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INDEPENDENT JOURNAL OF MANAGEMENT & PRODUCTION
(IJM&P)
http://www.ijmp.jor.br
v. 12, n. 2, March-April 2021
ISSN: 2236-269X
DOI: 10.14807/ijmp.v12i2.1321
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711
CEOS' INFLUENCES ON THE STOCK PERFORMANCE ON
COMPANIES
Maria Augusta Soares Machado
IBMEC-RJ, Brazil
E-mail: fuzzy-consultoria@hotmail.com
Ana Beatriz de Mello Moraes
IBMEC-RJ, Brazil
E-mail: ana.moraes@ibmec.edu.br
Alberto Jacobsen
Fuzzy Consultoria Ltda, Brazil
E-mail: fuzzy-consultoria@hotmail.com
André Machado Caldeira
Fuzzy Consultoria Ltda, Brazil
E-mail: amcaldeira@yahoo.com.br
Bruno Roberto Santos
Fuzzy Consultoria Ltda, Brazil
E-mail: bruno.roberto.2019@gmail.com
Submission: 3/30/2020
Revision: 5/13/2020
Accept: 6/3/2020
ABSTRACT
Investors are not concerned with subjective internal measures, employees’
satisfaction or internal policies regarding the CEO’s evaluation and their
compensation. For the investor, the most important aspect is the return of their
investment. This paper focuses filling the gap left generically and quantitatively
in evaluating the CEOSs influence on the stock performance on their companies
during their management. The measurement of the CEOSs influence on the stock
performance of the most important North American companies is this paper’s
proposal. Assuming an efficient market and observing these companies’ stock
performance during a specific period, it is possible to know with accuracy what
these institutions created during the same period, as well as, expectation changes
on their future profits. In this study, it was used some statistical tests described
along the paper. This study demonstrated that, completely assume that the
CEOSs of the main American companies were a determinant factor in the success
of these corporations is a widely committed mistake.
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DOI: 10.14807/ijmp.v12i2.1321
Keywords: stock; performance; the investors’ interests; dividends; adjustment
1. INTRODUCTION
Intellectual capital has recently obtained a growing recognition as the great assets of
companies and economies. “While during a time the main production factor was land and latter
on capital… Today the main factor is man on its own, in other words, his knowledge”. Pope
John Paul II (1991). Centesimus Annus.
Great company leaders are continuously in evidence on newspapers, magazines and
news. Their leadership recognition is enhanced as are their remuneration. Besides these
leaders’ attributed importance, another disturbing characteristic of this phenomenon is the
emphasis placed on how their leadership influence companies X, Y and Z to become references
in the market. Nevertheless, few questions should be inquired:
• Was company X really instigated by its superior leadership or was it predestined to
succeed simply because its products were greater than the competitions’?
• Should Y Company’s growth be attributed to its superior leadership or in fact its growth
is due to the widespread difficulty faced by the competition?
• Was the Z pharmaceutical company really stimulated by its superior leadership or its
performance should be really accredited to its researchers for they were capable of
developing new drugs?
Regardless of a superior leadership, in the three examples above, those companies could
have prospered, for their success is a result of independent factors, regardless of leadership. As
a result, an opportunity to develop studies that aim the measurement of these leaders’
importance to the companies arises. Nonetheless, many prior researches subjectively analyzed
the Chief Executive Officers’ (CEO) performance. According to Silva (2004, p. 87-102) until
the present moment most developed works (Newman 2001; Tyler and Biggs 2001; Lear 1999)1;
Ittner, 1997; Verespej, 1994; Longenecker and Goia 1988; Goldstein 1985) displayed
speculative CEO evaluation methods and discussed the relation between salary and
performance disregarding the financial aspect. These leaders’ financial (investor’s perspective)
influence has been neglected till the present moment.
1 Cf. other references to the topic, Ittner, 1997; Verespej, 1994; Longenecker and Goia
1988; Goldstein 1985
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De Matos (2001, p. 3) affirms that the investors’ main interest is the valuation of their
assets. Investors are not concerned with subjective internal measures, employees’ satisfaction
or internal policies regarding the CEOSs evaluation and their compensation. For the investor,
the most important aspect is the return of their investment.
