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  • 1. Earnings Smoothing, Governance and Liquidity: International Evidence Ryan LaFond Massachusetts Institute of Technology Mark Lang University of North Carolina Hollis A. Skaife University of Wisconsin January 2008 CAUTION: REVISION IN PROGRESS!!! We thank the seminar participants at University of British Columbia, Carnegie Mellon University, University of Chicago, University of Connecticut, University of North Carolina Global Issues in Accounting Conference, Ohio State University, University of Pennsylvania, and University of Texas Issues in Financial Reporting Conference for their comments.
  • 2. Earnings Smoothing, Governance and Liquidity: International Evidence Abstract We examine the relation between earnings smoothing, governance and liquidity for a sample of non-U.S. firms. We divide smoothing into innate and excess components, and find that excess smoothing is increasing in incentives to smooth (greater tax-book conformity, concentrated ownership, related party transactions and weak overall governance) and decreasing in oversight (investor protection, analyst following, ADR listing, Big-5 auditor and international accounting standards). Given the potential for smoothing to affect transparency, we examine the relation between smoothing and investors’ willingness to transact in the stock as reflected in liquidity. After controlling for other liquidity determinants, we find that firms with greater levels of excess smoothing experience lower liquidity as evidenced by higher bid-ask spreads, greater frequency of zero returns days and lower trading volume. Further, the effect of excess smoothing on liquidity and transactions costs is greatest in contexts where incentives are strongest and oversight is weakest. In contrast, results for innate smoothing suggest that innate smoothing is positively correlated with liquidity. Taken together, our results suggest that smoothing is affected by firms’ governance environments and that excess smoothing reduces investors’ willingness to transact in the stock, particularly when firms’ incentives to manage earnings are strong and oversight is weak.
  • 3. Earnings Smoothing, Governance and Liquidity: International Evidence I. Introduction In this paper, we investigate the relation between the earnings smoothing characteristics of accruals, transparency and liquidity in the firm’s stock. Ideally, accruals increase the transparency of reported accounting data relative to cash flows by better reflecting the underlying economics of the firm. However, because accruals have a significant discretionary component, their opportunistic application may decrease transparency. One potential economic consequence of transparency is that it will affect investors’ willingness to transact in the firm’s shares. Prior research suggests that reduced transparency will tend to result in lower liquidity, raising transactions costs and increasing the firm’s cost of capital (Bekaert et al. 2006). We estimate smoothing based on two measures from the prior literature: the variability of net income relative to cash flows (Leuz et al. 2003, Francis et al. 2004), and the correlation between cash flows and accruals (Lang et al., 2006, Barth et al, 2006). We begin by dividing smoothing into innate and excess components following the approach in Francis et al. 2004 because much of the smoothness in earnings is a function of firms’ inherent operating characteristics and the natural role of accruals.1 For example, the smoothing properties of accruals relative to cash flows naturally vary with differences in firm characteristics such as the firm’s industry, operating cycle, growth, size and the inherent variability of the operating environment. Innate accruals have the potential to be associated with increased transparency to the extent that they reflect the underlying 1 We use the terms “innate smoothing” and “excess smoothing” for convenience. We estimate innate smoothing based on predicted values from a regression of our smoothing measures on firms’ operating characteristics and industries in the spirit of the Jones (1991) model of discretionary accruals. Excess smoothing is smoothing beyond that which would be predicted based on the firms’ innate characteristics.
  • 4. operating characteristics of the firm. Accruals that excessively smooth earnings, on the other hand, are more likely to reflect managerial discretion, potentially decreasing transparency.2 To assess whether excess earnings smoothing reflects managerial discretion, we analyze its relation with various incentive and oversight measures. We consider three types of measures, (1) internal, firm-specific incentives, as reflected in concentrated ownership and the alignment of tax and financial reporting, (2) external oversight, such as investor protection, cross listing on US markets, Big-5 auditor, and nonlocal accounting standards, and (3) market oversight, as reflected in analyst following. After controlling for firm operating characteristics, we find, consistent with predictions, that excess earnings smoothing tends to be more pronounced when managers have greater incentives to smooth (concentrated managerial ownership and greater tax-book conformity) and face fewer impediments (weaker investor protection, less analyst coverage and for firms not cross listed on US markets, with a non-Big-5 auditor and following local GAAP). We also investigate whether a broad governance measure based on ratings provided by Governance Metrics International (GMI), which reflects not only the assessment of a firm’s ownership structure, but also captures a firm’s governance related to board structure, financial information quality, and shareholder rights, is related to excess smoothing. In addition, we explore the relation between smoothing and the GMI ranking for related party transactions, because related party transactions provide 2 It is possible that managers sometimes use their discretion to smooth earnings to increase the informativeness of earnings. However, our evidence suggests that, on average, excess smoothing behaves as though it decreases transparency. In particular, excess smoothing is more pronounced when managers have greater incentives to create opacity and oversight is weaker. More directly, transactions costs are higher and liquidity is lower when there is excess smoothing. 4
  • 5. particular opportunities for expropriation and, therefore, particular incentives for opacity. While the sample size drops substantially using the GMI data as we are limited to the largest firms where governance is likely to be less of an issue, our results are consistent in suggesting that excess smoothing is lower for firms with strong governance, especially those with fewer related party transactions. Given that excess smoothing appears to be predictably correlated with incentives and impediments for managers to smooth, we examine whether excess smoothing has effects on trading in a firm’s shares. In particular, if excess smoothing creates opacity, it should affect investors’ willingness to trade. As argued in Lesmond (2005), investors will be hesitant to trade if there are concerns over the adequacy of information available to them and bid-ask spreads will increase, increasing transactions costs.3 Bekaert et al. (2006) develops a model to illustrate that liquidity can affect expected returns even under full market integration. Following research like Lesmond (1999) and Bekaert et al. (2006), we use two measures of liquidity: bid-ask spreads and the proportion of zero-return days. Bid-ask spreads reflect transactions costs, which affect investors’ willingness to trade in a firms shares, while zero-return days reflect the frequency with which investors transact. We examine the relation between excess smoothing, innate smoothing, and these measures of liquidity because both measures have advantages and disadvantages as an empirical proxy for liquidity in international markets.4 3 The disclosure literature contains numerous studies suggesting that transparency can affect investors’ willingness to transact in capital markets. See, for example, Verrecchia (2001) for an overview of the literature. 4 As discussed later, we also conduct the analysis using volume as a measure, with similar results. Research such as Lesmond (2005) and Bekaert, Harvey and Lundblad (2006) suggests that, in international settings, volume tends to be a relatively weak proxy because it is not be computed consistently across exchanges, and does not behave like a priced liquidity factor or correlate highly with other liquidity measures. 5
