混合型人工類神經網路應用於台灣50隔日收盤價預測之研究

文化大學機構典藏 CCUR > 商學院 > 資訊管理學系暨資訊管理研究所 > 博碩士論文 > Item 987654321/27992

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题名:	混合型人工類神經網路應用於台灣50隔日收盤價預測之研究 Next Day Closing Price Prediction of the Taiwan 50 Exchange Traded Funds with Hybrid Artificial Neural Networks
作者:	周澤伯 Chou, Tse-Po
贡献者:	資訊管理學系碩士在職專班
关键词:	倒傳遞類神經網路移動視窗法臺灣50指數ETF Back-Propagation Neural Network Moving Window Taiwan 50 Index ETF
日期:	2014-06
上传时间:	2014-09-05 09:51:46 (UTC+8)
摘要:	股票市場已成為現在大多數人的投資工具之一，如若有方法可以得知隔日的收盤價，就可於今日先制定好投資策略及做資金調動的準備，故本研究的目標為預測隔日收盤價。在近期的文獻中發現有越來越多人是以類神經網路來預測非線性的股市指數及股價，故本研究也採用此模式來預測，並比較複迴歸模型的預測數據。本研究使用類神經網路中的倒傳遞類神經網路來預測「元大寶來台灣卓越50基金」，簡稱「台灣50」，是一種ETF（Exchange Traded Funds指數股票型證券投資信託基金），以十年的歷史資料來當作要訓練的資料，以並以一個月的期間來驗證所預測的數據是否正確。由過去的文獻中發現，若以類神經網路為工具來當作預測未來股票指數，少有同時切割訓練期資料及測試期資料，本研究將訓練期資料切割為7份，測試期資料切割為5份，共計組合35種預測模型，以「時間軸移動法」及以其中的七個模型用「移動視窗法」移動100個連續交易日方式來預測，並分析其預測數據，以找出最佳預測模型。研究結果證實倒傳遞類神經網路模型比複迴歸模型的預測效果好，若以移動視窗法預測，使用最佳模型來預測隔日收盤價的漲跌命中率達56%。 Securities such as stocks and bonds are examples of most peoples’ investment instruments. If there was a way to predict tomorrow’s closing price, one can draft the investment strategy today in advance, and prepare for essential funding. Therefore, the objective of this study is to predict the next day's closing price. This study uses back-propagation neural network model to predict and compare the performance data with "multiple regression model". The research target is "Polaris Taiwan Top 50 Tracker Fund", a.k.a. the "Taiwan 50", which is an Exchange Traded Fund (ETF). We use 10 years historical data as the training dataset, and use one month out of sample period to verify whether it is correctly predicted. In the previous literature, very few researches simultaneously split "training information" and "test period data", if using neural networks to predict the stock index. In this study, we divide the training data into seven parts, and split the test data into five datasets, producing a total of 35 combinations of forecasting models. We then apply the "time axis shift method" on seven models using "moving window method" to shift 100 consecutive trading days in order to collect the prediction results. Finally, we analyze the generated data to obtain the best forecasting model. The results confirmed that prediction effectiveness of the "back-propagation neural network model" is better than the "multiple regression models". Our best model can predict the rise or fall of the next day closing price with 56% accuracy using the “moving window method”.
显示于类别:	[資訊管理學系暨資訊管理研究所 ] 博碩士論文

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