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Modeling and measuring intraday overreaction of stock prices

Author

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  • Klößner, Stefan
  • Becker, Martin
  • Friedmann, Ralph

Abstract

We introduce a model for stock prices consisting of a fundamental price process and a news impact curve, which allows for either overreaction, underreaction, or correct response to changes of the fundamental value. We further develop statistics based on OHLC data, which separately measure upside and downside overreaction. The distribution of these statistics under the hypothesis of correct response and fundamental prices following Brownian motions is used to derive tests for upside and downside overreaction. We show that more realistic and frequently used fundamental price processes with correct response leave the distribution of the test statistics widely unaffected or lead to conservative tests. Empirical application to different stock markets provides strong evidence for intraday overreaction, particularly to bad news. The economic significance of the discrimination induced by the proposed statistics is further illustrated by analyzing the performance of a simple buy on bad news strategy.

Suggested Citation

  • Klößner, Stefan & Becker, Martin & Friedmann, Ralph, 2012. "Modeling and measuring intraday overreaction of stock prices," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1152-1163.
    • Handle: RePEc:eee:jbfina:v:36:y:2012:i:4:p:1152-1163
    DOI: 10.1016/j.jbankfin.2011.11.005
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    References listed on IDEAS

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    Cited by:

    1. Andrey Kudryavtsev, 2012. "Short-Term Stock Price Reversals May Be Reversed," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 5(3), pages 129-146, December.
    2. Lijian Wei & Lei Shi, 2020. "Investor Sentiment in an Artificial Limit Order Market," Complexity, Hindawi, vol. 2020, pages 1-10, June.
    3. Todorova, Neda, 2017. "The intraday directional predictability of large Australian stocks: A cross-quantilogram analysis," Economic Modelling, Elsevier, vol. 64(C), pages 221-230.
    4. Muneer Shaik & S. Maheswaran, 2016. "Modelling the Paradox in Stock Markets by Variance Ratio Volatility Estimator that Utilises Extreme Values of Asset Prices," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 15(3), pages 333-361, December.
    5. Camilleri, Silvio John, 2015. "Do call auctions curtail price volatility? Evidence from the National Stock Exchange of India," MPRA Paper 95301, University Library of Munich, Germany.

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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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