Browsing by Author "Chen, Yu-Ying"

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Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets
(2016-01-01)
Tung, Hui-Hsuan
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Chen, Yu-Ying
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Huang, Szu-Hao
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Cheng, Chiao-Chun
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Chen, Yu-Fu
This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine learning, and binary classification, are used to improve the system performance and make the evaluation of trading simulation trustworthy. Through experimenting on the component stocks of S&P 500, the results show that the accuracy can achieve 70% when adopting two discriminant feature representation methods, including "multi-day technical indicators" and "intra-day trading profile." The binary classification method based on LDA-Linear Prior kernel outperforms than other learning techniques and provides the investor a stable and profitable portfolios with low risk. In addition, we believe this paper is a FinTech example which combines advanced interdisciplinary researches, including financial anomalies and big data analysis technology.
Developing Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithm
(2018-01-01)
Chen, Yu-Ying
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Chen, Wei-Lun
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Huang, Szu-Hao
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Pairs trading is a statistical arbitrage strategy, which selects a set of assets with similar performance and produces profits during these asset prices far away from rational equilibrium. Once this phenomenon exists, traders can earn the spread by longing the underperforming asset and shorting the outperforming asset. This paper proposed a novel intelligent high-frequency pairs trading system in Taiwan Stock Index Futures (TX) and Mini Index Futures (MTX) market based on deep learning techniques. This research utilized the improved time series visualization method to transfer historical volatilities with different time frames into 2D images which are helpful in capturing arbitrage signals. Moreover, this research improved convolutional neural networks (CNN) model by combining the financial domain knowledge and filterbank mechanism. We proposed Filterbank CNN to extract high-quality features by replacing the random-generating filters with the arbitrage knowledge filters. In summary, the accuracy is enhanced through the proposed method, and it proves that the integrated information technology and financial knowledge could create the better pairs trading system.