Comparison of Neural Network and Ordinary Least Squares Models in Forecasting Chinese Stock Prices

Ojoung Kwon, Sasan Rahmatian, Alicia Iriberri, Zijian Wu


With the recent entrance of China’s stock market into the global financial system, a renewed interest emerges in discovering the one framework that will reliably forecast changes in stock market prices. This research presents evidence of the forecasting performance of ANN models compared to linear regression models, specifically ordinary least square (OLS) models. Using a 10-year data set from 2002 to 2012 of 154 companies in the A-share of the Shanghai Stock Exchange, the study demonstrates the use of ANNs in forecasting price changes in China’s stock market. The data set used includes observations on daily closing prices and data on 25 indicators, namely, macroeconomic indicators, market sentiment indicators, institutional investors, and microeconomic indicators of factors believed to contribute to the unpredictability of the Chinese stock exchange. A t-test was calculated to compare the performance of the ANN model with the performance of the OLS model in predicting daily stock price change in China’s stock markets.The results of the t-test indicate that the ANN model performed significantly better.

Keywords: Business Intelligence, Financial Forecasting, Investment Strategies, Behavioral Finance, Technical Analysis, Data Mining, Neural Networks, and Artificial Intelligence.

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Anwar, S., & Watanabe, K. (2010). Predicting Future Depositors Rate of Return Applying Neural Network: A Case-study of Indonesian Islamic Bank. International Journal of Economics and Finance, 2(3), 170.

Cao, Q., Parry, M. E., &Leggio, K. B. (2011). The three-factor model and artificial neural networks: predicting stock price movement in China. Annals of Operations Research, 185(1), 25-44.

Cao, Q., Leggio, K. B., &Schniederjans, M. J. (2005). A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32(10), 2499-2512.

Chan, K. C., Fung, H. G., &Thapa, S. (2007). China financial research: A review and synthesis. International Review of Economics & Finance, 16(3), 416-428.

Chen, C. I., & Huang, S. J. (2013). The necessary and sufficient condition for GM (1, 1) grey prediction model. Applied Mathematics and Computation, 219(11), 6152-6162.

Chen, X., Kim, K. A., Yao, T., & Yu, T. (2010). On the predictability of Chinese stock returns. Pacific-Basin Finance Journal, 18(4), 403-425.

Chi, L., Zhuang, X., & Song, D. (2012). Investor sentiment in the Chinese stock market: an empirical analysis. Applied Economics Letters, 19(4), 345-348.

Chiang, T. C., Li, J., & Tan, L. (2010). Empirical investigation of herding behavior in Chinese stock markets: Evidence from quantile regression analysis. Global Finance Journal, 21(1), 111-124.

Chung, H. Y. (2006). Testing weak-form efficiency of the Chinese stock market. Working paper presented on February 14th, 2006, at Lappeenranta University of Technology Department of Business Administration Section of Accounting and Finance.

Dawson, C. W., &Wilby, R. L. (2001). Hydrological modelling using artificial neural networks. Progress in physical Geography, 25(1), 80-108.

Demirer, R., &Kutan, A. M. (2006). Does herding behavior exist in Chinese stock markets?. Journal of international Financial markets, institutions and money, 16(2), 123-142.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.

Fan, J. P., Wong, T. J., & Zhang, T. (2007). Politically connected CEOs, corporate governance, and Post-IPO performance of China's newly partially privatized firms. Journal of financial economics, 84(2), 330-357.

Ferson, W. E., & Harvey, C. R. (1993). The risk and predictability of international equity returns. Review of financial Studies, 6(3), 527-566.

Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information & Management, 24(3), 159-167.

Gordon, R. H., & Li, W. (2003). Government as a discriminating monopolist in the financial market: the case of China. Journal of Public Economics, 87(2), 283-312.

Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.

Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., &Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine pollution bulletin, 64(11), 2409-2420.

Groenewold, N., Tang, S. H. K., & Wu, Y. (2003). The efficiency of the Chinese stock market and the role of the banks. Journal of Asian Economics, 14(4), 593-609.

He, Z. (2000). Corruption and anti-corruption in reform China. Communist and Post-Communist Studies, 33(2), 243-270.

Hecht-Nielsen, R. (1987). Kolmogorov's mapping neural network existence theorem. In Proceedings of the international conference on Neural Networks (pp. 11-14). IEEE Press.

