Does Corporate Investment Improve Stock Liquidity?

Transcription

1 Does Corporate Investment Improve Stock Liquidity? Moonsoo Kang 1 December 15, 2013 Moonsoo Kang is an assistant professor at the Hagan School of Business at Iona College. Mail: 715 North Avenue, Iona College, New Rochelle, NY 10801, Tel: (914) , mkang@iona.edu.

2 Does Corporate Investment Improve Stock Liquidity? Abstract Corporate investment affects the risk of a firm which, in turn, changes stock liquidity. Moreover, stock liquidity also influences corporate investment. This study analyzes the interaction between stock liquidity and corporate investment. We demonstrate that corporate investment improves stock liquidity even after controlling for the feedback effect. Moreover, stock liquidity improves more apparently for firms with financial constraints because those firms are more likely to experience great risk shift. Finally, the effect of corporate investment is robust to equity financing, confirming that the liquidity improvement is attributable to the risk shift from corporate investment. JEL Classification: G14, G31 Keywords: Stock Liquidity, Corporate Investment, Financial Constraints - 2 -

3 1. Introduction Corporate investment decision can affect stock liquidity. As Berk, Green, and Naik (1999) demonstrate, optimal corporate investment decision has an impact on the risk of a stock. Moreover, a change in the risk affects the behavior of traders, whether informed or not. Therefore, a change in the trading behavior can lead to a change in stock liquidity. On the other hand, the literature also addresses that stock liquidity influences corporate investment. As a determinant of required returns (Acharya and Pedersen, 2005), stock liquidity expands the set of profitable investment opportunities (Becker-Blease and Paul, 2006). In this study, we analyze the interaction between corporate investment and stock liquidity and provide empirical evidence on the role of corporate investment in stock liquidity. Conforming to the existing investment literature, our research frame follows a panel data analysis with both firm and year fixed effects. Moreover, to control for the feedback effect, we estimate the portion of corporate investment orthogonal to stock liquidity for all the companies on the COMPUSTA and CRSP data set over the period of 1962 to Our empirical analysis shows that corporate investment indeed improves stock liquidity and that this effect still holds even after controlling for the feedback effect of stock liquidity on corporate investment. Specifically, we find that an increase in corporate investment leads to an increase in stock liquidity. Moreover, high stock liquidity is associated with high corporate - 3 -

4 investment, consistent with the literature. Finally, we observe that corporate investment, orthogonal to stock liquidity, indeed contributes to stock liquidity, confirming that corporate investment plays an important role in improving stock liquidity despite the feedback effect. Moreover, we also find that the effect of corporate investment is stronger for firms with financial constraints. We use three variables as a measure of financial constraints: the Kaplan and Zinglales (1997; KZ) index, the Whited and Wu (2006; WW) index, and asset size. Following Baker, Stein, and Wurgler (2003), we use the revised KZ index, a composite index based on cash flow, cash dividend, cash balance, and leverage. The WW index is also a composite index, consisting of cash flow, dividend dummy, leverage, beginning-year-of book asset, industry sales growth, and firm sales growth. Specifically, we classify a universe of stocks into two groups every year: High and Low and compare two groups of stocks. Our empirical analysis shows that the improvement of stock liquidity is more apparent for financiallyconstrained firms such as high-kz/ww and small firms. In our robustness check, we explore an alternative explanation for the corporate investment-stock liquidity relation. That is, firms sometimes raise equity capital to meet the financing need for new investment opportunities. Thus, one can raise the possibility that improved stock liquidity is attributable to an increase in outstanding shares from new equity financing, not corporate investment. Therefore, we control net equity financing in a dollar - 4 -

5 amount to see whether equity financing improves stock liquidity and find that corporate investment improves stock liquidity, regardless of equity financing. This finding confirms that a change in stock liquidity comes from a risk shift due to corporate investment, consistent with our conjecture. Our study shares with Gopalan, Kadan, and Pevsner (2012) the notion that a firm s corporate investment decision is related to stock liquidity. While Gopalan, Kadan, and Pevsner (2012) focus on the role of asset liquidity associated with investment decision, the current study emphasizes the risk shift of corporate investment contributing to stock liquidity. This paper is also related to a line of recent researches explaining stock return behaviors based on a real-option or investment-based approach. This strand of literature stems from Berk, Green, and Naik (1999). They argue that optimal investment choices change a firm s assets and growth options in predictable ways and that a firm s book-to-market, determinant variable of corporate investment, serves as a state variable summarizing the firm s risk relative to the scale of its asset base. The rest of the paper is organized as follows. Section 2 develops our testable hypotheses while Section 3 presents our research framework for the analysis and introduces a variety of stock liquidity measures. Section 4 discusses the data and presents our empirical analysis. Section 5 presents robust test results. Finally, Section 6 concludes the paper

