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Journal articles on the topic 'Time series momentum'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Handley, Nicholas J. "Time Series Momentum." CFA Digest 42, no. 3 (2012): 179–81. http://dx.doi.org/10.2469/dig.v42.n3.47.

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2

Moskowitz, Tobias J., Yao Hua Ooi, and Lasse Heje Pedersen. "Time series momentum." Journal of Financial Economics 104, no. 2 (2012): 228–50. http://dx.doi.org/10.1016/j.jfineco.2011.11.003.

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3

Shen, Dehua, Andrew Urquhart, and Pengfei Wang. "Bitcoin intraday time series momentum." Financial Review 57, no. 2 (2021): 319–44. http://dx.doi.org/10.1111/fire.12290.

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4

He, Xue-Zhong, and Kai Li. "Profitability of time series momentum." Journal of Banking & Finance 53 (April 2015): 140–57. http://dx.doi.org/10.1016/j.jbankfin.2014.12.017.

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5

Borgards, Oliver. "Dynamic time series momentum of cryptocurrencies." North American Journal of Economics and Finance 57 (July 2021): 101428. http://dx.doi.org/10.1016/j.najef.2021.101428.

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6

Chevallier, Julien, and Florian Ielpo. "“Time series momentum” in commodity markets." Managerial Finance 40, no. 7 (2014): 662–80. http://dx.doi.org/10.1108/mf-11-2013-0322.

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Purpose – The purpose of this paper is to contain an empirical application of the concept of “time series momentum” – as developed by Moskowitz et al. (2012) – to commodity markets with daily data during 1995-2012. Design/methodology/approach – The paper applies the new concept of “time series momentum” to the sphere of commodity markets. Findings – The paper extends the results previously obtained by Moskowitz et al. (2012) to a second category labeled “breakout strategy.” Research limitations/implications – Further management strategies can be elaborated for investment management purposes, b

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7

Kim, Abby Y., Yiuman Tse, and John K. Wald. "Time series momentum and volatility scaling." Journal of Financial Markets 30 (September 2016): 103–24. http://dx.doi.org/10.1016/j.finmar.2016.05.003.

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8

Huang, Dashan, Jiangyuan Li, Liyao Wang, and Guofu Zhou. "Time series momentum: Is it there?" Journal of Financial Economics 135, no. 3 (2020): 774–94. http://dx.doi.org/10.1016/j.jfineco.2019.08.004.

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9

Hutchinson, Mark C., and John O'Brien. "Time series momentum and macroeconomic risk." International Review of Financial Analysis 69 (May 2020): 101469. http://dx.doi.org/10.1016/j.irfa.2020.101469.

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10

Lee, Minkyu. "A Study on Time Series Momentum and Technical Analysis." Dongguk Business Research Institute 46, no. 1 (2024): 23–50. http://dx.doi.org/10.55685/bcr.2024.46.1.23.

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This study comprehensively analyzes time series momentum and moving average strategies for the Korean stock market. The results of the empirical analysis are as follows. The correlation coefficients between the returns of time series momentum and moving average strategies are on average above 0.8, indicating that the two strategies are closely related. When calculating risk-adjusted returns and abnormal returns, Sharpe ratios and Jensen alphas were generally higher for the moving average strategy compared to the time series momentum strategy. Decomposing the difference in returns between time-

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11

Duan, Siyao. "Performance of Time-series Momentum Strategy: US Evidence." Advances in Economics, Management and Political Sciences 35, no. 1 (2023): 45–54. http://dx.doi.org/10.54254/2754-1169/35/20231722.

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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 strate

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12

Liu, Zhenya, Shanglin Lu, and Shixuan Wang. "Asymmetry, tail risk and time series momentum." International Review of Financial Analysis 78 (November 2021): 101938. http://dx.doi.org/10.1016/j.irfa.2021.101938.

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13

Fague, Jeremy, and Caio Almeida. "Robust optimization of time series momentum portfolios." Brazilian Review of Finance 19, no. 1 (2021): 52–69. http://dx.doi.org/10.12660/rbfin.v19n1.2021.82045.

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Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in

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14

Cai, Haotian, and Anatoly Schmidt. "What’s so special about time series momentum?" Journal of Investment Strategies 9, no. 2 (2020): 41–52. http://dx.doi.org/10.21314/jois.2020.122.

