Title: Hedging strategy for commodity futures based on SVM-KNN

Authors: Mei Sun; Yan Zhang; Rongpu Chen; Yulian Wen; Peiyao Nie

Addresses: School of Public Finance and Taxation, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China; School of Economics, Renmin University of China, Beijing, 100872, China ' School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China ' School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China; Sanya University, Sanya, 572022, China

Abstract: In view of the problem of excessive exposure to risks in the field of quantitative investment in commodity futures and policy failure in the low volatility market environment, a new quantitative investment strategy using SVM-KNN combined classifier to hedge multi-factor futures is proposed and applied to the management of quantitative fund. The quantitative investment strategy can not only reduce the overall systemic risk of the investment portfolio, but also adapt to the long-term environment of the commodity futures market. The retest data and the results of real trading show that the SVM-KNN-based hedging strategy of commodity futures is significantly higher than the traditional CTA trend tracking strategy in the annual rate of return and the Sharpe ratio, and the retracting of the cross period is greatly reduced.

Keywords: quantitative investment; commodity futures; multi-factor hedging; support vector machine; K-nearest neighbours.

DOI: 10.1504/IJRIS.2021.117077

International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.3, pp.139 - 146

Received: 23 Jan 2019
Accepted: 23 Sep 2019
Published online: 16 Aug 2021 *

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