6 2. Hypothesis Development We motivate the current research as follows. Stock liquidity is endogenously determined. That is, stock liquidity is governed by trade volume from different motives such as private information and liquidity. While both trade motives are influenced by the risk of underlying asset, the underlying risk is, on average, negatively associated with stock liquidity, as in Kyle (1985). In the meantime, corporate investment decreases the risk of a stock and therefore negatively relates to expected returns, producing the predictable relationship between book-to-market ratios and stock returns (Berk, Green, and Naik, 1999). 1 Thus, combining these two facts, we conjecture that corporate investment decision leads to a lower risk, which in turn improves stock liquidity. Moreover, this notion is also consistent with Eisfeldt (2004) suggesting that liquidity is pro-cycle. That is, high stock liquidity accompanies a positive economic condition which is characterized by a high marginal q, market-to-book ratio. In turn, a high marginal q leads to high corporate investment according to the corporate investment literature. 2 After all, corporate investment can contribute to stock liquidity. However, there is also the feedback effect of stock liquidity on corporate investment. When there is a positive shock on stock liquidity, the cost of equity decreases, which in turn expands the set of profitable investment opportunities (Becker-Blease and Paul, 2006). Stock 1 See Carlson, Fisher, and Giammarino (2004), Zhang (2005), and Liu, Whited, and Zhang (2009) for the literature. 2 See Hubbard (1998) for the classical corporate investment literature review

7 liquidity also affects the sensitivity of corporate investment to stock price through manager s information acquisition from stock price (Chen, Goldstein, and Jiang, 2007). Moreover, high stock liquidity reduces the cost of raising capital for investment through lower investment banker s fee for seasonal equity offerings (Butler, Grullon, and Weston, 2005). 3 Therefore, we empirically examine whether corporate investment indeed improves stock liquidity given the feedback effect of stock liquidity on corporate investment 4. Moreover, we also postulate that stock liquidity improves more apparently for firms with financial constraints. It is because those firms are more likely to experience apparent risk shift. As financial constraints prevent firms from financing all the desired investments, financiallyconstrained firms are less likely to respond to investment opportunities, as shown in Kaplan and Zinglales (1997) and Baker, Stein, and Wurgler (2003). Therefore, the fact that firms with financial constraints increase corporate investment suggests that those firms see value-enhancing investment opportunities. Accordingly, one can hypothesis that a given change in corporate investment leads to a greater change in the risk for firms with financial constraints (Li and Zhang, 2010). 3 The following is a partial list of studies linking corporate investment and stock markets: stock mispricing (Polk, Christopher, and Sapienza, 2009; Baker, Stein, and Wurgler, 2003), informative stock price (Foucault and Fresard, 2012; Bakke and Whited, 2010; Fang, Noe, and Tice, 2009; Khanna and Sonti, 2004; Subrahmanyam and Titman, 1999; Dow and Gorton, 1997). 4 Bond, Edmans, and Goldstein (2012) provide an excellent survey on the feedback effect of stock market to real economy

8 3. A model for stock liquidity and its feedback effect Following Gopalan et al. (2012), we estimate stock liquidity using a panel model with both firm fixed effects and time effects as follows, LIQ i, t RET 5 INV i i, t 1 t Q 6 0 i, t 1 i, t CF 7 LIQ o i, t i, t 1 i, t SIZE 1 i, t 1 PRC 2 i, t 1 TNV 3 i, t 1 VOL 4 i, t 1 (1) where LIQ i, t is firm i s stock liquidity in year t, i and t represent year and firm-fixed effects. The equation includes well-known control variables, such as lagged LIQ, SIZE (firm s capitalization), PRC (stock s price), TNV (stock s turnover), and VOL (lagged return volatility). Moreover, we also add RET (lagged return), Q (market-to-book ratio), and CF (cash flow) to capture the effect of corporate investment orthogonal to these variables. We employ three different stock liquidity measures: the Amihud (2002; AMH) illiquidity ratio, the Hasbrouck (2009; GBS) Gibbs sampler estimate, and the Fong, Holden, and Trzcinka (2011; FHT) measure. The AMH ratio is a cost-to-volume measure for liquidity. While the GBS estimate is Gibbs sampler estimate of the Roll(1988) s measure for the implicit bid-ask spread, the FHT measure is a cost-to-price measure for liquidity. Specifically, the AMH ratio is the average of a square root of daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. It is defined as follows

