Experimenting with 3 different input-output mapping structures of ANN models for predicting CSI 300 index
Forecasting the stock market price index is a challenging task. Many scholars have tried on many kinds of models to predict the stock index, mainly autoregressive integrated moving average model (ARIMA), artificial neural networks (ANN) with genetic algorithms (GA). This paper documents a set of thorough empirical tests of ANN's with different choices of inputs and different numbers of hidden neurons for forecasting the CSI 300 - the benchmark stock index of China. The prediction accuracy is measured in terms of hit rate and mean square error. The trend of the hit rate is observed by adjusting the window length and the number of hidden neurons. The results show that the hit rate is highest when the window length is between 14 days to 20 days.
Barbulescu, A., & Bautu, E. (2012). A ybrid approach for modeling financial time series. International Arab Journal of Information Technology, 9(4), 327-335.
Bekiros, S. D. (2010). Fuzzy adaptive decision-making for boundedly rational traders in speculative stock market. European Journal of Operational Research, 202(1), 285-293.
Bodyanskiy, Y., & Popov, S. (2006). Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research, 175(3), 1357-1366.
Brooks, C. (2002). Introductory Econometrics for Finance. Cambridge, UK: Cambridge University Press.
Chavarnakul, T., & Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural network. Expert Systems With Applications, 34(2), 1004-1017.
Chen, W. H., Shi, J. Y., & Wu, S. S. (2006). Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. International Electronic Finance, 1(1), 49-67.
Chen, A. S, Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901-923.
Ding, S. F., Su, C. Y., & Yu, J. Z. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review, 36(2), 153-162.
Freitas, P. S. A., & Rodrigues, A. J. L. (2006). Model combination in neural-based forecasting. European Journal of Operational Research, 173(3), 801-814.
Granger CWJ. (1989). Invited review combining forecasts—twenty years later. Journal of Forecasting 8(3),167-173.
Jacquier, E., Polson, N., & Rossi, P. (2004). Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, 122(1), 185-212.
Kanas, A. (2003). Non-linear forecasts of stock returns. Journal of Forecasting, 22(4), 299-315.
Koulouriotis, D. E., Diakoulakis, I. E., Emiris, D. M., & Zopounidis, C. D. (2005). Development of dynamic cognitive networks as complex systems approximators: Validation in financial time series. Applied Soft Computing Journal, 5(2), 157-179.
Kwon, Y. K., & Moon, Byung-Ro. (2007). A hybrid neurogenetic approach for stock forecasting. IEEE Transactions on Neural Networks, 18(3), 851-864.
Li, T., Li, Q., Zhu, S., & Ogihara, M. (2002). A survey on wavelet applications in data mining. SIGKDD Explorations 2002,4(2), 49-68.
Melin, P., Mancilla, A., Lopez, M. (2007). A hybird modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Applied Soft Computing, 7(4), 1217-1226.
Moreno, D. & Olmeda, I. (2007). Is the predictability of emerging and developed stock markets really exploitable?. European Journal of Operational Research, 182(1), 436-454.
Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. OMEGA-International Journal of Management Science, 33(6), 497-505.
Pan, H., Haidar, I., & Kulkarni, S. (2009). Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics. Frontiers of Computer Science in China, 3(2), 177-191.
Pan, H., Titakaratne, C., & Yearwood, J. (2005). Predicting Australian stock index using neural networks exploiting dynamical swings and intermarket influences. Journal of Research and Practice in Information Technology, 37(1), 43-55.
Refenes, A. N., Zapranis, A., & Francis, G. (1994). Stock performance modeling using neural networks: A comparative study with regression models. Neural Networks, 7(2), 375-388.
Ruxanda, G. (2010). Learning perceptron neural network with backpropagation algorithm. Economic Computation and Economic Cybernetics Studies and Research, 44(4), 37-54.
Wang, J. J., Wang, J. Z. & Zhang, Z. (2012). Stock index forecasting based on a hybrid model. Omega-International Journal of Management Science, 40(6), 758-766.
Yaser S., & Atiya A. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205-213.
Zhang, G., & Berardi, V. (2001). Time series forecasting with neural network ensembles: An application for exchange rate prediction. Journal of the Operational Research Society, 52(6), 652-664.
- There are currently no refbacks.
We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
email@example.com; firstname.lastname@example.org; email@example.com
Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures
Address: 730, 77e AV, Laval, Quebec, H7V 4A8, Canada
Telephone: 1-514-558 6138