Paper Details

Name: Rohan Korgaonkar
Country: India

Year: 2025
Volume: Special Issue: Volume-12, Issue-1, Year: 2025 (January-June)
Page Number: 239-244
DOI: https://doi.org/10.5281/zenodo.15357328

The integration of Artificial Intelligence (AI) in stock trading and risk management has revolutionized financial markets by enhancing predictive accuracy, automating trading strategies, and mitigating risks. AI-driven models, including machine learning and deep learning algorithms, process vast amounts of data in real time, enabling traders to identify patterns, optimize portfolio management, and execute high-frequency trades with minimal human intervention. Moreover, AI enhances risk assessment by analyzing macroeconomic indicators, market sentiments, and historical trends to predict volatility and potential downturns.
Despite its advantages, AI adoption in stock trading presents significant challenges. Algorithmic biases, data privacy concerns, regulatory uncertainties, and the potential for market manipulation pose risks that require robust governance frameworks. Additionally, AI models rely on historical data, making them vulnerable to unprecedented market events or black swan occurrences. Striking a balance between automation and human oversight is crucial to ensuring ethical and transparent financial decision-making.
This paper explores the opportunities and challenges of AI in stock trading and risk management, assessing its impact on market efficiency, investor behavior, and financial stability. It also discusses regulatory considerations and the future trajectory of AI-driven financial markets. The findings highlight the need for continuous advancements in AI governance, model transparency, and risk mitigation strategies to harness the full potential of AI while safeguarding market integrity.
Keywords: Artificial Intelligence, Stock Trading, Risk Management, Algorithmic Trading, Market Volatility, Financial Regulation