The effect of dividend payout policy on the volatility of ...
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The effect of dividend payout policy on the volatility
of stock:
Research on the New York Stock Exchange.
Ralph Willem Frederik van Tulder
Student number: 10434666
Bachelor thesis
Economics and finance
Faculty of Economics and Business
Supervisor: dr. P.J.P.M. Versijp
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Abstract
In this thesis the relationship between the dividend payout policies of companies and the
volatility of their common stock is researched. Data from 1997 till 2012 was used of the
companies listed on the New York Stock Exchange.
The dividend payout policy is divided into two variables: the dividend yield and the
payout ratio. Based on dividend related mechanisms a negative relationship is predicted and
control variables are defined. Furthermore dummies for the sector the firm is active in were
added.
By using a panel data regression on data from 1998 till 2012 on all the companies
listed on the NYSE a negative relationship is shown between both the dividend yield and the
dividend payout ratio and the volatility of stock. Furthermore the company size, the leverage
of a company, the earnings volatility and the sector that a company operates in show
significant relationships with the volatility.
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Table of content Abstract ................................................................................................................................................... 1
Chapter 1: Introduction ........................................................................................................................ 31.1 Dividend policy .............................................................................................................................. 31.2 Volatility of stock ........................................................................................................................... 5
Chapter 2: Literature review ................................................................................................................ 62.1 Relationship between dividend and stock characteristics ............................................................. 62.2 Baskin’s Research .......................................................................................................................... 7
2.2.1 The arbitrage realization effect .............................................................................................. 72.2.2 The information effect ............................................................................................................ 92.2.3 The duration effect. .............................................................................................................. 102.2.4 The rate of return effects ...................................................................................................... 112.2.5 Baskin’s regression .............................................................................................................. 11
2.3 Earlier findings on other developed and undeveloped markets .................................................. 12
Chapter 3: Methodology ...................................................................................................................... 143.1 Data selection .............................................................................................................................. 143.2 Regression ................................................................................................................................... 153.3 Variable definition ....................................................................................................................... 16
Chapter 4: Results of the regressions ................................................................................................. 194.1 Summary of variables .................................................................................................................. 194.2 Correlations ................................................................................................................................. 194.3 Regression with only dividend yield and dividend pay-out ratio ................................................ 204.4 Regression with control variables ............................................................................................... 214.5 Regression with control variables and dummy variables ............................................................ 234.5 Regression with control variables, dummy variables and dummies for dividend and leverage . 25
Chapter 5: Conclusions ....................................................................................................................... 27
Appendix A ........................................................................................................................................... 28
Appendix B ........................................................................................................................................... 31
Bibliography ......................................................................................................................................... 33
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Chapter 1: Introduction
In this thesis the relationship between dividend payout policy and the volatility of common
stock is researched. This relationship is important for directors of companies as well as for
investors. The understanding of this relationship could give directors and investors advantages
and could give directors an instrument to influence the volatility of their stock. This thesis
could also shed some light on the “dividend puzzle”: even though 85% of the companies of
the S&P 500 pay out dividend according to report from Andrew Birstingl (2015), there is no
common sense on what the ideal dividend payout policy is or the exact effects that dividend
has.
In this thesis the following research question is answered:
Is there a significant negative relationship between the dividend payout policy
and the volatility of common stock?
The dividend payout policy is defined as two variables: the dividend yield and the dividend
payout ratio. This thesis focuses on the New York Stock Exchange and thus the American
stock market.
1.1 Dividend policy
Dividend policy is a widely researched subject, but there is still no consensus on both the
view from the company’s side and from the consumer’s side. Before 1960 the common
consensus was that the higher the dividend the company pays, the more attractive the stock
was. The “bird in the hand fallacy” was the common theory that described the added value of
dividend in that time. This theory states that investors would rather have two dollars paid out
in dividends than have a two-dollar stock price increase and thus will dividend be more worth
to investors.
Miller & Modigliani (1961) found proof in their research that this theory does not hold
in perfect capital markets. Their theory shows that in perfect capital markets, keeping the
investment policy of the firm constant, dividend payout policy does not influence the stock
price and the return on stock for the investors. They show that consumers could always create
their own homemade dividend by selling their stocks and thus is the choice of dividend policy
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irrelevant. They also state that the present value of future cash flow is the only factor
determining the stock price.
“The dividend puzzle” (Black, 1976) however states that the theory of Miller &
Modigliani does not hold in the real economic market. They show that dividend can influence
the irrational investor and thus influence the stock price and the return on stock. Black
describes how companies use their dividend policy to indirectly influence their shareholders,
who act irrational in the real economic market.
Miller & Rock (1985) show in their research that under imperfect information,
dividend payout policy can be used as a signaling device to investors. Directors use the
dividend to sign information they have, but investors don’t have access to, to the investors.
Berk & DeMarzo (2007) state this effect as the dividend signalling hypothesis. Grullon,
Michaely, and Swaminathan (2002) found results in line with this hypothesis. They show that
an announcement of a 10% increase in dividend will be followed by a 1,34% increase in
stockprice. An announcement of a decrease in dividend, and thus giving out a bad signal, will
be followed by a 3,71% decrease in stock value.
