A substantial literature establishes a link between transparency and stock market liquidity (see, e.g., Glosten and Milgrom , Kyle , Welker , Healy, Hutton, and Palepu , and Leuz and Verrecchia ). Amihud and Mendelson  provide theoretical and empirical evidence that higher liquidity can lower expected returns and, thus, a firm's cost of capital.1 Our interest is in investigating the link between transparency, liquidity, cost of capital, and firm valuation across countries and, in particular, assessing the extent to which it is influenced both by institutional and firm-level factors and by time series variation in uncertainty (we review the related literature in the next section). The international setting is especially interesting because there is substantial variation in country-, firm-, and time period-level factors that permits an examination of interactions between firm-level transparency and various aspects of the firm's economic environment.
Our sample includes 97,799 firm-year observations across 46 countries over the period 1994–2007. In our first set of tests, we relate transparency to transaction costs and stock market liquidity. To measure transparency, we employ several firm-choice variables from prior cross-country research including earnings management (Fan and Wong  and Leuz, Nanda, and Wysocki ), auditor quality (Fan and Wong ), and adoption of global accounting standards (Daske et al. [2008, 2009]).2 We also employ two transparency variables that capture external information gathering by intermediaries: the number of analysts who cover a firm and the accuracy of analyst forecasts.3
To capture transaction costs and liquidity, we use two measures that are readily available for large samples of firms across many countries and that have been shown to correlate well with actual transaction costs for trading in a firm's shares: (1) the proportion of zero-return trading days over the fiscal year, and (2) the median bid-ask spread over the fiscal year. Bid-ask spreads speak more directly to transaction costs, while zero-return days measure liquidity more directly and are available for a wider sample of firms.4
We find that greater transparency—as measured by each of our components of transparency—is significantly associated with lower transaction costs and higher liquidity. In particular, liquidity is higher and transaction costs are lower for firms with less evidence of earnings management, that are audited by top-tier audit firms and, consistent with Daske et al. , that seriously commit to following international accounting standards (IAS). Also, liquidity is higher and transaction costs are lower when analyst following is higher and analysts’ forecasts are more accurate. Further, the economic magnitudes suggest that transparency has an economically meaningful association with liquidity.5
Next, we examine the extent to which the importance of firm-level transparency differs across economic settings. We expect firm-level transparency to be especially important when investor demand for information is higher because particular aspects of the firm's environment create greater uncertainty. We examine three specific aspects: country-level, firm-level, and time period characteristics.
First, at the country level, we argue that firm-level transparency will matter more in countries with greater overall opacity. We split our sample based on several institutional measures and find that the relation between firm-level transparency and liquidity is particularly strong in countries where there is likely to be significant self-dealing, disclosure requirements are relatively weak, and media penetration is low.6
Second, we consider time period–specific factors by examining whether transparency is particularly important during times of greater investor uncertainty, focusing on periods in which recent country-level share price volatility was especially high. Our results confirm that liquidity tends to dry up during periods of high volatility, as would be expected. Specific to our research question, we find that the effect of high volatility on liquidity is substantially mitigated for firms with high levels of transparency, suggesting that transparency is particularly important in periods of greater uncertainty.
Third, in terms of firm-level variation, we examine whether transparency matters most for firms expected to have internal governance problems (Leuz, Lins, and Warnock ). Prior research such as McConnell and Servaes  suggests that, while firm value is enhanced by concentrated ownership to a point, too much ownership concentration increases agency costs and reduces firm value. As a result, we expect transparency to be particularly important to liquidity for firms with high levels of ownership concentration. To assess this, we split our sample based on ownership concentration and find that the association between transparency and liquidity is substantially stronger for firms with expected governance problems, as reflected in more concentrated ownership. Finally, we examine whether country-level institutions interact with firm-level governance and find that firm-level transparency is particularly important for firms with concentrated ownership in countries with relatively weak minority investor protection.
While drawing causal inference is difficult, our results are robust to estimation using firm fixed effects, as well as to estimation using lagged transparency measures and lagged transparency with firm fixed effects, suggesting that our transparency variables explain within-firm variation in liquidity and do not simply reflect differences in characteristics across firms. Our results are also robust to simultaneous estimation of transparency and liquidity and to controls for self-selection. Further, our results remain strong when we specifically account for changes in firms’ growth opportunities and financing needs. Finally, similar results hold for short estimation windows around liquidity shocks, suggesting that reverse causality from liquidity to transparency is less likely to drive the empirical results.
In our final set of tests, we consider the linkages between liquidity, cost of capital, and valuation to assess the extent to which transparency is associated with firm valuation through liquidity. We find that liquidity is negatively correlated with ex ante cost of capital (as measured using an analyst-forecast-based valuation model) and positively correlated with firm valuation (as measured by Tobin's Q). Mediation analysis provides evidence that liquidity significantly mediates the association between transparency and cost of capital, suggesting that liquidity is an important channel through which transparency is linked to cost of capital. We also find that transparency is associated with Tobin's Q both through liquidity and other channels, likely reflecting the fact that firm valuation is affected by cash flow effects (e.g., expected expropriation of assets) as well as cost of capital. Further, the economic magnitudes of the effects we document suggest that transparency may materially affect cost of capital and valuation through its effect on liquidity.7
Our results make several potential contributions. First, our analyses highlight firm-specific channels through which transparency may be associated with liquidity and valuation. Our results indicate that oversight by reputable auditors, reductions in earnings management, and increases in analyst following and forecast accuracy, as well as accounting standards, are all incrementally associated with greater liquidity.8
Second, and more importantly, our tests are specifically designed to examine cross-setting variation in the importance of firm-level transparency to liquidity, focusing on differences in country-level institutions, firm-level governance, and time-period uncertainty. Our results suggest that firm-level transparency is especially important when country-level investor protection, disclosure, and media penetration are weak, firm-level governance is weak, and a country has experienced recent volatility shocks. These results are consistent with the notion that firm-level transparency matters most when other aspects of the firm's environment increase uncertainty.
Third, our results potentially shed light on the link between liquidity, cost of capital, and valuation in international settings. We find that greater transparency is associated with higher firm valuation and lower cost of capital around the world. More importantly, the mediation analysis suggests that a substantial portion of that relation is driven through the link between transparency and liquidity. Transparency can be costly for managers, both in terms of direct costs (e.g., hiring higher quality auditors and committing to follow IAS) and indirect costs (e.g., limiting their ability to expropriate assets and disclosing potentially sensitive information to competitors). Our results suggest that potential benefit from greater transparency may accrue to a firm's shareholders through increased liquidity and lower cost of capital.
In the next section, we review the related literature on transparency, liquidity, cost of capital, and valuation. In section 3, we discuss the data and methodology and present the results of our tests. Section 4 provides conclusions.9
2. Discussion of Related Literature
Our paper builds on several strands of the international literature.10 Most closely related are studies that analyze the effects of changes in accounting standards on measures of liquidity or cost of capital. Leuz  finds no evidence that the choice of U.S. GAAP versus IAS matters to the liquidity of firms trading on the German Neuer Market. Similarly, Daske  finds no evidence that adoption of International Financial Reporting Standards (IFRS) matters to cost of capital for European firms. On the other hand, Leuz and Verrecchia  show that a conversion to IAS or U.S. GAAP is associated with an increase in liquidity and reduction in cost of capital for German DAX firms. Daske et al.  document, for a broad global sample of firms, that capital market benefits to IFRS adoption accrue only in countries where firms have incentives to be transparent and where legal enforcement is strong. Similarly, Daske et al.  find that the benefits of IFRS adoption accrue only to “serious” adopters of IFRS. Taken together, these studies suggest that, while accounting standards have the potential to affect liquidity and the cost of capital, the effects are limited to contexts in which there is greater commitment to high-quality implementation.
Additional research such as Karolyi, Lee, and Van Dijk , Lang and Maffett , and Ng  examines determinants of liquidity risk (liquidity variability and covariability) based on a variety of firm- and country-level characteristics. Collectively, these papers draw on prior work such as Acharya and Pedersen  and Brunnermeier and Pedersen  that emphasizes the importance of time variation in liquidity and find that the variability and covariability of liquidity tend to be higher for firms and countries with greater opacity, especially during crisis periods. However, these studies focus on liquidity risk and uncertainty and not on determinants of the overall level of liquidity.
