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Why investors use technical analysis? Information discovery versus herding behavior

Author

Listed:
  • Tiandu Wang
  • Qian Sun

Abstract

Purpose - – The purpose of this paper is to establish two competitive models to explain why investors use technical analysis (TA). Design/methodology/approach - – Information Discovery Model suggests that technical traders are able to infer non-public information; Herding Behavior Model argues that TA is a kind of irrational herding behavior that can make profit when other noise traders exist. Findings - – The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns. Research limitations/implications - – The empirical results from Chinese stock market show that some technical trading rules generate significant excess returns. Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient. Originality/value - – Stocks with stronger information asymmetry and lower liquidity experiences higher excess return, which support the Information Discovery Model that TA is a method of information discovery for rational investors when the market is not fully efficient.

Suggested Citation

  • Tiandu Wang & Qian Sun, 2015. "Why investors use technical analysis? Information discovery versus herding behavior," China Finance Review International, Emerald Group Publishing Limited, vol. 5(1), pages 53-68, February.
    • Handle: RePEc:eme:cfripp:v:5:y:2015:i:1:p:53-68
    DOI: 10.1108/CFRI-08-2014-0033
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    Citations

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    Cited by:

    1. Dayong Lv & Wenfeng Wu, 2020. "Margin trading and price efficiency: information content or price‐adjustment speed?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(3), pages 2889-2918, September.
    2. Yang, Junmin & Cao, Zhiguang & Han, Qiheng & Wang, Qiyu, 2019. "Tactical asset allocation on technical trading rules and data snooping," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    3. Swamy, Vighneswara & Dharani, M. & Takeda, Fumiko, 2019. "Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis," Research in International Business and Finance, Elsevier, vol. 50(C), pages 1-17.

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