Stock Market Liquidity and Firm Performance: Wall Street Rule or Wall Street Rules? 1

Transcription

1 Stock Market Liquidity and Firm Performance: Wall Street Rule or Wall Street Rules? 1 Vivian W. Fang Thomas H. Noe Sheri Tice Tulane University Tulane University Tulane University First Draft: November 2006 This Draft: January 2008 ABSTRACT This paper investigates the relation between stock liquidity and firm performance. We find that firms with liquid stocks have better firm performance as measured by the market-to-book ratio. This result holds even when we include industry or firm fixed effects, control for idiosyncratic risk, control for endogenous liquidity with instrumental variables, or use alternative measures of liquidity. To identify the causal effect of liquidity on firm performance, we study an exogenous shock to liquidity---the decimalization of stock trading---and document that the increase in liquidity around decimalization improved firm performance. We next investigate the causes of liquidity s beneficial effect and find support for liquidity enhancing performance by increasing the information content of market prices, and strengthening the incentive effects of performance based compensation contracts. We find no evidence that liquidity enhances blockholder intervention. Finally, momentum trading, analyst coverage, investor overreaction and liquidity s valuation effects do not appear to drive our results. Key Words: Stock Market Liquidity; Firm Performance; Feedback Mechanism; Managerial Compensation; Blockholder Intervention. 1. Introduction 1 We welcome all comments. Fang, Noe and Tice are at the A.B. Freeman School of Business, Tulane University, New Orleans, LA, vfang@tulane.edu (Vivian Fang), tnoe@tulane.edu (Thomas Noe) and stice@tulane.edu (Sheri Tice). We thank Andrew Metrick for providing GIM index data with CRSP PERMNO numbers. We also thank Naveen Khanna, Zhanhui Chen, Vladimir Gatchev, Laura Li, Donald Monk, Onur Bayar, Prasun Agarwal, seminar participants at Tulane University and seminar participants at the 2007 FMA Doctoral Student Seminar for useful comments and suggestions. All errors are our own. 1

2 There are strong theoretical reasons to suspect that market liquidity will positively affect firm performance. Because stock shares are the currency which commands both cash flow and control rights, the tradability of this currency plays a central role in the governance, valuation, and performance of firms. In theoretical analyses, liquid markets have been shown to permit non-blockholders to intervene and become blockholders (Maug (1998)), facilitate the formation of a toehold stake (Kyle and Vila (1991)), promote more efficient management compensation (Holmstrom and Tirole (1993)), and stimulate trade by informed investors, thereby improving investment decisions by making share prices more informative (Subrahmanyam and Titman (2001) and Khanna and Sonti (2004)). Thus, a priori, a positive relation between liquidity and performance is quite plausible. However, despite the plethora of theoretical papers that have implications related to liquidity s effect on performance, empirical researchers have not made this relation the centre of systematic empirical investigation. Our paper aims to fill this lacuna by examining whether and why liquidity affects firm performance. First, we find that stocks with high liquidity have better performance as measured by the firm market-to-book ratio. We then break the market-to-book ratio into the following components: price-to-operating earnings ratio; leverage ratio; and operating return on assets ratio. 2 We find that more liquid stocks have higher operating returns on their assets and more equity in their capital structure. In contrast, their price-to-operating earnings ratios are similar to less liquid stocks. These results hold when we control for industry and firm fixed effects, the level of shareholder rights, momentum, idiosyncratic risk, and endogeneity using an instrumental variables procedure to instrument for liquidity. They also hold using an alternative measure of liquidity. Furthermore, we document that the increase in liquidity, caused by an exogenous shock due to decimalization, improved firm performance. Having established a relation between liquidity and performance, we turn to the problem of identifying its cause. We find that the positive effect of liquidity on firm performance is the greatest for liquid stocks with high business uncertainty (high 2 We define the firm market-to-book ratio as (Vd + Ve)/(Assets). The components of the market-to-book ratio are: (Vd + Ve)/(Assets) = [(Vd + Ve)/Op. Income] [Op. Income /Assets] = [Ve/Op Income] [(Ve + Vd)/Ve] [Op. Income/Assets] = (Price to Op. Income Ratio) (Leverage Ratio) (Operating Income to Assets). 2

