Oh Jangmin
School of AI Convergence, Sungshin Women's University, Seongbuk-gu, Seoul, South Korea.
PeerJ Comput Sci. 2022 Mar 2;8:e915. doi: 10.7717/peerj-cs.915. eCollection 2022.
This paper proposes a pattern-based stock trading system using ANN-based deep learning and utilizing the results to analyze and forecast highly volatile stock price patterns. Three highly volatile price patterns containing at least a record of the price hitting the daily ceiling in the recent trading days are defined. The implications of each pattern are briefly analyzed using chart examples. The training of the neural network was conducted with stock data filtered in three patterns and trading signals were generated using the prediction results of those neural networks. Using data from the KOSPI and KOSDAQ markets, It was found that that the proposed pattern-based trading system can achieve better trading performances than domestic and overseas stock indices. The significance of this study is the development of a stock price prediction model that exceeds the market index to help overcome the continued freezing of interest rates in Korea. Also, the results of this study can help investors who fail to invest in stocks due to the information gap.
本文提出了一种基于模式的股票交易系统，该系统使用基于人工神经网络的深度学习，并利用结果来分析和预测高度波动的股票价格模式。定义了三种高度波动的价格模式，这些模式在最近交易日中至少包含一次价格触及每日上限的记录。使用图表示例简要分析了每种模式的含义。神经网络的训练使用了按三种模式过滤后的股票数据，并利用这些神经网络的预测结果生成交易信号。利用韩国综合股价指数（KOSPI）和韩国创业板指数（KOSDAQ）市场的数据，发现所提出的基于模式的交易系统能够实现比国内外股票指数更好的交易表现。本研究的意义在于开发一种超越市场指数的股价预测模型，以帮助克服韩国利率持续冻结的问题。此外，本研究结果可以帮助那些因信息差距而未能投资股票的投资者。