9 AMH i, t 1 D i, t D RET i, d VOLUME d 1 i, d (2) The GBS estimate is the implicit bid-ask spread, s, proposed in Roll (1984) and revised by Hasbrouck (2009). This measure is calculated as the square root of the negative daily autocorrelation of individual stock returns, that is, GBS COVR, R ) (3) i, t ( i, d i, d 1 Since the auto-covariance of stock returns is often positive, this measure is not well defined in many cases. To overcome this problem, Hasbrouck (2009) introduces a Gibbs sampler estimate of the Roll s measure. 5 On the other hand, the FHT measure combines two features of transaction costs: return volatility and the proportion of zero returns. 6 It is estimated as FHT i, t 2 i, t 1 1 Zerosi 2, t (4) 5 We obtain this measure from Joel Hasbrouck s Web site ( 6 For details, see Fong et al. (2011). The measure is similar to the LOT measure in Lesmond, Ogden, and Trzcinka (1999) and the LOT Y-split measure in Goyenko, Holden, and Trzcinka (2009). Recent studies such as Marshall, Nguyen, and Visaltanachoti (2012), and Edmans, Fang, and Zur (2013), have already used the FHT measure

10 where i, t is the standard deviation of firm i s daily returns in year t, 1 is a probit function, that is, the inverse cumulative distribution function, associated with the standard normal distribution and Zeros i,t is the proportion of firm i s zero returns, calculated as the number of zero-return days divided by the number of total trading days in year t. The use of Zeros i,t is based on the idea that a zero return arises because transactions costs deter marginal investors from trading, and thus the frequency of zero returns signals illiquidity. On the other hand, to address the feedback effect of stock liquidity on corporate investment (Becker-Blease and Paul, 2006; Chen et al., 2007), we employ the portion of corporate investment orthogonal to stock liquidity. That is, we model corporate investment and estimate the residuals of corporate investment based on Chen et al. (2007). Conforming to the existing corporate investment literature, Chen et al. (2007) uses the following baseline model, INV i, t i t 1Qi, t 1 2CFi, t 3LIQi, t 1 4Qi, t 1 LIQi, t 1 5CFi, t LIQi, t 1 i, t (5) where INV i, t is firm i s investment in year t, i and t represent year and firm-fixed effects. We use two different investment measures: capital expenditures (Compustat Annual Item 128) and the sum of capital expenditure and R&D expenses (Compustat Annual Item 46), both scaled by beginning-of-year book assets (Item 6)

11 Corporate investment increases with two economic fundamentals: investment opportunity, measured as Q, and financial constraints, measured as CF. Moreover, corporate investment is also affected by stock liquidity directly and indirectly. 7 As Eisfeldt (2004) argues, liquidity magnifies the effect of changes in productivity on corporate investment. Since higher liquidity makes long-term risky investment more attractive, corporate investment increases more when stock liquidity improves. 4. Corporate investment and stock liquidity 4.1 Data We use common stocks (share code of 10 or 11) on the COMPUSTAT and CRSP data set over the period of 1969 to Conforming to the investment literature (Bakke and Whited, 2010), the dataset excludes all the firms whose primary SIC classification is between 4900 and 4999 or between 6000 and 6999 because our corporate investment model is inappropriate for regulated or financial firms. We also include only those whose book equity is greater than 10million dollars. As a result, the sample contains total 107,528 firm-year observations. Table 1 reports descriptive statistics. AMH, the Amihud illiquidity ratio, varies from to While the GBS estimate is from to 0.049, FHT ranges from to While Chen et al. (2007) focus on information acquisition through a liquidity proxy, our study employs stock liquidity as a state variable affecting corporate investment

12 To minimize the effect of outliers, we winsorize corporate investment variables, CAP and CRD, values outside the and fractiles equal to these fractiles. Q is defined as the market value of equity plus book value of assets minus book value of equity, scaled by beginning-ofyear assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. We use several control variables. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. Table 2 presents correlations among variables. We find that all the stock liquidity measures are highly correlated with each other (i.e. correlations of 0.662, 0.743, and 0.860). Moreover, consistent with the liquidity literature, stock liquidity measures are very persistent based on unreported results. For example, while AMH shows a serial correlation of 0.811, GBS and FHT exhibit a similar magnitude with correlations of and On the other hand, we observe that corporate investment variables are strongly related to each other (i.e. a correlation of 0.617) and that they are weakly related to stock liquidity, however. While a correlation between AMH and CAP (CRD) is (-0.106), a correlation with GBS (FHT) is (-0.017) for CAP and (-0.028) for CRD

