dividend suspensions and cash flow risk during the covid-19 … · 2020. 11. 28. · dividend...

Dividend Suspensions and Cash FlowRisk during the Covid-19 Pandemic

Davide Pettenuzzoa Riccardo Sabbatuccib Allan Timmermannc

aBrandeis UniversitybStockholm School of Economics

cUC San Diego

IAAE

October 21, 2020

Introduction

1 Introduction

2 Data and Econometric ModelingDataEconometric approach

3 A Dividend Growth Model with Suspensions

4 Dividend Suspensions and Macroeconomic Growth

5 Dividend Suspensions and Firm CharacteristicsComparison with Global Financial Crisis

6 Event study: Stock Market Reaction

7 Conclusion

Pettenuzzo, Sabbatucci & Timmermann (2020) Dividend Suspensions during Covid-19 October 21, 2020 2 / 48

Introduction

Introduction: Pandemic was a massive cash flow shock

The Covid-19 pandemic, lockdowns and social distancing measures hadunprecedented effects on economic activity and financial markets

Massive increase in firms’ cash flow risks and uncertainties about

virus trajectory (medical risks)

responses of governments and central banks (policy risks)

household spending, savings, elevated risk aversion (behavioral risks)

firm behavior: sectoral trend shifts (business risks)

freeze in credit markets (financial market risks)

Pandemic duration risk has made firms’ capital budgeting difficult

Cutting or suspending dividends preserves short-term capital


Introduction

Questions examined

Firms use dividends as a key signaling device for long-run earnings prospects

How did the pandemic affect firms’ decisions to suspend dividends?

Which firm characteristics affected the propensity to suspend dividends?

Did firms’ dividend suspensions, in turn, affect expectations of futureaggregate dividend growth?

Broader impact on expected macroeconomic growth

How did the stock market react to changes in firms’ dividend policies?

Shift in importance of aggregate (sector) vs. firm-specific dividend signal


Introduction

High frequency cash flow growth measure

Adopt the econometric approach for analyzing high-frequency (daily)dividend growth dynamics proposed by Pettenuzzo, Sabbatucci, andTimmermann (PST, 2020)

Generalize approach to account for dividend suspension signal

Firm-matched, bottom-up approach that accounts for

Firm fixed effectsSeasonality

Decompose daily dividend growth data into

jumps

smooth, highly persistent growth component

temporary Gaussian component with time-varying volatility (SV)


Introduction

Existing Literature on dividend suspensions

Existence of dividend payments remains a puzzle

inefficient from a tax perspective

costly, but credible device for signalling confidence in earnings prospects

Fama and French (2001) and Hoberg and Prabhala (2008) document asecular decline in the proportion of firms paying dividends

firms suspending dividends tend to be distressed, have low earnings and makefew investments

suspenders also have higher (return) risk

How do large, sudden shocks to firms’ cash flows impact their propensity topay dividends?


Data and Econometric Modeling

1 Introduction






7 Conclusion


Data and Econometric Modeling Data

Data Sources and Construction

Data on dividend announcements and suspensions from a variety of sources

Pre-Covid (01/2005 - 12/2019): CRSP data on daily stock prices and dividendannouncementsCovid (01/2020-): daily stock prices and dividend announcements from GlobalFinancial Data (GFD)

These data sources do not provide information on dividend suspensions

Combine information from textual data sources using automated text scraper:

K-8 SEC forms (EDGAR)Company press releases from NASDAQ news platformManual reviews of each case

375 suspensions which we merge with price and accounting data fromCompustat



Counts of K-8 filings, press releases and dividendsuspensions



Dividend announcements during 2020



Dividend suspenders in 2020



Daily dividend growth series

Our data consist of daily firm-level dividend announcements

D it : total dividends declared by firm i on day t

I it = 1 if company i announces quarterly dividends on day t, 0 otherwise

t̃−i : same-quarter, prior-year dividend announcement date for firm i

Nt : number of firms in existence on day t∆dt : Year-over-year growth in firm-, fiscal-quarter matched aggregatedividends on day t

∆dt = ln

∑Nti=1 I itD it∑Nti=1 I

itD

it̃−i



Daily dividend growth rates: very ”jumpy”



Bottom-up Approach is Key

Firm fixed effect is very important: individual firms must be carefullymatched prior to calculating dividend growth

Microsoft announced Q4 dividends on June 12, 2019 and July 22, 2020Annual dividend growth computed from July 22, 2019 to July 22, 2020 wouldbe meaningless

Bottom-up approach is also needed to assess the direct effect of dividendsuspenders on dividend growth expectations

Aggregate top-down approaches do not distinguish between smaller dividendsdue to dividend reductions vs dividend suspensions

Firms A and B both cut dividends from $2 to $1 versusFirm A suspends dividends, firm B keeps dividends at $2

