Stock Market Prediction using Bi-Directional LSTM

Abstract

Stock market prediction is quite challenging as the market is volatile and its direction is stochastic. The stock market gets driven by several factors like investor sentiment, economic strength, market rumors, inflation. All these aspects together make the stock market quite turbulent and hence very difficult to predict with accuracy. In this paper, we analyzed traditional Machine Learning prediction models and figured out the drawbacks associated with them. Hence we scrutinized a range of stock prediction models and finally singled out the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network. It intends to find out the title role of time series by analyzing historical data of different stocks and predict stock price trends. They form a unified framework for depth and time calculation learning faster than the one-directional approach. It can capture the temporal evolution of information which allows this model to attain the best performance.