Utilization of Artificial Intelligence in Investment Decisions Under Market Volatility : Manager vs. Machine

The adoption of artificial intelligence (AI) in the financial sector has consistently increased, driven by the AI boom that began in 2015. However, the amount of prior study of AI-powered instruments is quite limited, especially in volatile market conditions. Furthermore, the number of empirical studies comparing the performance of AI portfolio managers to their human counterparts is notably scarce. Thus, the study aims to fill this gap in the existing literature and determine whether AI outperforms a traditional portfolio manager under volatile market conditions, considering the Ukrainian conflict and Silicon Valley Bank (SVB) collapse. The study divided the sample data into two categories: AI-managed funds and human-managed funds. The event study method was selected as the research approach, with the aim of identifying possible abnormal returns during the events. Abnormal returns were calculated for ±20 days around the event date. Additionally, cumulative and holding period returns for eight separate observation periods were determined. All returns were risk-adjusted using the S&P500 index. Furthermore, a systematic literature review was conducted to examine previous empirical studies. The purpose was to answer a specific research question about psychological and other factors that influence the investment decision-making process and its outcomes between AI and human. The results indicate that neither management approach, AI nor a human portfolio manager, consistently outperformed the other. AI funds exhibited higher abnormal returns during the Ukrainian conflict, while human-managed funds had higher abnormal returns during the SVB collapse. Conversely, AI funds demonstrated higher long-term performance after both events. Furthermore, the findings imply that the selection of better management approach in the investment decision-making depends on specific circumstances. The results highlight that neither decision-making method—AI or human-driven—is mutually exclusive; instead, they serve different purposes, each with distinct strengths and weaknesses. A hybrid approach, combining the strengths of both AI and human portfolio managers, could optimize performance across various investment situations, results state. Moreover, the study introduced two hypotheses: H0, the null hypothesis, assumed that the utilization of artificial intelligence has no impact on investment performance in volatile market conditions. Alternatively, H1, the alternative hypothesis, posited that the utilization of artificial intelligence does impact investment performance in volatile market conditions. Based on the results, the null hypothesis, H0, was rejected and while the results suggested strong support to the alternative hypothesis, H1. In addition, it should be noted that the sample size of this thesis could have been larger, but that would have been challenging due to the comprehensive research approach focusing on each individual fund. As a further research proposal, the number of samples and the observation period should be increased in order to create more significant results. Also, another proposal is to enhance this research by integrating AI algorithms into investment decisions at a practical level. However, the implementation can bring challenges due to companies' data encryption principles