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題名 運用Elman類神經網路與時間序列模型預測LME銅價之研究
A study on applying Elman neural networks and time series model to predict the price of LME copper
作者 黃鴻仁
Huang, Hung Jen
貢獻者 楊建民
Yang, Jiann Min
黃鴻仁
Huang, Hung Jen
關鍵詞 銅價
Elman類神經網路
時間序列
GARCH模型
向量自我迴歸模型
Copper price
Elman neural networks
Time series
GARCH model
Vector Autoregressive model
日期 2011
上傳時間 30-Oct-2012 11:21:16 (UTC+8)
摘要 　　銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。

　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。

本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。
　　The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model.

　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model.

　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.
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描述 碩士
國立政治大學
資訊管理研究所
99356004
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099356004
資料類型 thesis
dc.contributor.advisor 楊建民zh_TW
dc.contributor.advisor Yang, Jiann Minen_US
dc.contributor.author (Authors) 黃鴻仁zh_TW
dc.contributor.author (Authors) Huang, Hung Jenen_US
dc.creator (作者) 黃鴻仁zh_TW
dc.creator (作者) Huang, Hung Jenen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 11:21:16 (UTC+8)-
dc.date.available 30-Oct-2012 11:21:16 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 11:21:16 (UTC+8)-
dc.identifier (Other Identifiers) G0099356004en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54561-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 99356004zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 　　銅價在近年來不斷的創下歷史新高，由於台灣蓬勃的電子、半導體、工具機產業皆需要銅，因此銅進口量位居全球第五(ICSG,2009)，使得台灣企業的生產成本受國際銅價的波動影響甚鉅，全球有70%的銅價是按照英國倫敦金屬交易所(London Metal Exchange, LME)的牌價進行貿易，因此本研究欲建置預測模式以預測銅價未來趨勢。

　　本研究之資料來源為2003年1月2日至2011年7月14日的LME三月期銅價，並依文獻探討選取LME的銅庫存、三月期鋁價、三月期鉛價、三月期鎳價、三月期鋅價、三月期錫價，以及金價、銀價、石油價格、美國生產者物價指數、美國消費者物價指數、聯邦資金利率作為影響因素的分析資料。時間序列分析、類神經網路已被廣泛的用於預測股市及期貨，本研究先藉由向量自我迴歸模型篩選出有影響力的變數，同時建置GARCH時間序列預測模型與具有遞迴的Elman類神經網路預測模型，再整合兩者建置GARCH-Elman類神經網路預測模型。

本研究之向量自我迴歸模型顯示銅價與金、鋁、銅庫存前第1期；自身前第2期；鎳、錫前第3期；鋅前第4期的變動有負向的影響；受到石油前第2期的變動有正向的影響，這其中以銅的自我解釋變異最高，銅庫存最低，推測其影響已有效率地反映到銅價上。也驗證預測模型必須考量總體經濟變數，且變數先經向量自我迴歸模型的篩選能因減少雜訊而提升類神經網路的預測能力。依此建置的GARCH模型有33.81%的累積報酬率、Elman類神經網路38.11%、整合兩者的GARCH-Elman類神經網路56.46%，皆優於實際銅價指數的累積報酬率。對銅有需求的企業者，能更為準確的預測漲跌趨勢，依此判斷如何跟原物料供應商簽訂合約的價格與期間，使其免於價格趨勢的誤判而提高生產成本，並提出五點建議供未來研究者參考。		zh_TW
dc.description.abstract (摘要) 　　The recent copper price in London Metal Exchange (LME) has breaking the historical high. Taiwan’s booming electronics, semiconductor and machine tool industry causing copper import volume ranked fifth in the world (ICSG, 2009). Because of 70% of copper worldwide trade in accordance with the price of the London Metal Exchange, this study using time series and neural networks to build the LME copper price forecast model.

　　This study considering copper, copper stocks, aluminum, lead, nickel, zinc, tin, gold, silver, oil ,federal funds rate, CPI and PPI during the period of 2003/1/2 to 2011/7/14. Time series model and neural networks have been widely used for forecasting the stock market and futures. In this study, using Vector Autoregressive (VAR) model screened influential variables, building GARCH model and Elman neural network to forecast the LME copper price; and further, integrating this two models to build GARCH-Elman neural network prediction model.

　　This study’s VAR models show that the copper has negative effect with gold, aluminum, copper stocks, nickel, tin, zinc and itself. And has positive impact with oil prices. The highest of explained variance is copper. Copper stocks are lowest, speculating that its impact has been efficiently reflecting on the price of copper. Verifying the prediction model must consider the macroeconomics variables. Using VAR model screened influential variables can reduce noise to enhance the predictive ability of the neural network. This study’s GARCH model has 33.81% of the cumulative rate of return, Elman neural network has 38.11% and the GARCH-Elman neural network has 56.46%. All of them are better than the actual price of copper.		en_US
dc.description.tableofcontents 中文摘要　　I
Abstract　　II
誌謝　　　　III
圖目錄　　　VI
表目錄　　　VII
第一章　緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第二章　文獻探討 3
第一節 影響銅價因素 3
一、 供給和需求關係 5
二、 總體經濟環境 7
三、 相關原物料 9
四、 小結 9
第二節 效率市場 10
第三節 GARCH時間序列與類神經網路模型 11
一、 GARCH時間序列模型 11
二、 類神經網路模型 14
第四節 相關研究 16
一、 應用類神經網路於預測之相關文獻 17
二、 應用類神經網路與時間序列模型之相關文獻 18
三、 小結 19
第三章　研究方法 21
第一節 本研究架構 21
第二節 資料與變數 22
第三節 變數檢定與變數選取 25
一、 變數檢定 25
二、 變數選取 29
第四節 本研究預測模型 32
一、 GARCH模型 32
二、 類神經網路模型 33
三、 評估預測能力 37
四、 小結 39
第四章　研究結果 40
第一節 預測模型變數選取分析 40
第二節 預測結果分析 43
五、 GARCH模型預測結果 43
二、 Elman類神經網路預測結果 45
三、 GARCH-elman類神經網路 46
四、 小結 48
第五章 結論與建議 51
第一節 結論 51
第二節 建議 52
第三節 未來研究方向 53
參考文獻 54
附錄一 變數檢定結果 60
附錄二 GRANGER因果關係檢定結果 61
附錄三 向量自我迴歸檢定結果 65		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099356004en_US
dc.subject (關鍵詞) 銅價zh_TW
dc.subject (關鍵詞) Elman類神經網路zh_TW
dc.subject (關鍵詞) 時間序列zh_TW
dc.subject (關鍵詞) GARCH模型zh_TW
dc.subject (關鍵詞) 向量自我迴歸模型zh_TW
dc.subject (關鍵詞) Copper priceen_US
dc.subject (關鍵詞) Elman neural networksen_US
dc.subject (關鍵詞) Time seriesen_US
dc.subject (關鍵詞) GARCH modelen_US
dc.subject (關鍵詞) Vector Autoregressive modelen_US
dc.title (題名) 運用Elman類神經網路與時間序列模型預測LME銅價之研究zh_TW
dc.title (題名) A study on applying Elman neural networks and time series model to predict the price of LME copperen_US
dc.type (資料類型) thesisen
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