| Abstract: | 台灣的股票市場隨著經濟成長與主管機關改革開放已經趨於成熟，多樣化的金融工具及便利的網路科技使得交易市場日趨活絡。早期由Sharp (1964) 等人提出的資本資產定價模型到Fama and French (1993) 的三因子模型，都是在衡量股票報酬率的影響因子，因此能發現一個可以解釋股價報酬的因子對投資人來說是非常重要的。
本研究首先以Whited和Wu (2006) 所建構的WW指數區分台灣上市、櫃公司財務受限程度由小至大分為前40% (另劃分35%、30%、25%、20%組合)的樣本為高受限公司，後40% (另劃分35%、30%、25%、20% 組合)的樣本為低受限公司；再依照持有現金比率大小前40% (另劃分35%、30%、25%、20% 組合)的樣本為高持有現金公司，後40% (另劃分35%、30%、25%、20% 組合)的樣本為低持有現金公司建構四組投資組合，分別為高財務受限-高持有現金、 高財務受限-低持有現金、 低財務受限-高持有現金、 低財務受限-低持有現金，發現低財務受限-高持有現金公司組合股票報酬表現最亮眼。
另外，本篇以個股公司報酬為探討，並以公司持有現金比率(CH)、財務受限程度(WW)、市場風險溢酬 (Rm-Rf)、公司規模(SIZE)、市場帳面價值比(MB)探討其與股票報酬之間關係。
Taiwan''s stock market has become increasingly active and mature because of the economic growth, diversified financial instruments, convenient network technology and improvement of polices. The capital asset pricing model proposed by Sharp (1964) and others like the three-factor model proposed by Fama and French (1993) all measured the factors that impact the rate of stocks return. Therefore, finding factors that can be used to explain the stock return is very important for investors.
This study first uses WW index constructed by Whited and Wu (2006) to identify the degree of constraints. Of whole sample, companies ranked in top 40% (35%, 30%, 25%, 20%) are defined as constrained companies whereas bottom 40% (35%, 30%, 25%, 20%) are defined as unconstrained companies.
Then, this paper uses cash holding ratio to do a further grouping. Of whole sample, companies ranked in top 40% (35%, 30%, 25%, 20%) are defined as high cash holding companies while bottom 40% (35%, 30%, 25%, 20%) are defined as low cash holding companies.
Finally, there are four portfolios of investment, i.e. High constrained - High cash holding, High constrained - Low cash holding, Low constrained - High cash holding, Low constrained - Low cash holding. This paper compares the long-term stock price performance of four portfolios and discusses the relationship between cash holdings(CH), finance constraint index, market risk premium (Rm-Rf), company size (SIZE), market-to-book ratio (MB) and stock returns through doing regression analysis using data during 1986 to 2015. |
