Dividend policy and probability of extreme returns

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Dividend policy and probability of extreme returns

Mohammed Bouaddi

The American University in Cairo, Cairo, Egypt

Omar Farooq

School of Business, ADA University, Baku, Azerbaijan, and

Neveen Ahmed

Institute of National Planning, Cairo, Egypt

Abstract

Purpose–This study examines the effect of dividend policy on theex anteprobability of stock price crash and theex anteprobability stock price jump.

Design/methodology/approach–We use the data of publicly listed non-financial firms from France and the ex antemeasures of crash and jump probabilities (based on the Flexible Quadrants Copulas) to test our hypothesis during the period between 1997 and 2019.

Findings–Our results show that dividend payments are negatively associated with theex anteprobability of crash and positively associated with theex anteprobability of jump. Our results are robust across various sub-samples and across different proxies of dividend policy. Our findings also hold when we use ex-post measures of crash and jump probabilities.

Originality/value–Unlike prior literature, we useex antemeasures of crash and jump probabilities. The main advantage of this forward looking measure is that it allows for more flexibility by modeling the dependence between market returns and stock returns as functions of their actual state. Our measure is also consistent with the behavior of investors and market participants in a way that the market participants do not know the future outcome with certainty, but rather they are anticipating the future.

KeywordsDividend policy, Agency problems, Jumps, Crashes, Flexible quadrants copulas Paper typeResearch paper

1. Introduction

The stock price crashes and jumps are defined as the occurrences of extreme negative and extreme positive stock returns, respectively. They can result not only in significant losses (in case of crashes) and significant gains (in case of jumps) in investors’wealth but also in their confidence in capital markets (Kimet al., 2016). Prior literature identifies number of factors that can affect the occurrence of extreme returns (Kim and Zhang, 2016;Callen and Fang, 2015;Kim et al., 2014;Aman, 2013;Huttonet al., 2009). Most of these papers, however, focus on one aspect of extreme returns, which is the stock price crash.Kimet al.(2014), for instance, document that firms with better corporate social responsibility (CSR) performance have lower stock price crash risk. In another related study,Kim and Zhang (2016)show that accounting conservatism reduces the likelihood of a crash in stock prices[1].Kimet al.(2011a)also come to similar conclusions when they show that tax avoidance is positively related to the occurrence of extreme negative returns. They argue that tax avoidance provides managers with tools to hide bad news from shareholders and overstate financial performance. Bad news, eventually, comes out and leads to stock price crash.Huttonet al.(2009)also document that opaque firms are more prone to stock price crashes. All of the above papers consider agency problems as the main underlying source of stock price crashes. These papers argue that any factor (such as CSR performance, accounting conservatism and opaqueness of financial reports) that reduces the agency problems will lower the stock price crash risk.Callen and Fang (2015), for example,

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JEL Classification—G35

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/1743-9132.htm

Received 12 January 2020 Revised 10 May 2020 12 August 2020 Accepted 3 September 2020

International Journal of Managerial Finance

Vol. 17 No. 4, 2021 pp. 640-661

DOI10.1108/IJMF-01-2020-0023

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show that the stock price crash risk is negatively affected by the presence of strong governance mechanisms.Andreouet al.(2016)andHonget al.(2017)also conclude that the stock crash risk is mitigated by the presence of strong monitoring mechanisms. Unlike the evidence on the determinants of stock price crashes, the literature on the determinants of stock price jumps is relatively scant. Furthermore, most of this scant literature identifies no impact of firm-specific characteristics on stock price jumps.Aman (2013), for example, shows no impact of media coverage on jump frequency. Similarly,Huttonet al.(2009)also show no relationship between opaqueness of financial reports and stock price jumps.

In this paper, we identify another factor–dividend policy–that can affect the occurrence of extreme returns (crashes and jumps). Our paper is similar toKimet al.(2016)in a way that we also document the impact of dividend policy on the occurrence of extreme returns. However, unlikeKimet al.(2016)and most of other papers on the determinants of occurrence of extreme returns, we use a different proxy for the stock price crash and jump. Instead of using the actual occurrence of extreme returns (jumps and crashes), we use the probability of occurrence of extreme returns. Our proxy is better suited than the ones used in the prior literature in a way that it is anex antemeasure of crash and jump. It permits to analyze the effect of dividend policy on the likelihood of having extreme returns. This forward looking measure is consistent with the behavior of investors in a way that they do not know the future outcome with certainty, but rather they are anticipating the future. To estimate theex anteprobability of crash and jump, we use the Flexible Quadrants Copulas to model the dependence between the market returns and the stock returns in different quadrant of distribution. The main advantage of this copula is that it allows for more flexibility by modeling the dependence between the market returns and the stock returns as functions of their actual state. This family of copula allows us to model whether the dependence between market returns and stock returns differ when both of them are in good or in bad state or they are in different states. Given that the effect of dividend policy on the probability of extreme returns may depend on which state the market conditions and the firm situations are, this measure ofex anteprobability is very important.