13 4.2 The effect of corporate investment on stock liquidity Corporate investment decision is often evaluated in a real options context because the decision to invest changes the ratio of growth options to assets in place. Thus, option exercise can change the risk of a firm in various ways. As a result, the risk of a firm relates to current and historical investment decisions of the firm (Carlson et al., 2004). On the other hand, a change in the risk of a firm also affects the trade motive of investors, leading to a change in stock liquidity. In this section, we examine a direct relation between corporate investment and stock liquidity in a panel analysis after controlling for year and firm-fixed effects. Specifically, we analyze the relation based on three stock liquidity measures: AMH, GBS, and FHT. Moreover, confirming to the corporate investment literature, we also employ two corporate investment measures normalized by a firm s asset: CAP and CRD. Table 3 presents the empirical analysis based on nominal corporate investment. Our findings are summarized as follows. First, lagged stock liquidity is strongly related to current stock liquidity, which is not surprising. This result just confirms that stock liquidity is persistent. Second, large firms and high trade activity are associated with high stock liquidity while we find mixed results for stock price or return volatility. Third, both lagged returns and current cash flows positively relate to stock liquidity. Finally, we find that corporate investment positively affects stock liquidity. That is, both corporate investment measures, CAP and CRD, significantly

14 contribute to stock liquidity for all the three stock liquidity measures. Given the well-known determinants for stock liquidity and both year and firm-fixed effects, this finding is interesting. Overall, primary panel data analysis shows that corporate investment seems to decrease the risk of a firm which, in turn, increases stock liquidity. However, we need to control for the feedback effect of stock liquidity on corporate investment, as addressed shortly in the next analysis. 4.3 The feedback effect of stock liquidity on corporate investment The liquidity literature addresses that stock liquidity affects corporate investment through several channels. For example, Becker-Blease and Paul (2006) argue that exogenous stock liquidity shock leads to an increase in firms profitable investment opportunities by showing that addition to the Standard and Poor s 500 Index enhances firm s growth opportunities. On the other hand, Butler et al. (2005) find that high stock liquidity reduces the cost of raising capital for investment by demonstrating that SEO investment banks fees are significantly lower for firms with more liquid stock. Finally, Chen et al. (2007) also observe the information role of stock liquidity in determining corporate investment. In this section, we start by investigating the feedback effect of stock liquidity on corporate investment. Table 4 presents the panel data analysis on corporate investment using

15 Equation (5). Consistent with the literature, stock liquidity positively affects corporate investment while the interaction with other variables shows mixed evidence. In the next step, we control the feedback effect. Specifically, we employ the portion of corporate investment orthogonal to stock liquidity. That is, we model corporate investment and estimate the residuals of corporate investment based on Chen et al. (2007). Table 5 exhibits our analysis based on corporate investment orthogonal to liquidity. Our findings are similar to those in Table 3. More importantly, we confirm that corporate investment indeed improves stock liquidity even after controlling for the feedback effect. Specifically, the coefficients for all the three liquidity measures are still significant for CAP or CRD. Taken together, empirical evidence in Table 5 leads us to conclude that corporate investment indeed contributes to stock liquidity significantly The interaction effect with financial constraints We also postulate that stock liquidity improves more apparently for firms with financial constraints. It is because those firms are more likely to experience apparent risk shift. As financial constraints prevent firms from financing all the desired investments, financiallyconstrained firms are less likely to respond to investment opportunities, as shown in Kaplan and Zinglales (1997) and Baker, Stein, and Wurgler (2003). Therefore, the fact that firms with

16 financial constraints increase corporate investment suggests that those firms see value-enhancing investment opportunities. Accordingly, one can hypothesis that a given change in corporate investment leads to a greater change in the risk for firms with financial constraints (Li and Zhang, 2010). We use three variables as a measure of financial constraints: the Kaplan and Zinglales (1997; KZ) index, the Whited and Wu (2006; WW) index, and asset size. Following Baker, Stein, and Wurgler (2003), we use the revised KZ index, a composite index based on cash flow (CF), cash dividend (DIV), cash balance (CB), and leverage (LEV), normalized by beginning-of-year book asset. It is defined as follows. KZ i, t 1.002CFi, t DIVi, t 1.315CBi, t LEVi, t (6) The WW index is also a composite index, consisting of cash flow (CF), dividend dummy (DDIV), leverage (LEV), logarithm of beginning-year-of book asset (LTA), industry sales growth (ISG), and firm sales growth (SG). It is estimated as follows. WW i, t 0.091CFi, t 0.062DDIVi, t 0.021LEVi, t 0.044LTAi, t 0.102ISGi, t SG i, t (7)

