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Modelling and Forecasting Stock Returns:Does Neural Network Model Perform Better than GARCH Model?
|關鍵字:||GARCH;類神經網路模型;預測區間;GARCH;Neural network;Prediction interval||引用:||一、中文部分 楊奕農，2009，時間序列分析：經濟與財務上之應用，台北：雙葉書廊。 羅華強，2011，類神經網路：MATLAB的應用，台北：高立圖書。 二、英文部分 Arroyo, J. and C. Maté, 2006, “Introducing interval time series: Accuracy measures.,” proceedings in computational statistics, 1139-1146. Babu, C. N. and B. E. Reddy, 2014, “A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data,” Applied Soft Computing, 23: 27-38. Choudhary, M. A. and A. Haider, 2012,. “Neural Network Models for Inflation Forecasting: An Appraisal,” Applied Economics, 44: 2631-2635. Claveria, O. and S. Torra, 2014, “Forecasting Tourism Demand to Catalonia: Neural Networks vs. Time Series Models,” Economic Modelling, 36: 220-228. Denton, J. W., 1995, “How Good are Neural Networks for Causal Forecasting?” The Journal of Business Forecasting, 14: 17-20. Foster, W. R., F. Collopy and L. H. Ungar, 1992, “Neural Network Forecasting of Short, Noisy Time Series,” Computers & chemical engineering, 16: 293-297. Fouladgar, M. M., M. Yazdani, S. Khazaee, E. K. Zavadskas and V. Fouladgar, 2013, “Comparison of Vector Time Series and ANN Techniques for Forecasting of WTI Oil Price” Economic Computation and Economic Cybernetics Studies and Research (ECECSR), 47: 19-35. Hornik, K., M. Stinchcombe and H. White, 1989, “Multilayer Feedforward Networks are Universal Approximators,” Neural networks, 2: 359-366. Hyndman, R., 2013, The difference between prediction intervals and confidence intervals. Hyndsight Blog, http://robjhyndman.com/hyndsight/intervals. Khashei, M. and M. Bijari, 2011, “A Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting,” Applied Soft Computing, 11: 2664-2675. Khashei, M. and M. Bijari, 2010, “An Artificial Neural Network (P, D, Q) Model for Timeseries Forecasting,” Expert Systems with applications, 37: 479-489. Khosravi, A., S. Nahavandi and D. Creighton, 2011, “Prediction Interval Construction and Optimization for Adaptive Neurofuzzy Inference Systems,” IEEE Transactions on Fuzzy Systems, 19: 983-988. Khosravi, A., S. Nahavandi and D. Creighton, 2010, “A Prediction Interval-Based Approach to Determine Optimal Structures of Neural Network Metamodels,” Expert systems with applications, 37: 2377-2387. Khosravi, A., S. Nahavandi and D. Creighton, 2010, “Construction of Optimal Prediction Intervals for Load Forecasting Problems,” IEEE Transactions on Power Systems, 25: 1496-1503. Maia, A. L. S., F. D. A. de Carvalho and T. B. Ludermir, 2008, “Forecasting Models for Interval-Valued Time Series,” Neurocomputing, 71: 3344-4352. Nikolopoulos, K., 2010, “Forecasting with Quantitative Methods: the Impact of Special Events in Time Series,” Applied Economics, 42: 947-955. Quan, H., D. Srinivasan and A. Khosravi, 2014, “Uncertainty Handling Using Neural Network-Based Prediction Intervals for Electrical Load Forecasting,” Energy, 73: 916-925. Ruiz-Aguilar, J. J., I. J. Turias and M. J. Jiménez-Come, 2014, “Hybrid Approaches Based on SARIMA and Artificial Neural Networks for Inspection Time Series Forecasting,” Transportation Research Part E: Logistics and Transportation Review, 67: 1-13. Taskaya-Temizel, T. and M. C. Casey, 2005, “A Comparative Study of Autoregressive Neural Network Hybrids,” Neural Networks, 18: 781-789. Weron, R., 2014, “Electricity Price Forecasting: A Review of the State-of-the-art With a Look into the Future,” International Journal of Forecasting, 30: 1030-1081. Xiong, T., Y. Bao and Z. Hu, 2013, “Beyond One-step-ahead Forecasting: Evaluation of Alternative Multi-step-ahead Forecasting Models for Crude Oil Prices” Energy Economics, 40: 405-415. Zhang, G. P., 2003, “Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model,” Neurocomputing, 50: 159-175.||摘要:||
Statistical methods have often been used in literature to predict stock price return, but the past statistical methods have their own limitations such as it might assume the data is a linear relation and residual is a white noise. As stock price return contains characters of leptokurtic, reject normality assumption, and volatility clustering; therefore in past literature, GARCH model had been used to predict stock price return datasets. However, in recent literature has shown the neural network model is more accurate. In addition, the primary problem for point prediction is it cannot properly handle the variability with datasets. As a result, this study aims at describing the interval prediction of GARCH model and neural network model in order to compensate the disadvantages of point prediction. The objective of this study is to compare GARCH model and neural network model for stock price return rate of point prediction and intervals prediction respectively by using S&P500, NASDAQ and DJIA stock price; and make further efforts on discussing the prediction performance between two sets of explanatory variables. According to obtained results, GARCH models have smaller predicting error on point prediction; in the case of interval prediction, it has different outcome by adopting different explanatory variables of GARCH models and neural network separately. Furthermore, GARCH model can generate well-informative and coverage probability by adopting Radii and midpoint as a dependent variable; while using the highest return rate and the lowest return rate, neural network model greatly outperforms than GARCH model.
|Appears in Collections:||應用經濟學系|
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