Two studies examine transparency and liquidity at the country level. Eleswarapu and Venkataraman  use exchange-listed ADRs to study the impact of macro-level institutions on liquidity across a range of countries and find that trading costs are lower when countries have better accounting standards and legal systems. However, by construction, all of the sample firms fall under the U.S. regulatory system. Bhattacharya, Desai, and Venkataraman  investigate a number of country-level relations such as those between earnings attributes, turnover, and cost of capital, finding mixed results depending on the particular measures employed.
Several studies assess determinants of liquidity for non-U.S. securities in specific settings. Amihud, Mendelson, and Uno  show that, when 66 Japanese firms reduced the lot size required for trading, the price of their shares increased, while Amihud, Lauterbach, and Mendelson  show a similar outcome for a small set of Israeli stocks that moved to a more liquid trading regime. Chaplinsky and Ramchand  find that yields on similar non-U.S. debt issues are higher on the less liquid Rule 144A U.S. market than on the U.S. public bond market. Bekaert, Harvey, and Lundblad  document that liquidity behaves like a priced risk factor in 19 emerging market countries in the sense that unexpected liquidity shocks are positively correlated with contemporaneous return shocks and negatively correlated with shocks to dividend yield.
Finally, several studies examine determinants of cost of capital in an international setting. Hail and Leuz  provide evidence that countries with better legal institutions and investor protection enjoy a lower cost of capital and Hail and Leuz  suggest that firms that cross list experience a reduction in cost of capital. However, these studies do not directly examine the effect of liquidity on cost of capital nor do they focus directly on the link to transparency.
3. Data, Methodology, and Results
3.1 sample construction
Accounting and market data are collected from Thomson Reuter's Datastream Advance database (a collaboration of market statistics from Datastream and accounting data from WorldScope) over the 1994–2007 time period. We require firm-year observations to have the necessary income statement and balance sheet data to calculate cash flows, accruals, and operating characteristic variables and to have sufficient market data to calculate the annual percentage of zero-return days. We exclude any country with less than 50 firm-year observations. In total, our sample contains 97,799 firm-year observations from 46 countries. Table 1 reports the frequency of observations by country. An advantage of our sample is that it includes a wide range of firms and is thus not dominated by the largest, most heavily followed multinational firms. As a result, our sample contains firms for which transparency issues are potentially more pronounced and the substantial variability within the sample should increase the power of our tests. Further, the wide range of institutional settings permits us to examine cross-setting variation in the strength of the relation between transparency and liquidity.
Sample by Country
|HONG KONG||3,386||3.46||SRI LANKA||95||0.10|
| || || || ||97,799||100.00|
3.2 transparency and liquidity
Our first hypothesis is that increased transparency will be associated with reduced transaction costs and increased liquidity. Because transparency is inherently difficult to measure and has many potential facets, we consider several measures.
For our first transparency variable, we estimate the degree to which a firm engages in discretionary earnings management. As discussed further in appendix A, we combine two commonly used measures of earnings management: variability of net income relative to cash flow and correlation between accruals and cash flows (e.g., Leuz, Nanda, and Wysocki  and Barth, Landsman, and Lang ). The underlying argument is that earnings management is manifested in the use of accruals to smooth out fluctuations in underlying cash flows.
There clearly are nondiscretionary components to earnings smoothness as well. Therefore, following the discretionary accruals literature (e.g., Jones ), we first regress out a set of fundamental determinants of earnings smoothness and use the resulting residuals to form our measure of discretionary earnings smoothness. Our analyses include both the portion of smooth earnings explained by the intrinsic fundamental factors (FUND_SMTHC) as well as the excess portion (DIS_SMTHC). Our primary interest is in DIS_SMTHC as a measure of transparency, and we predict that greater discretionary smoothing will be indicative of greater earnings management and associated with greater opacity. However, we expect FUND_SMTHC to be positively correlated with liquidity to the extent that there is less potential for asymmetric information in firms whose profits are naturally smooth. To provide further assurance that our measure captures elements of managerial discretion, we demonstrate in appendix A that DIS_SMTHC is positively correlated with incentives to manage earnings (concentrated ownership and high book-tax accounting conformity) and negatively correlated with impediments to earnings management (high quality auditor, strong investor protection, global accounting standards, and high analyst following).
Additional transparency variables are also likely to be important determinants of liquidity. To the extent that analysts serve as information intermediaries, their presence should increase transparency. Lang, Lins, and Miller  argue that, in an international setting, analysts are likely to play a particularly important oversight and information processing role.11 We therefore include ANALYST, the number of analysts forecasting current-year earnings, as our second firm-level transparency indicator.
In addition to the number of analysts following a firm, greater accuracy of their forecasts likely reflects greater transparency of the firm's information environment. Forecast accuracy captures both the information acquisition activities of analysts as well as the disclosure policies of firms (Lang and Lundholm ). Following Lang and Lundholm , we measure forecast accuracy after controlling for the size of the earnings surprise and bias during the period. Thus, R_ACCURACY, our third transparency measure, captures, for a given magnitude of earnings surprise and bias, the extent to which analysts were able to forecast earnings.12
The informativeness of accounting data is likely to be higher if such data are audited by an affiliate of a global accounting firm, so we include a firm-year specific indicator variable, BIG5, if a firm's auditor is affiliated with a Big-5 audit firm as our fourth transparency measure (Francis  and Fan and Wong ).13 Because our primary data source (Datastream) maintains firm-specific auditor data for only the most current fiscal year, we collect time-series data on firm auditor from a variety of additional sources, including historical point-in-time data from Datastream and Compustat Global. Auditor descriptions from these sources are classified as “Big-5” by hand.14
Accounting data may also be more informative if a firm follows IAS, particularly if the firm is from a country with relatively low-quality local accounting regulations. However, as Daske et al.  highlight, it is relatively straightforward for firms to voluntarily “adopt a label” of IAS, rather than making substantive changes, without any real economic effects. Similarly, Daske et al.  find that mandatory IFRS adoption results in capital market benefits only in countries where firms have “incentives to be transparent and legal enforcement is strong” (p. 1086). Accordingly, we define serious adopters (INTGAAP_S= 1), our final transparency indicator, to be adopting firms that have an above-median aggregate transparency score (calculated excluding the INTGAAP variable) and either a) are mandated by country regulations to adopt international accounting standards, or b) voluntarily adopted international standards (see also Barth, Landsman, and Lang  and Bradshaw and Miller ).15 The overall idea is that firms with large auditors, a large and accurate analyst following, and less evidence of earnings smoothing are more likely to be adopting international accounting standards in substance rather than in form only.16
Our models include controls for firm size as measured by the log of a firm's market value of equity (LNMVE), book-to-market (BM), whether the firm had a loss during the period (LOSS), and return variability (STDRET), as is typical in empirical tests of liquidity (Stoll ).17 We further include indicator variables for whether the stock trades in the U.S., either on an exchange (ADR_EX) or on the OTC or PORTAL markets (ADR_NEX). U.S. trading is likely to improve transparency (Lang, Lins, and Miller ), but it may also draw liquidity from local markets to the extent that shares are less costly to trade in the U.S. (Baruch, Karolyi, and Lemmon ).
Transparency is also likely to be affected by a firm's corporate governance environment. While it is difficult to measure firm-level governance precisely, one approach is to base it on concentrated ownership. McConnell and Servaes , among others, suggest that concentrated ownership may improve governance up to a certain level, but that high levels of ownership concentration are detrimental because they create sufficient control to permit, for example, expropriation of assets. To the extent that managers are entrenched, they have incentives to create opacity to hide their actions, whereas managers whose incentives are aligned with shareholders would instead want to make their firms more transparent. We therefore incorporate the percentage of closely held shares in our models, but, because the McConnell and Servaes  results suggest that the relation is nonmonotonic, we split this variable into high and low ownership ranges, where %CLHLD_H is equal to the percentage of the firm's shares that are closely held if that proportion is above the sample median value of %CLHLD, and zero otherwise, and %CLHLD_L is equal to the percentage of the firm's shares that are closely held if that proportion is below the sample median value of %CLHLD, and zero otherwise.18
Finally, for our main specifications, we include country, year, and industry fixed effects. While transparency likely differs across countries, market microstructure does as well, so country fixed effects are important. Given potential concerns about omitted variables and endogeneity, we also report results using firm fixed effects. Within-firm comparisons have the disadvantage of ignoring potentially interesting cross-firm variation, thus weakening the power of the tests, but they have the advantage of fewer potential econometric issues.
