臺灣流民拳之研究

[[abstract]]在台灣流傳的眾多武術中，最令我感到好奇的是強調「招不過三」的「流民拳」。於是就開始收集與「流民拳」有關的文獻，起初本想藉著歷史經驗以抓住門派的重要成因，以形成本研究的基本發展方向。然而，所獲得的歷史文本不多，因此本研究輔以訪談法及參與觀察法，來彌補史料的闕漏。用以探究「流民拳」的技術層次、制度層次、精神層次，所得結果如後：技術層次：強調能打的武術，但由於近來學武目的改變，以致於較少實戰對打訓練。制度層次：俱樂部與師徒制的並行下，雖利於推廣，但對於門派道統的傳承卻有些阻礙。精神層次：不離「教武育仁」維護門派榮譽期勉弟子。

[[alternative]]The effects of investors' trading behavior on the rise and fall of intraday stock market

碩士[[abstract]]本研究採用NBINGARCH（1,1）計次資料模型分析交易人行為對日內股市漲跌的影響，以機構投資人（外資、投信、自營商）買賣超餘額、融資餘額變動率、融券餘額變動率等變數，探討其分別對於台灣加權股價指數大盤、開盤後一小時、盤中、收盤前一小時之上漲（下跌）次數是否具有顯著性的影響。 實證結果發現：1.外資在大盤下跌（上漲）時會增加（出脫）持股的負向回饋交易，與其他交易人受大盤下跌（上漲）影響反而有恐慌性賣出（買進）之行為有所不同，而且在收盤時段，以及開盤時段大盤下跌時的日內效果特別顯著，推論其握有私有資訊。2.投信則與外資相同，與收盤時段上漲（下跌）次數有顯著負向（正向）關係，推論其也具有私有資訊，但與大盤上漲（下跌）次數不顯著。3.自營商雖然同為機構投資人，卻有不同的表現，與大盤，以及開收盤、盤中時段之上漲（下跌）次數有正向（負向）關係，漲時助長、跌時助跌之正向回饋交易，推論其為雜訊交易者。4.融資餘額變動率常被作為散戶投資情緒的觀察指標，其與大盤、開收盤及盤中時段之上漲（下跌）次數呈現顯著正向關係，所以認為散戶為雜訊交易者。5.融券餘額變動率僅與收盤時段之下跌次數有正向關係。[[abstract]]This paper used NBINGARCH(1, 1) count data model to analyze the effects of investors’ trading behavior on the rise and fall of intraday stock market. We examined whether the variables including the net buy/sell for institutional investors (foreign investors, investment trust and dealers), the change of balance of margin loan and stock loan had significantly influence on the rise and fall of TAIEX among the first hour, the middle and the last hour trading period. Empirical results showed as follows：1. Foreign investors engaged in negative feedback trading by increasing (decreasing) share holdings when TAIEX fell (rose). They were different from other traders who were affected by TAIEX to sell shares with panic when TAIEX fell and buy shares when TAIEX rose. Meanwhile, the intraday effects were especially significant among the closing trading period no matter what the rise and fall of TAIEX, and the fall of TAIEX among the beginning trading period. So, we inferred foreign investors held private information. 2. Investment trust had the same trading behavior as foreign investors. Their trading behavior was negative (positive) relation with the rise (fall) of TAIEX among the closing trading period. So, we inferred they also held private information. But, it wasn’t significant relation with the rise (fall) of TAIEX among the whole trading day. 3. Although dealers were institutional investors too, their trading behavior was different from others. It was positive (negative) relation with the rise (fall) of TAIEX among the whole trading day, the beginning, the closing and the middle trading period. They engaged in positive feedback trading, therefore we inferred they were noise traders. 4. The change of balance of margin loan often used as the sentiment index of individual investors. Their trading behavior indicated significant relation with the rise (fall) of TAIEX among the whole trading day, the beginning, the closing and the middle trading period. They engaged in positive feedback trading. We thought individual investors were noise traders. 5. The change of balance of stock loan was positive relation with the fall of TAIEX among the closing period only.[[tableofcontents]]目 錄 第一章 緒論…………………………………………………………………………1 第一節 研究背景與動機……………………………………………………………1 第二節 研究目的……………………………………………………………………6 第三節 研究架構……………………………………………………………………6 第二章 文獻探討……………………………………………………………………8 第一節 各類型投資人之介紹………………………………………………………8 第二節 投資人交易行為……………………………………………………………14 第三章 研究方法……………………………………………………………………26 第一節 研究期間及研究對象………………………………………………………26 第二節 單根檢定……………………………………………………………………27 第三節 計次資料模型………………………………………………………………33 第四章 實證結果與分析……………………………………………………………38 第一節 資料檢定及模型設定………………………………………………………38 第二節 實證結果……………………………………………………………………51 第五章 結論與建議…………………………………………………………………55 第一節 結論…………………………………………………………………………55 第二節 未來研究建議………………………………………………………………56 參考文獻……………………………………………………………………………57 表 目 錄 表1-1 集中交易市場投資人交易金額比例表……………………………………4 表1-2 上市證券信用交易值分析表………………………………………………5 表2-1 集中交易市場投資法人交易概況表………………………………………9 表2-2 我國開放外資投資國內股市歷程…………………………………………10 表2-3 外資投入我國股市概況表…………………….……………………………11 表4-1 基本統計分析表……………………………………………………………41 表4-2 ADF單根檢定法（水準項）………………………………………………46 表4-3 PP單根檢定法（水準項）…………………………………………………46 表4-4 ADF單根檢定法（差分項）……………………………………………47 表4-5 PP單根檢定法（差分項）………………………………………………47 表4-6 變數定義……………………………………………………………………48 表4-7 NBINGARCH（1, 1）實證表（1）………………………………………49 表4-8 NBINGARCH（1, 1）實證表（2）………………………………………50 圖 目 錄 圖1-1 集中交易市場投資人交易金額比例趨勢圖………………………………4 圖1-2 上市證券各項信用交易比率趨勢圖………………………………………5 圖1-3 本論文研究架構圖…………………………………………………………7 圖4-1 台灣加權股價指數日內上漲次數圖………………………………………42 圖4-2 台灣加權股價指數日內下跌次數圖………………………………………42 圖4-3 每日外資買賣超金額圖……………………………………………………42 圖4-4 每日投信買賣超金額圖……………………………………………………43 圖4-5 每日自營商買賣超金額圖…………………………………………………43 圖4-6 融資餘額和台灣加權股價指數走勢圖……………………………………43 圖4-7 融券餘額和台灣加權股價指數走勢圖……………………………………44 圖4-8 法人交易比重與台灣加權股價指數走勢圖………………………………44 圖4-9 融資占成交量比重與台灣加權股價指數走勢圖…………………………44 圖4-10 融券占成交量比重與台灣加權股價指數走勢圖………………………45[[note]]學號: 799530356, 學年度: 10

Predicting Anticancer Drug Resistance Mediated by Mutations

[[abstract]]Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance