Predicting NEPSE index price using deep learning models

Abstract

Stock price prediction is a prevalent research field in both industry and academia. There is a pressing demand to develop a prediction model that captures the pattern of the financial activities with high precision to make an informed decision. Stock price prediction is challenging due to the complex, incomplete, fuzzy, nonlinear, and volatile nature of financial data. However, developing a robust model is possible due to advancements in artificial intelligence, availability of large-scale data, and increased access to computational capability. This study performs a comparative analysis of three deep learning models—the Long Short-term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in predicting the next day’s closing price of the Nepal Stock Exchange (NEPSE) index. A set of sixteen predictors is carefully chosen under the domain of the fundamental market data, macroeconomic data, technical indicators, and financial text data of the stock market of Nepal. The performances of employed models are compared using the standard assessment metrics—Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). The experimental results show that the LSTM model architecture provides a superior fit with high prediction accuracy. Moreover, statistical evidences are presented to validate the models’ reliability and robustness.