Valuing Dividends
Charles G. Ham Assistant Professor Washington University in St. Louis [email protected]
Zachary R. Kaplan Assistant Professor Washington University in St. Louis [email protected]
Steven Utke Assistant Professor University of Connecticut [email protected]
April 2019
Preliminary, please don’t circulate without author consent
Valuing Dividends
Abstract
In frictionless markets dividends are irrelevant to firm value (Miller and Modigliani 1961), but in practice they affect valuation and stewardship, roles traditionally filled by accounting. We examine whether the firm decision to supply information through dividends substitutes for the use of accounting information. Using a variety of econometric methods to control for differences between dividend paying and non-dividend paying firms, we show that dividend payers have smaller price and volume reactions to earnings announcements than non-payers. We show the probability of just meeting or beating (missing) earnings estimates is lower (higher) for dividend payers relative to non-payers, suggesting that the presence of dividends shifts investors’ focus to dividends rather than earnings, thereby decreasing managerial myopia. Both analysts and managers forecast earnings less frequently for dividend payers, consistent with the decreased market reactions to earnings information for these firms reducing both the demand for, and supply of, earnings information. Collectively, this evidence suggests dividends impact the information environment, and although costly, could be an efficient way for profitable companies to satisfy investors’ demand for information.
Keywords: dividends, information environment
1. Introduction
The two major functions of accounting in market-based economies are stewardship and valuation (Beyer et al. 2010). Although irrelevant to firm value in perfect and frictionless markets (Miller and Modigliani 1961), in practice the firm decision to pay dividends affects both the valuation and stewardship of the firm. With respect to valuation, the level of dividends provides a measure of permanent earnings and markets react strongly to changes in dividends, which signal a shift to a new level of permanent earnings.1 With respect to stewardship, both survey and empirical evidence show dividend cuts lead to a decrease in managers’ reputation and an increase in the probability of turnover (Brav et al. 2005; Wu 2016). Given the potential overlap between the information conveyed through dividends and earnings, we examine the extent to which the two information sources serve as substitutes or complements with respect to valuation and stewardship. As such, our paper takes a first step toward understanding how the supply of information conveyed through dividends affects the firm’s overall information environment.
Our investigation of the implications of dividend paying for the firm’s information environment also contributes to our understanding of the reason firms pay dividends, the
‘dividend puzzle’ (Black 1976). The literature has documented numerous costs of paying dividends – they are tax disadvantaged and managers sacrifice positive NPV projects to maintain the dividend (Brav et al. 2005; Wu 2016) – but the literature been less successful in documenting the benefits that offset these costs. We argue dividends supply information about permanent earnings,2 and thereby substitute for the supply of information through other sources such as
1 See DeAngelo et al. 1992; DeAngelo et al. 1996; Benartzi et al. 1997; Koch and Sun 2004; Skinner and Soltes 2011; Ham et al. 2019. 2 Several studies argue that dividends provide information about unexpected changes in earnings (e.g. Aharony and Dotan 1994; Yoon and Starks 1995; Nissim and Ziv 2011; Ham et al. 2019). Others argue that dividends
1 earnings announcements (“dividend substitution”). The permanent earnings information from dividends increases the precision of investors’ prior expectations about future earnings, so investors update their expectations less in response to each earnings realization. The supply of information through dividends may also reduce the stewardship role of earnings. For example, when stakeholders can infer the quality of managerial leadership using both dividends and earnings, the weight stakeholders place on earnings could decline.
Despite our view that dividends and earnings serve as substitute sources of information, at least two explanations would predict a complementary relation. First, under the view that the investor base drives dividend paying (Grinblatt and Michaely 2005), investors of dividend paying firms could demand more overall information about earnings, with managers supplying a portion of the information through dividends. That is, dividend information could represent an ancillary component of a firm’s overall disclosure strategy that is used to complement and reinforce other signals (DeAngelo et al. 2008). Second, under the agency view, non-dividend paying firms waste a higher proportion of earnings than dividend paying firms (e.g., Easterbrook
1984; Jensen 1986), so investors should react more to each unit of earnings from dividend payers.3
An empirical challenge to investigating the effect of paying a dividend on the firm’s information environment is that dividend payout is an endogenous firm choice (e.g., DeAngelo et al. 2008). Large, profitable companies that tend to pay dividends could also simply have different earnings informativeness without the supply of information through the dividend
provide information about the persistence of past earnings changes or about future cash flow volatility and do not provide information about the level of earnings (DeAngelo et al. 1996; Benartzi et al. 1997; Koch and Sun 2004). 3 Waymire and Sivakumar (1993) show that before stock exchanges mandated accounting disclosures, firms that paid dividends had larger market reactions to earnings, consistent with a complementary relation.
2 directly affecting the informativeness of earnings. We address this empirical challenge using several features of the information environment. First, we use two matching techniques to compare dividend paying firms to non-dividend paying firm with similar traits: propensity score matching and entropy balancing. These approaches allow us to compare firms with similar propensities to pay dividends, but which differ in their actual intensity of dividend payout. We verify our matched firms have similar future growth in earnings and market value, suggesting we are comparing similar firms while varying payout choice. Second, we use payout from share buybacks as a counter-factual to payout from dividends. The method of returning cash to shareholders is irrelevant from a capital structure perspective, but not from an informational perspective. The implicit commitment to maintain dividends provides a credible signal about permanent earnings (Lintner 1956; Brav et al. 2005) whereas buybacks are highly variable and provide only transitory earnings information (Guay and Harford 2000). Thus, relative to repurchases, dividends are more likely to convey sufficient future earnings information to affect the firm’s overall information environment.
Our initial analyses show that when firms pay dividends, markets react less to their earnings. After propensity score matching non-dividend paying firms to dividend paying firms, we find dividend payers have a statistically significant 20-25 basis point reduction in absolute returns at the earnings announcement. In addition to matching, we control for average return volatility, suggesting our results are not just an artifact of dividend paying firms having less volatile returns. Instead, our results suggest that a lower proportion of price changes occur at earnings announcements (EA) for dividend paying firms. We then provide more direct evidence on how dividends affect the market’s response to new earnings information by documenting lower earnings response coefficients for dividend paying firms. Although dividend payers could
3 have larger earnings response coefficients (ERCs) because they have more persistent earnings
(Kothari 2001), we document the opposite, consistent with dividend substitution. We also provide evidence on the mechanism underlying dividend substitution by exploiting variation in the proximity of the most recent dividend declaration to the most recent EA. Consistent with the dividend conveying more information about future earnings when more time has elapsed since the most recent earnings signal, we show weaker ERCs during quarters with dividend declarations further from the prior EA. Taken together, our evidence suggests the availability of dividend information – a signal about permanent earnings – decreases the extent to which investors update valuation in response to unexpected earnings.
We also document lower abnormal volume around EAs for dividend paying firms, consistent with the lower quantity of pricing information leading fewer investors to adjust their portfolios in response to the EAs of dividend paying firms (e.g., Beaver 1968; Bamber 1986).
This suggests that dividend paying firms have a richer information environment, such that not only does valuation respond less to EAs, but fewer investors decide to shift their holdings.
Our next set of analyses seeks to link dividend paying to the stewardship role of earnings by examining the incentives of management to meet or beat earnings expectations. Survey and empirical evidence demonstrate that performance relative to earnings benchmarks affects managers’ stewardship of the firm (Matsunaga and Park 2001; Graham et al. 2005). These incentives lead managers to sacrifice economic resources in order to meet or beat expectations
(Bhojraj et al. 2009). We examine whether the increased information available due to dividends decreases the manager’s focus on earnings performance relative to the analyst consensus. We show firms that pay dividends just meet or beat earnings estimates less frequently and just miss estimates more frequently relative to non-dividend paying firms. We also find less real earnings
4 management activity to just meet or beat (Bhojraj et al. 2009), consistent with our theory that dividend paying firms benefit less from meeting or beating, and thus exhibit less myopic behavior. We use a difference-in-difference research design that compares growth in R&D expenditures for firms that just missed vs. just met or beat the consensus (first difference), across dividend paying and non-dividend paying firms (second difference). The results suggest dividend paying firms are less likely to cut R&D expenditures to beat the consensus forecast.
Our final set of tests focuses on the forecasting behavior of dividend paying firms. We predict that the availability of a substitute signal for valuation translates into lower demand for, and therefore supply of, earnings forecasts. This reduced demand affects both the benefits to firms in forecasting their own earnings, and the incentives of financial analysts to invest resources in information production. We find suggestive evidence in support of this prediction: in most specifications, dividend paying firms have fewer manager and analyst forecasts. In addition to an overall decline in information production for dividend paying firms, we observe a shift toward forecasting metrics that are more focused on cash flows, which can be used to predict dividends, rather than earnings. Conditional on issuing a forecast, managers of dividend paying firms are more likely to forecast capital expenditures and less likely to forecast top-line metrics such as gross margin and revenues.
Our findings make several contributions to the literature. First, our findings suggest dividend paying affects the firm’s information environment and should be considered simultaneously with voluntary disclosure and analyst activity when assessing the quality of the information environment. Our findings also suggest that buybacks have a limited effect on the information environment. Although these forms of payout are substitutes from a capital structure perspective, from an informational perspective they are not substitutes because the implicit
5 commitment to maintain the dividend provides information about permanent earnings. Overall, our findings answer the call for additional research that considers the interdependencies between firm decisions that affect the information environment, and is particularly important given that dividend information has not been previously considered (Healy and Palepu 2001; Beyer et al.
2010).
Second, we contribute to the dividend literature by arguing dividends provide a signal about permanent earnings that competes with earnings announcements to provide valuation information. Fama and French (1998) note that while dividends are tax-disadvantaged, dividend payers have higher valuations than can be explained by their book values. They conclude that dividends convey information about profitability. Consistent with this, our tests show that investors of dividend paying firms update less in response to new earnings information, suggesting they have stronger priors about future profitability.
Third, we show that attention to dividends decreases managerial myopia. A large number of researchers lament that the ‘numbers game’ leads to a short-term earnings focus on the part of managers (Matsunaga and Park 2001). We show that paying a dividend leads to a decrease in that myopic focus. However, we leave to future research whether dividend payers myopically focus on the dividend threshold (i.e., Skinner 2014), such that there is merely a shift in myopia rather than an overall reduction.
Fourth, we contribute to the valuation literature. Financial statement analysis textbooks frequently offer formulas that can be used to estimate firm value using either dividends or earnings. Our tests examining the returns at the EA suggest that, in practice, investors use them as substitutes. We also document the presence of a dividend leads to less earnings forecasting
6 and a shift to forecasting items such as investment, whose value can be used to estimate the dividend (e.g., Kothari 2001).
2. Literature review and hypothesis development
Our central hypothesis is that dividends supply information to the market about the firm’s permanent earnings (Fama and French 1998; Skinner and Soltes 2011), and thus affect how investors update expectations in response to new earnings information. Because of this additional signal, investors will form more precise earnings expectations before the earnings announcement, and will thus update less in response to new earnings information. We also argue the incremental earnings information supplied by dividend payments will affect the demand and supply of earnings information, possibly leading managers to make different decisions about both forecasting and managing earnings. These hypotheses lie at the intersection of the dividend literature and the literature on the role of accounting information in the valuation and stewardship of the firm. In this section, we review each of these literatures and more formally develop our hypotheses.
