How to cite this paper
Selmi, N., Chaabene, S & Hachicha, N. (2015). Forecasting returns on a stock market using Artificial Neural Networks and GARCH family models: Evidence of stock market S & P 500.Decision Science Letters , 4(2), 203-210.
Refrences
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Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
Bollerslev, T. (1988). On the correlation structure for the generalized autoregressive conditional heteroskedastic process. Journal of Time Series Analysis, 9(2), 121-131.
Chen, C. H. (1994). Neural networks for financial market prediction. Proceedings of the IEEE International Conference on Neural Networks, 2, 1199-1202.
Chen, A., Leung, M. T., & Hazem, D. (2003). Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers and Operations Research, 30, 901-923.
Choi, H., Lee, E., & Lee, C. (2006). Neural Network Deinterlacing Using Multiple Fields. Lecture Notes in Control and Information Sciences, 345, 970-975.
Choi, H., Lee, C., & Rhee, M. W. (1995). Trading S & P 500 stock index futures using a neural network. The Third Annual International Conference on Artificial Intelligence Applications on Wall Street, 63-72.
Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica: Journal of the Econometric Society, 391-407.
Gradojevic, N., & Yang, J. (2000). The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure variables, Working Paper n° 2000-23, Bank of Canada.
Hamid, S. A., & Iqbal, Z. (2004). Using neural networks for forecasting volatility of S & P 500 Index futures prices. Journal of Business Research, 57(10), 1116-1125.
Hornick, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal
Approximators. Neural Networks, 2, 359-366.
Huang, W., Lai, K. K., Nakamori, Y., Wang, S. Y., & Yu, L. (2007). Neural networks in finance and
economics forecasting. International Journal of Information Technology and Decision Making, 6, 113-140.
Huarng, K., & Yu, T. H. K. (2006). The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications, 363(2), 481-491.
Kamijo, K., & Tanigawa, T. (1990). Stock price pattern recognition: A recurrent neural network approach. Proceedings of the International Joint Conference on Neural Networks, 215-221.
Kim, N. H., Yoo, S. K., & Lee, K. S. (2003). Polygon reduction of 3D objects using Stokes’ theorem. Computer methods and programs in biomedicine, 71(3), 203-210.
Kunhuang, H., & Yu, T.H.K. (2006). The application of neural networks to forecast fuzzy time series.
Physical A: Statistical Mechanics and Its Applications, 363(2), 481-491.
Majhi, R., Panda, G., Sahoo, G., Dash, P. K., & Das, D. P. (2007, September). Stock market prediction of S & P 500 and DJIA using bacterial foraging optimization technique. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 2569-2575). IEEE.
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock Price Forecast Based on Bacterial Colony RBF Neural Network [J]. Journal of Qingdao University (Natural Science Edition), 20 (2), 50-54 (in Chinese).
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370.
O & apos; Connor, N., & Maddem, M. G. (2006). A neural network approach to predicting stock exchange movements using external factors: Applications and innovations in intelligent network to investment analysis. Financial Analysts Journal, 78-80.
Tan, T. Z., Quek, C., & Ng, G. S. (2005). discovery using complementary learning fuzzy neural learning memory structures. Neural Networks, 18(5-6), 818-825.
Trippi, R. R., & DeSieno, D. (1992). Trading equity index futures with a neural network. The Journal of Portfolio Management, 19(1), 27-33.
White, H. (1989). Some asymptotic results for learning in single hidden-layer feedforward network models. Journal of the American Statistical Association, 84(408), 1003-1013.
Yu, T. H. K., & Huarng, K. H. (2008). A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications, 34, 2945-2952.
Zhang, G. P., & Qi, G. M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, 501-514.
Zhang, D., Patuwo, E., & Hu, Y.M. (1998). Forcasting with artificial neural networks the state of the art. International journal of forecasting, 14(1), 35-62.
Zhu, X., Wang, H., Xu, L., & Li, H. (2007). Predicting stock index increments by neural networks: The role of trading volume under different horizons. Journal of Expert Systems with Applications, 34(4), 3043-3054.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307-327.