Considering the previous paragraph true, the following can be stated: No other work has
exclusively studied the CEOSs influence from the stock holders’ perspective, that is, no prior
work attempted to quantify the CEOSs influence on the stock performance of their companies.
Thus, this work had the ambition of filling the gap left open by its predecessors,
generically and quantitatively evaluating the CEOSs influence on the stock performance of
their companies during their management. The measurement of the CEOSs influence on the
stock performance of the most important North American companies was this paper’s proposal.
Assuming an efficient market and observing these companies’ stock performance
during a specific period, it is possible to know with accuracy2 what these institutions created
during the same period, as well as, expectation changes on their future profits. According to
Brealey (2003, p. 60) this is possible because the most common value determination method is
the discounted3 cash flow method, hence the market will price stocks according to the
companies’ current and future profits, in other words, any alteration in the company policies
capable of changing its future profits will cause an impact on its stock value today.
2. METHODOLOGY
2.1. The selection of a reference index
An important part of this paper was the selection of which public companies will have
their leadership evaluated. The first step was to determine which companies were the most
representative in the economy or capital market; this research has been extensively performed
by many institutions.
An important part of this study was the selection of which public companies had their
leadership evaluated. The first step was to determine which companies were the most
2 According to Peters (1996, p.5) in an efficient market all assets are evaluated according to the
available information.
3 According to Brealey and Myers (2003, p. 60) the discounted cash flow formula is the same as the
present value’s formula for any other assets after deducing the cash flows that may be gained in the
capital market by an interest rate that represents the associated risk.
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representative in the economy or capital market; this research has been extensively performed
by many institutions.
Companies and specialized institutions such as Dow Jones & Company, Fundação
Getúlio Vargas, JP Morgan, Standard & Poors, as well as, stock exchanges such as Bovespa,
developed methodologies in order to create different market indexes, as for example: Dax 30
from Frankfurt, FTSE 100 from London, Nikkei 225 from Tokio and Ibovespa from São Paulo.
The objective of these indexes was to represent the performance of economies or specific
sectors through the alteration of a stock “package”.
There are various representative indexes in many significant economies; hence, the
second step was the establishment of the most appropriate market indexes to test the hypothesis
of this work.
Since this was the first study to tests the hypothesis of CEOs from large companies as
essential factors in their companies’ stock performance, an analysis of the world’s largest
economy, the United States, is understandable. The American stock market is the most
developed in the world; consequently the largest and most representative companies have their
stocks negotiated there (regardless of the criterion, Marker Value, Accounting Value, Sales
Price, Revenues and Profits among others).
After the country selection and which companies to analyze, it still remained to choose
the most adequate index for this research, for the American economy is represented by many
indexes: Wilshire 5000, Russel 2000, S&P 500, Nasdaq, Dow Jones Industrial Average.
The Dow Jones Industrial Average index was chosen for many reasons; first, this index
is an exception among all other indexes because it has a strict components selection process
regulation. The components of the Dow Jones Industrial Average were selected by “The Wall
Street Journal” editors. There weren’t any pre-established criteria except for their headquarters
to be stationed in the United States4.
The second motive was that according to the Dow Jones & Company5 the companies
included in this index must be recognized as leaders in their industries and always pass through
a severe analysis before their inclusion.
4 More details on the index methodology may be found on the Dow Jones & Company website: Available
at:
<http://www.djindexes.com/mdsidx/index.cfm?event=showAvgMethod>. Accessed on: 13th of October
of 2019.
5 Dow Jones & company. Available at: <http://www.djindexes.com/jsp/avgMethod.jsp>. Accessed on: 27th
of October of 2019
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The third positive characteristic of the Dow Jones Industrial Average index is the
maintenance of its components. Unlike other market indexes the companies that compose the
DJIA are rarely changed. Modifications only occur after the announcement of a drastic6 event
involving one of the companies that compose the index.
