Asymmetric Information and Dividend Policy

Asymmetric Information and DividendPolicy

Kai Li and Xinlei Zhao

We examine how informational asymmetries affect firms dividend policies. We find that firms thatare more subject to information asymmetry are less likely to pay, initiate, or increase dividends,and disburse smaller amounts. We show that our main results are not driven by our sample andthat our results persist after accounting for the changing composition of payout over the sampleperiod, the increasing importance of institutional shareholdings, and catering incentives. Weconclude that there is a negative relation between asymmetric information and dividend policy.Our results do not support the signaling theory of dividends.

In this paper, we study how informational asymmetries affect firms dividend policies by exam-ining the relation between a firms dividend policy and the quality of its information environment.Dividends have long puzzled financial economists. Miller and Modigliani (1961) prove that

dividend policy is irrelevant to share value in a perfect and efficient capital market. However, theobservation that share prices typically rise when firms increase dividend payments suggests that,on the contrary, dividends do matter after all.Various studies have proposed various explanations for firms dividend behavior (see Allen

and Michaely, 2003, for a comprehensive review of the literature). Among them, the div-idend signaling theory is one of the dominant explanations. Under the signaling models ofBhattacharya (1979), John and Williams (1985), and Miller and Rock (1985), managers knowmore about the firms true worth than do its investors and use dividends to convey information tothe market. Thus, these models suggest a positive relation between information asymmetry anddividend policy. Other studies have developed tests to examine the dividend signaling models.However, our study may be the first to specifically examine the testable implications of the sig-naling models in the context of the relation between information asymmetry and firms dividendpolicies.To conduct our research, we ask the following questions: Are corporate dividend policies

affected by the degree of information asymmetry that firms face? Is the relation consistentwith the signaling view of asymmetric information? Given that information asymmetry is a

We thank Xia Chen for her help in obtaining the analyst coverage data, Bill Christie (the editor), an anonymous referee,Nalinaksha Bhattacharyya, Laurence Booth, Jason Chen, Qiang Cheng, Ming Dong, Charles Gaa, Ron Giammarino,Rob Heinkel, Harrison Hong, Alan Kraus, Rafael La Porta, Ranjan DMello, Hernan Ortiz-Molina, Gordon Phillips,Antoinette Schoar, Carina Sponholtz, John Thornton, seminar participants at Kent State University, University of BritishColumbia, and participants of the Northern Finance Association Meetings in Vancouver, the FMA European Conferencein Stockholm, and the FMA Annual Meetings in Salt Lake City for valuable comments. We gratefully acknowledgethe contribution of Thomson Financial for providing analyst data, available through the Institutional Brokers, EstimateSystem. These data have been provided as part of a broad academic program to encourage earnings expectations research.Li acknowledges the financial support from the Social Sciences and Humanities Research Council of Canada. Li alsowishes to thank the MIT Sloan School of Management for its hospitality and support when this paper was initially written.All errors are our own.

Kai Li is the W.M. Young Professor of Finance at the University of British Columbia in Vancouver, BC, Canada. XinleiZhao is an Associate Professor of Finance at Kent State University in Kent, OH.

Financial Management Winter 2008 pages 673 - 694

674 Financial Management Winter 2008

major market imperfection and that dividend policies are among the most important corporatedecisions, these are important questions.We use analyst earnings forecast errors and the dispersion in analyst forecasts to gauge the

degree of information asymmetry between managers and investors. We find that both analystearnings forecast errors and the dispersion in forecasts are negatively, and very often significantly,associated with a firms likelihood of paying dividends, initiating or increasing dividends, andwith the level of dividends paid. Overall, our findings suggest that firms with more transparentinformation environments pay out more dividends. This evidence does not support the signalingtheory of dividends.We also examine the relation between the quality of a firms information environment and

measures of total payout that include both dividends and repurchases. We do not find a positiveassociation between information asymmetry and repurchase activities. Signaling theory predicts astronger positive relation between asymmetric information and dividends than between asymmet-ric information and repurchases. Our finding of a stronger negative relation between asymmetricinformation and dividends confirms our evidence on the lack of support for the signaling theory.Our results are not broadly consistent with the dividend signaling models.The paper is organized as follows. In Section I, we discuss the signaling theory, describe

the sample and variables, and provide summary statistics. Section II presents our empiricalresults on firms dividend policies. Section III provides robustness checks on our main findings.Section IV presents our investigation of repurchase activities and total payout policies, andSection V concludes.

I. Variable Construction and Sample Characteristics

Since dividends provide a costly way of resolving asymmetric information, we examine therelation between information asymmetry and firms dividend policies under the signalingmodels.Because the resolution of asymmetric information is valuable, firms with greater asymmetricinformation should be more active dividend payers. Therefore, after controling for other dividenddeterminants, if the signaling theory of dividends is valid, we would observe a positive relationbetween information asymmetry and firm dividend policy. Further, because dividends imply afirm commitment and are also historically tax disadvantaged relative to repurchases, dividendsconstitute a more costly signal and investors should perceive them as having stronger informationcontent. Thus, the signaling theory predicts a stronger positive relation between asymmetricinformation and dividends than between asymmetric information and repurchases.1

Following earlier studies, we use Compustat and CRSP to examine dividend policy in industrialfirms. We exclude utilities (SIC 4900-4949) and financial firms (SIC 6000-6999). We note thatdoing so does not change our main conclusions (results available on request). To constructmeasures of asymmetric information, we merge our initial sample with Institutional BrokersEstimate System (IBES). Due to the availability of Detailed History Files from IBES, our sampleperiod is from 1983 to 2003. Our final sample is an unbalanced panel comprising 22,413 firm-yearobservations.

A. Measures of Dividend Policies

To explore the role of asymmetric information in dividend policy, we focus on quarterly regulardividends to common shareholders, the dividends with the greatest possible information content.

1We thank an anonymous referee for pointing this out to us.

Li & Zhao Asymmetric Information and Dividend Policy 675

The dividend items in Compustat (e.g., data items 21 and 26) include nonregular dividendpayments, such as special dividends and liquidation dividends, and thus they may not carry thesame information content as predicted in the models of Bhattacharya (1979), Miller and Rock(1985), and John and Williams (1985). We note that using the dividend variables in Compustatto define our dependent variables has no material effect on our main conclusions. We followGrullon, Michaely, Benartzi, and Thaler (2005) and Amihud and Li (2006) by using the CRSPdatabase to identify dividend and nondividend payers.We collect all regular quarterly dividends on ordinary common stocks in the CRSP daily file

(CRSP distribution code first digit = 1 (ordinary dividend); second digit = 2 (cash, US dollars);third digit = 3 (quarterly dividend); fourth digit = 2 (normal taxable at same rate as dividend)).After adjusting for changes in number of shares outstanding, we aggregate the quarterly dividendsinto an annual dividend amount. We set our first dividend variable, the payer dummy, equal toone for firm i in year t if the annual amount of dividends paid is positive, and zero otherwise.Our second dividend variable captures the initiation decisions of nondividend payers. For firm i

in year t, we set the initiation dummy equal to one if this is the first time firm i pays dividends, andzero for all the years prior to year t. The dividend initiation sample includes only the firm-yearsuntil the non-dividend-paying firm makes its first dividend payment, or when the sample periodends, whichever comes earlier. Over the sample period, if a firm omits and then resumes dividendpayments, our initiation dummy variable captures only the very first time that the firm initiateddividend payment.The decision that dividend payers have to make on a regular basis is whether or not to increase

dividends. Lintner (1956) shows that dividends are sticky and firms usually are reluctant to cutor omit dividends. Thus, our next measure of dividend policy examines dividend increases bydividend payers. We set the increase of dummy equal to one for firm i in year t if the percentageincrease in dividends is greater than 15%, and zero otherwise. To exclude any minor changes inthe sample, we use a cutoff point of 15% when we define dividend increases. Our rationale isthat if the signaling models hold, then we are more likely to find a negative relation between thequality of a firms information environment and a large increase in dividends. Our main resultsare not sensitive to the level of cutoff used in defining the dividend increase dummy.We obtain our fourth dividend variable, dividend payout, by scaling the annual dividend amount

by total assets. To ensure that our results are not driven by price variation or affected by the factthat a significant proportion of firms with negative earnings are paying dividends, we normalizethe amount of dividends by book assets, instead of market capitalization or earnings followingAllen and Michaely (2003).Table I provides summary statistics of our dividend policy variables. Column (1) shows that the

proportion of dividend payers declines steadily over the sample period, starting at 80.0% in 1983and reaching 30.7% in 2002, with a slight rebound in 2003.We note that the proportion of dividendpayers is higher in our sample than in the one used by Fama and French (2001), suggesting that oursample firms are on average larger and more mature than the general population of firms coveredin Compustat/CRSP. (We note that as a robustness check, we examine the effect of our sampleselection criterion on our main results.) This difference is due to our sample requirement for theavailability of analyst forecast data. Nonetheless, the same declining trend in the propensity topay dividends is evident throughout most of our sample period.Column (2) in Table I reports the fraction of first-time payers in year t among surviving

nondividend payers from year t 1. In our sample, the fraction of firms that initiate dividendsstarts at 7.3% in 1983. This measure drops steadily throughout most of the sample period andthen rises again beginning in 2002. Column (3) shows that there is no apparent time trend in thefraction of dividend payers increasing dividends. Column (4) shows that the average dividend


Table I. Time Series Characteristics of Dividend Policy

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, dividendinformation from CRSP, and analyst forecasts from IBES. We define dividend payers as firms that payquarterly dividends to common shareholders (CRSP four-digit distribution code = 1232) in year t. Wedefine nondividend payers as firms that do not pay dividends in year t. Dividend initiation takes the valueof one if the firm makes its first dividend payment in year t, and zero for all the years prior to t. Dividendincrease takes the value of one if the percentage increase in dividends is greater than 15%. We presentfrequency counts for dividend payers, nondividend payers, and payers that increase dividends. Dividendpayout is the ratio of annual aggregation of quarterly dividends paid to common shareholders to total assetsat the end of year t measured in percentages. We present annual averages for this measure.

Year (1) (2) (3) (4)Proportion Proportion Proportion Dividendof Dividend of Nondividend of Payers Payout

Payers Payers Initiating IncreasingDividends Dividends

1983 0.800 0.073 0.194 2.1201984 0.755 0.060 0.320 1.8481985 0.716 0.056 0.286 1.6881986 0.668 0.016 0.240 1.5221987 0.623 0.040 0.344 1.4651988 0.620 0.039 0.401 1.4611989 0.586 0.049 0.416 1.3351990 0.564 0.027 0.323 1.3361991 0.550 0.009 0.211 1.2991992 0.557 0.043 0.235 1.3221993 0.505 0.031 0.247 1.1711994 0.447 0.009 0.255 1.0411995 0.429 0.015 0.264 0.9451996 0.380 0.009 0.292 0.8681997 0.360 0.009 0.202 0.8091998 0.317 0.004 0.169 0.5871999 0.316 0.008 0.450 0.5492000 0.325 0.006 0.213 0.6152001 0.308 0.005 0.364 0.5582002 0.307 0.010 0.318 0.5352003 0.330 0.037 0.276 0.569

payout appears to decline steadily over the sample period, from 2.12% in 1983 to 0.54% in 2002,before rising in 2003. The increasing use of dividends as cash payout toward the end of our sampleperiod is probably partly due to the tax reform in 2003, after which most dividends were taxed ata lower 15% rate.

B. Measures of Firms Information Environments

We use analyst earnings forecast errors and the dispersion in analyst earnings forecasts tocapture the quality of a firms information environment. Elton, Gruber, and Gultekin (1984) showthat a large fraction of analyst forecast error is attributable to misestimation of firm-specificfactors rather than to misestimation of economy or industry factors. Their finding suggests thatanalyst forecast errors are a reasonable proxy for the degree of information asymmetry about thefirm.


The dispersion in analyst earnings forecasts represents the dispersion among analysts abouta consensus estimate of the forecast. Since disagreement among analysts is an indication of alack of available information, we use this standard deviation as another metric of the degree ofinformation asymmetry for a firm.We define analyst earnings forecast error as the absolute value of the difference between the

mean earnings forecast and actual earnings, divided by the absolute value of actual earnings.Dispersion of analyst earnings forecast is the standard deviation of the earnings forecast scaledby the absolute value of the mean earnings forecast. We require our sample firms to have both ofthese measures available.We have one reservation regarding our use of analyst forecast errors and forecast dispersion

as measures of asymmetric information, which is that forecast errors and dispersion might notso much capture asymmetries in information as levels of uncertainty that are common to bothmanagers and outside investors. For example, our measures might pick up a more risky environ-ment for the firm, implying a greater deviation than what we expected (and a larger variance ofsuch deviations). We argue that this concern does not pose any serious problems for our analysis.First, this is because other studies show that our measures for information asymmetry do capturedimensions beyond firm risk. Ajinkya, Atiase, and Gift (1991) and Lang and Lundholm (1993,1996) show that as firms enhance information disclosure, analyst earnings forecast accuracyincreases while forecast dispersion decreases. Bowen, Davis, and Matsumoto (2002) show thatconference calls improve analyst forecast precision and reduce forecast dispersion, and Chen andMatsumoto (2006) find that better access to management is associated with more accurate analystforecasts. Second, the concern about risky environments does not pose serious problems for ouranalysis because the positive correlation between firm risk and our two measures for asymmetricinformation is quite low (to be shown later). And to further lessen this concern, we control forfirm risk in all of our regression specifications. Thus, our results are not contaminated by thecommonality between information asymmetry and uncertainty, which is captured by firm risk.Panel A of Table II reports summary statistics for our two measures of asymmetric information.

The mean (median) analyst earnings forecast error is 21.7% (3.8%) of actual earnings, but themean (median) analyst forecast dispersion is 14% (3.2%) of the mean earnings forecast. The largedifference between mean and median values suggests that the distributions of these two measuresare highly skewed.Panel B presents summary statistics grouped by firm dividend policies. We find that both

measures of asymmetric information are significantly lower for dividend payers than are thoseobserved for nondividend payers. In addition, nondividend payers who initiate dividends anddividend payers with above-median payouts have lower forecast errors and forecast dispersionthan do nondividend payers who do not initiate dividends and dividend payers with below-medianpayout, respectively. The univariate results suggest a negative association between the degree ofinformation asymmetry and dividend policies.

C. Other Firm Characteristics

We also control for other firm characteristics that may affect a firms dividend policy: size,growth potential (the market-to-book ratio (M/B ratio), and asset growth), profitability, andfirm risk. Fama and French (2001) show that firms paying dividends are usually larger, withlower growth potential and higher cash flows. We add firm risk because Grullon, Michaely, andSwaminathan (2002), Hoberg and Prabhala (2008), and Bulan, Subramanian, and Tanlu (2007)suggest that firms pay dividends as a signal of firm maturity and declining risk.


We follow Fama and French (2001) in the construction of our first four variables that describefirm characteristics. We define profitability as earnings before extraordinary items (data 18) +interest expense (data 15) + income statement deferred taxes (data 50, if available)/total assets(data 6). We use both the M/B ratio and asset growth as growth opportunity measures. We definethe M/B ratio as the ratio of the market value of total assets to the book value of total assets. Wedefine the market value of total assets as the market value of equity plus the book value of totalassets minus the book value of equity, and the book value of equity is defined as stockholdersequity (data 216) or common equity (data 60) + preferred stock par value (data 130) or totalassets (data 6) total liabilities (data 181), plus balance sheet deferred taxes and investment taxcredit (data 35, if available) and postretirement benefit liabilities (data 330, if available), minusthe book value of preferred stocks (estimated in the order of the redemption (data 56), liquidation(data 10), or par value (data 130), depending on availability). We define firm size as the annualpercentile of market capitalization and use NYSE firms to calculate cutoff points. We do so toneutralize any effects of the growth in typical firm size through time, with the largest (smallest)firm taking the value of one (0.01). For firm risk, we follow Hoberg and Prabhala (2008) by usingthe standard deviation of residuals from a regression of firm daily stock returns on returns ofthe market portfolio. Our main results remain the same if we use the standard deviation of dailyreturns or the standard derivation of residuals from a regression of daily excess returns on thethree Fama and French (1992) factors.Panel A of Table II presents the summary statistics for firm characteristics. We show that the

