台灣特殊的兩黨對立政治環境及幾乎每年都會有的固定選舉，使得政治的不確定性深深的影響著國內的投資環境及投資人心態。本研究便是要探討，2002/1/1~2006/1/16 研究期間台灣的投資人在選舉前後的投資行為，是否真如大家所預期的，會受到台灣選舉事件的影響。 本研究首先利用適當的機率密度函數模型及選擇權市場資訊來導出隱含的風險中立密度值。再利用這些風險中立密度值，求出各個選舉事件相對應的機率分配圖形，並透過其機率分配圖形及波動率指數等統計值於投票日前後的變化來觀察某一選舉事件前後投資者的反應。 研究結果發現：1. 選舉事件的發生確實會影響投資者的心理，且投資者會透過選擇權市場有效率的反應預期的未來股價指數分佈情況。2. 越大型、越具爭議且全國性的選舉結果，其選舉期間機率分配圖形及波動率指數具有較高的波動性。3. 一般而言，選舉過後市場不確定因素降低，將使投資者對於股市的預期較為一致和樂觀。而若這個選舉結果使投資者感到意外，因而增加了市場的不確定性，則選後機率分配圖形及波動率指數的改變反而會更為明顯。4. 在此研究下對數常態混合法比傳統的 Black-Scholes 方法產生較低的誤差值，因此就實證的分析上能提供更好的配適。 / This research examines the behavior of investors during election periods from January 1st 2002 to January 6th 2006 in Taiwan. The research includes a few steps. First, we adopted a proper probability density function composed of stock index options data to construct the implied distribution. Then, when changing the whole shape of the risk-neutral implied distribution, the volatility indexes, and the statistics of the implied distribution, we observed investors' response around a specific election event. According to the empirical results, we found that: 1. An election event would influence investors’ behavior, and investors tend to reflect their expectation of future stock index in the option market in an efficient way. 2. The result of a large-scale and more disputed nationwide election will cause a higher fluctuation in both the implied distribution and the volatility index. 3. In general, the factor resulting from investors’ uncertainty of the market is likely to reduce after the election, which makes investors’ relatively unanimous and optimistic expectation of the stock market. However, if this election result surprises investors, their uncertainty of the market will increase, and thus the changes of the implied distribution and the volatility index become quite obvious. 4. The in-sample performance of the lognormal mixtures method employed in the research is considerably better than that of the traditional Black-Scholes model by having a lower root mean squared error.

本系統係藉由分析歷史交易資料來預測S&P500期貨市場之漲幅。 我們改進了Tsaih et al. (1998)提出的混和式AI系統。 該系統結合了Rule Base 系統以及類神經網路作為其預測之機制。我們針對該系統在以下幾點進行改善：(1) 將原本的日期資料改為使用分鐘資料作為輸入。(2) 本研究採用了“移動視窗”的技術，在移動視窗的概念下，每一個視窗我們希望能夠在60分鐘內訓練完成。(3)在擴增了額外的變數 – VIX價格做為系統的輸入。(4) 由於運算量上升，因此本研究利用TensorFlow 以及GPU運算來改進系統之運作效能。 我們發現VIX變數確實可以改善系統之預測精準度，但訓練的時間雖然平均低於60分鐘，但仍有部分視窗的時間會小幅超過60分鐘。 / The system is made to predict the Futures’ trend through analyzing the transaction data in the past, and gives advices to the investors who are hesitating to make decisions. We improved the system proposed by Tsaih et al. (1998), which was called hybrid AI system. It was combined with rule-based system and artificial neural network system, which can give suggestions depends on the past data. We improved the hybrid system with the following aspects: (1) The index data are changed from daily-based in into the minute-based in this study. (2) The “moving-window” mechanism is adopted in this study. For each window, we hope we can finish training in 60 minutes. (3) There is one extra variable VIX, which is calculated by the VIX in this study. (4) Due to the more computation demand, TensorFlow and GPU computing is applied in our system. We discover that the VIX can obviously has positively influence of the predicting performance of our proposed system. The average training time is lower than 60 minutes, however, some of the windows still cost more than 60 minutes to train.

