Dividends, trust, and firm value

Abstract

We find evidence that investors value dividends differently depending on their level of trust. Our tests indicate that investor demand for dividend-paying stocks increases as trust decreases, and that this relationship affects market values. We begin with survey evidence showing that people think accounting fraud is less likely among dividend payers and that people with low trust are more likely to hold dividend-paying stocks. We then empirically exploit accounting fraud discoveries within a mutual fund’s portfolio as a shock to trust. In response to these shocks, we show that mutual funds tilt their portfolios toward dividend-paying stocks. This result is not explained by a shift in risk preferences, indicating that these institutional investors are seeking dividends in particular rather than stable firms that just happen to pay dividends. Finally, we provide evidence that dividend payers experience a premium in their market values relative to non-payers when their investor base becomes less trusting.

1 Introduction

Dividends are fundamental to how investors value firms (e.g., Miller and Rock 1985; Fama and French 1998). While prior literature has discovered much about what investors can learn from dividends, it largely focuses on how dividends transmit information about firms in a standard agency or signaling framework where investors are rational, capital markets are competitive, and the interesting economics are at the firm level (e.g., Hail et al. 2014; Ham et al. 2020, 2023; Michaely et al. 2021; Ellahie and Kaplan 2021; Kaplan and Pérez-Cavazos 2022). While this literature is productive and insightful, it largely ignores the reality that investors are heterogeneous, as evidenced by the fact that dividend investors tend to cluster in the same stocks (e.g., Hotchkiss and Lawrence 2007). Such heterogeneity may cause investors to interpret dividends differently. One important aspect of this heterogeneity, hitherto unexplored, is the level of trust investors place in firms and their managers. In this paper, we find that low-trust investors have greater demand for dividend-paying stocks, as revealed by their tendency to allocate more of their portfolios to dividend payers. We further find that negative shocks to trust increase the stock market values of dividend payers relative to non-payers.

Trust is known to be an important factor in many areas of economics.¹ We hypothesize that it also plays a role in investor demand for dividends, such that low-trust investors are more likely to invest in dividend payers and value them more highly. The dividend literature is broadly consistent with firms paying dividends to establish trust. Dividends are highly persistent; dividend payers are less likely to manage earnings (e.g., Caskey and Hanlon 2013; Ham et al. 2023); and dividends predict future earnings changes and have implications for earnings persistence (e.g., Skinner and Soltes 2011; Ham et al. 2020). Low-trust investors may have trouble believing a firm’s financial statements, either because they do not trust the numbers or because they doubt that the income will ultimately flow to them (e.g., DeAngelo and DeAngelo 2006). If the firm paid dividends, this would address both concerns because dividends provide information that substitutes for earnings (e.g., Ham et al. 2023), and dividends signal to investors that they will not be expropriated by insiders (e.g., Ellahie and Kaplan 2021).

Conceptually, we can think of an investor with low trust as placing a higher probability on managers lying about earnings and misusing money that should be paid out to shareholders (Guiso et al. 2008). Thus, a low-trust investor places a higher probability on each of their investments suffering a big loss due to fraud or expropriation. If investors think fraudulent stocks are less likely to pay dividends than honest stocks, a drop in their trust will induce them to value dividend-paying stocks more (relative to non-payers), resulting in a shift in demand toward dividend payers. For example, suppose an investor thinks dividends would be less likely among fraudulent stocks than honest stocks if fraud existed, but they believe the probability of fraud is zero. In this stark case, dividend payers and non-payers have an equal conditional probability of fraud: zero. Then, suppose the investor has an experience that revises their probability of fraud up to greater than zero. After this revision, dividend payers will have a lower conditional probability of fraud than non-payers, and the expected value of holding dividend payers will increase relative to non-payers. Thus, we expect investors experiencing a loss of trust to have greater demand for dividend payers relative to non-payers.² This shift in demand will be exacerbated further if, apart from the monetary cost, investors are averse to being cheated.

We begin with survey evidence indicating that people feel that dividend payers are more trustworthy. Prior empirical work shows that dividend payers have a lower probability of committing accounting fraud than non-payers (Caskey and Hanlon 2013). We check whether investor perceptions align with this empirical evidence and find that they do. In a survey conducted by us, respondents rate dividend payers as significantly less likely to commit fraud than the average firm. Furthermore, we find support for the notion that low-trust investors gravitate toward dividend-paying stocks. In data from a preexisting survey where respondents rate their own level of suspicion, we find that less-trusting respondents are more likely to receive dividends even after we control for the respondent’s age, gender, and risk aversion. However, because trust and dividends are endogenously determined alongside many other factors, we acknowledge that these results are only suggestive.

Next, we provide better-identified evidence that low-trust investors gravitate toward dividend payers. To isolate the effect of trust on the demand for dividend-paying stocks, we use accounting fraud events as a negative shock to investor trust (Giannetti and Wang 2016).³ Our test exploits variation in exposure to these shocks by comparing mutual funds that did and did not hold the fraudulent firm in their portfolios at the time of the fraud. We find that funds holding the fraudulent firm (i.e., the treatment group) increase the fraction of dividend payers in their portfolios after the fraud by about 0.6 percentage points (t = 2.80) relative to funds that were not holding the fraudulent firm (i.e., the control group). This estimate plausibly isolates the accounting fraud’s impact on the demand for, rather than the supply of, dividend payers. This is because the test compares treatment and control funds with the same investment style that are likely choosing from the same set of investable stocks.