2.1 Dividends and their effect on valuation
Over the life of the firm, inflows of cash must equal outflows, so with perfect foresight valuing the firm using earnings or payout (i.e., dividends and buybacks) will yield identical valuations. However, at any point in time, stakeholders have uncertainty about future values of both payout and earnings, so payout can provide information about earnings and vice-versa
(Skinner et al. 1992; Aharony and Swary 1994; Guay and Harford 2000; Skinner and Soltes
2011). The relative information content of payout and earnings will be a function of how they resolve that uncertainty, given the availability of the other signal.
7 Dividends provide information about the level of earnings and inform the market about permanent changes in earnings (DeAngelo et al. 1992; DeAngelo et al. 1996; Guay and Harford
2000; Skinner and Soltes 2011; Ham et al. 2019). Other aspects of dividend policy, such as firms rarely cutting dividends and managers facing penalties from the capital market for cutting the dividend (Lintner 1956; Brav et al. 2005; Wu 2016), provide an economic rationale for dividend permanence. Given the penalties for cutting the dividend, managers will not declare dividends they cannot sustain with future earnings.4 Because of the permanent earnings information supplied through dividends, we argue that investors should update their expectations less in response to earnings realizations, and demand less information to help predict those realizations.
We formalize our first hypothesis as follows:
H1: Dividend paying firms have smaller price and volume reactions to earnings and earnings surprises.
We also contrast our predictions for dividend payments with those for repurchases. From a capital structure perspective, dividends and repurchases are equivalent because each form of payout removes cash from the control of managers, which can increase or decrease firm value depending on the return on cash (Faulkender and Wang 2006; Kaplan and Perez-Cavazos 2019).
However, from an informational perspective, the two forms of payout have very different properties. Buybacks tend to be transitory and correlate with temporary earnings changes whereas dividends tend to be persistent and correlate with permanent earnings changes (Guay and Harford 2000). We thus argue that dividends better substitute for earnings information than buybacks.
2.2 The substitution of dividend information for information from other sources
4 The alternative hypothesis that firms fund dividends out of existing cash holdings is inconsistent with the low cash balances of dividend paying firms (e.g., Farre-Mensa et al. 2018).
8 Prior literature on both dividends and earnings shows that stakeholders monitor managers through their performance relative to benchmarks. With respect to earnings, managers are judged relative to the analyst consensus and firms who miss these benchmarks suffer penalties in the form of reduced market value and human capital (Matsunaga and Park 2001; Skinner and
Sloan 2002). With respect to dividends, managers’ smooth payout to avoid penalties associated with cutting the dividend (Wu 2016).
To avoid these negative consequences, firms manage both reported earnings as well as the market’s earnings expectations. Consistent with this, the proportion of firms just beating earnings benchmarks substantially exceeds the fraction that just miss them (Burgstahler and
Dichev 1997; Degeorge et al. 1999). Managing either payout or earnings to achieve benchmarks demanded by investors entails costs (Stein 1990), and performance relative to benchmarks has been linked to a sacrifice of resources (Bhojraj et al. 2009; Wu 2016). In the presence of a dividend, which provides information about expected earnings, we expect that investors will fixate less on earnings. Managers of dividend paying firms will respond to this lack of fixation by delivering fewer earnings reports that just meet or beat analysts’ earnings forecasts relative to non-dividend paying firms. Thus, we state our second hypothesis as follows:
H2: Dividend paying firms are less (more) likely to just meet or beat (miss) earnings targets.
The provision of both dividends and voluntary disclosures are costly to firms (e.g.,
Rozeff 1982; Verrecchia 1983; Brav et al. 2005). Firms have incentives to optimally trade off the costs and benefits of voluntary disclosure and to produce the efficient level of information for investors in the economy (Healy and Palepu 2001). Because dividends affect the information environment, the presence of dividends can affect the level of additional voluntary disclosure
9 firms need to provide to reach this efficient level.5 The costs of disclosure generally arise from several sources, such as: (a) the costs of producing information, (b) proprietary costs incurred from competitors viewing disclosure, or (c) ex-ante commitments to meet certain earnings benchmarks that can lead to myopic behavior ex-post (Trueman 1986; Graham et al. 2005). The benefits of disclosure arise from reductions in information asymmetry (Diamond 1985; Diamond and Verrecchia 1991), cost of capital (Francis et al. 2008; Baginski and Rakow 2012), or reductions in litigation risk (Skinner 1994). Numerous studies document that the investor base affects the quantity and type of voluntary disclosure (Ajinkya et al. 2005; Chen et al. 2008), consistent with the supply of disclosure responding to investor demand for information.
Similarly, analysts have incentives to produce information only when doing so benefits themselves and/or their brokerage (e.g., McNichols and O’Brien 1997; Hayes 1998; Irvine
2001). In particular, analysts produce information when the demand for information from investors will generate trading revenues sufficient to justify information production.
We hypothesize that the supply of earnings information in the form of dividends will substitute for the supply of additional earnings-related disclosures from both analysts and managers. The basic intuition is that the dividend provides information about the firm’s earnings, which attenuates the demand for forecasts. Because forecasting is costly, and the supply of forecasts is not perfectly elastic, the reduced demand results in a reduction in forecasting.
H3: Managers and analysts issue fewer forecasts for dividend paying firms.
While we make directional predictions for all of our hypotheses, we highlight that these are not certain to hold. Specifically, to the extent that dividends play a complementary role to
5 Specifically, Beyer et al. (2010, p. 298) highlight the possibility of considering real decisions such as capital structure (e.g., dividends) in disclosure analyses.
10 other disclosures, we would expect the opposite relation to our predictions (DeAngelo et al.
2008). Further, under the agency view of dividends, non-payers waste a higher proportion of earnings than payers (e.g., Easterbrook 1984; Jensen 1986), so investors should react more to each unit of earnings from dividend payers and non-payers may forecast less to allow more flexibility for rent extraction (see, e.g., Hutton et al. 2003; Hirst et al. 2007).
3. Data and methodology
3.1 Sample
We obtain our data from Compustat, CRSP, and I/B/E/S. The sample includes firms on the CRSP/Compustat Merged file between 1971 and 2017 with available quarterly EA dates and total assets and market capitalization greater than $10 million. We further limit the sample to: non-financial and non-utility firms, firms trading on the NYSE, NYSE MKT (formerly AMEX), or Nasdaq exchanges, and ordinary common U.S. shares. We impose these restrictions, which are standard in the dividend literature, because regulations by industries, exchanges and foreign countries can affect dividend payout and we want our sample to only include a set of firms operating under homogenous regulations. Finally, we require the control variables that we use across all of our tests, as well as returns or volume at the EA, which are required for our initital analyses. This leaves a maximum of 400,171 firm-quarter (108,119 firm-year) observations for
10,954 firms for the first set of tests.
Our next set of tests requires quarterly analyst forecast data from I/B/E/S. After imposing this restriction on our initial dataset, we obtain a maximum of 239,201 firm-quarter (68,397 firm- year) observations for 7,649 firms. For our last set of tests, we require analyst and manager forecasts, including the specific forecast metric, to observe the volume and mix of forecasts.
Because the quality of management forecast data is low prior to the early 2000s (see Chuk et al.
11 2013) and many analyst forecast items were not collected prior to this time (see Hand et al.
2019), we retain only data from 2002 and later for these tests. We analyze forecasts made over a one-year period, and therefore conduct these analyses at the firm-year level. Table 1 presents the sample selection process.
INSERT TABLE 1 HERE
3.2 Descriptive Statistics
Table 2, Panel A presents descriptive statistics for dividend paying and non-paying firms separately. We find significant differences across all control variables in our analyses, highlighting the differences between dividend paying firms and non-paying firms. Consistent with the maturity hypothesis, dividend paying firms are older (AGE) and have more retained earnings (RE) than non-paying firms (DeAngelo et al. 2006; Denis and Osobov 2008). They also are larger (LogMVE), but have weaker investment opportunities as measured by book-to-market
(BTM), consistent with capital structure theory (Myers and Majluf 1984; Bolton et al. 2011).
They are more profitable (ROA, OP) and have lower earnings volatility (EARN_VOL), consistent with dividend paying firms having permanent earnings they can use to fund payout (Skinner and
Soltes 2011). Finally, they have lower return volatility (RET_VOL) which is consistent with cash flow uncertainty affecting the volatility of returns (Chay and Suh 2009).
INSERT TABLE 2 HERE
The empirical goal of our study is to examine changes in the information environment associated with the decision to pay a dividend, holding constant other firm characteristics. The substantial differences across firms in our descriptive statistics highlights the difficulty of this identification challenge, which we take several approaches to address. While we do not argue our empirical approach is perfect, we believe there are several features of the decision to pay a
12 dividend that we can exploit to provide convincing evidence of differences in the information environment between dividend payers and non-payers. For instance, dividend paying tends to be a persistent firm decision, so while there are differences in firm characteristics between dividend paying and non-dividend paying firms, these differences are likely to appear, and be accounted for, in the financial statement variables we use as controls. In other words, although the decision to pay a dividend likely incorporates unobservable information about profitability, this information will be recorded in financial statements in subsequent years, at which time the information will be incorporated in our control variables. Our use of lagged dividend paying as a proxy for current dividend paying further mitigates these concerns by increasing the time for any information associated with the decision to pay a dividend to affect the financial statements.
Finally, because we know many properties of dividends, we also can form expectations regarding how the decision to pay a dividend should correlate with our outcome variable. For a number of tests, these priors go in the opposite direction to our results, making it less likely unobserved heterogeneity in firm characteristics drives the results.
We use three empirical approaches to control for the observable differences between dividend paying and non-paying firms when testing the effect of dividend paying on the information environment. First, we use OLS regressions following the advice of Angrist and
Pischke (2009, p. 69-70) who note that including control variables in multivariate regression is generally sufficient to address differences in observable characteristics across firms. In addition to a full set of controls identified in the prior literature as determinants of the decision to pay a dividend, we also augment our OLS regressions with industry by year fixed effects, so our regression results compare firms who pay a dividend to those that do not, controlling for time varying shocks affecting all firms in the same industry. We also undertake two matching
13 strategies to better address potential functional form misspecification in our models related to differences between dividend paying and non-paying firms (e.g., Shipman, Swanquist, and
Whited 2017).
First, we perform propensity score matching (PSM) based on a firm’s propensity to pay dividends, which addresses potential issues related to non-linearities in control variables or lack of common support (i.e., overlap) between dividend payers and non-payers. For each dividend paying firm, we identify and match a non-paying firm with a similar propensity to pay. The PSM model eliminates both payers and non-payers without a sufficiently close match. We estimate our propensity score model by regressing a firm’s decision to pay a dividend on our control variables (defined later: LogMVE, BTM, LogAGE, ROA, OP, RE, EARN_VOL, and RET_VOL), as well as industry and year fixed effects. We use a one-to-one match, without replacement, with common support, and with a caliper distance of 0.03.
Second, we acknowledge that PSM is subject to limitations, including imperfect matches, and deletion of both treatment (dividend payer) and control (non-payer) firms that fail to match
(e.g., DeFond, Erkens, and Zhang 2017).6 Entropy balancing uses an iterative process to re- weight observations in the control sample to match the observations in the treatment sample.
Thus, in these tests, the first three moments of the control variables detailed above are nearly identical across the divided paying and non-dividend paying samples. Effectively, non-dividend paying firms with characteristics similar to dividend payers receive more weight in our estimations relative to those with dissimilar characteristics. Entropy balancing does not discard
6 To address some of these concerns, Hainmueller (2012) and Hainmueller and Xu (2013) developed the entropy balancing technique. Entropy balancing is an “equal percent bias reducing” matching method and as a result, unlike PSM, ensures that covariate imbalance improves after matching.
