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: | 1-一月-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 |
顯示於類別: | 會議論文 |