AI-Driven Portfolio Optimization: Redefining Risk and Return Dynamics in Modern Investment Strategies

Abstract

The main idea of this research work is to solve the problems related to the inadequacy of the traditional models of portfolio optimization, like MPT, in adapting to the changing and volatile nature of the market environment. The research goal is the development and evaluation of a new hybrid approach to portfolio optimization, which involves the combination of AI and classic theories of finance. The suggested methodology is based on a comparative experiment using historical stock market data in different market states. The hybrid AI model will use LSTM, Random Forest, CNN, and Reinforcement Learning for predictions in combination with mean-variance optimization and risk constraints like VaR and volatility for portfolio allocation. The following metrics will be used for performance evaluation: ROI, Sharpe Ratio, Maximum Drawdown, Volatility, and RMSE. The suggested model will prove its efficiency in comparison with traditional methods and AI methods and will have an Average Annual ROI equal to 18.9% and a higher Sharpe Ratio equal to 1.21. Furthermore, the risk metrics exhibit increased stability in the form of decreased volatility of 11.8% as well as improved performance in different market regimes, such as bull market ROI of 23.5%, bear market ROI of 12.7%, and volatile market ROI of 17.6%. Moreover, the model demonstrates minimized prediction error in the form of an RMSE of 0.028, which proves increased forecasting accuracy. In summary, the study has established that using AI-based prediction models along with traditional portfolio management techniques will improve the efficiency of the investment strategy.