A theoretical solution for the dividend puzzle, which states that there is no ideal
dividend policy, was found in a research paper by Rozef (1982). He found the optimal
dividend payout policy by finding the equilibrium of agency costs of exteranal financing and
the transaction costs associated with issuing external finance. He states that increasing the
dividends will lower the agency costs, but raises the cost of external finance. By increasing
the dividend pay untill the two factors reach a minimal total costs, companies can find their
optimal dividend ratio.
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1.2 Volatility of stock
The volatility of stock is a rate at which the price of a stock increases or decreases. It is
calculated by taking the standard deviation from the return on stock for a given time period.
French, Schwert, & Stambaugh (1987) report a positive relationship between the
expected market risk premium and the predictable volatility of stock returns. They also give
emperical evidence that shows that an unexpected increase in the volatility of stock return
increases the expected market premium and thus directly lowers the stock price.
Schwert (1989) tries to explain volatility from a historical macro-economical
perspective, but only finds weak evidence that macro-economic volatility can explain stock
price volatility. He does show evidence for a positive relation between leverage and stock
price volatility.
Cutler, Poterba, & Summers (1989) try to explain the changes in the volatility of stock
returns with macro-economic news releases that affect the fundamental company values. They
are able to explain slightly less than 50% of the volatility with the news releases. They state
the hypothesis that the volatility is also influenced by a re-assessment of excisting
fundamental values. This hypothesis is backed up by results from earlier research (Schwert,
1987 & French & Roll, 1986)
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Chapter 2: Literature review
2.1 Relationship between dividend and stock characteristics
A lot of research has been done on the relationship between dividend payout policy and
fundamental firm values as earnings, stock prices and volatility of stock. Black and Scholes
(1974) test their theory that firms with a higher dividend yield will have higher stock returns.
They try to show that managers can increase the return on their stock by increasing their
dividend pay-out policy. A significant relationship between the dividend yield and the return
is not found in the research. Black and Scholes do however find a relationship between
dividend yield and dividend pay-out policy; companies with high dividend yield tend to have
a high pay-out ratio.
Black and Scholes (1974) also state that after a dividend raise the stock price will
temporarily increase, but will return to its old value when investors find that the dividend
raise was not because of increased future cash flow. They suggest that companies in need of
cash can decrease their dividend, because after the temporary decrease the value will return to
its old value.
Fama and French (1988) find that a company’s dividend yield explains more than 25%
of the 2 to 4 year stock returns. They found that the longer the horizon, the more the dividend
yield explains.
The relation between stock price volatility and future real dividend is
researched by Shiller (1981). In his research he finds that the measure of stock price volatility
is way to high to be attributed to new information about future dividend. In addition to his
findings he states that the volatility has to be explained by more than the future dividend.
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2.2 Baskin’s Research
The base for this research paper was laid by Baskin (1989). He was the first to
research the relation between dividend payout policy and the volatility of stock. He aimed to
find empirical proof for a relationship between these two variables, which would help
investors to predict the risk of a company’s stock and give directors an instrument that can
change the volatility. He based his hypothesis on four mechanisms that describe the relation
between dividend and volatility: the arbitrage realization effect, the information effect, the
duration effect and the rate of return effect.
2.2.1 The arbitrage realization effect
The arbitrage realization effect makes use of the main assumption that markets aren’t perfect.
This contradicts the theory of Miller & Modigliani (1961). Baskin’s (1989) theory states that
although they might be difficult to exploit, arbitrage opportunities are present in real life
(imperfect) markets. This is due to the fact that not every investor knows the true value of a
stock, which can cause some stocks to be underpriced. Baskin argues that investors will take
advantage of this underpricing and thus increase the stock price. He also shows
mathematically that when a company does not pay dividends, there is no arbitrage
opportunity. This is explained by the formula’s below. This will cause a lower volatility for
non- or low dividend paying stock.
This theory is based on the following mathematical functions and definitions:
P = the value of the stock.
P* = the present value of future dividend (anticipated with full information).
A = the discount from the intrinsic value of the stock.
This gives us the formula by definition:
1 𝑃 = 𝑃∗ ∗ (1− 𝐴)
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Where (1-A) denotes the underpricing of the stock. Note that one should have more
information about the company’s value than the market has in order to know that a stock is
underpriced.
The rate of return for someone exploiting this arbitrage opportunity is described by:
2 𝑅𝑎𝑡𝑒 𝑜𝑓 𝑟𝑒𝑡𝑢𝑟𝑛 = 𝐷/𝑃 + 𝑔𝑟𝑜𝑤𝑡ℎ 𝑜𝑓 𝑠𝑡𝑜𝑐𝑘 𝑣𝑎𝑙𝑢𝑒.
Where D/P is the dividend divided by the current stock price, thus the dividend yield. The
growth of the value of the stock is the amount gained from the stock returning from P to its
intrinsic value P*. These two add up to the total rate of return.
The discount rate formula used in this thesis is denoted as:
3 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒 = 𝐷/𝑃 ∗ + 𝑔𝑟𝑜𝑤𝑡ℎ 𝑜𝑓 𝑠𝑡𝑜𝑐𝑘 𝑣𝑎𝑙𝑢𝑒.
