Predicting Mutual Fund Performance based on LSTM Models

Abstract

Accurate prediction of mutual fund performance is becoming crucial for investors and fund managers to make informed investment decisions and earn profits. This paper utilized Long Short-Term Memory (LSTM) models to forecast the future performance of mutual funds in this research paper. This paper began by collecting a comprehensive dataset comprising historical fund prices and relevant financial indicators from Yahoo Finance. The dataset is preprocessed to handle missing values and format errors. LSTM models are known for their ability to retain and utilize information from earlier time steps in a sequence to make predictions or decisions at later time steps. This capability enables them to capture and understand long-term dependencies or relationships between elements in the sequential data, which are employed to understand and model the changes and trends in the performance of a mutual fund over time. The models are trained on a subset of the dataset, and hyperparameters are optimized to enhance their predictive capabilities. Evaluation metrics such as Mean Squared Error (MSE), the mean absolute error (MAE), root mean square error (RMSE), and R² are employed to evaluate how accurately the models can predict future outcomes or events based on the available data. In conclusion, this research presents a robust methodology for predicting mutual fund performance using LSTM models. The findings highlight the potential of LSTM models as valuable tools for fund managers and investors, offering enhanced accuracy and providing valuable insights for informed decision-making in the dynamic and competitive mutual fund market.