  • 6. Our results provide evidence of a relation between innate smoothing, excess smoothing and liquidity. In particular, our evidence suggests that firms with greater evidence of excess smoothing experience significantly lower liquidity as reflected in more frequent zero-return days and higher bid-ask spreads, controlling for other factors. In contrast, our results suggest that, if anything, greater innate smoothing is associated with lower information asymmetry and lower transactions costs, as reflected in lower bid- ask spreads and fewer zero return days. This result is striking because it suggests that innate aspects of smoothing behave differently than the portion that is more likely to be influenced by managerial discretion. In other words, firms for which earnings are naturally smooth because of inherent firm characteristics (e.g., operating cycle, growth, size, profitability and sales variability) are associated with greater transparency and liquidity. Only excess, discretionary smoothing appears to reduce transparency. Finally, we examine the interaction between excess smoothing and the governance environment of the firm. We expect that smoothing has the greatest potential to reduce liquidity and increase transactions costs in situations in which incentives are strong and oversight is particularly weak. Our results suggest that, while excess smoothing reduces liquidity and increases transactions costs for the sample in general, the effects are most pronounced in environments where managers have greater incentives to smooth earnings and face less oversight (i.e., for firms that are not cross listed on the US markets, are not followed by analysts, do not file under IFRS, are not audited by a big five auditor and have highly concentrated ownership). A potential question is why managers would choose to smooth if it could have negative effects on investors’ willingness to hold shares. In some cases, it may result 6
  • 7. from managers creating opacity for personal gain. However, it is important to note that smoothing, even if it reduces investors’ willingness to trade, may be optimal for the firm. For example, in many countries stakeholders other than shareholders create incentives to report smoother earnings. Taxing authorities, for instance, may create incentives to smooth earnings because higher profits typically attract higher tax rates and losses may not provide full tax benefits. Similarly, firms may smooth earnings to reduce perceived risk and attract lower interest rates on debt, reduce pressure from labor or limit political costs. Our results suggest that a firm’s tendency to undertake those types of activities is particularly pronounced when financial reporting oversight is weak, few analysts follow the firm and ownership is more concentrated. While smoothing may be optimal in those situations, our results suggest a tradeoff in that excessive smoothing can reduce transparency, resulting in lower liquidity.5 Our results make several contributions to the literature.6 First, and most importantly, we focus on an economic consequence of earnings smoothing. As noted above, managers face tradeoffs, especially in international contexts, in applying discretion. The consequences of managerial discretion on transparency and liquidity are likely to be particularly strong in international settings where incentives to smooth earnings are stronger, financial reporting oversight is weaker and liquidity issues are more pronounced. 5 Desai and Dharmapala (2006) make a similar point in reference to tax avoidance and earnings management. They observe that earnings management to reduce taxes can create opacity that also reduces equity holders’ ability to assess firm performance. 6 Our measures of earnings smoothing can also be interpreted in the context of timely loss (and gain) recognition. As discussed in Ball and Shivakumar (2005, 2006), timely loss recognition will tend to result in a less negative correlation between accruals and cash flows because periods of poor cash flows also typically indicate likely decreases in the present value of future cash flows. As a consequence, timely loss recognition will be reflected in negative accruals in periods of poor cash flows, attenuating the natural negative relation between accruals and cash flows and creating greater volatility in the earnings stream. 7
  • 8. There is little existing evidence on the relation between transparency, transactions costs, liquidity and trading activity in international settings. Bhattacharya et al. (2003) investigates, at the country level, the association between country-wide aggressive loss recognition, loss avoidance and smoothing, and cost of equity capital and turnover. They provide mixed evidence on the relation between earnings attributes, turnover and cost of capital depending on the measure of cost of capital and the earnings attribute employed. Our analysis differs from theirs in several ways. First, because our analysis focuses on across-firm comparisons, we abstract from cross-country differences that may affect equity markets and are able to control for a variety of other firm-specific factors that likely affect earnings attributes and equity markets. Our liquidity analysis includes country fixed effects, so cross country differences are naturally controlled. Also, our results suggest a differential effect for the components of smoothing. We find that only excess smoothing appears associated with reduced transparency, while inherent smoothing appears to increase transparency. Finally, our results illustrate that the effects of smoothing on liquidity and transactions costs tend to be particularly pronounced in environments in which managers have particularly strong incentives to create opacity and there is relatively little oversight. Further, our results are consistent for both measures of trading costs (bid-ask spreads) and measures of liquidity (zero return days). Measures like bid-ask spreads and zero returns days focus more explicitly on the potential effects of information assymetry on transactions costs. Results in Lesmond (2005) suggest that the correlation between transactions cost measures such as bid-ask spreads and zero returns days is relatively low. Further, Bekaert, Harvey and Lundblad (2006) find that liquidity measures such as zero 8
  • 9. returns days (and, to a lesser extent, bid-ask spreads) behave like priced risk factors in international contexts. While we do not attempt to directly measure cost of capital, our results suggest that a mechanism by which earnings smoothing might affect cost of capital is through its effect on liquidity. While liquidity is difficult to measure, especially in international contexts, our results are consistent across our measures, suggesting a robust relation between excess smoothing and liquidity. Our results indicate that excess smoothing is associated with higher transactions costs and less trading. While it is always difficult to draw strong inferences on causality, our results suggest that one potential consequence of smoothing is a reduction in transparency as reflected in increased transactions costs. Because reduced transparency is likely to affect investors’ willingness to hold a stock and the required expected return on the stock, our results provide evidence on a potential cost of earnings smoothing. Second, we differentiate between innate and excess earnings smoothing. By their very nature, accruals affect the variability of earnings relative to cash flows, and that relation can affect the information environment of the firm. Therefore, it is potentially important to separate out the smoothing effects of accruals that occur naturally in the firm’s operating environment from those that reflect managerial discretion. The fact that our measures of excess smoothing are correlated with variables that likely reflect incentives for, and limits on, earnings management, and behave differently in the liquidity tests than do our innate smoothing measures provides greater confidence that our results do not reflect omitted correlated variables. 9
  • 10. Further, our results potentially help bridge the gap between the notion that smoothing can convey information about the underlying economics of the firm and the notion that smoothing can create opacity. Perspectives on smoothing differ in the literature, with research such as Dichev and Tang (2005) suggesting that smoothing is a function of matching and can enhance transparency while research such as Leuz, Nanda and Wysocki (2003) argues that opportunistic smoothing reduces transparency. Our results suggest that both factors may be at work in practice, with innate smoothing increasing transparency and excess smoothing increasing opacity. Our results, which are based on smoothing by non-U.S. firms, suggest that the former is representative of firms with strong governance and the latter effect may be particularly pronounced in settings where incentives to smooth earnings tend to be relatively strong and oversight relatively weak. Finally, our earnings smoothing analysis is at the individual firm level. Research such as Leuz et al. (2003) provides evidence of effects of earnings smoothing at the country level with a focus on across country variation driven by things like differences in the value of control rights and investor protection environment.7 While there are advantages in that approach, we posit and provide evidence that there is also substantial within-country variation in incentives to smooth and the consequences of innate and excess smoothing for liquidity. Our analysis complements prior research by incorporating a menu of firm-specific determinants of smoothing and provides consistent evidence of a link between governance-related factors and smoothing. 7 Burgstahler, Hail and Leuz (2006) document that earnings management in general is greater for private than for public firms and that strong legal systems result in less earnings management for both public and private firms. 10
