Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models

Abstract

Accurately predicting stock price trends is of critical importance in the financial sector, enabling both individuals and enterprises to make informed and profitable decisions. In recent years, researchers have employed a variety’ of techniques to forecast stock market trends, yet the challenge of improving accuracy remains. This research introduces an innovative approach to predicting stock prices, employing two sophisticated models: Long Short-Tenn Memory (LSTM) and Bidirectional Long Short-Tenn Memory (Bi-LSTM) networks. Through rigorous analysis, the research demonstrates that, with proper hypeiparameter tuning. LSTM models are capable of making highly accurate predictions of future stock trends, a capability’ that is also exhibited by Bi-LSTM models. The study’ evaluates the models by’ measuring the Root Mean Square Error (RMSE) while varying key factors. Publicly available stock market information. such as the highest and lowest prices, and opening and closing prices, is utilized for evaluating model effectiveness. The results indicate that the Bi-LSTM model is superior to the LSTM model in terms of RMSE. making it a more effective methodology for stock price forecasting and aiding in strategic decision-making.