Using (1) to rewrite (3) and substituting in (2) the following is computed:
4 𝑅𝑎𝑡𝑒 𝑜𝑓 𝑅𝑒𝑡𝑢𝑟𝑛 = 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒 + 𝐴 (𝐷𝑃)
Baskin (1989) shows that the return is the underpricing multiplied by the dividend yield. The
higher the dividend yield, the higher the rate of return is for investors who exploit this
underpricing. When there is no dividend and the stock shows no tendency to return to its true
value, there is no rate of return gained from arbitrage and thus there is no arbitrage
opportunity. Dividend yield thus increase the possible return from arbitrage on a stock.
Investors who exploit these arbitrage opportunities will cause the stock to rise and thus
increase the volatility.
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2.2.2 The information effect
The information effect is also known as the signing effect as described by Berk & DeMarzo
(2014). They state that a high dividend might signal to the investors that the company has
enough money to keep growing and to return money to the investors. As dividends tend to be
sticky they signal to the investors that the directors trust that the company can keep up with
paying these dividends in the future (Berk & DeMarzo, 2007).
This theory is also described by Allen and Rachim (1996) and researched by Miller and Rock
(1985). Miller and Rock state that a company can signal that the directors trust that the
company is doing well by issuing (high) dividend and simultaneously releasing the earnings
announcement. Investors will believe in the stability of the company. When investors believe
the company is able to sustain their income and continue to create value, the expected future
cash flow will be more stable and thus helps sustain the company’s value. When the
company’s value remains stable, keeping all else equal their stock value will remain stable
and thus the volatility will decrease.
As Berk & DeMarzo (2014) showed the value of a stock (read company) is calculated as
followed:
5 𝑃! =𝐷𝑖𝑣! + 𝑃!1+ 𝑟!
Assuming that the dividend and 𝑟! are stable, the price will change when 𝑃!changes. 𝑃! is the
future stock price and is unknown in the real markets, so it can be replaced by E(𝑃!) , the
expected future stock price.
So it is shown that ceteris paribus when the expected stock price is stable, thus when the
investors trust the company will sustain its performance, the stock price itself will be stable
and volatility will be low.
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2.2.3 The duration effect.
The duration effect states that high dividend yield stock is less sensitive to fluctuations in the
discount ratio. Baskin argues in his paper (1989) that high-dividend paying stock implies
more short-term cash flow. As the short-term cash flows can be compared to a short-term
debt, he argues that the high dividend paying stock will, as does the short-term debt, always
stay close to par.
Baskin (1989) supports this theory by the use of the Gordon growth model. The
Gordon growth model is a model designed by Gordon and Shapiro published in 1956. It
calculates the intrinsic value of a stock by using the present value of growing future
dividends.
Baskin uses the Gordon growth model to establish a relationship between dividend and
volatility as followes.
The stock price as calculated by the Gordon growth model:
6 𝑃! =𝐷!!!
(𝐾𝑒 − 𝑔)
Assuming that growth (g) and the equity discount rate (Ke) are constant.
Taking the first derivative to Ke to get the change in 𝑃!:
7 𝑑𝑃!𝑑𝐾𝑒 =
−(𝐷!!!)(𝐾𝑒 − 𝑔)!
If the formula is rewritten to a form where the equity discount rate is shown as a ratio of the
dividend yield the following formula is received:
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𝑑𝑃!𝑑𝐾𝑒𝑃!/𝐾𝑒
=𝐾𝑒
𝐷!!!/𝑃!
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As can be concluded from formula 8, a stock with a high dividend yield is less sensitive to
changes in Ke than a stock with low dividend yield. So a higher dividend yield reduces the
volatility of a stock.
2.2.4 The rate of return effects
The rate of return effect makes use of the assumption that companies with high growth
opportunities retain their earnings for reinvestment. It is thus likely that companies that pay
out less of their income (lower pay-out ratio) will create value due to new investment
opportunities. Baskin argues that creating value trough existing assets will be less vulnerable
to prediction errors.
As can be seen in equation (5), when P1 is predicted correct P0 will be stable. If the
expected value of P1 contains more errors, P0 will change when those errors are discovered
(and thus the expected P1 changes). A change in P0 will create volatility in the stock (P0).
Baskin states that high dividend paying stock has a better predictable future value, which
helps the stock price to stay stable.
2.2.5 Baskin’s regression
Based on the four mechanisms mentioned above Baskin designed a formula with the two
main components, dividend yield and pay-out ratio, and added some control variables. The
control variables that he used are the earnings volatility, the firm size, the yearly asset growth
of the company and the leverage ratio. Furthermore he added dummy variables for the
industries that the companies are active in: Mining/Oil, Utility/Railroad, Wholesale/Retail,
Financial and Service. The findings of his research are reported in chapter 2.3.
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2.3 Earlier findings on other developed and undeveloped markets
Baskin was the first to test the relationship between dividend pay-out policy and the
volatility of stock. In 1989 he did his research on 2344 American companies that were
available through Compustat files of 1986. He defined the dividend payout policy as two
variables: the dividend yield and the dividend payout ratio. The dividend yield is the ratio of
the dividend per stock divided by the stock price. This ratio shows how much of the
investment is returned to the shareholder as dividend per year. The dividend payout ratio is
the total amount of cash that is paid out as dividend to the stockholders divided by the total
earnings before income taxes of the company. These ratios are further discussed in chapter 3.