If dividend-paying firms are also less risky, the preceding result could be capturing a relationship between low trust and a preference for low risk rather than a preference for dividends. We address this potential explanation in three ways. First, in our main regressions in the mutual fund analysis, we control for changes in the riskiness of the fund’s portfolio to control for any changes in the fund’s risk preferences. Second, we replace the outcome variable in this regression with the variables capturing the change in the portfolio’s riskiness. In this test, we fail to detect any shift in risk preferences stemming from exposure to fraud. Instead, we find that funds holding the fraudulent firm make no significant changes to the riskiness of their portfolios relative to funds not holding the fraudulent firm. Third, we conduct a test at the mutual fund-firm-level that allows us to directly control for firm traits. We find that mutual funds with a shock to trust seek dividends in particular rather than old, stable firms with less volatile cash flows that just happen to pay dividends. Collectively, these results suggest that the increased preference for dividend-paying stocks is because of their dividend policy rather than their overall riskiness. This finding is in line with prior work that views trust and risk-aversion as distinct—notably Guiso et al. (2008), who model trust and risk aversion as separate constructs; and Ahern et al. (2014), who find that distinct cognitive processes govern risk-aversion and trust.

We next provide evidence that investor trust influences stock prices. We use a similar strategy as in the previous test, except now we show how a shock to the trust of a firm’s investor base affects the firm’s stock market value in terms of its market-to-book ratio. We use two analyses to triangulate this question. First, we aggregate the mutual fund data to the firm-year level to examine how a firm’s valuation is affected, depending on whether or not it pays dividends, when it is held by more mutual funds that have a negative shock to trust. Given our previous results, firms held by more funds experiencing shocks to trust should have higher valuations if they pay dividends and lower valuations if they do not. Consistent with this prediction, we find that firms with more investors hit by shocks to trust have lower valuations when they do not pay dividends (t = -3.25) and higher valuations when they do (t = 4.79 for the difference with non-payers, with the sum of the two coefficients significantly positive at the 1% level).

In our second analysis, we examine how an accounting fraud in a given US state impacts the relative valuation of the state’s dividend-paying firms versus its non-paying firms. An accounting fraud is more likely to affect other firms in the same state because they are more likely to share an investor base, given the tendency for investors to hold local stocks (Seasholes and Zhu 2010). In states with accounting frauds, we find that the dividend premium increases by about 4.9% (t = 2.59) in the year following the fraud. This indicates that a fraud-induced drop in trust in a given state leads to a greater value premium for dividend payers versus non-payers. The reported changes in the value premium in both of these tests are plausible to the extent that asset pricing follows a demand system (Koijen and Yogo 2019), where the higher relative demand for dividend payers causes them to have higher relative valuations. They are also plausible under a model of limited investor attention, such as Merton (1987), where a negative shock to trust induces investors to add dividend payers to their investable universe and remove non-payers.

We corroborate the above result of the state-level analysis with an associational test, which shows that US regions with less trust (according to surveys) place a higher relative value on dividend-paying stocks.⁴ While we caution that this result may be confounded by other factors, such as differences in corruption (e.g., Smith 2016), it points in the same direction as our finding, where shocks to trust increase the valuation of dividend payers relative to non-payers.

Our paper contributes to the literature on the information investors receive when a firm pays dividends.⁵ Prior literature has argued that dividends signal that outsiders are safe from insider expropriation (e.g., Ellahie and Kaplan 2021) or signal positive information about future earnings and cash flows (e.g., Michaely et al. 2021; Ham et al. 2020; Kaplan and Pérez-Cavazos 2022).⁶ Our results indicate that there may be heterogeneity in how investors perceive these signals from dividends, with less-trusting investors placing more weight on them relative to other information provided by the firm.

Other papers have explored how various factors increase or decrease the need for dividends in the agency context. For example, Hail et al. (2014) find evidence that dividends are less needed after an improvement in the information environment reduces information asymmetry between managers and investors, and Ellahie and Kaplan (2021) find that dividends are more needed in countries with weak institutions for firms early in their life cycles.⁷ The results in these papers suggest that firms use dividends to increase the trust investors place in them. We address trust from a different angle, showing that an investor whose trust is hurt by one firm will change how much they value the dividend policies of other firms. We further provide evidence that heterogeneity in trust can differentially affect dividend and non-dividend payers depending on their investor bases.

In addition to the literature on the information conveyed by dividends, our paper contributes to the literature on dividend clienteles. Dividend investors tend to cluster in the same stocks (e.g., Hotchkiss and Lawrence 2007). Several studies in this literature argue that at least part of the explanation relates to taxes (e.g., Desai and Jin 2011), but this does not explain all of the differences. For example, older investors tend to prefer dividend payers (Becker et al. 2011). We contribute to this literature by showing that less trusting investors are attracted to dividend payers, meaning that heterogeneity in investor trust contributes to the dividend clientele phenomenon.

Finally, we contribute to the literature on financial reporting fraud (see a review by Amiram et al. (2018)). Giannetti and Wang (2016) show that household stock market participation decreases after the revelation of fraud. Our evidence shows that the type of investments changes, with frauds pushing investors toward dividend payers.

2 Trust, dividends, and investor behavior

2.1 Dividends and the perceived likelihood of fraud

We begin with a survey in which investors tell us they perceive dividend payers as more trustworthy than the average firm (in the sense of being less likely to commit fraud). We run a survey on MTurk with 87 respondents.⁸ In the survey, we give the participants a series of characteristics and ask them to assess how likely firms with each characteristic are to commit accounting fraud relative to the average firm. For each characteristic, the participants mark their answers on a Likert scale from 1 to 7, where 1 indicates fraud is “much less likely,” 7 indicates fraud is “much more likely,” and 4 is “neutral” (because it is the midpoint between 1 and 7). One of the characteristics in the series is whether the firm is a dividend payer. We include other characteristics and randomize the list of characteristics presented to participants to mitigate any confounding effects from question order.

We present the results in Table 1. The participants think dividend payers are less likely to commit fraud than the average firm. On average, they rated dividend payers 3.483 on the Likert scale, which is on the “less likely” side of “neutral.” Because “neutral” is 4, we test whether the average rating differs significantly from 4 and find that it does at the 1% level. Table 1 shows that paying dividends is the financial variable least associated with fraud. Some characteristics increased the perception of fraud, such as being in the finance industry and beating analyst expectations (with average scores of 4.724 and 4.805, respectively, again significantly different from 4 at the 1% level). The appendix contains the actual questions that generated these findings.

