
Performance of Time-series Momentum Strategy: US Evidence
- 1 University of Glasgow
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Abstract
This paper examines the effectiveness of the time series momentum strategy in generating positive returns in the US stock market, with a focus on exploring its dynamics and performance using different moving average methods. The author conducted an empirical analysis of the time series momentum strategy using S&P500 data from 2000 to 2022. A regression model was applied to estimate the expected returns and volatility of each as-set, and then an evaluation of momentum trading strategy based on different moving average methods was developed. The author evaluates the performance of the strategy with and without transaction costs. The study contributes to the literature by providing empirical evidence on the effectiveness of the time series momentum strategy in the US stock market and by exploring the performance of different moving average methods on the strategy. The findings of this study can provide insights for investors and portfolio managers interested in implementing momentum strategies in their investment portfolios.
Keywords
momentum strategy, time-series momentum, quantitative portfolio
[1]. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
[2]. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
[3]. Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.
[4]. Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international stock returns. Journal of Financial Economics, 105(3), 457-472.
[5]. Hutchinson, M. C., & O'Brien, J. (2020). Time series momentum and macroeconomic risk. International Review of Financial Analysis, 69, 101469. https://doi.org/10.1016/j.irfa.2020.101469
[6]. Hartley, J. (2020). Interest rate momentum everywhere across global yield curves. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3542813
[7]. Molyboga, M., Swedroe, L., & Qian, J. (2020). Short-Term Trend: A Jewel Hidden in Daily Returns. The Journal of Portfolio Management, 46(3), 80-93. https://doi.org/10.3905/jpm.2020.1.186
[8]. Onishchenko, O., Zhao, J., Kuruppuarachchi, D., & Roberts, H. (2021). Intraday time-series momentum and investor trading behavior. Journal of Behavioral and Experimental Finance, 31, 100557. https://doi.org/10.1016/j.jbef.2021.100557
[9]. Molyboga, M., Qian, J., & He, C. (2021). Practical applications of carry and time-series momentum: A match made in heaven. The Journal of Alternative Investments. https://doi.org/10.4324/9781315100944-5
[10]. Levy, B. P. C., & Lopes, H. F. (2021). Trend-following strategies via dynamic momentum learning. https://doi.org/10.48550/arXiv.2106.08420
Cite this article
Duan,S. (2023). Performance of Time-series Momentum Strategy: US Evidence. Advances in Economics, Management and Political Sciences,35,45-54.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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