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On Testing the Random-Walk Hypothesis: A Model-Comparison Approach

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  • Darrat, Ali F
  • Zhong, Maosen

Abstract

The main intention of this paper is to investigate, with new daily data, whether prices in the two Chinese stock exchanges (Shanghai and Shenzhen) follow a random-walk process as required by market efficiency. We use two different approaches, the standard variance-ratio test of Lo and MacKinlay (1988) and a model-comparison test that compares the ex post forecasts from a NAIVE model with those obtained from several alternative models: ARIMA, GARCH and the Artificial Neural Network (ANN). To evaluate ex post forecasts, we utilize several procedures including RMSE, MAE, Theil's U, and encompassing tests. In contrast to the variance-ratio test, results from the model-comparison approach are quite decisive in rejecting the random-walk hypothesis in both Chinese stock markets. Moreover, our results provide strong support for the ANN as a potentially useful device for predicting stock prices in emerging markets. Copyright 2000 by MIT Press.

Suggested Citation

  • Darrat, Ali F & Zhong, Maosen, 2000. "On Testing the Random-Walk Hypothesis: A Model-Comparison Approach," The Financial Review, Eastern Finance Association, vol. 35(3), pages 105-124, August.
    • Handle: RePEc:bla:finrev:v:35:y:2000:i:3:p:105-24
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    References listed on IDEAS

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    1. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    2. Nicholas Barberis & Ming Huang & Tano Santos, "undated". "Prospect Theory and Asset Prices," CRSP working papers 494, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
    3. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    4. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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