We use two measures of liquidity and transaction costs: zero return days and bid-ask spreads. As Amihud and Mendelson  note, transaction costs and liquidity are related but separate concepts. From an investor's perspective, both the direct transaction costs of trading in a firm's shares as well as the ability to form and liquidate a substantial portfolio in a timely manner are potentially important determinants of the price one is willing to pay for a stock. To measure liquidity, we follow Bekaert, Harvey, and Lundblad  and define the zero-return metric (ZERORET) as the number of zero-return trading days over the firm's fiscal year divided by the total trading days of the fiscal year. Using the zero-return measure in our setting is advantageous because stock prices are widely available and measured consistently across markets relative to other measures such as volume or bid-ask spreads. Lesmond, Ogden, and Trzcinka  argue that a manifestation of illiquidity will be infrequent trading reflected in days without price movements. As such, higher values correspond to greater illiquidity.19
We also estimate our models with the bid-ask spread (BIDASK) as a proxy for transaction costs using the median bid-ask spread over the fiscal year, where the bid-ask spread is calculated as (ASK−BID)/((ASK+BID)/2), and the value is included in log form in the models following prior literature (see, e.g., Daske et al. ). Given that higher values of the ZERORET and BIDASK measures correspond to greater illiquidity, we predict a negative relation between these measures and our previously described transparency measures.
3.2.1. Descriptive Statistics.
Table 2, panel A, provides descriptive statistics for the variables we use in our tests, as well as several intermediate variables, grouped in the order in which they appear in subsequent tables. Our sample firms have a median market value of equity (MVE) of $127 million and median total assets (ASSETS) of $269 million. Median leverage (LEV), measured as debt to total assets, is 0.54, and the median book-to-market ratio (BM) is 0.76. The median sample firm reports losses infrequently and has experienced 6.9% sales growth (SG) over recent years. The table also shows that 2.2% of the sample firms have a U.S. exchange–listed ADR (ADR_EX) while 5.2% have an ADR that is not exchange traded (ADR_NEX).20
|BIG5||−0.10||.|| 0.18||−0.01|| 0.17|
|ANALYST||−0.15|| 0.21||.|| 0.09|| 0.07|
|R_ACCURACY|| 0.00||−0.01|| 0.11||.|| 0.04|
|INTGAAP_S||−0.04|| 0.17|| 0.09|| 0.06||.|
Statistics for our core transparency-related variables are as follows. Mean and median analyst following levels (ANALYST) for our sample are 3.2 and one, respectively, 36.7% of the firms are audited by Big-5 affiliates (BIG5), and 9.7% prepare their financial statements under nonlocal GAAP (INTGAAP). Turning to our liquidity variables, the median sample firm has a zero return (ZERORET) on 21.8% of the trading days in the year and has a bid-ask spread (BIDASK) of 1.3%. Data requirements to calculate BIDASK reduce the sample size relative to the ZERORET metric, with this measure available for 62,900, rather than 97,799, firm-years. We discuss average cost of capital, Tobin's Q, and related control variables later in the paper when we present valuation tests.
Table 2, panel A, also reports statistics for our measures of country-level institutions. The median Anti-Self Dealing Index score (ASDI, obtained from Djankov et al. ) for our sample is 0.50 and the median country-level disclosure score (DISCLOSE, obtained from La Porta, Lopez-de-Silanes, and Shleifer ) is 0.75. Each measure has a range of possible scores from 0 to 1, where higher values represent stronger investor protection against managerial self-dealing and greater required disclosure, respectively. MEDIA ranks a country's media penetration based on the World Bank's World Development Indicators. Following Bushman, Piotroski, and Smith , our MEDIA measure features data on newspaper circulation and television ownership per capita from 1994 to 2004 and we additionally include Internet connections per capita over the same time period. The median MEDIA ranking in our sample is 9, where lower scores correspond to better media penetration (the best possible score is one).
Table 2, panel B, presents a correlation matrix for our transparency measures, with Pearson correlation coefficients above the diagonal and Spearman coefficients below the diagonal. No correlations exceed 0.21, suggesting that multicollinearity is unlikely to be a serious issue for our tests. That said, most measures are significantly correlated (in the predicted direction), suggesting that they capture a shared underlying construct.
3.3 liquidity results
Tables 3 and 4 display the test results for our primary hypothesis using our two measures of liquidity as dependent variables. We report results separately with and without the analyst forecast accuracy variable because its inclusion substantially reduces the sample size and limits the sample to the largest firms.
Zero Return Days and Transparency Determinants
|DIS_SMTHC||0.018||0.00||0.012||0.00||0.013||0.00||0.007||0.04|| || || || |
|BIG5||−0.005||0.05||−0.003||0.15||0.001||0.70||−0.003||0.31|| || || || |
|ANALYST||−0.003||0.00||−0.003||0.00||−0.001||0.00||−0.004||0.00|| || || || |
|R_ACCURACY|| || || || ||−0.088||0.00||−0.018||0.35|| || || || |
|INTGAAP_S||−0.045||0.00||−0.019||0.00||−0.031||0.00||−0.029||0.00|| || || || |
|TRANS|| || || || || || || || ||−0.189||0.00||−0.081||0.00|
|S.E. Clusters (#)||F (15,681)||F (15,681)||F (8,781)||F (8,781)||F (15,681)||F (15,681)|
Bid-Ask Spread and Transparency Determinants
|DIS_SMTHC||0.047||0.00||0.063||0.00||0.054||0.00||0.066||0.00|| || || || |
|BIG5||−0.048||0.00||−0.079||0.00||−0.018||0.12||−0.041||0.01|| || || || |
|ANALYST||−0.010||0.00||−0.006||0.00||−0.010||0.00||−0.010||0.00|| || || || |
|R_ACCURACY|| || || || ||−0.419||0.00||−0.209||0.04|| || || || |
|INTGAAP_S||−0.096||0.00||−0.094||0.00||0.005||0.74||−0.040||0.01|| || || || |
|TRANS|| || || || || || || || ||−0.533||0.00||−0.356||0.00|
|S.E. Clusters (#)||F (12,382)||F (12,382)||F (6,863)||F (6,863)||F (12,382)||F (12,382)|
Recall that larger values of our liquidity measures represent higher illiquidity. In terms of our control variables, the results suggest that larger firms (LNMVE) with higher book-to-market ratios (BM) tend to be more liquid, although the relations are not always statistically significant. As discussed earlier, disclosure versus migration issues lead to ambiguous predictions for the two ADR coefficients. The signs of the nonexchange-traded ADR coefficients in our models are mixed, suggesting that the liquidity migration and disclosure effects tend to be offsetting when cross listing is not accompanied by an increase in mandated disclosure, but the coefficients on the exchange-traded ADR variable are generally negative, suggesting that cross listing increases liquidity when accompanied by increases in mandated disclosure and oversight.
In addition, the %CLHLD_H variable is significantly positive, suggesting that increases in ownership when ownership concentration is especially high are associated with lower liquidity. While this could reflect a lower level of transparency for firms with relatively concentrated ownership, it may also capture differences in free float.21 The %CLHLD_L variable is (in some specifications) significantly negative, suggesting that increases in ownership when ownership concentration is relatively low have a positive net effect on governance and, hence, on firm liquidity over and above any offsetting free float effect.
