V2I12P20

AI Reliability in Financial Decisions: Investor Sentiment and Portfolio Risk Management

Niyatha^1*, Dr. Geeta Joshi²

Abstract

Artificial intelligence (AI) is reshaping financial decision-making by helping investors better understand market behaviour and manage portfolio risks. Unlike traditional approaches, AI can rapidly analyze enormous amounts of data from markets, news, and social media, providing insights that would be impossible to gather manually. One of its most important applications is in identifying investor sentiment—the mood or attitude of investors—which strongly influences market movements. By analyzing these patterns, AI can help predict short-term trends and reduce the effect of emotional or biased decisions. It also plays a key role in portfolio risk management, suggesting how to diversify investments, simulating performance in stressful scenarios, and highlighting hidden vulnerabilities. These capabilities are particularly valuable during periods of uncertainty, when markets are highly volatile and risks are harder to measure. Despite its promise, the reliability of AI in finance still faces challenges. Its effectiveness depends on the quality of data it uses, and sentiment data can often be noisy or misleading. Many AI systems are also complex and lack transparency, making it difficult for investors to fully understand how results are generated. In some cases, algorithms may overreact to sudden changes in sentiment or market conditions, unintentionally increasing risks. This highlights the need for a balanced approach. Rather than replacing human judgment, AI should complement it—offering speed and analytical strength, while human expertise contributes context, ethics, and long-term perspective. A hybrid model that combines both can create more reliable and trustworthy outcomes. Ultimately, the success of AI in finance depends not only on advanced technology but also on responsible use, strong oversight, and continuous adaptation to changing market environments.

Keywords:

Artificial intelligence; investor sentiment; portfolio risk management; market volatility