Binary classification and data analysis for modeling calendar anomalies in financial markets

Hui Hsuan Tung, Chiao Chun Cheng, Yu Ying Chen, Yu Fu Chen, Szu-Hao Huang, An-Pin Chen

研究成果: Conference contribution同行評審

13 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面116-121
頁數6
ISBN(電子)9781509035557
DOIs
出版狀態Published - 13 7月 2017
事件7th International Conference on Cloud Computing and Big Data, CCBD 2016 - Taipa, Macau, China
持續時間: 16 11月 201618 11月 2016

出版系列

名字Proceedings - 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016

Conference

Conference7th International Conference on Cloud Computing and Big Data, CCBD 2016
國家/地區China
城市Taipa, Macau
期間16/11/1618/11/16

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