博碩士論文 106453011 詳細資訊




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姓名 張心和(Hsin-Ho Chang)  查詢紙本館藏   畢業系所 資訊管理學系在職專班
論文名稱 遞迴類神經網路結合先期工業廢水指標之股價預測研究
(Recurrent Neural Network using Advanced Industrial Wastewater Indexes for the research of stock price prediction)
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摘要(中) 本研究主要以深度學習之遞迴類神經網路方法,實作具時間序列性之LSTM(Long Short-Term Memory)長短期記憶遞迴類神經網路(Recurrent Neural Network,RNN)模型,接收來自產業工廠即時製造過程中排放工業廢水監測數據,經本研究設計的實驗預測未來股價趨勢,並依據研究標的公司於台灣證券交易所公開之股價,驗證各期資料訓練出模型之預測準確率。
並提出產業製程排放廢水量與下期營收成正相關之假說,以科學實驗方法驗證假說可靠度。
本研究貢獻,主要驗證以當期廢污水排放各項監測數值及對應之股價,加以訓練類神經網路模型,進而以當期數據去預測未來股價趨勢,以評定本研究所創建的類神經網路模型之準確率及假說。 最終期望創建出具備領先性的“生產資源消耗面”之非財務性科技預測指標—新河指標,提供投資者作為研析標的公司未來股價走勢之依據。
摘要(英) This research uses Deep Learning technology, LSTM Network, to solve the prediction issue of future stock price. In contrast to traditional methods, it uses industrial wastewater dataset to train LSTM model. In experiment, it is designed to different models by deferred periods of the affected stock price and finds the most accurate model for stock price prediction.
Moreover, this paper designs experiments to ascertain the hypothesis, industrial wastewater of factories influencing its future stock price trend, whether they have the positive correlation.
The contribution of this research proves the future stock price prediction of manufacturing industry can use the leading index, industrial wastewater, effectively. And it also finds out using industrial wastewater dataset to intensify the accuracy of LSTM network in stock price prediction is a useful way.
Ultimately to produce a non-finance leading index of stock prediction, New River index, by LSTM approach that helps investors to judge investment in advance is this research contribution.
關鍵字(中) ★ 金融科技
★ LSTM
★ 深度學習
★ 長短期記憶
★ 類神經網路
關鍵字(英) ★ FinTech
★ LSTM
★ Long Short-Term Memory
★ Deep Learning
★ Neural Network
論文目次 摘要 i
Abstract ii
誌謝 iii

第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 2
1-3 研究貢獻 4
1-4 研究架構 4

第二章 文獻探討 6
2-1 財務報導相關文獻探討 6
2-2 污水排放監測相關文獻探討 8
2-3 類神經網路相關文獻探討 11
2-4 文獻回顧小結 19

第三章 研究方法 20
3-1 問題本質分析 20
3-2 各行業日用水量分析 21
3-2-1 產業用水量分析 21
3-2-2 實證研究對象選擇 22
3-3 工業廢水排放與股價反應之遞延時間研究 23
3-4 系統架構 26
3-5 長短期記憶遞迴類神經網路 28
3-6 LSTM模型實作架構 32

第四章 實驗結果 33
4-1 實驗設計 33
4-2 實驗一:找出標的公司總廢水量與股價走勢趨近的遞延期間 36
4-3 實驗二:LSTM預測模型訓練成果 41
4-3-1 實驗二各模型訓練成果紀錄 41
4-3-2 工業廢水資料預測股價模型與傳統單一股價預測模型之預測績效比較 55

第五章 研究結論與未來研究方向 59

參考文獻 61
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指導教授 陳以錚(Yi-Cheng Chen) 審核日期 2019-6-26
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