Empirically, this distinction is really important



Daily dividend growth: PST vs CRSP top-down approach


Data and Econometric Modeling Econometric approach

Econometric model

Stylized features of daily dividend growth series

1 jumps are important to daily cash flow dynamics

reflect heterogeneity in news on individual firms’ cash flow growth and bigchanges in the composition of firms announcing dividends on any given day

2 small, persistent component driving cash flow growth

3 time-varying volatility

Econometric specifications should account for all three features



Mean-reverting, stochastic volatility model with jumps

Baseline specification for the daily dividend growth series (∆dt+1):

∆dt+1 = µdt+1 + ξdt+1Jdt+1 + εdt+1

µdt+1: smooth, mean-reverting componentJdt+1 ∈ {0, 1}: jump indicator variableξdt+1 ∼ N

(0, σ2ξ

): jump size

εdt+1: temporary cash flow shock

Persistent component follows a mean-reverting process (∣∣φµ∣∣ < 1)

µdt+1 = µd + φµ (µdt − µd ) + σµεµt+1, εµt+1 ∼ N (0, 1)

Jump component captured through a Probit model:

Pr (Jdt+1 = 1) = Φ (λ1 + λ2Ndt+1)

Ndt+1: number of firms announcing dividends on day t + 1



Mean-reverting, stochastic volatility model with jumps, II

Stochastic volatility dynamics:

εdt+1 ∼ N (0, ehdt+1)

hdt+1: log-variance of εdt+1 follows a mean-reverting process (|φh| < 1)

hdt+1 = µh + φh (hdt − µh) + σhεht+1, εht+1 ∼ N (0, 1)

εdt+1, εµt+1, and εht+1: uncorrelated at all times



Estimation

Adopt a Bayesian approach and estimate all model parameters and latentstates using a Gibbs Sampler augmented with a number ofMetropolis-Hastings steps

Combine sampler with Albert and Chib (1993) data augmentationprocedure to estimate the parameters of the Probit jump intensity model

Use standard conjugate priors and, whenever possible, specify uninformativepriors

Exceptions: persistence parameters of the µt and ht processes, φµ and φh,whose priors are centered at 0.99



Accounting for dividend suspensions

Generalize the model to include as an extra covariate the proportion of firmssuspending dividends on a given day

µdt+1 = µd + φµ (µdt − µd ) + βµ(

Nst+1Nst+1 +Ndt+1

)+ σµεµt+1

Nst+1: number of dividend suspenders on day t + 1

Ndt+1: number of firms announcing dividends on day t + 1

Dividend suspensions lead to lower future dividend growth if βµ < 0

Jump probability also depends on the proportion of dividend suspenders:

Pr (Jdt+1 = 1) = Φ(

λ1 + λ2Ndt+1 + λ3

(Nst+1

Nst+1 +Ndt+1

))

Dividend suspensions lead to higher jump probability if λ3 > 0


A Dividend Growth Model with Suspensions

Log-dividend growth, persistent component µdt , and SVcomponent hdt

1971 1976 1982 1987 1993 1998 2004 2009 2015 2020

-4

-2

0

2

4

6

yt & mu

t

1971 1976 1982 1987 1993 1998 2004 2009 2015 2020

-0.05

0

0.05

0.1

0.15

0.2

mut

1971 1976 1982 1987 1993 1998 2004 2009 2015 2020

0

0.1

0.2

0.3

0.4

0.5

0.6

exp(ht/2)



Dividend growth decomposition: Jumps



Persistent Dividend Growth Component

Consider three cases for the persistent component µdt+1baseline specification fitted to dividend data that includes suspensions toconstruct ∆dt+1

baseline specification fitted to dividend data that excludes suspensions

generalized model fitted to dividend data that includes dividend suspensions

For the vast majority of the sample, the three cases are indistinguishable

During Covid, large differences emerge, particularly for the generalized model



Parameter estimates

Parameter estimates

Without suspenders With suspenders With suspenders dynamics

Mean Std 90% Credible Set Mean Std 90% Credible Set Mean Std 90% Credible Setµd 0.075 0.019 0.049 0.100 0.071 0.024 0.038 0.099 0.078 0.015 0.058 0.099φµ 0.997 0.001 0.994 0.999 0.997 0.001 0.995 0.999 0.996 0.002 0.993 0.999βµ -0.001 0.001 -0.002 -0.000σµ 0.003 0.000 0.002 0.003 0.003 0.000 0.002 0.003 0.003 0.000 0.002 0.003µh -5.006 0.116 -5.194 -4.814 -5.004 0.118 -5.202 -4.816 -5.016 0.118 -5.206 -4.816φh 0.899 0.009 0.885 0.913 0.900 0.008 0.886 0.913 0.899 0.008 0.886 0.913σh 0.719 0.046 0.644 0.797 0.727 0.049 0.647 0.809 0.727 0.048 0.644 0.807σξ 2.869 0.043 2.799 2.939 2.897 0.043 2.827 2.967 2.896 0.044 2.825 2.970λ1 -1.360 0.061 -1.459 -1.260 -1.277 0.058 -1.374 -1.183 -1.296 0.059 -1.393 -1.199λ2 -0.015 0.003 -0.021 -0.010 -0.015 0.003 -0.020 -0.011 -0.015 0.003 -0.020 -0.010λ3 0.686 0.222 0.309 1.032



