时间：2022-11-30
新加坡南洋理工大学包特教授、法国里昂商学院Brice Corgnet教授，日本大阪大学Nobuyuki Hanaki教授, 新加坡南洋理工大学Yohanes E. Riyanto教授与天津大学马寅初经济学院助理教授朱家华合作的文章“Predicting the Unpredictable: New Experimental Evidence on Forecasting Random Walks”被国际知名经济学期刊Journal of Economic Dynamics and Control 录用，并已在线发表。
本文研究了个人如何使用价格图表中的明显可预测性度量来预测未来市场价格。文章使用实验室实验的方法，让实验中的被试者预测与 Bloomfiled & Hales(2002) (BH) 的开创性实验中相同的随机游走时间序列, 以及股票价格时间序列。我们的实验结果与BH的实验结果一致，即当随机游走序列中存在更多反转时，被试者会减少依赖图表中的趋势规律做预测。
在股票价格预测任务中，当股票价格时间序列中出现更多反转时，实验结果没有证明被试者会减少依赖股票价格序列中的趋势规律做预测。此外，文章还发现，被试者也使用其他因素，如价格序列中的自相关系数、振幅和波动率作为可预测性的衡量标准。最后，文章表明，正如随机游走理论预测的那样，依赖过去数据中的明显模式并不能提高预测的准确性。
Journal of Economic Dynamics and Control (JEDC)涵盖的研究领域主要包括计算经济学、动态经济模型和宏观经济学。1979年出版至今，在经济学界一直有非常好的口碑。Kalaitzidakis et al. (2003)对经济学期刊学术影响的排名中，JEDC在159 种期刊中位列第 23 位。Kodrzycki 和 Yu (2006)对经济学期刊学术影响的排名中，JEDC在181种期刊中位列第 27 位。
Predicting the Unpredictable:
New Experimental Evidence on Forecasting Random Walks*
Te Baoa, Brice Corgnetb, Nobuyuki Hanakic, Yohanes E. Riyantoa, and Jiahua Zhud
a School of Social Sciences, Nanyang Technological University, Singapore
b EM Lyon Business School, Lyon, France
c Institute of Social and Economic Research, Osaka University, Osaka, Japan
d Ma Yinchu School of Economics, Tianjin University, Tianjin, China
Abstract
We investigate how individuals use measures of apparent predictability from price charts to predict future market prices. Subjects in our experiment predict both random walk times series, as in the seminal work by Bloomfield and Hales (2002)(BH), and stock price time series. We successfully replicate the experimental find-ings in BH that subjects are less trend-chasing when there are more reversals in random walk times series. We do not find evidence that subjects overreact less to the trend when there are more reversals in the stock price prediction task. Our subjects also appear to use other variables such as autocorrelation coefficient, amplitude and volatility as measures of predictability. However, as random walk theory predicts, relying on apparent patterns in past data does not improve their prediction accuracy.
Keywords: Asset Prices, Regime-switching, Price Prediction, Experimental Finance
JEL Classification: C91, D91, D84, G41
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