本文以臺灣證交所上市的前九檔槓桿及反向指數股票型基金(LETFs)作為 樣本,根據 Charupat and Miu(2014)研究方法衡量其績效。傳統衡量指數股票 型基金(ETFs)績效的方式,為單純將基金淨值報酬對指數累積報酬做簡單迴 歸,若將此方法應用在衡量 LETFs 之績效上,由於許多影響基金淨值報酬的因素 沒被分離出來,常造成迴歸結果存在嚴重偏誤,或是難以解釋。本文是第一篇研 究國內 LETFs 績效的著作,透過在迴歸式中納入複利效果、融資效果,以更精確 的方式比較分析影響 LETFs 基金淨值報酬的因素,及各 LETFs 之管理績效。本 文實證結果除了證實融資效果確實存在,也證實大部分複利效果及融資效果的理 論性質,最重要的是,顯示出追蹤上証 180 指數的三檔 LETFs 在準確複製報酬槓 桿倍數上比較傑出,而追蹤台灣 50 指數的三檔 LETFs 在基金管理效率方面有比 較優秀的表現。 / Using Leveraged and Inverse Exchange-Traded Funds (LETF) listed in the Taiwan Stock Exchange, this thesis evaluates the performance of these LETFs based on the methodology proposed by Charupat and Miu (2014). The traditional approach of performance evaluation of ETFs is to regress the fund’s net asset value (NAV) returns on the underlying index’s returns. However, such an approach fails to account for important factors, such as compounding and financing effects, that affect the NAV of the LETFs, and unavoidably leads to serious estimation biases. This is the first thesis which evaluates the performance of LETFs listed in the Taiwan Stock Exchange. By considering compounded effect and financing effect in the regression model, the proposed method is more precise and appropriate in disentangling factors that affect the performance of the LETFs. Our empirical evidence shows how compounding effect, financing costs, and management factors influence LETFs’ tracking errors. Most of all, the three LETFs tracking the SSE180 index have the best tracking ability of the underlying asset return, while the LETFs tracking the FTSE TWSE Taiwan 50 index have the best management performance among all LETFs examined in this these.

