第一節 結論
本研究採取直接分解法將個別股票報酬當中不能由市場模式所解釋的部分，
做為公司特有風險波動，藉此來研究公司特有風險的特性。對於無法建構一個納 入足夠多投資標的股票而達到分散非系統性風險的投資組合的投資人而言，公司 特有風險對於股價報酬的影響也許是決定該股票報酬的主要因素之一。
本研究採用日股價報酬資料做為樣本來估計公司特有風險變數，觀察公司特 有風險是否對於股價報酬具有解釋能力。研究期間取自1986年7月至2007年6月之 股票交易日，共計252個月的資料。
在將總風險與公司特有風險與股票超額報酬做迴歸分析之後發現，我們發現 兩變數在解釋當其股票超額報酬上，具有顯著的正向關係，亦即當樣本公司的公 司特有風險上升，其股票超額報酬也會上升的。本研究發現與 Fu（2009）的研 究結果一致。而本研究當中也發現以往在 CAPM 理論下的 Beta 係數在本研究樣 本之下，呈現不顯著的情況，且其係數為負，相對來說在台灣股票市場上，公司 特有風險對於股價超額報酬的解釋能力勝過於屬於市場風險的 Beta 係數。且台 灣股票市場當中，散戶交易占大多數，而散戶一般持有的股票標的數目有限，往 往沒辦法去建構一個可以適度分散風險的投資組合，因此公司特有風險對於股價 報酬所帶來的影響在台灣市場尤為重要。此外，研究也發現公司特有風險相對整 體市場風險有往上的趨勢。因此，在對於股票價格的定價上，公司特有風險具有 越來越重要的影響，不可忽略。
第二節 研究限制與建議
在往後研究建議上，因為以往在做股價報酬研究時，其資料為時間序列（time series）之型態，因此其序列的波動常會有「波動叢集現象」（volatility clustering）
產生。即前期波動有大幅的變化時，下一期也會有同方向大幅的變化；前期有小 幅的變化時，下一期亦會有同方向小幅之變化，也就是說大波動跟隨大波動，小 波動跟隨小波動。而 Bollerslev （1986）曾提到股票報酬序列之間，殘差項的變 異數不是固定常數，而是有異質變異數的現象。因此在估計模型殘差項時可採取 Bollerslev（1986）GARCH （ generalized autoregressive conditional
heteroskedasticity ）模型或是 Nelson （1991）的 EGARCH（ exponential generalized autoregressive conditional heteroskedasticity ）來估計，來減少以 OLS 法估計上的 誤差。因此建議後續研究方面，在估計公司特有風險變數上可以採取上述兩個方 法。
在研究限制上，由於研究期間的前幾年，樣本公司數目較後面為少，在民國 75年的期間估計樣本只有81家，且研究期間初期股市波動劇烈，大盤指數屢破高 峰又大跌，股市震盪大，故在估計公司特有風險上對於整體期間影響較為深刻。
此外，在短期價格記憶（momentum）變數上也可以改用較短期的累積報酬進行 衡量。
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