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Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry

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  • Smith, David M.
  • Wang, Na
  • Wang, Ying
  • Zychowicz, Edward J.

Abstract

This article presents a unique test of the effectiveness of technical analysis in different sentiment environments by focusing on its usage by perhaps the most sophisticated and astute investors, namely, hedge fund managers. We document that during high-sentiment periods, hedge funds using technical analysis exhibit higher performance, lower risk, and superior market-timing ability than nonusers. The advantages of using technical analysis disappear or even reverse in low-sentiment periods. Our findings are consistent with the view that technical analysis is relatively more useful in high-sentiment periods with larger mispricing, which cannot be fully exploited by arbitrage activities because of short-sale impediments.

Suggested Citation

  • Smith, David M. & Wang, Na & Wang, Ying & Zychowicz, Edward J., 2016. "Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 51(6), pages 1991-2013, December.
    • Handle: RePEc:cup:jfinqa:v:51:y:2016:i:06:p:1991-2013_00
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    Cited by:

    1. Urquhart, Andrew & Zhang, Hanxiong, 2019. "The performance of technical trading rules in Socially Responsible Investments," International Review of Economics & Finance, Elsevier, vol. 63(C), pages 397-411.
    2. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
    3. Ikhlaas Gurrib, 2022. "Technical Analysis, Energy Cryptos and Energy Equity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 249-267, March.
    4. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    5. Robert Hudson & Andrew Urquhart, 2021. "Technical trading and cryptocurrencies," Annals of Operations Research, Springer, vol. 297(1), pages 191-220, February.
    6. Czudaj Robert L., 2020. "The role of uncertainty on agricultural futures markets momentum trading and volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-39, June.
    7. Yao-Tsung Wu & Chien-Hung Liu & Kuo-Hao Lin & Dun-Yao Ke, 2024. "Does media coverage matter for the performance of technical trading strategies? Evidence from Taiwan," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(1), pages 147-166, January.
    8. Chandrinos, Spyros K. & Lagaros, Nikos D., 2018. "Construction of currency portfolios by means of an optimized investment strategy," Operations Research Perspectives, Elsevier, vol. 5(C), pages 32-44.
    9. Chen, Kuan-Hau & Su, Xuan-Qi & Lin, Li-Feng & Shih, Yi-Cheng, 2021. "Profitability of moving-average technical analysis over the firm life cycle: Evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 69(C).
    10. Ponta, Linda & Carbone, Anna, 2018. "Information measure for financial time series: Quantifying short-term market heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 132-144.
    11. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    12. Ikhlaas Gurrib & Mohammad Nourani & Rajesh Kumar Bhaskaran, 2022. "Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-27, December.
    13. Gao, Lei & Wang, Ying & Zhao, Jing, 2017. "Does local religiosity affect organizational risk-taking? Evidence from the hedge fund industry," Journal of Corporate Finance, Elsevier, vol. 47(C), pages 1-22.
    14. Osman Kilic & Joseph M. Marks & Kiseok Nam, 2022. "Predictable asset price dynamics, risk-return tradeoff, and investor behavior," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 749-791, August.
    15. Ikhlaas Gurrib & Firuz Kamalov & Elgilani Elshareif, 2021. "Can the Leading US Energy Stock Prices be Predicted using the Ichimoku Cloud?," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 41-51.
    16. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    17. Mingwei Sun & Paskalis Glabadanidis, 2022. "Can technical indicators predict the Chinese equity risk premium?," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 114-142, March.

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