Finally, the coefficient on FUND_SMTHC is consistently negative. This result is interesting because it suggests that firms for which accruals naturally smooth earnings relative to cash flows have higher liquidity, consistent with research, such as Francis et al. , indicating that transparency may be greater for firms for which accruals naturally smooth earnings.22
In terms of our primary relations of interest, we find consistent results across most specifications, indicating that our transparency measures are significantly associated with increased liquidity. For both zero-return days and bid-ask spreads, we find a significantly positive coefficient on discretionary earnings smoothing, suggesting that transaction costs are higher and investors are less willing to trade in firms’ shares when managers report earnings that are excessively smoothed relative to underlying cash flows. Results in appendix A suggest that managers tend to smooth earnings more when incentives to create opacity are strongest and oversight is weakest. The results in table 3 complement those results by suggesting that earnings smoothing is associated with less liquidity. Further, the fact that the coefficient on DIS_SMTHC is the opposite sign from the coefficient on FUND_SMTHC suggests that the split between discretionary and fundamental components is not arbitrary, but instead that the two measures appear to capture different aspects of smoothness.
In addition, we find a significant negative coefficient on the indicator variable for Big-5 auditor in most specifications, suggesting that a high-quality auditor is associated with greater transparency and an increased willingness by investors to transact in a firm's shares. The coefficient on INTGAAP_S is negative in most specifications, suggesting, consistent with Daske et al. , that use of nonlocal accounting standards by firms that are likely to be serious adopters is associated with greater liquidity.23
In terms of the analyst variables, both are consistent with expectations. Across all specifications, the number of analysts following the firm and analyst forecast accuracy are each positively correlated with liquidity. The results suggest that the oversight and information acquisition roles of analysts increase investors’ willingness to transact in the firm's shares.
Overall, the liquidity analysis is consistent with our predictions regarding transparency. It suggests that, controlling for country, industry, year and many other factors, transaction costs are lower and liquidity is higher for firms with less evidence of earnings management, a higher quality auditor, a serious commitment to international GAAP, greater analyst following, and more accurate analyst earnings forecasts. It is noteworthy that the results are most consistent with firm fixed effects, suggesting that the findings do not simply reflect fundamental firm-level differences across the sample. Further, the fact that each measure has an incremental effect on transparency suggests that, while the measures are unlikely to be independent (e.g., auditor quality and accounting standards influence earnings smoothing), none subsumes the others.
For parsimony in our later analyses, we combine the transparency variables to create an aggregate measure, TRANS, by ranking each variable, summing the percentile ranks, and taking the average.24 If R_ACCURACY is not available, the measure captures the percentile rank of the four remaining variables (DIS_SMTHC, BIG5, ANALYST, and INTGAAP_S).25 This aggregation also reflects the fact that the various transparency measures are unlikely to be independent, so the aggregated variable captures the combined effect. We report the coefficients for models using this aggregate measure in columns 5 and 6 of tables 3 and 4. When we replace the individual transparency components with TRANS, the coefficient on TRANS is highly significant in both the ZERORET and LN(BIDASK) specifications with coefficients of −0.189 and −0.533, respectively. Appendix B reports results for the disaggregated transparency components for the analyses that follow. While the disaggregated results are generally consistent with our primary findings, the individual transparency components are not always significant.
Although our TRANS measure is imprecise, the economic significance of our results appears to be meaningful.26 The results in tables 3 and 4 suggest that an interquartile shift in TRANS (i.e., a shift from the 25th to the 75th percentile) is associated with a nearly 17% decrease in the proportion of zero return trading days and about a 10% reduction in the bid-ask spread.27
3.4 when does firm-level transparency matter most?
The prior section establishes a baseline association between transparency and liquidity. However, one of our primary goals is to document the extent to which the importance of firm-level transparency varies with a firm's characteristics and its environment. We expect that firm-level transparency will be more important in countries, companies, and time periods in which there is likely to be more investor demand for information because, for example, inherent uncertainty is greater. We consider three aspects of the firm's environment that are likely to increase investor demand for information: poor country-level institutions, time periods with high aggregate levels of investor uncertainty, and firm-level governance problems.
3.4.1. Transparency and Country-Level Institutions.
Country-level institutions are likely to influence the extent to which firm-level transparency affects liquidity. In particular, firm-level transparency likely matters more when investor protection (ASDI), disclosure (DISCLOSE), and media penetration (MEDIA) are weak. Because we estimate models with country fixed effects in our initial analysis, we cannot test for country effects using country-level variables across our full sample. To execute our tests while still controlling for country fixed effects, we split our sample into countries that score below and above the median value of these three measures, respectively.28 Given the similarity of results for both of our liquidity measures, for parsimony we report our subsequent results using a combined liquidity measure (ILLIQ). ILLIQ is equal to the average, scaled percentile rank of the available liquidity measures, ZERORET and LN(BIDASK). If BIDASK is unavailable, the measure captures the percentile rank of ZERORET.29
Table 5, panel A, reports the results of these analyses. Our primary interest is in comparing the strength of the relation between TRANS and ILLIQ across the high and low ASDI, DISCLOSE, and MEDIA subsamples. The results are consistent with our expectations. First, in terms of controls, most relations are similar across the two subsamples. Where the coefficients differ, they are generally consistent with expectations. For example, cross listing has a greater positive effect on liquidity for firms from countries with weak investor protection, consistent with bonding being more important in those environments. Similarly, concentrated ownership is more of an issue for liquidity in environments with weaker investor protection.
Illiquidity and Country-Level Governance
|HIGH–LOW (p-value)|| ||0.216 (0.00)|| || ||0.081 (0.00)|| || ||0.061 (0.03)|| |
|S.E. Clusters (#)|| ||C–I (589)|| || ||C–I (589)|| || ||C–I (589)|| |
|S.E. Clusters (#)||F (4,714)||F (10,967)||F (4,940)||F (10,741)||F (8,194)||F (7,487)|
|RIGHT-LEFT (p-value)|| ||0.123 (0.00)||0.069 (0.08)||0.008 (0.82)|| |
|S.E. Clusters (#)|| ||C–I (436)||C–I (348)||C–I (201)|| |
|Number of Countries||18||16||8||4|
|S.E. Clusters (#)||F (2,530)||F (3,255)||F (3,748)||F (6,148)|
In terms of our primary relation of interest, the association between transparency and illiquidity is significantly negative for countries with both weak and strong country-wide institutions across all specifications. However, consistent with our hypothesis, the relation is substantially stronger for countries with low ASDI, DISCLOSE, and MEDIA scores.30 Further, the results obtain consistently regardless of whether our measure captures minority investor protection (ASDI), opacity (DISCLOSE), or the amount of information available from sources external to the firm (MEDIA). Coefficient estimates relating transparency to liquidity are two to three times as large for firms in countries with weak investor protection, disclosure, and media penetration, consistent with the notion that, in environments in which country-wide institutions are weak, firm-level factors such as choice of auditor, accounting standards, extent of earnings smoothing, and oversight by analysts become substantially more important.31
Next, we attempt to capture the incremental importance of the general governance provided by a country's institutions by creating an aggregate governance measure, GOV_SCORE, which combines our three country-level institutional variables. GOV_SCORE is calculated by summing, for each country, the number of instances ASDI, DISCLOSE, or MEDIA is above the sample median and, as such, ranges from zero to three. In panel B of table 5, we report results across each of the four possible GOV_SCORE value groups. We split the sample by group to allow coefficient estimates to vary across groups.
There are two primary takeaways from this analysis. First, the number of observations is substantial in each partition, suggesting that our sample firms and countries are relatively well dispersed amongst the four GOV_SCORE groupings and that the three governance measures in panel A capture different institutional attributes. More importantly, the magnitude of the TRANS coefficient increases monotonically, moving across partitions from strongest to weakest overall governance environments. Further, the coefficients indicate economic importance as well, with a move from the best to the worst GOV_SCORE group being associated with a coefficient on TRANS that is almost three times as large in magnitude (−0.117 vs. −0.317). Overall, this analysis suggests that firm-level transparency is increasingly important as additional aspects of country-level governance become worse.
