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On Testing the Random‐Walk Hypothesis: A Model‐Comparison Approach
Author(s) -
Darrat Ali F.,
Zhong Maosen
Publication year - 2000
Publication title -
financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.621
H-Index - 47
eISSN - 1540-6288
pISSN - 0732-8516
DOI - 10.1111/j.1540-6288.2000.tb01423.x
Subject(s) - random walk , econometrics , autoregressive integrated moving average , random walk hypothesis , autoregressive conditional heteroskedasticity , economics , stock (firearms) , variance (accounting) , statistics , stock market , mathematics , time series , volatility (finance) , engineering , geography , mechanical engineering , context (archaeology) , accounting , archaeology
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 NAÏVE 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.