In this paper, we use the data of non-financial firms from France and document a significant impact of dividend policy on the likelihood of stock price crashes and jumps during the period between 1997 and 2019. Our results show that firms paying higher proportion of their earnings as dividends are less likely to experience stock price crashes in future. We argue that dividend payouts reduce agency conflicts within the firms. As a result, it becomes less likely for managers of dividend-paying firms to hide negative information.

Timely incorporation of negative information in stock prices lowers the occurrences of crashes. Our results also show that firms paying higher proportion of their earnings as dividends are more likely to experience stock price jumps in future. Lower agency problems associated with dividend-paying firms allow stock market participants to upgrade their expectations (Healyet al., 1999). We argue that it is very likely that investors overreact to positive information in these firms, thereby increasing the likelihood of extreme positive returns (jumps). Our results remain qualitatively the same for different sub-samples and for different proxies of dividend policy. We also show that the relationship between dividend policy and the extreme returns hold when we use ex-post measures of crash and jump.

Our paper has significant implications for stock market participants. Our results show that dividend policy can reduce the downside risk of stock prices and improve their upside potential by constraining managerial opportunism. Investors, therefore, should use dividend policy as an indicator of whether to expect extreme negative returns or extreme positive returns. Managers can also use dividend policy as a mechanism to reduce the downside risk and improve the upside potential of the stock prices. The remainder of paper is structured as follows:Section 2briefly presents the motivation for this study.Section 3summarizes the data andSection 4presents assessment of our arguments.Section 5presents additional tests.

The paper ends withSection 6where we present our conclusions.

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2. Motivation and background

2.1 Dividend policy and extreme negative returns (crashes)

Prior literature argues that the occurrence of extreme negative returns is a result of agency problems and information asymmetries embedded in modern corporations (Changet al., 2017;

Andreouet al., 2016; Jin and Myers, 2006;Huttonet al., 2009). Kim and Zhang (2016), for example, document negative relationship between accounting conservatism and stock price crash risk. Their findings indicate that managers of firms with high information opacity have greater incentives to hide negative information. The accumulation of negative information, eventually, leads to a stock price crash. In another related study,Andreouet al.(2016)show that CEO stock option incentives and the percentage of directors holding shares are important determinants of stock price crash risk. Both of these factors are found to be associated with greater incentives to hide information[2].Honget al.(2017)compliment the above findings when they find that firms with large ownership-control wedge (difference between cash flow rights and voting rights) are at a higher risk of stock price crash than firms with a small ownership-control wedge. Habib and Hasan (2017) highlight another important factor by documenting a negative association between managers’ability and stock price crash risk. They argue that the more able the managers are, the more likely they would act for their self-interest and take decision that are good for their careers, but not necessarily for the best interest of the shareholders. They believe that these actions expose firms to higher price crash risk.

Most of the above findings are motivated fromJensen (2004)andJensen (2005)who suggest that managers have incentives to hide negative information.Kothariet al.(2009)also argue that managers strategically delay the release of bad news relative to good news[3]. Motivated by this behavior of managers,Jin and Myers (2006)develop a theoretical model where they argue that managers can hide negative information for an extended period of time. However, their ability to hide negative information is not infinite. After a while, it becomes too costly for managers to conceal negative information. They, therefore, eventually give up and let all of the unobserved negative information become public at once. Over flooding of negative information makes investors overreact to this unfavorable information and lead to extreme negative returns (Jin and Myers, 2006;Huttonet al., 2009;Kim and Zhang, 2016).

An important outcome of above papers is that any mechanism that limits the ability of managers to withhold negative information should reduce crash risk. In this paper, we hypothesize that the dividend policy adopted by firms may one such mechanism that constrains the managers from hiding negative information. And in doing so, dividend policy also helps in timely incorporation of negative information in stock prices, thereby reducing the occurrence of extreme negative returns. Our arguments are based on plentiful of prior literature that considers dividend policy as a mechanism via which quality of information disclosure can be improved.Skinner and Soltes (2011)andFarooqet al.(2018), for instance, find that earnings quality of dividend-paying firms is higher than that of non-dividend- paying firms. In another related study,Caskey and Hanlon (2013)suggest that dividend payments lead to higher financial reporting quality. Lawson and Wang (2016) also compliment the above findings by noting that specialized agents, such as auditors, perceive the information disclosed by dividend-paying firms to be more reliable than that of non- dividend-paying firms. Another reason that is cited for dividend’s role in improving quality of disclosure is based on the increased scrutiny that accompanies dividend-paying firms by stock market participants (financial analysts, institutional investors, commercial and investment banks and credit rating agencies). Prior literature argues that dividend payments improve information disclosure by subjecting managers to external monitoring and disciplinary actions. Kim et al. (2016) argue that the possibility of being subject to increased scrutiny reduces managers’incentives to hide bad news, thereby leading to better disclosure and lower risk of stock price crash.