  • 11. In the next section, we discuss the methodology and data. Section III presents the results, followed by conclusions in Section IV. II. Methodology We quantify earnings smoothing using two measures common in the literature. The first earnings smoothing measure (SMTH1) captures the volatility of earnings relative to the volatility of cash flows (Leuz et al. 2003, Francis, LaFond, Olsson and Schipper 2004). Specifically, SMTH1 is 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. We calculate the standard deviations 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. SMTH1 is multiplied by negative one so that larger values, i.e., values closer to zero, represent more smooth earnings. The second earnings smoothing measure (SMTH2) is equal to the correlation between the cash flow from operations scaled by total assets and total accruals scaled by total assets (Lang et al., 2006, Barth et al., 2006).8 SMTH2 is multiplied by negative one so that firms with larger SMTH2 values (i.e., values closer to one) represent firms with more smoothed earnings. 8 Leuz et al. (2003) and Bhattacharya et al. (2003) calculate the 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. 11
  • 12. III.I Sample Table 1 presents the number of firm-year observations and descriptive statistics on SMTH1 and SMTH2 for the 21 sample countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Singapore, Spain, Sweden, Switzerland, and the U.K. We select these 21 countries because they have relatively well-developed capital markets and managers face diverse incentives to smooth earnings because of the differences in governance attributes across firms domiciled in these countries. Accounting and market data are collected from Datastream Advanced (a collaboration of market statistics from Datastream and accounting data from WorldScope) over the 1994-2005 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. Table 1 highlights that, on average, firms domiciled in Greece, Austria, and Italy report the most smoothed earnings whereas Norwegian, Swedish, and Canadian firms report the least smoothed earnings. The descriptive statistics on SMTH1 and SMTH2 and rank ordering of countries are generally consistent with those reported in Leuz et al. (2003) and Bhattacharya et al. (2003), respectively. III.2 Determinants of Smoothing III.2.a Innate Controls We base our innate measures of smoothing on variables identified in prior research such as Dechow and Dichev (2002) and, especially, Francis et al (2004). As in those papers, we are interested in the types of variables that are likely to affect the 12
  • 13. inherent smoothing properties of accruals. Our goal is to develop a model for the “expected” level of accruals smoothing for a firm, recognizing that the characteristics of accruals levels are naturally a function of factors like the firm’s size, growth, operating characteristics and industry. By the nature of the accounting system, much of the smoothing effect of accruals reflects the underlying nature of a firm’s operating environment and provides information on the volatility of the underlying fundamentals. As a result, firms with operating characteristics that naturally lead to earnings-smoothing accruals may have fewer informational issues.9 We include LNTOTASS, log of total assets, as a measure of firm size, to reflect the scale and likely diversification of the firm. OPCYLE, measured as log of days of accounts receivable plus inventories, captures the length of the firm’s operating cycle and OPLEV, measured as net property, plant and equipment divided by total assets captures capital intensity. STD_SALES is the standard deviation of sales and measures the volatility of a firm’s underlying operating environment. BM is the ratio of book value to market value of equity and is intended to reflect the extent of intangible assets and expected earnings growth. %LOSS is the proportion of years that a firm experiences losses over the last five years since loss firms likely have different accruals properties. LEV is total debt divided by total assets, because financing is likely correlated with earnings attributes.10 AVECFO is average cash from operations divided by total assets over the last five years and reflects the notion that a firm’s general level of profitability likely affects its accruals attributes. Finally, we include indicator variables for a firm’s industry since the properties of accruals are likely to depend on a firm’s industry. 9 This is the notion inherent in papers like Dechow and Dichev (2002) and Francis et al (2004) in which earnings-smoothing accruals can increase the informativeness of earnings and reduce cost of capital. 10 Leverage could also affect incentives to smooth earnings. Results are not sensitive to exclusion of LEV or to including it as determinants of discretionary smoothing in the liquidity tests. 13
  • 14. III.2.a Discretionary Determinants We base our measure of excess smoothing on the residuals from the regression of our smoothing measures on the innate controls. To the extent that accruals characteristics also reflect managerial discretion, there should be predictable correlations between excess smoothing, managerial incentives to smooth earnings and institutional constraints on those incentives. Ball et al. (2000) suggests that country-specific institutional factors contribute to a manager’s set of reporting incentives. The first governance variable we consider is the antidirector rights index of LaPorta et al. (1998) label RIGHTS. Prior research documents that firms domiciled in countries with weak investor protection report smoother earnings (Leuz et al. 2003), which is typically interpreted as suggesting that, in countries with weak investor protection, managers face greater incentives and have the ability to smooth earnings to conceal opportunistic behavior. We add RIGHTS to our smoothing model as a proxy for greater constraints on managers’ ability to smooth earnings when there is strong investor protection.11 Another institutional factor posited to contribute to managers' reporting incentives is the alignment between tax and financial reporting (Alford et al. 1993; Ali and Hwang 2000; Kasanen et al. 1996). In some countries, tax-reporting rules permit managers to use similar accounting methods for tax reporting and financial reporting, i.e., there is a high degree of tax-book conformity.12 In general, it is optimal for managers to smooth 11 As a sensitivity test, we replace RIGHTS with LAW; a categorical variable coded one for firms domiciled in common law countries and zero otherwise (La Porta et al. 1997). We do not include both measures in our model because the Pearson (Spearman) correlation between LAW and RIGHTS is 0.64 (0.76). When we substitute LAW for RIGHTS the results of the analysis are consistent with those reported in the tables. 12 In many countries, the explicit link between tax and financial accounting in the consolidated accounts has been loosened over time. For example, consolidated reports may be prepared under International Financial Reporting Standards (IFRS) for financial reporting purposes, while parent-entity reports are the basis for 14
  • 15. earnings for tax reporting in order to minimize the likelihood of large tax payments or to avoid tax losses that may provide reduced benefits to the firm. We posit that when there is a high alignment between tax reporting and financial reporting, the incentives managers face to smooth earnings for taxes will carry over to smoother accounting earnings. Thus, the second governance attribute studied is TXBKCONFORM, which is coded one for firms domiciled in countries that have high tax-book conformity, and zero otherwise. Our measure of TXBKCONFORM is based on Ashbaugh and LaFond (2003) and details of its construction by sample country are provided in the Appendix. As noted above, we posit that there is likely to be substantial within-country variation in managers’ incentives to smooth earnings, and there are factors that can mitigate or enhance managers’ ability to act on those incentives. First, the effective regulatory environment of firms may vary depending on whether they list on US exchanges. The scrutiny of regulators over a firm’s financial reporting affects the quality of firm’s financial information (Securities and Exchange Commission [SEC] 2000). The US regulatory environment is considered one of the most demanding in the world and, therefore, managers of U.S. foreign issuers are likely to have less of a tendency to manage earnings because they fall under the jurisdiction of the SEC.13 Thus, we posit that the incentives to smooth earnings are less for U.S. foreign issuers relative to firms not listed with the SEC and use ADR, coded one for sample firms that are U.S. foreign issuers and zero otherwise, to capture the disincentives to smooth earnings. tax reporting. However, managers may still align the choices made in the financial statements with those in the tax books to reduce scrutiny and ease record keeping. To the extent the misclassification of tax-book conformity adds noise, the noise would bias against finding any significant relation between TXBKCONFORM and smoothing. 13 While firms trading in US markets are not required to report local accounts that comply with US GAAP, Pownall and Schipper (1999), Ashbaugh and Olsson (2002) and Lang, Ready and Wilson (2006) 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. 15