Table 1 Perception of Fraud and Firm Characteristics

2.2 Trust and the likelihood of receiving dividends

We next provide evidence that investors are more likely to receive dividends when they believe themselves to be less trusting. We use data from CentER Savings Surveys that ask individuals how trusting they are and whether they hold investments that pay dividends.⁹ For the regressions used in this analysis, our outcome variable is Received Dividends, which is an indicator that equals 1 if the individual says they received dividends in the past year.¹⁰ Our variable of interest is a self-reported measure of trust where the individuals rate themselves on a scale of 1–7, with 1 representing “trusting, credulous” and 7 representing “suspicious.” We label this self-reported trust variable Level of Suspicion because higher values indicate greater suspicion. As an alternative variable of interest, we also create a dummy variable, Trust Dummy, that equals one if the participant rates herself as less than a 4, which means that she is more trusting than suspicious. For both trust variables, we use the individual’s self-reported trust from the previous year because the dividends received are reported for the previous year.¹¹ Thus, in the regressions, we regress dividends received in a given year on the self-reported trust in that same year. In the regressions, we control for each participant’s self-assessed risk tolerance,¹² total assets (converted to deciles),¹³ and number of owned stocks.¹⁴ Some specifications also include controls for demographics, including age, number in the household, number of children, and gender.

Our sample is at the individual-year level; it runs from 1997 to 2003 and includes heads of household.¹⁵ We limit the sample to investors by keeping only individuals who report positive holdings in stocks, growth funds, or mutual funds in the past year.¹⁶ In Table 2, Panel A, we present descriptive statistics. About half of the sample receives dividends in any given year. On average, the sample is at about the midpoint of the 1 to 7 scale in terms of Level of Suspicion and Risk Tolerance. The average Number of Stocks is 0.619. This last measure is a logarithm; when not in log form, the average number of stocks owned by individuals in the sample is 1.759, with a standard deviation of 4.216.

Table 2 Individual Investors and Dividends

Figure 1 shows the percentage of sample individuals that receive dividends at each value of Level of Suspicion. The graph shows a monotonic increasing relationship between suspicion and dividends. Among the most trusting individuals (Level of Suspicion = 1), only about 25% hold investments that pay dividends. The percentage holding dividends increases with each increase in the Level of Suspicion, up to about 70% for the least trusting individuals (Level of Suspicion = 7).

Fig. 1

Dutch Dividend Demand. This figure plots the percentage of survey respondents receiving dividends, broken out by Level of Suspicion, which equals the rating the respondent assigned herself on a scale of 1 to 7, where 1 represents “trusting, credulous” and 7 represents “suspicious.” The figure pools together all individual-year observations in our sample from the Dutch survey data

We next show that the positive relationship between dividends and suspicion holds after including controls. Table 2, Panel B shows the results from the following linear probability model for individual i in year t¹⁷:

$$Received\; Dividend{s}_{i,t}={\beta }_{0}+{\beta }_{1}Trust\; Variabl{e}_{i,t}+{\varvec{\gamma}}{\varvec{C}}{\varvec{o}}{\varvec{n}}{\varvec{t}}{\varvec{r}}{\varvec{o}}{\varvec{l}}{{\varvec{s}}}_{{\varvec{i}},{\varvec{t}}}+{\varepsilon }_{i,t}$$

where “Trust Variable” is either Level of Suspicion or Trust Dummy. The vector of controls always includes controls for total assets, risk tolerance, and the number of stocks owned.¹⁸ Sometimes it also includes controls for demographic characteristics. Standard errors are clustered by individual, since individuals are likely to give similar survey responses over time.

In Table 2, Panel B, we find evidence consistent with less-trusting investors being more likely to receive dividends. Significant at the 5% level or better, we find that Received Dividends is positively associated with Level of Suspicion and negatively associated with Trust Dummy. Focusing on the Trust Dummy results, we estimate that trusting individuals (Trust Dummy = 1) are between 7.1 and 13.6 percentage points less likely than suspicious individuals (Trust Dummy = 0) to hold investments that pay dividends. These results are consistent with low-trust investors tilting their portfolios toward dividend-paying stocks. We acknowledge, however, that these results are only associations and, therefore, only suggestive. In our main analysis, which we cover next, we turn to a setting that better identifies the impact of trust on the demand for dividend-paying stocks.

3 Trust and mutual fund manager behavior

3.1 The effect of trust on mutual fund investment in dividend-paying stocks

We now turn to our main analysis, in which we design a test to isolate the effect of changes in trust on changes in investor demand for dividend-paying stocks. We use accounting frauds as negative shocks to investor trust (Giannetti and Wang 2016). These shocks presumably affect trust more for investors who hold the stock than for those who have not been exposed to the shocks through their investments (Tversky and Kahneman 1973). This creates variation in the change in trust, which we can exploit to examine the effect of trust on the demand for dividend payers. We focus on mutual funds, whose portfolios are visible, and we compare the investment decisions of funds that do versus do not own stock in the fraudulent firm. We find that the mutual funds that own the fraudulent firm—and therefore have a larger negative shock to trust—tilt their portfolios more towards dividend-paying stocks after the fraud is discovered. This provides evidence that a reduction in investor trust increases their demand for dividend payers.

Our data on accounting fraud discoveries comes from the Journal of Accounting Research’s website (Call et al. 2018).¹⁹ The data contains over 1,000 frauds revealed since 1978. After matching the companies in the dataset with Compustat, we are left with 773 frauds revealed from 1978 to 2011.²⁰ We assume that frauds are discovered and revealed to the public in the year that the SEC starts investigating the fraud.²¹ We use these fraud discoveries as our shocks to trust. We measure a mutual fund’s exposure to these shocks in a given year with the indicator variable Fraud Investment, which takes a value of 1 if a fraud was discovered for one of the stocks in the mutual fund’s portfolio that year.