3.4.2. Transparency and Time Period Uncertainty.
A second environmental factor that is likely to matter for transparency is time period uncertainty. For example, when there are exogenous shocks that increase uncertainty, effects are likely to be mitigated for more transparent firms. One method for capturing market uncertainty in the U.S. is to use a measure such as the Chicago Board Options Exchange Volatility Index (VIX), which reflects expected volatility of the S&P Index over the next 30 days (e.g., Dreschler ). Unfortunately, similar measures are not available for all of the markets we study here. However, investors are likely to set their expectations of future volatility based on currently observed volatility. While our preceding analyses are based on annual observations because of our measures of transparency, we can measure volatility over a much shorter window. We base our measure of expected future volatility on the last 30 days of country-level return volatility.32 As a result, the question we ask is whether, following a month of high country-level uncertainty, firm-level illiquidity is more sensitive to transparency.33
Table 6 reports the empirical results of three models interacting TRANS with lagged country-level volatility, where HVOL designates a time period of above-median recent volatility. Several points are worth noting. First, HVOL is strongly positive across all specifications, suggesting that increases in uncertainty tend to be associated with substantial reductions in liquidity. Second, the TRANS variable is significantly negative across all specifications, suggesting that transparency is important to liquidity, even controlling for high investor uncertainty. Most important for our research question, the TRANS*HVOL coefficient is strongly negative, suggesting that transparency is particularly important to liquidity when uncertainty is high and it helps to mitigate the effect of uncertainty on liquidity.34 In other words, opaque firms appear to suffer substantially more from volatility shocks than do more transparent firms.
Illiquidity and Country-Level Volatility
|S.E. Clusters (#)||F, Y-M (10,751, 84)||F, Y-M (10,751, 84)||F, Y-M (10,751, 84)|
The differences in specifications are also interesting. Model 1 includes controls for only industry and year fixed effects, retaining cross-country variation. Results suggest that transparency is more important for liquidity in country/time periods with high uncertainty than those with low uncertainty. Model 2 includes controls for industry, year, and country with similar results, suggesting that the findings do not simply reflect cross-country variation in uncertainty. Model 3 includes year and firm fixed effects, showing that, even within a firm, transparency matters more in time periods with higher uncertainty, which suggests that our results do not simply reflect other cross-firm differences. The firm-fixed-effects finding is also important because, as discussed later, it suggests that the relation we observe between transparency and liquidity is not the result of reverse causality. From the firm's perspective, the country-wide uncertainty shock is exogenous and our firm-level transparency measure is lagged, so it does not change. As a result, the differential relation between liquidity and uncertainty as a function of transparency cannot be caused by transparency responding to liquidity.
To further assess the effect of volatility on the strength of the relation between transparency and liquidity, we disaggregate the HVOL indicator into quartiles. Results (not tabulated) provide evidence that the mitigating effect of transparency is monotonically increasing in the extremity of the uncertainty. These results are particularly interesting in light of the recent global economic crisis because they suggest that transparent firms are less likely to be affected by increases in investor uncertainty than are opaque firms.
3.4.3. Transparency and Firm-Level Governance.
A final setting in which transparency is likely to be particularly important is where firm-level governance is weak, which can occur when ownership is highly concentrated and managers are thus more likely to be entrenched. To examine the effect of firm-level governance, we follow our earlier approach of including separate coefficients for low and high ownership concentration, but we now interact these piecewise coefficients with transparency. Results are reported in table 7. As before, we find that, at higher levels, increased ownership concentration appears to exacerbate agency problems and increase investor uncertainty. However, the interactions between ownership concentration and transparency remain significantly negative for both low and high ownership-concentration firms, suggesting that transparency is associated with less uncertainty irrespective of the ownership level. More important for our hypotheses, the effect of transparency is particularly strong among firms with the highest levels of ownership concentration, suggesting that, while transparency is associated with greater liquidity at all levels of ownership, the effects are particularly pronounced when ownership concentration (and, hence, the extent of expected agency problems) is high.
Illiquidity and Firm-Level Governance
|HIGH–LOW (p-value)|| || ||0.209 (0.00)|| |
|HIGH–LOW (p-value)|| || ||0.188 (0.00)|| |
|S.E. Clusters (#)|| || ||C-I(589)|| || |
|S.E. Clusters (#)||F (15,681)||F (4,714)||F (10,967)|
One final issue is whether firm-level governance interacts with country-level institutions. The idea is that concentrated ownership is likely to be particularly problematic when minority investor protection is weak and, hence, transparency is likely to be particularly important in those contexts. To assess this, we repeat the analysis, splitting between high- and low-ASDI firms, including the ownership concentration interaction. The last two columns of table 7 report the results of the analysis. Irrespective of country-level investor protection, transparency is more important for firms with more concentrated ownership. However, transparency matters most when concentrated ownership is combined with weak investor protection. The transparency coefficient is more than six times as large for observations with highly concentrated ownership in weak investor protection environments relative to observations with more diffuse ownership in strong investor protection environments.35
Overall, results from these analyses suggest important interactions between transparency and other aspects of the firm's environment. If overall uncertainty is high, either because of weak country-level institutions, economic volatility, weak firm-level governance, or a combination of these factors, we find that firm-level transparency appears to be particularly important to liquidity. Further, the fact that all of the interactions are consistent with expectations provides some comfort that our measures do, in fact, capture aspects of transparency and liquidity.
3.5 endogeneity and reverse causality
As noted earlier, a challenge for research on transparency is assessing causality. For example, the firm's choice of transparency may be a function of unobservable omitted variables (an endogeneity issue) or of the underlying liquidity in a firm's shares (a reverse causality issue). Our results are based on associations and, while we view them as descriptively interesting, it is inappropriate to draw strong conclusions about causality. That being said, we use several approaches to attempt to provide assurance that our results are not a product of endogeneity or reverse causality.36
First, our analyses include a wide range of controls which should capture many of the reasons that transparency might be endogenous. For example, suppose one is concerned that transparency and liquidity are both high because a firm trades on a U.S. exchange or because it attracts greater investor interest. Inclusion of variables such as cross listing, size, market-to-book, and growth should mitigate this concern. Similarly, our primary analyses use country, industry, and year fixed effects, which should control for country, industry, and time period factors.
Second, the robustness to inclusion of firm fixed effects makes it unlikely that our results simply reflect unmodeled cross-firm differences in transparency and liquidity. For example, if it were the case that some firms are of inherently higher liquidity and higher transparency, the relation between liquidity and transparency should not be evident in a within-firm comparison. If anything, our results are more consistent after controlling for cross-firm differences, suggesting that within-firm variation contributes meaningfully to the results.
Although the inclusion of firm fixed effects controls for time-invariant omitted variables, the possibility remains that firms experience a significant change not explicitly captured by our control variables, such as growth opportunities or financing needs, and that this change simultaneously affects both firm liquidity and our transparency proxies. To provide further assurance that such a change is unlikely to drive our results, as an untabulated robustness test we include several additional growth and financing variables in our analyses. The first is analysts’ long-term growth forecasts from I/B/E/S. These forecasts, made during the firm's current fiscal year, capture analyst expectations of earnings growth over the next three to five years and, as such, should serve as a control for any predictable changes in the firm's growth opportunities. In addition, we include period t+ 1 sales growth (thus implicitly assuming perfect foresight by the market). To control for shocks to a firm's financing needs, we employ two additional variables: capital raising and capital expenditures in period t+ 1 (again assuming perfect foresight). Although these additional control variables substantially reduce our sample size and there is some slight attenuation of the coefficient on TRANS, our inferences throughout the paper remain unchanged by their inclusion, either individually or as a group.37
Third, the results are robust to the inclusion of lagged transparency. Un-tabulated results indicate similar conclusions when the contemporaneous transparency values are replaced by lagged values of transparency, again suggesting that our baseline results do not simply reflect an unobservable variable that drives both transparency and liquidity. Further, fixed effects results are robust to inclusion of lagged transparency, suggesting that innovations in transparency tend to be associated with higher subsequent liquidity.
Fourth, the analyses in the preceding section suggest that the effects are strongest following changes in investor uncertainty. Ideally, we would be able to observe exogenous shocks to transparency to help to identify the system. While it is difficult to identify exogenous transparency shocks, we can examine exogenous changes in country-level uncertainty over periods during which transparency is unlikely to change appreciably (e.g., one month windows). Given transparency is measured over the preceding year and is thus being held constant during those periods, and changes in liquidity remain a function of transparency, it is unlikely to be the case that the observed association between transparency and liquidity is simply the result of transparency changing in response to changes in liquidity.