Hill, T., Marquez, L., O'Connor, M., & Remus, W. (1994). Artificial neural network models for forecasting and decision making. International journal of forecasting, 10(1), 5-15.

Iskyan, K. (2016). China's stock markets have soared by 1,479% since 2003. Business Insider. November 16, 2016.

Jarrett, J. E., Pan, X., & Chen, S. (2009). Do the Chinese bourses (stock markets) predict economic growth?. International journal of business and economics, 8(3), 201.

Jiang, F. (2011). How predictable is the Chinese stock market?(Doctoral dissertation, Singapore Management University (Singapore)). n.p.: ProQuest, UMI Dissertations Publishing.

Kang, S. H., Cheong, C., & Yoon, S. M. (2010). Long memory volatility in Chinese stock markets. Physica A: Statistical Mechanics and its Applications, 389(7), 1425-1433.

Khare, K., Darekar, O., Gupta, P. and Attar, V.Z. (2017). Short term stock price prediction using deep learning. In 2nd IEEE International Conference On Recent Trends in Electronics Information and Communication Technology (RTEICT), May 19-20, 2017, India

Kiani, K. M. (2007). Asymmetric business cycle fluctuations and contagion effects in G7 countries. International Journal of Business and Economics, 6(3), 237.

Kiani, K. M. (2009). Asymmetries in macroeconomic time series in eleven Asian economies. International Journal of Business and Economics, 8(1), 37.

Kimoto, T., Asakawa, K., Yoda, M., &Takeoka, M. (1990, June). Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 1-6). IEEE.

Knight, J., &Yueh, L. (2008). The role of social capital in the labour market in China. Economics of transition, 16(3), 389-414.

Kryzanowski, L., Galler, M., & Wright, D. W. (1993). Using artificial neural networks to pick stocks. Financial Analysts Journal, 49(4), 21-27.

Kwon, O., Wu, Z., & Zhang, L. (2016). Study of the forecasting performance of China stock’s prices using business intelligence: Comparison between normalized and denormalized data. Academy of Accounting and Financial Studies Journal, 20(1), 53.

Kwon, O., Tjung, L., and Tseng, K. (2013). Comparison Study of the Forecasting Performance of China and USA Stocks’ Prices using Business Intelligence (BI), the Proceedings of the 2013 Trilateral Conference, Seoul, Korea, October 16-18, 2013, pp. 183-216.

Lee, C. F., &Rui, O. M. (2000). Does trading volume contain information to predict stock returns? Evidence from China's stock markets. Review of Quantitative Finance and Accounting, 14(4), 341-360.

Yeh, Y. H., & Lee, T. S. (2000). The interaction and volatility asymmetry of unexpected returns in the greater China stock markets. Global Finance Journal, 11(1-2), 129-149.

Leigh, W., Hightower, R., &Modani, N. (2005). Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike. Expert Systems with Applications, 28(1), 1-8.

Li, X., & Zhang, B. (2013). Spillover and cojumps between the US and Chinese stock markets. Emerging Markets Finance and Trade, 49(sup2), 23-42.

Lim, T. C., Huang, W., Yun, J., & Zhao, D. (2013). Has stock market efficiency improved? evidence from China. Journal of Finance & Economics, 1(1), 1-9.

Liu, F., & Wang, J. (2012). Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomputing, 83, 12-21.

Liu, H., & Wang, J. (2011). Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market. Mathematical Problems in Engineering, 2011.

Lü, L. (2004). What Motivate Investors to Sell?: Evidence from China's Stock Market (Doctoral dissertation, Chinese University of Hong Kong). n.p.: ProQuest, UMI Dissertations Publishing.

Ma, S., and Barnes, M. (2001). Are China’s stock markets really weak-form efficient? Centre for International Economic Studies, Discussion paper, No. 0119.

Mamaysky, H., Spiegel, M., & Zhang, H. (2008). Estimating the dynamics of mutual fund alphas and betas. The Review of Financial Studies, 21(1), 233-264.

Mamudi, S. and Bartenstein, B., 2017. China Stocks win MSCI Inclusion. Bloomberg. June 20, 2017.

Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental modelling & software, 15(1), 101-124.

Mostafa, M. M. (2010). Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert Systems with Applications, 37(9), 6302-6309.

