Optimizing Mutual Fund Portfolio Management through the Application of Advanced Soft Computing Techniques

The effective management of mutual fund portfolios is paramount for investors seeking to optimize returns while minimizing risk. However, traditional optimization techniques often struggle to accurately forecast portfolio performance, leading to suboptimal investment decisions. To address this challenge, this paper offers a novel solution to this problem by combining the Quantum Neural Network (QNN) for performance prediction with the Quantum-Inspired Evolutionary Algorithm (QEA) for portfolio optimisation. Building a flexible and dynamic framework to improve portfolio management's accuracy and efficiency is the main goal. With the utilisation of quantum computing concepts, the QNN uses real-time market data to make more accurate performance predictions, while the QEA effectively searches the solution space to find the best possible portfolio configurations. The suggested integrated framework outperforms conventional approaches, as evidenced by empirical testing results, which show a mean Absolute Percentage Error (MAPE) of 5.45% versus 5.68% for Traditional approaches. This indicates enhanced decision-making ability and forecast accuracy made possible by the integrated method. One potential path towards transforming mutual fund portfolio management is the implementation of quantum-inspired strategies. This novel paradigm has the potential to transform portfolio management techniques by providing investors with improved risk-adjusted returns and optimized investment strategies under turbulent market situations.