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2.2 Dividend policy and extreme positive returns (jumps)

This paper also hypothesizes that dividend policy can also increase the occurrence of extreme positive returns (jumps). We note that higher disclosure levels and lower information asymmetries associated with dividend-paying firms help these firms to improve their reputation vis-a-vis investors (La Portaet al., 2000). High dividends constrain managers from squandering cash flows on value destroying activities (such as investing in unprofitable projects, “empire-building” activities and extracting private perquisite), thereby building the reputation of dividend-paying firms as firms with lower agency problems (Lie, 2000;Officer, 2011). Manos (2003) highlight the agency reducing role of dividends by arguing that dividends act as a bonding mechanism with external stakeholders. Firms opt to use this bonding mechanism to boost their reputation. We argue that lower agency problems help stock market participants to upgrade their expectations regarding these firms. Dividends provide missing pieces of information (such as, the information about future earnings) about the firm to investors.Healyet al.(1999) note that investors revise their valuations upward for firms with lower agency problems.

This paper hypothesizes that it is more likely that investors overreact to good information for these firms than to good information for firms with higher agency problems. Therefore, we expect these firms to have higher likelihood of extreme positive returns (jumps).

Consistent with our arguments,Douchet al.(2015)show that stock prices of firms with better information environment have heavier positive tails (positively skewed) than stock prices of firms with poor information environment.

3. Data

This paper uses the data of non-financial firms from France to document the effect of dividend policy on theex anteprobability of stock price crashes and theex anteprobability of stock price jumps. The period of analysis is between 1997 and 2019. Following sub-sections will explain the data in detail.

3.1 Ex-ante probability of crash and jump

The relationship between random variables can be estimated by modeling the conditional mean of regressors as a function of regressands or by computing the joint relationship by separating the marginal behavior of each variable from the joint behavior. The later approach is appealing and more general than the former because it separates the marginal distributions from the dependence structure.

The copulas are, usually, used to separate the marginal behavior of variables from the joint behavior. A copula, C(u,v), is a cumulative distribution function (cdf) with uniform marginals on the unit interval (Nelsen, 2006;Joe, 1997). According toSklar (1973), ifF(r_s) and G(r_m) are univariate continuous cdfs of stock return (r_s) and market return (r_m), then C(F(r_s), G(r_m)) is a distribution of R 5(r_s, r_m)’ with marginal distributions F and G. Furthermore, ifH is a continuous bivariate cdf with continuous univariate marginal cdfsFandG, then there exists a unique copulaCthat satisfies the following relationship:

Hðrs;rmÞ ¼CðFðrsÞ;GðrmÞÞ (1) The density ofH(denoted byh) is defined by the following expression. In the following expression,c(u,v) is the copula density,u5F(r_s) andv5G(r_m) are distributed uniformly on the interval [0,1], andfandgare the corresponding marginal densities ofFandG.

hðrs;rmÞ ¼fðrsÞgðrmÞcððrsÞ;GðrmÞÞ (2)

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Sklar (1973)introduced the copula to model the dependence structure. A non-decreasing and right-continuous bivariate function C is called a copula if it satisfies the following conditions:

(1) The function C is grounded function:C(u, 0)5C(0,v)50 (2) The function C has uniform marginals:C(u, 1)5uandC(1,v)5v

(3) The function C is non-decreasing:C(u₂,v₂)C(u₁,v₂)C(u₂,v₁)þC(u₁,v₁)≥0 for u₂≥u₁andv₂≥v₁

This paper uses a new family of copulas–Flexible Quadrants Copulas (FQC)–to model the joint relationship between variables (Bouaddiet al., 2016)[4]. In order to define FQC, suppose the following:

(1) u5F(r_s) andv5F(r_m) are the set of non-negative random variables in unit square (i.e.

(u,v)∊^{(0, 1)),}

(2) ƙ1 andƙ2 are threshold levels belonging to (0, 1) (3) Ci(i51, 2, 3, 4) are any well-known copulas

(4) θi(i51, 2, 3, 4) are their dependence parameters for which the range depends on the structure of the copulasC_i

For above parameters, the following function is a copula, which is grounded, has uniform margins and is non-decreasing.

Cvðu;vÞ ¼ 8>

>>

><

>>

: ƙ1ƙ2C1

u ƙ¹;v

ƙ²jθ1

ifu≤ƙ1and v≤ƙ2

ƙ²uþƙ¹ð1ƙ²ÞC2

u ƙ1;vƙ2

1ƙ2jθ²

ifu≤ƙ¹and v>ƙ² ƙ1vþƙ2ð1ƙ1ÞC3

uƙ1

1ƙ1

;v ƙ2

jθ3

ifu>ƙ1andv≤ƙ2

ƙ1ƙ2þƙ2ðuƙ1Þ þƙ1ðvƙ2Þ þ ð1ƙ1Þ ifu>ƙ1andv>ƙ2

3ð1ƙ2ÞC4

uƙ1

1ƙ1

;vƙ2

1ƙ2

jθ4

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The copula defined inEquation (3)is termed Flexible Quadrants Copulas (FQC). The FQC family density is given by the following:

cðu;vÞ ¼v²Cðu;vÞ vuvv ¼

8>

>>

><

>>

: c1

u ƙ1;v

ƙ2jθ¹

ifu≤ƙ¹andv≤ƙ² c2

u ƙ1

;vƙ²

1ƙ2

jθ2

ifu≤ƙ1andv>ƙ2

c3

uƙ1

1ƙ1

;v ƙ2

jθ3

ifu>ƙ1andv≤ƙ2

c4

uƙ1

1ƙ¹;vƙ2

1ƙ²jθ4

ifu>ƙ1andv>ƙ2

(4)