17 Specifically, we classify a universe of stocks into two groups every year based on financial constraints and compare the sensitivity of stock liquidity to corporate investment. Tables 6 to 8 present the analysis sorted on KZ, WW, and asset size, respectively. Indeed, our analysis shows that the effect of corporate investment is stronger for firms with financial constraints. 8 That is, stock liquidity improves more apparently for high-kz/ww index-scored and small firms. Taken together, along with the main hypothesis shown in Section 4.3, the current analysis strengthens our interpretation on the role of corporate investment. That is, corporate investment changes the risk of stocks. Moreover, when firm is financially constrained, a decrease in the risk is more apparent, which boosters the liquidity motive of traders and leads to a greater improvement in stock liquidity. 5. Robustness check In this section, we investigate whether the effect of corporate investment is explained by any alternative economic force. Specifically, we examine if equity financing accompanied by corporate investment decisions contributes to improved stock liquidity. The reason is as follows. Equity financing can often accompany corporate investment. Moreover, an increase in equity 8 The only exception is for asset size-based sorting with CRD measure in Table

18 capital itself can lead to an increase in stock liquidity, as shown in Eckbo, Masulis, and Norli (2000). Thus, someone might suspect that an increase in stock liquidity is attributable to equity financing, not corporate investment decision. To exclude this possibility, we conduct a robustness test by classifying stocks into two groups based on equity financing. Table 9 presents the analysis based on equity financing. Overall, we find that the effect of corporate investment is significant regardless of equity financing. For example, corporate investment improves stock liquidity for firms with no equity financing as well as firms with equity financing. Interestingly, if any, the effect of corporate investment is more apparent for the former, not the latter. Taken together, this analysis confirms that corporate investment indeed improves stock liquidity and that the effect is robust to equity financing. Thus, we can conclude that improved stock liquidity is attributable to the risk shift by corporate investment, not equity financing. 6. Conclusions Corporate investment has an impact on the risk of a stock which, in turn, affects stock liquidity. Moreover, stock liquidity also influences corporate investment. This study analyzes the interaction between stock liquidity and corporate investment. Using the panel data analysis with year and firm-fixed effects, we show that corporate

19 investment improves stock liquidity. Moreover, to control for the feedback effect, we employ the portion of corporate investment orthogonal to stock liqudity and still confirm the effect of corporate investment on stock liquidity even after controlling for the feedback effect. We also find the interaction of corporate investment with financial constraints by showing that stock liquidity improves more apparently for financially-constrained firms. This is because those firms are more likely to experience great risk shift. Finally, our analysis shows that the effect of corporate investment is robust to equity financing, suggesting that improved stock liquidity is attributable to the risk shift from corporate investment, not equity financing. On the other hand, we would like to point out the following. The effect of corporate investment on stock liquidity complicates managerial investment decision, since their investment decision is likely to affect future investment opportunities through stock liquidity. Therefore, when firm s managers make an investment decision, they need to consider not only the current investment opportunity but also how their investment decision changes future stock liquidity which in turn affects further investment opportunities. Thus, this dynamic relation between corporate investment and stock liquidity opens a fruitful venue for future research

20 7. References 1. Acharya, Viral and Lasse H. Pedersen, 2005, Asset pricing with liquidity risk, Journal of Financial Economics 77, Amihud, Yakov, 2002, Illiquidity and stock returns: cross-section and time-series effects, Journal of Financial Markets 5, Baker, Malcolm, Jeremy Stein, and Jeffery Wurgler, 2003, When does the market matter? stock prices and the investment of equity dependent firms, Quarterly Journal of Economics 118, Bakke, T. and Toni Whited, 2010, Which firms follow the market? an analysis of corporate investment decisions. Review of Financial Studies 23, Berk, Jonathan, Richard C. Green, and Vasant Naik, 1999, Optimal investment, growth options, and security returns, Journal of Finance 54, Bond, Philip, Alex Edmans, and Itay Goldstein, 2012, The real effects of financial markets, Annual Review of Financial Economics 4, Becker-Blease, John and Donna Paul, 2006, Stock liquidity and investment opportunities: evidence from index additions, Financial Management 35, Butler, A., G. Grullon, and J. Weston, 2005, Stock market liquidity and the cost of issuing equity, Journal of Financial and Quantitative Analysis 40, Carlson, Murray, Adlai Fisher, and Ron Giammarino, 2004, Corporate investment and asset price dynamics: Implications for the cross-section of returns, Journal of Finance, Chen, Q., Itay Goldstein, and Jiang Wang, 2007, Price informativeness and investment sensitivity to stock price, Review of Financial Studies 20,