The only negative aspect that could be pointed out against the use of the Dow Jones
Industrial Average index in this study was that, it is only composed by thirty companies. On
the other hand, according to the Dow Jones & Company on June of 2003, the DJIA represented
29%7 of all that could be invested in the American market, besides daily appearing in most
papers around the world, in the news throughout the globe, it is often transmitted during the
day in many television networks and financial websites. The Dow Jones agency also affirms8
that “even though there are many market indexes, the DJIA remains as the one instinctively
checked by professionals during the day”.
2.2. Data Gathering
At this point it’s necessary to know which companies constituted the Dow Jones
Industrial Average index on September 28th of 2019. This is a public domain information,
available in many publications, constantly transmitted throughout the day by television
networks, websites9, or even still, in the Dow Jones & Company website there is a document
with the historical composition of the DJIA10 since its establishment on the 3rd of July of 1884
6 Fusions and acquisitions, changes in the Core Business are examples of drastic events capable of
justifying such alterations. Available at: <http://www.djindexes.com/jsp/avgMethod.jsp>. Accessed on: 27th
of October of 2019.
7 Dow Jones & Company. Available at <http://www.djindexes.com/jsp/avgKeyBene.jsp> Accessed on:
9th of October of 2019.
8 Dow Jones & Company. Available at:
<http://www.djindexes.com/mdsidx/index.cfm?event=showAvgBenefits>. Accessed on: 13th of October
of 2019.
9 Few sites were these information may be found:
• Yahoo Finance. Available at: <http://finance.yahoo.com/q/cp?s=^DJI>. Accessed on: 13th of
October of 2019.
• Bloomberg. Available at: <http://www.bloomberg.com/markets/stocks/movers_index_dow.html>
Accessed on: 13th of October of 2014.
• Dow Jones & Company. Dow Jones Indexes. Available at:
<http://www.djindexes.com/mdsidx/index.cfm?event=showComponentWeights&rptsymbol=DJI&sit
emapid=1>. Accessed on: 13th of October of 2019.
10 Dow Jones & Company. Available at http://www.djindexes.com/downloads/DJIA_Hist_Comp.pdf
Accesses on 27th of September of 2019.
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till its latest change on April the 8th of 2019. According to this reference, on September 28th of
2014 the DJIA was composed by the following companies (Table 1):
Table 1: Dow Jones Average Index
Name of the Company
Negotiation Code
Alcoa
AA
Altria Group
MO
American International Group
AIG
American Express
AXP
Boeing
BA
Caterpilar
CAT
Citigroup
C
Coca Cola
KO
DuPont
DD
Exxom Mobile
XOM
General Eletric
GE
General Motors
GM
Hewllet Packard
HPQ
Home Depot
HD
Honeywell International
HON
IBM
IBM
Intel
INTC
Johnson & Johnson
JNJ
JP Morgan Chase
JPM
Mc Donald's
MCD
Merck
MRK
Microsoft
MSFT
SBC Communications
SBC
3M
MMM
United Technologies
UTC
Pfizer
PFE
Procter & Gamble
PG
Verizon
VZ
Wall Mart
WMT
Walt Disney Company
DIS
Now that all of the companies’ names and are their respective CEOs are known, the
date in which they assumed position remains to be disclosed. Different methods were
employed to obtain these data: initially it was necessary to access the websites of the thirty
companies and the Yahoo Finance11 website was used to obtain their addresses.
It was necessary to access the Websites and find the leadership pages from this point
forward. Some of these companies shared the desired information; others presented in a
fragmented manner or simply didn’t display any reference to it’s leadership. The partners’
pages were accessed and e-mails were written or request forms were filled, with the inquired
11 Yahoo. Yahoo Finance. Available at: <http://finance.yahoo.com/?u>. Accessed on: 10th of October
of 2019.
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information, in the websites of the companies that didn’t offer the necessary data on leadership.
The summary of this research12 is shown on the table 2.