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The distribution ofuconditional onvis given by the following:

Cvðu;vÞ ¼dCðu;vÞ

dv ¼

8>

>>

><

>>

: τ¹^C¹;v

u τ¹^;

v τ²^jθ¹

ifu≤τ¹^and^v^≤τ² τ¹^C²;v

u τ¹^;

vτ² 1τ²^jθ²

ifu≤τ¹^and^v>τ² τ¹þ ð1τ¹ÞC3;v

uτ¹ 1τ¹^;

v τ²^jθ³

ifu>τ¹^and^v≤τ² τ¹þ ð1τ¹ÞC4;v

uτ¹ 1τ¹^;

vτ² 1τ²^jθ⁴

ifu>τ¹^and^v>τ² (5)

In this paper, we specify our elementary copula to be of the Clayton form:

Ci¼ ðu^−θⁱþv^−θⁱ1Þ⁻¹^θⁱ Where i¼1;2;3;4 (6) The set of parametersθi∊^(0,þ∞) (wherei51, 2, 3, 4) capture the degree of dependence in different directions. That is,θ1measures the degree of dependence between stock return and market return when both are down,θ2measures the degree of dependence between stock return and market return when stock return is down and market return is up,θ3measures the degree of dependence between stock return and market return when stock return is up and market return is down, whileθ4measures the degree of dependence between stock return and market return when both are up. If any ofθi’s is zero, the two variables exhibit independence in that direction.

The distribution ofU5uconditional onV5vusing the Clayton form is given as follows:

CvðU≤vÞ ¼ 8>

>>

><

>>

: τ¹

v τ²

_−θ₁₋1 u τ¹

_θ₁ þ

v τ²

_θ₁ 1

⁻¹_θ

1−1

ifu≤τ¹^and^v≤τ² τ¹^vτ²

1τ²

−θ2−1 u τ¹

θ2

þ vτ²

1τ² θ2

1 ⁻¹_θ

2−1

ifu≤τ¹^and^v>τ² τ¹þ ð1τ¹Þ

v τ²

_−θ₃₋1 uτ¹ 1τ¹

_θ₃ þ

v τ²

_θ₃ 1

⁻¹_θ

3−1

ifu>τ¹^and^v≤τ² τ¹þ ð1τ¹Þ

vτ² 1τ²

−θ4−1 uτ¹ 1τ¹

θ4

þ vτ²

1τ² θ4

1 ⁻¹_θ

4−1

ifu>τ¹^and^v>τ² (7) We deduce fromEquation (7)that the conditional probability of crash (probability of variable Uto be in the extreme lower tail, given thatV5v) is given as follows:

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PðU<ƙ1Þ ¼PðU <ƙ1jV¼v;ƙ1≤τ¹^;^v^≤τ²^{Þ þ}^P^ð^U ^<^ƙ¹^j^V^¼^v^;^ƙ¹^≤τ¹^;^v^>τ²^Þ

¼τ¹ v

τ²

_−θ₁₋1 ƙ1

τ¹ _−θ₁

þ v

τ² _−θ₁

1 ⁻¹_θ

1−1

Iv≤τ2

þ 2

64ƙ1τ¹^vτ² 1τ²

−θ2−1ƙ1

τ¹ −θ2

þ vτ²

1τ² −θ2

1 ⁻¹_θ

2−1

3 75

1Iv≤τ2

(8) In the above equation,ƙ15Φ(μs–3.2σs),Φis the cumulative distribution of standard normal distribution,μsis the conditional expected stock return andσsis its conditional standard deviation. The threshold value of 3.2 is the 0.1% upper quantile of the normal distribution.

The value of 3.2 extreme quantile is used byHuttonet al.(2009)andKimet al.(2011a,b). In this case, the crash is defined as those periods in which the stock experiences the firm-specific returns that are 3.2 standard deviations below the expected value of return.

Similarly, we deduce fromEquation (7)that the conditional probability of jump (probability of the variableuto be in the extreme upper tail, given thatV5v) is given as follows:

PðU >ƙ2Þ ¼PðU >ƙ2jV¼v;ƙ2>τ¹;v≤τ²Þ þPðU>ƙ2jV¼v;ƙ2>τ¹;v>τ²Þ

¼ 2

64vτ¹þ ð1τ¹Þ v

τ²

−θ3−1ƙ2τ¹ 1τ¹

−θ3

þ v

τ² −θ3

1 _θ⁻¹

31−1

3 75Iv≤τ2

þ 2

641ƙ2vþτ¹þ ð1τ¹Þ vτ²

1τ²

−θ4−1 ƙ²τ¹

1τ¹ −θ4

þ vτ²

1τ² −θ42

1 _θ⁻¹

24−1

3 75

3

1Iv≤τ2

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In the above equation,ƙ25Φ(μsþ3.2σs). Above equation defines jump as those periods in which the stock experiences the firm-specific returns that are 3.2 standard deviations above the expected value of return.

