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Overreaction diamonds: precursors and aftershocks for significant price changes

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  • Ahmet Duran
  • Gunduz Caginalp

Abstract

Overreactions and other behavioral effects in stock prices can best be examined by adjusting for the changes in fundamentals. We perform this by subtracting the relative price changes in the net asset value (NAV) from that of market price (MP) daily for 134 406 data points of closed-end funds trading in US markets. We examine the days before and after a significant rise or fall in price deviation and MP return and find evidence of overreaction in the days after the change. Prior to a spike in deviation we find a gradual two- or three-day decline (and analogously in the other direction). Overall, there is a characteristic diamond pattern, revealing a symmetry in deviations before and after the significant change. Much of the statistical significance and the patterns disappear when the subtraction of NAV return is eliminated, suggesting that the frequent changes in fundamentals mask behavioral effects. A second study subdivides the data depending on whether the NAV or market price is responsible for the spike in the relative difference. In a majority of spikes, it is the change in market price rather than NAV that is dominant. Among those spikes for which there is little or no change in NAV, the results are similar to the overall study. Furthermore, the upward spikes are preceded by one or two days of declining market price while NAV rises slightly or is relatively unchanged. This suggests that a cause of the spike may be due to over-positioning of traders in the opposite direction in anticipation.

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  • Ahmet Duran & Gunduz Caginalp, 2007. "Overreaction diamonds: precursors and aftershocks for significant price changes," Quantitative Finance, Taylor & Francis Journals, vol. 7(3), pages 321-342.
    • Handle: RePEc:taf:quantf:v:7:y:2007:i:3:p:321-342
    DOI: 10.1080/14697680601009903
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    References listed on IDEAS

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    Cited by:

    1. Mynhardt, H. R. & Plastun, Alex, 2013. "The Overreaction Hypothesis: The Case of Ukrainian Stock Market," MPRA Paper 58941, University Library of Munich, Germany.
    2. Guglielmo Maria Caporale & Alex Plastun & Viktor Oliinyk, 2021. "The frequency of one-day abnormal returns and price fluctuations in the forex," Journal of Applied Economics, Taylor & Francis Journals, vol. 24(1), pages 401-415, January.
    3. Bommarito, Michael J. & Duran, Ahmet, 2018. "Spectral analysis of time-dependent market-adjusted return correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 273-282.
    4. Guglielmo Maria Caporale & Alex Plastun, 2020. "Momentum effects in the cryptocurrency market after one-day abnormal returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 251-266, September.
    5. G. Caginalp & M. Desantis, 2011. "Stock price dynamics: nonlinear trend, volume, volatility, resistance and money supply," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 849-861.
    6. Frank Thomas Seifried, 2010. "Optimal Investment for Worst-Case Crash Scenarios: A Martingale Approach," Mathematics of Operations Research, INFORMS, vol. 35(3), pages 559-579, August.
    7. Ahmet Duran & Michael Bommarito, 2011. "A profitable trading and risk management strategy despite transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 829-848.
    8. Caginalp, Gunduz & DeSantis, Mark & Sayrak, Akin, 2014. "The nonlinear price dynamics of U.S. equity ETFs," Journal of Econometrics, Elsevier, vol. 183(2), pages 193-201.
    9. Ramiah, Vikash & Xu, Xiaoming & Moosa, Imad A., 2015. "Neoclassical finance, behavioral finance and noise traders: A review and assessment of the literature," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 89-100.
    10. 子, 鬼谷, 2022. "Humanoid psychological sentiments and enigma of investment," OSF Preprints rm9gu, Center for Open Science.

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