mean (median) profitability of our sample firms is about 7.1% (9.6%), and the mean (median)M/B ratio is 1.99 (1.49). The mean (median) growth rate of assets is 23.6% (10.1%), suggestinga highly skewed distribution for asset growth among sample firms. The mean (median) firm riskis 2.79% (1.37%). Summary statistics of firm size suggest that on average, our sample firms areslightly smaller than the median NYSE firm but larger than the average publicly traded firm. Interms of the risk measure, our sample firms are less risky than an average public firm as examinedin Hoberg and Prabhala (2008). The standard deviations indicate that there are large variationsacross firms.In Panel C, Table II, we report the pairwise correlations between firm characteristics and the

asymmetric information measures. The two asymmetric information measures have a correlationof 0.37, suggesting that when analysts cannot agree on a firms earnings forecast, they are lesslikely to provide accurate forecasts. Neither of the asymmetric information measures is highlycorrelated with the firm characteristics that we find are important determinants of dividend policy.In particular, the correlations between firm risk and the two measures of information asymmetryare below 0.09. This result confirms that there is some overlap between firm risk and ourmeasuresof information asymmetry. However, it also indicates that the extent of overlap is limited, whichsuggests that our two measures do pick up aspects of a firms information environment that arenot captured by firm risk. Thus, our two measures are more likely to be exogenous proxies forasymmetric information, implying that our model specification should be a relatively clean testof the relation between information asymmetry and dividend policy.

II. Main Results

Given that most of our analyses involve panel data, our estimates are based on robust standarderrors. We estimate these errors by assuming independence across firms, but we account forpossible autocorrelation within the same firm. The robust standard errors are frequently muchlarger than conventional estimates, which assume independence among firm-year observations,


Table II. Summary Statistics

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, dividendinformation from CRSP, and analyst forecasts from IBES. We define profitability as earnings beforeextraordinary items (data 18) + interest expense (data 15) + income statement deferred taxes (data 50,if available)/total assets (data 6). The market-to-book (M/B) ratio is the ratio of the market value of totalassets to the book value of total assets. Asset growth is the rate of growth of total assets. Firm size is theNYSE market capitalization percentile. Firm risk is the standard deviation of residuals from the marketmodel measured in percentages. We define forecast error as the absolute value of the difference betweenmean analyst earnings forecasts and actual earnings, divided by the absolute value of actual earnings. Wedefine forecast dispersion as the standard deviation of analyst earnings forecast scaled by the absolute valueof the mean earnings forecast. Panel A presents summary statistics of firm characteristics and measuresof firms information environment. Panel B presents summary statistics of measures of firms informationenvironment for firms with different dividend characteristics. Panel C presents a correlation matrix of firmcharacteristics and measures of firms information environment. p-values appear in parentheses.

Panel A. Firm Characteristics and Information Environment

Mean Median Standard 25th 75thDeviation Percentile Percentile

Profitability 0.071 0.096 0.203 0.046 0.146M/B ratio 1.992 1.494 1.742 1.137 2.199Asset growth 0.236 0.101 1.103 0.012 0.252Firm size 0.473 0.450 0.289 0.220 0.720Firm risk 2.786 1.371 1.771 2.480 3.479Forecast error 0.217 0.038 0.622 0.013 0.125Forecast dispersion 0.140 0.032 0.357 0.013 0.095

Panel B. Measures of Firms Information Environment Grouped by Dividend Policy

Mean Standard 25th Median 75thDeviation Percentile Percentile

Forecast errorDividend payers 0.162 0.517 0.010 0.030 0.090Nondividend payers 0.267 0.700 0.016 0.049 0.167Difference 0.104p-value


Table II. Summary Statistics (Continued)

Panel B. Measures of Firms Information Environment Grouped by Dividend Policy (Continued)

Mean Standard 25th Median 75thDeviation Percentile Percentile

Dividend payers with above-median payouts 0.072 0.209 0.010 0.023 0.051Dividend payers with below-median payouts 0.161 0.389 0.014 0.037 0.117Difference 0.089p-value


growth potential are more likely to pay dividends. Moreover, we show that risky firms are lesslikely to pay dividends. This finding confirms the result in Grullon et al. (2002) and Hoberg andPrabhala (2008).More importantly, after controlling for the usual determinants of a firms propensity to pay

dividends, our results show negative coefficients on both measures of information asymmetry,which suggests that firms in a poorer information environment are less likely to pay dividends.This evidence does not support the signaling models of dividends.In Panel B, we report the results from the logistic regressions that we use to examine the

nondividend payers decisions to initiate dividends. Similar to our results on the decision to pay,we find that larger, more profitable firms are more likely to initiate dividend payments. Thepropensity to initiate dividends is negatively associated with the M/B ratio. After we control forthe M/B ratio, we find that the asset growth rate is positively associated with the propensity toinitiate dividends. Also, risky firms are less likely to initiate dividends, a result that is consistentwith the maturity and risk argument. Both measures of asymmetric information are negativelyassociated with the likelihood of dividend initiation.Panel C presents regression results from our examination of the decision to increase dividends

among payers. We find that more profitable firms are more likely to increase dividends. Thepositive effect of M/B ratios on the likelihood of increasing dividends appears to contradictthe growth opportunity argument. However, this result can be explained by the dual role played bytheM/B ratio. Fama and French (2002) suggest that theM/B ratio is ameasure of both profitabilityand growth potential. It is likely that theM/B ratio ismore ameasure of profitability than ameasureof growth opportunities among dividend-paying firms. Both measures of asymmetric informationare negatively associated with the likelihood of increasing dividends.In Panel D, we examine the determinants of the level of dividend payout. We show that larger,

more profitable firms with lower risk pay more cash dividends. Consistent with the findings fromthe other panels, both asymmetric information measures are negatively related to the amount ofdividends paid.Our findings lead us to conclude that there is a negative relation between asymmetric informa-

tion and measures of dividend policy. Our results do not support the signaling theory of dividends.We note that using insider returns as a proxy for information asymmetry, Khang and King (2006)show that the amount of dividends is negatively related to returns to insider trades across firms.They thus conclude that their results do not support the signaling theory of dividends either.

