Author(s)

Shotaro Minami

Asuka Asset Management Co., Ltd., Tokyo, Japan.

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.

LSTM, Long-Short Term Memory(LSTM-RNN), Recurrent Neural Network (RNN), Prediction of Single Stock Price, Artificial Intelligence Finance

Minami, S. (2018) Predicting Equity Price with Corporate Action Events Using LSTM-RNN. Journal of Mathematical Finance, 8, 58-63. doi: 10.4236/jmf.2018.81005.