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15

Chakrabarti, Gagari, and Chitrakalpa Sen. "Time series momentum trading in green stocks." Studies in Economics and Finance 37, no. 2 (2020): 361–89. http://dx.doi.org/10.1108/sef-07-2019-0269.

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Purpose The purpose of this study is to explore the inherent instability, if any, in the context of investment in stocks of environment friendly companies (or the “green” stocks) across the globe using the time series momentum (TSM) trading strategies. Design/methodology/approach Using the monthly data for the Green Indexes from the USA, the Europe and the Asia-Pacific region over 2003-2019, the authors construct TSM trading strategies to examine the efficacy of regional Green Indexes as well as two diversified global green portfolios to offer abnormal return to attract investors, particularly

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16

Pitkäjärvi, Aleksi, Matti Suominen, and Lauri Vaittinen. "Cross-asset signals and time series momentum." Journal of Financial Economics 136, no. 1 (2020): 63–85. http://dx.doi.org/10.1016/j.jfineco.2019.02.011.

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17

Struck, Clemens, and Enoch Cheng. "Time-Series Momentum: A Monte Carlo Approach." Journal of Financial Data Science 1, no. 4 (2019): 103–23. http://dx.doi.org/10.3905/jfds.2019.1.012.

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18

Jin, Muzhao, Fearghal Kearney, Youwei Li, and Yung Chiang Yang. "Intraday time‐series momentum: Evidence from China." Journal of Futures Markets 40, no. 4 (2019): 632–50. http://dx.doi.org/10.1002/fut.22084.

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19

Chen, Wei. "Comparison of Cross-sectional Momentum Strategy and Time-Series Momentum Strategy." Highlights in Business, Economics and Management 39 (August 8, 2024): 462–66. http://dx.doi.org/10.54097/p2fhxd83.

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There are all kinds of quantitative portfolios used in the stock investing such as momentum, mean reversion, liquidity and so on. Cross-sectional momentum and Time-series momentum are the two main method of momentum strategies. They are also the basic methods that were used to construct the portfolio of investing. The essay analyses the difference of the theoretical definition and compares the two methods’ return, sharp ratio, maximum drawdown and some other indicators in a relative stable and prosperous environment which is simulated by well performed stocks in the 10 years’ period without co

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20

Mu, Yuandong, and Chaohua He. "Re-examining differences between momentum and time series momentum among individual stocks." Applied Economics Letters 26, no. 18 (2019): 1537–43. http://dx.doi.org/10.1080/13504851.2019.1584362.

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21

Hutahaean, Syawaluddin. "Applying Weighted Taylor Series on Time Series Water Wave Modeling." International Journal of Advanced Engineering Research and Science 11, no. 2 (2024): 38–47. http://dx.doi.org/10.22161/ijaers.112.6.

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This study develops a time series model for water waves using a weighted Taylor series approach to analyze water wave dynamics. The model incorporates the continuity equation, Euler's momentum conservation equation, and the Kinematic Free Surface Boundary Condition, all formulated through the weighted Taylor series. By integrating the modified continuity equation across the water depth, utilizing depth-averaged velocity concepts, we derive the water surface elevation equation. Similarly, applying the Euler momentum conservation principle to the water surface yields an equation for the horizont

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22

Onishchenko, Olena, Jing Zhao, Duminda Kuruppuarachchi, and Helen Roberts. "Intraday time-series momentum and investor trading behavior." Journal of Behavioral and Experimental Finance 31 (September 2021): 100557. http://dx.doi.org/10.1016/j.jbef.2021.100557.

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23

Hong, K. J., and S. Satchell. "Time series momentum trading strategy and autocorrelation amplification." Quantitative Finance 15, no. 9 (2015): 1471–87. http://dx.doi.org/10.1080/14697688.2014.1000951.

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24

Marshall, Ben R., Nhut H. Nguyen, and Nuttawat Visaltanachoti. "Time series momentum and moving average trading rules." Quantitative Finance 17, no. 3 (2016): 405–21. http://dx.doi.org/10.1080/14697688.2016.1205209.

