Journal of Mathematical Finance, 2018, 8, 58-63
http://www.scirp.org/journal/jmf
ISSN Online: 2162-2442
ISSN Print: 2162-2434
DOI:
10.4236/jmf.2018.81005 Jan. 31, 2018 58
Journal of Mathematical Finance
Predicting Equity Price with Corporate Action
Events Using LSTM-RNN
Shotaro Minami
Asuka Asset Management Co., Ltd., Tokyo, Japan
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 forecas
t-
ing 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 mo
del is expected to
be a pr
omising method in the stock price prediction of a single stock with
variables like corporate action and corporate publishing.
Keywords
LSTM, Long-Short Term Memory(LSTM-RNN), Recurrent Neural
Network (RNN), Prediction of Single Stock Price, Artificial Intelligence
Finance
1. Introduction
In the practice of stock asset management business, forecasting the stock price is
one of the important tasks. Stock prices are ultimately determined by supply and
demand, but some investors judge buying and selling by examining the compa-
ny’s future performance, and some investors judge only by looking at the
movement of stock prices. Also, there are some investors who judge the under
and over-valued stocks with reference to stock market index such as PER and
PBR. Some investors buy because it is a favorite company. Thus, it is not easy to
predict future stock prices because stock prices are determined by market par-
ticipants with various perspectives and ideas.
The method of predicting stock prices from the company’s future perfor-
How to cite this paper:
Minami, S. (2018
)
Predicting Equity Price with Corporate
Action Events Using LSTM
-RNN.
Journal
of Mathematical Finance
,
8
, 58-63.
https://doi.org/10.4236/jmf.2018.81005
Received:
November 28, 2017
Accepted:
January 28, 2018
Published:
January 31, 2018
Copyright © 201
8 by author and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
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