Abstract

Predicting the critical net asset value (NAV) in the financial market is difficult for investors and fund agencies. The present study introduces machine learning (ML) and deep learning (DL) models such as linear regression, deep long short-term memory recurrent neural network (DLSTM-RNN), and autoregressive integrated moving averages (ARIMA) for predicting the NAV. The five different equity sectoral technology mutual fund direct growth plans from January 2013 to December 2022 have been collected. The novelty of the current study is deeply examining, which ML or DL model devotedly predicts the NAV closing price. The major key findings of the experimental results proved that the DLSTM-RNN model makes statistically viable predictions, whereby the mean absolute percent error (MAPE) average prediction accuracy value is 0.02. Based on the accuracy of a superior model, we compute the annualized return volatility to compare the risk of investments with annual return periods over different time horizons. The Jarque-Bera statistics of the return volatility over time Gaussian distribution is rejected at the 0.01 level. Statistical paired t-test and Pearson correlation coefficient are used to compare the effects of the proposed three models. In addition, the benchmark portfolio strategy yields a Sharpe ratio of 7.0193 and the maximum drawdown is 0.3743. The AI performed deep LSTM neural network model simulation, especially when using a daily and monthly MAPE strategy giving 81% and 84% highest NAV prediction consistency than the linear regression and ARIMA models.

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