TITLE:
Predicting Equity Price with Corporate Action Events Using LSTM-RNN
AUTHORS:
Shotaro Minami
KEYWORDS:
LSTM, Long-Short Term Memory(LSTM-RNN), Recurrent Neural Network (RNN), Prediction of Single Stock Price, Artificial Intelligence Finance
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.8 No.1,
January
31,
2018
ABSTRACT: Forecasting the stock price of a particular has been a difficult task for many analysts and researchers. In fact, investors are highly interested in the research area of stock price prediction. However, to improve the accuracy of forecasting a single stock price is a really challenging task; therefore in this paper, I propose a sequential learning model for prediction of a single stock price with corporate action event information and Macro-Economic indices using LTSM-RNN method. The results show that the proposed model is expected to be a promising method in the stock price prediction of a single stock with variables like corporate action and corporate publishing.