Local Hurst index timing strategy

Qingzheng Shi, Wenzhuo Li, Jincheng Wang, Xuezhen Zhang, Hui Zou
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:707-716, 2025.

Abstract

This paper proposes a technical analysis strategy based on the Hurst index to predict stock price trends in uncertain markets. As a robust timing tool requiring minimal assumptions, the Hurst index effectively captures market memory effects. We apply this method to the CSI 300, mathematically analyze its properties, and empirically validate its profitability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-shi25b, title = {Local Hurst index timing strategy}, author = {Shi, Qingzheng and Li, Wenzhuo and Wang, Jincheng and Zhang, Xuezhen and Zou, Hui}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {707--716}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/shi25b/shi25b.pdf}, url = {https://proceedings.mlr.press/v278/shi25b.html}, abstract = {This paper proposes a technical analysis strategy based on the Hurst index to predict stock price trends in uncertain markets. As a robust timing tool requiring minimal assumptions, the Hurst index effectively captures market memory effects. We apply this method to the CSI 300, mathematically analyze its properties, and empirically validate its profitability.} }
Endnote
%0 Conference Paper %T Local Hurst index timing strategy %A Qingzheng Shi %A Wenzhuo Li %A Jincheng Wang %A Xuezhen Zhang %A Hui Zou %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-shi25b %I PMLR %P 707--716 %U https://proceedings.mlr.press/v278/shi25b.html %V 278 %X This paper proposes a technical analysis strategy based on the Hurst index to predict stock price trends in uncertain markets. As a robust timing tool requiring minimal assumptions, the Hurst index effectively captures market memory effects. We apply this method to the CSI 300, mathematically analyze its properties, and empirically validate its profitability.
APA
Shi, Q., Li, W., Wang, J., Zhang, X. & Zou, H.. (2025). Local Hurst index timing strategy. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:707-716 Available from https://proceedings.mlr.press/v278/shi25b.html.

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