14 observations, which increases power relative to PSM. McMullin and Schonberger (2018) provide an excellent discussion of entropy balancing and an application in an accounting setting.
Table 2, Panel B presents the descriptive statistics for the PSM and entropy balanced samples. We find that covariate balance improves in the PSM sample relative to our unmatched descriptive statistics, but several statistically significant differences remain (although several of these go the opposite direction as in Panel A). However, assessing covariate balance does not rely solely on statistical significance (Shipman et al. 2017). We highlight that, while some of the differences across the dividend paying and non-paying samples are statistically significant, they are generally economically small at both the mean and median. In the entropy balanced sample, there are no statistically significant differences and the descriptive statistics across dividend paying and non-paying firms are virtually identical. In these matched samples, by ensuring that our dividend paying and non-paying firms are relatively similar, we reduce concerns that any results are driven by inherent differences between payers and non-payers.
Our variable of interest in most of our analyses is a dummy variable set equal to one if the firm paid a quarterly dividend in the year before we measure our outcome variables.7 We primarily measure dividend paying using a dummy variable for three reasons: (i) our matching techniques are calibrated to dichotomous variables, (ii) firms resist cutting dividends so high dividend yields are often a function of distress, which we do not want to capture in our test variable, and (iii) few firms pay trivial dividends, so for most firms the decision to pay a dividend is a dichotomous choice to make a commitment to return their excess capital. In Figure
7 Other dividend frequencies, such as annual, semi-annual or monthly are very uncommon for the U.S. non- financial firms in our sample, and the properties of these dividends have not been studied extensively. The second most common form of dividend during our sample period is the special dividend. Firms paid 35 times more in quarterly dividends than special dividends over the period of our sample – and most of the special dividends are paid by firms with regular quarterly dividends.
15 1, we show a histogram which depicts the proportion of firms paying a dividend. The lower mass of firms at 0 – 0.5% yields than at higher yields is consistent with few firms choosing to pay a trivial dividend. Nonetheless, in our analyses comparing buybacks to dividends, we use a continuous measure of dividends and generally find the substitution effect is increasing in yield.
4. Valuation results
Our first hypothesis predicts that market participants will update valuation less in response to earnings news for dividend payers. Specifically, because the dividend payment increases the precision of investors’ earnings expectations, investors update less in response to the earnings realization. We provide evidence in support of H1 by examining absolute returns at the EA, trading volume at the EA, and the association between the earnings surprise and the signed EA return (the ERC).
4.1 Dividends and absolute abnormal earnings announcement returns
We first test H1 by examining whether markets update their assessment of valuation in response to earnings more or less in the presence of dividends. We measure investor updating using abnormal absolute returns over the three-day window centered on the quarterly EA.
Because absolute returns over an event window measure the amount of change in value associated with the release of information, H1 predicts markets should update less in response to
EAs for dividend paying firms. Alternatively, if dividends provide information about the level of agency conflicts within a firm (Easterbrook 1984; Jensen 1986), we might predict larger returns at EAs for dividend paying firms because more of the income recorded in the financial statements will ultimately be transmitted to investors (i.e., Sivakumar and Waymire 1993). We test this hypothesis using the following model:
16 ABS_ABN_RETi,q = β0 + β1DIVi,t + β2LogMVEi,t + β3BTMi,t + β4LogAGEi,t + (1)
β5ROAi,t + β6OPi,t + β7REi,t + β8EARN_VOLi,t + β9RET_VOLi,t
+ β10YEAR*IND_FEi,t + β11MONTH_FEq + ε where i represents the firm, q represents the current quarter, and t represents the most recent year prior to the quarterly EA date. ABS_ABN_RET measures the information content in the EA as the absolute value of the market-adjusted return in the three day EA window [-1,1] minus the average of the same values one week before and after the EA. Adjusting EA returns for average firm volatility allows us to measure the incremental volatility associated with the release of earnings. Our variable of interest is a dummy variable set equal to one for dividend paying firms
(DIV). We expect a negative relation between dividend payment and information at the EA under the hypothesis that dividends substitute for earnings information.
We include several control variables likely to be correlated with both firms’ decisions to pay dividends and with the information in their EAs. We control for size (LogMVE), book-to- market (BTM), firm age (LogAGE), return on assets (ROA), operating profitability (OP), retained earnings scaled by assets (RE), and both earnings and return volatility (EARN_VOL and
RET_VOL). We also include year-by-industry (two-digit SIC) fixed effects to account for common variation within an industry over time, as well as month fixed effects to account for variation attributable to the period when the EA occurs. Appendix A provides full variable definitions.
Table 3 presents results from estimating equation (1). Column (1) reports results from estimating OLS regressions on the unmatched sample, which controls for differences between dividend paying and non-paying firms. We find dividend paying firms have abnormal returns
0.46% below non-payers, a statistically significant difference (t=13.09). In columns (2) and (3) we use matching techniques to control for differences between payers and non-payers. Our
17 column (2) and (3) results (using PSM and entropy balancing, respectively) show significantly lower EA returns for dividend paying firms of 0.25% and 0.23%, respectively.8 The results are also economically meaningful: in column (3), the results suggest a reduction in abnormal absolute EA returns of approximately 10% (i.e., 0.23%/2.62%).
We conduct three additional analyses, using our entropy-balanced specification, to ensure the robustness of our results. First, in column (4), we control for return volatility over the same year in which we measure the EA returns. Including this control addresses the concern that our results are driven by total volatility being lower for dividend paying firms in the year of the EA for some unobservable reason. We continue to find a large statistically significant coefficient on our variable of interest and little attenuation in its magnitude. One concern with our results reflecting decreased information content is that there is an unobservable firm characteristic that affects both the decision to pay a dividend and earnings information content simultaneously. In column (5), we address this concern by replacing the dividend indicator with an indicator for whether the firm paid a dividend five years ago. We argue that it is less likely that the decision to pay a dividend five years ago is correlated with a current year firm trait that reduces the information content of the EA. The correlation between the two dividend paying indicators is
0.76, consistent with prior research that dividend paying is highly persistent. Using the five year lagged indicator, we continue to find a highly significant coefficient. After accounting for the imperfect correlation between past payout and current payout, the coefficient on the dividend indicator is comparable to our column (3) estimate (i.e., 0.15%/0.76 ≈ 0.2%).
Finally, in our main analysis, we use an indicator for whether the firm pays a dividend to
8 Results are robust to using size-adjusted rather than market-adjusted returns. In addition, approximately 23% of the firms in our sample bundle dividend declarations with EAs. In untabulated analyses, we find slightly larger reductions in earnings information content when removing these firms from the sample for all analyses in section four.
18 better accommodate our matching techniques. Nonetheless, our theory also would predict that the intensity of dividend information content would increase the degree of substitution. In column (6), we include a measure of the intensity of dividend information content, the dividend yield (Yield), and find a highly significant coefficient. This specification also allows us to contrast dividend yield with the amount of share buybacks. From a capital structure perspective, buybacks and dividends are equivalent as they return cash from managers to shareholders.
However, from an informational perspective they are very different because buybacks convey only transitory information while dividends convey permanent earnings information (Guay and
Harford 2000). In contrast to our findings on dividend yield, we find a positive but insignificant coefficient on repurchases, consistent with information rather than capital structure considerations driving the substitution between dividends and earnings.9
INSERT TABLE 3 HERE
4.2 Dividends and abnormal earnings announcement share volume
The second test of H1 examines whether investors turn over their portfolios more or less at the EA for dividend paying firms. We expect that, not only will dividends lead to less change in prices, but that trading decisions will evolve less in response to earnings news as a result of paying a dividend. While prices reflect an aggregation of investors’ beliefs, studying volume is also important because it accounts for the information content of an announcement as measured by the aggregate activity of investors, which can differ from the average effects (e.g., Bamber
1986). We measure EA volume as the abnormal volume around the EA, calculated as the difference between volume over the three days of the EA and the average volume over the period
9 When removing EAs announced within two trading days of dividend declarations from the sample, we obtain similar inferences for all results in Tables 3 through 5. Further, in all analyses we find a higher coefficient on the variable of interest, which is consistent with the dividend increasing EA information content when bundled with the EA.
19 one week before and one week after the EA, scaled by shares outstanding (EA_Volume). We test the hypothesis that dividends substitute for earnings information by estimating equation (1), replacing ABN_ABS_RET with EA_Volume.
The results are reported in Table 4. The hypothesis of less trading for dividend paying firms predicts a negative coefficient on DIV, similar to Table 3. We estimate an identical set of specifications as in Table 3. Across all six columns, we find significantly lower abnormal volume for dividend paying firms. We also find higher abnormal volume for firms with significant repurchases, further highlighting the information substitution role of dividends.
INSERT TABLE 4 HERE
4.3 Dividends and earnings response coefficients
In Table 5, we examine the effect of dividend paying on earnings response coefficients
(ERCs) to provide further evidence that dividends substitute for earnings information. Relative to the previous tests, this analysis makes two points. First, we know from prior literature that dividend paying firms tend to have more persistent earnings (Skinner and Soltes 2011). Because
ERCs increase in earnings persistence (Kothari 2001), if our results are the result of unobserved heterogeneity between dividend payers and non-payers, we would expect higher ERCs for dividend paying firms. Conversely, the dividend substitution hypothesis predicts the opposite.
Second, by examining how markets impound a specific piece of information (i.e., earnings news), we can ensure our results are not driven by variation in the amount of additional disclosures firms make with earnings.
If dividends substitute for earnings information, we expect the ERC to be lower for dividend paying firms than non-dividend paying firms. We present our model below:
20 CARi,q = β0 + β1UEi,q + β2DIVi,t + β3UE*DIVi,q + β4CONTROLSi,t + (2)
β10YEAR*IND_FEi,t + ε where CAR represents the market-adjusted signed abnormal return in the three day [-1,1] EA window. Unlike our first measure of information content (ABS_ABN_RET), this measure is a signed measure of the market reaction to earnings. We compute unexpected earnings (UE) as the decile rank of the difference between reported earnings and the most recent median estimate on the consensus file, scaled by share price at the beginning of the quarter. H1 predicts that, because the dividend provides market participants with a measure of permanent earnings, markets will react less to earnings surprises for dividend paying firms and we will observe a negative coefficient on the interaction UE*DIV.
Table 5, Panel A presents results. In column (1), we estimate our OLS regression results on the unmatched sample and find a statistically significant negative coefficient on the interaction, consistent with H1. The coefficient magnitude suggests that dividend paying firms have market reactions that are 8% smaller per unit of earnings news, relative to non-payers (i.e.,
0.0007/0.0085). While the coefficient could be affected by the endogenous choice to pay a dividend, as discussed earlier we would expect that, because of the persistence of earnings for dividend paying firms, this endogenous choice predicts a positive coefficient on UE*DIV, in contrast to the significant negative coefficient we find.
In column (2), we present results using PSM and continue to find a statistically significant coefficient, slightly larger in magnitude than the coefficient in column (1). In column
(3), after using entropy balancing, we find a much larger statistically significant coefficient. Our entropy balanced results suggest dividend payers have market reactions to earnings 25% smaller
(0.0027/0.0105) than non-payers. In columns (4) and (5), we find this result is robust to
21 including the full set of controls interacted with unexpected earnings and lagging the decision to pay a dividend five years, respectively.10
In column (6), we compare the effect of share repurchases to the effect of dividend paying on ERCs. We use the dividend yield (Yield) in place of DIV so the scaling of the variables will be comparable. In this specification, we find a negative and statistically significant coefficient on the interaction between yield and unexpected earnings, but an insignificantly positive coefficient on the repurchases interaction. This provides further evidence that the informational properties of dividends drive the substitution effect.
