A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

  • Authors:

  • Qing Cao;Karyl B. Leggio;Marc J. Schniederjans

  • Affiliations:

  • Henry W. Bloch School of Business and Public Administration, University of Missouri-Kansas City, Kansas City, MO;Henry W. Bloch School of Business and Public Administration, University of Missouri-Kansas City, Kansas City, MO;College of Business Administration, University of Nebraska-Lincoln, Lincoln, NE

  • Venue:
  • Computers and Operations Research
  • Year:
  • 2005

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Abstract

Evidence exists that emerging market stock returns are influenced by a different set of factors than those that influence the returns for stocks traded in developed countries. This study uses artificial neural networks to predict stock price movement (i.e., price returns) for firms traded on the Shanghai stock exchange. We compare the predictive power using linear models from financial forecasting literature to the predictive power of the univariate and multivariate neural network models. Our results show that neural networks outperform the linear models compared. These results are statistically significant across our sample firms, and indicate neural networks are a useful tool for stock price prediction in emerging markets, like China.