Interpretation of Estimates

Daily dividend growth contains a small, highly persistent mean componentwith an estimated φµ = 0.997

This component is very smooth at the daily frequency (σµ = 0.003) comparedto the far more volatile, temporary shocks to the dividend process (σh = 0.71)

The stochastic volatility process is far less persistent than the mean process(φh = 0.9)

Estimates of βµ and λ3 are highly significant:

Suspensions important to dividend dynamicsβµ < 0, λ3 > 0: more suspensions is associated with lower future dividendgrowth and higher jump probability



Comparing µdt with and without dividend suspenders



Evolution in dividend dynamics

The sharp increase in the number of suspensions from mid-March onwardshas a large effect on µdt

Decline in µdt is much faster than during the GFC and is a direct consequenceof including the fraction of dividend suspensions

Cumulative impact of daily dividend suspensions from March 11 through April30 amounts to -10.8% per annum

The jump probability depends negatively on both the number of announcingfirms and the proportion of dividend suspensions

Varying the proportion of dividend suspensions from zero (no suspensions) to100%, the jump probability fluctuates from 5% to almost 70%

Dividend suspensions are a key driver of the likelihood of jumps in thedividend growth process



Jump probability vs fraction of dividend suspenders



Volatility of Dividend Growth

Dividend suspensions are important for the level of uncertainty surroundingdividend growth during the pandemic

Ignoring dividend suspensions, dividend growth volatility peaks at 15% in earlyApril

Incorporating dividend suspensions, dividend growth volatility climbs to 26%on March 16, peaks at a historical high of 70% on March 19, and remainselevated above 30% until March 30

Volatility of daily dividend growth trails the VIX by a few days



Daily dividend growth volatility, VIX, and Google Trendssentiment index


Dividend Suspensions and Macroeconomic Growth

1 Introduction






7 Conclusion




Can µdt be used to forecast broad measures of economic activity?

Firms’ cash flows were hit by a large common (pandemic) shock, causinghigher-than-normal correlations between cash flows

Estimate regressions

yt = β0 + β1yt−1 + β2µdt−1 + β3Nst−1 + εt , t = 2, ...,T ,

yt : growth in monthly (IP) or quarterly (GDP) variable

µdt−1: measured on the last day of the previous period

Nst−1: number of dividend suspensions in period t − 1T : end of sample. Goal is to predict yT+1



Macro Forecasts (cont.)

µdt and Nst are observed daily, so we can update macro forecasts for all mdays T + i/m, i = 1, ...,m in period T + 1:

ŷT+i/m = β̂0 + β̂1yT + β̂2µ̂dT+i/m + β̂3Ns,T+i/m, i = 1, ...,m,

m = 22(66) for the monthly (quarterly) data

Dividend suspensions is a key variable



IP growth forecasts, daily updates



GDP growth forecasts, daily updates


Dividend Suspensions and Firm Characteristics

1 Introduction






7 Conclusion




Fama and French (2001): profitability, size, and investment opportunities areimportant drivers of dividend suspensions

Hoberg and Prabhala (2008): idiosyncratic and systematic risk measurescaptured from stock returns have a negative and highly significant effect onfirms’ propensity to pay dividends

We implement Probit regressions

Dependent variable: indicator for whether a firm suspends its dividends

Covariates: firm size, market capitalization, leverage, cash, ROA, cumulativereturns, and idiosyncratic volatility

12 Industry dummies



Probit regressions for dividend suspensions

Probit of dividend suspenders

(1) (2)

2008-2009 2020

Firm size -.19*** -.071[-4.39] [-1.17]

Leverage 1.02*** 0.65***[3.32] [3.02]

Cash -1.42** 0.73[-2.47] [1.25]

ROA -1.98*** -2.06**[-3.03] [-2.00]

30-days cumulative returns -.26 -.97**[-0.79] [-2.39]

idiosyncratic vol 8.39*** 4.71[2.62] [0.87]

Industry FE Y Y

R2 14.26% 19.18%Observations 1,308 1,134



Empirical results

The pandemic caused a big shift in the drivers of firms’ suspension decisions

Firm size and cash holdings matter less for suspensions

Leverage (positive effect) and profitability (negative) retain their significance