INSERT TABLE 5 HERE
4.3.1 The timing of dividends and earnings response coefficients
In this section, we examine whether the ERCs vary based on the timing of the dividend announcement within dividend paying firms. If dividends substitute for earnings information, the timing of the dividend relative to the most recent EA should affect the ERC. Evidence that the timing of dividends relative to EAs matters would provide support for a substitution effect and establish a mechanism underlying our results thus far. We test this using the following model:
CARi,q = β0 + β1UEi,q + β2DIV_TIMEi,t + β3UE*DIV_TIMEi,q + (3)
β4CONTROLSi,t + β10YEAR*IND_FEi,t + ε where DIV_TIME is a measure of the time between the prior quarter EA date and the dividend declaration date.11 If a firm provides earnings information and declares a dividend shortly
10 In untabulated analyses, we find no evidence that the smaller reaction to earnings surprises for dividend paying firms arises because of under-reaction to earnings news which would result in higher post-earnings announcement drift (Bernard and Thomas 1990). Specifically, we replace our dependent variable (i.e., announcement returns) with returns over the sixty trading days following the EA and re-estimate columns (1) – (3). In all specifications we find UE*DIV loads with a statistically insignificant coefficient. 11 We restrict the sample to quarters with a dividend between the current and prior EAs. Thus, this measure calculates the distance between the most recent earnings signal and the dividend.
22 thereafter, that dividend may not provide substantial new information. However, as the dividend declaration moves further away from the prior EA, it is likely to convey new information to the market that management has learned during the quarter. As such, we expect that the interaction
UE*DIV_TIME will be negative. A negative relation indicates that a dividend announcement well after the prior EA substitutes for information in the upcoming EA. Our estimates of equation (3) only use the sample of dividend paying firms, and thus avoids endogeneity concerns related to the decision to pay a dividend. We further remove bundled observations for which the dividend news and earnings news are co-mingled.
We present our results in Table 5, Panel B, and find dividend announcements pre-empt this quarter’s earnings information significantly more when they occur further from the prior quarter’s EA. In column (1), we find that moving the declaration from just after the prior quarter’s EA to just before the current quarter’s EA decreases the market reaction to the earnings surprise by an average of 25% (0.0022/0.0085). In column (2), we include a full set of interactions between UE and our other control variables. If our results are driven by firms with larger unexpected earnings endogenously choosing to declare dividends earlier in the quarter, we would expect attenuation after controlling for these interactions. Instead, we find a statistically significant coefficient that is larger in magnitude than the coefficient in column (2).
5. The substitution of dividend information with other information sources
Our second hypothesis predicts that supplying earnings information through the dividend will attenuate the demand for other sources of information from stakeholders, thereby affecting the information environment. First, we examine whether dividend paying firms are less focused on narrowly meeting or beating earnings benchmarks as well as the mechanism by which this manifests (e.g., real earnings management). If shareholders place more weight on dividends, we
23 expect managers will focus less on developing a reputation for exceeding earnings benchmarks
(Trueman 1986), which will translate into less earnings management. We also test whether managers of, and analysts following, dividend paying firms provide less earnings-related information to the market via forecasts.
5.1 The effect of dividend paying on meeting or beating
We test H2, examining the relation between dividends and the likelihood of just meeting or beating, using the following model:
Surp[X,Y]i,q= β0 + β1DIVi,t + β2LogMVEi,t + β3BTMi,t + β4LogAGEi,t + β5ROAi,t + (4)
β6OPi,t + β7REi,t + β8EARN_VOLi,t + β9RET_VOLi,t +
β10YEAR*IND_FEi,t + ε where Surp represents a series of indicator variables to capture earnings that fall just above or below the median of the outstanding consensus forecast. We obtain both actuals and forecasts from the I/B/E/S unadjusted file to avoid imprecision associated with rounding the consensus
(Payne and Thomas 2003) and split-adjust these values where necessary (Robinson and
Glushkov 2006). Our hypothesis proposes that, because investors place more weight on dividends, managers are less myopic and expend fewer resources to just meet or beat earnings expectations. Thus, we predict a negative coefficient on DIV when earnings just meet or beat analysts’ forecasts. That is, we expect that dividend paying firms are less likely to manage earnings to just meet or beat. Conversely, we predict a positive coefficient when earnings are just below the consensus. Because the non-payers whose unmanaged earnings would be just below zero will manage earnings upward to just exceed the benchmark, dividend payers will be more likely to appear just below the threshold. We predict the magnitude of the coefficient on
DIV will decline as we move away from the zero threshold, as incentives to manage earnings are most intense around zero.
24 We begin our analysis by plotting the impact of DIV on meeting or beating around the consensus. Specifically, we estimate thirteen regressions, one for each penny beginning with actual earnings six cents below the consensus through six cents above the consensus. For example, Surp[-0.01] is set to one if the forecast error falls in the range [-0.01,0.00), Surp[0] is set to one if the forecast error is equal to zero, and Surp[0.01] is set to one if the forecast error falls in the range (0.00,0.01]. All other variables are as previously defined. This specification allows us to determine whether dividend paying firms are less (more) likely than non-dividend paying firms to report earnings that just meet or beat (miss) the analyst consensus.
In Figure 2, we graph the coefficient on DIV, as well as the corresponding 95% confidence interval, for the thirteen specifications over the forecast error range [-0.06,0.06]. The results are stark: relative to non-dividend paying firms, dividend paying firms are more likely to just miss the consensus forecast and less likely to just meet or beat the consensus. As the forecast error moves away from zero, in both directions, the coefficient on DIV moves towards, and becomes statistically indistinguishable from, zero. The significantly positive coefficient just below the consensus and the significantly negative coefficient just above are inconsistent with alternative explanations, such as dividend paying firms having different earnings dispersion.
In Table 6, we examine whether we obtain similar results after using our matching approaches to control for observable differences across dividend payers and non-payers. For this analysis we define the dependent variable as Surp[-0.03,0.03] which equals negative one if the forecast error falls in the range [-0.03,0.00), positive one if the forecast error falls in the range
[0.00,0.03], and zero otherwise. The results are reported in Panel A of Table 6. Column (1) reports the results using our baseline OLS regressions. The coefficient on DIV is negative and statistically significant. Columns (2) and (3) also report significantly negative coefficients using
25 the propensity score matched sample and the entropy balanced sample. In terms of economic magnitudes, our coefficient estimates of 2.0% to 3.3% indicate an 8.3% (2.0/24.2) to 13.6%
(3.3/24.2) drop in just meeting or beating relative to missing for dividend paying firms.12
Finally, in column (4), we replace the dividend indicator (DIV) with the dividend yield
(Yield) and include separate control variables to capture share repurchases and issuances
(REPURCH and ISSUE, respectively). The coefficient on Yield is negative and statistically significant whereas the coefficient on REPURCH is insignificant and the coefficient on ISSUE is positive and statistically significant.
INSERT TABLE 6 HERE
5.1.1 Dividend paying and earnings management
We would like to interpret our meet or beat findings as dividend paying shifting the benefits of managing earnings, so that dividend payers are less willing to incur the associated costs. In this section, we present preliminary evidence that lower earnings management contributes to the lower propensity to just meet or beat for dividend paying firms. If stakeholders of dividend paying firms place less weight on earnings, we suspect managers of dividend paying firms will be less willing to sacrifice long-term value to hit short-term earnings targets (Graham et al. 2005). Evidence of differential earnings management behavior would provide further support for our contention that the results in Table 6, Panel A are driven by less myopic behavior for dividend paying firms.
12 We compute these economic magnitudes by first calculating the difference between the percentage of non- payers that just meet or beat and the percentage of non-payers that just miss (38.6% - 14.4%) and then dividing that percentage by the coefficient on DIV, our estimate of the difference between payers and non-payers.
26 Specifically, we examine whether dividend paying firms are less likely to cut R&D expenses to meet or beat earnings expectations, by estimating the following difference-in- differences model (Bhojraj et al. 2009):
R&Di,q = β0 + β1DIVi,t + β2 Surp[-0.03,0.03]i,t + β3DIVi,t*Surp[-0.03,0.03] (5)
+ β4LogMVEi,t + β5BTMi,t + β6LogAGEi,t + β7ROAi,t + β8OPi,t
+ β9REi,t + β10EARN_VOLi,t + β11RET_VOLi,t
+ β12YEAR*IND_FEi,t + ε
Our dependent variable, R&D is the change in R&D expenses scaled by lagged assets. The first-difference is whether the firm pays a dividend. The second difference is Surp[-0.03,0.03], which captures the likelihood that firms manage earnings to meet earnings expectations. We expect firms just above the threshold will be more likely to reduce R&D to achieve the benchmark, so we predict a negative coefficient on Surp[-0.03,0.03]. Our variable of interest is
DIV*Surp[-0.03,0.03], for which we would expect a positive coefficient if dividend payers are less willing to cut R&D to achieve earnings benchmarks relative to non-payers.
We report the results in Panel B of Table 6. In column (1), we report baseline results without the dividend indicator or the interaction to validate that firms cut R&D to meet benchmarks (Bhojraj et al. 2009). The coefficient on Surp[-0.03,0.03] is negative and statistically significant, suggesting the firms in our sample do in fact cut R&D expenses to meet or beat. In column (2), we include the dividend indicator and its interaction with
Surp[-0.03,0.03]. The main effect on Surp[-0.03,0.03] remains negative and statistically significant, but the interaction with DIV is positive and statistically significant. As expected, this suggests dividend paying firms are less likely to cut discretionary expenses to reach the consensus earnings expectation.
5.2 Dividend paying and forecasting
27 We test H3, examining whether dividend paying firms provide less earnings guidance, using the following model:
#Fore_Manageri,t+1 = β0 + β1DIVi,t + β2LogMVEi,t + β3BTMi,t + β4LogAGEi,t + (6)
β5ROAi,t + β6OPi,t + β7REi,t + β8EARN_VOLi,t +
β9RET_VOLi,t + β10YEAR*IND_FEi,t + ε where #Fore_Manager is the log of the number of days in year t+1 that management issues guidance.13 We exclude firm-years with no guidance and we include management forecasts of all items except dividends, although the results are similar if we only consider forecasts of earnings.
All other variables are as previously defined.
Panel A of Table 7 reports the results. In column (1), we estimate equation (6) on the unmatched sample. The coefficient on DIV is negative and statistically significant, suggesting dividend paying firms issue less guidance. In column (2), we use the PSM sample and find similar results. In column (3), we use the entropy balanced sample and the coefficient on DIV remains negative, but is statistically insignificant.
In Panel B of Table 7, we estimate analogous specifications to those in Panel A, but we replace the dependent variable with #Fore_Analyst, which captures the number of forecast days by sell side analysts rather than managers. Similar to Panel A, we find a negative and statistically significant coefficient on DIV in columns (1) and (2). In column (3), using the entropy-matched sample we find a negative coefficient falls just short of conventional levels of statistical significance.
While the results in Panel A of Table 7 capture management forecast volume, we next examine the mix of management guidance provided by dividend paying firms. We expect
13 Specifically, if management forecasts earnings, sales, and capital expenditures all on the same day, we treat this as one forecast. Thus, before the log transformation, the variable has a possible range of [1,365].
