Online portfolio selection of integrating expert strategies based on mean reversion and trading volume

Lin, Hong; Zhang, Yong; Yang, Xingyu

doi:10.1016/j.eswa.2023.121472

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…We conduct a comprehensive analysis of the parameter setting of PAE through experiments using benchmark data sets. Similar to previous studies [9,17,19,35,36], we adopt an empirical method to determine the parameters based on their CW computed by (4).…”

Section: Parameter Settingmentioning

confidence: 99%

Passive Aggressive Ensemble for Online Portfolio Selection

Xie,

Yin,

et al. 2024

Mathematics

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Developing effective trend estimators is the main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield the maximum final cumulative wealth in online portfolio selection tasks. This paper studies an online ensemble approach for online portfolio selection by leveraging the strengths of multiple trend estimators. Specifically, a return-based loss function and a cross-entropy-based loss function are first designed to evaluate the adaptiveness of different trend estimators in a financial environment. On this basis, a passive aggressive ensemble model is proposed to weigh these trend estimators within a unit simplex according to their adaptiveness. Extensive experiments are conducted on benchmark datasets from various real-world stock markets to evaluate their performance. The results show that the proposed strategy achieves state-of-the-art performance, including efficiency and cumulative return.

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Section: Parameter Settingmentioning

confidence: 99%

Passive Aggressive Ensemble for Online Portfolio Selection

Xie,

Yin,

et al. 2024

Mathematics

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An asset subset-constrained minimax optimization framework for online portfolio selection

Yin,

Zhong,

Xiao

et al. 2024

Expert Systems with Applications

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Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis

Zeng,

Xu,

Zhong

2024

International Journal of Fuzzy System Applications

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Intelligent online portfolios have become an important research topic in the field of quantitative finance. This paper proposes an online portfolio based on trend trading strategy using fuzzy logic technology analysis method and considering investor sentiment. Firstly, the paper uses SVM classification algorithm to analyze stock comment text data in online forums and constructs investor sentiment indicators. Secondly, the paper transforms the heuristic algorithm of technical trading into corresponding fuzzy IF-THEN rules and combines them into a fuzzy investment decision system. Thirdly, the paper constructs a new online portfolio based on trend trading strategy. Finally, the paper uses the text data of investor reviews and stock trading data on the internet for empirical analysis to illustrate the effectiveness of the online portfolio strategy constructed. The results indicate that online portfolios constructed based on trend trading strategies can achieve higher performance than benchmark strategies while considering transaction costs.

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Online portfolio selection of integrating expert strategies based on mean reversion and trading volume

Cited by 3 publications

References 53 publications

Passive Aggressive Ensemble for Online Portfolio Selection

Passive Aggressive Ensemble for Online Portfolio Selection

An asset subset-constrained minimax optimization framework for online portfolio selection

Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis

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