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The three-factor model and artificial neural networks: predicting stock price movement in China

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  • Qing Cao
  • Mark Parry
  • Karyl Leggio

Abstract

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is significant, given the volatility of the Chinese stock markets and the added risk that arises from the Chinese legal and regulatory environment. In this paper we address this research gap by examining the predictive ability of several well-established forecasting models, including dynamic versions of a single-factor CAPM-based model and Fama and French’s three-factor model. In addition, we compare the forecasting ability of each of these models with that of an artificial neural network (ANN) model that contains the same predictor variables but relaxes the assumption of model linearity. Surprisingly, we find no statistical differences in the forecasting accuracy of the CAPM and three-factor model, a result that may reflect the emerging nature of the Chinese stock markets. We also find that each ANN model outperforms the corresponding linear model, indicating that neural networks may be a useful tool for stock price prediction in emerging markets. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Qing Cao & Mark Parry & Karyl Leggio, 2011. "The three-factor model and artificial neural networks: predicting stock price movement in China," Annals of Operations Research, Springer, vol. 185(1), pages 25-44, May.
  • Handle: RePEc:spr:annopr:v:185:y:2011:i:1:p:25-44:10.1007/s10479-009-0618-0
    DOI: 10.1007/s10479-009-0618-0
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    as
    1. Adam Fadlalla & Chien-Hua Lin, 2001. "An Analysis of the Applications of Neural Networks in Finance," Interfaces, INFORMS, vol. 31(4), pages 112-122, August.
    2. Harvey, Campbell R, 1995. "Predictable Risk and Returns in Emerging Markets," The Review of Financial Studies, Society for Financial Studies, vol. 8(3), pages 773-816.
    3. Young, Michael N. & McGuinness, Paul B., 2001. "The missing link: why stock markets have been ineffective in Chinese SOE reform," Business Horizons, Elsevier, vol. 44(4), pages 55-62.
    4. Barry, Christopher B. & Goldreyer, Elizabeth & Lockwood, Larry & Rodriguez, Mauricio, 2002. "Robustness of size and value effects in emerging equity markets, 1985-2000," Emerging Markets Review, Elsevier, vol. 3(1), pages 1-30, March.
    5. Zong-Jun Wang & Xiao-Lan Deng, 2006. "Corporate Governance and Financial Distress: Evidence from Chinese Listed Companies," Chinese Economy, Taylor & Francis Journals, vol. 39(5), pages 5-27, October.
    6. Wang, Xiao Lu & Shi, Kan & Fan, Hong Xia, 2006. "Psychological mechanisms of investors in Chinese Stock Markets," Journal of Economic Psychology, Elsevier, vol. 27(6), pages 762-780, December.
    7. Wu, Desheng(Dash) & Liang, Liang & Yang, Zijiang, 2008. "Analyzing the financial distress of Chinese public companies using probabilistic neural networks and multivariate discriminate analysis," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 206-220, September.
    8. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    9. Kie Wong & Ruth Tan & Wei Liu, 2006. "The Cross-Section of Stock Returns on The Shanghai Stock Exchange," Review of Quantitative Finance and Accounting, Springer, vol. 26(1), pages 23-39, February.
    10. Watts, Rl & Leftwich, Rw, 1977. "Time-Series Of Annual Accounting Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 15(2), pages 253-271.
    11. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Kang, Joseph & Liu, Ming-Hua & Ni, Sophie Xiaoyan, 2002. "Contrarian and momentum strategies in the China stock market: 1993-2000," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 243-265, June.
    14. Wang, Yuenan & Di Iorio, Amalia, 2007. "The cross section of expected stock returns in the Chinese A-share market," Global Finance Journal, Elsevier, vol. 17(3), pages 335-349, March.
    15. Corradi, Valentina & Swanson, Norman R., 2002. "A consistent test for nonlinear out of sample predictive accuracy," Journal of Econometrics, Elsevier, vol. 110(2), pages 353-381, October.
    16. Xiaming Liu & Haiyan Song & Peter Romilly, 1997. "Are Chinese stock markets efficient? A cointegration and causality analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 4(8), pages 511-515.
    17. Wayne E. Ferson & Campbell R. Harvey, 1999. "Conditioning Variables and the Cross Section of Stock Returns," Journal of Finance, American Finance Association, vol. 54(4), pages 1325-1360, August.
    18. Bhattacharya, Utpal & Daouk, Hazem & Jorgenson, Brian & Kehr, Carl-Heinrich, 2000. "When an event is not an event: the curious case of an emerging market," Journal of Financial Economics, Elsevier, vol. 55(1), pages 69-101, January.
    19. Brennan, Michael J. & Chordia, Tarun & Subrahmanyam, Avanidhar, 1998. "Alternative factor specifications, security characteristics, and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 49(3), pages 345-373, September.
    20. repec:bla:jfinan:v:53:y:1998:i:6:p:1975-1999 is not listed on IDEAS
    21. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    22. Shen, Chung-Hua, 1996. "Forecasting macroeconomic variables using data of different periodicities," International Journal of Forecasting, Elsevier, vol. 12(2), pages 269-282, June.
    23. Wei, Zuobao & Varela, Oscar, 2003. "State equity ownership and firm market performance: evidence from China's newly privatized firms," Global Finance Journal, Elsevier, vol. 14(1), pages 65-82, May.
    24. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    25. Mookerjee, Rajen & Yu, Qiao, 1999. "Seasonality in returns on the Chinese stock markets: the case of Shanghai and Shenzhen," Global Finance Journal, Elsevier, vol. 10(1), pages 93-105.
    26. Paul McGuinness & Michael Ferguson, 2005. "The ownership structure of listed Chinese State-owned enterprises and its relation to corporate performance," Applied Financial Economics, Taylor & Francis Journals, vol. 15(4), pages 231-246.
    27. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    28. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    29. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    30. Ng, Lilian & Wu, Fei, 2007. "The trading behavior of institutions and individuals in Chinese equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2695-2710, September.
    31. Church, Keith B. & Curram, Stephen P., 1996. "Forecasting consumers' expenditure: A comparison between econometric and neural network models," International Journal of Forecasting, Elsevier, vol. 12(2), pages 255-267, June.
    32. Qi, Min, 1999. "Nonlinear Predictability of Stock Returns Using Financial and Economic Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 419-429, October.
    33. Griffin, Pa, 1977. "Time-Series Behavior Of Quarterly Earnings - Preliminary Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 15(1), pages 71-83.
    34. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    35. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    36. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    37. repec:sae:ausman:v:28:y:2003:i:2:p:119-139 is not listed on IDEAS
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