21 11. Dow, J. and G. Gorton, 1997, Stock market efficiency and economic efficiency: Is there a connection? Journal of Finance 52, Durnev, A., R. Morck, and B. Yeung, 2004, Value-enhancing capital budgeting and firmspecific stock return variation, Journal of Finance 59, Eckbo, B. E., R.W. Masulis, and O. Norli, 2000, Seasoned public offerings: resolution of the new issues puzzle, Journal of Financial Economics 56, Edmans, A., Vivian Fang, Emanuel Zur, 2013, The effect of liquidity on governance, Review of Financial Studies 26, Eisfeldt, L. Andrea, 2004, Endogenous liquidity in asset markets, Journal of Finance 59, Fang, V.W., Thomas Noe, and S. Tice S, Stock market liquidity and firm value. Journal of Financial Economics. 94, Fong, K., Craigh Holden, and Charles Trzcinka, 2013, What are the best liquidity proxies for global research? SSRN working paper. 18. Foucault, T. and L. Fresard, 2012, Cross-listing, investment sensitivity to stock price and the learning hypothesis, Review of Financial Studies 25, Gopalan, R., O. Kadan, and M. Pevzner, 2012, Asset liquidity and stock Liquidity, Journal of Financial and Quantitative Analysis 47, Goyenko, R., Craig Holden, and Charles Trzcinka, 2009, Do liquidity measures measure liquidity? Journal of Financial Economics 92, Hasbrouck, J., 2009, Trading costs and returns for U.S. equities: estimating effective costs from daily data, Journal of Finance 64, Hubbard, Glenn, 1998, Capital market imperfections and investment, Journal of Economic Literature 36, Kaplan, Steven and Luigi Zingales, 1997, Do investment-cash flow sensitivities provide - 2 -

22 useful measures of financing constraints? Quarterly Journal of Economics 112, Khanna, N. and R. Sonti, 2004, Value creating stock manipulation: feedback effect of stock prices on firm value, Journal of Financial Markets 7, Kyle, Albert, 1985, Continuous auctions and insider trading, Econometrica 53, Lesmond, David, Joseph Ogden, and Charles Trzcinka, 1999, A new estimate of transaction costs, Review of Financial Studies 12, Liu, Laura, Toni Whited, and Lu Zhang, 2009, Investment-based expected stock returns, Journal of Political Economy 117, Li, D., and L. Zhang, 2010, Does Q-theory with investment frictions explain anomalies in the cross-section of returns? Journal of Financial Economics 98, Livdan D., H. Sapriza, and L. Zhang, 2009, Financially constrained stock returns, Journal of Finance 64, Polk, Christopher, and Paola Sapienza, 2009, The stock market and corporate investment: a test of catering theory, Review of Financial Studies 22, Roll, Richard, 1988, R 2, Journal of Finance 43, Subrahmanyam A. and S. Titman, 1999, The going public decision and the development of financial markets, Journal of Finance 54, Zhang, Lu, 2005, The Value Premium, Journal of Finance 60,

23 Table 1. Descriptive Statistics This table presents descriptive statistics for the panel data set. AMH is the average of a square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two. CAP (or CRD) is capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. Q is the market value of equity plus book value of assets minus book value of equity, scaled by beginning-of-year assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. The sample spans 1962 to Mean Std. Dev. 1 percent Median 99 percent AMH GBS FHT CAP CRD Q CF SIZE PRC TNV VOL RET

24 Table 2. Correlations This table presents correlations among variables. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two. CAP (or CRD) is the capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. Q is the market value of equity plus book value of assets minus book value of equity, scaled by book value of assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. SIZE is the firm size defined as the logarithm of capitalization in year t- 1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. The sample spans 1962 to FHT GBS CAP CRD Q CF SIZE PRC TNV VOL RET AMH GBS FHT CAP CRD Q CF SIZE PRC TNV VOL 0.031

25 Table 3. The Effect of Corporate Investment on Stock Liquidity This table presents the analysis for the panel data with both firm and year effects. The dependent variable is AMH/GBS/FHT. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two in year t. INV is CAP (or CRD), capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. LLIQ is AMH/GBS/FHT in year t-1. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. Q is the market value of equity plus book value of assets minus book value of equity, scaled by beginning-of-year assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. The t-values are presented in parentheses. The sample spans 1962 to CAP CRD AMH GBS FHT AMH GBS FHT INV (-14.25) (-10.31) (-8.40) (-15.50) (-10.70) (-11.99) LLIQ (175.9) (135.51) (187.15) (175.34) (135.40) (186.61) SIZE (-27.03) (-11.52) (-26.25) (-26.34) (-10.89) (-26.16) PRC (5.11) (-9.40) (0.62) (3.37) (-10.89) (-0.31) TNV (-9.45) (-6.28) (-4.74) (-8.91) (-5.76) (-4.27) VOL (-1.14) (21.60) (-10.17) (-0.57) (21.95) (-9.70) RET (-45.80) (-39.69) (-52.46) (-46.07) (-39.94) (-52.58) Q (0.35) (2.35) (-0.78) (0.99) (2.83) (-0.19) CF Within R- Squared(%) (-22.50) (-26.51) (-25.42) (-25.33) (-28.25) (-27.41)

