ASMODS: Intelligent Detection of Abnormal Stock Price Movements in Response to Social Media Postings

摘要

In recent years, the spreading of malicious social media messages about financial stocks has threatened the security of financial market. However, identifying these threats from noisy social media datasets challenge both research and practitioner communities. This paper describes a system named ASMODS for intelligent detection of abnormal stock price movements in response to social media postings. The system consists of intelligent modules to develop synchronized data collections of social media messages and stock prices, to identify abnormal price movements, and to balance the datasets for machine learning. Empirical findings show that ASMODS could enhance the effectiveness of logistic regression, artificial neural networks, and decision trees on detecting abnormal stock returns. The results have strong implication for cybersecurity in the financial market.