Our mutual fund data runs from 1984 to 2011. Our data on mutual fund characteristics comes from the Center for Research in Securities Prices (CRSP) Survivor Bias-Free US Mutual Fund database. Our data on funds’ quarterly holdings come from the Thomson Reuters mutual fund holdings database.²² Our sample consists of US equity mutual funds that are open-ended, diversified, and actively managed.²³ We also exclude very small and very young funds from the sample.²⁴ CRSP provides information on multiple share classes issued by the same fund. To avoid multiple counting, we aggregate share-class-level data to the portfolio level by taking the value-weighted average of a fund’s characteristics across share classes.²⁵ After requiring non-missing observations for the main fund-level variables,²⁶ our final sample includes 21,722 mutual fund-year observations.²⁷ In addition, we obtain data on the stocks held by the mutual fund sample from CRSP. We only consider stocks with share codes 10, 11, 12, and 18 and exchange codes 1, 2, and 3.²⁸ To determine the prices of each stock, we use the end-of-month prices from CRSP.

To determine the impact of shocks to trust on the demand for dividends, we run regressions with the following functional form:

$${Y}_{i,t}={\alpha }_{s,t}+\beta Fraud\; Investmen{t}_{i,t}+{\varvec{\gamma}}{\varvec{C}}{\varvec{o}}{\varvec{n}}{\varvec{t}}{\varvec{r}}{\varvec{o}}{\varvec{l}}{{\varvec{s}}}_{{\varvec{i}},{\varvec{t}}}+{\varepsilon }_{i,t}.$$

The observations are at the mutual fund-year level. As already discussed, our variable of interest is $Fraud\; Investmen{t}_{i,t}$, which takes a value of 1 if a fraud was discovered for one of the stocks in the mutual fund’s portfolio that year. We use three measures for the left-hand-side variable, ${Y}_{i,t}$. The first is $\Delta\; Dividend\; Shar{e}_{i,t}$, which equals the change from the last calendar quarter of year t-1 to the last calendar quarter of year t in the fraction of the fund’s stocks that are dividend payers. The second and third left-hand-side variables both capture the change in average dividend yield from the last calendar quarter in year t-1 to the last calendar quarter in year t: $\Delta\; Dividend\; Yield {\;(VW)}_{i,t}$ is the change in the value-weighted average of the dividend yield, where value weights are based on the fund’s equity investment values in each company; and $\Delta\; Dividend \;Yield {\;(EW)}_{i,t}$ is the change in the equal-weighted dividend yield, where the average is equal-weighted across all companies in the fund’s portfolio.²⁹ All three variables are measured excluding the fraud firm from the fund portfolios; that is, the fraud firm is excluded in both years, t-1 and t.³⁰

Conceptually, $\Delta\; Dividend\; Shar{e}_{i,t}$ is a measure of whether the fund manager increases the number of dividend payers in the portfolio, and both measures of $\Delta\; Dividend\; Yiel{d}_{i,t}$ capture whether the fund manager shifts from stocks with low dividend yields to stocks with high dividend yields. $\Delta\; Dividend\; Shar{e}_{i,t}$ captures the extensive margin, in the sense that it shows how much mutual funds tilt toward firms paying any dividends; and $\Delta\; Dividend\; Yiel{d}_{i,t}$ captures both the extensive and intensive margins, in the sense that it also captures whether the mutual funds prefer stocks paying more dividends as opposed to less. Table 3, Panel A contains summary statistics for all three of the left-hand-side variables, along with the variable of interest.

Table 3 Fraud and Mutual Fund Portfolio Decisions

We also include fund style-by-year fixed effects to control for any changes in the propensity to pay dividends among the firms in the fund’s investable universe.³¹ In addition, all specifications include controls related to the fund’s performance.³² In some specifications, we also include controls for the change in the riskiness of the mutual fund’s portfolio, in an attempt to control for changes in risk aversion that might be induced by exposure to the fraud. The risk controls include (i) the change in the portfolio’s co-movement with the market, (ii) the change in the portfolio’s idiosyncratic volatility, (iii) the change in the portfolio’s overall volatility, (iv) the change in the portfolio’s ability to track its target index, and (v) the intended risk change of a fund’s portfolio.³³

The results for this test are in Table 3, Panel B, and they show evidence that mutual funds tilt their portfolios toward dividend payers when a fraud has been detected in a firm that was part of the fund’s portfolio. When the left-hand-side variable is $\Delta\; Dividend\; Shar{e}_{i,t}$, we find that funds increase the fraction of stocks that are dividend payers by about 0.6 percentage points compared to funds not invested in the fraud firm. When the left-hand-side variable is $\Delta\; Dividend\; Yield \;{(VW)}_{i,t}$ [$\Delta\; Dividend\; Yield\; {(EW)}_{i,t}$], we find that funds increase the average dividend yield among their stock investments by about 0.03 [0.04] percentage points compared to funds not invested in the fraud firm. These results are broadly consistent with investors increasing their demand for dividend-paying stocks after they lose some of their trust that earnings numbers are real and will eventually be paid out to shareholders.

In Table 3, Panel C, we evaluate both the parallel trends assumption and the timing of the effect. These tests are the same as in Panel B, except that the right-hand side includes $Fraud\; Investmen{t}_{i,t-1}$ and $Fraud\; Investmen{t}_{i,t+1}$. These indicator variables are equal to one in the year before (i.e., t-1) and the year after (i.e., t + 1) the fraud year, respectively, for funds that hold the fraudulent firm in year t. Regarding the parallel trends assumption, the coefficient on $Fraud\; Investmen{t}_{i,t-1}$ is never statistically significant and never positive, indicating that there was no general trend toward holding more dividends before the mutual fund was exposed to the fraud. Regarding the timing of the effect, our evidence suggests that there may be a modest additional move toward dividend payers in the near future after the fraud exposure, because the coefficient on $Fraud \;Investmen{t}_{i,t+1}$ is positive. However, the coefficient is never more than half the coefficient on $Fraud\; Investmen{t}_{i,t}$, and never statistically significant. This indicates that most of the effect happens immediately after exposure to the fraud.