26 Table 4. The Effect of Stock Liquidity on Corporate Investment This table presents the analysis for the panel data with both firm and year effects. The dependent variable is INV, where INV is CAP (or CRD), capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. Q is the market value of equity plus book value of assets minus book value of equity, scaled by book value of assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. LLIQ is AMH/GBS/FHT in year t-1. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two in year t. The t-values are presented in parentheses. The sample spans 1962 to CAP CRD AMH GBS FHT AMH GBS FHT Q (12.01) (9.70) (7.40) (24.23) (21.53) (21.94) CF (11.64) (12.88) (11.88) (-59.65) (-44.18) (-57.73) LLIQ (-10.87) (-14.64) (-13.84) (-17.76) (-2.20) (-16.08) Q*LLIQ (7.36) (-0.21) (14.71) (10.18) (-0.75) (12.42) CF*LLIQ (8.14) (-1.67) (7.13) (3.80) (-2.02) (2.41) Within R- Squared(%)

27 Table 5. The Effect of Unexpected Corporate Investment on Stock Liquidity This table presents the analysis for the panel data with both firm and year effects. The dependent variable is AMH/GBS/FHT. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two in year t. UINV or EINV is unexpected or expected CAP (or CRD), capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. LLIQ is AMH/GBS/FHT in year t-1. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. Q is the market value of equity plus book value of assets minus book value of equity, scaled by beginning-of-year assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. The t-values are presented in parentheses. The sample spans 1962 to Panel A: CAP AMH GBS FHT UINV (-12.29) (-13.20) (-10.47) (-10.25) (-7.19) (-7.42) EINV (-55.98) (27.14) (-23.24) LLIQ (176.13) (134.14) (135.78) (43.07) (187.16) (170.04) SIZE (-26.74) (-30.26) (-11.54) (-11.90) (-26.07) (-27.78) PRC (4.72) (5.84) (-9.37) (-10.33) (0.39) (0.01) TNV (-9.39) (-13.63) (-6.28) (-5.94) (-4.75) (-4.47) VOL (-1.18) (3.51) (21.6) (22.5) (-10.16) (-10.11) RET (-45.92) (-42.44) (-39.69) (-39.79) (-52.57) (-50.00) Q (-0.33) (45.33) (1.87) (-26.68) (-1.19) (15.09) CF Within R- Squared(%) (-23.16) (34.31) (-27.06) (-29.89) (-25.80) (-0.93)

28 Panel B: CRD AMH GBS FHT UINV (-13.92) (-14.39) (-10.93) (-11.06) (-11.14) (-11.32) EINV (-46.99) (30.68) (-21.54) LLIQ (176.08) (81.37) (135.50) (107.65) (187.04) (139.71) SIZE (-26.20) (-28.33) (-10.90) (-11.63) (-26.07) (-27.48) PRC (3.28) (2.31) (-10.88) (-11.82) (-0.35) (-0.75) TNV (-8.89) (-14.01) (-5.75) (-5.34) (-4.31) (-3.88) VOL (-0.67) (4.28) (21.96) (23.00) (-9.72) (-9.93) RET (-46.16) (-41.62) (-39.93) (-39.63) (-52.66) (-50.02) Q (-0.42) (43.94) (1.76) (-30.46) (-1.32) (19.32) CF Within R- Squared(%) (-22.70) (-50.14) (-26.64) (29.72) (-25.54) (-26.07)

29 Table 6. Financial Constraints and the Corporate Investment-Liquidity Pattern for KZ index This table presents the analysis for the panel data with both firm and year effects. The dependent variable is AMH/GBS/FHT. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two in year t. UINV is unexpected CAP (or CRD), capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. LLIQ is lagged AMH/GBS/FHT in year t-1. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. Q is the market value of equity plus book value of assets minus book value of equity, scaled by beginning-of-year assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. The coefficients (excluding LGBS/LFHT) on Panel B/C are multiplied by The t-values are presented in parentheses. The sample spans 1962 to Panel A: AMH CAP CRD High Low Diff High Low Diff UINV (-8.87) (-6.68) (-3.45) (-9.99) (-7.63) (-4.94) LLIQ (117.03) (137.18) (-0.05) (117.10) (137.02) (0.07) SIZE (-15.84) (-24.36) (-1.36) (-15.51) (-24.11) (-1.21) PRC (2.16) (6.10) (-1.25) (1.36) (5.17) (-1.47) TNV (-5.93) (-8.77) (-0.37) (-5.46) (-8.57) (-0.09) VOL (-0.22) (-2.44) (1.06) (0.15) (-2.15) (1.23) RET (-32.45) (-34.18) (-10.66) (-32.53) (-34.37) (-10.62) Q (-0.95) (-1.80) (-0.28) (-0.69) (-1.93) (0.04) CF Within R- Squared(%) (-15.98) (-19.33) (-6.55) (-15.29) (-19.24) (-5.96) No. of Obs. 46,812 48,175 46,812 48,175 6