電子工業為台灣最具競爭力之產業,使得電子類股在集中市場成交比重高達 69.49%,可見電子類股的波動足以對整個台股市場造成相當大的影響。而許多研究指出,網路上的文本訊息藉由社會網路的催化而快速傳遞,會對群眾情緒造成影響,進而影響股價波動,故對於投資者而言,如果能快速分析大量網路財經文本來推測投資大眾情緒進而預測股價走勢,即可提升獲利。然而,每天有近百篇的財經文本產生,傳統的人工抽樣分析方式效率不彰且過於耗力, 已不足以負荷此巨量資料。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果,但監督式學習方法所使用的訓練資料集須有事先定義好的已知類別,故其有無法預期未知類別的限制,造成無法判斷文本中可能存在的未知主題,所以本研究提出一套針對財經文本的混合監督式學習與非監督式學習之情感分析方法,透過非監督式學習將 2014 整年度的電子工業財經文本進行文本主題判別、情緒指數計算與情緒傾向標注。之後配合視覺化工具作趨勢線圖分析,找出具有領先指標特性之主題,接著再用監督式學習將其結合國際指標、總體經濟指標、台股指標、技術指標等,建立分類模型以預測台灣電子類股價指數走勢。 在實驗結果中,主題標注方面,本研究發現因文本數量遠大於議題詞數量造成 TFIDF 矩陣過於稀疏,使得 TFIDF-Kmeans 主題模型分類效果不佳;而文本具有多主題之特性造成 NPMI-Concor 分群之議題詞過於複雜不易歸納,然而LDA 主題模型基於所有主題被所有文章共享的特性,使得在字詞分群與主題分類準確度都優於 TFIDF-Kmeans 和 NPMI-Concor 主題模型,分類準確度高達 98%,故後續採用 LDA 主題模型進行主題標注。情緒傾向標注方面,證實本研 究擴充後的情感詞集比起 NTUSD 有更好的字詞極性判斷效果,計算出的情緒 指數之趨勢線也較投資人常用的 MACD 之趨勢線更符合電子類股價指數之趨 勢。此外,亦發現並非所有文本的情緒指數皆具有領先特性,僅企業營運主題與總體經濟主題之文本的情緒指數能提前反應電子類股價指數趨勢,故本研究用此二主題之文本的情緒指數來建立分類模型。 接著,本研究透過比較情緒指數結合技術指標之分類模型與單純技術指標分類模型的準確率發現,前者較後者高出 7%的準確率。進一步結合間接情緒指標的分類模型更有高達 71%準確率,故證實了情感分析確實能有效提升電子股價類股指數趨勢預測準確度,以提升投資人之投資報酬率。 / The electronic industry is the most competitive industry in Taiwan, and its large volume could have strong influence on the whole stock market. Many research show that text documents on the Internet have great effect on public emotion, and the public emotion could also affect the stock price. For investors, it is important to know how to analyze the potential emotion in text documents then use this information to predict the stock trend. However, the traditional way to analyze text documents by human resource cannot afford the large volume of financial text documents on the Internet. In past Sentimental Analysis research, supervised method is proven as a method could reach high accuracy, but there are limits about predicting the future trend. This research found a solution which mixed supervised and unsupervised methods to deal with these large financial text documents. First, we use unsupervised method to find out the topic of documents, and then calculate the sentimental index to judge the document’s emotional direction. After that we will produce trend line charts by visualization tools to find out which theme documents’ sentiment index are leading indicators. Furthermore, we use supervised method to integrate the sentimental index with other 24 indirect sentimental index to build the prediction model. According to the result, we found that LDA model’s performance is better than TFIDF-Kmeans model and NPMI-Concor mode because of document characteristic. Besides, sentimental dictionary I build has higher accuracy than NTUSD on judging word polarity. The trend of sentimental index and Taiwan electronic sub-index(TE) to each other is more similar than MACD line and TE to each other. We also discover that the sentiment index produced from documents about enterprise operation and macroeconomics are leading indicators, so we use these to build prediction model. Moreover, we found that the prediction model which include the sentiment index better than which only include the technical indicators. As mentioned above, the sentimental index could make the prediction of Taiwan electronic sub-index trend be more accurate and promote the return of investment.

