LEVERAGING EMOTION RECOGNITION TO OPTIMIZE INVESTMENT STRATEGIES IN THE FINANCIAL MARKET - Jadhav, Dhaval; Patel, Ankit D. (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Investor emotions often lead to oversized decision-making in the stock market, resulting in unrealistic or impulsive trading. In this paper, we present an application developed through this research that utilizes real-time emotion recognition via a webcam to guide investors toward more rational and informed investment choices. This application employs advanced facial expression analysis powered by machine learning to detect emotional states such as fear, greed, and confidence. Once the user's emotion is identified, the system offers customized investment guidance that mitigates emotional biases and ensures fair judgment. By integrating emotional awareness with personalized financial strategies, this tool aims to achieve better investment outcomes, avoid emotional trading mistakes, and cultivate more disciplined trading behaviour. The study highlights the potential of emotion-aware technologies to transform investment practices under varying market conditions and illustrates the impact of such technology on the financial strategies adopted.