Empowering Robo-Advisors: Data-Driven Mutual Fund and Stock Market Price Prediction with Deep Learning Techniques

In the contemporary financial landscape, the integration of deep learning techniques has revolutionized the capabilities of robo-advisors in providing data-driven insights for mutual fund and stock market price prediction. This study explores the application of deep learning methods, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, in empowering robo-advisors to offer personalized investment recommendations and enhance decision-making processes for investors. Leveraging vast amounts of historical financial data, including market trends, asset prices, and economic indicators, LSTM and GRU networks are adept at capturing complex temporal patterns and dependencies within sequential data, enabling more accurate predictions of future stock prices and market trends. By employing these deep learning techniques, robo-advisors can analyze market dynamics in real-time, adapt investment strategies to changing conditions, and provide tailored recommendations aligned with individual investor preferences and risk profiles. Through a comprehensive review of existing literature and empirical studies, this paper evaluates the performance and effectiveness of LSTM and GRU networks in mutual fund and stock market price prediction tasks. The findings suggest that LSTM and GRU networks offer significant advantages over traditional forecasting methods, such as autoregressive models and technical analysis, by effectively capturing long-term dependencies and nonlinear relationships within financial time series data. Moreover, the integration of additional data sources, such as news sentiment analysis and social media trends, further enhances the predictive accuracy and robustness of the models. Overall, the application of deep learning techniques in empowering robo-advisors holds immense potential for revolutionizing investment management practices, democratizing access to financial markets, and empowering investors with actionable insights for informed decision-making. The proposed method is implemented in Python and has an accuracy of about 99.12%.