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- W2735685736 abstract "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." @default.
- W2735685736 created "2017-07-21" @default.
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- W2735685736 date "2016-11-01" @default.
- W2735685736 modified "2023-10-16" @default.
- W2735685736 title "Binary Classification and Data Analysis for Modeling Calendar Anomalies in Financial Markets" @default.
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- W2735685736 doi "https://doi.org/10.1109/ccbd.2016.032" @default.
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