Abstract
In the context that the tails of security returns obey an asymmetric power-law distribution, this paper constructs two fractal statistical measures based on fractal theory: fractal expectation and fractal variance. Subsequently, a new momentum strategy is constructed by introducing the fractal measures into the momentum strategy as measures of returns and risks to optimize the selection criterion. Finally, the empirical results show that the new momentum strategy outperforms the traditional momentum strategy and the risk-adjusted momentum strategy, confirming the effectiveness of fractal expectation and fractal variance.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 71903017
Funding source: National Social Science Foundation of China
Award Identifier / Grant number: 17BJY188
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: National Natural Science Foundation of China, Funder ID: https://doi.org/10.13039/501100001809 (Grant Number: 71,903,017); National Social Science Fund of China, Funder ID: https://doi.org/10.13039/501100012456 (Grant Number: 17BJY188).
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2022-0020).
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Articles in the same Issue
- Frontmatter
- Research Article
- Analysis of heterogeneous duopoly game with information asymmetry based on extrapolative mechanism
- Review
- Modelling volatility dependence with score copula models
- Research Articles
- A new test for non-linear hypotheses under distributional and local parametric misspecification
- Optimization study of momentum investment strategies under asymmetric power-law distribution of return rate
- Comparison of Score-Driven Equity-Gold Portfolios During the COVID-19 Pandemic Using Model Confidence Sets
- Integrated variance of irregularly spaced high-frequency data: A state space approach based on pre-averaging