  • 16. Oversight by informational intermediaries may affect firms’ incentives to smooth earnings. We use analyst following (ANALYST) as a proxy for the demand for transparent financial information by capital market participants.14 Analysts depend on relevant financial reports, as well as other pieces of information, to develop their forecasts of firms’ future earnings and stock recommendations. In this context, analysts may serve as a proxy for increased capital market monitoring of managers’ financial reporting thereby potentially mitigating earnings management. Thus, we expect a negative relation between ANALYST and the two smoothing measures. Further, we expect the level of auditing to affect managers’ ability to smooth earnings. 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 to be associated with less discretionary smoothing. Thus, we expect a negative relation between BIG5 and smoothing. Also, we expect accounting standards to affect the ability to smooth earnings. While our ADR variable captures cross listing on US markets, many firms adopt US GAAP or IFRS without cross listing. Research such as Bradshaw and Miller (2007) and Barth et al. (2007) suggests that firms adopting IFRS or US GAAP are subjecting themselves to more restrictive accounting standards and, therefore, have less flexibility to manage earnings. As a result, we expect a negative relation between adoption of IFRS or US GAAP (INTGAAP) and smoothing. 14 Analysts may potentially increase incentives for managers to smooth earnings to meet analysts’ earnings expectations, or may be attracted to firms that smooth less for other reasons. That is less of a concern with other measures such as regulatory environment, tax-book conformity, cross listing and ownership structure since those measures are less likely to be caused by smoothing. Research on US firms in Yu (2006) investigates the endogeneity of analyst following and suggests that analyst following serves primarily as a source of capital market oversight and that analyst following appears to mitigate earnings management. Our results are robust to exclusion of analyst following from our analyses. 16
  • 17. The third firm-specific governance attribute that we consider is insider ownership. We use the percent of shares that are closely held, %CLHLD, to proxy for insider ownership (Himmelberg, Hubbard and Love, 2002; Lins and Warnock, 2004). While, in theory, more concentrated ownership could result in increased monitoring of managers’ discretionary accounting practices, research like Lang, Lins and Miller (2003), Leuz, Lins and Warnock (2005) and Leuz (2006) suggest that more concentrated ownership is associated with increased agency issues internationally. As a consequence, we predict a positive relation between %CLHLD and the smoothing measures. In summary, we model earnings smoothness as a function of a firm’s operating characteristics and governance attributes. The OLS regression model with industry fixed effects used to test the relation between smoothing and governance is as follows: SMTH t = β 0 + β1 LNTOTASS t + β 2 LEVt + β 3 BM t + β 4 STD _ SALES t + β 5 % LOSS t + β 6 OPCYCLEt + β 7 SGt + β 8OPLEVt + β 9 AVECFOt + β10 RIGHTS t + β11TAXCONFORM t + β12 ADRt + β13 ANALYSTt + β14 BIG5t (1) + β15 INTGAAPt + β16 %CLHLDt + ∑a =1α a INDi + ε t 60 SMTH is set equal to SMTH1or SMTH2 and the definitions of the operating characteristic variables are as follows: LNTOTASS is equal to the natural log of total assets measured in US dollars; LEV is equal to total debt divided by total assets; BM is equal to book value of common equity divided by market value of equity; STD_SALES is the standard deviation of sales scaled by total assets calculated requiring a minimum of three and maximum of five fiscal years; %LOSS is the proportion of years that a firm reports negative earnings, calculated requiring a minimum of three and maximum of five fiscal years; OPCYCLE is the natural log of the operating cycle measured in days, defined as 365*(average accounts receivable /sales)+365*(average inventory/cost of goods sold); SG is the average sales growth over the past three to five years; OPLEV is net property, plant and equipment over total assets; AVECFO is equal to the average cash flow from operations divided by average total assets over the past three to five fiscal years. All other variables are as previously defined. 17
  • 18. III.3 Descriptive Statistics Table 2 reports the descriptive statistics on the two smoothing measures as well as the operating characteristic variables used to explain differences in the innate portion of earnings smoothing. The mean (median) values of SMTH1 and SMTH2 after pooling all firm-year observations are -0.634 (-0.513) and 0.721 (0.911) respectively. A typical sample firm is relatively large (median LNTOTASS 12.317 USD), has a significant amount of debt in its capital structure (median LEV 0.213) and is a fairly mature firm as captured by a median BM value of 0.689. The median sample firm has sales volatility of 0.100, reports losses infrequently, has an operating cycle of about 139 days and has experienced 5.8% sales growth over recent years. The average firm has 26.7% of its assets invested in net property, plant, and equipment. Table 2 also reports the descriptive statistics on the five governance attributes we predict will affect managers’ incentives to report smooth earnings. The average firm in our sample has an investor’s right score of 4.000 out of six, where six represents the strongest investor protection. The mean TXBKCONFORM value of 0.586 indicates that 58.6% of our sample firms are from countries with a high degree of conformity between tax and financial reporting. The descriptive statistics indicate that just over 3% of the observations are associated with firms trading ADRs in the U.S. The mean and median analyst following indicate that our sample firms are, on average, followed by relatively few analysts. We also see that, for the median firm, 44.6% of shares are closely held, suggesting that there are potential agency issues that create greater incentives to smooth. Of our sample firms, 35.2% are audited by Big-5 18
  • 19. firms and 7.1% prepare their financial statements under nonlocal GAAP (US GAAP or IFRS). IV. Results IV.1 Incentives to Smooth Our first general hypothesis is that earnings smoothing is affected by managers’ incentives to smooth and that there exist firm-specific and institutional governance attributes that enhance or mitigate managerial incentives to smooth earnings. Table 3 reports the regression results of estimating the earnings smoothing model using SMTH1 and then SMTH2. Significance levels are based on Fama-MacBeth (1973) t-statistics to control for potential cross correlation in residuals. Because of the reduction in sample size due to data limitations on %CLHLD, we table the results of estimating equation (1) with and without the closely held incentive attribute. However, we discuss the results of both analyses together because there are no significant differences in the findings. Results for out innate controls are generally as expected given the nature of accruals and the results in Francis et al. (2004). Regardless of smoothing measure, the results indicate that firms that are larger have more earnings-smoothing accruals, consistent with the notion of increased stability and diversification for larger firms. Similarly, firms with higher BM ratios generally have smoother earnings, reflecting lower levels of intangible assets and lower expected growth rates for those firms, as do firms with higher LEV, reflecting the notion that firms with more stable fundamentals have higher levels of debt financing. Consistent with expectations, firms that have more 19
  • 20. volatile sales and more frequent losses have more volatile earnings. In addition, firms with longer operating cycles and lower operating leverage tend to have smoother earnings relative to cash flows. One potentially surprising result is that the smoothing effect of accruals is greater for firms with relatively high sales growth, but that result is conditional on sales variability, firm size and operating cycle. The effect of profitability on smoothing depends on the earnings specification. Turning to our primary incentive variables of interest, all variables enter into the regression significantly and as predicted. We find a negative relation between RIGHTS and earnings smoothing indicating that managers report relatively smoother earnings in countries with weak investor protection. This finding is consistent with prior research that examines the relation between earnings smoothing and investor protection (Leuz et al., 2003). Consistent with expectations, the positive and significant coefficient on TXBKCONFORM indicates that managers are more likely to smooth earnings when reporting financial information that is more closely linked to taxes. This finding suggests that managers in high tax book conformity countries face incentives to smooth earnings for tax purposes. As expected based on research like Lang, Raedy and Wilson (2006) and Leuz (2006), we find a negative relation between ADR and earnings smoothing. This finding indicates that managers of non-U.S. firms that face the regulatory oversight of the SEC and restricted accounting measurement choices under U.S. GAAP have less of a tendency to smooth earnings. The results also indicate that higher analyst following is associated with less smoothing as the coefficient on ANALYST is negative and highly significant. This finding suggests that capital market monitoring plays an important role in 20