By applying a regression on the data mentioned above, Baskin came to a significant
relationship between dividend yield and stock price volatility. He also shows that there is no
evidence for a relationship between the dividend payout ratio and the volatility of stock. He
did find a significant relationship for size, earnings volatility and leverage. These findings
provide proof for the theories mentioned in 2.2.
After Baskin’s research this method was repeated several times. The first notable
researchers to reproduce this investigation in a different area were Allen & Rachim in 1996.
They repeated Baskin’s research on companies listed in Australia from 1972 till 1985. They
found a significant negative relationship between the pay-out ratio and the volatility, whereas
Baskin had found that this relationship was not significant. They also found that the firm size,
leverage and earnings volatility have significant influence on the volatility. Allen and Rachim
reject the duration and the arbitrage effect because these theories use the dividend yield as a
crucial factor. They also reject the rate of return effect because there is no significant effect
from the variable growth on the dividend yield and the pay-out ratio. If the rate of return
effect works in real life, they say, than this would be the case.
Hussainey, Oscar Mgbame, & Chijoke-Mgbame reproduced Baskin’s research in 2011
regarding the UK market. They did not include the financial sector in their research. In their
research they found emperical evidence for both the dividend yield and the dividend pay-out
ratio. They further found as expected a negative relationship between size and volatility and a
positive relationship between leverage and volatility.
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Lashgari & Ahmadi (2014) researched the relationship between dividend on the
Tehran stock market in Iran. They found a significant effect of dividend pay-out ratio, but no
significant effect for size, leverage and earnings volatility.
Furthermore there are some more bachelor theses and master theses on this subject, all
with different outcomes with regards to the relationship between dividend payout policy and
the volatility of stock.
The later researches thus show different results compared to Baskin. Some of the theses find a
strong correlation between dividend yield and dividend pay-out ratio and decide to drop one
of those variables. The choice of which variable they drop will have influenced their outcome.
Baskin has kept both and found that only the dividend yield was significant.
This research uses more data than the research of Baskin and uses a different
regression tactic than the older researches. All the old researches used Ordinary Least Squares
on averaged data per company. In this research a regression on panel data will be used, as will
be explained in chapter 3.
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Chapter 3: Methodology
3.1 Data selection
The research area of this thesis is limited to the New York Stock Exchange. All the
equity data was collected from the Compustat database by Wharton WRDS. The yearly data
of all NYSE listed companies was retrieved. However, not all the data was complete. When
companies missed essential data, their data was omitted and not taken into account in the
regression.
A second dataset with quarterly equity data was received from Compustat in order to
calculate the yearly earnings volatility (see paragraph 3.2). This dataset was merged with the
previous mentioned yearly data after the calculation of the earnings volatility.
For all the NYSE companies the following yearly data was gathered from 1997 till
2012: dividends per share, earnings before income taxes, total dividends, total market value,
total asset value and long term debt. Furthermore the companies were divided into different
industrial sectors denoted by the official Global Industry Classification Standard (GIC) sector
codes. These were also received from Compustat. The GIC codes were used to create dummy
variables.
The data on the stocks of the companies was received from the CRSP database by
Wharton WRDS. Again the stock data for all active NYSE companies was retrieved. The data
was received monthly in order to calculate the yearly volatility and the average yearly stock
price. After calculating these two variables the data was converted to yearly data and merged
with the equity data. This created the final dataset that was used for this thesis.
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3.2 Regression
Based on the earlier mentioned mechanisms described by Baskin (1989) the following
regression was designed in order to research the relationship between dividend pay-out policy
and the volatility of stock:
𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 = 𝛽! + 𝛽!𝐷𝑌𝑖𝑒𝑙𝑑 + 𝛽!𝑃𝑟𝑎𝑡𝑖𝑜 + 𝛽!𝐸𝑣𝑜𝑙 + 𝛽!𝑆𝑖𝑧𝑒 + 𝛽!𝐺𝑟𝑜𝑤𝑡ℎ + 𝛽!𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒
Where:
Volatility The volatility of the stock
DYield The dividend yield
Pratio The pay-out ratio
Evol The quarterly earnings volatility
Size The firm size (total market value)
Growth The yearly asset growth
Leverage The leverage ratio
In addition dummy variables regarding the following industrial sectors were added:
Energy, Industrials, Materials, Consumer discretionary, Consumer staples, Healthcare,
Financials, Information technology, Telecommunication services and Sector Unknown.
In the regression the dividend yield and the payout ratio are the two variables this research is
about, the other variables are control variables.
The data is a panel data set divided per company and per year. By using panel data instead of
averaging all the values, like Baskin and previous researchers did, the amount of observations
will be significantly higher. Also panel data analysis allows us to control for changing
characteristics of a company over the years. The GLS random effects model will be used to
analyze the data. Because the variables used tend to be sticky, we will use robust standard
errors.
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3.3 Variable definition
The regression mentioned in 3.2 contains a series of variables, from which the dividend yield
and the dividend payout ratio are the main components, whereas the rest are control variables.
These variables are chosen based on the mechanisms described in chapter 2. To get each
variable in the right form to perform the regression, some transformations were done. How
each variable is computed and defined is described below:
Volatility
To calculate the volatility of the stock monthly closing prices were gathered. The monthly
return on these stock prices was computed and used to calculate the standard deviation of the
return on the stocks, which is by definition the volatility. The volatility was calculated per
year.
𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛 = 𝑝! − 𝑝!!!
