Financial Market Forecasting: Enhancing RNNs for Improved Stock Prediction

Stock prediction, a fundamental area of research in machine learning and finance, aims at predicting future variations in stock values based on historic data. For the traders, investors, and financial institutions, stock prediction forms the basis of sound financial decisions. It facilitates all parties to predict the advance of market trends, risks diminishment, improvement in investment approaches, and returns maximization. In this paper, the authors test predictive capability of two machine learning models on stock prices of two firms listed on NIFTY 50 stock exchange: SBI and HDFC. The authors accomplish this by using a variety of indicators. The most successful model among those tested was Recurrent Neural Network (RNN), which achieved highest level of robustness and lowest Mean Absolute Error (MAE) score. The authors also enrich this RNN model with a self-attention mechanism and compare its efficiency with that of the simple RNN. By utilizing enormous volumes of historical stock data, the researchers could generate models that can capture complex patterns and the financial markets dynamics through modern machine learning techniques such as RNNs. This enhances predictive capabilities and helps investors make proper decisions.