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http://dx.doi.org/10.5392/JKCA.2022.22.03.081

A Study on Stock Trading Method based on Volatility Breakout Strategy using a Deep Neural Network  

Yi, Eunu (동국대학교 일반대학원 핀테크블록체인학과)
Lee, Won-Boo (동국대학교 일반대학원 핀테크블록체인학과)
Publication Information
The Journal of the Korea Contents Association / v.22, no.3, 2022 , pp. 81-93 More about this Journal
Abstract
The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.
Keywords
Stock Trading; Deep Learning; Volatility Breakout; Bi-directional Long Short Term Memory;
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Times Cited By KSCI : 1  (Citation Analysis)
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