本研究之目的為探討台灣30檔產業指數之近十年的12個月報酬期間結構，以其報酬水準、變動與曲度衡量之，並觀察隨持有期間移動之逐期變化。再檢定所有產業指數回復一般報酬水準的期間，比較其效率性。
　　首先以B樣條為基底轉換的泛函資料分析作為靜態模型，發現：大部分的指數都在短期期間有波動較大的報酬，隨著持有時間拉長其報酬率才會逐漸收斂於零附近。且以一階微分的函數可發現，多數產業指數存在第一個月即負報酬、並在持有二至三季時上下震盪，於第四季開始有更明顯的正報酬；其二階微分函數探討出，多數產業指數在持有至第2期時會產生期間結構的最小報酬，並又在第12個月時有直線向上的趨勢。筆者推論短期的持有期間易受短線散戶的影響，其股價波動會因動能效應而造成劇烈的波動，但隨期間拉長，以基本面為主的價值投資才開始顯現出影響力。而多數產業指數在第2期有著最小報酬，推論在持有期間兩個月之後，平均將開始跌深反彈，才造成此期間結構之特性。
　　接著再以利率期間結構的泛函模型作為動態模型，此計量模型可以透過迴歸檢定的結果，觀察其分布來比較不同產業指數的所有報酬水準、變動與曲度。接著以模型參數q之分配中位數來看，電器電纜類指數、玻璃陶瓷類指數、化學工業指數平均之異常報酬收斂時間短、效率性較佳；通訊網路業指數、鋼鐵工業類指數、塑膠工業類指數平均之異常報酬收斂時間長、效率性較差。
　　比較兩種模型的研究結果，筆者認為：在從傳統研究利率期間結構模型，至此篇將模型修改配適於股票市場上，B樣條轉換之靜態模型較具參考性，因泛函模型之動態模型在急劇波動的股票市場其深受各自的解釋力所影響，若解釋力不佳，那觀察出來的期間結構特性也較不具參考性，而靜態模型不受此影響。

　　This thesis studies 12 months period of term structure for 30 industry indices’ return by two different ways, adopting B-spline of functional data analysis as static model, and functional model of term structure as dynamic model individually.
　　After functional transformation, we find that most of industry indices’ return have bigger fluctuation in short-term and converge to zero in long-term. And by mean function of B-spline transformation, we also find that most of industry indices are negative return at first period, and have a minimum return at second period, then increasing by month gradually. I supposed that the short-term holding period is susceptible to short-term investors, and its stock price fluctuations will cause severe fluctuations due to momentum effect. However, with the extension of the period, the investment based on fundamental analysis will begin to show influence. Most industry indices have the minimum compensation in the second period. It is inferred that after two months of holding, the average will begin to fall deeper and rebound, and it had result in the characteristics of the structure during this period.
　　By using statistic results of functional model. I found most of the central distribution type of indices have positive slope and negative curvature. Otherwise, we can use the parameter q to check the market efficiency separately. Industry index of electrical cable, glass ceramic, and chemical industry converge abnormal return faster, and regard as more efficient; Industry index of communication network, steel industry, and plastic industry converge abnormal return slower, and regard as less efficient.

論文審定書 i
摘 要 ii
Abstract iii
目 錄 iv
圖 次 viii
表 次 ix
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究背景 2
第三節 研究預期貢獻 2
第四節 研究流程與架構 3
第二章 文獻探討 4
第一節 泛函資料分析 4
第二節 期間結構模型 5
第三節 效率市場理論 6
第三章 研究方法 8
第一節 實證資料與處理 8
第二節 泛函資料轉換 12
第三節 泛函模型 14
第四章 實證結果 17
第一節 函數轉換與平均函數 17
1. 以B樣條進行函數轉換 17
2. 以B樣條檢驗報酬水準 18
3. 以B樣條檢驗報酬變動 21
4. 以B樣條檢驗報酬曲度 23
5. 靜態函數模型之結論 23
第二節 泛函模型分析 26
1. 以泛函模型之c0觀測報酬水準 30
2. 以泛函模型之c1觀測報酬變動 31
3. 以泛函模型之c2觀測報酬曲度 32
4. 以泛函模型之q衡量指數效率性 33
5. 以泛函模型衡量三因子曝險程度 36
6. 動態函數模型之結論 40
第五章 結論 41
第六章 參考資料 43
附錄一 30檔產業指數之原始資料圖形 46
附錄二 B樣條轉換後之30檔產業指數圖形 47
附錄三 B樣條轉換後之30檔產業指數一次微分圖形 48
附錄四 B樣條轉換後之30檔產業指數二次微分圖形 49
附錄五 B樣條轉換後之30檔產業指數平均函數 50
附錄六 B樣條轉換後之30檔產業指數一階微分平均函數 51
附錄七 B樣條轉換後之30檔產業指數二階微分平均函數 52

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