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標題: 股價報酬區間預測:類神經網路模型與GARCH模型之比較
Modelling and Forecasting Stock Returns:Does Neural Network Model Perform Better than GARCH Model?
作者: 陳芊邑
Cian-Yi Chen
關鍵字: GARCH;類神經網路模型;預測區間;GARCH;Neural network;Prediction interval
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本研究使用S&P500、Nasdaq 、DJIA三個股價資料,來檢驗GARCH模型與類神經網路模型,兩者在股價報酬率上之點預測及區間預測的預測結果,並且進一步比較兩組不同解釋變數之預測優劣。實證結果顯示,在點預測上GARCH模型有較低的預測誤差,在區間預測上GARCH與類神經網路模型因採用的解釋變數不同,造成最佳預測模型有所不同。GARCH模型採用半徑、中位數為解釋變數,可得到高資訊量及覆蓋率的預測區間;若採用最高報酬率、最低報酬率為解釋變數時則建議採用類神經路模型來建構預測區間。

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.
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