本研究利用事件研究法探討自2005年9月~2011年9月為止,台灣上市櫃公司不同類型減資之長期異常報酬,採用累積異常報酬(CAR)、買進持有異常報酬(BHAR)、平均每月曆月異常報酬(CTAR),以及因子模式作為衡量方法,並以規模淨值市價比(MVPB)、規模(MVR)、等權市場(EWRM)及加權市場(VWRM)為對照基準,偵測現金減資、彌補虧損減資以及庫藏股減資是否具有長期異常報酬。 實證結果發現:上市櫃公司無論是實施現金減資、彌補虧損減資抑或是庫藏股減資,都無法得到異常報酬,其中,以庫藏股減資之上市櫃公司,僅每月曆月異常報酬法未偵測出異常現象,此似乎意味著本研究結果會受到異常報酬衡量方法之影響。而Loughran and Ritter利用模擬分析發現,每月曆月異常報酬法在偵測異常報酬方面的檢定力較弱,亦即較難取得顯著之平均異常報酬。以上市櫃公司實施現金後進場投資,未出現明顯的獲利或虧損。若在上市櫃公司彌補虧損減資後進場,則會有些許的虧損。而在庫藏股減資後進場,會出現不少的獲利。
The event study methodology was adopted in order to know the long-term abnormal remuneration for different types of capital reduction of listed companies in Taiwan from September 2005 to September 2011. it using Cumulative Abnormal Return (CAR), Buy-and-Holdings Abnormal Return (BHAR), and Mean Monthly Abnormal Returns (CTAR) and Factor Model are used as a measure, and they are measured against the MVPB, MVR, EWRM, and VWRM benchmarks. Whether the mode of capital reduction, capital reduction for making up loss and stock repurchase for capital reduction have abnormal long-term compensation. The empirical results show that listed companies can't get the abnormal compensation for cash reductions, capital reductions for making loss and stock repurchase for capital reduction, which the Public-Listed Companies of the stock repurchase for capital reduction only do not detect the Mean Monthly Abnormal Returns. The anomalous phenomenon in which seems to imply that the results of this study will affected by the measure of abnormal compensation. Loughran and Ritter used simulation analysis to find that the Mean Monthly Abnormal Returns has weaker detection power in detecting abnormal returns, that is, it is difficult to obtain significant average abnormal return. Public-Listed Companies implemented cash reduction and entering the market, no obvious profit or loss. If the public-listed companies implement capital reductions for making loss and enter the market, there will be a slight loss. After entering into the market, the stock repurchase for capital reduction will show a lot of profit.