  • 21. diminishing managers’ incentives to smooth earnings. Also consistent with increased oversight reducing earnings management, we find a strong negative association between the presence of a Big-5 auditor and earnings smoothing. Finally, smoothing tends to be less pronounced for firms that report under IFRS or US GAAP in their local accounts, incremental to the effect of cross listing. Consistent with the notion that concentrated ownership can create incentives for opacity, the results also suggest that non-U.S. firms’ ownership structures are associated with earnings smoothing. Specifically, we find, after controlling for innate operating characteristics, firms with more closely held shares engage in relatively more earnings smoothing. Taken together, the results reported in Table 3 suggest that concentrated ownership and tax book conformity encourage earnings smoothing, but the incentives to smooth are mitigated in the presence of strong investor protection, regulatory oversight over financial reporting, and monitoring by capital market participants. These results are important because they suggest that, after controlling for innate determinants, excess smoothing is correlated with incentive variables as would be expected if excess smoothing reflects managerial discretion. IV.2 Overall Internal Governance In the preceding analyses, we examine the effects of internal and external incentives and oversight to draw inferences on whether governance mitigates the propensity for managers to smooth earnings. However, measures like ownership concentration have potentially countervailing effects and a broader range of factors could be important to incentives. An alternative approach is to consider firms’ governance structures as a 21
  • 22. whole based on a more general governance index. To examine governance more generally, we replace %CLHLD with an overall measure of governance (GOVSCORE) that is equal to the governance rating of the firm as reported in Governance Metrics International (GMI). The GMI score reflects not only the assessment of a firm’s ownership structure, but also captures a firm’s governance related to board structure, financial information quality, and firm level shareholder rights. The GMI data are only available for the fiscal 2004 and 2005 reporting periods and only available for 1,122 firms, substantially reducing the sample size and limiting the analysis to large, widely- followed firms. However, an advantage of the measure is that it includes a wider range of factors and reflects more judgment in assessing the likely effects of governance differences in practice Within the GMI scoring system, one particular category of interest is the scrutiny and disclosure of related party transactions (RELATEDPARTY), where higher values indicate a greater existence and less scrutiny being placed on related party transactions. If managers engage in related party transactions to expropriate the firm's resources, then they have incentives to manage earnings to mask such expropriation (Gordon and Henry 2005). When there is no or little scrutiny over related party transactions, the manager has greater incentives to expropriate firm resources and smooth earnings to create opacity. Thus, we predict a positive relation between RELATEDPARTY and SMTH. To test whether internal governance as a whole affects managers’ incentives to smooth earnings and whether related party transactions have incremental smoothing effects, we estimate the following OLS regression: 22
  • 23. SMTH t = β 0 + β1 LNTOTASS t + β 2 LEVt + β 3 BM t + β 4 STD _ SALES t + β 5 % LOSS t + β 6 OPCYCLEt + β 7 SGt + β 8OPLEVt + β 9 AVECFOt + β10 ADRt + β11 ANALYSTt + β12 BIG5t (2) + β13 INTGAAP + β14 GOVSCOREt + β15 RELATEDPARTYt + β16YR04 t + ε t where all variables are previously defined. Table 4 presents the results of estimating equation (2). As noted above, the sample size is substantially reduced because we are estimating equation (2) for only two years and the GMI data only cover the largest, most widely held companies. The explanatory power of the operating characteristic and governance variables for SMTH using the smaller sample is lower than when the model is estimated using the full sample, perhaps reflecting the fact that the GMI firms tend to be larger and more homogeneous. While the signs are generally consistent with the earlier models, the significance of the estimated coefficients on the innate determinants of smoothing is reduced consistent with the smaller sample size and greater homogeneity. In terms of our primary variables of interest, after controlling for innate operating characteristics we find evidence of a negative relation between SMTH and GOVSCORE in both models, although it is only significant in the SMTH2 model, suggesting that firms with stronger internal governance engage in less earnings smoothing. Further, the evidence suggests that a lack of scrutiny and disclosure of related party transactions increases managers’ incentives to smooth earnings as reflected in a positive relation between SMTH and RELATEDPARTY in both models. While the inferences from the analyses presented in Table 4 should be viewed with caution due to the sample and time period limitations, the overall results presented 23
  • 24. in Table 4 are consistent with those in the preceding analyses and support the notion that smoothing is more pronounced when governance is weak. IV.3 Smoothing and Liquidity The preceding analyses provide evidence that excess smoothing is higher for firms where governance and oversight are weaker and incentives to manage earnings are stronger. If so, excess smoothing has the potential to affect transparency and, ultimately, the willingness of investors to transact in a stock. Our second general hypothesis is that excess smoothing is expected to have negative capital market consequences in terms of reduced liquidity. In particular, if excess earnings smoothing is associated with reduced information available to market participants, they will be less willing to transact in a firm’s stock because of potential information asymmetries and resulting higher transaction costs. We use the predicted value of equation (1) estimated using only the operating characteristic variables as a measure of innate smoothing (INNATE_SMTH1 or INNATE_SMTH2). We define the difference between a firm’s predicted value and reported smoothing measure (i.e., SMTH1 or SMTH2) as excess smoothing (EXCESS_SMTH1 or EXCESS _SMTH2, respectively), which we posit is affected by managers’ incentives to smooth earnings and will result in reduced transparency and less liquidity. To test our second hypothesis, we estimate the following OLS model with industry and country fixed effects: LIQUIDITYt = β 0 + β1 LNMVEt + β 2 LOG ( PRC ) t + β 3 BM t + β 4 LOSS + β 5 STD _ RETt (3) + β 6 RINNATE _ SMTH t + β 7 REXCESS _ SMTH t + ∑a =1 α a INDi + ∑b=1 γ b COUNTRYi + ε t 60 20 24
  • 25. Where LIQUIDITY is set equal to one of the three proxies for liquidity defined below, LNMVE is equal to the natural log of market value of equity at the fiscal year end, measured in US dollars; LOGPRC is the natural log of the firm’s share price as of the fiscal year end, measured in US dollars; LOSS is equal to one if net income before extraordinary items is negative, zero otherwise; ROA is equal to net income before extraordinary items divided by average total assets. All other variables are as previously defined. We use two measures of liquidity. First, we consider the bid-ask spread (BID_ASK_SPRD), measured as the average bid-ask spread over the fiscal year, where the bid-ask spread is calculated as (ASK-BID)/((ASK+BID)/2). As noted in research like Glosten and Milgrom (1985), information asymmetry can lead to increased bid-ask spreads and reduced share prices (Amihud and Mendelson, 1996). In their international study, Lesmond et al. (1999) argue that a scarcity of information will increase information asymmetry and, hence, the bid-ask spread. If excessive smoothing results in less useful financial information, we predict a positive relation between BID_ASK_SPRD and excess smoothing. Our second proxy for liquidity is the proportion of zero return days. As discussed in Bekaert, Harvey and Lundblad (2006), an advantage of using the zero return measure in an international setting is that stock prices are widely available and measured consistently across markets relative to other measures such as volume or bid-ask spreads.15 Lesmond et al. (1999) argues that a manifestation of high transaction costs will be infrequent trading reflected in days without price movements. Bekaert, Harvey and Lundblad (2006) apply the zero return measure in international contexts and find that the measure predicts future returns and behaves like a priced returns factor. Lesmond (2005) provides evidence that zero returns are a better proxy for liquidity than is volume in 15 We include country fixed effects in the model to control for potential cross country differences in the measurement of the liquidity variables. 25