3 operating income volatility or high R&D intensity). This finding supports the feedback theory as Subrahmanyam and Titman (2001) show that feedback is more important when the relationship between stakeholders and the firm is fragile or there is high cash flow uncertainty with respect to existing projects. Since the feedback mechanism does not rely on a reduction in the shareholder/manager agency conflicts, shareholder rights should not be a complement to stock price feedback. In support, we find that high stock liquidity improves firm performance for all shareholder rights quintiles. We conclude that stock liquidity improves firm operating performance through a feedback effect mechanism as proposed in Subrahmanyam and Titman (2001). We also find that pay-for-performance sensitivity and stock-market liquidity are complements. This is consistent with Holmstrom and Tirole (1993), and suggests that liquidity, by increasing signal-to-noise ratio in stock prices, increases reliance on stock based compensation. We do not find support for any of the other agency based operating performance theories. Moreover, more liquid stocks price-to-operating earnings ratios are similar to those of less liquid stocks. Hence, liquidity does not appear to improve firm performance through its effect on manager myopic or hypermetropic investment selection. A trade-off of current profits with long-term prospects should result in different price-to-operating earnings ratios for firms with liquid stocks. We also do not find any evidence that liquidity improves firm performance through blockholder intervention as the relation between liquidity on firm performance is similar for stocks with high and low levels of outside blockholdings as well as for stocks with high and low levels of institutional holdings. In summary, liquidity s positive effect on performance appears to stem from improving the compensation and investment policies of corporate insiders rather than by facilitating market discipline of insiders. Valuation effects of liquidity are a possible alternative explanation for our results as prior empirical work finds a negative correlation between stock liquidity and stock returns. 3 The most prevalent explanation given for a negative correlation between stock liquidity and returns is that illiquid stocks have higher transaction costs or a higher sensitivity to a liquidity risk factor (see, for instance, Amihud and Mendelsen (1986) and 3 See for example Stoll and Whaley (1983), Amihud and Mendelson (1986), Brennan and Subrahmanyam (1996), Chalmers and Kadlec (1998), Pastor and Stambaugh (2003), and Hasbrouck (2005). 3

4 Acharya and Pedersen (2005)). If liquid stocks have lower expected returns they will trade at a premium all else held constant. 4 More recently, Baker and Stein (2004) suggest that liquidity could be a sentiment indicator. In their model high liquidity stocks are overvalued which is why they trade at a premium and have lower expected returns in the future. However, if higher valuations of firms with more liquid stocks is based only on lower expected returns, we should see higher price-to-operating income ratios for high liquidity stocks but similar financial leverage and operating profitability for high and low liquidity stocks. Since on average, liquid stocks have similar price-to-operating income ratios as less liquid stocks but different financial leverage and profitability ratios, illiquidity risk and sentiment do not appear to be explanations for our results. 5 We conclude that stock liquidity improves firm performance through a feedback effect where liquidity stimulates the entry of informed investors who make prices more informative to stakeholders. Liquidity also improves firm performance by increasing the efficiency of manager pay-for-performance sensitivity. The paper outline is as follows: In section 2, we review prior work and discuss the various ways in which stock market liquidity might affect governance and thus firm performance. Section 3 describes our sample, data sources, and variable measurements. Section 4 contains our empirical tests while section 5 concludes. 2. Liquidity and Firm Performance The relation between liquidity and performance has received considerable attention in financial economics from a variety of perspectives. Researchers have considered both the effect of liquidity on performance as well as the dependence of liquidity on performance. The causative theories advance many distinct mechanisms through which liquidity affects performance. Most focus on the effect of liquidity on operating performance and 4 Since firm performance is typically measured with Tobin s Q (proxied by the market value of equity plus the book value of debt standardized by total assets), a firm will most likely exhibit a higher firm market-tobook ratio if its equity trades at a premium. 5 There is some support for investor overreaction or illiquidity risk within the sub-sample of large cap stocks. Within the large-cap tercile more liquid stocks have a higher price to operating income ratio than less liquid stocks. 4

5 are agency-based causative theories. Important theories in this vein include Maug (1998) which models a large relationship investor s monitoring decision. The investor monitors and trades with an aim to profit from the price appreciation caused by his monitoring activities. Maug concludes that liquid stock markets, far from being a hindrance to corporate control, tend to support effective corporate governance. Another causal mechanism through which liquidity may discipline management is identified in Admati and Pfleiderer (2006) and Palmiter (2002)---If management s compensation is tied to current stock prices, then increased liquidity increases the cost of opportunism to managers by facilitating informed selling or dumping. 6 The distinguishing characteristic of all of these causative agency theories is that they predict that the effect of liquidity on performance will be proportional to the extent of the agency conflict within the firm. In contrast to the agency-based causative theories, Subrahmanyam and Titman (2001) show liquidity can positively affect firm cash flows even when agency conflicts are absent. In this setting liquidity stimulates the entry of informed investors who make prices more informative to stakeholders. Their decision to stay or go has an impact on firm cash flows. This is particularly valuable when the relationship between stakeholders and the firm is fragile or there is high cash flow uncertainty with respect to existing projects. This is because positive cascades (success or good news begets more success) will be most valuable in this setting. Furthermore, as shown in Khanna and Sonti (2004), informed traders factor the effect of their trades on managerial behavior into their trading strategy, trading more aggressively, and thus making prices more informative. This feedback effect improves operating performance and relaxes financial constraints. Both effects can increase firm performance. Feedback theories imply that the effect of liquidity is proportional to the sensitivity of firm operations to the information content of stock prices. While many models focus on the positive role of liquidity in resolving manager/shareholder agency problems, other researchers have noted potential adverse effects of market liquidity on agency problems within the firm. Edmans (2007) shows 6 See Holmstrom and Tirole (1993), Kyle and Vila (1991) and, Attari, Bannerjee and Noe (2006) for additional arguments for how liquidity can reduce the costs of insider/outsider agency problems through increasing the threat of activism or increasing the incentive effects of compensation contracts. 5