Finally, Panel D of Table 3 evaluates whether exposure to the fraud increases risk aversion. This test uses the same specification as Panel B, except it removes the risk controls, which proxy for changes in risk aversion, and puts them on the left-hand side, one by one. We fail to detect any increase in risk aversion, as mutual funds exposed to the fraud do not make any detectable decrease in their co-movement with the market (Δ R2 CAPM), their idiosyncratic volatility (Δ S.D. Res CAPM), their overall volatility (Δ Fund Vol), their tracking error (Δ Tracking Error), or their risk shifting (Risk Shifting). If their risk aversion had increased, we would have expected to see them reduce the riskiness of their portfolios. Thus our evidence uncovers no change in risk aversion, indicating that the main results are due to a drop in trust.

Our inference that there was a change in trust but no change in risk aversion is consistent with prior work that views trust and risk aversion as distinct. For example, Guiso et al. (2008) model trust and risk aversion as two separate constructs, and Ahern et al. (2014) find evidence that distinct cognitive processes govern risk aversion and trust. Under the model provided by Guiso et al. (2008), being low-trust and being risk-averse are conceptually distinct, in that being low-trust means the investor places a high probability on being cheated by a firm’s manager, whereas being risk-averse means the investor has a concave utility function. Thus, investors could, in theory, simultaneously be risk-neutral and low-trust, which would translate to having both a linear utility function and a belief that managers are highly likely to cheat. We also note that investors could be averse to being defrauded, apart from any monetary consequences; this could be additional motivation to invest in dividend payers.

3.2 Firm-level characteristics and mutual funds subject to trust shocks

We next analyze which dividend payers have increased demand within mutual funds using a regression of the following general form, for mutual fund i, firm j, and year t:

$${\Delta\;Holdings}_{i,j,t}=\beta{\;Dividend\;Payer}_{j,t}\times Fraud\;Investment_{i,t}+\lambda_{i,t}+\delta_{j,t}+\varepsilon_{i,j,t}.$$

The observations are at the mutual fund-firm-year level. As in the previous section, $Fraud\; Investmen{t}_{i,t}$ takes a value of 1 if a fraud was discovered for one of the stocks in the mutual fund’s portfolio that year. The left-hand-side variable, ${\Delta\; Holdings}_{i,j,t}$, is defined as the logarithm of the ratio (1 + shares_t)/(1 + shares_t-1) held by fund i in firm j’s stock. ${Dividend\; Payer}_{j,t}$ is a dummy variable that indicates whether a firm paid dividends in that year. We include fund-by-year fixed effects to absorb time-varying fund characteristics. We also include firm-by-year fixed effects to absorb time-varying firm characteristics. Standard errors are clustered by mutual fund-year. Continuous variables are winsorized at the 1st and 99th percentiles each year. We first run the model as specified above, to document our baseline result for this specification. After that, we interact the model with firm characteristics to examine which dividend payers have increased demand within mutual funds. Summary statistics of the variables employed in this analysis are reported in Table 4, Panel A.

Table 4 Fraud and Mutual Fund Portfolio Changes

The baseline results are reported in the first column of Table 4, Panel B. The positive and significant coefficient on Fraud Investment × Dividend Payer of 0.129 (t = 3.32) implies that when a mutual fund gets exposed to fraud, it increases its stake in a dividend-paying firm by 12.9% on average. We also run two tests to examine which dividend-paying firms experience the increase in demand from funds experiencing fraud.

First, we test whether other firm characteristics are driving the increase in demand for dividend-paying stocks. It could be that experiencing fraud makes mutual fund managers more risk-averse, and this leads them to invest in established firms with stable cash flows that just happen to pay dividends. To examine this, we not only interact $Fraud\; Investmen{t}_{i,t}$ with ${Dividend\; Payer}_{j,t}$ but also interact it with ${ln\_Firm\_Age}$, ${CFO\; volatility}_{j,t}$, and ${\mathrm{ln}\_\mathrm{SIZE}}_{j,t}$. ${ln\_Firm\_Age}_{j,t}$ is the natural logarithm of one plus a firm’s number of years available in Compustat up to and including year t. ${CFO\; volatility}_{j,t}$ is the standard deviation of a firm’s last five years of cash flows from operations. Cash flow from operations is a firm’s operating income after depreciation minus accruals. Accruals are calculated using the balance sheet method. ${\mathrm{ln}\_\mathrm{SIZE}}_{j,t}$ is the natural logarithm of a firm’s equity market value. The estimation results are reported in the second column of Table 4, Panel B. We find that dividend payments continue to explain changes in mutual fund portfolios when allowing for firm age, cash flow volatility, and size to explain the effect. The positive and significant coefficient on ${Dividend\; Payer}_{j,t}\times Fraud\; Investmen{t}_{i,t}$ of 0.120 (t = 3.15) implies that, even when we control for firm characteristics related to stability, a mutual fund that gets exposed to fraud increases its stake in dividend paying firms by 12%. The coefficients of the other firm-specific variables are not statistically significant. Hence, we conclude that mutual funds with fraud shocks seek dividend-paying stocks rather than stable, established stocks that just happen to pay dividends.