  • 26. international settings. Ashbaugh-Skaife, Gassen and LaFond (2006) provide evidence that a zero return metric is a summary measure of the extent to which firm-specific information is impounded in share price. Lesmond (2005) demonstrates that more traditional measures of transactions costs such as bid-ask spreads, where available, tend to be correlated with zero return days. Following Bekaert, Harvey and Lundblad (2006), we define the zero-return metric (ZR) as the number of zero-return trading days over the fiscal year divided by the total trading days of the firm’s fiscal year and use it as our second LIQUIDITY measure. If excess smoothing results in greater transaction costs, we expect a positive relation between ZR and our EXCESS_SMTH measures. The control variables are added to the model for consistency with prior literature (Lee, Mucklow and Ready 1993; Welker 1995; Chordia, Roll, and Subrahmanyam 2000; and Ertimur 2004). We transform INNATE_SMTH and EXCESS_SMTH into scaled percentile ranks, where values range from zero to one, with higher values representing greater smoothing. The transformation is necessitated by our earlier definition of SMTH, which cast SMTH as a non-positive value to facilitate interpretation of the SMTH results.16 Table 5 displays the descriptive statistics for the dependent and independent variables of equation (3). As stated above, data requirements to calculate BID_ASK_SPREAD reduce the sample size relative to the %ZERORET metric. The descriptive statistics indicate that sample firms, on average, have zero returns on 36.0% of the trading days in the year and have a spread of 2.8%. 16 Results are consistent for the raw (unranked) smoothing variables. 26
  • 27. Panels A and B of Table 6 display the results of estimating equation (4) using the two measures of liquidity. For both measures, the signs and significance of the coefficients on the control variables, in general, allow us to draw similar inferences across the analyses and are consistent with the prior literature, so we discuss them only once. The results suggest that larger firms (LNMVE), with lower book-to-market ratios (BM), trading at a lower price per share (LOG(PRC)), with more frequent losses (LOSS) and more volatile returns (STD_RET) tend to be more liquid, although the relations are not always statistically significant. Turning to the variables of interest, panel A of Table 6 reports the results of the bid-ask spread analysis. The results indicate that there is a negative and significant coefficient on RINNATE_SMTH, suggesting that expected smoothing as a result of firms operations is associated with reduced information asymmetry. A potential interpretation is that industries and operating environments where accruals naturally smooth earnings are also characterized by reduced uncertainty and potential for information asymmetry. In contrast, the excess portion of smoothing is positively associated with bid-ask spreads suggesting that excessive smoothing is associated with more opaque financial information that increases transaction costs. Coupled with our previous results, it appears that firms where managers have incentives to increase opacity and there is relatively little oversight tend to smooth more aggressively and that the excess smoothing is associated with reduced transparency reflected in higher transactions costs. Further, the fact that the two components of smoothing have opposite signs indicates that the effect of smoothing depends on its source and provides some assurance that our approach for splitting smoothing into components identifies substantive differences. 27
  • 28. Similar results obtain for the zero-return analysis in table 6, panel B. When we bifurcate earnings smoothing into innate and excess components, we find a significantly positive coefficient on the excess portion of earnings smoothing, i.e., REXCESS_SMTH. This finding suggests that investors are less willing to trade in firms’ shares when managers report earnings that are excessively smooth relative to underlying cash flows. We also find a marginally significant negative coefficient on the innate portion of smoothing, i.e., RINNATE_SMTH, suggesting that the smoothing that comes about as a result of operating characteristics increases investors’ willingness to transact. In summary, the results presented in Table 6 for both of our liquidity measures and both of our smoothing measures support the notion that excess smoothing is associated with reduced liquidity and higher transactions costs. Results for the relation between innate smoothing and bid-ask spreads and zero return days suggest that innate smoothing is associated with lower transactions costs and enhanced liquidity. Taken together, the results highlight the potential countervailing effects of innate and excess smoothing. IV.4 Interaction between Excess Smoothing and Governance A final question is whether there is an interaction effect between smoothing and governance. In particular, the potential effects of smoothing on transparency are likely to be less pronounced for firms that are in otherwise rich information environments with fewer incentives to manage earnings and higher levels of attestation and regulatory oversight. For example, we expect that, for, large cross-listed firms with significant analyst following, Big-5 auditors and dispersed ownership that report under IFRS, the 28
  • 29. effect of smoothing on the information environment is unlikely to be as pronounced as for smaller firms with less average coverage, local auditors and concentrated ownership that trade only on the local market and file under local GAAP. To examine that issue we create a governance composite variable (GOV) under which firms receive one point for each of the following: (1) if they are cross listed in the US, (2) if they are followed by more than one analyst, (3) if they report under IFRS or US GAAP, (4) if they are audited by a Big 5 auditor and (5) if they have closely held ownership of less than 44%. As a consequence, GOV takes on lower values the more likely it is the case that the firm faces significant incentives to manage earnings and relatively weak oversight. Table 7 reports results interacting GOV with our excess smoothing measure in our transaction cost and liquidity regressions. Results for the control variables and innate smoothing are similar to table 6. In terms of the variables of interest, excess smoothing enters positively as before, indicating that firms with greater levels of excess smoothing experience lower liquidity and higher transactions costs. Further, the coefficient on GOV is positive, indicating, consistent with expectations, that firms that are more likely to have incentive issues and reduced oversight experience lower liquidity and higher transactions costs. Finally, the interaction between GOV and REXCESS is significantly negative, indicating that the effects of smoothing tend to be less pronounced in environments in which governance is otherwise strong. 29
  • 30. Taken together, the results suggest that excess smoothing tends to create opacity, which raises transactions costs and reduces liquidity, and that the relation is strongest in cases where other governance issues are likely to be most pronounced. IV.5 Other Analyses First, as noted above, we replicate the results using trading volume, number of shares traded over the fiscal year divided by the number of shares outstanding, (VOLUME) as a proxy for liquidity. An opaque information environment can lead to lower trading volume because of higher transaction costs and greater information asymmetry, so we expect a negative relation between excess smoothing and VOLUME. However, research such as Lesmond (2005) and Bekaert, Harvey and Lundblad (2006) suggests that volume tends to be a relatively weak proxy for transactions costs and liquidity relative to bid-ask spreads and zero returns days in that it may not be computed consistently across exchanges, and does not behave like a priced liquidity factor or correlate highly with other liquidity measures. Results for volume are consistent with those for bid-ask spread and zero return days in that excess smoothing tends to reduce trading volume, particularly in cases where other governance factors are weak. Second, we repeat the entire analysis after eliminating Japanese and UK firms because these two countries add the most firms to our sample, potentially threatening the external validity of our results. We draw similar inferences from the results after eliminating Japanese and UK firms. Specifically, we continue to find support for both hypotheses that excess smoothing is significantly lower when firms have better governance and excessive smoothing is significantly associated with reduced liquidity. 30
  • 31. Third, in testing for liquidity effects, rather than pooling firm-year observations from all countries, we estimate the smoothing model within each country. We then use the firm-specific residuals and predicted values from the within-country estimates to test the relation between excess and innate smoothing, and measures of liquidity. The results of these analyses are similar to those reported in the tables. Specifically, we continue to find that excessive smoothing is significantly associated with lower liquidity regardless of the liquidity measure used. In addition, we find that innate smoothing, when significant, is associated with greater liquidity. Fourth, in testing our second hypothesis, we replicate the analysis including the governance variables from the first smoothing analysis as controls. In particular, a potential concern is that our excess smoothing measures may be capturing the notion that poor governance in general is associated with greater opacity rather than the effects of smoothing. We do not include the governance variables as controls in our primary analysis because it is difficult to disentangle the effects of governance overall from the effects of governance through smoothing. However, our conclusions are robust to replicating the liquidity analysis including the governance controls. Fifth, in testing for liquidity effects, we include controls for the overall level of accruals and for the absolute value of accruals. In particular, papers like Bhattacharya et al (2007) and Jayaraman (2007) suggest that, in US contexts, larger accruals generally may be associated with greater informed trading and higher transactions costs. Intuitively, that could be the case because extreme accruals may indicate unusual circumstances for the firm that are associated with greater uncertainty and more information asymmetry. Extreme accruals should not affect our analysis directly because 31
