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Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test (Revision of 5-87)

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  • Andrew W. Lo
  • Craig A. MacKinlay

Abstract

In this paper, we test the random walk hypothesis for weekly stock market returns by comparing variance estimators derived from data sampled at different frequencies. The random walk model is strongly rejected for the entire sample period (1962-1985) and for all sub-periods for a variety of aggregate returns indexes and size-sorted portfolios. Although the rejections are largely due to the behavior of small stocks, they cannot be completely attributed to the effects of infrequent trading or time-varying volatilities. Moreover, the rejection of the random walk for weekly returns does not support a mean-reverting model of asset prices.

Suggested Citation

  • Andrew W. Lo & Craig A. MacKinlay, "undated". "Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test (Revision of 5-87)," Rodney L. White Center for Financial Research Working Papers 29-87, Wharton School Rodney L. White Center for Financial Research.
    • Handle: RePEc:fth:pennfi:29-87
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    Cited by:

    1. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    2. Juan Jose Echavarria & Mauricio Villamizar-Villegas, 2016. "Great expectations? evidence from Colombia’s exchange rate survey," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 25(1), pages 1-27, December.
    3. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    4. Ben Moews & J. Michael Herrmann & Gbenga Ibikunle, 2018. "Lagged correlation-based deep learning for directional trend change prediction in financial time series," Papers 1811.11287, arXiv.org, revised Nov 2018.
    5. Matheus José Silva de Souza & Danilo Guimarães Franco Ramos & Marina Garcia Pena & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Examination of the profitability of technical analysis based on moving average strategies in BRICS," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-18, December.

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