𝑝!
𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 = 1𝑁 (𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛 −𝑀𝑒𝑎𝑛 𝑚𝑜𝑛𝑡ℎ𝑙𝑦 𝑟𝑒𝑡𝑢𝑟𝑛 )!
!
!!!
Dividend Yield
The dividend yield is defined as the ratio of dividend paid out to the average stock price. To
compute the dividend yield the total dividend per share is used as received from Compustat.
The average share price, calculated as yearly average of the monthly stock closing prices, is
used as share price in this ratio. By dividing the dividend per share by the share price the
dividend yield was computed. The dividend yield is used as a ratio in the regression.
𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑 𝑦𝑖𝑒𝑙𝑑 = 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑 𝑝𝑒𝑟 𝑠ℎ𝑎𝑟𝑒𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑠ℎ𝑎𝑟𝑒 𝑝𝑟𝑖𝑐𝑒
Payout ratio
The payout ratio of a company is the percentage of its yearly income it returns to the investors
in the form of dividend. A higher payout ratio means the company pays out a larger part of its
profit to their owners/shareholders. To calculate this value the total dividend paid out that
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year was divided by the total earnings before taxes. Both values were received from the
Compustat database.
𝑃𝑎𝑦𝑜𝑢𝑡 𝑟𝑎𝑡𝑖𝑜 =𝑇𝑜𝑡𝑎𝑙 𝑑𝑖𝑣𝑖𝑑𝑒𝑛𝑑 𝑝𝑎𝑖𝑑
𝐸𝐵𝐼𝑇
Earnings volatility
The volatility of Earnings is calculated by taking the quarterly EBIT to total asset ratio. This
ratio is used to calculate the volatility of the earnings per year. The return on the earnings was
calculated per quarter, which was used to measure the yearly standard deviation. This
standard deviation is the earnings volatility.
𝐸𝑞𝑢𝑖𝑡𝑦 𝑡𝑜 𝐴𝑠𝑠𝑒𝑡 𝑟𝑎𝑡𝑖𝑜 = 𝑃𝑟𝑒𝑡𝑎𝑥 𝑖𝑛𝑐𝑜𝑚𝑒𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠
𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑒𝑞𝑢𝑖𝑡𝑦 = 𝐸 𝑡𝑜 𝐴 𝑟𝑎𝑡𝑖𝑜!𝐸 𝑡𝑜 𝐴 𝑟𝑎𝑡𝑖𝑜!!!
− 1
𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑣𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦 = 1𝑁 (𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑒𝑞𝑢𝑖𝑡𝑦 −𝑀𝑒𝑎𝑛 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝑒𝑞𝑢𝑖𝑡𝑦 )!
!
!!!
Size
The size of the firm is measured by the total market value in millions of dollars. On this total
market value a base 10 log transformation is applied to receive an order of magnitude.
𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒 = log!"(𝑀𝑎𝑟𝑘𝑒𝑡 𝑣𝑎𝑙𝑢𝑒)
Growth
The variable growth is the yearly growth of the assets. It is calculated by subtracting the value
of the total assets at the beginning of the year from the value at the end of the year and
dividing that by the beginning value. The growth is thus defined as a percentage.
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𝐺𝑟𝑜𝑤𝑡ℎ = 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠! – 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠!!!
𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠!
Leverage
For leverage the ratio of long-term debt to total assets is used in order to compare the
companies.
𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 = 𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑛𝑔 𝑡𝑒𝑟𝑚 𝑑𝑒𝑏𝑡
𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠
Restrictions were set on some of these variables in order to avoid irregular outliers that
could bias the regression. These limits were carefully selected in order to only omit the
outliers and to not bias the regression. The limitations that were set with their corresponding
histograms are included in appendix B.
19
Chapter 4: Results of the regressions
4.1 Summary of variables
In table 1 the summary of the used variables is given. This gives a clear view of the variables
with their means, standard deviations, minimum and maximum. The data denoted below are
after adjustments for outliers as explained in paragraph 3.3.
Variable Obs Mean Std, Dev, Min Max
Volatility 6542 0,106 0,060 0,018 0,499
Dyield 6542 0,019 0,023 0,000 0,149
Dpayout 6542 0,182 0,218 0,000 1,000
Evol 6542 0,011 0,014 0,000 0,100
Firmsize 5536 3,308 0,694 1,016 5,706
Leverage 6542 0,235 0,184 0,000 0,997
Growth 6542 0,105 0,183 -0,493 0,999 Table1
As can be seen the dataset consists of 6542 observations. These observations come from 650
different companies. Firmsize has only 5536 observations due to missing data from the
Compustat database.
4.2 Correlations
To test for multicollinearity and other correlation errors a correlation test was ran. The results
are displayed in table 2.
Volatility Dyield Dpayout Evol Firmsize Leverage Growth
Volatility 1
Dyield -0,29 1
Dpayout -0,27 0,79 1
Evol 0,20 -0,08 -0,04 1
Firmsize -0,27 0,03 0,06 -0,14 1
Leverage -0,01 0,19 0,16 -0,03 -0,12 1
Growth 0,03 -0,08 -0,08 -0,05 0,056 -0,04 1 Table2
As can be seen the dividend payout ratio and the dividend yield show the highest correlation.
This is no surprise, because of their shared dependence on the value of the dividend paid out.