Firms with low 30-day prior returns are more likely to suspend dividends

Information story: stock market identifies companies most adversely affected bythe outbreak of the pandemic and most likely to suspend dividends

Causal story: Large negative stock returns make it more difficult for firms toraise capital through the equity market and trigger tighter loan conditionsthrough existing bond covenants


Dividend Suspensions and Firm Characteristics Comparison with Global Financial Crisis

Word clouds from K-8 filings, press releases: GFC vs.Pandemic

Figure: January 2, 2020 – April 30, 2020Pettenuzzo, Sabbatucci & Timmermann (2020) Dividend Suspensions during Covid-19 October 21, 2020 40 / 48


Number of dividend suspensions by period and industry

Dividend Suspensions by Industry and Year

Industry 2008-2009 2020

Consumer NonDurables – Food, Tobacco, Textiles, Apparel, Leather, Toys 11 3Consumer Durables – Cars, TVs, Furniture, Household Appliances 13 8Manufacturing – Machinery, Trucks, Planes, Off Furn, Paper, Com Printing 7 13Oil, Gas, and Coal Extraction and Products 1 10Chemicals and Allied Products 1 3Business Equipment – Computers, Software, and Electronic Equipment 3 4Telephone and Television Transmission 3 0Utilities 0 0Wholesale, Retail, and Some Services (Laundries, Repair Shops) 17 24Healthcare, Medical Equipment, and Drugs 2 2Finance 79 6Other – Mines, Constr, BldMt, Trans, Hotels, Bus Serv, Entertainment 13 32

Total 150 105



Comparison with the Global Financial Crisis

Determinants of firms’ decision to suspend dividends were quite differentduring the pandemic compared to during the GFC

Industry composition also very differentDuring GFC:

Banks, Insurance Companies and Other Financials counted for more than half ofall suspensions (79 of 150)Wholesale, Retail and Services (17) and Consumer Durables (13) were secondand third most affected

During Covid-19 pandemic:

Financial firms did not announce many dividend suspensions (6)Wholesale, Retail, and services (24), Manufacturing (13) and Oil and Gas (10)were harder hit


Event study: Stock Market Reaction

1 Introduction






7 Conclusion



Event Study

Estimate a three-factor Fama-French model on firms’ excess returns

100-day estimation window from 115 to 15 days prior to each firm’s dividendannouncement date

abnormal returns are computed from ten days before each firm’sannouncement or suspension date (day 0) to ten days after

cumulate abnormal residuals to obtain cumulative abnormal returns (CARs)

compute cross-sectional averages of the CARs across firms in four categories

increases in dividends

no change or small reductions in dividends

substantial dividend cuts

dividend suspensions



Cumulative abnormal returns during Covid-19



Empirical findings

CAR values rise two days prior to the announcement date for firms thatreduce but do not suspend dividends, peaking at 5% on the day after theannouncement, before stabilizing around 3%

very different from conventional effects of dividend reductions (negative)many firms were expected to suspend dividends due to the pandemic

For dividend suspenders, CAR values decline five days prior to theannouncement day and bottom out at -6% two days after the announcement

Large portion of the negative CARs predate the dividend announcement

Firms that increased dividends had significantly positive CAR values on thedividend announcement date and the two adjacent days


Conclusion

Conclusion

Daily dividend announcements contain important forward-looking information

Dividend suspensions have a large effect on cash flow dynamics

impact expected future dividend growth and jump probability

impact goes far beyond the mere dollar amount - strong signal

effects can only be uncovered from a bottom-up (disaggregate) approach

Dividend suspensions have predictive power over aggregate economic growth

Timing and magnitude of stock market’s reaction is very different forsuspensions (strongly negative) and dividend reductions (positive)

What happens next? Dividend hiatus continues or payments re-start?


Conclusion

Looking ahead: Dividend Restarters

A few companies reinstated their dividend payments after May 2020

May 21 - Air Products and Chemicals (APD)June 10 - Dick’s Sporting Goods (DKS)July 15 - Bassett Furniture Industries (BSET)July 22 - Bluegreen Vacations (BXG)July 29 - Dunkin’ Brands Group (DNKN)July 31 - Jack In The Box (JACK)August 11 - Crown Crafts (CRWS)August 18 - La-Z-Boy (LZB)August 20 - Estee Lauder (EL)August 21 - Foot Locker (FL)September 14 - Buckle (BKE)September 15 - Herman Miller (MLHR)September 24 - Darden Restaurants (DRI)


IntroductionData and Econometric ModelingDataEconometric approach

A Dividend Growth Model with SuspensionsDividend Suspensions and Macroeconomic GrowthDividend Suspensions and Firm CharacteristicsComparison with Global Financial Crisis

Event study: Stock Market ReactionConclusion

dividend suspensions and cash flow risk during the covid-19 … · 2020. 11. 28. · dividend...

Documents