Table 2: Name of the CEOSs and the date they assumed their position
Company
Name of the CEO
Assumed position on
Alcoa
Alain J. Belda
1st of January 2001
Altria Group
Louis C. Camilleri
25th of April 2002
American International Group
Maurice R. Greenberg
1967
American Express
Kenneth I. Chenault
1st of April 2001
Boeing
Harry C. Stonecipher
1st of December 2003
Caterpilar
James W Owens
1st of February 2004
Citigroup
Charles Prince
8th of September 2002
Coca Cola
E.Neville Isdell
1st of June 2004
DuPont
Chad Holliday Jr
1st of January 1999
Exxom Mobile
Lee R. Raymond
1st of April 1993
GE
Jeffrey R. Immelt
7th of September 2001
GM
Rick Wagoner
1st of May 2003
Hewllet Packard
Carly Fiorina
1st of July 1999
Home Depot
Robert L. Nardelli
1st of December 2000
Honeywell International
David M. Cote
1st of February 2001
IBM
Samuel J. Palmisano
1st of March 2002
Intel
Craig R. Barrett
26th of March 1998
Johnson & Johnson
William C. Weldon
1st of April 2002
JP Morgan Chase
William B. Harrison, Jr.
1st of December 2000
Mc Donald's
Charlie Bell
19th of April 2004
Merck
Raymond V. Gilmartin
1st of June 1994
Microsoft
Steve. Ballmer
1st of January 2000
SBC Communications
Edward E. Whitacre Jr
1st of January 1990
3M
W. James Mcnerney, Jr.
1st of January 2001
United Technologies
George. David
1st of April 1994
Pfizer
Hank McKinnell
1st of January 2001
Procter & Gamble
A.G Lafley
8th of June 2000
Verizon
Ivan Seidenberg
1st of April 2002
Wall Mart
Lee Scott
14th of January 2000
Walt Disney Company
Michael D. Eisner
1st of September 1984
Since the market index was chosen, the names of the companies and their respective
CEOSs were known, to construct the tests two information were still missing:
12 The website of the American International Group did not inform the date their current CEO assumed
position. As indicated above, an e-mail was sent inquiring this information and the company refused to
answer, the last alternative was a phone call to the partners’ section and the same information was
given.
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• The historical series adjusted by splits13 and dividends for a period of three years14,
previous and posterior to the CEOs change for each one of the companies.
• The DJIA historical series for a period of three years prior to the oldest CEO change
(Michael D. Einsner on the 1st of September of 1984). Hence it was necessary to obtain
a complete series since the 1st of September of 1981.
The Yahoo Finance15Website data base was used to obtain these data and other
information including daily opening, maximum, minimum, closing and closing adjusted by
splits and dividends, for all companies.
Since the used data was adjusted by splits and dividends16, based on prices from 19th of
October of 2019, a problem occurred with three of the twenty nine series. The stock historical
series adjusted by splits and dividends for Exxom Mobile, SBC Communications and Walt
Disney Company shows values around a few cents of the dollar, thus the estimated returns for
these stocks achieved imprecise values17. These companies faced this problem for two reasons:
1. These companies uphold a good history of dividends18 paid to stock holders;
therefore it was imperative to make constant adjustments to their historical series.
13 According to the American law, a Split is an event that needs to be approved by the stock holders
and board of directors. It’s an event that increases the number of stocks by dividing each stock by a
smaller amount of stocks. When it occurs the market prices for these stocks fall proportionally to the
number in which the division was made.
14 Among the thirty companies that compose the DJIA, there were nine cases of CEO change in less
the three years, thus, for these nine companies there aren’t any dada for the years subsequent the last
CEO change. Hence, the series prior and posterior to a CEO change will be asymmetrical for these
companies.
15 The thirty companies’ dividends history can be accessed filling the company’s negotiation code at the
end of the link. Yahoo. Yahoo Finance at http://finance.yahoo.com/q/hp?s=> Accesses on 19th of
October of 2019. i.e. to access General Motors’ dada (negotiation code GM) the following link should
be accessed: http://finance.yahoo.com/q/hp?s=gm. Yahoo. Yahoo Finance. Available on 19th of
October of 2019.
