Deep Learning in Stock Market Forecasting: Comparative Analysis of Neural Network Architectures Across NSE and NYSE

Abstract

This research explores the application of four deep learning architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN)—in predicting stock prices using historical data. Focusing on day-wise closing prices from the National Stock Exchange (NSE) of India and the New York Stock Exchange (NYSE), the study trains the neural network on NSE data and tests it on both NSE and NYSE stocks. Surprisingly, the CNN model outperforms the others, successfully predicting NYSE stock prices despite being trained on NSE data. Comparative analysis against the ARIMA model underscores the superior performance of neural networks, emphasizing their potential in forecasting stock market trends. This research sheds light on the shared underlying dynamics between distinct markets and demonstrates the efficacy of deep learning architectures in stock price prediction.

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