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25

He, Xue-Zhong, Kai Li, and Youwei Li. "Asset allocation with time series momentum and reversal." Journal of Economic Dynamics and Control 91 (June 2018): 441–57. http://dx.doi.org/10.1016/j.jedc.2018.02.004.

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26

Ham, Hyuna, Hoon Cho, Hyeongjun Kim, and Doojin Ryu. "Time‐series momentum in China's commodity futures market." Journal of Futures Markets 39, no. 12 (2019): 1515–28. http://dx.doi.org/10.1002/fut.22053.

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27

Uzun, I. S., and M. V. Lobachev. "INTELLIGENT TIME SERIES DATA ANALYSIS OF CRYPTOCURRENCY MARKET DYNAMICS BASED ON OHLCV DATASET AND MOMENTUM TECHNICAL INDICATORS." ELECTRICAL AND COMPUTER SYSTEMS, no. 39(115) (2024): 65–76. http://dx.doi.org/10.15276/eltecs.39.115.2024.7.

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Abstract. This article presents an analysis of the historical dynamics of the cryptocurrency market based on time series data using the OHLCV dataset. The study presents the results of calculations of the main cryptocurrency market momentum technical indicators. Using intelligent computational methods, the paper assesses patterns and trends in the data of major cryptocurrencies. The study emphasizes the importance of technical analysis in understanding the volatile landscape of digital currencies. Key words: time-series, data analysis, cryptocurrency market, momentum indicators, technical anal

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28

Zhang, Qingsen. "Exploit momentum in Cryptocurrency Market." BCP Business & Management 42 (March 20, 2023): 8–12. http://dx.doi.org/10.54691/bcpbm.v42i.4547.

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Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for i

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29

Schmid, Olivier, and Patrick Wirth. "Optimal Allocation to Time-Series and Cross-Sectional Momentum." Journal of Portfolio Management 47, no. 4 (2021): 160–79. http://dx.doi.org/10.3905/jpm.2021.1.213.

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30

Hong, KiHoon, KiBong Park, and Yong Woong Lee. "Risk reduction in a time series momentum trading strategy." Journal of Risk Model Validation 10, no. 4 (2016): 55–70. http://dx.doi.org/10.21314/jrmv.2016.162.

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31

Jegadeesh, Narasimhan, and Sheridan Titman. "Cross-Sectional and Time-Series Determinants of Momentum Returns." Review of Financial Studies 15, no. 1 (2002): 143–57. http://dx.doi.org/10.1093/rfs/15.1.143.

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32

Lim, Bryan, Stefan Zohren, and Stephen Roberts. "Enhancing Time-Series Momentum Strategies Using Deep Neural Networks." Journal of Financial Data Science 1, no. 4 (2019): 19–38. http://dx.doi.org/10.3905/jfds.2019.1.015.

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33

Han, Yichen. "Momentum Quantification and Prediction of Tennis Match Based on Time Series and Logistic Regression." Highlights in Science, Engineering and Technology 101 (May 20, 2024): 555–63. http://dx.doi.org/10.54097/0kkx3840.

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In the highly competitive sport of tennis, it is crucial to understand and capitalize on every small match advantage, and despite the attention paid to the concept of momentum, its quantification, analysis, and prediction remain a challenge. The purpose of this study was to examine the role of momentum quantification, momentum influencing factors, and accurate prediction of momentum shifts in enhancing athletes' performance in tennis. By analyzing the competitive data of tennis players, a tennis-based "Big Data 'Momentum' Shift Prediction Model" was constructed, which not only specifically qua

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34

Lobão, Júlio, and Ana Rosário. "Time-Series Momentum in a Small European Stock Market: Evidence from a New Historical Financial Dataset." Scientific Annals of Economics and Business 70, no. 3 (2023): 335–52. http://dx.doi.org/10.47743/saeb-2023-0021.

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In this paper, we examine the Portuguese stock market for indication of time-series momentum effects using a new historical financial dataset that covers about 120 years of data. We find strong time-series momentum effects that cannot be explained by conventional risk factors. The positive return continuation seems to last for a period of 12 months, being heavily concentrated at the first month. At longer investment horizons, returns tend to mean-revert. The market exhibited significant time-series momentum for all look-back and holding periods of 12 months or less. A strategy with a 1-month l

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35

Ming, Lei, Wuqi Song, and Minyi Dong. "Revisiting time series momentum in China's commodity futures market: Evidence on sources of momentum profits." Economic Modelling 128 (November 2023): 106522. http://dx.doi.org/10.1016/j.econmod.2023.106522.

