標題: Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets
作者: Tung, Hui-Hsuan
Chen, Yu-Ying
Huang, Szu-Hao
Cheng, Chiao-Chun
Chen, Yu-Fu
Chen, An-Pin
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: day-of-the-week effect;calendar anomalies;linear discriminant analysis;support vector machine;back-propagation neural networks;technical indicators
公開日期: 1-Jan-2016
摘要: 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.
URI: http://dx.doi.org/10.1109/CCBD.2016.35
http://hdl.handle.net/11536/146143
ISSN: 2378-3680
DOI: 10.1109/CCBD.2016.35
期刊: 2016 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CCBD)
起始頁: 116
結束頁: 121
Appears in Collections:Conferences Paper