28 investors of dividend paying firms to more intensively demand information about expected dividends. As a result, we expect more capital expenditure forecasts that provide the amount of capital being used for investment, which is thus not available for payout. We expect less demand for top-line items, such as revenue and gross margin, which are more useful for projecting profits than payout. We test for systematic differences in forecast mix using the following model:
%Fore_Manageri,t+1 = β0 + β1GuidanceTypei,t + β2GuidanceType*DIVi,t + (7)
β10Year*Firm + ε where %Fore_Manager is percentage of manager forecasts for a given metric. We calculate this as the number of days in year t+1 that management issues an earnings, sales, gross margin, or capital expenditure forecast, scaled by the number of days that management issues any guidance.
We capture forecast mix by stacking firm-years for each forecast type, including indicator variables for the forecast type (e.g., SALE for sales forecasts, GM for gross margin forecasts, and
CPX for capital expenditure forecasts, while earnings forecasts serve as the base case), and interacting the forecast type with DIV. Because each firm-year has repeated observations, we include firm by year fixed effects in the model, and thus exclude the main effect of DIV (which is absorbed by the fixed effects).
The results are reported in Panel C of Table 7. In column (1) (column (2)), we report the results without (with) control variables. Our variables of interest are the interactions between the forecast type and the dividend paying indicator. The coefficient on SALE (GM, CPX) is positive
(negative) and significant, suggesting managers of non-dividend paying firms issue a greater
(smaller) percentage of sales (gross margin, capital expenditure) forecasts relative to earnings forecasts. The coefficient on the interaction between DIV and CPX is positive and significant, consistent with dividend paying firms, who invest less, forecasting investment more frequently.
We find the coefficients on the interactions with SALE and GM are negative and significant,
29 suggesting that, relative to non-dividend paying firms, dividend paying firms provide fewer forecasts of top-line items, which are less readily convertible into payout. Overall, our analysis suggests the mix of managerial forecasts shifts toward predicting payout, when firms pay dividends.
INSERT TABLE 7 HERE
6. Additional analysis
Although we match on numerous observable firm characteristics using both PSM and entropy balancing, we perform additional analyses to rule out the possibility that the endogenous choice to pay a dividend affects our results. For instance, firms may choose to pay a dividend based on their knowledge of future outcomes so dividend paying firms could inherently differ from non-payers. Under this scenario, dividend paying would predict the change in firm performance from year t to year t+1. However, if our matching techniques are effective, we should find few differences in future changes in performance within our matched samples.
We therefore examine the change in size (Log_MVE) and performance (ROA, OP) with our PSM matched sample and our entropy balanced sample. In these analyses, we add net payout back to market value and assets to avoid any mechanical relation induced by dividend payments that would drive differences across dividend paying and non-paying firms. The market value of equity regressions include our main control variables. The performance regressions follow the prior literature and include non-linear controls for the level of income (Fama and French 2000;
Nissim and Ziv 2001; Grullon et al. 2005). Specifically our regression controls include the lagged change in income, an indicator for the sign of the lagged change, and the interaction between these two variables to account for the fact that the change in these variables is not expected to be zero and is a function of the past change (Freeman and Tse 1992). Table 8
30 presents results for the PSM (columns (1) to (3)) and entropy balanced (columns (4) to (6)) samples. Overall, we find little evidence of differences in future changes in firm performance between dividend paying and non-paying firms.
INSERT TABLE 8 HERE
While our matching techniques control for observable differences across firms by matching on firm traits, which reduces bias from functional form misspecification (Shipman et al. 2017), this falsification test provides additional evidence on the quality of the matches.
However, our matching procedures are unable to account for unobservables. By showing that firm performance changes generally do not vary across dividend paying and non-paying firms after matching, it suggests that any unobservable variable that we do not account for in our main tests would have to (a) affect all of our main dependent variables and (b) not affect changes in firm size or performance.
7. Conclusion
For decades, researchers have been investigating the dividend puzzle – i.e., the reason firms pay dividends in spite of their irrelevance for valuation (Miller and Modigliani 1961; Black
1976). Presumably, managers pay dividends only when the benefits exceed the costs, but researchers have more thoroughly documented the costs. Dividends are tax disadvantaged because of the taxation of re-invested profits, and for much of recent economic history, they were also tax disadvantaged through their treatment as ordinary income as opposed to capital gains. Investors impose penalties on managers for cutting dividends, and empirical and survey evidence suggest managers respond by making sub-optimal investment decisions (Brav et al.
2005; Wu 2016). While extensive literatures in corporate finance document that removing
31 excess cash from the control of the manager increases firm value, buybacks can accomplish this without the costly implicit commitment to maintain the dividend.
In this paper, we hypothesize that managers of dividend paying firms benefit by signaling the level of permanent earnings to investors. Because boards will punish managers for not maintaining the dividend, managers only declare dividends they expect to maintain. Investors of dividend paying firms thus update to a more precise prior when they receive earnings information through the dividend, thereby decreasing their response at the earnings announcement. Non-payers consist of both firms unwilling and unable to provide a similar signal, so investors’ prior expectations are less precise, and investors update their assessment of valuation more in response to earnings realizations. In tests examining both earnings response coefficients, the absolute value of earnings announcement returns and abnormal volume, we show investors update less in response to earnings information, consistent with this dividend substitution view.
We note the more precise prior earnings expectation for investors of dividend paying firms provides an incentive to pay dividends. Fama and French (1998) show dividend paying firms have higher valuations and earnings than would be expected based on other financial statement variables. While studies examining dividend changes in event-time find most of the earnings information in dividends is explained by earnings in the next few quarters (Ham et al.
2019), which is inconsistent with signaling, one possible explanation is the cumulative effect of these changes, or the level of dividends, has a significant effect on earnings expectations and valuation.
In addition to providing evidence on how investors update their expectations at earnings announcements, we also show that dividends affect the supply and demand for other sources of
32 information. First, we show the probability of just meeting or beating (missing) earnings estimates is lower (higher) for dividend payers relative to non-payers, suggesting that the presence of dividends shifts investors’ focus to dividends rather than earnings, thereby decreasing managerial myopia. Second, we show both analysts and managers forecast earnings less frequently for dividend payers, consistent with the decreased market reactions to earnings information for these firms reducing both the demand for, and supply of, earnings information.
Collectively, this evidence suggests dividends impact the information environment, and although costly, could be an efficient way for profitable companies to meet investors’ demands for information.
33 References Aharony, Joseph and Amihud Dotan, 1994, Regular dividend announcements and future unexpected earnings: An empirical analysis, Financial Review, 29: 125 – 151. Ajinkya, Bipin, Sanjeev Bhojraj, and Partha Sengupta. "The association between outside directors, institutional investors and the properties of management earnings forecasts." Journal of accounting research 43.3 (2005): 343-376. Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton university press, 2009. Baginski, Stephen P., and Kenneth C. Rakow. "Management earnings forecast disclosure policy and the cost of equity capital." Review of Accounting Studies 17.2 (2012): 279-321. Bamber, Linda Smith. "The information content of annual earnings releases: A trading volume approach." Journal of Accounting Research (1986): 40-56. Beaver, William H. "The information content of annual earnings announcements." Journal of Accounting Research (1968): 67-92. Benartzi, Shlomo, Roni Michaely, and Richard Thaler, 1997, Do Changes in Dividends Signal the Future or the Past?, Journal of Finance, 52: 1007 – 1034. Bernard, Victor L., and Jacob K. Thomas. "Evidence that stock prices do not fully reflect the implications of current earnings for future earnings." Journal of Accounting and Economics 13.4 (1990): 305-340. Beyer, Anne, et al. "The financial reporting environment: Review of the recent literature." Journal of Accounting and Economics 50.2-3 (2010): 296-343. Bhattacharya, Sudipto, 1979, Imperfect Information, Dividend Policy, and `The Bird In The Hand’ Fallacy, Bell Journal of Economics, 10 (1), 259-270. Bhojraj, Sanjeev, et al. "Making sense of cents: An examination of firms that marginally miss or beat analyst forecasts." The Journal of Finance 64.5 (2009): 2361-2388. Black, Fischer. "The dividend puzzle." Journal of portfolio management 2.2 (1976): 5-8. Bolton, Patrick, Hui Chen, and Neng Wang. "A unified theory of Tobin's q, corporate investment, financing, and risk management." The journal of Finance 66.5 (2011): 1545- 1578. Brav, Alon, John Graham, Campbell Harvey, and Roni Michaely, 2005, Payout Policy in the 21st Century, Journal of Financial Economics 77:483–527. Burgstahler, David, and Ilia Dichev. "Earnings management to avoid earnings decreases and losses." Journal of Accounting and Economics 24.1 (1997): 99-126. Chay, Jong-Bom, and Jungwon Suh. "Payout policy and cash-flow uncertainty." Journal of Financial Economics 93.1 (2009): 88-107. Chuk, Elizabeth, Dawn Matsumoto, and Gregory S. Miller. "Assessing methods of identifying management forecasts: CIG vs. researcher collected." Journal of Accounting and Economics 55.1 (2013): 23-42. DeAngelo, Harry, Linda DeAngelo, and Douglas J. Skinner. "Dividends and losses." The Journal of Finance 47.5 (1992): 1837-1863. DeAngelo, Harry, Linda DeAngelo, and Douglas J. Skinner, 1996. Reversal of fortune, dividend signaling, and the disappearance of sustained earnings growth. Journal of Financial Economics, 40, 341–371. DeAngelo, Harry, Linda DeAngelo and Douglas J. Skinner, 2009, Corporate Payout Policy, In Foundations and Trends in Finance
34 DeAngelo, Harry, Linda DeAngelo, and René M. Stulz. "Dividend policy and the earned/contributed capital mix: a test of the life-cycle theory." Journal of Financial economics 81.2 (2006): 227-254. DeFond, Mark, David H. Erkens, and Jieying Zhang. "Do client characteristics really drive the Big N audit quality effect? New evidence from propensity score matching." Management Science 63.11 (2016): 3628-3649. Denis, David J., and Igor Osobov. "Why do firms pay dividends? International evidence on the determinants of dividend policy." Journal of Financial economics 89.1 (2008): 62-82. Degeorge, Francois, Jayendu Patel, and Richard Zeckhauser. "Earnings management to exceed thresholds." The Journal of Business 72.1 (1999): 1-33. Diamond, Douglas W. "Optimal release of information by firms." The Journal of Finance 40.4 (1985): 1071-1094. Diamond, Douglas W., and Robert E. Verrecchia. "Disclosure, liquidity, and the cost of capital." The Journal of Finance 46.4 (1991): 1325-1359. Easterbrook, Frank H. "Two agency-cost explanations of dividends." The American economic review 74.4 (1984): 650-659. Faulkender, Michael, and Rong Wang. "Corporate financial policy and the value of cash." The Journal of Finance 61.4 (2006): 1957-1990. Fama, Eugene F., and Kenneth R. French. "Taxes, financing decisions, and firm value." The Journal of Finance 53.3 (1998): 819-843. Fama, Eugene, and Kenneth French, 2000, Forecasting profitability and earnings, Journal of Business, 73, 161-175. Farre-Mensa, Joan, Roni Michaely, and Martin C. Schmalz. "Financing payouts." Ross School of Business Paper 1263 (2018). Francis, Jennifer, Dhananjay Nanda, and Per Olsson. "Voluntary disclosure, earnings quality, and cost of capital." Journal of Accounting Research 46.1 (2008): 53-99. Freemen, Robert N., and Senyo Y. Tse. "A nonlinear model of security price responses to unexpected earnings." Journal of Accounting Research (1992): 185-209. Guay, Wayne, and Jarrad Harford. "The cash-flow permanence and information content of dividend increases versus repurchases." Journal of Financial Economics 57.3 (2000): 385- 415. Graham, John R., Campbell R. Harvey, and Shiva Rajgopal. "The economic implications of corporate financial reporting." Journal of Accounting and Economics 40.1-3 (2005): 3-73. Grinstein, Yaniv, and Roni Michaely. "Institutional holdings and payout policy." The Journal of Finance 60.3 (2005): 1389-1426. Hainmueller, Jens. "Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies." Political Analysis 20.1 (2012): 25-46. Hainmueller, Jens, and Yiqing Xu. "Ebalance: A Stata package for entropy balancing." Journal of Statistical Software 54.7 (2013). Ham, Chad., Zachary Kaplan and Mark Leary, 2019, Do dividends convey information about future earnings. Accepted Journal of Financial Economics. Hand, John RM, et al. "Analyst Forecast Data Feeds Are Not What They Used to Be." Available at SSRN 3117122 (2018). Hayes, Rachel M. "The impact of trading commission incentives on analysts' stock coverage decisions and earnings forecasts." Journal of Accounting Research 36.2 (1998): 299-320.
