Download pdf
A Kelly Quantitative Trading Investment Strategy Improved by Overfitting Rate
- 1 Beijing University of Posts and Telecommunications, Haidian, Beijing, China
- 2 Beijing University of Posts and Telecommunications, Haidian, Beijing, China
- 3 Beijing University of Posts and Telecommunications, Haidian, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
With the improvement and maturity of China's capital markets, data-driven quantitative trading, known for its objectivity, high frequency, and automation, has gradually developed into an important trading method in the financial market. This paper addresses the overfitting phenomenon in quantitative trading, quantifying the overfitting rate using the concept of information coefficient. By applying the overfitting rate to optimize the Kelly formula extended to continuous time, the paper derives the overfitting rate-improved Kelly score. Finally, a specific order placement application example is provided based on the theory of linear regression, and a comparative analysis with the fractional Kelly criterion is conducted.
Keywords
quantitative trading, information coefficient, overfitting, Kelly Formula
[1]. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
[2]. Kelly, J. (1956). A new interpretation of the information rate. Bell System Technical Journal, 35(4), 917-926.
[3]. Rotando, L. M., & Thorp, E. O. (1992). The Kelly criterion and the stock market. The American Mathematical Monthly, 99(10), 922-931.
[4]. Maclean, L. C., Thorp, E. O., & Ziemba, W. T. (2010). Long-term capital growth: The good and bad properties of the Kelly and fractional Kelly capital growth criteria. Quantitative Finance, 10(7), 681-687.
[5]. Davis, M. H. A., & Lleo, S. (2010). Fractional Kelly strategies for benchmarked asset management. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 127-152). World Scientific.
[6]. Jacquier, E., & Polson, N. G. (2012). Asset allocation in finance: A Bayesian perspective. In Hierarchical models and MCMC: A Tribute to Adrian Smith (pp. 56-59).
[7]. Wu, M. E., Tsai, H. H., Chung, W. H., et al. (2020). Analysis of Kelly betting on finite repeated games. Applied Mathematics and Computation, 373, 125028.
[8]. Carta, A., & Conversano, C. (2020). Practical implementation of the Kelly criterion: Optimal growth rate, number of trades, and rebalancing frequency for equity portfolios. Frontiers in Applied Mathematics and Statistics, 6, 577050.
Cite this article
Zhang,Z.;Chen,X.;Li,X. (2025). A Kelly Quantitative Trading Investment Strategy Improved by Overfitting Rate. Journal of Applied Economics and Policy Studies,17,50-55.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Journal of Applied Economics and Policy Studies
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).