20
When a company raises its dividend, keeping all else equal both the dividend yield and the
dividend payout ratio will rise. The same high correlation was found in the paper of Allen &
Rachim (1996). This correlation however will not cause a multicollinearity problem due to the
high amount of observations used in this research.
The dividend yield and dividend payout ratio both show a negative relationship with
the volatility. This concludes with the expectations and the mechanics described earlier.
The negative correlation between firmsize and volatility can be explained by the fact
that larger companies have more divers investments, which can be seen as risk spreading. The
more spread of the investments and income, the less the stock will react to a shock in a certain
market. Because of this reduced risk the volatility will be lower as the size of the company
gets bigger.
The relationship between leverage and both dividend variables is somehow
remarkable. It shows that the higher the leverage is, the higher the dividend of a company is.
Also the negative relationship between leverage and firm size is remarkable. One would
expect bigger companies with more diversified activities to be able to bear more debt and thus
have a higher leverage. However, when one looks at the calculations of both variables, it can
be seen that the variable firmsize is computed using market value and leverage is computed
using total assets. Running a correlation test on these variables shows a correlation of 0.49. So
if the firm size is higher, market value must be higher, which will go hand in hand with a
higher total asset value. Keeping the debt level equal, the leverage will thus drop. If the
leverage would rise with the firm size, the company has to issue relatively more debt than the
total assets rise.
4.3 Regression with only dividend yield and dividend pay-out ratio
At first the core regression containing only the variables from the research question was
executed. As explained in chapter 3 the regression will be a panel data regression with the
random effects model. The random effects model is applied in order to allow for differences
between companies. One of these differences is for instance the sector that the company
operates in. Assuming the effects of dividend increase are constant for all the companies, and
thus implicating the fixed effects model, is not valid. Therefore the effects are assumed to be
random and the random effects model is used.
21
There is no reason to assume the standard errors are homoscedastic, so this thesis uses
robust error terms in its regressions. The regression of the random effects method using only
the core variables is displayed below in table 3.
Volatility Coef, Robust Std,
Err, z P>z [95% Conf, Interval]
Dyield -0,298 0,072 -4,160 0,000 -0,438 -0,158
Dpayout -0,027 0,007 -3,800 0,000 -0,042 -0,013
_cons 0,119 0,002 72,490 0,000 0,116 0,122 Table3
As can be seen both the dividend yield and the dividend payout have a significant effect on
the volatility of stock. A one-point increase of the dividend yield will lead to a 0.298 decrease
in volatility. Regarding the value of both means (see table 1) the dividend yield is able to
influence the volatility with only small amounts. The coefficient of the dividend payout ratio
is 10 times as low as that from the dividend yield. Its mean however is 10 times larger. This
means, assuming both variables are able to change in the same way and with the same
percentages, the influence of the dividend payout ratio is about the same as that of the
dividend yield. For the complete regression output including R2 , the amount of observations
and the chi2 test see appendix A.
4.4 Regression with control variables
The second regression consists of the core variables as described in 4.3 with the variables
added as stated in chapter 3. These variables are all based on economic theories and the
results will therefore show the validity of these theories. As with the base regression a random
effects panel regression was performed. The results of this regression are given in table 4.
22
Volatility Coef, Robust Std,
Err, z P>z [95%Conf, Interval]
Dyield -0,456 0,079 -5,750 0,000 -0,611 -0,300
Dpayout -0,021 0,008 -2,760 0,006 -0,037 -0,006
Evol 0,567 0,079 7,160 0,000 0,412 0,723
Firmsize -0,029 0,002 -14,750 0,000 -0,032 -0,025
Leverage 0,010 0,007 1,550 0,121 -0,003 0,024
Growth 0,007 0,005 1,440 0,150 -0,002 0,016
_cons 0,204 0,007 29,460 0,000 0,190 0,217 Table4
The core variables show a very different relationship with the volatility in comparison with
the core regression under paragraph 4.3. Dividend yield has a coefficient of -0.46, which is
significant at the 95% level. This means that when the dividend yield goes up with 1 point,
the volatility will decrease with -0.46 points. Increasing the dividend yield by increasing the
dividend policy of a firm seems to be an effective way to decrease the volatility of a stock.
The biggest difference is the coefficient of the dividend yield, which is 1,5 times as
high as in the first regression without the control variables. It is likely to be caused by an
omitted variable bias in the first regression.
The dividend payout policy decreases with 0.006 from 0.027 to 0.021. This decrease
might also be caused by the omitted variable bias in the first regression.
The control variables differ from each other in results. The earnings volatility shows a
highly positive relationship with the volatility as expected. This relationship is significant at
the 99% level. The variable for firm size seems to have a smaller influence with the volatility,
but due to the way this variable is computed, with a mean value of 3.3 its influence on the
volatility will even be bigger than that of the other variables.
The last two variables, growth and leverage show no significant relationship with the
volatility, even not at the 90% level.
Because of potential multicollinearity errors this test is also run with the dividend yield and
the dividend payout ratio omitted.
The two separate regressions do not differ much from the original regression. Both the
dividend yield and the dividend payout ratio show higher effects on the volatility. This is
easily explained by the omission of the other core variable. The dividend yield seems to take
over the effects of the dividend payout ratio and vice versa. The other variables stay the same
in influence and significance.