III. Additional Investigation

Here, we address other possibilities that may lead to our results. First, we ask if our sampleconstruction, which requires firms to have data available on analyst earnings forecasts, couldsystematically bias our findings. Second, we ask if a significant part of our results could beexplained by the increasing use of share repurchases as a form of payout. Third, we ask howsensitive are our results to other factors that have been suggested in the literature to explaindividend policy, such as institutional monitoring and catering.

A. Sample Selection

As mentioned before, our sample firms are different from the general population covered inCompustat/CRSP as examined in Fama and French (2001). So the important question is, doesthis sample difference drive the results?2

2We thank an anonymous referee for suggesting this analysis to us.


Table III. Explaining Dividend Policy

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, dividendinformation from CRSP, and analyst forecasts from IBES. We define profitability as earnings beforeextraordinary items (data 18) + interest expense (data 15) + income statement deferred taxes (data 50, ifavailable)/total assets (data 6). The market-to-book (M/B) ratio is the ratio of the market value of total assetsto the book value of total assets. Asset growth is the rate of growth of total assets. Firm size is the NYSEmarket capitalization percentile. Firm risk is the standard deviation of residuals from the market modelmeasured in percentages. We define forecast error as the absolute value of the difference between meananalyst earnings forecasts and actual earnings, divided by the absolute value of actual earnings. We defineforecast dispersion as the standard deviation of analyst earnings forecast scaled by the absolute value of themean earnings forecast. The dependent variable in Panel A is the payer dummy set equal to one for firm iin year t if the annual amount of dividends paid is positive, and zero otherwise. The dependent variable inPanel B is the initiation dummy set equal to one if this is the first time firm i pays dividends, and zero for allthe years prior to year t. The dependent variable in Panel C is the increase dummy set equal to one for firmi in year t if the percentage increase in dividends is greater than 15%, and zero otherwise. The dependentvariable in Panel D is dividend payout, which we define as the ratio of annual aggregation of quarterlycommon dividends obtained from CRSP to total assets measured in percentages. The estimation includesindustry and year dummies. We base the reported p-values on White (1980) heteroskedasticity-consistentstandard errors, adjusted to account for possible correlation within a (firm) cluster.

Panel A. The Decision to Pay Dividends

(1) (2) (3)

Profitability 2.446 2.413 2.370[


Table III. Explaining Dividend Policy (Continued)

Panel B. The Decision to Initiate Dividends (Continued)

(1) (2) (3)

Firm risk 0.592 0.586 0.581[


Table III. Explaining Dividend Policy (Continued)

Panel D. The Decision on the Amount of Dividends (Continued)

(1) (2) (3)

Forecast error 0.052 0.021[0.001] [0.146]

Forecast dispersion 0.173 0.161[


Table IV. Sample Selection

The sample period is from 1983 to 2003. To assess the impact of sample selection criterion on our mainresults, we expand the sample to include all firms from the Compustat/CRSP merged file. For firms withoutinformation on analyst forecasts, we assign zero to their forecast errors and forecast dispersion. We also addto the regression model the no analyst coverage dummy, which we set equal to one for firms without anyanalyst coverage, and zero otherwise in year t. We define profitability as earnings before extraordinary items(data 18) + interest expense (data 15) + income statement deferred taxes (data 50, if available)/total assets(data 6). The market-to-book (M/B) ratio is the ratio of the market value of total assets to the book value oftotal assets. Asset growth is the rate of growth of total assets. Firm size is the NYSE market capitalizationpercentile. Firm risk is the standard deviation of residuals from the market model measured in percentages.We define forecast error as the absolute value of the difference between mean analyst earnings forecastsand actual earnings, divided by the absolute value of actual earnings. We define forecast dispersion as thestandard deviation of analyst earnings forecast scaled by the absolute value of the mean earnings forecast.The dependent variable in Column (1) is the payer dummy, set equal to one for firm i in year t if the annualamount of dividends paid is positive, and zero otherwise. The dependent variable in Column (2) is theinitiation dummy, set equal to one if this is the first time firm i is paying dividends, and zero for all theyears prior to year t. The dependent variable in Column (3) is the increase dummy, set equal to one for firmi in year t if the percentage increase in dividends is greater than 15%, and zero otherwise. The dependentvariable in Column (4) is dividend payout, which we define as the ratio of annual aggregation of quarterlycommon dividends obtained from CRSP to total assets measured in percentages. The estimation includesindustry and year dummies. We base the reported p-values on White (1980) heteroskedasticity-consistentstandard errors adjusted to account for possible correlation within a (firm) cluster.