6 that liquidity, by facilitating informed trade, can lead managers to be less myopic. Also, as Coffee (1991) and Bhide (1993)) note, liquidity, as well as being a lubricant for share purchases by outside activists also facilitates exit by current blockholders who are potentially activists. Goldstein and Guembel (2002) show that negative feedback trading is also possible where informed investors exploit liquidity with short-selling strategies that harm firm performance. Both agency-based and feedback-based causative theories focus on the effect of liquidity on operating performance. However, liquidity might also affect firm value by changing the discount rate. If the marginal investor values liquidity as in Holmstrom and Tirole (2001), then illiquid stocks should trade at a discount. This implies a positive relation between stock liquidity and market-price based performance measures such as Tobin s Q. More recently, Baker and Stein (2004) suggest that liquidity might be related to valuation as a sentiment indicator. In their model, high liquidity stocks are overvalued. Since they trade at a premium they have lower future expected returns. Valuation theories are plausible and predict that the effect of liquidity on performance should be via the markets valuation or discounting of firm operating cash flows rather than via firm cash flows per se. In summary, causative theories are either operating-performance based, asserting that liquidity improves operating performance or valuation based, asserting that the performance effect stems from liquidity s effect on the valuation of cash flows. Operating performance theories, in turn, can be divided into agency or feedback theories. A further complication is an assumption of the causative role of liquidity on firm performance may be premature. Liquidity may be correlated with other variables that affect firm value. For example, Speigel and Wang (2005) show that including stock idiosyncratic risk along with liquidity in equations that predict stock returns renders liquidity insignificant. Similarly, a strong case can be made for liquidity being the dependent variable in the liquidity/performance relation rather than the independent variable. The logic supporting dependent liquidity is that high performance firms will have high market-to-book ratios and high market-to-book ratios may attract institutional investors. For example, Gutierrez and Pirinsky (2007) find that institutions chase relative returns as they tend to buy cross-sectional return winners. They show that on average, 6

7 these stocks tend to have high market-to-book ratios. Such trades increase market depth and thus augment stock liquidity. Thus high firm performance generates liquidity by producing institutional investor demand. Under this theory of dependent liquidity, the relation between liquidity and performance should be driven by those manifestations of high performance that are most attractive to institutional investors. Empirical tests need to be designed to account for both a causative and dependent role for liquidity. 3.1 Sample Selection 3. Data We collect our data from several databases. We obtain daily and monthly stock return data from Center for Research in Security Prices (CRSP), shareholder rights data from Investor Responsibility Research Center (IRRC), firm financials data from Compustat industry annual file, analyst coverage data from I/B/E/S, institutional holdings data from CDA/Spectrum Institutional Holdings database, managerial compensation data from Executive Compensation, and Fama French factors and blockholder ownership data through WRDS Variable Construction Table 1 presents the definition and summary statistics of each variable we use in our study. The variables are defined and discussed below. For brevity, we do not describe summary statistics for all variables. When constructing variables, missing daily returns were deleted from the sample and missing financials were either deleted or set to zero. 8 Following Lesmond (2005), if the number of missing daily returns and zero returns exceeds 80% of the annual trading days 7 For details on the construction of the Blockholder database, please see Dlugosz, Jen, Ruediger Fahlenbrach, Paul A. Gompers, and Andrew Metrick, 2006, Large Blocks of Stock: Prevalence, Size, and Measurement, Journal of Corporate Finance, p We also collect intraday trades and quotes from TAQ database to construct proportional bid-ask spread as an alternative measure of liquidity. It is defined as bid-ask spread divided by the midpoint of bid and ask price. 8 We set missing values of book value of inventory, book value of current assets, book value of current liabilities, book value of long-term debt, book value of preferred shares, book value of advertising expenditure, book value of R&D expenditure, and book value of deferred taxes to zero. 7

8 for a firm s fiscal year, we drop that firm-year from our sample. We further require that a stock has at least 120 trading days in a fiscal year to be included in the sample. Given data limitations associated with the index of shareholder rights we focus on a sample that covers the seven years in which the IRRC has published volumes. Our final sample consists of 11,243 firm-year observations with 3,174 firms for the following years: 1990, 1993, 1995, 1998, 2000, 2002, and Liquidity Index Liquidity and transaction costs are difficult to measure as they often require the availability of detailed microstructure data. In this paper, we follow Bekaert, Harvey, and Lundblad (2005) to construct our main proxy for liquidity as a transformation of the proportion of zero daily firm returns. Specifically, for each stock-year, we calculate the proportion of zero daily returns, ZR, of the stock over the firm s fiscal year. We then compute our liquidity proxy, ZRINDEX, by taking the natural logarithm of 1-ZR. Thus, ZRINDEX is constructed to be non-positive and positively related to stock market liquidity. For our sample, ZRINDEX ranges from to 0 with a mean value of -0.10, a median value of -0.06, and a standard deviation of Lesmond et al. (1999) provides the theoretical foundation for this liquidity measure. They argue that if the value of an information signal is insufficient to outweigh transaction costs, market participants will elect not to trade, resulting in an observed zero return. Thus, the higher the level of transaction costs, the more zero returns will be observed. They also document that this measure is highly correlated with other more traditional measures of transaction costs, such as bid-ask spread and spread plus commission. The use of this liquidity proxy allows us to keep substantially more observations than other proxies for liquidity. As a robustness check we also run our specifications using an alternative proxy for liquidity. The use of proportional bid-ask spreads as a proxy for liquidity results in a smaller sample but yields similar results. These results are not tabulated but are available from the authors. We conclude that our results are not sensitive to our choice of a proxy for liquidity Firm Performance 8