Fifth, given that the association between liquidity and transparency varies predictably across subsamples, it is more difficult to envision a consistent theory in which causality is reversed yet the subsample results hold. For example, the theory would need to explain why, in low investor protection environments, in firms with higher ownership concentration, and in periods of high uncertainty, relatively lower levels of liquidity result in especially low levels of transparency.
Next, we attempt to assess endogeneity and causality by explicitly modeling transparency and liquidity in a two-stage least squares framework. Statistical approaches for dealing with simultaneity/causality concerns are not easy to implement because of the difficulty in obtaining appropriate instruments. In designing our system, we are guided primarily by analyst following research since we are not aware of attempts to model our other variables simultaneously. However, given that investor demand for information provides the motivation for the choice of variables in modeling analyst following, such a motivation is likely to be shared when selecting related variables to model such as auditor choice, accounting standards, and earnings smoothing, and a similar estimation approach seems appropriate.38
In terms of analyst following, Roulstone  provides some evidence that causality runs from analyst following to liquidity. He jointly estimates analyst following and liquidity for a sample of U.S. firms and concludes that analysts provide information to capital markets and do not simply chase liquidity. Similarly, Yu  provides evidence that analysts discipline accounting choices around earnings management. In our two-stage least squares analysis (untabulated), we build on Roulstone  and Yu  and estimate a first-stage model that features transparency (TRANS) as a function of two sets of variables: potentially endogenous variables present in table 3 liquidity regression (LNMVE, BM, STDRET, ADR_EX, ADR_NEX,%CLHLD_H,%CLHLD_L, and FUND_SMTHC), and those suggested by research such as Lang and Lundholm , Roulstone , and Yu  as instruments for transparency (return-earnings correlation and asset growth, both computed over the prior three- to five-year window, and one-year lagged return on assets).39 Our second-stage model uses the same independent variables as the liquidity equations from column 5 of tables 3 and 4 and uses ILLIQ as the dependent variable. Analysis of the first-stage model suggests that our instruments are significantly related to transparency and the Kleibergen-Paap statistic indicates that we do not suffer from weak instruments (see Stock and Yogo ). Results from the two-stage estimation are consistent with those reported earlier in that transparency remains significantly negatively correlated with illiquidity.40
Subject to the caveat that dealing with endogeneity is difficult in these types of environments, the overall evidence presented in this section provides some confidence that our results do not simply reflect endogeneity or reverse causality.
3.6 other analyses
In this section, we discuss the results of several untabulated robustness tests also designed to bolster confidence in the interpretation of our results. First, because they represent such a significant portion of our sample, and thus threaten the generalizability of our results, we repeat our analyses eliminating Japanese and U.K. firms. Our inferences are robust to the exclusion of both of these countries. In fact, the relation between transparency and liquidity holds for 44 out of 46 sample countries, suggesting that the results are not driven by a small subset of countries. Similarly, to examine whether the results are sensitive to time period trends, we rerun the analysis by year. Results are consistent for each of the years in the sample.
Next, we implement two alternative estimation procedures for our discretionary smoothness variable, DIS_SMTHC. First, rather than pooling firm-year observations from all countries, we estimate DIS_SMTHC using the residuals from within-country regressions. We continue to find that discretionary smoothing is significantly negatively related to liquidity in all specifications. Second, we construct an alternative measure of DIS_SMTHC based on the predicted value from a regression of the residual value from the first-stage fundamental smoothness regression on determinants of discretionary smoothness discussed in appendix A. The residual from the fundamental smoothness regression consists of at least two components: discretionary smoothing and noise. This alternative estimation attempts to reduce noise in the discretionary component by capturing only the components of the residual related to likely determinants of discretionary smoothness. A limitation of this specification is that our first-stage fitted values are a linear function of a number of the other variables in the second-stage regression, so there is substantial multicollinearity. Including the fitted value in the liquidity regressions, the coefficient estimate on the fitted value remains strongly positive as predicted (p-value of 0.00) in all specifications; the fundamental smoothing variable remains significantly negative; and the analyst following, international GAAP, and accuracy measures remain significantly negative in all specifications. The Big-5 variable remains negative, but becomes insignificant in several specifications, likely reflecting multicollinearity.
Third, we include share turnover, defined as the annual volume of shares traded for the firm's fiscal year divided by the firm's total number of shares outstanding, as an additional control in the analysis. We do not include turnover in our primary analysis for two reasons. First, calculation of share turnover requires firm-level trading volume data, which materially reduces the size of our sample, and the missing observations are clustered in a subset of country/years limiting the breadth of the sample. Second, it is not clear whether including a control for turnover is appropriate since turnover is itself a measure of liquidity (see, e.g., Lesmond ) and reflects the effects of trading costs and infrequent trading. However, including share turnover as an additional control variable does not affect any of our conclusions.
Fourth, we consider regression specifications excluding FUND_SMTHC and BM. Our motivation for including these variables in our main analyses is to control for factors other than transparency, such as business risk, likely to affect liquidity. However, fundamental earnings smoothness and book-to-market have not typically been used as controls in liquidity regressions. Results excluding these variables are very similar to those presented in the tabulated analyses.
Fifth, we assess robustness to alternative constructions of the TRANS and ILLIQ variables. In our primary analyses, TRANS is based on the average percentile rank of DIS_SMTHC, BIG5, ANALYST, and INTGAAP_S and includes R_ACCURACY only where it is available. ILLIQ is similarly constructed as the average of the percentile rank of ZERORET and BIDASK when available. We construct the TRANS and ILLIQ variables in this fashion to preserve sample size and retain a broad cross section of firms and economic environments. To ensure that results are not driven by our treatment of missing observations, we re-estimate all of our analyses with modified TRANS and ILLIQ variables. The modified TRANS variable excludes R_ACCURACY, and is calculated as the average percentile rank of the four other proxies that are available for the entire sample. The modified ILLIQ variable is calculated only when both ZERORET and BIDASK are both nonmissing. Overall, results are very similar to our tabulated analyses, alleviating concerns that the construction of the TRANS variable unduly influences our results.
Finally, we include controls for the overall level and absolute value of accruals in our tests of liquidity effects. Jayaraman  and Bhattacharya, Desai, and Venkataraman  suggest that, in a U.S. setting, a higher level of accruals may be suggestive of greater informed trading and higher transaction costs. To ensure that our analysis is not affected by such circumstances, we replicate our analysis including the level and absolute value of accruals. We find that including either (or both) accruals measures has no effect on the inferences drawn in our liquidity analysis.
3.7 linking transparency and liquidity to valuation
We conclude our empirical section by specifically examining the relation between liquidity and both ex-ante cost of capital and firm valuation, as well as the role played by transparency. While it is true in theory that liquidity should affect cost of capital and valuation, as discussed earlier, there is little empirical evidence for an international set of firms on the significance or economic magnitude of this relation and, to our knowledge, there is no evidence that directly ties in the linkage with transparency.
We assess the effects of liquidity using two modeling approaches: implied cost of capital and Tobin's Q. There is debate in the empirical literature as to the optimal approach to estimating an implied cost of capital (see, e.g., Botosan and Plumlee , Hail and Leuz , and Lee, Ng, and Swaminathan ). For this reason, we estimate four separate models frequently cited in the literature: 1) the modified PEG ratio model by Easton ; 2) the Ohlson and Juettner-Nauroth  model; 3) the Gebhardt, Lee, and Swaminathan  model; and 4) the Claus and Thomas  model. Following Hail and Leuz , we take the average of these four models as our firm-specific measure of cost of capital.
We include the following control variables: LNASSETS, LEV, STDRET, ADR_EX, ADR_NEX, BIAS, and RF_RATE (the country-specific yearly risk-free interest rate). These controls are typical for the literature (see, e.g., Easton , Botosan and Plumlee , and Daske et al. ). In addition, we include country, industry, and firm fixed effects.41 Descriptive statistics for our cost of capital measure are reported in table 2, panel A. For our sample, the mean cost of capital is 11.4%, the median is 10.2%, and the interquartile range is from 8.0% to 13.3%. Overall, the relative order of magnitude seems reasonable based on prior studies and there is substantial variation among sample firms.