In our second analysis of firm-level characteristics, we examine whether mutual funds seek high dividend payments per se, or whether the funds also account for sustainable funding of the dividend payments. For this analysis, we interact $Fraud\; Investmen{t}_{i,t}$ with three dummies that indicate firms with low, mid, or high dividend yields instead of interacting $Fraud\; Investmen{t}_{i,t}$ with ${Dividend\; Payer}_{j,t}$. Low/Mid/High Yield is a dummy that indicates dividend payers in the lowest/second/highest yearly dividend yield tertile, where a firm’s dividend yield is its dividend payment divided by its equity market value. Only firms with non-zero dividend payments are used to form the tertiles.³⁴ Hence, the results are estimated relative to non-payers. The results are reported in the third column of Table 4, Panel B. We find that the effect of trust shocks on the change in mutual fund holdings in dividend-paying firms is concentrated in firms that pay a moderate or high dividend yield. The coefficient on ${Low\; Yield}_{j,t}\times Fraud \;Investmen{t}_{i,t}$ is positive (0.05) but not significant (t = 1.35). The coefficient on ${Mid\; Yield}_{j,t}\times Fraud \;Investmen{t}_{i,t}$ (${High\; Yield}_{j,t}\times Fraud\; Investmen{t}_{i,t}$) is 0.145 (0.212) and statistically significant with a t-statistic of 3.12 (4.10). This is consistent with the notion that low dividend yield stocks generate less trust. At the same time, the demand for high dividend yield stocks shows that mutual funds seek higher dividends per se and do not necessarily account for possible dividend cuts in the future. The results hold when looking at extreme deciles instead of tertiles of dividend yield (untabulated).

4 Trust, dividends, and market valuation

We now examine whether the trust level of a firm’s investor base differentially affects the firm’s market value if the firm does or does not pay dividends. To motivate a prediction that lower trust leads to higher relative values for dividend payers, we can draw upon either of the two asset pricing models. Through the lens of Koijen and Yogo (2019), we can view stock prices as following a characteristics-based demand system. We believe our story fits well with both a key assumption of this model—that investors have heterogeneous beliefs—and the main result of this model—that the optimal portfolio consists of a characteristics-based demand function.³⁵ The heterogeneous beliefs could include heterogeneity in the investors’ belief about how likely managers are to cheat them—which is to say, heterogeneity in investor trust. Furthermore, the characteristics-based demand function could include a stock’s dividend payout among the characteristics considered by investors; indeed, Koijen and Yogo (2019) themselves include a dividend characteristic in their specification of the demand function. Consistent with these assumptions, our evidence shows that investors who lose trust increase their demand for dividend payers relative to non-payers. If asset pricing follows a characteristics-based demand system, this greater relative demand should increase the stock prices of dividend payers relative to non-payers.

Through the lens of another model (Merton 1987), we can view investors as being limited to an investable universe of firms based on the total number of firms they can pay attention to at any given time. Firms with fewer investors paying attention to them have fewer investors holding their stock; this means each investor must take on more of the firm’s risk, lowering the price the investors are willing to pay for the stock.³⁶ Under this model, when investors lose trust, their preference for dividend payers grows, and they allocate their attention more to dividend payers and less to non-payers. As a result, if a dividend payer and a non-payer compete for the same investors’ attention, a drop in trust among those investors will cause more of them to pay attention to the dividend payer rather than the non-payer. Such a heterogeneous response will increase the dividend payer’s stock price relative to the non-payer.

4.1 Fraud events and firm valuation

In our main tests on the valuation implications of trust and dividends, we again use accounting frauds as negative shocks to trust. We use two sets of analyses to study this question. For the first analysis, we aggregate the mutual fund data to the firm-year level to measure the fraction of a firm’s investors experiencing negative shocks to trust because of exposure to fraud. We use this aggregated measure to study how having more investors exposed to fraud affects the stock market values of dividend-paying firms versus non-paying firms. For the second analysis, we examine how accounting fraud in a given US state impacts the stock market values of the state’s dividend- and non-dividend-paying firms.

Turning to the first analysis, we expect firms that are held by more funds experiencing shocks to trust will have higher values if they pay dividends and lower values if they do not. For this test, we use the same accounting fraud and mutual fund data as in our previous tests to determine which mutual funds are investing in a given firm and which mutual funds are holding a stock that is committing fraud in a given year. For firm-level data, we use Compustat. Our sample for this test runs from 1985 to 2011, based on the availability of our mutual fund data and our fraud data.

For the first analysis, we conduct a regression of the following model:

$$\begin{array}{l}Log{\left(M/B\right)}_{i,t}=\beta_1DividendPayer_{i,t}+\beta_2Frau{dFundInvestment}_{i,t-1}\\\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+\;\beta_3DividendPayer_{i,t}\times Frau{dFundInvestment}_{i,t-1}+\boldsymbol\gamma\boldsymbol C\boldsymbol o\boldsymbol n\boldsymbol t\boldsymbol r\boldsymbol o\boldsymbol l{\mathbf s}_{\boldsymbol i\boldsymbol,\boldsymbol t}+\lambda_i\\\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;+\;\delta_t+\varepsilon_{i,t}\end{array}$$

The observations are at the firm-year level. The dependent variable is the natural logarithm of the market-to-book ratio, which is calculated by dividing market equity by common equity. Dividend Payer is an indicator equal to one if the firm pays dividends in that year. ${Fraud\; Fund\; Investment}_{i,t-1}$ is the natural logarithm of one plus the number of funds that both invested in firm i in the previous year and experienced fraud shocks that year. We include firm and year fixed effects. Standard errors are clustered by firm and year. Table 5, Panel A reports the summary statistics of the variables.

Table 5 Fraud Investment and Firm Valuation

Table 5, Panel B reports the estimation results. Consistent with our expectations, non-dividend-paying firms with more funds hit by a trust shock have lower valuations, while dividend-paying firms with more funds hit by a trust shock have higher valuations. The lower valuations for non-dividend payers can be seen from the coefficient on ${Fraud\; Fund\; Investment}_{i,t-1}$, which is significantly negative at -0.036 (t = -3.25). The dividend payers have higher valuations than the non-dividend payers, as can be seen from the significantly positive coefficient on $Dividend\; Paye{r}_{i,t}\times {Fraud\; Fund\; Investment}_{i,t-1}$ of 0.059 (t = 4.79). Indeed, the dividend payers with more funds hit by a trust shock have higher valuations overall, because the sum of the two coefficients is significantly positive.³⁷ The inferences are similar when we control for yearly return on equity deciles and yearly leverage deciles. These results indicate that decreasing the trust of a firm’s investor base will increase the investors’ valuation of the firm’s dividends. The results further indicate that a drop in trust that affects some investors more than others will in turn differentially affect firms based on their dividend policies and their investor bases.

