Shakeel - Department of Management Studies, Faculty of Management studies, Jamia Millia Islamia, New Delhi-۱۱۰۰۲۵, India.
Siddiqui - Department of Management Studies, Faculty of Management studies, Jamia Millia Islamia, New Delhi-۱۱۰۰۲۵, India.
Alam - Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia,New Delhi-۱۱۰۰۲۵, India.
Kashif - Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia,New Delhi-۱۱۰۰۲۵, India.
Khan - Department of Computer Engineering, Jamia Millia Islamia University, Delhi, India.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of ۱۵, ۲۰, ۴۵, ۶۳, ۱۲۶, and ۲۵۲ days using RMSE, MAPE, and R۲ as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R۲ value throughout the longest forecast duration.

Net Asset Value, NAV prediction, Mutual Funds, N-BEATS, FLANN, LSTM