(1) (2) (3) (4)Decision Decision Decision Decision onto Pay to Initiate to Increase the Amount

Dividends Dividends Dividends of Dividends

Profitability 3.170 4.007 5.426 0.077[


Table V. Repurchase, Institutional Ownership, and Catering

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, dividendinformation from CRSP, and analyst forecasts from IBES. We define profitability as earnings beforeextraordinary items (data 18) + interest expense (data 15) + income statement deferred taxes (data 50,if available)/total assets (data 6). The market-to-book (M/B) ratio is the ratio of the market value of totalassets to the book value of total assets. Asset growth is the rate of growth of total assets. Firm size is theNYSE market capitalization percentile. Firm risk is the standard deviation of residuals from the marketmodel measured in percentages. We define forecast error as the absolute value of the difference betweenmean analyst earnings forecasts and actual earnings, divided by the absolute value of actual earnings. Wedefine forecast dispersion as the standard deviation of analyst earnings forecast scaled by the absolutevalue of the mean earnings forecast. The repurchase amount is the product of the split-adjusted changein shares outstanding and the average of the split-adjusted stock price at the beginning and the end of theyear, normalized by total assets and measured in percentages. Institutional ownership is the fractional shareownership by institutions. Dividend premium is the difference between log(M/B ratio) for dividend payersand the same measure for nondividend payers. The dependent variable in Panel A is the payer dummy, setequal to one for firm i in year t if the annual amount of dividends paid is positive, and zero otherwise.The dependent variable in Panel B is the initiation dummy, set equal to one if this is the first time firm i ispaying dividends, and zero for all the years prior to year t. The dependent variable in Panel C is the increasedummy, set equal to one for firm i in year t if the percentage increase in dividends is greater than 15%,and zero otherwise. We define the dependent variable in Panel D, dividend payout, as the ratio of annualaggregation of quarterly common dividends obtained from CRSP to total assets measured in percentages.The estimation has industry and year dummies included. We base the reported p-values on White (1980)heteroskedasticity-consistent standard errors adjusted to account for possible correlation within a (firm)cluster.

Panel A. The Decision to Pay Dividends

(1) (2) (3) (4)

Profitability 2.190 2.948 3.209 2.873[


Table V. Repurchase, Institutional Ownership, and Catering (Continued)

Panel B. The Decision to Initiate Dividends

(1) (2) (3) (4)

Profitability 7.148 7.089 8.255 8.059[


Table V. Repurchase, Institutional Ownership, and Catering (Continued)

Panel D. The Decision on the Amount of Dividends

(1) (2) (3) (4)

Profitability 0.460 0.691 1.283 1.131[0.018] [0.001] [


C. Institutional Shareholdings

Allen, Bernardo, andWelch (2000) present amodel in which they use dividends to attract better-informed, monitoring, institutional shareholders. Their theory predicts a positive correlationbetween dividends and institutional shareholdings.To explore whether our results are driven by institutional monitoring, we control for institutional

holdings in our regression specifications, and report the results in Table V, Column (2). We findthat measures of asymmetric information remain negatively related to firms dividend policies.Further, contrary to the monitoring argument in Allen et al. (2000), but mostly consistent with theempirical findings in Grinstein and Michaely (2005), we show that, after we control for firm risk,firms with higher institutional shareholdings are less likely to pay dividends and are associatedwith lower dividend payouts. It is clear that the negative relation between asymmetric informationand dividend policy is not explained by the presence of (monitoring) institutional shareholders.Again, our results fail to lend support to the signaling theory of dividends.

D. The Catering Theory of Dividends

Using aggregate data, Baker and Wurgler (2004) develop their catering theory of dividends.They find that investor demand for dividend-paying stocks is time-varying. Managers cater toinvestor demand for dividends by paying dividends when investors place a premium on dividend-paying stocks, and vice versa.We use the dividend premium measure provided in Baker and Wurgler (2004), which they

define as the difference in the value-weighted averageM/B ratio of payers and the value-weightedaverage M/B ratio of nondividend payers. We add this measure to Equation (1). Column (3) ofTable V presents the results. (We note that because the sample in Baker and Wurglers, 2004,study ends in 2000, the sample size with the catering measure is smaller.) We find that adding thedividend premium into our model specification has no material effect on the role of asymmetricinformation in dividend policy.Moreover, the coefficient estimate of dividend premium contradicts the argument in Baker and

Wurgler (2004). We show that this finding is mainly due to our inclusion of the year dummiesand firm risk. Once we remove these dummies and the risk variable, the coefficient on dividendpremium is significant and positive. This result is consistent with Baker and Wurglers argumentthat the dividend premium primarily captures the temporal variation in market sentiment.Column (4) of Table V presents our results when we use all additional dividend factors. It is

clear that our main results on asymmetric information do not change with this expanded modelspecification. Thus, we conclude that the negative relation between information asymmetry anddividend policy is not driven by other factors that may affect a firms dividend policy. And again,our evidence does not support the signaling theory of dividends.