35 Healy, Paul M., and Krishna G. Palepu. "Information asymmetry, corporate disclosure, and the capital markets: A review of the empirical disclosure literature." Journal of Accounting and Economics 31.1-3 (2001): 405-440. Hirst, D. Eric, Lisa Koonce, and Shankar Venkataraman. "How disaggregation enhances the credibility of management earnings forecasts." Journal of Accounting Research 45.4 (2007): 811-837. Hutton, Amy P., Gregory S. Miller, and Douglas J. Skinner. "The role of supplementary statements with management earnings forecasts." Journal of Accounting Research 41.5 (2003): 867-890. Irvine, Paul JA. "Do analysts generate trade for their firms? Evidence from the Toronto stock exchange." Journal of Accounting and Economics 30.2 (2000): 209-226. Jensen, Michael C. "Agency costs of free cash flow, corporate finance, and takeovers." The American economic review 76.2 (1986): 323-329. Kalay, Avner. "Signaling, information content, and the reluctance to cut dividends." Journal of financial and Quantitative Analysis 15.4 (1980): 855-869. Kaplan, Zachary R. and Gerardo Perez-Cavazos. "Dividend signaling with excess cash." Unpublished working paper (2019). Koch, Adam S., and Amy X. Sun. "Dividend changes and the persistence of past earnings changes." The Journal of Finance 59.5 (2004): 2093-2116. Kothari, S. P. "Capital markets research in accounting." Journal of Accounting and Economics 31.1-3 (2001): 105-231. Lintner, John. "Distribution of incomes of corporations among dividends, retained earnings, and taxes." The American Economic Review 46.2 (1956): 97-113. Matsunaga, Steven R., and Chul W. Park. "The effect of missing a quarterly earnings benchmark on the CEO's annual bonus." The Accounting Review 76.3 (2001): 313-332. McMullin, Jeff L., and Bryce Schonberger. "Entropy-balanced discretionary accruals." Kelley School of Business Research Paper 15-31 (2018): 15-01. McNichols, Maureen, and Patricia C. O'Brien. "Self-selection and analyst coverage." Journal of Accounting Research 35 (1997): 167-199. Miller, Merton H., and Franco Modigliani. "Dividend policy, growth, and the valuation of shares." The Journal of Business 34.4 (1961): 411-433. Myers, Stewart C., and Nicholas S. Majluf. "Corporate financing and investment decisions when firms have information that investors do not have." Journal of financial economics 13.2 (1984): 187-221. Nissim, Doron, and Amir Ziv, 2001, Dividend changes and future profitability, Journal of Finance, 61 (6), 2111-2134. Payne, Jeff L., and Wayne B. Thomas. "The implications of using stock-split adjusted I/B/E/S data in empirical research." The Accounting Review 78.4 (2003): 1049-1067. Robinson, David, and Denys Glushkov. "A note on IBES unadjusted data." Wharton Research Data Services (2006): 1-15. Rozeff, Michael S. "Growth, beta and agency costs as determinants of dividend payout ratios." Journal of financial Research 5.3 (1982): 249-259. Sivakumar, Kumar N., and Gregory Waymire. "The information content of earnings in a discretionary reporting environment: Evidence from NYSE industrials, 1905-10." Journal of Accounting Research 31.1 (1993): 62-91.
36 Skinner. 538. https://fivethirtyeight.com/features/corporate-america-is-enriching-shareholders- at-the-expense-of-the-economy/ Skinner, Douglas J., and Richard G. Sloan. "Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio." Review of accounting studies 7.2-3 (2002): 289-312. Skinner, Douglas J., and Eugene Soltes. "What do dividends tell us about earnings quality?." Review of Accounting Studies 16.1 (2011): 1-28. Shipman, Jonathan E., Quinn T. Swanquist, and Robert L. Whited. "Propensity score matching in accounting research." The Accounting Review (2016). Trueman, Brett. "Why do managers voluntarily release earnings forecasts?." Journal of Accounting and Economics 8.1 (1986): 53-71. Verrecchia, Robert E. "Discretionary disclosure." Journal of Accounting and Economics 5 (1983): 179-194. Wu, Yufeng. "What’s behind Smooth Dividends? Evidence from Structural Estimation." The Review of Financial Studies (2016). Yoon, Pyung and Laura Starks, 1995, Signaling, Investment Opportunities, and Dividend Announcements, Review of Financial Studies 8 (4), 995-1018.
37 APPENDIX A Variable Definitions Variable Definition Dependent Variables
ABS_ABN_RETi,q equals the abnormal return around firm i's quarter q earnings announcement (day = 0), defined as the absolute value of the 3-day market-adjusted abnormal earnings announcement return 1 (푎푏푠(∑푑푎푦=−1(푟푒푡 − 푣푤푟푒푡푑))) minus the average of the absolute value of the equivalent 3-day abnormal return centered one week prior to and one week after the earnings announcement. We require that the firm have 3 days of returns in each window.
EA_VOLUMEi,q equals the abnormal volume around firm i's quarter q earnings announcement (day = 0), defined as the cumulative 3-day earnings 1 announcement volume in 000s (∑푑푎푦=−1(푣표푙/1000)) minus the average of the equivalent 3-day cumulative volume centered one week prior to and one week after the earnings announcement. This abnormal volume is then scaled by the average shares outstanding over the 3-day earnings announcement window. We require that the firm have 3 days of volume in each window.
CARi,q equals the abnormal return around firm i's quarter q earnings announcement (day = 0), defined as the absolute value of the 3-day market-adjusted abnormal earnings announcement return 1 (푎푏푠(∑푑푎푦=−1(푟푒푡 − 푣푤푟푒푡푑))).
#Fore_Analyst t+1 equals the logged number of IBES analyst forecasts for firm i in year t+1. If an analyst makes multiple forecasts in a single day (or multiple analysts forecast in a single day), we collapse them to represent one forecast. That is, this equals the number of days in year t+1 where any analyst issued any forecast.
#Fore_Managert+1 equals the logged number of IBES manager forecasts for firm i in year t+1. If a manager makes multiple forecasts in a single day, we collapse them to represent one forecast. That is, this equals the number of days in year t+1 where management issued any forecast.
%Fore_Managert+1 equals the percentage of IBES manager forecasts for firm i in year t+1 for a given forecast metric. We scale the number of days in year t+1 that management issues an earnings, sales, gross margin, or capital expenditure forecast, by the number of days that management issues any guidance.
Salet+1 is an indicator for a Sales forecast (measure = SAL) for firm i in year t+1.
%CPXt+1 is an indicator for a CAPX forecast (measure = CPX) for firm i in year t+1.
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APPENDIX A Variable Definitions Variable Definition %GRM,t+1 is an indicator for a gross margin forecast (measure = GRM) for firm i in year t+1. Surp[X] represents a series of indicator variables to capture earnings that fall just above or below the median of the outstanding consensus forecast. For example, Surp[-0.01] is set to one if the forecast error falls in the range [- 0.01,0.00), Surp[0] is set to one if the forecast error is equal to zero, and Surp[0.01] is set to one if the forecast error falls in the range (0.00,0.01].
Surp[-0.03,0.03]i,q equals negative one if the forecast error falls in the range [-0.03,0.00), positive one if the forecast error falls in the range [0.00,0.03], and zero otherwise. Variables of Interest
DIVi,t is an indicator variable that equals one if firm i paid a dividend in year t and zero otherwise.
DIV_LAG5i,t is an indicator variable that equals one if firm i paid a dividend in year t- 5 and zero otherwise.
Yieldi,t equals the quarterly dividend yield as of the end of year t annualized to represent a full year equivalent amount.
DIV_TIMEi,q equals the time between firm i's earnings announcement for quarter q-1 and the dividend declaration date (dclrdt) prior to the quarter q earnings announcement. This variable is then scaled so that all values fall between 0 and 1. Control Variables
LogMVEi,t equals the natural logarithm of (1 + MVE) for firm i in year t. MVE equals firm i’s market value of equity from CRSP, converted to millions of dollars to match Compustat ([prc*shrout]/1000), at the end of year t.
BTMi,t equals firm i’s book equity to market equity (MVE) as of the end of year t. Book equity equals Compustat’s common shareholders’ equity (CEQ) plus deferred taxes (TXDB) plus investment tax credit (ITCB). If CEQ is missing, we replace CEQ with total shareholders’ equity (SEQ) minus preferred stock (the first available of PSTKRV, PSTKL, or PSTK). If SEQ is missing, we calculate total shareholders’ equity (AT – LT). If TXDB, ITCB, or preferred stock are missing, we set them to zero. MVE is described above.
LogAGEi,t equals the natural logarithm of (1 + AGE) for firm i in year t. AGE equals the number of years that firm i has appeared on Compustat as of the end of year t.
ROAi,t equals firm i’s return on assets (OIADP/AT) at the end of year t.
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APPENDIX A Variable Definitions Variable Definition OPi,t equals the firm i’s operating profitability in year t (REVT – COGS – (XSGA – XRD) – XINT) scaled by book equity as defined in the BTM calculation. We remove R&D expense from SG&A expenses. If any item is missing, we set it to 0.
REi,t equals firm i’s total retained earnings scaled by assets (RE/AT) at the end of year t.
EARN_VOLi,t equals the standard deviation of firm i's income (IB) from year t to year t-4, requiring all 5 years of data, scaled by assets (AT) at the end of year t (Dichev and Tang 2009).
RET_VOLi,t equals the average of the absolute value of the daily abnormal return for firm i in year t ([(∑푦푒푎푟 푡 푎푏푠(푟푒푡 − 푣푤푟푒푡푑)]/number of daily returns over year t). FUT_RET_VOLi,t equals RET_VOLi,t+1. We use this alternative variable name for ease of presentation in the tables.
UEi,q the decile rank, by year, of the value of the I/B/E/S actual earnings minus the I/B/E/S analysts’ most recent median quarterly earnings forecast for firm i in quarter q, scaled by stock price at the beginning of quarter q. REPURCHi,t equals purchases of common and preferred shares scaled by MVE for firm i in year t (PRSTKC/MVE). If missing PRSTKC, we set it equal to 0. ISSUEi,t equals issuance of common and preferred shares scaled by MVE for firm i in year t (SSTK/MVE). If missing SSTK, we set it equal to 0.
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Figure 1: Histogram of Yield
Years
- Percent of Firm of Percent
Yield
This figure presents a histogram of the distribution of Yield for the firm-years in our full sample (108,119 firm-years), with bin widths of 0.5%. Yields above 8% are set to 8%. Yields are based on the last quarterly dividend announced during the year.
