Artificial Intelligence in Stock Market Investment: Enhancing Decision-Making through Predictive Analytics and Behavioral Insights

Abstract

This research explored how artificial intelligence (AI) (and behavioral insights) could be used to improve the process of determining investments in the stock market. The study used mixed-method research design to analyze data containing historical market data and sentiment data collected in social media and financial news and assessing predictive models, such as Long Short-Term Memory (LSTM) networks, random forests, and gradient boosting machines. The findings showed that the AI-driven models were highly effective in comparison to conventional methods in prediction of stock price variations and that LSTM models are the most accurate. The addition of sentiment analysis also enhanced the level of prediction in all models, which shows substantial importance of investor sentiment and market action in determining the price of an asset. Moreover, AI models that combined behavioral knowledge attained better risk neutral returns, as well as, lower portfolio volatility. In spite of these benefits, issues surrounding model interpretability, data privacy, and regulatory compliance remained the problem, with the overall lack of significant adoption. Findings of the case study have also identified that major investment firms had high implementation costs besides finding it difficult to balance predictive power and transparency. The research has come to the conclusion that, although AI has a transformative nature in investment strategies, ethics and regulatory compliance should be at the forefront of focus. Future study in these aspects should include further interpretation of explainable and fair AI framework, tests of cross-market soundness, and further placements of viewpoints of behavioral finance to enhance that of sustainable and responsible investment.