Artificial intelligence techniques and their impact on abnormal stock returns

Abstract

This study aims to analyze the extent to which artificial intelligence (AI) technologies influence the abnormal returns of bank stocks, by applying the research to a sample of ten banks listed on the Iraq Stock Exchange over the period from 2014 to 2023. The study collected primary data by evaluating the level of AI adoption across various dimensions, including machine learning, natural language processing, robotic process automation (RPA), and automation in internal audit functions. Abnormal stock returns were measured using the market model, which defines abnormal return as the difference between the actual return and the expected return of a stock. The study was based on the central hypothesis that there is no direct impact of AI technologies on the abnormal returns of Iraqi bank stocks. To test this hypothesis, a set of statistical methods was employed using SPSS, EViews, and Amos software. The results of the statistical analysis indicated that there is no statistically significant relationship between the level of AI adoption and abnormal stock returns within the Iraqi context. This suggests that, in the studied environment, AI technologies do not exert a direct influence on abnormal returns. In light of these findings, the study recommends conducting more detailed future research to examine the impact of each AI technology individually. Furthermore, it is important to explore potential challenges that may hinder the effective adoption of AI, such as cybersecurity concerns, privacy protection, and the efficiency of AI applications in forecasting stock prices, in order to attract investors and enhance market efficiency.