Our valuation regression estimates that feature implied cost of capital as the dependent variable are reported in table 8. It is important to note that the number of observations has dropped from 97,799 in the preceding analysis to 23,605 here, reflecting the limited availability of long-term analyst forecast data for our sample of firms. This is likely to bias against our finding strong results because the sample is limited to the largest, most heavily followed firms, which are likely to have fewer transparency issues and therefore a weaker relation between transparency and cost of capital. The table shows that cost of capital behaves as one would expect with respect to the control variables; cost of capital is higher the greater is the risk free rate, and is higher for smaller firms, more highly leveraged firms, and more volatile firms. Although not statistically significant at conventional levels, the exchange-listed ADR variable is negative, consistent with the prior literature.42 More importantly, our variable of interest, TRANS, is negatively and significantly associated with cost of capital, which indicates that, as predicted, firms with higher levels of opacity tend to face a higher cost of equity capital. Results are consistent in the firm fixed effects estimations (column 3), suggesting that the analysis does not simply capture differences in underlying characteristics across firms.
Cost of Capital, Illiquidity, and Transparency
|ILLIQ|| || ||0.021||0.00|| || ||0.012||0.00|
|Mediating Effect (p-value)||0.009 (0.00)|| || ||0.004 (0.00)|| |
|Fixed Effects||C, I||C, I||F||F|
|S.E. Clusters (#)||F (5,387)||F (5,387)||F (3,387)||F (5,387)|
Beyond documenting that transparency is negatively associated with cost of equity capital, we also examine whether liquidity is an important channel for this effect using a mediation analysis (e.g., Hammersley ).43 The results of the mediation analysis (with country, industry, and year fixed effects in column 2 and with firm fixed effects in column 4) confirm our predictions. Specifically, the coefficient on TRANS decreases significantly when ILLIQ is added to the regression analyses, suggesting that liquidity is a significant channel through which transparency affects cost of capital.44 Moreover, we can use the coefficients on our variables to broadly gauge economic significance. They indicate that a shift from the 75th to the 50th illiquidity percentile is associated with a decrease in cost of capital of just less than half of a percentage point (45 basis points) and that a shift from the 75th to the 50th transparency percentile is associated with an increase in cost of capital of approximately one-third of a percentage point (32 basis points). These results suggest that the liquidity and transparency effects we document are economically important as well as statistically significant.45
We also estimate the relation between liquidity and valuation as measured with Tobin's Q. While a cost of capital approach is more direct, it limits our analysis to a small subsample of the largest firms and relies crucially on the assumptions around analyst forecasts. This is particularly an issue in international settings where analyst following, when it exists, tends to be limited. For our Tobin's Q analysis, the sample size increases substantially to 76,936 and includes a much wider range of firms.46
Tobin's Q is defined as: (book value of assets + (market value of equity – book value of equity))/book value of assets. It is designed to reflect the valuation placed on the assets by the market relative to their book value and inherently incorporates the cost of capital used in discounting future cash flows. Table 2, panel A, provides statistics for Tobin's Q (Q). The median Q is about 1.10, indicating that investors value assets slightly above their book value.
Table 9 reports results for Tobin's Q. We include control variables from the prior literature, along with country, industry, year and firm fixed effects.47 Results for the controls are consistent with prior literature. Tobin's Q tends to be higher for firms that are smaller, more profitable, more highly levered, have higher growth, are cross listed on U.S. exchanges, and pay dividends. In terms of our variable of interest, the coefficient on TRANS is strongly positive, suggesting that investors place higher valuations on more transparent firms.
Firm Value, Illiquidity and Transparency
|ILLIQ|| || ||−0.811||0.00|| || ||−0.515||0.00|
|Mediating Effect (p-value)||0.233 (0.00)|| || ||0.07 (0.00)|| |
|S.E. Clusters (#)||F (13,466)||F (13,466)||F (13,466)||F (13,466)|
Again, we estimate a mediation analysis to disentangle the valuation effect of transparency that occurs through liquidity from the direct effect of transparency on valuation. The mediation analysis suggests that transparency is important to valuation, both through the liquidity channel as well as through other channels. Including ILLIQ in the regression significantly mediates the effect of transparency on valuation, although the effect is only partial because the coefficient on TRANS remains significantly positive. Finally, the relation between liquidity and Q appears to be economically meaningful as well, with a shift from the 75th to the 50th percentile of ILLIQ associated with about a 16% increase in Tobin's Q and a similar shift in TRANS associated with a 6.5% decrease in Tobin's Q.48
Reductions in the liquidity and valuation of securities traded in global capital markets during the recent financial crisis have demonstrated the importance of understanding more fully the drivers of a firm's stock market liquidity and associated linkages to valuation. In this paper, we examine whether reduced transparency is associated with increased transaction costs and lower liquidity in a firm's shares and, therefore, increased cost of capital and reduced valuation. We also investigate the extent to which the relation between transparency and liquidity is influenced by institutional and firm-level factors and by time series variation in uncertainty.
For a global sample of firms, our evidence suggests that increased transparency, as reflected in reduced earnings management, higher quality auditing, a serious commitment to international accounting standards, increased analyst following, and smaller analyst forecast errors, is associated with lower bid-ask spreads and greater liquidity. The relation is particularly pronounced in environments in which there is likely to be more inherent uncertainty (weak country-level institutions, time periods of increased country-level volatility, and when ownership is concentrated), suggesting that firm-level transparency is most important in the presence of other informational issues. Our results also provide evidence that liquidity represents an important channel through which transparency becomes associated with a lower cost of capital and higher valuation. Taken together, our results suggest that a focus on the transparency provided to those who invest in a firm's securities could be a fruitful component of an effort to more fully understand the increases in illiquidity and decreases in valuation for many assets worldwide associated with the recent financial crisis.
Our results are subject to several caveats. First, we focus on only one potential consequence of increased transparency, improved liquidity. Of course, increased transparency entails other costs and benefits. As a consequence, our results do not imply that managers would be better off by increasing transparency, only that benefits may accrue through reduced transaction costs and increased liquidity. It is possible that other costs associated with increased transparency more than offset the liquidity benefits. There is room for future research examining more specifically the tradeoffs in establishing an optimal transparency level.
Second, as discussed earlier, it is difficult to ascertain causality. Our analyses are based on associations, and we cannot be certain to what extent the relations are causal. While we attempt to control for a wide range of potentially important factors and to account for possible endogeneity, conclusions should be drawn with caution. There is substantial scope for additional research identifying more specifically the channels through which transparency may affect liquidity. Overall, though, we view our paper as providing interesting initial evidence on the potentially important effects of transparency on liquidity and valuation in a global setting.
Computation and Tests of the Discretionary Earnings Management Proxy: DIS_SMTHC
We compute DIS_SMTHC based on two earnings smoothness measures commonly used in the literature. The first earnings smoothness measure (SMTH1) captures the volatility of earnings relative to the volatility of cash flows with the idea being that, the more firms use accruals to manage earnings, the smoother net income will be relative to cash flows (Leuz, Nanda, and Wysocki  and Francis et al. ). SMTH1 is measured as the standard deviation of net income before extraordinary items divided by the standard deviation of cash flow from operations, where net income before extraordinary items and cash flow from operations are scaled by average total assets and the standard deviations are calculated using rolling time intervals requiring a minimum of three and a maximum of five years of data. Cash flow from operations is equal to net income before extraordinary items minus accruals, where accruals are defined as the change in current assets minus the change in current liabilities minus the change in cash plus the change in current debt in current liabilities minus depreciation and amortization expense.
The second earnings smoothness measure (SMTH2) is the correlation between the cash flow from operations scaled by total assets and total accruals scaled by total assets. The idea behind this measure is that, to the extent managers create accrual reserves in good times and use them to compensate for poor cash flows in bad times, accruals and cash flows will be more negatively correlated (Lang, Raedy, and Wilson , Barth, Landsman, and Lang ). We note that Leuz et al.  and Bhattacharya, Daouk, and Welker  calculate their correlation-based measure using the change in cash flows from operations and the change in total accruals, whereas our correlation measure is based on the level. We draw identical inferences when defining SMTH2 based on changes; however, the sample sizes are smaller due to the additional data requirements of the change measures. Both smoothing measures (SMTH1, SMTH2) are multiplied by negative one so that larger values represent firms with smoother earnings.
