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http://dx.doi.org/10.30693/SMJ.2022.11.1.38

Trading Algorithm Selection Using Time-Series Generative Adversarial Networks  

Lee, Jae Yoon (인하대학교 전기컴퓨터공학과)
Lee, Ju Hong (인하대학교 컴퓨터공학과)
Choi, Bum Ghi (인하대학교 컴퓨터공학과)
Song, Jae Won (밸류파인더스)
Publication Information
Smart Media Journal / v.11, no.1, 2022 , pp. 38-45 More about this Journal
Abstract
A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.
Keywords
Trading Algorithm; TimeGAN; Stock Market; Algorithm Selection; Finance;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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