Improving Mutual Fund Performance Analysis through the Fusion of CNN-LSTM and Explainable AI Techniques

Analysis of mutual fund performance is crucial for fund managers and investors to make wise choices. Predictions made using traditional approaches are frequently not as accurate since they are unable to identify intricate patterns in financial data. Convolutional neural networks (CNN) and long short-term memory (LSTM) networks are two examples of deep learning approaches that provide promising ways to improve the predictive accuracy of financial forecasting jobs. Incorporating explainable AI techniques can also help with risk management and decision-making by offering insights into the fundamental causes influencing mutual fund performance. By utilizing the combined strength of explainable AI techniques and CNN-LSTM architecture, this work seeks to improve mutual fund performance analysis. The aim is to create a strong framework that can forecast mutual fund performance with accuracy and offer comprehensible explanations of the underlying elements. This work is interesting because it combines explainable AI methods which are particularly useful for analyzing mutual fund performance with CNN-LSTM architecture. In this work, the dual challenges of prediction accuracy and model transparency in financial forecasting are addressed by integrating deep learning with interpretability. The proposed framework for mutual fund performance analysis uses historical data, CNN-LSTM architecture, and explainable AI methods. The model outperforms traditional methods, achieving higher predictive accuracy and providing actionable insights into fund performance drivers. The model's interpretability enhances trustworthiness and utility for investors and fund managers, empowering stakeholders with better decision-making in dynamic financial markets.

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