We estimate the model parameters by using the method of inference functions for margins (Joe, 1997). This method has two steps and relies on the assumption of parametric univariate marginal distributions. The parameters of margins are first estimated, and then each parametric margin is plugged into the copula likelihood, and this full likelihood is maximized with respect to the copula parameters. To be more specific, we model the margins as follows:

first we specify the conditional mean of stock return as follows:

rs;t¼δ0þX^p

i¼1

δiðrs;t−iÞ þX^q

j¼1

γjðrm;t−jÞ þε^s;t (10) In the above equation,r_s,tandr_m,tare stock return and market return at timet,δ’s andγ’s are constant parameters and εs,t is firm-specific return (news component). We model the conditional mean of market return as an AR(4). In the following equation,α’s are constant parameters andεm,tis the market-specific return (news component).

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rm;t¼α⁰þX¹

j¼1

α^jðrm;t−jÞ þε^m;t (11)

Using the BIC criterion, the maximal lags are selected to be 4, i.e.p5q5l54. Since the returns exhibit time varying conditional variance, we use an asymmetric EGARCH to model the conditional variances ofεs,tandεm,t. The conditional variances are then given as follows:

ln σ²k;t

¼σþθ ε^k;t−1

σ^k;t−1

þδε^k;t−1

σ^k;t−1

þβln σ²k;t−1

(12) In the above equation, θ captures the leverage effect,δcaptures the news effect, and β captures the volatility persistence (k 5 s, m). To estimate the marginal parameters in Equation (10),Equation (11)andEquation (12), we use the maximum likelihood estimator (MLE) with an assumption that the errors are normally distributed. Once the marginal parameters are estimated, we compute the estimated marginal cdfs by plugging the MLE estimated values to get the following:

b ut¼Φ

εc^s;t

σc^s;t

(13) b

vt¼Φ εc^m;t

dσ^m;t

(14) In the second step, the copula parameters are estimated by replacinguandvas follows by the above parameters.

θ¼argmaxX^T

t¼1

cðubt;vbtÞ (15)

where θ¼ ðθ1;θ2;θ3;θ4Þand cðubt;vbtÞ is given byEquation(4)

Once the copula parameters are estimated, their estimates are substituted inEquation (8) andEquation (9)to get the time varying crash and jump probabilities.Table 1documents the descriptive statistics forex anteprobability of crash andex anteprobability of jump. The results show that, on average, the firms in our sample have higher probability of jump than probability of crash.

Statistic Probability of jump Probability of crash

Minimum 0.0040 0.0494

25th percentile 0.1365 0.1248

Mean 0.1617 0.1422

Median 0.1664 0.1377

75th percentile 0.1887 0.1546

Maximum 0.3145 0.3566

Standard deviation 0.0411 0.0268

Observations 4,574 4,574

Table 1.

Descriptive statistics for the probability of extreme returns

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3.2 Dividend policy

This paper defines dividend policy (DIV) by the percentage of earnings paid out as dividends.

The dividend payouts are supposed to alleviate agency conflicts through the reduction of free cash flow available to managers (La Portaet al., 2000). We obtain the data for dividend policy from the Worldscope.

3.3 Control variables

This paper uses number of firm-specific characteristics as control variables. These variables are as follows:

(1) SIZE: We define SIZE as the log of firm’s market capitalization.Chenet al.(2001) argue that large firms are more likely to have extreme returns.

(2) LEVERAGE: This paper defines LEVERAGE as the total debt to total asset ratio. We argue that higher financial risk associated with highly levered firms should affect the probability of having extreme returns (Huttonet al., 2009;Kimet al., 2011a).

(3) EPS: We define EPS as earnings per share.Huttonet al.(2009)andKimet al.(2011a) show that performance of firms is a significant determinant of the probability of having extreme returns.

(4) GROWTH: This paper defines GROWTH as the growth in total assets.Choyet al.

(2019) argue that due to managerial incentive for empire-building, higher asset growth is significantly related with higher crash risk.Douchet al.(2015)also use growth in total assets as a control variable while examining the likelihood of extreme returns.

(5) ANALYST: We define ANALYST as the total number of analysts issuing earnings forecast for a firm. Higher analyst coverage is associated with better information environment. We expect information environment to have a significant effect on the probability of having extreme returns.

(6) BETA: This paper defines BETA as the sensitivity of stock returns to the market returns.Park and Song (2018)use it as a determinant of extreme returns.

(7) SIGMA: In this paper, SIGMA measures the idiosyncratic risk. This is found by subtractingR-square of the market model from 1. Firms with higher idiosyncratic risk are more likely to have extreme returns (Li and Cai, 2016).

(8) SKEW: We define SKEW as the skewness of stock returns. We expect positively skewed stocks to experience lower crashes risk and higher jumps.

The data for the above mentioned variables are obtained from the Worldscope and Datastream.Table 2reports the descriptive statistics for variables used in this study. The results show that the average value of total debt to total asset ratio is around 20% for our sample firms. It indicates relatively lower financial risk for firms included in our analyses.