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Figure 2: Dividends and meeting or beating 1.5
1.0
0.5
0.0
100*Coefficient -0.5
-1.0
-1.5 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 EPS Surprise
This figure presents the coefficient, and 95% confidence interval, on DIV for thirteen regressions. The dependent variable is an indicator for each penny earnings per share surprise beginning with actual earnings six cents below the consensus and ending with actual earnings six cents above the consensus. The model, including control variables, is detailed in equation (4) in section 5.1.
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Table 1: Sample Selection Firm- Firm- Data Restrictions Quarters Years Firms CRSP/Compustat merged firm-quarter observations for all firms reporting quarterly earnings announcements and positive total assets from 1970-2017 806,875 221,897 21,563
Less: Firm-quarters with less than $10 million of assets or market value (80,107) (25,984) (1,848) Financial and utility firm-quarters (SIC 6000-6999 or 4900-4999) (165,602) (43,836) (3,776) Firm-quarters with share code other than 10 or 11 (62,899) (18,218) (2,239) Firms-quarters with exchange code other than 1, 2, or 3 (118) (31) (1) Firm-quarters missing required variables (97,978) (25,709) (2,745)
Maximum Sample Size for Earnings Announcement Tests 400,171 108,119 10,954
Less: Firm-quarters missing IBES analyst data (160,970) (39,722) (3,305)
Maximum Sample Size for ERC and Meet-or-Beat Tests 239,201 68,397 7,649
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Table 2: Descriptive Statistics Panel A: Unmatched Sample Dividend Paying Firms Non-Dividend Paying Firms Variable N Mean StdDev Median N Mean StdDev Median Mean Diff % Median Diff % Yield 159,820 0.0285 0.0194 0.0242 240,351 0.0000 0.0000 0.0000 0.0285 N/A *** 0.0242 N/A *** DIV_LAG5 159,820 0.7941 0.4044 1.0000 240,351 0.0573 0.2324 0.0000 0.7368 N/A *** 1.0000 N/A *** ABS_ABN_RET 159,820 0.0155 0.0433 0.0068 240,351 0.0262 0.0678 0.0126 -0.0107 -69.45% *** -0.0059 -87.02% *** EA_VOLUME 159,820 0.0076 0.0197 0.0018 240,327 0.0149 0.0327 0.0041 -0.0073 -96.44% *** -0.0023 -130.90% *** CAR 159,820 0.0017 0.0580 0.0007 240,351 0.0009 0.0911 -0.0012 0.0007 42.82% *** 0.0019 281.82% *** Surp [0.00, 0.03] 90,428 0.3403 0.4738 0.0000 149,227 0.3860 0.4868 0.0000 -0.0457 -13.43% *** 0.0000 N/A *** Surp [-0.03, 0.00) 90,428 0.1345 0.3411 0.0000 149,227 0.1443 0.3514 0.0000 -0.0098 -7.29% *** 0.0000 N/A *** UE 90,359 4.5350 2.5194 5.0000 149,052 4.4828 3.0604 4.0000 0.0522 1.15% *** 1.0000 20.00% *** #Fore_Analyst 11,732 3.7318 1.0832 3.7612 25,965 3.1690 1.0621 3.1781 0.5628 15.08% *** 0.5831 15.50% *** #Fore_Manager 9,794 1.7043 0.4460 1.7918 19,282 1.5760 0.4625 1.6094 0.1283 7.53% *** 0.1824 10.18% *** LogMVE 159,820 6.2793 2.0216 6.1941 240,351 5.1647 1.7433 4.9468 1.1146 17.75% *** 1.2473 20.14% *** BTM 159,820 0.8249 0.6658 0.6536 240,351 0.6877 0.7042 0.5242 0.1372 16.63% *** 0.1294 19.80% *** LogAGE 159,820 3.1738 0.5671 3.2581 240,351 2.6011 0.6100 2.5649 0.5727 18.04% *** 0.6931 21.27% *** ROA 159,820 0.1150 0.0771 0.1089 240,351 0.0106 0.1979 0.0574 0.1044 90.80% *** 0.0516 47.33% *** OP 159,820 0.3200 0.3261 0.2973 240,351 0.2067 0.4971 0.2178 0.1133 35.40% *** 0.0795 26.75% *** RE 159,820 0.3534 0.2740 0.3529 240,351 -0.4576 1.7326 0.0645 0.8109 229.50% *** 0.2884 81.71% *** EARN_VOL 159,820 0.0292 0.0356 0.0202 240,351 0.0945 0.1441 0.0464 -0.0653 -223.31% *** -0.0262 -129.16% *** RET_VOL 159,820 0.0149 0.0060 0.0138 240,351 0.0257 0.0116 0.0236 -0.0108 -72.69% *** -0.0098 -71.52% ***
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Table 2: Descriptive Statistics Panel B: Propensity Score Matched Sample Dividend Paying Firms Non-Dividend Paying Firms Variable Mean StdDev Median Mean StdDev Median Mean Diff % Median Diff % LogMVE 5.7149 1.8493 5.5859 5.7977 1.9702 5.6995 -0.0827 -1.45% *** -0.1136 -2.03% *** BTM 0.8049 0.6657 0.6450 0.7997 0.7395 0.5993 0.0052 0.65% 0.0456 7.07% *** LogAGE 2.9018 0.6002 2.9444 2.9357 0.5830 2.9957 -0.0339 -1.17% *** -0.0513 -1.74% *** ROA 0.0987 0.0846 0.0935 0.0982 0.0841 0.0914 0.0005 0.54% 0.0021 2.23% *** OP 0.2895 0.3593 0.2692 0.2909 0.3456 0.2639 -0.0014 -0.47% 0.0053 1.97% *** RE 0.2721 0.3243 0.2767 0.2597 0.3278 0.2638 0.0125 4.59% *** 0.0129 4.66% *** EARN_VOL 0.0374 0.0480 0.0242 0.0390 0.0421 0.0273 -0.0016 -4.24% *** -0.0031 -12.67% *** RET_VOL 0.0180 0.0068 0.0170 0.0179 0.0067 0.0169 0.0001 0.63% *** 0.0000 0.19% **
Panel C: Entropy Balanced Sample Dividend Paying Firms Non-Dividend Paying Firms Variable Mean StdDev Median Mean StdDev Median Mean Diff % Median Diff % LogMVE 6.2795 2.0215 6.1941 6.2795 2.0215 6.2401 0.0000 0.00% -0.0460 -0.74% BTM 0.8250 0.6658 0.6537 0.8250 0.6658 0.6562 0.0000 0.00% -0.0025 -0.39% LogAGE 3.1739 0.5669 3.2581 3.1739 0.5669 3.2189 0.0000 0.00% 0.0392 1.20% ROA 0.1150 0.0771 0.1090 0.1150 0.0771 0.1030 0.0000 0.00% 0.0059 5.45% OP 0.3201 0.3262 0.2974 0.3201 0.3262 0.2738 0.0000 0.00% 0.0235 7.91% RE 0.3534 0.2740 0.3530 0.3534 0.2740 0.3344 0.0000 0.00% 0.0186 5.27% EARN_VOL 0.0292 0.0356 0.0202 0.0292 0.0356 0.0207 0.0000 0.00% -0.0005 -2.40% RET_VOL 0.0149 0.0060 0.0138 0.0149 0.0060 0.0138 0.0000 0.00% -0.0001 -0.50% This table reports descriptive statistics. Panel A (B, C) reports the unmatched (propensity matched, entropy balanced) sample. ***, **, * indicate significant differences in means or medians between dividend paying and non-paying firms at the one percent, five percent, or ten percent level, respectively. Differences in means are estimated using t tests. In Panels A and B, differences in medians are estimated using Wilcoxon tests. In Panel C, differences in medians are estimated using the Hodges-Lehmann estimator to allow for weighted observations. The Hodges-Lehmann estimator is a non-parametric method to estimate the difference in the center of two distributions and is based on the Wilcoxon test (e.g., Hershberger 2011). Continuous variables are winsorized at 1% and 99%, by year. See Appendix A for variable definitions.