In the second analysis, we examine how an accounting fraud in a given US state impacts the relative valuation of the state’s dividend-paying firms versus its non-paying firms. Given the tendency for investors to hold local stocks (Seasholes and Zhu 2010), two firms from the same state are more likely to have overlapping investor bases. Thus, if a firm commits fraud, other firms from the same state will likely experience a drop in investor trust that is greater than the drop experienced by firms from other states. In turn, firms in the same state as the fraud will likely be more affected by an increase in demand for dividend payers, relative to non-payers. In line with this story, we find that dividend payers become more valuable (in terms of market-to-book ratio) relative to non-payers in the same state when another firm in the state commits fraud.³⁸

We again use the same accounting fraud data as our test showing that shocks to trust influence demand for dividends. For firm-level data, we again use Compustat. Since our sample of fraud events is from 1978 to 2011, we restrict our firm financial database to the years 1979 to 2012. In doing so, we make it possible for there to have been a fraud in the firm’s state in the previous year. Table 6, Panel A shows that about half of the firm-year observations in our sample have a fraud revealed in their state in the previous year. Additionally, our fraud intensity measure, which equals the total number of frauds in the state in the previous year divided by the number of firms in that state, has an average of about 0.4%.

Table 6 Fraud Events and Firm Valuation

We consider the following regression, with firm-year observations, for firm i in state j in year t:

$$\begin{array}{l}Log{\left(M/B\right)}_{i,t}=\alpha_i+\alpha_t+\beta_1Dividend\;Payer_{i,t}+\beta_2Fraud_{j,t-1}+\beta_3Fraud_{j,t-1}\\\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\times \;Dividend\;Payer_{i,t}+\boldsymbol\gamma\boldsymbol C\boldsymbol o\boldsymbol n\boldsymbol t\boldsymbol r\boldsymbol o\boldsymbol l{\mathbf s}_{\boldsymbol i\boldsymbol,\boldsymbol t}+\varepsilon_{i,t}.\end{array}$$

The dependent variable is the logarithm of the market-to-book ratio, calculated by dividing market equity by common equity. Dividend Payer is an indicator that turns on if the firm’s total dividends (other than stock dividends) that year are positive. The fraud variable is measured for the state where the firm is headquartered, taking on two forms. The first is Fraud Dummy, an indicator that turns on if, in the previous year, a fraud was discovered at one of the firms headquartered in the state; the second is Fraud Intensity, a continuous variable equal to the previous year’s number of frauds discovered in the state divided by the state’s total number of firms. The sample for these tests excludes the firms that committed fraud, to avoid confounding our results with the direct market reaction to the news of the fraud. We include firm fixed effects to control for time-invariant firm characteristics that could be related to the market-to-book ratio. We include year fixed effects to account for factors such as market sentiment. We cluster standard errors by firm to account for autocorrelation in the firm’s market-to-book ratio, and by year to account for correlated shocks within a year that affect valuation.

The results in Table 6, Panel B provide evidence that a negative shock to trust in the state leads to an increase in market value for dividend payers relative to non-payers. This can be seen from the positive coefficient on the interaction between Dividend Payer and the fraud variables Fraud Dummy and Fraud Intensity. In the first column, the interaction with Fraud Dummy has a coefficient of 0.056 (t = 2.64). This implies that the dividend premium—the market-to-book valuation spread between dividend payers and non-dividend payers—increases by 5.6% for firms in states that had a fraud in the previous year. We also estimate that non-payers in the state see their market-to-book ratios decrease by 2.1% in the year after the fraud (though this estimate is not statistically significant). This is consistent with a negative demand shock for non-payers following a drop in investor trust.³⁹ An untabulated analysis fails to find evidence of different underlying trends before the year of the fraud in the market-to-book ratios of dividend payers versus non-payers, indicating that the parallel trends assumption is satisfied.⁴⁰

The results are consistent when the fraud variable is Fraud Intensity. In the fourth column of Table 6, Panel B, the interaction between Dividend Payer and Fraud Intensity has a positive coefficient of 3.144 (t = 3.76). The standard deviation of the fraud intensity measure is about 0.0074. This implies that a one standard deviation increase in the fraud intensity measure is associated with a 2.3% increase in the market-to-book ratios of dividend payers relative to non-payers. In this specification, we again find a decrease in values for the non-dividend-paying firms, based on the coefficient on Fraud Intensity of -1.591 (t = -2.75).

These results change little when we add controls. In the second and fifth columns of Table 6, Panel B, we add controls for other determinants of firm value. These include the firm’s return on equity decile and its leverage decile.⁴¹ After adding these controls, the coefficients of interest are almost the same. In the third and sixth columns, we further add a control for economic conditions. It is plausible that more frauds are revealed during bad economic times. If bad economic times are also associated with a greater dividend premium, then our results may capture the correlation between bad economic times and the dividend premium. Thus, we control for state-level GDP growth rates and an interaction term of this growth rate and Dividend Payer.⁴² Again, after adding this control in the third and sixth columns, the coefficients of interest are almost the same.⁴³ In the last column of Table 6, Panel B, we restrict our sample to firms that had a fraud event in their state in the previous year. Consistent with columns four through six, fraud intensity is associated with larger market-to-book ratios for dividend payers relative to non-payers. This shows that there is an effect at the intensive margin, in addition to at the extensive margin.

4.2 Valuation of dividend payers based on regional trust

We corroborate the valuation results with an associational test of whether US regions with less trust place higher relative values on dividend-paying stocks. These tests should be interpreted cautiously since the results may be confounded by other factors, such as differences in corruption across regions (e.g., Smith 2016). However, given these caveats, we find that regions with less trust do indeed have dividend payers with higher market-to-book ratios relative to non-payers, consistent with low trust increasing the relative value of dividend payers.

