Abstract
The accurate prediction of stock prices is not an easy task. The long short-term memory (LSTM) neural network and the transformer are good machine learning models for times series forecasting. In this paper, we use LSTM and transformer to predict prices of banking stocks in China’s A-share market. It is shown that organizing the input data can help get accurate outcomes of the models. In this paper, we first introduce some basic knowledge about LSTM and present prediction results using a standard LSTM model. Then, we show how to organize the input data during the training period and give the comparison results for not only LSTM but also the transformer model. The numerical results show that the prediction results of LSTM and transformer can be improved after the input data are organized when training.
Highlights
Predicting stock prices is a very important and difficult research topic for researchers from financial and academic fields
We observe that there was no big fluctuation in Chinese banking stocks, and predicting price changes of Chinese banking stocks can be handled as a time series forecasting problem
We show how to organize the data to make the input have more periodic information when using long short-term memory (LSTM) and transformer to predict the prices of Chinese banking stocks
Summary
Predicting stock prices is a very important and difficult research topic for researchers from financial and academic fields. We understand that the stock prices are affected by many uncontrolled factors, and we may need to study the annual share report, company performance, and so on to get the meaningful prediction for a stock. We observe that there was no big fluctuation in Chinese banking stocks, and predicting price changes of Chinese banking stocks can be handled as a time series forecasting problem. Long shortterm memory (LSTM) neural networks [1] and the transformer model [2] are two powerful deep learning tools for times series forecasting. We show how to organize the data to make the input have more periodic information when using LSTM and transformer to predict the prices of Chinese banking stocks
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