IV. Repurchase and Total Payout

Although our main focus is on the relation between information asymmetry and firms divi-dend policies, we also examine whether information asymmetry is an important consideration forrepurchases. Vermaelen (1984), Ofer and Thakor (1987), and McNally (1999) extend the modelsin Bhattacharya (1979) and Miller and Rock (1985) to repurchases, suggesting that the signalingmotive may also determine firms repurchase decisions. However, the inherent inflexibility individends implies that dividends have stronger informational content than do repurchases. Thus,if the signaling models are valid, we expect to find a weaker (less positive or more negative)


relation between asymmetric information and repurchases (or total payouts) than between asym-metric information and dividends. Therefore, we examine the relation between repurchases andinformation asymmetry separately, and we opt not to combine repurchase and dividend policiesin our main analysis.We use the two repurchase measures defined in Section III, the repurchase dummy and the

repurchase amount. We define the total payout policy variables in a comparable way. We set thepayout dummy equal to one for firm i in year t if the firm either pays dividends or repurchasesshares, and zero otherwise. The total payout aggregates all dividends paid and the amountrepurchased during the year, scaled by total assets.We report the time-series characteristics of repurchase and payout measures in Table VI,

Panel A. We see that the proportion of repurchasing firms increases from 1983 to 1990, declinesafterward, and then peaks again toward the end of the technology bubble. The proportion of firms

Table VI. Summary Statistics of Repurchase and Payout Policies

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, divi-dend/repurchase information from CRSP, and analyst forecasts from IBES. We define repurchasing firms asthose firms that make nontrivial repurchases in year t and nonrepurchasing firms as those firms that makeno repurchases in year t. The repurchase amount is the ratio of the product of the split-adjusted change inshares outstanding and the average of the split-adjusted stock price at the beginning and the end of the year,normalized by total assets. Payout firms are firms that either pay dividends or make repurchases or both inyear t and nonpayout firms are those that make no payout in year t. We define total payout as the ratio ofthe sum of the dividend payout and repurchase amount in year t to total assets.

Panel A. Time-Series Characteristics of Repurchase and Payout Policies

Year (1) (2) (3) (4)Proportion of Repurchase Proportion of TotalRepurchasing Amount Payout Payout

Firms Firms

1983 0.121 0.262 0.817 2.3811984 0.283 1.053 0.808 2.9011985 0.210 0.779 0.743 2.4661986 0.252 1.179 0.712 2.7011987 0.259 1.418 0.701 2.8831988 0.377 2.096 0.712 3.5571989 0.271 1.111 0.652 2.4461990 0.333 1.441 0.651 2.7771991 0.193 0.696 0.599 1.9961992 0.172 1.709 0.602 3.0311993 0.166 0.724 0.552 1.8941994 0.186 0.775 0.503 1.8161995 0.214 0.969 0.495 1.9141996 0.211 0.938 0.453 1.8061997 0.240 1.458 0.452 2.2671998 0.297 1.569 0.457 2.1561999 0.259 1.339 0.440 1.8892000 0.307 2.322 0.464 2.9372001 0.208 0.957 0.400 1.5152002 0.209 0.972 0.432 1.5072003 0.202 0.833 0.425 1.402


Table VI. Summary Statistics of Repurchase and Payout Policies (Continued)

Panel B. Measures of Firms Information Environment Grouped by Repurchasing or Not,and Paying Out or Not

Mean Median Standard 25th 75thDeviation Percentile Percentile

Forecast errorRepurchasing firms 0.174 0.553 0.010 0.029 0.090Nonrepurchasing Firms 0.231 0.641 0.014 0.042 0.139Difference 0.057p-value


Table VII. Explaining Repurchase and Payout Policies

The sample period is from 1983 to 2003. We obtain accounting information from Compustat, divi-dend/repurchase information from CRSP, and analyst forecasts from IBES. We define profitability asearnings before extraordinary items (data 18) + interest expense (data 15) + income statement deferredtaxes (data 50, if available)/total assets (data 6). The market-to-book (M/B) ratio is the ratio of the marketvalue of total assets to the book value of total assets. Asset growth is the rate of growth of total assets.Firm size is the NYSE market capitalization percentile. Firm risk is the standard deviation of residualsfrom the market model measured in percentages. We define forecast error as the absolute value of thedifference between mean analyst earnings forecasts and actual earnings, divided by the absolute value ofactual earnings. We define forecast dispersion as the standard deviation of analyst earnings forecast scaledby the absolute value of the mean earnings forecast. The dependent variable in Column (1) is the repurchasedummy, set equal to one for firm i in year t if the annual amount of repurchases is positive, and zerootherwise. The dependent variable in Column (2), repurchase amount, is the product of the split-adjustedchange in shares outstanding and the average of the split-adjusted stock price at the beginning and the endof the year, normalized by total assets. The dependent variable in Column (3) is the payout dummy, set equalto one for firm i in year t if the annual amount of payout is positive, and zero otherwise. We define thedependent variable in Column (4), total payout, as the ratio of the sum of the dividend payout and repurchaseamount to total assets. The estimation includes industry and year dummies. We base the reported p-valueson White (1980) heteroskedasticity-consistent standard errors adjusted to account for possible correlationwithin a (firm) cluster.

(1) (2) (3) (4)Decision to Repurchase Decision to TotalRepurchase Amount Pay Out Payout

Profitability 2.574 1.780 2.259 2.335[


Prior research shows that forecast errors and dispersion are positively correlated with the extentof information asymmetry that firms face. We conjecture that if the signaling theory of dividendsis an accurate description of reality, then firms dividend policies should be positively associatedwith analyst earnings forecast errors and forecast dispersion.Using a CRSP/Compustat/IBES combined sample over 1983-2003 and controlling for firm

characteristics, we find that, ceteris paribus, firms more subject to the problem of informationasymmetry are less likely to make dividend payments, to initiate dividends, and to increasedividends, and that these firms also distribute smaller amounts. Our conclusions are not drivenby sample selection criteria, and they hold after we control for contemporaneous repurchasingactivities, the presence of monitoring institutional investors, and catering incentives. Therefore,our evidence casts doubt on the validity of the dividend signaling models.We find a weak negative relation between repurchases andmeasures of information asymmetry.