動機與目的：傳統的統計迴歸模式假設觀測值的不確定性來自於隨機現象，而模糊迴歸則考慮不確定性來自於多重隸屬現象。不同的模型建構所得到的估計值也不一致。如何衡量模型的優劣程度，至今仍沒有一套嚴謹的標準。 研究方法：本研究以區間模糊數建構模糊迴歸模式，如此一來對樣本的解釋方式將更為貼近現實，並提出一套區間模糊數距離測度，以衡量估計值與實際值之間的差距。實證分析中（懸浮微粒PM_10濃度預測、台灣加權股價指數預測），我們藉由此距離測度衡量二維模糊迴歸與傳統二項最小平方法對於樣本的配適性。 創新與推廣：提出區間模糊數距離衡量估計值與原樣本之差異程度。在符合傳統統計迴歸精神之下，當距離最小就是差異最小的估計，最能符合所抽取的樣本，也是最佳估計。 重要發現：利用本區間模糊數距離測度，我們發現二維模糊迴歸方法比起傳統二項最小平方法更有效率且廣義殘差（generalized residual）將更小。 結論：過去以來，我們對於模糊迴歸架構一直都沒有完整的衡量標準。文中我們定義區間模糊數區間距離與平均距離，並推導賦距空間等性質。結合實例分析及應用，建構一合適模糊迴歸模式，以利統計決策分析參考。 / Objective: This study concerns how to develop effective fuzzy regression models. In the literature, little is addressed on how to evaluate the effectiveness of fuzzy regression models developed with different regression methods. We consider this issue in this work and present a framework for such evaluation. Method: We consider fuzzy regression models developed with different regression approaches. A method to evaluate the developed models is proposed. We then show that the proposed method possesses desirable mathematical properties and it is applied to compare the two-dimensional regression method and the traditional least square based regression method in our case studies: predicating the concentration of and the volatility of the weighted price index of the Taiwanese stock exchange. Innovation: We propose a new metric to define a distance between two fuzzy numbers. This metric can be used to evaluate the performance of different fuzzy regression models. When a prediction from one model is closest to the sample data measured in terms of the proposed metric, it can be recognized as the optimal predication. Results: Based on the proposed metric, it can be obtained that the two-dimensional fuzzy regression method is better than the traditional least square based regression method. Especially, its resulting generalized residual is smaller. Conclusion: In the literature, no unified framework has been previously proposed in evaluating the effectiveness of developed fuzzy regression models. In this work, we present a metric to achieve this goal. It facilitates the work to determine whether a fuzzy regression model suitably fits obtained samples and whether the model has potential to provide sufficient accuracy for follow-up analysis in a considered problem.

自從2007年發生金融海嘯後，歐盟貨幣整合問題持續被討論，其中，歐盟成員國各國經濟狀況差距過大被認為是這次經濟危機爆發出種種問題的主要原因。而近幾年，在國際其他地區區域性整合刺激之下，亞洲各國開始重視簽屬自由貿易協定的重要性，甚至未來朝區域貨幣整合作發展。 此論文主要是探討東亞國家是否具備成立亞元區的條件。我們探討東亞各國2004-2015年間對美元匯率以及消費者物價指數(CPI)變化是否有一致的趨勢，主要使用MST及HC、DCG tree機器學習分群演算法作為分析工具，對於東亞十個國家，包含台灣、日本、中國、南韓、香港、印尼、泰國、馬來西亞、菲律賓、新加坡以及用於對照的世界上其他國家作分析，以2008年金融海嘯以及2013年日本提出新經濟政策作為分界點，分為三個時期做討論。 研究結果顯示，三個時期，東亞國家匯率變動相較於世界其他國家皆較有一致的趨勢，而更進一步檢視，我們發現在日本提出新經濟政策前後，東亞國家匯率結構發生明顯的變化。物價指數方面同樣也是東亞國家有較集中的趨勢，但物價指數不向匯率，不同時期並無明顯變化。我們進而推論東亞國家以符合成立亞元區最基本的條件，匯率和物價指數的一致性，但是有幾個結果不能忽略，日本對於東亞國家匯率結構的影響力以及人民幣、日圓等強勢貨幣匯率變動趨勢在分析結果中和其餘東協國家還是有些差距，這些可能是在未來貨幣整合過程中市所需要注意的部分。 / After the global financial crisis, European Union (EU) faced a range of economic and political problems, including the Greek debt crisis and the upcoming UK referendum on EU membership. People start to think what factors cause these problems. A large literature has emerged discussing this issue and examining the future directions of monetary and exchange rate agreements of many countries in the region. One of the popular questions has raised researchers’ interests: Are East Asian countries possible to be the next monetary integrated area? This paper investigates the similarity of exchange rates and consumer price index (CPI) in the East Asian region, Taiwan, Japan, China, Hong Kong, South Korea and five ASEAN member countries by cluster analysis. We use HC tree and DCG tree to discuss the structures of these two macro economic factors and whether major economic events between 2004~2015 such as global financial crisis and Japan’s new economic policy influenced the structure. The exchange rates and CPI are both found to be more consistent among East Asian countries, including that these countries already have the basic condition to be the monetary integrated area. However, it is important to pay attention to Japan’s new economic policy, which influenced the structure a lot, and that Japanese yen and Chinese yuan still have gaps with ASEAN member countries.