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36

Singh, Simarjeet, and Nidhi Walia. "TIME-SERIES AND CROSS-SECTIONAL MOMENTUM IN INDIAN STOCK MARKET." Copernican Journal of Finance & Accounting 9, no. 3 (2021): 161. http://dx.doi.org/10.12775/cjfa.2020.018.

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37

Cheema, Muhammad A., Gilbert V. Nartea, and Yimei Man. "Cross-Sectional and Time Series Momentum Returns and Market States." International Review of Finance 18, no. 4 (2017): 705–15. http://dx.doi.org/10.1111/irfi.12148.

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38

Wang, Zhaoyuan, Shancun Liu, Haijun Yang, and Harris Wu. "An Agent-Based Approach for Time-Series Momentum and Reversal." Journal of Systems Science and Complexity 33, no. 2 (2020): 461–74. http://dx.doi.org/10.1007/s11424-020-8042-2.

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39

Cheema, Muhammad A., Mardy Chiah, and Yimei Man. "Cross-sectional and time-series momentum returns: Is China different?" Pacific-Basin Finance Journal 64 (December 2020): 101458. http://dx.doi.org/10.1016/j.pacfin.2020.101458.

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40

Chakrabarti, Gagari. "Time-Series Momentum Trading Strategies in the Global Stock Market." Business Economics 50, no. 2 (2015): 80–90. http://dx.doi.org/10.1057/be.2015.16.

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41

Molyboga, Marat, Junkai Qian, and Chaohua He. "Carry and Time-Series Momentum: A Match Made in Heaven." Journal of Alternative Investments 23, no. 2 (2020): 84–93. http://dx.doi.org/10.3905/jai.2020.1.106.

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42

Lim, Bryan Y., Jiaguo (George) Wang, and Yaqiong Yao. "Time-series momentum in nearly 100 years of stock returns." Journal of Banking & Finance 97 (December 2018): 283–96. http://dx.doi.org/10.1016/j.jbankfin.2018.10.010.

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43

Feissel, M., D. Gambis, and T. Vesperini. "Predicting Universal Time from astronomical and meteorological measurements." Symposium - International Astronomical Union 128 (1988): 269–73. http://dx.doi.org/10.1017/s0074180900119588.

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44

Yang, Dong-Hyuk, and Yong-Shin Kang. "Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification." Sensors 22, no. 14 (2022): 5095. http://dx.doi.org/10.3390/s22145095.

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Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular di

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45

Cheema, Muhammad A., and Gilbert V. Nartea. "Cross-sectional and time-series momentum returns: are Islamic stocks different?" Applied Economics 50, no. 54 (2018): 5830–45. http://dx.doi.org/10.1080/00036846.2018.1488068.

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46

Shi, Huai-Long, and Wei-Xing Zhou. "Time series momentum and contrarian effects in the Chinese stock market." Physica A: Statistical Mechanics and its Applications 483 (October 2017): 309–18. http://dx.doi.org/10.1016/j.physa.2017.04.139.

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47

Bird, Ron, Xiaojun Gao, and Danny Yeung. "Time-series and cross-sectional momentum strategies under alternative implementation strategies." Australian Journal of Management 42, no. 2 (2016): 230–51. http://dx.doi.org/10.1177/0312896215619965.

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48

Hambusch, Gerhard, KiHoon Jimmy Hong, and Ellenora Webster. "Enhancing Risk-Adjusted Return Using Time Series Momentum in Sovereign Bonds." Journal of Fixed Income 25, no. 1 (2015): 96–111. http://dx.doi.org/10.3905/jfi.2015.25.1.096.

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49

Moskowitz, Tobias, Yao Hua Ooi, and Lasse Heje Pedersen. "Time Series Momentum." SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.2089463.

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50

Kim, Saejoon. "Time-series residual momentum strategies." Applied Economics, September 2, 2021, 1–15. http://dx.doi.org/10.1080/00036846.2021.1967862.

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