سرمایه گذاری خصوصی و بازده های عمومی حقوق صاحبان سهام
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|10027||2012||25 صفحه PDF||41 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Economics and Business, Volume 64, Issue 2, March–April 2012, Pages 160–184
2-1- محرک تجربی
2-2- محرک تئوری
3-1- نیازهای خود تامین مالی سرمایه گذاری خصوصی
3-2- روش برآورد
3-4- برآورد سرمایه گذاری خصوصی برنامه ریزی شده
جدول 1: ساختار[Et[NCORt+1.
4-نتایج قیمت گذاری دارایی
4-1- نتایج اصلی
جدول 2: حساسیت جریان نقدی و شرایط اعتباری
جدول 3: تخمین GMM با بتاهای چند متغیره
جدول 4: تخمین GMM با بتاهای تک متغیره
4-2- مقایسه با مدل های دیگر و نیرومندی
جدول 5: رگرسیون فاما-مکبث.
4-3- بازده ها و بتاهای پورتفولیوی اضطرار
جدول 6: مقایسه با روش های کسب و کار خصوصی دیگر
4-4- دوره های رونق اعتباری و بحران اعتباری
جدول 7: رگرسیون رونق اعتباری و بحران اعتباری
5-تجزیه و تحلیل بوت استرپ
جدول 8: تنظیم سیستم بوت استرپ
جدول 9: تحلیل بوت استرپ مستقل
Because of external financing costs, private business owners often need to self-finance new investment projects. These self-financing needs create an incentive for business owners to hold financial assets whose payoffs are positively correlated with self-financing needs. If this effect is aggregated, expected returns on financial assets should be negatively correlated with aggregate private investment self-financing needs. To test the cross-sectional asset pricing implications of this conjecture, we use realized noncorporate investment growth and future forecasted noncorporate investment growth as proxies for self-financing needs. We find that our private investment model can explain a good share of the cross-sectional returns of size-, value- and distress-sorted equity portfolios, almost as well as the Fama–French factors. In contrast to the Fama–French model, however, we find the signs on our estimated coefficients to be consistent with our theoretical predictions.
The effect of the private business sector on the prices of public-traded stocks has received only scant attention in the finance literature. The most notable study is Heaton and Lucas (2000) who find that including aggregate private business profits in Jaganathan and Wang's (1996) labor-enhanced conditional CAPM can help explain the cross-section of size and value portfolio returns. Heaton and Lucas find that stocks which have positive correlation with aggregate private business income trade at a discount, and thus have higher average returns, relative to stocks that have low or negative correlation with aggregate private business income. This is in accordance with their prediction based on income-diversification incentives that background income risk commands a positive risk premium. However, when we test a version of the Heaton–Lucas model using an updated time horizon and a different set of test assets which includes distress-sorted portfolios, we find that labor income and proprietary business income are traded at a premium instead of a discount. One of the main purposes of this paper is to propose and test a theoretical framework in which this result can be related to a rational, economic incentive. Rather than starting from traditional diversification theory, we consider the hedging incentives that financially constrained private business owners face. If private business owners face external financing costs, they will have an incentive to inject money from their personal financial savings into their private business in order to either expand via new investment projects during up-cycles or to prevent inefficient downsizing during down-cycles. The private investment self-financing needs at work here are analogous to the hedging incentives analyzed by Froot, Scharfstein, and Stein (1993) who show that, in the presence of external financing costs, assets whose returns are correlated with investment opportunities make good hedging instruments. The implication of this result, applied to private business owners, is that assets whose returns have high correlation with self-financing needs should face higher demand by private business owners than assets whose returns have low correlation with self-financing needs. In aggregate, this extra demand implies that financial assets whose returns have high correlation with private investment self-financing needs should, all else equal, trade at a premium and thus exhibit lower average returns. Testing this conjecture would be straightforward if self-financing needs were directly observable. Since this is not the case, we infer self-financing needs using forecasted and realized noncorporate investment growth. These variables are used in order to approximate self-financing needs for, respectively, planned and contemporaneous private investment. We use forecasted noncorporate investment as a proxy for planned investment since there is typically a delay between the preliminary financing stages of investment planning and the actual implementation and reporting of investment projects. Realized noncorporate investment, on the other hand, captures self-financing needs associated with contemporaneously realized investment projects. For test assets, we use the 25 Fama–French size- and value-sorted portfolios plus 10 distress-sorted portfolios following Campbell, Hilscher, and Szilagyi (2008). Using Fama–Macbeth and generalized method of moment estimation procedures, we find that our model is able to explain the cross-section of expected returns about as good as the Fama–French size and value factors. More importantly, whereas the estimated sign on key risk premium coefficients is puzzling in the Fama–French specification, and other specifications, the sign on our risk premium coefficients is consistent with our private investment explanation. For example, the estimated coefficient for market returns in the Fama–French model using our test assets is significantly negative. This is in accordance with the findings in Campbell et al. (2008) who show that market beta is significantly related to their distress portfolios, but with higher betas corresponding to the more distressed portfolios which have lower average returns. Although this is puzzling from a traditional portfolio-diversification perspective, this is not surprising from the perspective of our private investment approach: positive market returns are an indicator of greater investment opportunities and self-financing needs, and since private business owners have an incentive to hedge these needs, the risk premium on market returns is negative. We also analyze the effect of time-varying credit conditions. In credit crunch periods, we find statistical evidence of investment-cash flow sensitivity in the noncorporate business sector, consistent with the hypothesis of Fazzari, Hubbard, and Petersen (1988) who