Challenge and Opportunity: Deep Learning-Based Stock Price Prediction by Using Bi-Directional LSTM Model

Abstract

Stock price prediction is a challenging and important task in finance, with many potential applications in investment, risk management, and portfolio optimization. In this paper, we propose a bi-directional long short-term memory (Bi-LSTM) model for predicting the future price of a stock based on its historical prices. The Bi-LSTM model is a variant of the popular LSTM model that is capable of processing input sequences in both forward and backward directions, allowing it to capture both short- and long-term dependencies in the data. We apply the Bi-LSTM model to historical stock price data for Apple Inc. and evaluate its performance using mean squared error (MSE) and visual inspection of actual vs. predicted prices. Our experiments show that the Bi-LSTM model is able to make accurate predictions on the testing data and capture some of the trends and patterns in the data, although it may struggle with sudden changes in the market. Overall, our results suggest that the Bi-LSTM model is a promising tool for stock price prediction and has many potential applications in finance and investment.