An integrated momentum strategy based on entropy and behavioral overreaction: Evidence from Vietnam
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DOIhttp://dx.doi.org/10.21511/imfi.23(1).2026.12
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Article InfoVolume 23 2026, Issue #1, pp. 154-171
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Type of the article: Research Article
Abstract
The increasing behavioral volatility and informational complexity of emerging stock markets such as Vietnam create a critical need for more advanced analytical approaches to identify reliable momentum signals. This study aims to develop and validate an integrated momentum-based trading strategy specifically designed for the Vietnamese stock market. Using price and trading volume data for all stocks listed on the VNINDEX from January 2015 to February 2025, the methodology combines permutation-based entropy measures to capture short-term structural patterns with a formation–holding period framework to analyze medium- and long-term dynamics through Continuing Overreaction. The empirical results reveal a pronounced structural divergence in momentum behavior across investment horizons. Short-term momentum is persistent and strongly associated with low-complexity price and volume patterns, indicating coordinated behavioral trading and temporary predictability. In contrast, medium- and long-term Continuing Overreaction effects exhibit consistently negative values across various formation and holding horizons, suggesting that excess trading intensity leads to systematic mean reversion rather than sustained momentum. Backtesting over the period from January 2023 to February 2025 demonstrates that the proposed integrated strategy substantially outperforms a passive VNINDEX buy-and-hold benchmark, achieving a Sharpe ratio of 3.96 compared to 0.64 for the market. The superior performance remains robust across alternative portfolio construction settings and reflects improved downside risk control rather than increased return volatility. These findings indicate that integrating entropy-based complexity measures with volume-driven behavioral indicators provides a more effective framework for enhancing risk-adjusted returns in emerging stock markets.
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JEL Classification (Paper profile tab)G11, G12, G14, C58
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References32
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Tables9
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Figures8
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- Figure 1. Permutation Entropy of price and volume measures for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 2. Permutation Transition Entropy of price and volume measures for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 3. Most frequent permutation transitions of daily closing prices for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 4. Most frequent permutation transitions of log returns for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 5. Most frequent permutation transitions of trading volume for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 6. Most frequent permutation transitions of log-volume changes for VNINDEX stocks (m = 2-5, 2015–2025)
- Figure 7. Continuing Overreaction (CO) values of Winner-minus-Loser portfolios across J/K horizons for VNINDEX stocks (2015–2025)
- Figure 8. Performance comparison between the VNINDEX benchmark and the integrated strategy (Jan 2023 – Feb 2025)
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- Table 1. Permutation Entropy and Permutation Transition Entropy for VNINDEX at different embedding dimensions (m = 2-5)
- Table 2. Continuing Overreaction results for J/K-month strategies (Dec 2015–Feb 2025)
- Table 3. VNINDEX benchmark performance (Jan 2023 – Feb 2025)
- Table A1. Annual summary of VNINDEX data (2015–2025)
- Table A2. Backtesting results for integrated Permutation Entropy – Permutation Transition Entropy – Continuing Overreaction model (m = 2)
- Table A3. Backtesting results for the integrated model (m = 3)
- Table A4. Backtesting results for the integrated model (m = 4)
- Table A5. Backtesting Results for the Integrated Model (m = 5)
- Table A6. Descriptive statistics for VNINDEX benchmark data (Jan 2023–Feb 2025)
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- Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61(4), 1645-1680.
- Bandt, C., & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Physical Review Letters, 88(17), 174102.
- Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307-343.
- Batten, J. A., & Vo, X. V. (2014). Liquidity and return relationships in an emerging market. Emerging Markets Finance & Trade, 50(1), 5-21.
- Byun, S. J., Lim, S. S., & Yun, S. H. (2016). Continuing overreaction and stock return predictability. Journal of Financial and Quantitative Analysis, 51(6), 2015-2046.
- Carhart, M. M. (1997). On persistence in mutual fund performance. The Journal of Finance, 52(1), 57-82.
- Chiang, I-H. E., Kirby, C., & Nie, Z. Z. (2021). Short-term reversals, short-term momentum, and news-driven trading activity. Journal of Banking & Finance, 125, 106068.
- Chui, A. C., Titman, S., & Wei, K. C. J. (2010). Individualism and momentum around the world. The Journal of Finance, 65(1), 361-392.
- Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and capital market under- and overreactions. The Journal of Finance, 53(6), 1839-1885.
- De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), 793-805.
- De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Positive feedback investment strategies and destabilizing rational speculation. The Journal of Finance, 45(2), 379-395.
- Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
- Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The Journal of Finance, 51(1), 55-84.
- Goyal, A., Jegadeesh, N., & Subrahmanyam, A. (2025). Empirical determinants of momentum: a perspective using international data. Review of Finance, 29(1), 241-273.
- Griffin, J. M., Ji, S., & Martin, J. S. (2003). Momentum investing and business cycle risk: Evidence from pole to pole. The Journal of Finance, 58(6), 2515-2547.
- Grinblatt, M., & Han, B. (2005). Prospect theory, mental accounting, and momentum. Journal of Financial Economics, 78(2), 311-339.
- Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of Finance, 54(6), 2143-2184.
- Hou, K., Xue, C., & Zhang, L. (2015). Digesting anomalies: An investment approach. The Review of Financial Studies, 28(3), 650-705.
- 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.
- Jegadeesh, N., & Titman, S. (2025). Short-Term Reversals and Longer-Term Momentum around the World: Theory and Evidence. Review of Financial Studies, 38(12), 3673-3728.
- Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
- Lee, C. M., & Swaminathan, B. (2000). Price momentum and trading volume. The Journal of Finance, 55(5), 2017-2069.
- Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
- Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708.
- Nguyễn Phương, Minh. (2025). Exploring momentum strategy: Applying the combination of Permutation Entropy, Permutation Transition Entropy, and continuing overreaction for the Vietnamese stock market. [Data set]. Zenodo.
- Rouwenhorst, K. G. (1999). Local return factors and turnover in emerging stock markets. The Journal of Finance, 54(4), 1439-1464.
- Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
- Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83-104.
- Stambaugh, R. F., & Yuan, Y. (2017). Mispricing factors. The Review of Financial Studies, 30(4), 1270-1315.
- Vo, X. V., & Phan, D. B. (2019). Herding and equity market liquidity in emerging market: Evidence from Vietnam. Journal of Behavioral and Experimental Finance, 24, 100189.
- Zhao, X., Ji, M., Zhang, N., & Shang, P. (2020). Permutation transition entropy: Measuring the dynamical complexity of financial time series. Chaos, Solitons & Fractals, 139, 109962.
- Zunino, L., Zanin, M., Tabak, B. M., Pérez, D. G., & Rosso, O. A. (2009). Forbidden patterns, permutation entropy and stock market efficiency. Physica A: Statistical Mechanics and its Applications, 388(14), 2854-2864.