The smoothness of earnings relative to cash flows is clearly a natural function of the fundamentals that affect a firm's operating environment, but we are interested in the portion in excess of naturally occurring earnings smoothness. As a consequence, we draw from prior research on the determinants of earnings smoothness and specify an equation designed to capture, to the extent possible, the expected level of earnings smoothness for a firm. We then measure discretionary (excess) smoothing using the residual from the regression specified below:
The right-hand side variables are: LNASSETS, the log of total assets measured in millions of U.S. dollars, a measure of firm size; LEV, total debt divided by total assets, to capture differences in financing choices; BM, the ratio of book value to market value of equity, to reflect the extent of the firm's intangible assets and expected earnings growth; STD_SALES, the standard deviation of sales, to capture the volatility of a firm's underlying operating environment; %LOSS, the proportion of years that a firm experiences losses over the last three to five years, to capture differences in the accruals properties of loss observations; OPCYCLE, the log of days of accounts receivable plus inventories, to capture the length of the firm's operating cycle; SG, the average sales growth over the past three to five years, to capture growth opportunities; OPLEV, net property, plant and equipment divided by total assets, to capture capital intensity; AVECFO, average cash flow from operations divided by total assets measured over the last five years, to capture a firm's general level of profitability; and indicator variables for a firm's industry because the properties of accruals are likely to depend on industry, as well as year indicator variables to control for macro economic cycles that could affect earnings cycles.
After we obtain each of the two discretionary smoothness regression residual measures for SMTH1 and SMTH2, they are then scaled into percentile ranks, and combined by taking the average. This variable is referred to as DIS_SMTHC and is used to proxy for earnings management in our liquidity regressions. We follow the same procedure in computing our proxy for fundamental earnings smoothness, which is used as a control variable in our tests. The predicted values obtained from the model for SMTH1 and SMTH2 are scaled into ranks and averaged—this variable is called FUND_SMTHC.
Because earnings management is inherently difficult to measure, we next conduct a set of tests designed to build confidence in the selection of our residuals-based measure of earnings management. For these tests, we benchmark our SMTH1 and SMTH2 measures against likely determinants of discretionary earnings management. If our measures reflect managerial discretion, there should be predictable correlations with managerial incentives to smooth earnings and with institutional constraints on those incentives that go beyond the correlations with fundamentals-based variables that we have specified in our model above.
Therefore, we conduct an additional analysis that assesses the association between our smoothing measures and a set of incentive and oversight proxies. As country-level measures of governance, we include either the recently developed Anti-Self-Dealing Index (ASDI) of Djankov et al. , which has been shown to be particularly useful in determining the extent to which managerial self-dealing is likely to be controlled by a country's institutional factors or DISCLOSE from La Porta, Lopez-de-Silanes, and Shleifer , which summarizes the disclosure requirements faced by firms in a given country. In addition, we include an indicator variable for the degree of alignment between tax and financial reporting (TXBKCONFORM from Ashbaugh and LaFond ) since, in countries with a high degree of alignment, the incentives managers face to smooth earnings for taxes will carry over to smoother accounting earnings (Alford et al. , Ali and Hwang , and Kasanen, Kinnunen, and Kiskanen ).
In terms of firm-level determinants, we include an indicator variable for whether the firm is listed on a U.S. exchange, ADR_EX, since the U.S. regulatory environment is considered one of the most demanding in the world. We note that, while firms trading in U.S. markets are not required to report local accounts that comply with U.S. GAAP, Pownall and Schipper , Ashbaugh and Olsson , and Lang, Ready, and Wilson  suggest that non-U.S. firms required to prepare U.S. GAAP financial information choose alternatives under IFRS or their domestic standards that are closer U.S. GAAP. We also include an indicator, ADR_NEX, for other types of U.S. cross listings (Level 1 and Rule 144A listings) that allow access to U.S. investors, but do not commit the firm to SEC registration requirements since firms may choose to curtail discretionary smoothing to enhance the informativeness of their accounting earnings by U.S. investors, even if they are not subject to additional regulatory oversight. Oversight by informational intermediaries likely affects firms’ incentives and ability to smooth earnings as well. We use analyst following (ANALYST) as a proxy for the demand for transparent financial information by capital market participants.49 Further, because larger auditing firms are likely to have greater resources and greater legal and reputational exposure, we expect attestation by a Big-5 auditing firm (BIG5) to be associated with less discretionary smoothing (Fan and Wong ). Finally, following Bradshaw and Miller  and Barth, Landsman, and Lang , we expect better accounting standards to reduce the ability to manage earnings and include an indicator, INTGAAP, for firms that have adopted either IAS or U.S. GAAP.
Results for our earnings smoothing measures (not tabulated) are all consistent with predictions. In particular, our measures of earnings management are lower for firms in countries with better investor protection and a weaker link between tax and financial reporting, and in firms with higher analyst following and a Big-5 auditor that report under IFRS or U.S. GAAP in their local accounts and trade in the U.S., particularly if they trade on a U.S. exchange. Taken together, these results provide some comfort that our smoothing measures behave as though they reflect managerial discretion in the sense that they are positively correlated with incentives to manage earnings and negatively correlated with impediments to earnings management.
Results Disaggregating the Transparency Variable
Throughout the analyses of the interactive transparency effects (i.e., tables 5–7), for parsimony we report all results using an aggregate measure of transparency. This treatment corresponds to the notion that our variables likely capture the same latent firm characteristic, transparency, and that they are unlikely to be independent of each other.50 Although our primary interest is overall transparency, as opposed to the individual components, in this appendix we report results disaggregating transparency into its components (i.e., DIS_SMTHC, BIG5, ANALYST, R_ACCURACY, and INTGAAP_S).51 In general, conclusions are consistent across the components of the transparency variable, although the relations for the subcomponents are not always statistically significant.
Beginning with panel A of table 5, for the ASDI split, each of the individual transparency variables is more negatively related to illiquidity in the low ASDI group, except for BIG5, where the difference between the sub-groups is insignificant. For the DISCLOSE split, ANALYST, R_ACCURACY and INTGAAP_S are more negatively related to ILLIQ in the low DISCLOSE group, while DIS_SMTHC and BIG5 are insignificantly different between the groups. For the MEDIA split, the difference between the low and high subgroups is significant for the ANALYST, DIS_SMTHC, and INTGAAP_S variables but not for BIG5 and R_ACCURACY. In panel B of table 5, disaggregation of the transparency variable shows that each of the individual variables is most important to illiquidity in the country grouping with the worst governance (GOV= 0) and least important in the group with the best governance (GOV= 3), except for the DIS_SMTHC and BIG5 variables, where these differences are not statistically significant.
In table 6, across all three columns, when disaggregating the TRANS variable into its individual components, we find that DIS_SMTHC, BIG5, and ANALYST are all significantly more negatively related to ILLIQ following periods of high uncertainty. While the interactions between HVOL and R_ACCURACY and INTGAAP_S generally maintain their negative signs, the coefficients are insignificant.
In terms of the closely held shares interactions in table 7, column 1, DIS_SMTHC and ANALYST are both most negatively related to ILLIQ when there is a larger proportion of shares that are closely held, while the remaining three variables are insignificantly different between the partitions. Splitting the sample into high and low ASDI groups, all of the variables, except for DIS_SMTHC, are most negatively related to illiquidity when more shares are closely held and protection against self-dealing is weak.
Following a similar logic to that used in constructing our aggregate transparency variable, we also use an aggregate illiquidity variable in tables 5–7, combining the illiquidity proxies ZERORET and BIDASK. All results in these analyses are robust to using the ZERORET measure. The same is true for BIDASK except in the following cases: (1) In table 5, panel B, the difference in the magnitude of the TRANS coefficient is larger in the low DISCLOSE subsample, but is insignificantly different from the high DISCLOSE coefficient, and (2) in table 6, column 3, the interaction between TRANS and HVOL is negative, but not significant.