  • 32. our interest is only in the excess component of earnings smoothing and our results suggest an asymmetric relation between smoothing and liquidity depending on the source of the smoothing. However, to ensure that our results are not affected by the general level of accruals overall, we replicate our analysis including the magnitude of accruals and the absolute value of accruals. Our conclusions are unaffected by inclusion of the magnitude of accruals and the absolute value of accruals. The results of these sensitivity analyses support our overall conclusions that better governance is associated with reduced smoothing and that excessive smoothing is associated with reduced liquidity. V. Conclusion We examine the relation between earnings management via earnings smoothing, governance and liquidity. Our evidence suggests that better governance mitigates earnings smoothing. In particular, earnings smoothing is more prevalent when there is weak investor protection, when fewer analysts follow the firm, when there is a greater proportion of closely held shares, and when there is less scrutiny over related party transactions. We also document that excess earnings smoothing has capital market effects as evidenced by a negative relation between our measure of excess smoothing and liquidity as measured by the frequency of zero-return days, bid-ask spreads, and share volume. Our results suggest that firms that excessively smooth earnings likely face lower liquidity and higher transactions costs, potentially increasing cost of capital. Results are particularly strong in environments in which excess smoothing is coupled with other governance issues. 32
  • 33. The results raise questions for future research. First, our study examined a limited set of governance attributes. Future research can explore whether alternative governance attributes reduce or increase firms’ smoothing. Second, we investigated only one set of capital market consequences – transactions costs, investors’ willingness to trade and resulting liquidity – that has implications for cost of capital. Future research can explore other capital market consequences and whether other economic events (e.g., mandatory dividend payouts) affect incentives and economic consequence of earnings management. 33
  • 34. APPENDIX Country-wide Governance Attributes Limited Sample Inventory Depreciation Tax TXBK- Investor Country Conformity Conformity Incentives CONFORM Protection Australia No No No 0 4 Austria Yes Yes Yes 1 2 Belgium Yes Yes No 1 0 Canada No No Yes 0 5 Denmark No No No 0 2 Finland Yes Yes Yes 1 3 France Yes Yes Yes 1 3 Germany Yes Yes Yes 1 1 Greece Yes Yes No 1 2 Hong Kong No No Yes 0 5 Ireland Yes No No 0 4 Italy Yes Yes Yes 1 1 Japan Yes Yes No 1 4 Netherlands No No No 0 2 New Zealand No No No 0 4 Norway No No Yes 0 4 Singapore No No No 0 4 Spain Yes Yes No 1 4 Sweden Yes No Yes 1 3 Switzerland Yes Yes Yes 1 2 UK No No Yes 0 5 The tax book conformity index (TXBKCONFORM) is developed from tax summaries provided in Corporate Taxes: A Worldwide Summary (Price Waterhouse 1995). Inventory conformity is noted yes when the inventory method used for tax reporting must also be used for financial reporting, and no otherwise. Depreciation conformity is noted yes when tax depreciation must also be recorded for financial reporting, and no otherwise. Limited tax incentives is noted yes when there are fewer than four tax incentives identified in the country summary, and no otherwise. Tax authorities that instill tax incentives such as a research and development tax credit, an employment tax credit, etc. are less likely to require tax-book conformity because tax credits are used to manage tax payments. Thus the lack of tax incentives is treated as an indicator of high tax-book conformity. TXBKCONFORM is coded one when two or three tax conformity measures are denoted yes. Investor Protection is as reported in La Porta et al. (1998). 34
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  • 39. TABLE 1 Sample and Country-wide Earnings Smoothing SMTH1 SMTH2 Country Mean Median Std Dev Mean Median Std Dev n Australia -0.729 -0.632 0.537 0.618 0.869 0.515 3,174 Austria -0.464 -0.358 0.375 0.826 0.957 0.322 636 Belgium -0.631 -0.550 0.454 0.725 0.891 0.374 680 Canada -0.799 -0.724 0.546 0.581 0.800 0.495 4,496 Denmark -0.567 -0.469 0.382 0.763 0.919 0.347 1,118 Finland -0.703 -0.599 0.472 0.697 0.858 0.384 916 France -0.603 -0.455 0.523 0.784 0.929 0.338 5,021 Germany -0.560 -0.423 0.479 0.783 0.940 0.361 4,951 Greece -0.388 -0.295 0.312 0.908 0.971 0.177 912 Hong Kong -0.793 -0.686 0.621 0.622 0.842 0.481 2,943 Ireland -0.716 -0.674 0.423 0.627 0.828 0.456 478 Italy -0.477 -0.365 0.396 0.819 0.955 0.317 1,521 Japan -0.540 -0.423 0.449 0.791 0.942 0.347 22,160 Netherlands -0.540 -0.384 0.470 0.781 0.948 0.371 1,348 New Zealand -0.586 -0.509 0.429 0.750 0.909 0.366 416 Norway -0.801 -0.735 0.536 0.610 0.788 0.442 961 Singapore -0.680 -0.498 0.634 0.720 0.922 0.426 2,235 Spain -0.517 -0.397 0.448 0.816 0.954 0.300 881 Sweden -0.802 -0.727 0.562 0.574 0.794 0.506 1,666 Switzerland -0.701 -0.505 0.683 0.737 0.917 0.382 1,556 UK -0.738 -0.648 0.504 0.626 0.835 0.469 11,741 SMTH1 is defined as the standard deviation of net income before extraordinary items scaled by average total assets divided by the standard deviation of cash flow from operations scaled by average total assets, where standard deviations are calculated using a minimum of three and maximum of five years of data. SMTH1 is multiplied by negative one so that larger values, i.e., values closer to zero represent more smooth earnings. SMTH2 is defined as the correlation between the cash flow from operations and total accruals where both measures are scaled by average total assets, where correlations are calculated using a minimum of three and maximum of five years of data. SMTH2 is multiplied by negative one so that larger values, i.e., values closer to one represent more smooth earnings. n represents the number of firm-year observations over 1994 – 2005. 39
  • 40. TABLE 2 Descriptive Statistics on Non-U.S. Firms’ Earnings Smoothing and Operating Characteristics Variable Mean Median Std Dev SMTH1 -0.634 -0.513 0.510 SMTH2 0.721 0.911 0.413 LNTOTASS 12.426 12.317 1.893 LEV 0.233 0.213 0.171 BM 0.858 0.689 0.700 STD_SALES 0.151 0.100 0.159 %LOSS 0.233 0.000 0.301 OPCYCLE 4.875 4.937 0.682 SG 0.304 0.058 2.357 OPLEV 0.291 0.267 0.217 AVECFO 0.049 0.057 0.109 RIGHTS 3.689 4.000 1.264 TXBKCONFORM 0.586 1.000 0.493 ADR 0.031 0.000 0.173 ANALYST 5.165 2.000 7.376 BIG5 0.352 0.000 0.478 INTGAAP 0.071 0.000 0.257 %CLHLDa 0.447 0.446 0.223 Descriptive statistics are based on all firms having sufficient data over the 1994 – 2005 time period (n=69,810). SMTH1 is defined as the standard deviation of net income before extraordinary items scaled by average total assets divided by the standard deviation of cash flow from operations scaled by average total assets, where standard deviations are calculated using a minimum of three and maximum of five years of data. SMTH1 is multiplied by negative one so that larger values, i.e., values closer to zero represent more smooth earnings. SMTH2 is defined as the correlation between cash flow from operations and total accruals where both measures are scaled by average total assets, where correlations are calculated using a minimum of three and maximum of five years of data. SMTH2 is multiplied by negative one so that larger values, i.e., values closer to one represent more smooth earnings. The definition of operating characteristic variables are as follows: LNTOTASS is equal to the natural log of average total assets measured in US dollars over the SMTH estimation period; LEV is equal to the average total debt divided by total assets over the SMTH estimation period; BM is equal the average to book value of common equity divided by market value of equity over the SMTH estimation period; STD_SALES is the standard deviation of sales scaled by total assets calculated requiring a minimum of three and maximum of five fiscal years; %LOSS is the proportion of years that a firm reports negative earnings, calculated requiring a minimum of three and maximum of five fiscal years; OPCYCLE is the natural log of the average operating cycle measured in days, defined as 365*(average accounts receivable /sales)+365*(average inventory/cost of goods sold) over the SMTH estimation period; SG is the average sales growth over the past three to five years; OPLEV the average is net property, plant and equipment over total assets over the SMTH estimation period; DIVIDEND is equal to the average cash dividends divided by average total assets over the SMTH estimation period; AVECFO is equal to the average cash flow from operations divided by total average total assets over the past three to five fiscal years. Governance variables are defined as: RIGHTS is the antidirector rights index developed by La Porta et al. (1998) for the country; TXBKCONFORM is equal to one if there is a high degree of conformity between tax and financial reporting in the country, and zero otherwise (see Appendix for details); ADR is equal to one if the firm trades in the U.S. during the fiscal year, and zero otherwise; ANALYST is equal to the average number of analysts making a forecast for fiscal year t’s earnings over the SMTH estimation period; and %CLHLD is the average proportion of 40