9 In studying the association between firm performance and stock market liquidity, we rely on a proxy for Tobin s Q as our main measure of firm performance. Tobin's Q (the ratio of the firm's market value to the replacement cost of its assets) has been used as a measure of firm value in an enormous number of studies (see, e.g., Morck, Shleifer, and Vishny (1988), Yermack (1996), and Gompers et al. (2003)). Our proxy for Q is taken from Kaplan and Zingales (1997). 9 To compute the numerator of Q, the market value of the firm, we first sum the book value of firm s assets and the market value of the firm s common equity, which is equal to the number of common shares outstanding times the price per share at the end of the fiscal year. We then subtract the sum of the book value of a firm s common equity and the balance sheet deferred taxes from this estimate. The denominator of Q, the replacement value of the firm's assets, is simply assumed to be the book value of firm s assets. For our sample, Q ranges from 0.26 to with a mean value of 1.73, a median value of 1.31, and a standard deviation of We next break the firm market-to-book ratio into three components: price-tooperating earnings, financial leverage, and operating profitability based on the following equation: Market Value of Assets11Book Value of AssetsititititQOIOAOIP (1) The operating earnings to price ratio, OIP, is equal to operating income after depreciation divided by market value of common equity. The financial leverage ratio, LEVERAGE, is defined as the fraction of the market value of a firm s assets coming from common equity. Operating return on assets, OIOA, is equal to operating income after depreciation divided by the book value of assets. We use operating income after depreciation instead of net income as a measure of operating profitability to exclude the effect of financial leverage on profits. Q and its three components are all measured at a firm s fiscal year end Control Variables in Baseline Specifications 9 See Table 1 Panel A for detailed information on the definition of Q, along with the other performance measures OIP, LEVERAGE and OIOA. 9

10 In our baseline specifications, we use all the control variables used by Gompers et al. (2003) in their regressions explaining firm performance. These controls include the natural logarithm of total assets (LOG_BVTA), a dummy variable indicating the inclusion in the S&P 500 (DUM_SP500), a dummy variable indicating whether a firm is incorporated in the state of Delaware (DUM_DE), and the natural logarithm of firm age (LOG_AGE). 10 A firm's age is approximated as the number of years of financial data available in Compustat prior to a firm s fiscal year end. As a robustness check, we use additional controls the literature has often used in Q regressions advertising expenditure, R&D expenditures, and long-term debt, all scaled by total assets. The results using the additional controls are not shown. However, inclusion of the additional control variables produces similar results as using only the Gompers et al. (2003) control variables. We use an index constructed by Gompers et al. (2003), GIMINDEX, to control for the level of shareholder rights. GIMINDEX is based on the 24 distinct provisions provided by the IRRC. 11 To construct the GIMINDEX, we add one point for each provision that restricts shareholder rights. Cumulative voting rights for shareholders and secret ballot are two provisions whose presence actually increases shareholder rights. Thus, for each one, we add one point to the GIMINDEX when firms do not have it. By construction, the GIMINDEX is negatively related to the strength of a firm's shareholder rights. For our sample, GIMINDEX ranges from 1 to 19 with a mean value of 9.04, a median value of 9, and a standard deviation of Our summary statistics on GIMINDEX are comparable to those reported by Gompers et al. (2003). We include industry fixed effects in most of the regressions. We use 49 industries as defined in Fama and French (1997) but use a slightly updated version provided by French's website. In regressions including firm fixed effects we exclude industry fixed effects but industry-adjust both dependent and independent variables (excluding the dummy variables DUM_SP500 and DUM_DE) by subtracting the median value in the firm s industry for the year. 10 We use the Compustat historical S&P major index code (Data276) to identify companies in S&P 500 Index. This two-digit code identifies the corresponding index constituents. Beginning January 1, 2002, the indexes were reclassified. Only the codes 10, 91 and 92 continue to exist. History was only reclassified back to December 31, In our paper, we identify companies with codes 10, 40, 49, 60 and 90 as S&P 500 companies before December 31, 1994 and companies with code 10 as S&P 500 companies after December 31, See Appendix A for detailed information on each of the provisions. 10

