On Testing the Random‐Walk Hypothesis: A Model‐Comparison Approach
用新日度数据，通过方差比检验和模型比较法（ARIMA、GARCH、人工神经网络）检验中国沪深股市价格是否遵循随机游走，发现模型比较法明确拒绝该假说，且神经网络对新兴市场股价预测有潜力。
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 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.