  • 41. a shares that are closely held as of the end of the fiscal year t over the SMTH estimation period. Requiring firms to have closely held ownership data reduces the sample size to 53,553. 41
  • 42. TABLE 3 Incentives to Smooth Earnings Incentives to Smooth - Annual Cross-Sectional Fama-MacBeth Regressions of SMTH1 and SMTH2 Regressed on Firm Operating Characteristics and Incentive Attributes SMTH t = β 0 + β1 LNTOTASSt + β 2 LEVt + β 3 BM t + β 4 STD _ SALES t + β 5 % LOSS t + β 6OPCYCLEt + β 7 SGt + β 8OPLEVt + β 9 AVECFOt + β10 RIGHTS t + β11TXBKCONFORM t + β12 ADRt + β13 ANALYSTt + β14 BIG 5 (2) + β15 INTGAAP + β16 %CLHLDt t + ∑a =1α a INDi + ε t 60 SMTH1 SMTH2 parameter parameter parameter parameter estimate p-value estimate p-value estimate p-value estimate p-value INTERCEPT -0.486 0.00 -0.576 0.00 0.737 0.00 0.652 0.00 Innate Characteristics LNTOTASS 0.020 0.00 0.023 0.00 0.015 0.00 0.018 0.00 LEV 0.075 0.00 0.059 0.01 0.073 0.00 0.059 0.00 BM 0.000 0.87 -0.003 0.24 0.021 0.00 0.021 0.00 STD_SALES -0.128 0.00 -0.131 0.00 -0.098 0.00 -0.101 0.00 %LOSS -0.650 0.00 -0.644 0.00 -0.423 0.00 -0.420 0.00 OPCYCLE 0.009 0.02 0.013 0.01 0.015 0.00 0.018 0.00 SG 0.003 0.01 0.004 0.00 0.003 0.06 0.004 0.04 OPLEV -0.135 0.00 -0.138 0.00 -0.106 0.00 -0.112 0.00 AVECFO -0.161 0.00 -0.145 0.00 0.276 0.00 0.294 0.00 Governance RIGHTS -0.029 0.00 -0.029 0.00 -0.021 0.00 -0.020 0.00 TAXCONFORM 0.039 0.00 0.020 0.02 0.023 0.00 0.011 0.07 ADR -0.058 0.00 -0.052 0.00 -0.075 0.00 -0.065 0.00 ANALYST -0.044 0.00 -0.039 0.00 -0.030 0.00 -0.026 0.00 BIG5 -0.023 0.00 -0.023 0.00 -0.026 0.00 -0.026 0.00 INTGAAP -0.059 0.00 -0.068 0.00 -0.030 0.00 -0.032 0.00 %CLHLD 0.073 0.00 0.061 0.00 Industry Dummies YES YES YES YES 2 Average Adj R 0.26 0.25 0.25 0.24 Average n 5,816 4,463 5,816 4,463 All variables are defined in Table 2. 42
  • 43. TABLE 4 Overall Governance and Incentives to Smooth Incentives to Smooth – Pooled Cross Sectional Regressions, firm cluster standard errors of SMTH1 and SMTH2 Regressed on Firm Operating Characteristics and Incentive Attributes SMTH t = β 0 + β1 LNTOTASSt + β 2 LEVt + β 3 BM t + β 4 STD _ SALES t + β 5 % LOSS t + β 6OPCYCLEt + β 7 SGt + β 8OPLEVt + β 9 AVECFOt + β10 ADRt + β11 ANALYSTt + β12 BIG 5 + β13 INTGAAP + β14 GOVSCORE t + β15 RELATEDPARTYt + β15YR 04 t + ε t SMTH1 SMTH2 parameter parameter estimate p-value estimate p-value INTERCEPT -0.568 0.00 0.447 0.01 Innate Characteristics LNTOTASS 0.020 0.09 0.026 0.00 LEV 0.023 0.79 0.103 0.15 BM -0.014 0.71 0.022 0.49 STD_SALES 0.054 0.62 0.005 0.96 %LOSS -0.786 0.00 -0.684 0.00 OPCYCLE 0.000 0.98 0.026 0.08 SG 0.004 0.39 0.004 0.15 OPLEV -0.115 0.06 -0.105 0.04 AVECFO -0.689 0.00 0.138 0.50 Governance ADR -0.022 0.42 0.003 0.87 ANALYST -0.055 0.01 -0.049 0.01 BIG5 0.011 0.68 0.017 0.46 INTGAAP -0.010 0.71 -0.021 0.31 GOVSCORE -0.009 0.11 -0.015 0.00 RELATEDPARTY 0.010 0.01 0.005 0.09 Year Dummies YES YES 2 Adj R 0.13 0.17 n 2,049 2,049 This table reports the results of a pooled cross-sectional regression using data from fiscal 2004 and 2005 using 2,071 firm-year observations for 1,122 firms. The smaller sample sizes are due to the requirement that firms be followed by Governance Metrics International (GMI). GOVSCORE is GMI’s global overall rating, where higher values represent stronger governance; RELATEDPARTY is equal to the GMI assessment of related party transactions where higher values indicate a greater existence and less scrutiny being placed on related party transactions. All other definitions are provided in Table 2. 43
  • 44. TABLE 5 Descriptive Statistics for Market Consequences Variables Variable n Mean Median Std Dev %ZERORET 69,721 0.360 0.273 0.260 BIDASK 23,698 0.028 0.014 0.035 LNMVE 69,721 11.862 11.703 1.958 LOG(PRC) 69,721 1.465 1.497 2.007 BM 69,721 0.858 0.689 0.700 LOSS 69,721 0.242 0.000 0.428 STD_RET 69,721 0.128 0.102 0.170 Variable definitions are as follows: %ZERORET is equal to the percent of days in fiscal year t for which the stock price does not change; BIDASK is equal to the average bid ask spread over the fiscal year, where the bid ask spread is equal to (ASK-BID)/((ASK+BID)/2)); LNMVE is equal to the natural log of market value of equity at the fiscal year end, measured in US dollars; LOG(PRC) is the natural log of firm’s share price as of the fiscal year end, measured in US dollars; LOSS is equal to one if net income before extraordinary items is negative, and zero otherwise; STD_RET is the standard deviation of monthly returns over the past three to five years. 44
  • 45. TABLE 6 Market Consequences of Smoothing Panel A: Annual Cross Sectional Fama-MacBeth Regressions of the Bid Ask Spread (BIDASK) Regressed on Expected and Excess Smoothing (2001-2005 time period) Log ( BIDASK t ) = β 0 + β1 LNMVEt + β 2 LN ( PRC ) t + β 3 BM t + β 4 LOSS + β 5 STD _ RETt + β 6 RINNATE _ SMTH t + β 7 REXCESS _ SMTH t + ∑a =1 α a INDi 60 + ∑b=1 γ b COUNTRYi + ε t 20 SMTH1 SMTH2 parameter parameter estimate p-value estimate p-value INTERCEPT 5.956 0.00 5.957 0.00 LNMVE -0.394 0.00 -0.394 0.00 LOG(PRC) 0.012 0.25 0.013 0.21 BM 0.004 0.33 0.007 0.15 LOSS -0.015 0.25 -0.017 0.21 STD_RET -0.002 0.97 -0.004 0.94 RINNATE_SMTH -0.270 0.00 -0.263 0.00 REXCESS_SMTH 0.075 0.00 0.063 0.00 Industry Dummies YES YES Country Dummies YES YES Average Adj R2 0.77 0.77 Average n 4,740 4,740 45
  • 46. TABLE 6 Continued Panel B: Annual Cross Sectional Fama-MacBeth Regressions of the Zero Return Metric (ZR) Regressed on Expected and Excess Smoothing ZRt = β 0 + β1 LNMVEt + β 2 LN ( PRC ) t + β 3 BM t + β 4 LOSS + β 5 STD _ RETt + β 6 RINNATE _ SMTH t + β 7 REXCESS _ SMTH t + ∑a =1 α a INDi + ∑b=1 γ b COUNTRYi + ε t 60 20 SMTH1 SMTH2 parameter parameter estimate p-value estimate p-value INTERCEPT 5.299 0.00 5.450 0.00 LNMVE -0.520 0.00 -0.519 0.00 LOG(PRC) 0.123 0.00 0.122 0.00 BM 0.040 0.00 0.044 0.00 LOSS -0.174 0.00 -0.169 0.00 STD_RET -1.254 0.00 -1.247 0.00 RINNATE_SMTH -0.100 0.04 -0.133 0.14 REXCESS _SMTH 0.046 0.00 0.059 0.00 Industry Dummies YES YES Country Dummies YES YES 2 Average Adj R 0.63 0.63 Average n 5,810 5,810 Each panel reports the mean results of estimating annual cross-sectional regressions over Panels A and C 1994 – 2005 and 2001-2005 Panel B, where p-values are based on the time-series standard errors of the coefficient estimates. ZR is equal to log(%ZERORET /(1-%ZERORET)), where %ZERORET is equal to the percent of days in fiscal year t for which the stock price does not change. RINNATE_SMTH is equal to the scaled percentile rank of INNATE_SMTH, where INNATE_SMTH is equal to the predicted value from the earnings smoothing model. REXCESS_SMTH is equal to the scaled percentile rank of EXCESS_SMTH, where EXCESS_SMTH is equal to the percentile rank residual value from the earning smoothing model. All other variables are defined in Table 5. 46
  • 47. TABLE 7 Market Consequences of Smoothing Panel A: Annual Cross Sectional Fama-MacBeth Regressions of the Bid Ask Spread (BIDASK) Regressed on Expected and Excess Smoothing and Governance (2001-2005 time period) Log ( BIDASK t ) = β 0 + β1 LNMVEt + β 2 LN ( PRC ) t + β 3 BM t + β 4 LOSS + β 5 STD _ RETt + β 6 RINNATE _ SMTH t + β 7 REXCESS _ SMTH t + β 8GOV + β 9GOV * REXCESS _ SMTH t + ∑a =1 α a INDi + ∑b=1 γ b COUNTRYi + ε t 60 20 SMTH1 SMTH2 parameter parameter estimate p-value estimate p-value INTERCEPT 5.708 0.00 5.702 0.00 LNMVE -0.344 0.00 -0.344 0.00 LOG(PRC) -0.002 0.78 -0.002 0.83 BM 0.004 0.41 0.007 0.21 LOSS 0.008 0.51 0.007 0.59 STD_RET 0.001 0.99 -0.001 0.99 RINNATE_SMTH -0.264 0.00 -0.260 0.00 REXCESS_SMTH 0.067 0.01 0.070 0.03 GOV -0.142 0.00 -0.138 0.00 REXCESS_SMTH* GOV -0.009 0.09 -0.017 0.04 Industry Dummies YES YES Country Dummies YES YES 2 Average Adj R 0.78 0.78 Average n 4,740 4,740 47
  • 48. TABLE 7 Continued Panel B: Annual Cross Sectional Fama-MacBeth Regressions of the Zero Return Metric (ZR) Regressed on Expected and Excess Smoothing ZRt = β 0 + β1 LNMVEt + β 2 LN ( PRC ) t + β 3 BM t + β 4 LOSS + β 5 STD _ RETt + β 6 RINNATE _ SMTH t + β 7 REXCESS _ SMTH t + β 8GOV + β 9 GOV * REXCESS _ SMTH t + ∑a =1 α a INDi + ∑b=1 γ b COUNTRYi + ε t 60 20 SMTH1 SMTH2 parameter parameter estimate p-value estimate p-value INTERCEPT 5.395 0.00 5.328 0.00 LNMVE -0.433 0.00 -0.433 0.00 LOG(PRC) 0.119 0.00 0.119 0.00 BM 0.020 0.10 0.023 0.07 LOSS -0.121 0.00 -0.119 0.00 STD_RET -0.943 0.00 -0.936 0.00 RINNATE_SMTH -0.159 0.00 -0.129 0.00 REXCESS_SMTH 0.049 0.09 0.139 0.00 GOV -0.247 0.00 -0.222 0.00 REXCESS_SMTH* GOV -0.030 0.00 -0.054 0.00 Industry Dummies YES YES Country Dummies YES YES 2 Average Adj R 0.63 0.63 Average n 5,810 5,810 GOV is equal to a governance composite, where firms receive one point for each of the following, 1 if they trade and ADR in the US, 1 if there are followed by more than one analyst, 1 if they report under either IFRS or US GAAP, 1 if they are audited by a big five auditor and 1 if there closely held ownership is less than 44 percent. Both analysts following and closely held ownership are based on the sample medians 48

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