本研究主要參考Ando and Bai(2017) 所建構之全新架構，透過因子 分析和SCAD(Smoothly clipped absolute deviation) 懲罰項， 且搭配 PIC(Panel information criterion) 準則所建構出的迭代收 模型，將台灣股市中的股票進行重分組；而此過程中我們運用到機器學習之技術來決定股票樣本初始分組之最適情形。我們希望探討的問題包括台灣的股市報酬率之最適分組情形為何? 哪些產業之類股在報酬上有相同的趨勢? 又哪些類股在報酬上其實並不適合以產業型態來做分類? 哪些解釋變數對台灣股市是重要的變數?

我們透過因子結構以探討台灣股市的共同因子，以及藉分組後的組內特定因子討論分組後組內的異質性；同時，我們透過SCAD 懲罰項，在迭代的過程中自動對台灣股市重要的解釋變數。透過這些研究，希望提供投資人在投資台灣股市時一些有別於傳統直接以產業為分類標籤之投資建議。

實證結果中，我們首先發現台灣股市中的傳統類股在報酬上有較為相近 之趨勢，間接證實了傳統產業具有一定的共榮性；此外，生技以及建 類股亦有它們各自的報酬趨勢，且組內異質性低，故對於此兩類股而 以產業來將它們分組是恰當的。另外，電子工業類股的分組複雜而分散，且異質性高；深入分析後，我們發現電子工業類股在產品鏈中之 類在報酬表現上無特定趨勢，判斷可能原因為受到產業鏈上、中、下游之影響，而使得公司之間的股價報酬相互反映；唯有產品鏈以外的服務業，包括電子通路以及資訊服務，有其特定之報酬趨勢。

最後，在解釋變數的篩選上，我們於公司面、市場面、總體經濟面以及匯率中選取 15 個解釋變數放入迭代之過程中。我們發現消費者物價指數對台股報酬率是最不重要之解釋變數；而貨幣供給、規模溢酬以及市價比溢酬為較重要之變數，對於台灣股市之影響則相對較為顯著。

1 前言---4
2 文獻回顧---6
2.1 分群方法---6
2.2 因子分析(Factor Analysis)---7
2.3 壓縮方法(Shrinkage Method)---8
2.4 類股表現之分析---8
3 研究方法---9
3.1 PIC 準則(Panel information criterion)---11
3.2 變數初始值之選擇---12
3.3 變數選擇之迭代---14
3.4 迭代收斂以及最終結果之選擇---16
4 實證分析和應用---16
4.1 台灣各類股之分群分析---16
4.1.1 研究資料與敘述統計---16
4.1.2 實證結果---20
4.2 電子工業類股之分群分析---26
4.2.1 研究資料與敘述統計---26
4.2.2 實證結果---28
5 結論與建議---34
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