The results also show that average value of analyst coverage is above 5, while the median value is 1. It indicates that most of the firms in our analyses have relatively low analyst following. The average value of earnings per share in 2.52 Euros and average growth rate is almost 14%.

The correlations between variables are presented inTable 3. Some of the variables appear to have high correlation, but the computation of Variance Inflation Factor (VIF) after every regression shows that the multicollinearity is not a problem in our analysis[5].

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4. Methodology and results

This paper argues that dividend policy is a significant determinant of stock price crashes and stock price jumps. In order to test this argument, we estimate various versions of the following pooled OLS regression equation. In the following equation, PROB can be either the ex anteprobability of crash (CRASH) or theex anteprobability of jump (JUMP). All other variables are as defined above. For the purpose of completeness, we also include the year dummies (YDUM) and the industry dummies (IDUM) in our analysis.

PROBt¼αþβ1ðDIVtÞ þβ2ðSIZEtÞ þβ3ðLEVERAGEtÞ þβ4ðEPStÞ þβ5ðGROWTHtÞ þβ6ðANALYSTtÞ þβ7ðBETAtÞ þβ8ðSIGMAtÞ þβ9ðSKEWtÞ

þX^N⁻¹

I¼1

γIðIDUMtÞ þX^T⁻¹

Y¼1

δYðYDUMtÞ þε^t

(16) Table 4documents the results of our analysis[6]. To mitigate the potential cross-sectional and time-series dependence, the results of our analysis are based on standard errors corrected for firm clustering (Petersen, 2009). As expected, our results show that dividend policy is a significant determinant of stock price crashes and stock price jumps. We show that higher dividend payout ratios are associated with lower probability of stock price crashes and higher probability of stock price jumps[7]. The table reports significantly negative (positive) coefficient of DIV when probability of stock price crash (jump) is used a dependent variable.

Our result is consistent withKimet al.(2016)who also show negative effect of dividend policy on stock price crash risk. The computation of elasticities at the average point of theex ante probability of jump and theex anteprobability of crash with respect to dividend payout ratio shows that an increase of 1% in dividend payout ratio leads to an increase of almost 1% in the probability of jump (0.95%) and a decrease of almost 2% of the probability of crash (1.99%) [8]. Therefore, an increase in dividends distribution will decrease the likelihood of crash more than it increases the likelihood of jump.

5. Additional tests

5.1 Effect of dividend policy (alternate measures) on the ex-ante probability of extreme returns

As an additional test, we re-estimateEquation (16)for alternate proxies of dividend policy.

For the purpose of this paper, our alternate measures of dividend policy are: (1) Decision to pay dividend, (2) Dividend yield, (3) Total cash dividends to market capitalization ratio and (4) Total cash dividends to total asset ratio. The results of our analysis are reported inTable 5.

Variables 25th percentile Mean Median 75th percentile Standard deviation Observations

DIV 0 0.2131 0.1515 0.3549 0.2418 4,574

SIZE 10.7312 12.5081 12.2085 13.9978 2.4180 4,574

LEVERAGE 8.6000 20.7655 19.2600 30.7200 15.0704 4,511

EPS 0.1100 2.5224 0.9700 2.8400 7.1296 4,517

GROWTH 14.5280 13.9052 7.4130 32.4900 47.9550 4,474

ANALYST 0 5.2009 1 7 7.9218 4,574

BETA 0.1068 0.3944 0.3070 0.6466 0.4091 4,554

SIGMA 0.8819 0.9009 0.9715 0.9953 0.1547 4,554

SKEW 0.2610 0.2514 0.1996 0.7653 1.7654 4,554

Note(s):All variables are as defined inSection 3

Table 2.

Descriptive statistics for control variables

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VariablesCRASHJUMPDIVSIZELEVERAGEEPSGROWTHANALYSTBETASIGMASKEW CRASH1.000 JUMP0.3221.000 DIV0.1880.1491.000 SIZE0.2360.5210.4111.000 LEVERAGE0.0300.1210.0260.2851.000 EPS0.0570.0400.1660.2150.0901.000 GROWTH0.0890.0010.0770.0050.0460.0661.000 ANALYST0.1730.6150.3320.7670.1500.0050.0321.000 BETA0.1660.5690.0560.4960.1160.0670.0300.5521.000 SIGMA0.1680.7240.2770.6960.1120.0270.0630.7990.6991.000 SKEW0.0840.1310.1030.0680.0420.0070.2640.0890.0040.0681.000 Note(s):AllvariablesareasdefinedinSection3 Table 3.

