ABSTRACT
This paper is the first study to present firm-level evidence that the time-series momentum (TSMOM) strategies with look-back-period k of 10 to 200 days outperform the buy-and-hold strategy (BH) on individual stocks in the Chinese stock market. We document that the optimal k* generating the best performance is different across assets and varies over time. We hence propose a model to predict the asset-specific and time-dependent k*, and examine the performance of the TSMOM strategies with the predicted k*. Our analysis shows that using the time-varying predicted k* substantially improves the predictability of the TSMOM strategies. Our new model and findings shed the light on trading strategy for both academia and applied investment practitioners.
Disclosure Statement
No potential conflict of interest was reported by the authors.
Notes
1 Such as Brock, Lakonishok, and LeBaron Citation1992; Blume, Easley, and O’Hara Citation1994; Zakamulin Citation2015; Lo, Mamaysky, and Wang Citation2000; Zhu and Zhou Citation2009; Neely et al. Citation2014; Kilgallen Citation2012; Huang and Zhou Citation2013; Glabadanidis Citation2014, Citation2015; Han, Yang, and Zhu Citation2016, etc.
2 Such specification is closer to the traditional moving average (MA) trading strategy. Prior literature shows that TSMOM and MA are closely related but different. More details regarding the analysis of these two strategies can be found in Marshall et al. (Citation2017), He and Li (Citation2015) and Zhou and Zhu (Citation2013).
3 The CSI 300 is a capitalization-weighted stock market index designed to replicate the performance of top 300 stocks traded in the Shanghai and Shenzhen stock exchanges.
4 Even though we calculate the Jensen’s alpha for the TSMOM strategies with various k values as an additional robustness check, and the results are available upon request.
5 As a further robustness check, we calculate the Jensen’s Alpha (the results are available upon request) for each of our sample stocks from each TSMOM strategy. The results are again consistent with those in Table 3.
6 Descriptive statistics are available upon request.
Brock, W., J. Lakonishok, and B. LeBaron. 1992. “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance 47: 1731–1764. doi:10.1111/j.1540-6261.1992.tb04681.x. Blume, L., D. Easley, and M. O’Hara. 1994. “Market Statistics and Technical Analysis: The Role of Volume.” Journal of Finance 49: 153–181. doi:10.1111/j.1540-6261.1994.tb04424.x. Lo, A., H. Mamaysky, and J. Wang. 2000. “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation.” Journal of Finance 55: 1705–1765. doi:10.1111/0022-1082.00265. Zhu, Y., and G. Zhou. 2009. “Technical Analysis: An Asset Allocation Perspective on the Use of Moving Averages.” Journal of Financial Economics 92: 519–544. doi:10.1016/j.jfineco.2008.07.002. Neely, C. J., D. E. Rapach, J. Tu, and G. Zhou. 2014. “Forecasting the Equity Risk Premium: The Role of Technical Indicators.” Management Science 60: 1772–1791. doi:10.1287/mnsc.2013.1838. Kilgallen, T. 2012. “Testing the Simple Moving Average across Commodities, Global Stock Indices, and Currencies.” Journal of Wealth Management 15: 82–100. doi:10.3905/jwm.2012.15.1.082. Huang, D., and G. Zhou. 2013. “Economic and Market Conditions: Two State Variables that Predict the Stock Market.” Working paper. Glabadanidis, P. 2014. “The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes.” International Review of Finance 14: 161–202. doi:10.1111/irfi.12018. Glabadanidis, P. 2015. “Market Timing with Moving Averages.” International Review of Finance 15: 387–425. doi:10.1111/irfi.12052. Han, Y., K. Yang, and Y. Zhu. 2016. “Trend Factor: Any Economic Gains from Using Information over Investment Horizons?” Journal of Financial Economics 122: 352–375. doi:10.1016/j.jfineco.2016.01.029. Marshall, B. R., N. H. Nguyen, and N. Visaltanachoti. 2017. “Time Series Momentum and Moving Average Trading Rules.” Quantitative Finance 17: 405–421. doi:10.1080/14697688.2016.1205209. He, X. Z., and K. Li. 2015. “Profitability of Time Series Momentum.” Journal of Banking & Finance 53: 140–157. doi:10.1016/j.jbankfin.2014.12.017. Zhou, G., and Y. Zhu, 2013. “An Equilibrium Model of Moving-average Predictability and Time-series Momentum.” Unpublished working paper, Washington University in St. Louis.