23
4.5 Regression with control variables and dummy variables
In order to efficiently research the relationship between dividend payout policy and the
volatility of stock in the best way possible dummy variables were added regarding the type of
business the company is in. Each type of business has its own risk characteristics and own
type of investments. Because of this there could be a significant difference between types of
business. As explained in chapter 3 the Global Industry Classification Standard is used to
determine in which industry the company operates. The utilities sector was omitted to avoid
the dummy trap. All the results are thus compared to the base dummy: the utilities sector. This
sector was chosen as base dummy it is expected to show a low volatility due to a stable supply
and demand and due to strict regulation. Thus was tested if the utilities sector shows a
significant different volatility than the other sectors.
As above the random effects regression was used with robust standard errors. The
results are displayed below.
Volatility Coef, Robust Std,
Err, z P>z [95%Conf. Interval]
Dyield -0,384 0,0793 -4,840 0,000 -0,540 -0,229
Dpayout -0,022 0,0078 -2,780 0,005 -0,037 -0,006
Evol 0,529 0,0814 6,490 0,000 0,369 0,688
Firmsize -0,027 0,0019 -14,290 0,000 -0,031 -0,024
Leverage 0,013 0,0067 1,960 0,050 0,000 0,026
Growth 0,006 0,0048 1,180 0,239 -0,004 0,015
Cenergy 0,042 0,0060 6,990 0,000 0,030 0,053
Cmaterials 0,034 0,0059 5,770 0,000 0,022 0,045
Cindustrials 0,025 0,0055 4,430 0,000 0,014 0,035
Cconsdis 0,030 0,0059 5,160 0,000 0,019 0,042
Cconsstap 0,023 0,0066 3,500 0,000 0,010 0,036
Chealth 0,026 0,0062 4,280 0,000 0,014 0,039
Cfinancials 0,019 0,0054 3,550 0,000 0,009 0,030
Cinfo 0,037 0,0064 5,820 0,000 0,025 0,050
Ctele 0,032 0,0076 4,240 0,000 0,017 0,047
_cons 0,172 0,0091 18,870 0,000 0,154 0,190 Table5
24
In table 5 the results of the regressions are displayed. As can be seen some small changes in
the variables occur compared to the regression without dummy variables. The influence of the
dividend yield drops, but this is corrected by all the positive dummy variables. Dividend
payout ratio, the firm size and the earnings volatility show no large changes.
What is remarkable is the significance of leverage. Where leverage wasn’t significant
at 90% at the regressions without the dummy variables for sectors, it now shows significance
at the 95% level. This could be a sign that there was an omitted variable bias before the
dummies were added.
As expected, all the dummy variables show a significantly higher volatility compared to the
utilities sector. The energy sector and the information technology sector show the highest
volatility. The relatively high volatility of the energy sector might be caused by the high
political importance, which might cause quickly changing expectations due to political
changes. Further research on the characteristics of these sectors might be necessary to explain
the outcome of these results.
To check if without the possible multicollinearity problem these findings still hold, again
two regressions were performed in which the dividend yield or the dividend payout ratio was
omitted. Both regressions show that with omitting one of the variables both the dividend yield
and the dividend payout ratio stay significant at 99%. The same as before can be seen; the
coefficients of both variables rise slightly to adjust for the missing variable. Some of the
dummy variables go from insignificant to significant, but this can be attributed to their higher
values due to the missing negative variable. This again shows us that multicollinearity is not a
problem in this regression.
25
4.5 Regression with control variables, dummy variables and dummies for dividend and
leverage
The last and most complete regression was performed with inclusion of dummies for
companies that pay no dividend and companies that are unleveraged. The no dividend dummy
was added because companies that pay no dividend might have different characteristics than
companies that do pay dividends. Also this might give a more detailed image about how
issuing dividend might decrease the volatility of a company.
The dummy for no leverage was added in order to determine if investors have more
trust in unleveraged companies. Through the earlier mentioned information effect companies
could signal to investors that they have enough capital to keep on growing without the need of
loans. Thus a negative value for the dummy for no leverage would be expected. The summary
of these dummies and the results of the regression are displayed below.
Variable Obs Mean Std, Dev, Min Max
Cnodiv 6542 0,327 0,469 0 1
Cnolev 6542 0,069 0,254 0 1 Table6
As can be seen, in 33% of the observations the company doesn’t pay out any dividend at all.
Also, in 7% of the observations the company is unleveraged.
26
Table7
Both dummy variables are added in this regression as Cnodiv and Cnolev. The dummy for no
dividend shows a significant relationship with the volatility. This means that the volatility of
companies that pay no dividend is significantly different than those who do pay dividend.
This was also concluded in the earlier regressions, where filling in zero for dividend yield and
dividend payout ratio would give a higher volatility, but now can be seen that by issuing
dividend a company might instantly lower its volatility. The regression shows us that in this
dataset the companies that pay no dividend at all have a volatility of 0.012 higher than
companies that do pay a small dividend.
The dummy for no leverage is very insignificant and thus shows that having no
leverage at all does not influence the volatility of the stock. This could mean that companies
being totally equity financed do not influence expectations of investors.
Furthermore the dividend yield and dividend payout ratio show a lower impact than
before, which is simply explained by the fact that the constant is lower by introducing the
dummy for no dividend paying firms.