11 3.2.4 Additional Variables To test whether our results are driven by momentum, analyst coverage, or idiosyncratic risk, we include additional control variables. To capture momentum we calculate a stock s RANK, in the last month of fiscal year t for stock i. To do this we rank all stocks in the sample by their compounded monthly return over the six months period ending with the last month of fiscal year t for stock i. We then record the decile rank for each stock where a rank of 1 is given to stocks with the lowest cross sectional momentum and a rank of 10 is given to stocks with the highest cross sectional momentum. Analyst coverage, #ANALYSTS is defined as the number of analysts covering firm i during its fiscal year t. Specifically, it is measured as the number of analysts who have issued at least one earnings forecast for firm i in the I/B/E/S database during firm i's fiscal year t. LOG_#ANALYSTS is the natural logarithm of #ANALYSTS. We follow Spiegel and Wang (2005) to estimate idiosyncratic risk. Specifically, we regress excess monthly return of firm i s stock on the market risk premium and two Fama-French factors, SMB and HML, using ordinary least squares (OLS) procedures. IDIORISK is then defined as the standard error of the regression. The regressions are estimated using 60 monthly returns prior to fiscal year end with a minimum of 24 monthly returns observations required. Next, to control for possible endogeneity of liquidity, we run two-stage least squares (2SLS) regressions using two different instrumental variables: ZRINDEX t-1 and Z1. ZRINDEX t-1 is a one period lag of our liquidity measure ZRINDEX. Z1 is the average ZRINDEX of the two firms in firm i s industry with the closest market cap for fiscal year t where closest is defined as the firm with the closest market cap above firm i and the firm with the closest market cap below firm i who operates in firm i s industry. Finally, to differentiate several theories which might explain why liquidity has an impact on firm performance, we include R&D intensity, operating income volatility, institutional ownership, pay-performance sensitivity and their interaction with the liquidity measure, ZRINDEX as independent variables. R&D intensity is a proxy for the business risk or uncertainty inherent in operating in a particular business. To measure R&D intensity, we follow Eberhart et al. (2004) and measure RDTA as R&D expenditure 11

12 during firm i s fiscal year t scaled by book value of total assets at fiscal year end. It is set to zero if missing. Due to many missing observations in R&D expenditure, we also include a dummy variable to indicate whether R&D expenditure is missing in the database. Operating income volatility is another proxy for the business risk. For each firm i, INCVOL it is defined as the standard deviation of quarterly operating income before depreciation divided by quarterly book value of assets. It is measured over twenty quarters prior to fiscal year t end with a minimum of eight quarterly observations. Institutional ownership is used as a proxy for blockholder ownership. INSTHOLD is defined as the mean percentage of common stock held by all institutional holders who hold at least 5% of the shares outstanding for the four quarters prior to the fiscal year end. PPS, pay-for-performance sensitivity, is defined similar to Yermack (1995) and Core and Guay (1999) as the change in value of a CEO s stock option award for every dollar change in the value of the firm s common equity Correlation Matrix Table 2 presents Pearson and Spearman rank correlations among the liquidity measure (ZRINDEX), the firm performance measures, and all control variables used in our baseline specifications. Pearson correlations are reported above the main diagonal and Spearman correlations are reported below the diagonal. As shown in the table, we find that stock liquidity, ZRINDEX, has significantly positive Pearson (Spearman) correlations with three firm performance measures: Q, LEVERAGE, and OIOA. In other words, firms with liquid stocks tend to have better firm performance, less debt in their capital structure, and higher operating profitability. Stock liquidity, ZRINDEX, has a significant negative Spearman correlation with operating income to price, but the Pearson correlation between the two variables is not significant. The different signs and significance levels could be due to nonlinearity or outliers. As we discussed in section 2, numerous theoretical studies predict a positive relation between stock market liquidity and firm market-to-book ratio. However, the different theories have different predictions regarding the relation between liquidity and the three components of Q. 12 See Appendix C for a detailed definition of our pay-for-performance sensitivity measure. 12

13 In addition, we find that the shareholder rights measure, GIMINDEX, has significant negative Pearson (Spearman) correlations with Q. Note that, GIMINDEX is constructed to be negatively related to the strength of a firm s shareholder rights, therefore significant negative correlations between GIMINDEX and Q would suggest that firms with stronger shareholder rights have higher value. This finding is consistent with the findings of Gompers et al. (2003). Moreover, the negative Pearson (Spearman) correlations between GIMINDEX and financial leverage, LEVERAGE, seem to suggest that firms with stronger shareholder rights tend to use more equity in their capital structure. We also document significant positive correlations between stock liquidity, ZRINDEX, and the shareholder rights measure, GIMINDEX. Although this result appears to imply that firms with liquid stocks tend to have weaker shareholder rights, we don t attempt to draw a conclusion about the causal direction. 4. Empirical Results This paper investigates the effect of liquidity on firm performance and the mechanisms through which liquidity affects firm performance. We first examine whether liquidity improves performance, lowers performance, or has no effect on performance. This is stated in the following hypotheses: Hypothesis 1: Liquidity improves firm performance Hypothesis 2: Liquidity harms firm performance Null Hypothesis: Liquidity does not impact firm performance. 4.1 Baseline Specification To assess whether stock liquidity improves, harms, or has no effect on firm performance we regress a proxy for Tobin s Q on our liquidity measure and several control variables. The baseline specification for the pooled regression is defined as follows: 13

