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Testing for Market Efficiency: A Comparison of the Cumulative Average Residual Methodology and Intervention Analysis

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  • Larcker, David F.
  • Gordon, Lawrence A.
  • Pinches, George E.

Abstract

During the past decade considerable empirical evidence has been accumulated suggesting the stock market adjusts to the arrival of new information in an efficient manner. The studies providing this evidence consist of announcement tests of new publicly available information (such as earnings, stock splits, accounting changes, etc.) on the risk-adjusted return of securities. The specific methodology employed is crucial since it directly affects the results of a test for market efficiency. Following the pioneering work of Ball and Brown [1] and Fama, et al. [15], many researchers [6, 12, 21, 22, 27] have employed a similar methodology in order to test for market efficiency. This cumulative average residual (CAR) methodology consists of: (1) estimating the parameters of the market model based on data in a time period prior (and sometimes subsequent) to an announcement, and (2) analyzing the residuals derived from applying this model to a time period which includes the announcement date.

Suggested Citation

  • Larcker, David F. & Gordon, Lawrence A. & Pinches, George E., 1980. "Testing for Market Efficiency: A Comparison of the Cumulative Average Residual Methodology and Intervention Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(2), pages 267-287, June.
    • Handle: RePEc:cup:jfinqa:v:15:y:1980:i:02:p:267-287_00
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    1. Kuchler, Fred & Vroomen, Harry, 1987. "Impacts of the PIK Program on the Farm Machinery Market," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 39(3), pages 1-10.
    2. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2021. "Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach," Papers 2103.06740, arXiv.org, revised Sep 2021.
    3. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2023. "Combining counterfactual outcomes and ARIMA models for policy evaluation," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-24.
    4. Boardman, Anthony & Vertinsky, Ilan & Whistler, Diana, 1997. "Using information diffusion models to estimate the impacts of regulatory events on publicly traded firms," Journal of Public Economics, Elsevier, vol. 63(2), pages 283-300, January.
    5. Jeffery A. Born & Seth C. Anderson, 1986. "A Comparison Of Intervention And Residual Analysis," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 9(3), pages 261-270, September.
    6. Canning, Patrick N. & Vroomen, Harry, 1994. "Welfare Impacts of a Trade Restriction: An Equilibrium Approach and Application in the Potash Industry," Technical Bulletins 156762, United States Department of Agriculture, Economic Research Service.
    7. Vitaly Guzhva, 2008. "Applying intervention analysis to financial performance data: The case of US airlines and September 11th," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 32(3), pages 243-259, July.
    8. Kentaka Aruga, 2014. "An intervention analysis on the Tokyo Grain Exchange non-genetically modified and conventional soybean futures markets," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-11, December.
    9. Michael Cichello & Douglas Lamdin, 2006. "Event Studies and the Analysis of Antitrust," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 13(2), pages 229-245.
    10. Rex Thompson, 1986. "Comment on “Standard setting and security returns: A time series analysis of FAS No. 8 eventsâ€," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 242-250, September.
    11. Lamdin, Douglas J., 2001. "Implementing and interpreting event studies of regulatory changes," Journal of Economics and Business, Elsevier, vol. 53(2-3), pages 171-183.
    12. Jin-Hong Park, 2012. "Nonparametric approach to intervention time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1397-1408, December.
    13. Thomas T. Cheng, 1986. "Standard setting and security returns: A time series analysis of FAS No. 8 events," Contemporary Accounting Research, John Wiley & Sons, vol. 3(1), pages 226-241, September.
    14. Akhigbe, Aigbe & Martin, Anna D. & Whyte, Ann Marie, 2005. "Contagion effects of the world's largest bankruptcy: the case of WorldCom," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(1), pages 48-64, February.
    15. Michael J. Gombola & Douglas R. Kahl & Kenneth P. Nunn Jr., 1988. "Valuation Of The Preferred Stock Sinking Fund Feature: A Time-Series Approach," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 11(1), pages 33-42, March.
    16. Lim Kai Jie, Shawn & Chadha, Pavneet & Lau, Joshua & Potdar, Nishad, 2012. "Is the Mongolian Equity Market Efficient? Empirical Evidence from Tests of Weak-Form Efficiency," MPRA Paper 41834, University Library of Munich, Germany.
    17. Wang, Nanying & Houston, Jack, 2015. "An intervention analysis on the relationship between futures prices of non-GM and GM contract soybeans in China," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196842, Southern Agricultural Economics Association.

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