Do trend following strategies work in Chinese futures markets?

Abstract

We examine the performance of trend following strategies in Chinese commodity futures markets. We provide evidence that trend following‐based technical trading rules yield better performance than the buy and hold strategy on both individual contracts and sorted portfolios. The outperformance is robust to transaction costs, data frequency, sub‐prime crisis, shorting constraint, delayed execution, liquidity and parameters. Finally, the profitability of the trend following strategy may be subject to data snooping bias.

The untold story of commodity futures in China

“…Since the intercepts remain large and significant, anchoring bias provides at least a partial explanation for the remarkable profits generated by the momentum strategy. Consistent with our hypothesis, this behavioral bias likely has been amplified in China due to the retail‐dominance (Li, Zhang, & Zhou, ). However, we fail to detect any significant relationship between the anchoring bias and other types of premia.…”

“…Recent studies have examined the broad market performance (Fung, Tse, Yau, & Zhao, 2013;Tu, Song, & Zhang, 2013), trend-following strategies (Li, Zhang, & Zhou, 2017), pricing implications (He, Jiang, & Molyboga, 2019), volatility (Jiang, Ahmed, & Liu, 2017;Tian, Yang, & Chen, 2017), diversification potential (Hammoudeh, Nguyen, Reboredo, & Wen, 2014;Liu, Tse, & Zhang, 2018), the impact of speculation (Fan, Mo, & Zhang, 2019;Wellenreuther & Voelzke, 2019), and high frequency trading (Zhao & Wan, 2018). For example, Tu et al (2013) conclude that the correlation between the Chinese and the US markets has increased during the period 2000. B. Li et al (2017 find that trendfollowing strategies outperform buy and hold strategies.…”

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

“…There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013). There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013).…”

“…In this article, we choose four common agricultural futures-corn, wheat, cotton, and soybean-and the CSI300 index to represent the Chinese agricultural futures market and the stock market. There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013). The raw transaction prices are obtained from the Wind Financial Terminal in Chinese markets during the period from April 8, 2005, to June 31, 2014.…”

A statistical learning approach for stock selection in the Chinese stock market

“…As Rapach and Zhou (2013) have indicated, forecasting stock returns is extremely challenging in general; this task is even more difficult when it involves the turbulent Chinese stock market. Kang et al (2002) studied contrarian and momentum strategies for the Chinese stock market in earlier years, while Li et al (2017) examined the performance of trends following strategies in Chinese commodity futures markets. The recent introduction of advanced statistical approaches to portfolio management, such as machine learning, has been highly successful (e.g.…”