14 Q i,t = a + bzrindex i,t + cgimindex i,t + ddum_sp500 it + edum_de it + flog_age it + glog_bvta it + IND j + YR t + error it, (2) where Q is our proxy for Tobin s Q and is measured at the end of firm i s fiscal year t. The liquidity measure, ZRINDEX, is measured for firm i over its fiscal year t. The control variables in the regression are an index of shareholder rights (GIMINDEX), a S&P 500 dummy (DUM_SP500), a Delaware incorporation dummy (DUM_DE), natural logarithm of firm age (LOG_AGE), natural logarithm of the book value of total assets (LOG_BVTA), unobserved industry effect for industry j (IND j ), and unobserved year effect for year t (YR t ). Industries are defined in Fama and French (1997) but we use a slightly updated version provided by French's website Baseline Q Specification We first estimate Eq. (2) using pooled OLS and all years for which shareholder rights data is available. The years are 1990, 1993, 1995, 1998, 2000, 2002, and We then estimate Eq. (2) for each year and drop year fixed effects. Panel A of Table 3 contains the OLS regression estimates of the baseline specification. As the table indicates, the coefficients on the liquidity measure, ZRINDEX, are positive and significant at the 1% level for each of the seven years in the sample period and in the pooled specification. These results support Hypothesis #1 since higher stock market liquidity is correlated with higher firm performance as measured by Q. The results appear economically significant as well. The marginal effects from the pooled specification suggest that an increase in liquidity, ZRINDEX, of.05 leads to an increase in Q of.162. The coefficient on the shareholder rights measure, GIMINDEX, is negative and significant at the 1% level in the pooled specification. It is also negative in every year and statistically significant in most of the years in the sample period, particularly the earlier years. This suggests that weaker shareholder rights are correlated with lower firm performance and is consistent with the findings of Gompers et al. (2003). Of the four control variables included in the regressions, LOG_BVTA has significantly negative coefficients in every year, which implies that small companies have 13 Our results are similar if we industry-adjust all variables instead of including industry fixed effects. 14

15 higher firm performance on average. S&P 500 companies have higher firm performance than non-s&p 500 companies, as DUM_SP500 has significant positive coefficients throughout. This is not surprising as S&P tends to select the leaders in each industry to be in the S&P 500. Younger firms tend to have higher firm performance as LOG_AGE has significant negative coefficients throughout. Delaware incorporation, DUM_DE, seems to have an insignificant impact on firm performance. Our results appear to be consistent with higher stock market liquidity improving firm performance. Liquidity may improve firm performance by: permitting non blockholders to intervene and become blockholders; facilitating the formation of a toehold stake; promoting more effective contracting on stock price regarding managerial compensation; or making prices more informative to stakeholders, which improves operating performance or relaxes financial constraints. To identify the source of the performance gains we next explore how liquidity impacts the various components of Q Baseline Specification Components of Q To gain further insight into the source of higher firm performance for stocks with high liquidity, we break the firm performance measure, Q, into three components: price-tooperating earnings ratio, financial leverage ratio, and operating return on assets ratio. We replace Q in Eq. (2) with each of its components. We show only the pooled OLS results for each of the three dependent variables for brevity. Year and industry fixed effects are included in each specification. We first replace the dependent variable in Eq. (2) with OIP, (operating income after depreciation divided by the market value of equity). This ratio captures investors perceptions of the future growth and riskiness of operating earnings. We use operating income rather than net income to eliminate the impact of financial leverage on the results. The coefficient estimates are shown in column 1 of Panel B of Table 3. As the panel shows, the liquidity measure does not significantly impact OIP. In fact, none of the independent variables are significant in this specification. Next we replace the dependent variable in Eq. (2) with LEVERAGE, (market value of common equity divided by the market value of equity plus book value of debt minus deferred taxes). LEVERAGE measures the fraction of equity in a firm s capital structure. The coefficient estimates of 15

16 this specification are shown in column 2 of Panel B of Table 3. We see that stocks with high stock market liquidity tend to have higher fraction of equity in their capital structure or less financial leverage. This finding is consistent with Weston, Butler and Grullon (2005) who show that the cost of issuing equity is significantly lower for firms with more liquid stock. Thus, firms with liquid stocks may choose to issue equity as opposed to debt. Lastly we replace the dependent variable in Eq. (2) with OIOA (operating income after depreciation divided by book value of total assets). Once again we use operating income rather than net income to eliminate the impact of financial leverage on the results. The coefficient estimates are shown in column 3 of Panel B of Table 3. Stocks with high stock market liquidity tend to have higher operating profitability. In summary, we find that stocks with high liquidity have better firm performance (higher firm Q), more equity in their capital structure (or low financial leverage), and higher operating profitability levels. Higher operating profitability for firms with higher liquidity could mean that managers exhibit myopic preferences. However, with manager myopia we would also expect to see a higher operating income to price ratio, OIP, since long term projects would be substituted with short term projects. Since high and low liquidity stocks have similar OIP ratios, it does not appear that myopic manager behavior is behind the higher operating profitability of firms with highly liquid stocks Baseline Specification Firm Fixed Effects An unobservable correlated with both stock market liquidity and firm performance may be present making coefficient estimates biased. For example, high quality managers may tend to manage companies with more liquid stocks. High quality managers should also result in high firm performance. In this case, manager quality is unobservable and correlated with both liquidity and firm performance. Thus, stock liquidity will be positively correlated with firm performance; however, better firm performance is not due to liquidity. Firm fixed effects can be used as an endogeneity control if the unobservable correlated with stock market liquidity and industry-adjusted performance is relatively constant over time. In Table 3 Panel C we replace industry fixed effects with firm fixed effects in the baseline specification Eq. (2) and industry-adjust all variables except the 16