McNelis, P. D. (1996). A neural network analysis of Brazilian stock prices: Tequila effects vs. pisco sour effects. Journal of Emerging Markets, 1, 29-44.

Meng, D. (2008, April). A neural network model to predict initial return of Chinese SMEs stock market initial public offerings. In Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on (pp. 394-398). IEEE.

Palani, S., Liong, S. Y., &Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597.

Panchal, G., Ganatra, A., Kosta, Y. P., &Panchal, D. (2010). Searching most efficient neural network architecture using Akaike’s information criterion (AIC). International Journal of Computer Applications, 1(5), 41-44.

Riedel, J., Jin, J., &Gao, J. (2007). How China grows: Investment, finance, and reform. Princeton University Press.

Sanddorf-Köhle, W. G. &Friedmann, R. (2002). Volatility clustering and nontrading days in Chinese stock markets. Journal of economics and business, 54(2), 193-217.

Shanghai Securities News and State Information Centre (SSNSIC), 2003. 2002 annual report of listed companies. Available from [in Chinese].

Sun, Q., & Tong, W. H. (2003). China share issue privatization: the extent of its success. Journal of financial economics, 70(2), 183-222.

Sun, Q., Tong, W. H., & Tong, J. (2002). How does government ownership affect firm performance? Evidence from China’s privatization experience. Journal of Business Finance & Accounting, 29(1‐2), 1-27.

Tjung, L. C., Kwon, O., & Tseng, K. C. (2012). Comparison study on neural network and ordinary least squares model to stocks’ prices forecasting. Journal of Management Information and Decision Sciences, 15(1), 1.

Tjung, L. C., Kwon, O., Tseng, K. C. and Brandly-Geist, J. (2010). Forecasting financial stocks using data mining. Global Economy and Finance Journal, 3(2), pp. 13-26.

Tseng, K.C., Kwon, O., and Tjung, L. C., (2012). Time series and neural networks forecast of daily stock prices. Investment Management and Financial Innovations, 3(1), pp. 32- 54.

Tondkar, R. H., Peng, S., &Hodgdon, C. (2003). The Chinese Securities Regulatory Commission and the regulation of capital markets in China. Advances in International Accounting, 16, 153-174.

Wei, Z., Varela, O., & Hassan, M. K. (2002). Ownership and performance in Chinese manufacturing industry1. Journal of Multinational Financial Management, 12(1), 61-78.

Wang, J., Pan, H., & Liu, F. (2012). Forecasting crude oil price and stock price by jump stochastic time effective neural network model. Journal of Applied Mathematics, 2012.

Wang, M., Qiu, C., & Kong, D. (2011). Corporate social responsibility, investor behaviors, and stock market returns: Evidence from a natural experiment in China. Journal of business ethics, 101(1), 127-141.

Wang, W. C., Silva, M. M. S., &Moutinho, L. (2016). Modelling Consumer Responses to Advertising Slogans through Artificial Neural Networks. International Journal of Business and Economics, 15(2), 89.

Watanabe, M. (2002). Holding company risk in China: A final step of state-owned enterprises reform and an emerging problem of corporate governance. China Economic Review, 13(4), 373-381.

Wei, J. R., Huang, J. P., & Hui, P. M. (2013). An agent-based model of stock markets incorporating momentum investors. Physica A: Statistical Mechanics and its Applications, 392(12), 2728-2735.

Xu, X., & Wang, Y. (1999). Ownership structure and corporate governance in Chinese stock companies. China economic review, 10(1), 75-98.

Yang, Z., Su, R., Zhang, Q., & Ying, S. (2014). Managers' Incentives, Earnings Management Strategies, and Investor Sentiment. International Journal of Business and Economics, 13(2), 157.

Yao, J., Ma, C., & He, W. P. (2014). Investor herding behaviour of Chinese stock market. International Review of Economics & Finance, 29, 12-29.

Yao, Y., and Yueh, L. (2009). Law, finance, and economic growth in China: an introduction. World Development [H.W. Wilson – SSA], 37(4), 753.

Yümlü, S., Gürgen, F. S., & Okay, N. (2005). A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction. Pattern Recognition Letters, 26(13), 2093-2103.

Zhang, H., Wei, J., & Huang, J. (2014). Scaling and predictability in stock markets: A comparative study. PloS one, 9(3), e91707.


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