Correlation matrix

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ProbabilityofjumpProbabilityofcrash VariablesModel(1)Model(2)Model(3)Model(1)Model(2)Model(3) DIV0.0241***(9.37)0.0094***(4.53)0.0072***(4.31)0.0233***(15.69)0.0121***(8.23)0.0133***(8.97) SIZE0.0091***(37.90)0.0003(1.08)0.0030***(18.31)0.0041***(13.75) LEVERAGE0.0001**(2.21)0.0001***(4.87) EPS0.0002***(4.77)0.0001(0.23) GROWTH0.0001***(11.57)0.0001***(9.19) ANALYST0.0007***(9.98)0.0001***(2.65) BETA0.0101***(5.71)0.0132***(8.87) SIGMA0.1384***(27.88)0.0350***(8.37) SKEW0.0023***(5.38)0.0020***(6.17) YeardummiesYesYesYesYesYesYes IndustrydummiesYesYesYesYesYesYes Observations4,5744,5744,3654,5744,5744,365 F-value40.0291.17274.3623.1928.9128.86 AdjustedR-square0.1830.4000.6560.1590.2160.270 Note(s):AllvariablesareasdefinedinSection3.Thet-valuesareinparenthesis.Thevariablesthataresignificantat1%arefollowedby***,at5%arefollowedby**, andat10%arefollowedby*

Table 4.

Effect of dividend policy on theex ante probability of extreme returns

Dividend

policy and

probability of

returns

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ProbabilityofjumpProbabilityofcrash VariablesDecisiontopay dividendDividendyieldTotaldividendstomarket capitalizationTotaldividendstototal assetsDecisiontopay dividendDividendyieldTotaldividendstomarket capitalizationTotaldividendstototal assets DIV0.0068***(6.78)0.0004***(2.65)0.0564***(3.58)0.1147***(4.06)0.0126***(14.07)0.0007***(4.63)0.0689***(4.80)0.2351***(9.71) SIZE0.0002(0.73)0.0004(1.63)0.0003(1.02)0.0004*(1.68)0.0030***(9.84)0.0043***(16.11)0.0039***(14.17)0.0040***(14.99) LEVERAGE0.0001*(1.71)0.0001**(1.97)0.0001***(2.31)0.0001(1.38)0.0001***(3.95)0.0001***(4.81)0.0001***(5.40)0.0001***(3.27) EPS0.0001***(3.79)0.0003***(5.79)0.0002***(4.80)0.0002***(4.82)0.0001(1.60)0.0001**(2.48)0.0001(1.07)0.0001(0.93) GROWTH0.0001***(12.03)0.0001***(12.75)0.0001***(12.15)0.0001***(12.64)0.0001***(10.05)0.0001***(10.07)0.0001***(9.50)0.0001***(10.00) ANALYST0.0007***(9.75)0.0008***(11.12)0.0008***(11.01)0.0008***(11.99)0.0001**(2.17)0.0001***(2.87)0.0001**(2.43)0.0002***(4.11) BETA0.0093***(5.24)0.0113***(6.97)0.0120***(7.12)0.0115***(6.89)0.0148***(9.97)0.0125***(8.93)0.0119***(8.38)0.0133***(9.48) SIGMA0.1379***(28.11)0.1370***(30.63)0.1337***(29.17)0.1347***(29.35)0.0340***(8.32)0.0330***(8.64)0.0288***(7.43)0.0312***(8.17) SKEW0.0023***(5.40)0.0019***(5.30)0.0021***(5.22)0.0021***(5.37)0.0020***(6.20)0.0017***(5.85)0.0019***(6.07)0.0020***(6.59) YearDummiesYesYesYesYesYesYesYesYes Industry DummiesYesYesYesYesYesYesYesYes Observations4,3655,0994,8494,8454,3655,0994,8494,845 F-Value275.06318.72322.49317.2232.8333.7831.7232.90 AdjustedR- Square0.6580.6470.6670.6690.2930.2650.2620.279 Note(s):AllvariablesareasdefinedinSection3.Thet-valuesareinparenthesis.Thevariablesthataresignificantat1%arefollowedby***,at5%arefollowedby**, andat10%arefollowedby*

Table 5.

Effect of dividend policy (alternate measures) on theex anteprobability of extreme returns

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The table shows that our results are robust across all proxies of dividend policy. We report significantly negative (positive) coefficient of DIV when the probability of stock price crash (jump) is used a dependent variable for all proxies of dividend policy. The computation of elasticities at the average point of theex anteprobability of jump and theex anteprobability of crash with respect to different proxies of dividend policy shows that an increase in dividends distribution will decrease the likelihood of crash more than proportional[9].

5.2 Effect of dividend policy on the ex-post probability of extreme returns

As a robustness check, we also use the ex-post measures of crash and jump as a dependent variable and re-estimateEquation (16). The results of our analysis are reported inTable 6.

The table confirms the findings of previous tables by reporting significantly negative relationship between dividend policy and stock price crash and significantly positive relationship between dividend policy and stock price jump.

5.3 Concerns regarding reverse causality and omitted variables

There may be concerns regarding reverse causality. It is possible that extreme returns can drive managers to increase dividends. In order to address this concern, we use the next period’s probability as a dependent variable inEquation (16). Furthermore, the concerns regarding the omitted variable bias are mitigated by including the lagged value of dependent variable as an independent variable inEquation (16). The results of our analysis are reported inTable 7. As was the case before, we report significantly negative (positive) coefficient of DIV when the probability of stock price crash (jump) is used a dependent variable for both estimations.