30 Panel B: GBS CAP CRD High Low Diff High Low Diff UINV (-8.71) (-3.32) (-4.62) (-9.57) (-2.78) (-6.45) LLIQ (88.13) (107.51) (-3.29) (87.93) (107.47) (-3.52) SIZE (-6.97) (-9.08) (-0.60) (-6.56) (-8.85) (-0.40) PRC (-7.63) (-4.84) (-3.49) (-8.59) (-5.47) (-3.92) TNV (-3.34) (-6.12) (0.92) (-2.80) (-5.98) (1.27) VOL (14.26) (16.16) (2.36) (14.60) (16.22) (2.58) RET (-28.03) (-28.46) (-7.22) (-28.13) (-28.57) (-7.23) Q (-0.36) (0.78) (-0.69) (-0.08) (0.72) (-0.39) CF Within R- Squared(%) (-20.06) (-19.46) (-8.12) (-19.39) (-19.37) (-7.59) No. of Obs. 41,534 42,529 41,534 42,529 7

31 Panel C: FHT CAP CRD High Low Diff High Low Diff UINV (-7.02) (0.99) (-6.23) (-9.48) (-1.84) (-7.57) LLIQ (116.51) (166.84) (-15.14) (116.47) (166.85) (-15.28) SIZE (-15.36) (-21.16) (-2.98) (-15.27) (-21.54) (-2.72) PRC (-5.27) (10.32) (-9.70) (-5.94) (10.79) (-10.52) TNV (-2.97) (-2.87) (-1.11) (-2.50) (-2.85) (-0.71) VOL (-5.03) (-13.90) (2.41) (-4.65) (-13.79) (2.70) RET (-34.97) (-42.19) (-10.01) (-34.96) (-42.19) (-9.99) Q (-3.54) (-2.15) (-2.91) (-3.28) (-2.23) (-2.60) CF Within R- Squared(%) (-18.72) (-21.08) (-9.03) (-18.08) (-21.26) (-8.35) No. of Obs. 46,800 48,166 46,800 48,166 8

32 Table 7. Financial Constraints and the Corporate Investment-Liquidity Pattern for WW index This table presents the analysis for the panel data with both firm and year effects. The dependent variable is AMH/GBS/FHT. AMH is the average of the square root of the Amihud s (2002) daily liquidity measure which is an absolute daily return scaled by daily dollar trade volume in year t. GBS is the Hasbrouck(2009) s Gibbs sampler estimate (divided by 100) of the Roll(1988) s measure for the implicit bid-ask spread in year t. FHT is the product of the standard deviation of daily stock return and the probit function of (1+the proportion of zero returns)/2 in year t, multiplied by two in year t. UINV is unexpected CAP (or CRD), capital expenditure (plus R&D) scaled by beginning-of-year assets in year t. LLIQ is lagged AMH/GBS/FHT in year t-1. SIZE is the firm size defined as the logarithm of capitalization in year t-1. PRC is the logarithm of a stock price in year t-1. TNV is the average of the logarithm of daily turnover in year t-1. VOL is the standard deviation of daily stock return in year t-1. RET is a stock return over year t-1. Q is the market value of equity plus book value of assets minus book value of equity, scaled by beginning-of-year assets in year t-1. CF is the sum of net income before extraordinary item and depreciation and amortization expenses, scaled by beginning-of-year assets in year t. The coefficients (excluding LGBS/LFHT) on Panel B/C are multiplied by The t-values are presented in parentheses. The sample spans 1962 to Panel A: AMH CAP CRD High Low Diff High Low Diff UINV LLIQ SIZE PRC TNV VOL RET Q CF Within R- Squared(%) No. of Obs. 46,402 47,393 46,402 47,393 9

33 Panel B: GBS CAP CRD High Low Diff High Low Diff UINV LLIQ SIZE PRC TNV VOL RET Q CF Within R- Squared(%) No. of Obs. 40,415 42,833 40,415 42,833 10

34 Panel C: FHT CAP CRD High Low Diff High Low Diff UINV LLIQ SIZE PRC TNV VOL RET Q CF Within R- Squared(%) No. of Obs. 46,398 47,381 46,398 47,381 11