Publication Details
Abstract
Artificial intelligence (AI) has significantly transformed investment strategies in financial markets by enabling data-driven decision-making at unprecedented scale and speed. However, its application in volatile market conditions raises critical questions—not only about financial performance but also ethical implications. This study explores the dual impact of AI-based investment strategies, focusing on both their behavior during periods of market volatility and the ethical challenges they present.
Leveraging the World Stock Prices (Daily Updating) dataset, which covers daily stock price data for major global brands from 2000 to 2025, we developed predictive AI models for stock forecasting and portfolio allocation. These models were evaluated against conventional investment benchmarks under varying volatility regimes. To support interpretability and examine regional and sectoral trends, a multi-tool visualization framework was employed using Python, Tableau, and Excel.
Key performance metrics included cumulative returns, Sharpe ratio, and maximum drawdown. Ethical considerations were assessed through indicators such as model bias, opacity, and risk exposure patterns. The results show that AI-driven strategies outperformed traditional benchmarks in moderately volatile conditions but struggled during periods of extreme market turbulence, largely due to limited model generalization.
Ethical analysis revealed critical concerns, including opaque decision-making, uneven asset allocation, and regional disparities, highlighting the need for improved transparency and fairness. This study contributes to the discourse on responsible AI in finance by addressing the tension between performance maximization and ethical responsibility. It recommends the development of transparent, auditable AI frameworks that minimize bias and promote long-term, equitable outcomes for investors and society.