5.4 Effect of information environment on the relationship between dividend policy and the ex-ante probability of extreme returns

Prior literature argues that dividends reduce agency problems and information asymmetries by reducing the cash flows available to managers (Easterbrook, 1984;Jensen, 1986). Lower agency problems curb managerial incentives to hoard bad news. However, the amount of value-relevant information available with managers to be hidden varies across firms.

Managers are likely to have more value-relevant information to hide in firms with higher agency problems. Therefore, we expect that the relationship between dividends and occurrence of extreme returns is more pronounced for firms with higher information asymmetry. In order to address this issue, we re-estimate Equation (16)for sub-samples characterized by different levels of agency problems. For the purpose of this paper, our sub- samples are based on the following characteristics:

(1) Stock Price Volatility: Prior literature argues that firms exhibiting higher stock price volatility are characterized by higher agency problems (Profilet and Bacon, 2013;

Farooqet al., 2012;Zainudin et al., 2018; Phan and Tran, 2019). These firms are supposed to have less efficient information environment, thereby leading to imprecise expectations of investors regarding firm values. It should, therefore, lead to higher volatility in stock prices. For the purpose of this paper, we divide the sample into two groups based on stock price volatility.

(2) Book Value to Market Value Ratio: Prior literature uses the book value to market value ratio to categorize stocks as glamour or value stocks (Chanet al., 1995). The value stocks are supposed to have high book value to market value ratio and the glamour stocks are supposed to have low book value to market value ratio. For the purpose of this paper, we consider the stocks that have book value to market value

Dividend

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ProbabilityofjumpProbabilityofcrash VariablesModel(1)Model(2)Model(3)Model(1)Model(2)Model(3) DIV0.0075(1.52)0.0387***(8.02)0.0300***(6.48)0.0328***(9.64)0.0305***(8.71)0.0236***(6.48) SIZE0.0085***(15.89)0.0029***(3.19)0.0006(1.56)0.0027***(3.64) LEVERAGE0.0001(1.10)0.0001***(2.78) EPS0.0017***(9.78)0.0013***(11.43) GROWTH0.0001***(2.76)0.0001**(2.15) ANALYST0.0014***(7.48)0.0006***(4.57) BETA0.0138***(3.30)0.0018(0.46) SIGMA0.1568***(3.30)0.0262***(2.65) SKEW0.0027***(2.65)0.0015*(1.71) YeardummiesYesYesYesYesYesYes IndustrydummiesYesYesYesYesYesYes Observations4,5744,5744,3654,5744,5744,365 F-value13.9921.1593.4235.6835.1339.50 AdjustedR-square0.0840.1400.3080.1840.1840.234 Note(s):AllvariablesareasdefinedinSection3.Thet-valuesareinparenthesis.Thevariablesthataresignificantat1%arefollowedby***,at5%arefollowedby**, andat10%arefollowedby*

Table 6.

Effect of dividend policy on the ex-post probability of extreme returns

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NextPeriod’sex-anteprobabilityasadependentvariableLastPeriod’sex-anteprobabilityasanindependentvariable VariablesProbabilityofjumpProbabilityofcrashProbabilityofjumpProbabilityofcrash DIV0.0114***(5.80)0.0147***(9.47)0.0042***(2.80)0.0063***(5.37) EXTREME(t1)0.4894***(26.36)0.6619***(36.74) SIZE0.0003(0.92)0.0041***(13.38)0.0006**(2.28)0.0013***(5.57) LEVERAGE0.0001**(2.16)0.0001***(5.26)0.0001**(2.54)0.0001***(2.75) EPS0.0003***(5.53)0.0001(0.99)0.0001*(1.80)0.0001(1.48) GROWTH0.0001***(3.14)0.0001*(1.92)0.0001****(12.79)0.0001***(10.80) ANALYST0.0011***(12.74)0.0002***(3.47)0.0001*(1.95)0.0001*(1.71) BETA0.0059***(3.25)0.0102***(6.49)0.0066***(4.43)0.0091***(7.76) SIGMA0.1114***(20.05)0.0254***(5.82)0.0887***(18.85)0.0174***(5.28) SKEW0.0014***(2.99)0.0014***(4.09)0.0015***(4.86)0.0008***(3.90) YeardummiesYesYesYesYes IndustrydummiesYesYesYesYes Observations4,2194,2194,0374,037 F-value188.5125.88320.8369.57 AdjustedR-square0.5880.2550.7490.600 Note(s):AllvariablesareasdefinedinSection3.Thet-valuesareinparenthesis.Thevariablesthataresignificantat1%arefollowedby***,at5%arefollowedby**, andat10%arefollowedby*

Table 7.

Effect of dividend policy onex ante probability of extreme returns: Sensitivity checks

Dividend

policy and

probability of

returns

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VariablesValuestocksGlamourstocksConcentrated ownershipDispersed ownershipLowstockprice volatilityHighstockprice volatility DIV0.0091***(4.29)0.0140***(6.73)0.0141***(7.58)0.0090***(3.71)0.0038**(2.14)0.0112***(3.90) SIZE0.0043***(10.34)0.0039***(8.45)0.0040***(11.51)0.0039***(6.67)0.0010**(2.55)0.0044***(9.79) LEVERAG