To proxy for the trust level of a firm’s investor base, we use the General Social Surveys for the region of the United States that contains the firm’s headquarters.⁴⁴ Exploiting the regional granularity of the General Social Survey, we separate the United States into nine regions: the Northeast, Mid-Atlantic, Northeast Central, Northwest Central, South Atlantic, Southeast Central, Southwest Central, Mountain, and Pacific. The trust measure we use in this test is Trusting Fraction, which is the fraction of respondents in the firm’s region that year who believe most people can be trusted. Our test utilizes variation across geographic regions and over time. Figure 2 shows that there is meaningful variation along both dimensions.

Fig. 2

US Trust Levels. This figure plots mean trust levels from the General Social survey by region (Panel a) and by decade (Panel b). We measure Trust in a specific region-year as the fraction of survey respondents who say “Most people can be trusted” in response to the question “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” To generate trust values for years when the survey is not run, we use interpolation. The region values, presented below, are the average values of annual means across all years. “All regions” is the average of all region values across the entire sample. The unconditional average of respondents that say “Most people can be trusted” without interpolated values is 0.391; with interpolation, it is 0.400

To examine how variation in trust might impact the relative value of dividend payers, we run another regression at the firm-year level that regresses Log(M/B) on Dividend Payer, Trusting Fraction, and the interaction of the two. The results in Table 7 show that the coefficient on the interaction term is significantly negative, meaning that higher-trust regions value dividend payers less than non-dividend payers.⁴⁵ This associational result is consistent with our previous results showing that negative shocks to trust increase the relative values of dividend payers. If we were to interpret these results as causal—which, we caution, may not be justified—then the interaction term coefficient in the third column has the following interpretation: lowering the fraction of trusting respondents by 10 percentage points increases the market-to-book ratios of dividend payers by 4.6% (t = 3.04) relative to non-payers.⁴⁶ Whether or not this causal interpretation is justified, the negative coefficient is consistent with our previous finding that negative shocks to trust increase the relative value of dividend payers.⁴⁷

Table 7 Valuation of Dividend-Paying Firms versus Non-Dividend-Paying Firms

Thus far, we have focused on the demand for dividends. It is also possible that management will respond to high dividend premiums by issuing dividends (Baker and Wurgler 2004). To the extent this occurs, it should weaken the valuation implications of the demand effect, since the most affected firms would likely be the first to start paying dividends when trust drops. We find weak evidence that managers base their dividend decisions on the trust of their investor bases. In unreported results, the relationship between the trust level and the probability of a dividend initiation is only marginally significant (at the 10% level) and economically small (i.e., a 10% drop in trust is associated with a 0.1% increase in the probability of initiating a dividend). We caution against taking this as evidence that managers do not respond to changes in trust when setting dividend policy. First, we think trust and dividends likely form an endogenous system where, even as low trust increases demand for dividends, having more dividends increases trust by showing that earnings are real and managers treat shareholders well. Second, pointing in favor of trust causing managers to adjust their payout policies, prior work shows a positive relationship between a firm’s cash holdings and the trust level of the firm’s home country (Dudley and Zhang 2016). Overall, the evidence from this test and the previous one indicate that dividend payers experience an increase in their market values relative to non-payers when a drop in trust causes investors to increase their demand for dividend-paying stocks.

5 Conclusion

We find, in surveys, that investors perceive dividend paying firms as less likely to commit accounting fraud than non-dividend paying firms. We expect that decreased trust, or a higher perceived likelihood of fraud, will push investors toward investments that are perceived as less likely to be fraudulent. This leads us to our primary hypothesis: trust levels will be negatively associated with the demand for dividends. We find evidence to support this hypothesis.

First, we show that less trusting households are more likely to receive dividends. Then, for sharper identification, we use accounting frauds as negative shocks to investor trust. We show that mutual funds that are exposed to the fraud (because they hold stock in the fraudulent firm) increase their portfolio allocations to dividend-paying stocks, relative to a control group of unexposed funds. This comparison isolates the change in the demand for dividends from the change in the supply of dividends because both the treatment and control groups are choosing from the same set of investment opportunities. We provide evidence against the notion that the increase in demand for dividend payers is driven by an increase in risk aversion, because we find no evidence that funds exposed to the fraud change the riskiness of their portfolios. This suggests that they are seeking the dividends in and of themselves.

We then provide evidence that low investor trust and the attendant increase in demand for dividend-paying stocks may cause dividend payers to increase in value relative to non-payers. Here, we focus on how changes to the trust of a firm’s mutual fund investor base might affect the firm’s value if the firm pays dividends relative to if it does not. We find that having more investors hit by frauds causes market-to-book ratios to increase for dividend-paying firms and decrease for non-dividend-paying firms. We also show that dividend payers have higher market-to-book ratios relative to non-payers in the same US state when one of the state’s firms committed fraud in the previous year. We then corroborate these tests with an associational test showing that less-trusting regions of the United States have higher relative market-to-book ratios for dividend payers versus non-payers.

Our results indicate that dividends and the information they provide are interpreted and valued differently depending on an investor’s level of trust. We already show evidence that a drop in trust among a firm’s investors increases the value of dividends for that firm. However, we have not fully explored the implications arising from heterogeneity in investor trust. We think this is a promising area for future research. Another promising direction for future research would be to further explore how drops in trust manifest in investors’ utility functions. Our hypothesis is based on trust entering the utility function as the investor’s perceived probability of suffering a large loss due to fraud. Another possibility, which would also drive investors toward dividends after a drop in trust, is that investors may have non-standard utility functions whereby they get direct disutility from being cheated or defrauded. This appears consistent with prior evidence that people get disutility from unequal outcomes, especially when they themselves are the ones coming up short (Loewenstein et al. 1989). However, to our knowledge, no prior research explicitly shows that people get direct disutility from being cheated. We encourage future research to investigate this and determine how much of the shift to dividends after a drop in trust is driven by this direct disutility.