Stock Market Prediction Based on BiLSTM

Abstract

In recent years, the application of deep learning techniques in financial forecasting has garnered increasing attention due to their potential to capture complex patterns in market data. This study employed a Bidirectional Long Short-Term Memory (BiLSTM) neural network to predict the stock price trends of Apple Inc. By analyzing data sourced from Yahoo Finance, a predictive model capable of capturing stock price trends and patterns was developed. The study demonstrated satisfactory performance on the test dataset, indicating the model's effectiveness in forecasting stock prices. The findings underscore the significance of utilizing deep learning techniques for stock price prediction, with implications for financial decision-making and risk management. Utilizing a Bidirectional Long Short-Term Memory (BiLSTM) neural network, this study successfully predicted Apple Inc.'s stock price trends with favorable accuracy, capturing complex temporal dependencies. Further research avenues include enhancing model robustness across market conditions and integrating sentiment analysis for improved predictive capabilities. Overall, this work contributes to advancing stock price prediction methods, facilitating informed financial decision-making.