A kernel-based trend pattern tracking system for portfolio optimization
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We propose a novel kernel-based trend pattern tracking (KTPT) system for portfolio optimization. It includes a three-state price prediction scheme, which extracts both of the following and reverting patterns from the asset price trend to make future price predictions. Moreover, KTPT is equipped with a novel kernel-based tracking system to optimize the portfolio, so as to capture a potential growth of the asset price effectively. The kernel measures the similarity between the current portfolio and the predicted price relative to control the influence of each asset when optimizing the portfolio, which is different from some previous kernels that measure the probability of occurrence of a price relative. Extensive experiments on 5 benchmark datasets from real-world stock markets with various assets in different time periods indicate that KTPT outperforms other state-of-the-art strategies in cumulative wealth and other risk-adjusted metrics, showing its effectiveness in portfolio optimization.
KeywordsKernel method Trend pattern analysis Tracking system Portfolio optimization
The authors would like to thank the Editor-in-Chief, the Area Editor, and the anonymous reviewers for the detailed and constructive comments and suggestions that help to significantly improve this paper. This work is supported by the National Natural Science Foundation of China [Grant Numbers 61703182, 61602211, 61603152]; the Fundamental Research Funds for the Central Universities [Grant Numbers 21617347, 21617404]; the Talent Introduction Foundation of Jinan University [Grant Numbers 88016653, 88016534]; Science and Technology Program of Guangzhou, China [Grant Number 201707010259]; the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET); Guangxi Key Laboratory of Trusted Software [Grant Number kx201606]; the Fundamental Research Funds for the Center for Mathematical Finance in Guangdong Province [Grant Number 50411628].
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