argue that invest-cash flow sensitivity is evidence of costly external financing. We also find evidence that suggests self-financing needs are more associated with investment plans during a credit boom, and more associated with contemporaneous investment projects in a credit crunch. That is, in periods where the credit spread is below its mean (a credit boom), we find that expected future noncorporate investment growth has a stronger effect on asset prices than when the credit spread is above its mean (a credit crunch). This is consistent with idea that business conditions and next-quarter investment opportunities are better in a credit boom than in a credit crunch. On the other hand, in periods where the credit spread is above its mean, we find that contemporaneous noncorporate investment growth has a stronger effect on asset prices than when the credit spread is below its mean. This is consistent with the idea that during a credit crunch there is reduced investment planning activity and a greater need for supplemental self-financing of projects in the latter stages of implementation. In related investment-based cross-sectional asset pricing work, Cochrane (1996) and Li, Vassalou, and Xing (2006) show that size and value premia can largely be explained by sectoral investment factors. These models, however, are motivated by macroeconomic models with multisector total factor productivity shocks and a linear pricing kernel specification relative to sectoral investment returns or growth rates. In contrast, we focus on the private investment sector, motivated by financial frictions associated with the private business sector. Another investment-based study is by Gomes, Yaron, and Zhang (2003) who show that corporate financing frictions can help explain the cross-section of expected returns. However, whereas Gomes et al. use corporate measures of investment and financial constraints and focus only on size-sorted portfolios, we focus on noncorporate measures and we are able to explain size-, value- and distress-sorted portfolios. Two other studies consider financial constraints from a corporate rather than noncorporate perspective. Lamont, Polk, and Saa-Requejo (2001) study a cross-section of financially constrained firms, as defined by the Kaplan and Zingales (1997) index, and find that financially constrained firms have lower expected returns. However, Whited and Wu (2006) construct a different measure of corporate financial constraints and find the opposite result, namely that financial constraints lead to higher expected returns. Petkova and Zhang (2005) use macroeconomic variables to predict future investment conditions and document a positive relationship between market betas for value portfolios and the expected market risk premium. Although the direction of the relationship found by Petkova and Zhang is consistent with conditional CAPM theory, Lewellen and Nagel (2006) show that the empirical variation in the expected market risk premium is too small to explain the magnitude of the observed value premium. In contrast to the conditional CAPM framework of Petkova and Zhang, forecasting variables in our private investment model are not used to predict changes in the expected market risk premium; rather, forecasting variables in our model capture expected future private investment self-financing needs. Thus, the Lewellen–Nagel critique does not apply in our framework. We also differ from Petkova and Zhang in that we include distress-sorted portfolios as test assets. In a recent working paper, Chen, Novy-Marx, and Zhang (2011) consider a 3-factor model based on market returns, a return-on-equity factor (high-profit firms minus low-profit firms), and an investment factor (low-investment firms minus high-investment firms). Their model is motivated by the “mechanical” valuation theory discussed in Fama and French (2006) and the q-theory of Liu, Whited, and Zhang (2009). Although these theoretical motivation are distinct from our approach, we test their model relative to ours, and vice-versa, and find that our models add statistically significant explanatory power to each other.1In Section 2 of the paper we discuss another strand of literature that documents the existence and source of costly external financing for private business owners. These costs arise from direct transaction costs as well as indirect costs associated with adverse selection and agency problems that are particularly significant with externally financed private business ventures.2 In Section 2 we also provide theoretical motivation for our empirical specification. Then, in Section 3 we explicate our estimation strategy. In Section 4 we present the results of our private investment asset pricing regressions and compare our results to other benchmark models. In Section 5 we present a bootstrap analysis of our empirical results. We conclude in Section 6.
نتیجه گیری انگلیسی
In this paper we consider a private investment asset pricing model where private investment self-financing needs create an incentive for private business owners to hedge with assets that are positively correlated with self-financing needs. To test this model, we use noncorporate investment growth and future forecasted noncorporate investment growth to proxy for self-financing needs. As evidence that self-financing needs can have significant effects, we find greater investment-cash flow sensitivity for the noncorporate business sector in credit crunch periods, when the credit spread is high, relative to credit boom periods when the credit spread is low. In asset pricing tests using size-, value- and distress-sorted portfolios, we find that the private investment model can explain about 70% of the cross-sectional variation in returns. More importantly, the estimated risk premium coefficients have the correct sign, as predicted by the private investment model. Other leading asset pricing models do not provide an economic rationale for the estimated risk premia that we obtain in our sample. For example, corroborating the findings of Campbell et al. (2008), we find that high-distress portfolios have higher market betas but lower average returns than low-distress portfolios. This finding is exactly the opposite of what portfolio-diversification-based theories predict. However, from the perspective of our private investment model, this is not surprising: portfolios with high market betas are good hedges for private investment self-financing needs, and thus trade at a premium and exhibit lower expected returns. More specifically, our results suggest that high-distress portfolios comprise a good hedge for expected future investment plans in a credit boom, and a good hedge for contemporaneous self-financing needs in a credit crunch.