to naturally adapt and manage non-linear and temporal based input data that classical neural networks struggle with. A feed-forward network of Leaky-Integrate and Fire (LIF) neurons is proposed for stock market manipulation detection. The data employed in this research was obtained from the LOBSTER project and the Bloomberg Newcastle Business School trading room. To find the ideal network design and encoding strategy for this task, extensive experiments are conducted and experimental results and their comparison against existing models revealed that the proposed method outperforms the chosen benchmark models and is more successful at identifying patterns of stock price manipulation.