動能效果是各國股票市場中最常見的異常現象，Sharpe (1964)提出資本資產定價模型(CAPM)，認為股票報酬與系統風險之間呈現正相關，而和其他非系統性風險無關，因此投資人透過投資所能獲得的超額報酬皆因承擔系統風險而得到的補償。然而近來許多實證研究的結果皆發現了一些非系統性風險能夠解釋股票報酬的異常現象，例如規模效應、本益比效應等等。若股票市場是具有效率的，那麼市場上所有已公開資訊皆應已充分反應在股價上，因此透過各種投資策略皆應無法獲得超額報酬，然而Jegadeesh and Titman (1993)卻發現利用買進過去報酬相對較佳之股票和賣出過去報酬表現相對較差之股票可獲得顯著的超額報酬，即所謂的動能投資策略，這種策略的獲利性很顯然的違背了效率市場假說，因此許多學者相繼提出理論來解釋造成此種現象之原因，其中有某些行為財務理論將此現象歸因於投資人對市場上之新訊息反應不足所致。 本文研究係以Grinblatt and Han (2005)的實證方法，透過建立資本利得與損失的代理變數來衡量由於處分效果造成股價反應不足之程度，並利用Fama-Macbeth橫斷面分析法來探討台灣股市的動能效果是否是因市場上存在處分效果，導致股價反應不足所引起。實證結果發現台灣股市在中長期(過去27到52週)存在顯著的動能效果，然而利用資本利得與損失的代理變數並無法成功的將該動能效果消除，顯示處分效果無法有效的解釋台灣股市中動能效果的來源。

1997年爆發亞洲金融風暴，隔年（1998年）起公司發生財務危機事件層出不窮，1998年至2005年間最為嚴重；2007年全球金融海嘯至今，投資人擔心買到地雷股，對於投資股票市場仍採觀望態度。在經過層層把關的財報背後究竟隱藏多少危機？這些危機難道是不可預測的嗎？其實，公司爆發財務危機並非一夕之間產生的問題，就如同人類的慢性病不是一天造成的，是長期忽略身體健康警訊造成的結果，事出必有因，因此許多學者便開始探究財務危機背後的成因，試圖找出一些指標供投資人作為投資前之考量因素。 本研究主要之目的在於探究財務危機之預測指標，分別探討Beta、公司規模、淨值市價比以及前一年平均報酬（負的動能效果）是否可作為財務危機之預測指標。本研究之樣本公司為1983年至2007年之台灣上市公司，利用Altman提出之Z-score模型將公司區分為危機公司以及正常公司，再將樣本公司依Beta、公司規模、淨值市價比以及前一年平均報酬分別分組，探討這些變數是否可作為財務危機之預測指標。實證結果指出Beta及淨值市價比無法作為財務危機之預測指標，但公司規模及前一年平均報酬（負的動能效果）可以作為財務危機之預測指標。 / With the Asian financial crisis breaking out in 1997, many companies began to suffer financial distress in the following year, and the situations were getting even worse during 1998 and 2005. Faced the new waves of financial tsunami across the world starting from 2007, the investor, therefore, have been adopting a wait-and-see attitudes towards the stock market, fearing of being hit by the “tank stocks”。How many financial problems hidden behind the carefully prepared financial statements? Are they unpredictable? As a matter of fact, just like the human chronic diseases which actually caused by long-term ignorance of health warning, corporate financial distress never happens suddenly. Thus a number of scholars are dedicated to study the reasons for financial problems, attempting to figure out certain indicators capable of being prior reference for investment decision-making. This paper aims to study the predictors of financial distress. Beta, firm size, book-to-market ratio and average monthly prior-year return (negative momentum effect) are to be considered respectively to determine their possibilities of being predictors. The sample companies discussed in this paper are chosen among the listed companies during 1983 and 2007 in Taiwan. They are grouped into two categories of crisis company and normal company by using the Z-score model developed by Altman. Then the sample companies are divided in terms of Beta, firm size, book-to-market ratio and average monthly prior-year return so as to trace these variables’ likelihood to predict bankruptcy. It eventually turns out that firm size and average monthly prior-year return could serve as predictors of financial distress, other than Beta and book-to-market ratio.