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題名 整體經驗模態分解在台灣期貨市場與選舉預測市場的應用
Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan作者 鄭緯暄 貢獻者 王信實
鄭緯暄關鍵詞 本質模態函數
預測市場
價格發現
整體經驗模態分解
濾波
雜訊
IMF
Prediction Market
Price Discovery
EEMD
Filter
Noise日期 2011 上傳時間 30-Oct-2012 11:47:57 (UTC+8) 摘要 金融市場常常受到政治、經濟與社會環境等因素所影響，所得到價格為眾多變數交互作用的結果，包含了許多雜訊。本文引進一套數據處理方法「整體經驗模態分解」(Ensemble Empirical Mode Decomposition，EEMD)來分析「期貨市場」以及「預測市場」。第一個實證利用EEMD處理台股期貨，分析對台股指數的解釋能力，並同時與原始台股期貨預測台股指數，比較預測結果；第二個實證利用EEMD來分析預測市場，判別是否能有效的消除雜訊，準確預測選舉結果。第一個實證結果發現，EEMD能有效地過濾期貨市場的雜訊，另外，在最後到期日前十二天或者是前九天，以週期為6.5日經EEMD處理的台股期貨對台股指數的預測較原始台股期貨預測準確；第二個實證結果指出，直接利用EEMD處理預測市場得到的長期趨勢「剩餘訊號」(Residue)來預測選舉並無優於原始預測市場，主因為預測市場參與者不只在乎長期趨勢，亦在乎短期事件的衝擊，故直接利用剩餘訊號預測選舉結果會有所失真，而將剩餘訊號由低頻率之「本質模態函數」(Intrinsic Modes Function，IMF)合併至週期為6日與12日的IMF，得到了EEMD週趨勢價格，分成選前一天和選前十天的資料並與原始預測市場以及民調預測做比較，從不同的準則來看，發現以EEMD週趨勢價格來做選舉預測，準確度較原始預測市場與民調預測的結果更好。根據中選會2012年初選前對選罷法做成的解釋，未來事件交易所在選前十日亦須停止交易，我們可將EEMD運用在日後的選舉預測，把預測市場的合約價格以EEMD處理，應可提高選舉預測的準確度。
The financial markets are usually affected by political, economic and social environment factors, and thus the volatilities of asset prices in these markets are subject to a lot of noises and shocks. To filter out noises and quantify shocks, this paper applies a data processing method, Ensemble Empirical Mode Decomposition (EEMD), and demonstrates its improved prediction to the futures and election prediction markets.While the first empirical application shows that the EEMD effectively filters out the noises in the futures market, the second one indicates that the Taiwanese election prediction using EEMD “residue” is not as accurate as that by original data from the prediction market. The reason why the residue cannot serve as a good predictor is that the market participants consider not only the long-term trend, but also shocks, especially those right before the elections. We then attempt to predict the election outcomes by the week trend series processed by EEMD. The prediction by the week EEMD trend series turns out to be more accurate than that by the poll and original prediction market. 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Huang (2009). “Ensemble Empirical Mode Decomposition: A Noise-assisted Data Analysis Method,” Advances in Adaptive Data Analysis, Vol.1, 1–41.Zhang, X., K.K. Lai, and S.Y. Wang (2008). “A New Approach for Crude Oil Price Analysis Based on Empirical Mode Decomposition,” Energy Economics, Vol.30, No.3, 905-918. 描述 碩士
國立政治大學
經濟學系
99258017
100資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099258017 資料類型 thesis dc.contributor.advisor 王信實 zh_TW dc.contributor.author (Authors) 鄭緯暄 zh_TW dc.creator (作者) 鄭緯暄 zh_TW dc.date (日期) 2011 en_US dc.date.accessioned 30-Oct-2012 11:47:57 (UTC+8) - dc.date.available 30-Oct-2012 11:47:57 (UTC+8) - dc.date.issued (上傳時間) 30-Oct-2012 11:47:57 (UTC+8) - dc.identifier (Other Identifiers) G0099258017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54812 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 99258017 zh_TW dc.description (描述) 100 zh_TW dc.description.abstract (摘要) 金融市場常常受到政治、經濟與社會環境等因素所影響，所得到價格為眾多變數交互作用的結果，包含了許多雜訊。本文引進一套數據處理方法「整體經驗模態分解」(Ensemble Empirical Mode Decomposition，EEMD)來分析「期貨市場」以及「預測市場」。第一個實證利用EEMD處理台股期貨，分析對台股指數的解釋能力，並同時與原始台股期貨預測台股指數，比較預測結果；第二個實證利用EEMD來分析預測市場，判別是否能有效的消除雜訊，準確預測選舉結果。第一個實證結果發現，EEMD能有效地過濾期貨市場的雜訊，另外，在最後到期日前十二天或者是前九天，以週期為6.5日經EEMD處理的台股期貨對台股指數的預測較原始台股期貨預測準確；第二個實證結果指出，直接利用EEMD處理預測市場得到的長期趨勢「剩餘訊號」(Residue)來預測選舉並無優於原始預測市場，主因為預測市場參與者不只在乎長期趨勢，亦在乎短期事件的衝擊，故直接利用剩餘訊號預測選舉結果會有所失真，而將剩餘訊號由低頻率之「本質模態函數」(Intrinsic Modes Function，IMF)合併至週期為6日與12日的IMF，得到了EEMD週趨勢價格，分成選前一天和選前十天的資料並與原始預測市場以及民調預測做比較，從不同的準則來看，發現以EEMD週趨勢價格來做選舉預測，準確度較原始預測市場與民調預測的結果更好。