由於房地產投機近年來對國內經濟造成極大的影響，而公共政策亦以打壓投機為目標。然而，房地產投機之意義為何?不同投機行為間有何差異?房地產投機的投機強度如何衡量?如何訂定抑制房地產投機之對策?這些均為本研究所討論的內容。 本研究首先分析房地產市場及其財貨特性，並配合相關法令以瞭解房地產投機、其他投機主體與投機標的物探討房地產投機行為，並歸納投機指標，然後以此指標建立「房地產投機指數模型」用以衡量投機強度。 最後對策研擬部份首先檢討全國土地問題會議之結論，然後針對健全房地產市場、減少市場失靈與政府失靈和房地走投機指數模型之運用等相關課題分析，最後以短、中、長期研擬房地產投機防制對策。

我國證券市場從民國七十五年起蓬勃發展以來, 到民國七十八年底止, 總投資開戶人 口已超過三百萬戶, 每日成交金額動輒壹千多億元, 其規模之大已躍居世界證券舞臺 重要地位, 僅次於日本及美國, 從理論上言, 任一理性投資或投機者所憑恃進出股市 者, 厥為基本分析或技術分析或兩者并用; 本論文從技術分析觀點對目前各種股價指 數之反應敏感程度作一深入研究, 欲探知在證券市場中長期波動走勢中, 何種股價指 數能在重要反轉時點儘早出現預警訊號, 得以警示或預知股市變動趨勢或走向, 那么 對於廣泛投資或投機大眾則貢獻良多矣。 本論文內容概分五章, 第一章緒論敘述研究動機與目的, 研究方法與限制, 并提出研 究架構; 第二章為對於各種股價指數編制方法之檢討, 因美國對股價指數之編制歷史 最悠久, 編制股價指數之機構最多, 且世界各國其主要證券市場對於股價指數之編制 大體師法美國編制方式, 而現今證券市場規模最大者依序為日本、美國及我國, 故本 章僅就美國、日本及我國證券市場主要股價指數之編制方法加以探討, 第三章則對於 各種技術分析方法加以探討, 并對於敏感度作一界定, 以便於技術分析方法之篩選, 第四章則選定民國七十五年起至七十九年二月止我國證券市場實際資料作一實證分析 , 并對其結果加以探討, 第五章則為結論與建議。

第一章概論，敘述研究目的，理論架構、分析方法及研究過程。 第二章理論探討及定義之設定，根據有關企業形象文獻及理論。就軍福中民特殊之角 色及擔負的功能，所形成之特定目標市場設定有關變數。 第三章企業形象與消費行為之關係。 (1) 根據有關理論，研究消費者之寵顧頻率、購買廣度及金額與企業形象指數之關係 。 (2) 消費者心理滿足程度對企業形象之影響。 第四章各關係群體總和企業形象之測度及差異分析，測度管理當局、高指數消費群( 經常購買) 、低指數消費者( 很少購買者) 之總和形象指數( 重要性程度×滿意程度 ) 在各變項之差異分析。 第五章根據研究之結果, 針對顯著差異變項及消費者心理滿足層面提出軍福中心企業 形象之改進方案。

近年來通貨膨脹厲害，在稅制規定中，均以名目所得表示，造成人民實質稅負上升， 尤其是寬免額及稅率級距問題更受重視，本文對此問題加以研討。討論所得稅指數化 之穩定面及公平面。研究結果，所得稅指數化並不會加重經濟之不穩定且其可達到租 稅中立原則。 第一章緒論。第二章學者對所得稅指數化的穩定分析。第三章所得稅指數化穩定分析 之模型設立。第四章各國實行所得稅指數化之狀況。第五章個人所得稅指數化對所得 分配之影響。第六章通貨膨脹對我國所得分配之影響。第七章所得稅指數化之特殊問 題研究。第八章結論與建議。