27
Chapter 5: Conclusions
The results of the regression give a clear answer on the research question. From the
regressions can be seen that the dividend yield and the dividend payout ratio have a
significant influence on the volatility of the stock. Compared to their both means they
influence the volatility comparably. Evidence is found that increasing the dividend yield with
1 percent point decreases the volatility with 0.3 percent point. A percent point increase in the
dividend payout ratio will decrease the volatility with 0.016 percent point. These results do
not prove any of the mechanisms described in chapter 3 wrong.
This research has also shown that companies that do not pay out dividend have a
significantly higher volatility than companies that do pay out dividend. Issuing dividend will
thus lead to a lower volatility.
Furthermore, the size of a company shows a significant and negative effect on the
volatility. The higher the market value of a company, the lower the volatility. In contrast, the
earnings volatility shows a strong positive relationship with the volatility. Companies that
have a more volatile income will have a more volatile stock price.
When the dummy for no dividend was not taken into account, the leverage of a
company also showed a significant positive relationship with the volatility. The more debt to
assets a company has, the higher the volatility.
Also, as expected, large differences were found between the sectors that the companies
operate in: the utilities sector displaying the lowest volatility and the energy sector displaying
the highest.
28
Appendix A
Regression 1
Regression 2
rho .19766568 (fraction of variance due to u_i) sigma_e .04957751 sigma_u .02460779 _cons .2037717 .0069165 29.46 0.000 .1902157 .2173278 Growth .0068595 .0047692 1.44 0.150 -.0024879 .0162069 leverage .0103869 .0066969 1.55 0.121 -.0027388 .0235125 Firmsize -.0285715 .0019365 -14.75 0.000 -.0323669 -.0247761 Evol .5673666 .0792866 7.16 0.000 .4119677 .7227656 dpayout -.0214741 .0077808 -2.76 0.006 -.0367242 -.0062239 dyield -.4558509 .0792822 -5.75 0.000 -.6112411 -.3004607 volatility Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for 572 clusters in CompanyID)
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(6) = 481.61
overall = 0.1662 max = 15 between = 0.3117 avg = 9.7R-sq: within = 0.0755 Obs per group: min = 1
Group variable: CompanyID Number of groups = 572Random-effects GLS regression Number of obs = 5536
29
Regression 3
.
rho .17320906 (fraction of variance due to u_i) sigma_e .04957751 sigma_u .02269197 _cons .171752 .0091 18.87 0.000 .1539165 .1895876 Ctele .0320305 .0075623 4.24 0.000 .0172086 .0468524 Cinfo .0373532 .0064146 5.82 0.000 .0247809 .0499256 Cfinancials .0191033 .0053851 3.55 0.000 .0085488 .0296578 Chealth .0264386 .0061772 4.28 0.000 .0143315 .0385458 Cconsstap .0232851 .0066482 3.50 0.000 .0102549 .0363153 Cconsdis .0302493 .005857 5.16 0.000 .0187699 .0417287 Cmaterials .0339361 .0058842 5.77 0.000 .0224034 .0454689Cindustrials .0245001 .0055362 4.43 0.000 .0136493 .0353509 Cenergy .0416636 .0059587 6.99 0.000 .0299848 .0533425 Growth .0056884 .0048277 1.18 0.239 -.0037737 .0151505 Leverage .0131904 .0067157 1.96 0.050 .0000279 .0263529 Firmsize -.0273398 .001913 -14.29 0.000 -.0310892 -.0235904 Evol .5286481 .0813981 6.49 0.000 .3691107 .6881854 Dpayout -.0217532 .0078216 -2.78 0.005 -.0370832 -.0064232 Dyield -.384385 .0793378 -4.84 0.000 -.5398843 -.2288857 Volatility Coef. Std. Err. z P>|z| [95% Conf. Interval] Robust (Std. Err. adjusted for 572 clusters in CompanyID)
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(15) = 678.22
overall = 0.1922 max = 15 between = 0.3778 avg = 9.7R-sq: within = 0.0770 Obs per group: min = 1
Group variable: CompanyID Number of groups = 572Random-effects GLS regression Number of obs = 5536
30
Regression 4
31
02
46
810
Density
0 .1 .2 .3 .4 .5Volatility
020
4060
8010
0D
ensi
ty
0 .05 .1 .15dividend yield - dividend per share/shareprice average yearly
05
1015
Den
sity
0 .2 .4 .6 .8 1dividend payout - total dividend to earnings ratio
Appendix B
Volat i l i ty with restr ict ionVolat i l i ty < 0 ,5 D iv idendy ie ld with restr ict ionDyie ld < 0 ,15
D iv idendpayout rat io with restr ict ion0 < Dpayout < 1 Earnings volat i l i ty with restr ict ions Evol < 0 ,1
F i rmsize with restr ict ions 0 > F i rmsize Leveragewith restr ict ions Leverage< 1
020
4060
80D
ensi
ty
0 .02 .04 .06 .08 .1Earnings volatility
0.2
.4.6
Den
sity
1 2 3 4 5 6Size - log10 market value
01
23
45
Den
sity
0 .2 .4 .6 .8 1Leverage - long term debt to asset ratio
32
HistogramofGrowthwithrestr ict ions-0,5>Growth>1
01
23
4D
ensi
ty
-.5 0 .5 1Growth in asset over the year
33
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