Forecasting the changes in daily stock prices in Shanghai Stock Exchange using Neural Network and Ordinary Least Squares Regression
-
DOIhttp://dx.doi.org/10.21511/imfi.17(3).2020.22
-
Article InfoVolume 17 2020, Issue #3, pp. 292-307
- Cited by
- 980 Views
-
241 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The research focuses on finding a superior forecasting technique to predict stock movement and behavior in the Shanghai Stock Exchange. The author’s interest is in stock market activities during high volatility, specifically 13 years from 2002 to 2015. This volatile period, fueled by events such as the dot-com bubble, SARS outbreak, political leadership transitions, and the global financial crisis, is of interest. The study aims to analyze changes in stock prices during an unstable period. The author used advanced computer sciences, Machine Learning through information processing and training, and the traditional statistical approach, the Multiple Linear Regression Model, with the least square method. Both techniques are accurate predictors measured by Absolute Percent Error with a range of 1.50% to 1.65%, using a data file containing 3,283 observations generated to record the daily close prices of individual Chinese companies. The t-test paired difference experiment shows the superiority of Neural Network in the finance sector and potentially not in other sectors. The Multiple Linear Regression Model performs equivalent to the Neural Network in other sectors.
- Keywords
-
JEL Classification (Paper profile tab)F37, C12, C45
-
References39
-
Tables4
-
Figures0
-
- Table 1. List of terms and abbreviations
- Table 2. Industry-wide summary for APE (Absolute Percent Error) for daily stock price change and p-value of t-test for paired differences
- Table 3. Summary for APE (Absolute Percent Error) for daily stock price change across eight industrial sectors
- Table A1. Summary tables of analysis by industries and by individual companies from A-share of the Shanghai Stock Exchange
-
- Alyuda Research. (2006). Alyuda Neuro Intelligence User Manual. Alyuda Research, LLC, 2001–2017.
- Anwer, S., & Watanabe, K. (2010). Predicting future depositor’s rate of return applying Neural Network: A Case-study of Indonesian Islamic Bank. International Journal of Economics and Finance, 2(3), 170-176.
- Burstein, F., & Holsapple, C. (2008). Handbook on Decision Support System 2. In International Handbooks on Information Systems (pp. 175-193). Springer Berlinn Heidelbert.
- Cao, Q., & Parry, M. (2011). The three-factor model and artificial neural networks: Predicting stock price movement in China. Annals of Operations Research, 185(1), 25-44.
- Cao, W., Han, Y., & Lam, S. (2013). Short-term stock price trend prediction of an emerging market using neural networks. IIE Annual Conference Proceedings, 50.
- Chen, X., Kim, K., Yao, T., & Yu, T. (2010). On the predictability of Chinese stock returns. Pacific-Basin Finance Journal, 18(4), 403-425.
- Chiang, T., 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.
- Dai, W., Wu, J., & Lu, C. (2012). Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert Systems with Applications, 39(4), 4444-4452.
- Dawson, C. W., & Wilby, R. L. (2001). Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25(1), 80-108.
- Fama, E. (1970). Efficient Capital Market: A Review of Theory and Empirical Work. Journal of Finance, 25, 383-417.
- Fan, J., Wong, T., & 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.
- Firth, M., Rui, O., & Wu, X. (2009). The timeliness and consequences of disseminating public information by regulators. Journal of Accounting and Public Policy, 28(2), 118-132.
- Fletcher, D., & Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information Management, 24(3), 159-167.
- 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.
- Gordon, R., & Li, W. (2003). Government as a discriminating monopolist in the financial market: The case of China. Journal of Public Economics, 87(2), 283-312.
- Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). Bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 29, 196-210.
- Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques (2nd ed.). Morgan Kaufmann.
- He, Z. (2000). Corruption and anti-corruption in reform China. Communist and Post-Communist Studies, 33(2), 243-270.
- Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 1-8.
- Huang, H. C., & Chen, C. C. (2013). A Study on the construction of the prediction model for exchange rate fluctuations. International Journal of Intelligent Information Processing, 4(4), 63-74.
- Kang, S., Cheong, C., & Yoon, S. (2010). Long memory volatility in Chinese stock markets. Physica A: Statistical Mechanics and Its Applications, 389(7), 1425-1433.
- Kwon, O., Wu, Z., & Zhang, L. (2016). Study of the forecasting performance of China stock’s prices using Business Intelligence (BI): Comparison between normalized and denormalized data. Academy of Information and management Sciences Journal, 20(1), 53-69.
- Li, X., Wang, S. S., & Wang, X. (2017). Trust and stock price crash risk: Evidence from China. Journal of Banking and Finance, 76, 74-91.
- Lim, T., 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, 1-15.
- 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 Modeling and Software, 15(1), 101-124.
- Meng, D. (2008). A neural network model to predict initial return of Chinese SMEs stock market initial public offerings. In 2008 IEEE International Conference on Networking, Sensing and Control (pp. 394-398).
- Palani, S., Liong, S., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56(9), 1586-1597.
- Riedel, J., Jin, J., & Gao, J. (2007). How China grows: Investment, finance, and reform. NJ: Princeton University Press.
- Tjung, L. C., Kwon, O., & Tseng, K. C. (2012). Comparison study on neural network and ordinary least squares model to stocks’ prices forecasting. Financial Management, 9(1), 32-54.
- Tjung, L.C., Kwon, O., Tseng, K. C., & Bradley-Geist, J. (2010). Forecasting financial stocks using data mining. Global Economy and Finance Journal, 3(2), 13-26.
- 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.
- Wei, J., Huang, J., & Hui, P. (2013). An agent-based model of stock markets incorporating momentum investors. Physica A-Statistical Mechanics and Its Applications, 392(12), 2728-2735.
- Yao, J., Ma, C., & He, W. (2014). Investor herding behavior of Chinese stock market. International Review of Economics & Finance, 29, 12-29.
- Yao, Y., & Yueh, L. (2009). Law, finance, and economic growth in China: an introduction. World Development [HW. Wilson – SSA], 37(4), 753.
- Zhang, H., Wei, J., & Huang, J. (2014). Scaling and predictability in stock markets: A comparative study. PloS One, 9(3), 91707-91704.
- Zhang, X., Zhang, Y., Wang, S., Yao, Fang, Y. B., & Yu, P. S. (2018). Improving stock market prediction via heterogeneous information fusion. Knowledge-Based Systems, 143, 236-247.