research-article

Selection of Market Window Size in Portfolio Strategies

Authors:

Jia-Yi Lie,

Hong-Yu Duan,

Yu-Luan Chen,

Zhao-Rong LaiAuthors Info & Claims

ICEME '18: Proceedings of the 2018 9th International Conference on E-business, Management and Economics

Pages 127 - 133

https://doi.org/10.1145/3271972.3271982

Published: 02 August 2018 Publication History

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Abstract

We propose a feasible selection method of market window size for four state-of-the-art portfolio strategies. Market window size is a common parameter in the machine learning strategy for portfolio selection. However, in previous researches, the selection of market window size often lacks the guidance of scientific theories. In this paper, we analyze the sensitivity of market window size for four strategies on six benchmark data sets respectively. We study the distribution rule of the best market window sizes, which bring the peak total wealth, and then present the market window size selection method that is effective whether there is ample history data or not. What's more, to appraise the result of our method, we divide the benchmark data sets into two parts. We select the appropriate window size in the first part by our method, while the second part is used to test. By comparing with the wealth achieved in the second part using original method, we find that our selection method can effectively optimize the final results.

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Index Terms

Selection of Market Window Size in Portfolio Strategies
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Online learning settings
2. General and reference
  1. Cross-computing tools and techniques
    1. Experimentation

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Published In

ICEME '18: Proceedings of the 2018 9th International Conference on E-business, Management and Economics

August 2018

169 pages

ISBN:9781450365147

DOI:10.1145/3271972

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Wilfrid Laurier University: Wilfrid Laurier University

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Association for Computing Machinery

New York, NY, United States

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Published: 02 August 2018

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National Natural Science Foundation of China
Fundamental Research Funds for the Center for Mathematical Finance in Guangdong Province
Talent Introduction Foundation of Jinan University
Fundamental Research Funds for the Central Universities

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ICEME 2018

ICEME 2018: 2018 9th International Conference on E-business, Management and Economics

August 2 - 4, 2018

ON, Waterloo, Canada

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Abstract

References

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Recommendations

Systemic Risk-Driven Portfolio Selection

Market Ecology: Trading Strategies and Market Volatility

Optimal Leveraged Portfolio Selection Under Quasi-Elastic Market Impact

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