Transfer Learning and LSTM to Predict Stock Price

Abstract

Predicting stock prices has always been an attractive issue. Past literature has focused on the impact of historical stock prices and social media sentiment on stock prices, ignoring the impact on the three major corporations that account for most stock transactions. In this paper, we add the three significant corporations as the dataset in the stock trading price, but the corporate trading data announced by the stock exchange has only been available since May 2012, so the data sample is less than ten years. In the target dataset, we compared the model with the ARIMA and LSTM for error, and the migration learning model outperformed the other two models.