Bollerslev, T. (1988). On the correlation structure for the generalized autoregressive conditional heteroskedastic process. Journal of Time Series Analysis, 9(2), 121-131.
Chen, C. H. (1994). Neural networks for financial market prediction. Proceedings of the IEEE International Conference on Neural Networks, 2, 1199-1202.
Chen, A., Leung, M. T., & Hazem, D. (2003). Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers and Operations Research, 30, 901-923.
Choi, H., Lee, E., & Lee, C. (2006). Neural Network Deinterlacing Using Multiple Fields. Lecture Notes in Control and Information Sciences, 345, 970-975.
Choi, H., Lee, C., & Rhee, M. W. (1995). Trading S & P 500 stock index futures using a neural network. The Third Annual International Conference on Artificial Intelligence Applications on Wall Street, 63-72.
Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987-1008.
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: the ARCH-M model. Econometrica: Journal of the Econometric Society, 391-407.
Gradojevic, N., & Yang, J. (2000). The Application of Artificial Neural Networks to Exchange Rate Forecasting: The Role of Market Microstructure variables, Working Paper n° 2000-23, Bank of Canada.
Hamid, S. A., & Iqbal, Z. (2004). Using neural networks for forecasting volatility of S & P 500 Index futures prices. Journal of Business Research, 57(10), 1116-1125.
Hornick, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal
Approximators. Neural Networks, 2, 359-366.
Huang, W., Lai, K. K., Nakamori, Y., Wang, S. Y., & Yu, L. (2007). Neural networks in finance and
economics forecasting. International Journal of Information Technology and Decision Making, 6, 113-140.
Huarng, K., & Yu, T. H. K. (2006). The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications, 363(2), 481-491.
Kamijo, K., & Tanigawa, T. (1990). Stock price pattern recognition: A recurrent neural network approach. Proceedings of the International Joint Conference on Neural Networks, 215-221.
Kim, N. H., Yoo, S. K., & Lee, K. S. (2003). Polygon reduction of 3D objects using Stokes’ theorem. Computer methods and programs in biomedicine, 71(3), 203-210.
Kunhuang, H., & Yu, T.H.K. (2006). The application of neural networks to forecast fuzzy time series.
Physical A: Statistical Mechanics and Its Applications, 363(2), 481-491.
Majhi, R., Panda, G., Sahoo, G., Dash, P. K., & Das, D. P. (2007, September). Stock market prediction of S & P 500 and DJIA using bacterial foraging optimization technique. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 2569-2575). IEEE.
Miao, K., Chen, F., & Zhao, Z. G. (2007). Stock Price Forecast Based on Bacterial Colony RBF Neural Network [J]. Journal of Qingdao University (Natural Science Edition), 20 (2), 50-54 (in Chinese).
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370.
O & apos; Connor, N., & Maddem, M. G. (2006). A neural network approach to predicting stock exchange movements using external factors: Applications and innovations in intelligent network to investment analysis. Financial Analysts Journal, 78-80.
Tan, T. Z., Quek, C., & Ng, G. S. (2005). discovery using complementary learning fuzzy neural learning memory structures. Neural Networks, 18(5-6), 818-825.
Trippi, R. R., & DeSieno, D. (1992). Trading equity index futures with a neural network. The Journal of Portfolio Management, 19(1), 27-33.
White, H. (1989). Some asymptotic results for learning in single hidden-layer feedforward network models. Journal of the American Statistical Association, 84(408), 1003-1013.
Yu, T. H. K., & Huarng, K. H. (2008). A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications, 34, 2945-2952.
Zhang, G. P., & Qi, G. M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, 501-514.
Zhang, D., Patuwo, E., & Hu, Y.M. (1998). Forcasting with artificial neural networks the state of the art. International journal of forecasting, 14(1), 35-62.
Zhu, X., Wang, H., Xu, L., & Li, H. (2007). Predicting stock index increments by neural networks: The role of trading volume under different horizons. Journal of Expert Systems with Applications, 34(4), 3043-3054.