Prediction Stock Price Based on Different Index Factors Using LSTM

Abstract

Predicting stock price has been a challenging project for many researchers, investors, and analysts. Most of them are interested in knowing the stock price trend in the future. To get a precise and winning model is the wish of them. Recently, Neural Network has been a prevalent means for stock prediction. However, there are many ways and different predicting models such as Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). In this paper, we propose a novel idea that average previous five days stock market information (open, high, low, volume, close) as a new value then use this value to predict, and use the predicted value as the average of the stock price information for the next five days. Moreover, we utilize Technical Analysis Indicators to consider whether to buy stocks or continue to hold stocks or sell stocks. We use Foxconn company data collected from Taiwan Stock Exchange for testing with the Neural Network Long Short-Term Memory (LSTM).