This finding further strengthens our evidence on the lack of support for the signaling theory ofdividends.

References

Ajinkya, B.B., R.K. Atiase, andM.J. Gift, 1991, Volume of Trading and the Dispersion in Financial AnalystEarnings Forecasts, Accounting Review 66, 389-401.

Alford, A.W. and P.G. Berger, 1999, A Simultaneous Equations Analysis of Forecast Accuracy, AnalystFollowing, and Trading Volume, Journal of Accounting, Auditing, and Finance 14, 219-240.

Allen, F., A. Bernardo, and I. Welch, 2000, A Theory of Dividends Based on Tax Clientele, Journal ofFinance 55, 2499-2536.

Allen, F. and R. Michaely, 2003, Payout Policy, in G. Constantinides, M. Harris, and R. Stulz, Eds.,Handbook in Economics, North-Holland, Elsevier. 337-429.

Amihud, Y. and K. Li, 2006, The Declining Information Content of Dividend Announcements and theEffect of Institutional Holdings, Journal of Financial and Quantitative Analysis 41, 637-660.

Baker, M. and J. Wurgler, 2004, A Catering Theory of Dividends, Journal of Finance 59, 1125-1165.

Bhattacharya, S., 1979, Imperfect Information, Dividend Policy, and the Bird in the Hand Fallacy, BellJournal of Economics 10, 259-270.

Bowen, R.M., A.K. Davis, and D.A. Matsumoto, 2002, Do Conference Calls Affect Analyst Forecasts?Accounting Review 77, 285-316.

Bulan, L., N. Subramanian, and L. Tanlu, 2007, On the Timing of Dividend Initiations, Financial Man-agement 36, 31-65.

Chen, S. and D.A. Matsumoto, 2006, Favorable versus Unfavorable Recommendations: The Impact onAnalyst Access to Management-Provided Information, Journal of Accounting Research 44, 657-689.

Elton, E.J., M.G. Gruber, and M.N. Gultekin, 1984, Professional Expectations: Accuracy and Diagnosis ofErrors, Journal of Financial and Quantitative Analysis 19, 351-363.

Fama, E.F. and K.R. French, 1992, The Cross-Section of Expected Stock Returns, Journal of Finance 47,427-465.

Fama, E.F. and K.R. French, 2001, Disappearing Dividends: Changing Firm Characteristics or LowerPropensity to Pay? Journal of Financial Economics 60, 3-44.


Fama, E.F. and K.R. French, 2002, Testing Trade-off and Pecking Order Predictions about Dividends andDebt, Review of Financial Studies 15, 1-33.

Fama, E.F. and K.R. French, 2005, Financing Decisions: Who Issues Stock? Journal of Financial Eco-nomics 76, 549-582.

Grinstein, Y. and R. Michaely, 2005, Institutional Holdings and Payout Policy, Journal of Finance 60,1389-1426.

Grullon, G. and R. Michaely, 2002, Dividends, Share Repurchases, and the Substitution Hypothesis,Journal of Finance 62, 1649-1684.

Grullon, G., R. Michaely, S. Benartzi, and R.H. Thaler, 2005, Dividend Changes Do Not Signal Changesin Future Profitability, Journal of Business 78, 1659-1682.

Grullon, G., R. Michaely, and B. Swaminathan, 2002, Are Dividend Changes a Sign of Firm Maturity?Journal of Business 75, 387-424.

Hoberg, G. and N.R. Prabhala, 2008, Dividend Policy, Risk, and Catering, Review of Financial Studies,forthcoming.

Hong, H., T. Lim, and J.C. Stein, 2000, Bad News Travels Slowly: Size, Analyst Coverage, and theProfitability of Momentum Strategies, Journal of Finance 55, 265-295.

John, K. and J. Williams, 1985, Dividends, Dilution, and Taxes: A Signaling Equilibrium, Journal ofFinance 40, 1053-1070.

Khang,K. andT.-H.D.King, 2006, DoesDividend PolicyRelate toCross-SectionalVariation in InformationAsymmetry? Evidence from Returns to Insider Trades, Financial Management 35, 71-94.

Lang, M.H. and R.J. Lundholm, 1993, Cross-Sectional Determinants of Analysts Ratings of CorporateDisclosures, Journal of Accounting Research 31, 246-271.

Lang, M.H. and R.J. Lundholm, 1996, Corporate Disclosure Policy and Analyst Behavior, AccountingReview 71, 467-492.

Lintner, J., 1956, Distribution of Incomes of Corporations among Dividends, Retained Earnings, andTaxes, American Economic Review 46, 97-113.

McNally, W.J., 1999, Open Market Stock Repurchase Signaling, Financial Management 28, 55-67.

Miller, M. and F. Modigliani, 1961, Dividend Policy, Growth and the Valuation of Shares, Journal ofBusiness 34, 411-433.

Miller, M. and K. Rock, 1985, Dividend Policy under Asymmetric Information, Journal of Finance 40,1031-1051.

Ofer, A.R. and A.V. Thakor, 1987, A Theory of Stock Price Responses to Alternative Corporate CashDisbursement Methods: Stock Repurchases and Dividends, Journal of Finance 42, 365-394.

Vermaelen, T., 1984, Repurchase Tender Offers, Signaling, and Managerial Incentives, Journal of Finan-cial and Quantitative Analysis 19, 163-181.

White, H., 1980, A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test forHeteroskedasticity, Econometrica 48, 817-883.

Documents

Asymmetric Information and Dividend Policy