17 S&P 500 dummy and the Delaware incorporation dummy. The estimates in column 1 of Table 3 show that an increase in industry-adjusted stock liquidity leads to an increase in the industry-adjusted Q ratio. The estimates shown in columns 2, 3, and 4 indicate that an increase in industry-adjusted liquidity leads to an increase in the fraction of equity in a firm s capital structure and an increase in industry-adjusted operating profitability. Therefore, the results are robust to the inclusion of firm fixed effects. 4.2 Robustness Tests In the previous section we show that high stock market liquidity is correlated with better firm performance as measured by Tobin s Q. We also find that more liquid stocks have more equity in their capital structure, higher operating profitability, but similar price-tooperating earnings ratios as less liquid stocks. In this section we test several alternative explanations for the results Reverse Causality One alternative explanation for the results is that high Q firms are sought after by institutions for prudent man (fiduciary responsibility) reasons. Institutions may want to hold stocks of good companies like Microsoft. If institutional investors are compelled to diversify across industries they will invest in stocks with high industry-adjusted market-to-book ratios. Liquidity is high for these stocks because institutions trade these stocks. In other words, there is reverse causality which results in a positive correlation between stock market liquidity and firm performance. To identify the causal effect of liquidity on firm performance we examine the effect of an exogenous shock to liquidity (decimalization) on firm performance. Specifically, we use the change in liquidity around decimalization as an instrument for liquidity. On January 29, 2001 the NYSE and AMEX began trading all listed stock issues in decimals. 14 Later, NASDAQ converted all stocks from fractional price form to decimal price form over the interval of March 12, 2001 April 9, The switch from fractional prices to decimal prices lowered spreads since there are now 100 price points 14 Over August 2000 January 2001 the NYSE ran pilot programs with selected stocks but only 158 out of 3,525 stocks were trading in decimals prior to January 29,

18 instead of 16 price points within a dollar where buyers and sellers can trade. Lower spreads will lower the cost of trading and should increase liquidity. 15 However, liquidity should increase more for some stocks than for others. Both predictions are supported by research. For example, using a sample of NYSE and NASDAQ stocks, Bessembinder (2003) documents that quoted bid-ask spreads fall following decimalization and the largest declines were for the most actively traded stocks. Similarly, Furfine (2003) uses a sample of NYSE stocks and concludes that decimalization resulted in an increase in liquidity as the price impact of a trade went down, particularly for the most actively traded stocks. Infrequently traded stocks had no change in liquidity following decimalization. In summary, liquidity increases following decimalization, increases more for more actively traded stocks, and is exogenous to the firm. Therefore, the change in liquidity around decimalization can be used as an instrument for liquidity to test the causal effect of liquidity on firm performance. To assess the effect of an exogenous shock to liquidity (caused by decimalization) on firm performance we regress the change in firm Q on the change in liquidity from the month before decimalization to the month after decimalization. Since decimalization for the vast majority of NYSE and AMEX stocks occurred on January 29, 2001, we examine the effect of the change in the average liquidity for stock i from the month of December 2000 to the month of February Of course the exclusion restriction is that the change in liquidity that occurs between December 2000 and Feb is due entirely to decimalization. We use the specification shown in Eq. (3) to estimate the effect of a change in liquidity on the change in firm Q around decimalization for NYSE and AMEX listed stocks: Q i, Dec.2000 to Feb.2001 = a + b LOG_PSPRD i, Dec.2000 to Feb IND j + error i, (3) where for each stock i traded on NYSE and AMEX, the change in our proxy for Tobin s Q, Q, is measured from the end of Dec to the end of Feb and the change in 15 Though bid-ask spreads were predicted to drop following decimalization, it was unclear what would happen to depth (# of shares at the quoted bid or ask price). To capture the impact of a change in spreads and depth, Furfine (2003) examines the price impact of a trade before and after decimalization. The price impact of a trade is defined as a revision to stock price following a trade. If market depth is low at the quoted price, the price will change as a large order is filled. 18

19 liquidity, LOG_PSPRD, is calculated as the natural logarithm of proportional bid-ask spread over the month Feb minus the natural logarithm of proportional bid-ask spread over the month Dec Similarly, we use the specification shown in Eq. (4) to estimate the effect of a change in liquidity on the change in firm Q around decimalization for NASDAQ stocks: Q i, Feb to May 2001 = a + b LOG_PSPRD i, Feb. 2001to May IND j + error i, (4) where for each stock i traded on NASDAQ, both Q and LOG_PSPRD measure the change from Feb to May We use PSPRD, the bid-ask spread divided by the midpoint of bid and ask price 16, as the measure of liquidity rather than our main liquidity measure ZRINDEX in both Eq. (3) and Eq. (4). We believe that PSPRD is a more reliable measure for this particular research design since ZRINDEX, which measures the incidence of zero returns, is much more likely to be affected by news or other random events over a short time period. We also include an unobserved industry effect for industry j (IND j ) in both equations. We estimate Eq. (3) for all NYSE and AMEX stocks and Eq. (4) for all NASDAQ stocks using ordinary least squares (OLS) procedures wherever data are available for the sample period. The results are shown in the columns 1 and 2 of Table 4. For both the NYSE & AMEX sample and the NASDAQ sample, a decrease in the proportional bidask spread surrounding decimalization results in an increase in firm Q. The coefficients on PSPRD are negative and significant at the 1% level. Our findings suggest that an increase in stock liquidity, caused by an exogenous shock due to decimalization, improves firm performance Idiosyncratic Risk Spiegel and Wang (2005) examine two well-known empirical findings: liquidity is negatively correlated with returns while idiosyncratic risk is positively correlated with returns. They examine whether they are one effect or two effects and find that 16 The formula is (ASK - BID)/ [(ASK+BID)/2]. It is an intraday measure and uses all quotes from time to time each trading day. The time is quoted using Consolidated Quote System (CQS) time stamp. Then the PSPRD at each quote time is weighted using elapsed time to calculate daily PSPRD. Finally in a month, each daily PSPRD is weighted equally to calculate monthly PSPRD and then taken natural logarithm to correct for non-normality in the distribution. 19

