Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks

摘要

With the growing importance of Internet-based businesses, malicious code attacks on information technology infrastructures have been on the rise. Prior studies have indicated that these malicious attacks are associated with detrimental economic effects on the attacked firms. On the other hand, we conjecture that more intense malicious attacks boost the stock price of information security firms. Furthermore, we use artificial neural networks and vector autoregression analyses as complementary methods to study the relationship between the stock market returns of information security firms and the intensity of malicious attacks, computed as the product of the number of malicious attacks and their severity levels. A major contribution of this work is the resulting time-delayed artificial neural network model that allows stock return predictions and is particularly useful as an investment decision support system for hedge funds and other investors, whose portfolios are at risk of losing market value during malicious attacks.