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Table 3: Dividends and absolute abnormal earnings announcement returns (1) (2) (3) (4) (5) (6) ABS_ABN_RET ABS_ABN_RET ABS_ABN_RET ABS_ABN_RET ABS_ABN_RET ABS_ABN_RET
DIV -0.005*** -0.003*** -0.002*** -0.002*** (-13.088) (-6.208) (-5.472) (-4.812) DIV_Lag5 -0.001*** (-3.227) Yield -0.051*** (-5.446) Repurch 0.003 (0.770) Issue -0.003 (-0.999) LogMVE 0.000*** 0.001*** 0.000 0.001*** 0.000 -0.000 (4.301) (3.553) (0.418) (3.164) (0.187) (-0.072) BTM 0.000 -0.001 -0.000 -0.000 -0.000 -0.000 (1.201) (-1.307) (-1.072) (-1.201) (-0.876) (-0.688) LogAGE -0.003*** -0.002*** -0.003*** -0.003*** -0.003*** -0.003*** (-10.276) (-4.746) (-8.472) (-7.249) (-7.244) (-8.280) ROA 0.017*** 0.009*** 0.006* 0.010*** 0.006* 0.006* (12.670) (2.825) (1.851) (3.035) (1.915) (1.895) OP 0.002*** 0.002** 0.001 0.000 0.001 0.001 (4.392) (2.423) (1.167) (0.691) (1.229) (1.166) RE 0.001*** 0.000 -0.002** -0.002** -0.002** -0.002*** (5.018) (0.605) (-2.570) (-2.097) (-2.509) (-2.781) EARN_VOL 0.009*** 0.020*** 0.020*** 0.014*** 0.019*** 0.020*** (5.386) (3.732) (4.091) (2.888) (3.945) (4.059) RET_VOL 0.257*** 0.465*** 0.469*** -0.229*** 0.466*** 0.462*** (12.727) (11.827) (12.680) (-4.814) (12.538) (12.304) FUT_RET_VOL 0.990*** (18.749)
Sample Unmatched Propensity Entropy Entropy Entropy Entropy Observations 400,082 144,177 400,082 381,832 400,082 400,082 Adjusted R2 0.047 0.057 0.077 0.083 0.077 0.077 This table reports OLS regression results. The dependent variable is absolute abnormal returns at the earnings announcement. The independent variables of interest include the dividend indicator, the lagged dividend indicator, and the dividend yield. The models include industry by year fixed effects and month fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 4: Dividends and abnormal earnings announcement share volume (1) (2) (3) (4) (5) (6) EA_VOLUME EA_VOLUME EA_VOLUME EA_VOLUME EA_VOLUME EA_VOLUME DIV -0.005*** -0.003*** -0.003*** -0.003*** (-16.050) (-8.927) (-7.858) (-7.389) DIV_Lag5 -0.002*** (-5.287) Yield -0.046*** (-6.835) Repurch 0.021*** (6.135) Issue 0.002* (1.691) LogMVE 0.003*** 0.003*** 0.002*** 0.002*** 0.002*** 0.002*** (28.402) (22.478) (15.779) (16.712) (15.777) (15.322) BTM -0.000*** -0.001*** -0.001*** -0.001*** -0.001*** -0.001*** (-2.953) (-2.926) (-3.542) (-3.797) (-3.229) (-3.518) LogAGE -0.004*** -0.003*** -0.004*** -0.004*** -0.004*** -0.004*** (-15.723) (-8.431) (-11.191) (-10.483) (-9.892) (-11.014) ROA 0.014*** 0.008*** 0.007** 0.009*** 0.007** 0.007** (14.324) (3.174) (2.402) (3.073) (2.465) (2.378) OP 0.001*** 0.001** 0.002*** 0.002*** 0.002*** 0.002*** (3.246) (2.465) (3.829) (3.350) (3.904) (3.520) RE 0.000*** 0.001* -0.001 -0.001 -0.001 -0.001 (2.935) (1.649) (-1.155) (-0.986) (-1.076) (-1.389) EARN_VOL 0.005*** 0.022*** 0.024*** 0.020*** 0.023*** 0.022*** (3.603) (5.168) (5.773) (5.013) (5.577) (5.506) RET_VOL 0.326*** 0.523*** 0.493*** 0.174*** 0.489*** 0.496*** (21.906) (18.841) (17.270) (6.285) (17.066) (17.113) FUT_RET_VOL 0.464*** (14.501)
Sample Unmatched Propensity Entropy Entropy Entropy Entropy Observations 400,058 144,206 400,058 381,808 400,058 400,058 Adjusted R2 0.164 0.219 0.237 0.244 0.235 0.236 This table reports OLS regression results. The dependent variable is abnormal share volume at the earnings announcement. The independent variables of interest include the dividend indicator, the lagged dividend indicator, and the dividend yield. The models include industry by year fixed effects and month fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 5: Dividends and earnings response coefficients Panel A: Dividend payers vs. non-dividend payers (1) (2) (3) (4) (5) (6) CAR CAR CAR CAR CAR CAR UE 0.009*** 0.010*** 0.011*** 0.008*** 0.010*** 0.007*** (76.404) (49.929) (42.611) (6.435) (47.143) (6.338) DIV 0.002** 0.003** 0.011*** 0.012*** (1.962) (2.556) (8.626) (8.768) DIV*UE -0.001*** -0.001*** -0.003*** -0.003*** (-3.894) (-3.280) (-9.607) (-9.579) DIV_Lag5 0.012*** (9.624) DIV_Lag5*UE -0.003*** (-10.751) Yield 0.337*** (9.885) Yield*UE -0.078*** (-11.165) Repurch -0.027* (-1.832) Repurch*UE 0.004 (1.541) Issue 0.026*** (2.866) Issue*UE -0.007*** (-3.926)
Controls Yes Yes Yes Yes Yes Yes Controls*UE No No No Yes No Yes Sample Unmatched Propensity Entropy Entropy Entropy Entropy Observations 239,201 86,197 239,201 239,197 239,201 239,201 Adjusted R2 0.086 0.108 0.125 0.128 0.125 0.128 This table reports OLS regression results. The dependent variable is abnormal signed returns at the earnings announcement. The independent variables of interest include the interactions between unexpected earnings and the dividend indicator, the lagged dividend indicator, and the dividend yield. The control variables include the standard set of control variables from Table 3. The models include industry by year fixed effects and month fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 5: Dividends and earnings response coefficients Panel B: Dividend timing (1) (2) CAR CAR UE 0.008*** 0.005*** (31.633) (4.105) DIV_TIME 0.011*** 0.013*** (4.500) (4.558) DIV_TIME*UE -0.002*** -0.003*** (-4.433) (-4.869)
Controls Yes Yes Controls*UE No Yes Sample Dividend payers only Dividend payers only Observations 56,734 56,734 Adjusted R2 0.110 0.115 This table reports OLS regression results. The dependent variable is abnormal signed returns at the earnings announcement. The independent variable of interest is the interaction between unexpected earnings and the time between the prior quarter earnings announcement and the dividend announcement. The control variables include the standard set of control variables from Table 3. The models include industry by year fixed effects and month fixed effects. Robust standard errors are clustered by firm. T- statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 6: Dividends and meeting or beating Panel A: Meet or beat threshold (1) (2) (3) (4) Surp[-0.03,0.03] Surp[-0.03,0.03] Surp[-0.03,0.03] Surp[-0.03,0.03] DIV -0.028*** -0.033*** -0.020*** (-4.619) (-4.780) (-2.620) Yield -0.748*** (-3.732) Repurch -0.075 (-1.079) Issue 0.135** (2.047) LogMVE 0.003 0.001 0.001 0.001 (1.106) (0.316) (0.243) (0.241) BTM -0.023*** -0.027*** -0.032* -0.031* (-5.285) (-3.240) (-1.901) (-1.885) LogAGE -0.021*** -0.020*** -0.017*** -0.016** (-4.889) (-3.236) (-2.707) (-2.507) ROA 0.303*** 0.359*** 0.335*** 0.338*** (17.471) (7.640) (5.491) (5.596) OP 0.002 -0.013* -0.007 -0.006 (0.547) (-1.786) (-0.841) (-0.718) RE -0.016*** -0.042*** -0.040*** -0.038*** (-6.299) (-3.535) (-2.690) (-2.624) EARN_VOL 0.001 -0.123* -0.205** -0.191** (0.046) (-1.670) (-2.251) (-2.086) RET_VOL -0.832** 0.324 0.078 -0.092 (-2.470) (0.456) (0.102) (-0.121) ABS_ABN_RET -0.310*** -0.382*** -0.436*** -0.438*** (-11.726) (-8.213) (-9.077) (-9.131) EA_Volume -0.746*** -0.966*** -1.088*** -1.082*** (-12.942) (-9.387) (-10.890) (-10.832) #Analyst 0.091*** 0.090*** 0.089*** 0.088*** (19.249) (12.124) (9.825) (9.902)
Sample Unmatched Propensity Entropy Entropy Observations 239,655 80,664 239,655 239,655 Adjusted R2 0.045 0.048 0.056 0.057 This table reports OLS regression results. The dependent variable is set to negative (positive) one if the firm just misses (beats) the consensus forecast. The independent variables of interest include the dividend indicator and the dividend yield. The models include industry by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 6: Dividends and meeting or beating Panel B: Earnings management (1) (2) ΔR&D ΔR&D Surp[-0.03,0.03] -0.060*** -0.072*** (-6.835) (-6.551) DIV -0.192*** (-10.093) DIV*Surp[-0.03,0.03] 0.045*** (3.471) LogMVE 0.061*** 0.073*** (5.710) (6.767) BTM -0.234*** -0.228*** (-10.560) (-10.525) LogAGE -0.163*** -0.129*** (-9.575) (-7.422) ROA 0.115 0.107 (0.993) (0.927) OP -0.015 -0.013 (-0.568) (-0.498) RE 0.015 0.016 (1.178) (1.290) EARN_VOL -0.981*** -0.974*** (-8.485) (-8.429) RET_VOL 18.079*** 16.381*** (10.842) (9.773) ABS_ABN_RET -0.227** -0.231** (-2.067) (-2.106) EA_Volume 1.084*** 0.979*** (4.533) (4.082) #Analyst 0.003 -0.010 (0.143) (-0.517)
Sample Unmatched Unmatched Observations 114,173 114,173 Adjusted R2 0.047 0.048 This table reports OLS regression results. The dependent variable is the change in R&D expenses. The independent variable of interest is the interaction between the dividend indicator and the just miss/meet indicator. The models include industry by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 7: Dividends and forecasting Panel A: Manager forecast volume (1) (2) (3) #Fore_Manager #Fore_Manager #Fore_Manager DIV -0.036*** -0.039*** -0.011 (-2.860) (-2.812) (-0.672) LogMVE 0.086*** 0.088*** 0.062*** (21.191) (14.852) (7.647) BTM 0.021** 0.043** 0.067*** (2.326) (2.362) (2.810) LogAGE -0.014 -0.024* -0.015 (-1.582) (-1.913) (-1.109) ROA 0.150*** -0.144 -0.005 (4.487) (-1.637) (-0.044) OP 0.018*** 0.035*** 0.022*** (3.313) (3.647) (2.684) RE 0.001 -0.014 -0.061** (0.238) (-0.747) (-2.508) EARN_VOL -0.005 -0.166 -0.305** (-0.162) (-1.548) (-2.368) RET_VOL -3.615*** -3.493** -8.395*** (-5.441) (-2.260) (-4.944)
Sample Unmatched Propensity Entropy Observations 29,076 11,358 29,076 Adjusted R2 0.229 0.229 0.212 This table reports OLS regression results. The dependent variable is the number of manager forecasts. The independent variable of interest is the dividend indicator. The models include industry by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 7: Dividends and forecasting Panel B: Analyst forecast volume (1) (2) (3) #Fore_Analyst #Fore_Analyst #Fore_Analyst DIV -0.111*** -0.047** -0.034 (-6.132) (-2.386) (-1.600) LogMVE 0.520*** 0.534*** 0.506*** (93.454) (63.731) (55.023) BTM 0.051*** 0.068** 0.067 (3.150) (2.540) (1.604) LogAGE -0.171*** -0.148*** -0.151*** (-12.854) (-7.768) (-8.496) ROA -0.146*** -0.360*** -0.309** (-3.473) (-2.725) (-2.116) OP 0.018** 0.025 0.031*** (2.204) (1.638) (2.712) RE -0.010** -0.111*** -0.124*** (-2.210) (-3.371) (-3.768) EARN_VOL -0.222*** -0.368** -0.331** (-5.181) (-2.057) (-1.996) RET_VOL 5.926*** 14.975*** 12.257*** (5.799) (6.493) (5.596)
Sample Unmatched Propensity Entropy Observations 37,697 14,004 37,697 Adjusted R2 0.658 0.660 0.680 This table reports OLS regression results. The dependent variable is the number of analyst forecasts. The independent variable of interest is the dividend indicator. The models include industry by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 7: Dividends and forecasting Panel C: Manager forecast mix (1) (2) %Fore_Manager %Fore_Manager Sale 0.056*** 0.266*** (5.466) (4.525) GM -0.441*** -0.706*** (-40.628) (-11.118) Capex -0.303*** -1.071*** (-23.337) (-12.179) DIV*Sale -0.252*** -0.130*** (-14.693) (-6.674) DIV*GM -0.102*** -0.054*** (-5.662) (-2.598) DIV*Capex 0.135*** 0.100*** (5.386) (3.573)
Controls No Yes Sample Stacked Stacked Observations 116,304 116,304 Adjusted R2 0.354 0.382 This table reports OLS regression results. The dependent variable is the percentage of manager forecasts. The independent variables of interest include the interactions between the dividend indicator and the forecast type. The control variables include the standard set of control variables from Table 3 and their interactions with the forecast type. The models include firm by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two-tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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Table 8: Future firm characteristics (1) (2) (3) (4) (5) (6) ΔLogMVE ΔOP ΔROA ΔLogMVE ΔOP ΔROA DIV -0.001 0.008 0.002** 0.002 0.007 0.001 (-0.298) (1.572) (2.462) (0.645) (1.565) (1.308)
Controls Yes Yes Yes Yes Yes Yes Sample Propensity Propensity Propensity Entropy Entropy Entropy Observations 34,843 32,159 32,159 94,999 84,665 84,665 Adjusted R2 0.245 0.254 0.213 0.275 0.236 0.257 This table reports OLS regression results. The dependent variable is the future change in firm size or performance. The independent variable of interest is the dividend indicator. The control variables include the standard set of control variables from Table 3 as well as total assets, total payout, and, in the performance regressions, the lagged change in performance, an indicator for a negative lagged change, and the interaction between the two. The models include industry by year fixed effects. Robust standard errors are clustered by firm. T-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels for two- tailed tests, respectively. Sample selection procedures are reported in Table 1. Variable definitions are reported in Appendix A.
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