20 idiosyncratic risk is a much stronger predictor of returns than liquidity. In other words, controlling for idiosyncratic risk eliminates the power of liquidity to explain returns. To control for the possibility that idiosyncratic risk is the underlying factor that drives the relation better firm performance and stock liquidity, we add a measure of a stock s idiosyncratic risk, IDIORISK, to Eq. (2). The results are shown in column 1 of Panel A of Table 5. The higher a stock s idiosyncratic risk, the lower the firm s Q. This is consistent with the predictions of Spiegel and Wang (2005) as stocks with high idiosyncratic risk have higher required returns and will sell at a discount all else held constant. However, the coefficient on the liquidity measure, ZRINDEX, is still positive and statistically significant at the 1% level after including a control for idiosyncratic risk. The results using the components of Q as the dependent variable are also robust to the inclusion of firm idiosyncratic risk, IDIORISK. Stocks with high idiosyncratic risk tend to use more financial leverage and have lower operating profitability. These results are shown in columns 2, 3, and 4 of Panel A of Table 5. High liquidity stocks continue to have less financial leverage and higher operating profitability even after we control for idiosyncratic risk. We conclude that the baseline model results are robust to controlling for idiosyncratic risk Analyst Coverage Analyst coverage may be an important omitted variable in our baseline specification. Analysts may tend to cover growth stocks more than value stocks. 17 Roulstone (2003) finds that stocks with more analyst coverage tend to be more liquid. The concern is that analyst coverage rather than liquidity improves firm Q. Since analyst coverage is correlated with liquidity, liquid stocks might appear to have better firm performance when analyst coverage is really driving the superior performance, not liquidity. To control for this possibility we add the natural log of the number of analysts following stock i during fiscal year t, LOG_#ANALYSTS, to Eq. (2). The results are shown in column 1 of Panel B of Table 5. The more analysts following a stock, the higher the firm s Q. However, the coefficient on the liquidity measure, ZRINDEX, is still positive 17 Since our measure of Q is the market-to-book ratio of the firm and the book value of debt is used as a proxy for the market value of debt, firms with high equity market-to-book ratios most likely have high firm Q ratios. 20

21 and statistically significant at the 1% level after including analyst coverage in the regression. We next use the components of Q as the dependent variable and include analyst coverage as an explanatory variable. These results are shown in columns 2, 3, and 4 of Panel B of Table 5. Stocks with more analyst coverage tend to use less financial leverage and have higher operating profitability. High liquidity stocks continue to have less financial leverage and higher operating profitability after we control for analyst coverage. In this specification they also have a higher operating income to price ratio Momentum The compensation structure of mutual fund managers may cause mutual fund managers to trade stocks of high Q firms. Mutual fund managers are compensated based on the dollar amount of assets under management. If investors have a behavioral preference for momentum stocks (cross sectional relative return winners), mutual fund managers will invest in them or risk losing assets under management. They will move around between various momentum stocks buying them when they are rising and selling them when they start to underperform. If momentum stocks tend to be stocks with high firm Q, there will be higher liquidity for stocks with relatively high Q ratios. Since stocks that have recently been relative return cross-sectional winners would experience a rise in firm Q, and momentum is predicted to be correlated liquidity, liquid stocks might appear to have better firm performance when momentum is really driving higher firm Q, not liquidity. Gutierrez and Pirinsky (2007) find empirical support for the prediction that institutions chase high relative returns and buy cross-sectional return winners. They also find that stocks that are cross-sectional return winners tend to be stocks with high marketto-book ratios. We control for this by including a cross-sectional momentum variable, RANK in Eq. (2). 18 The results are shown in column 1 of Panel C of Table 5. As is expected, the higher the relative momentum rank the higher firm Q. However the coefficient on the liquidity measure, ZRINDEX, is still positive and statistically 18 See Table 1 Panel A for a detailed definition of momentum variable RANK. We repeat this analysis using the decile cross-sectional momentum rank for the past 9 months or the past 12 months instead of the past 6 months. The results using the momentum rank for the past 9 months or the past 12 months are very similar to those using a stock s momentum rank for the past 6 months so they are not shown. 21