根據中選會2012年初選前對選罷法做成的解釋，未來事件交易所在選前十日亦須停止交易，我們可將EEMD運用在日後的選舉預測，把預測市場的合約價格以EEMD處理，應可提高選舉預測的準確度。 zh_TW dc.description.abstract (摘要) The financial markets are usually affected by political, economic and social environment factors, and thus the volatilities of asset prices in these markets are subject to a lot of noises and shocks. To filter out noises and quantify shocks, this paper applies a data processing method, Ensemble Empirical Mode Decomposition (EEMD), and demonstrates its improved prediction to the futures and election prediction markets.While the first empirical application shows that the EEMD effectively filters out the noises in the futures market, the second one indicates that the Taiwanese election prediction using EEMD “residue” is not as accurate as that by original data from the prediction market. The reason why the residue cannot serve as a good predictor is that the market participants consider not only the long-term trend, but also shocks, especially those right before the elections. We then attempt to predict the election outcomes by the week trend series processed by EEMD. The prediction by the week EEMD trend series turns out to be more accurate than that by the poll and original prediction market. Based on this study, we can apply the EEMD to the next election prediction and improve its accuracy. en_US dc.description.tableofcontents 謝辭 I摘要 IIABSTRACT III圖目錄 VI表目錄 VII專有名詞中英對照說明 X第一章 緒論 1第一節 研究動機與目的 1第二節 本文架構 2第二章 文獻回顧 4第一節 EEMD之理論 4第二節 期貨市場與價格發現之功能 6第三節 預測市場之理論 11第三章 研究方法與流程 15第一節 EEMD之數據處理 15第二節 期貨市場對台股指數之解釋能力分析 19第三節 預測市場之選舉結果預測分析 21第四節 研究流程 26第四章 EEMD在期貨市場的應用 28第一節 研究對象與資料處理 28第二節 EEMD處理之台股期貨對台股指數的解釋能力分析 33第三節 小結 38第五章 EEMD在預測市場的應用 39第一節 研究對象與資料處理 39第二節 當選人預測分析 40第三節 得票率預測分析 46第四節 小結 51第六章 結論與建議 53第一節 結論 53第二節 建議 54參考文獻 55附錄 61 zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099258017 en_US dc.subject (關鍵詞) 本質模態函數 zh_TW dc.subject (關鍵詞) 預測市場 zh_TW dc.subject (關鍵詞) 價格發現 zh_TW dc.subject (關鍵詞) 整體經驗模態分解 zh_TW dc.subject (關鍵詞) 濾波 zh_TW dc.subject (關鍵詞) 雜訊 zh_TW dc.subject (關鍵詞) IMF en_US dc.subject (關鍵詞) Prediction Market en_US dc.subject (關鍵詞) Price Discovery en_US dc.subject (關鍵詞) EEMD en_US dc.subject (關鍵詞) Filter en_US dc.subject (關鍵詞) Noise en_US dc.title (題名) 整體經驗模態分解在台灣期貨市場與選舉預測市場的應用 zh_TW dc.title (題名) Applications of ensemble empirical mode decomposition to future and election prediction markets in Taiwan en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 中文部分江鴻鑫、夏榮生（2005）。「標準化互相關應用於經驗模態分解法於之研究」。第三十三屆測量學術發表會論文，頁23-38。吳順德、陳思予、陳虹伯（2009）。「經驗模態分解法之研究趨勢探討與問題分析」。《國立台北科技大學學報》，第42卷，第1期。吳順德、陳思予、陳虹伯（2009）。「經驗模態分解法與小波分解法應用於濾除心電訊號之基準線飄移問題之比較」。2009年資訊技術與產業應用研討會，國立台灣師範大學機電科技學系。周靖秦、陳秀淋（2011）。「利用小波轉換分析美國總體指標與道瓊工業指數之關係」。《經濟論文》，第39卷，第3期，頁339 -367。林建中（2010）。「以經驗模態分解法來分析臺灣股票加權指數與世界經濟大國股票指數之相關性及趨勢研究」。碩士論文，國立台北大學企業管理學系碩士班。施雅菁（2003）。「小型台指期貨價格之研究」。碩士論文，私立淡江大學財務金融研究所。畢德成（2000）。「希伯特頻譜於地震資料之應用」。碩士論文，國立中央大學土木工程學系碩士班。陳振雄（2010）。「應用希爾伯特—黃轉換之訊號濾波研究」，《科學與工程技術期刊》，第6卷，第1期，頁75-84。童振源、周子全、林繼文、林馨怡（2011）。「2009年台灣縣市長選舉預測分析」，《選舉研究》，第18卷，第1期，頁63-94。童振源、周子全、林繼文、林馨怡（2011）。「選舉結果機率之分析—以2006年與2008年台灣選舉為例」，《台灣民主季刊》，第8卷，第3期，頁135-159。童振源、林馨怡、林繼文、黃光雄、周子全、劉嘉凱、趙文志（2009）。「台灣選舉預測—預測市場的運用與實證分析」，《選舉研究》，第16卷，第2期，頁131-66。黃台心（2009）。《計量經濟學》，二版。台北，新陸書局。詹錦宏、蔡建安（2005）。「台指期貨與摩台指期貨價格發現功能之研究」，《管理研究學報》。蔡茹鈴（2008）。「小波轉換與HHT轉換法在金融股價預測之應用」。碩士論文，逢甲大學應用數學系碩士班。謝文良（2002）。「價格發現、資訊傳遞、與市場整合－台股期貨市場之研究」，《財務金融研究季刊》，第十卷，第3期，頁1-31。羅飛雪、戴吾蛟（2010）。「小波分解與EMD在變形監測應用中的比較」，《大地測量與地球動力學》，第30卷，第3期，頁137-141。英文部分Berg, J., F. 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