16 The series adjusted by “splits” and dividends will be used, for they are the ones that trully demonstrate
the stock holders real return.
17 The greatest problem was in the oldest part of the series.
18 A good history of dividends is when companies have a clear dividends distribution policy, as well as,
a good profit percentage distribution history, or in other words, a high “pay out ratio”. According to Matos
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2. In the three cases, the last CEO change took place ten years earlier, increasing the
effects of the constant dividends adjustment.
The problem above was solved by manually modifying the calculus base (closing on
the 19th of October 2019) for three exact years after the CEO alteration, for there was19 a closing
series and a dividends history for each one of the companies.
2.3. Data Treatment
As mentioned previously, this study’s main idea was the analysis of the relevance of
CEOs on the stock performance of the most important American companies.
A possible hypothesis to establish if the CEOs were really determinant in the
companies’ success was to measure if the average return of the selected companies’ stock varies
when their CEOs were changed, in other words, test if the stock’s movement tendencies change
when these companies have their leadership altered.
Before carrying through with the analysis a problem needs to be solved; stocks prices
vary according to economic cycles and he perception of the economy’s future. Thus, the stocks’
prices tendencies are influenced by macroeconomic aspects, in other words, stock prices are
influenced by systematic risk perceptions.
The macroeconomic effects must be detached from the microeconomic ones in order to
observe the real performance of each companies’ stock without the macroeconomic effects and
thus analyze the CEOs’ real relevance in the stock’s performance, that is, its essential to know
the stocks performance regardless of the market index variation. Three stages must be attained
to achieve this goal:
The first step was to create a daily return series for the desired period for each one of
the analyzed companies based on the closing historical series adjusted by splits and dividends,
as well as, create a daily return series for the same period based on the DJIA’s historical series.
After the creation of the companies’ daily return series and the DJIA index daily return
series, for the same period, the second step would be the creation of a return series with daily
(2001, p. 97) during most of the twentieth century corporations used dividends as their main tool to
distribute the excess of liquidity to their stock holders.
19 These companies dividends history can be accessed filling the company’s negotiation
code at the end of the link. Yahoo. Yahoo Finance at http://finance.yahoo.com/q/hp?s=Accesses on
19th of October of 2019. Example: To obtain the dada for SBS Communications (negotiation code SBS)
the following address should be accessed. http://finance.yahoo.com/q/hp?s=sbs.
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returns discounted by the DJIA index’s daily return, in other words, for the selected period, a
excess return series with daily is being created for each company. It can also be affirmed that
companies’ daily alphas20 series were being created.
The last stage was the definition of a base21 and then create, for each company, a
historical closing series deduced by splits, dividends and the DJIA.
2.4. Testing the Hypothesis
This study has tested if the CEOs of the main American companies have significant
influence on their company’s stock return during their administration. To evaluate the CEOs
influence on the return of their stock time series two periods has been used. These time series
refer to the periods before and after the current CEO in each one of the companies.
From this point forward this paper had continued from the following premise: If a CEO
change was capable of causing variations in the series’ outcome during the periods constituted
by the prior to and posterior moments of the Chiefs change. This will demonstrate that, in these
cases, the CEOs had significant influence on the stocks’ return, otherwise, the CEOs were not
an important tool in their performances.
This study has tested in two ways the hypothesis that a CEO change causes a significant
alteration on their companies’ stock return. First, through a test called the Chow22 breakpoint
test. The objective of this test is to generate N regressions for N sub-periods and verify if there
is any significant difference among the estimated equations. A significant difference indicates
a breakpoint.
The companies’ stock return time series and the DJIA historical returns series’ (for a
period of six years, divided into two sub-periods, three of these years were prior to the CEO
change and the other three were subsequent to the alteration) it was used with the aid of the
Chow's breakpoint test to analyze the hypothesis proposed by this study.
20 According to Gastineau (1996, p. 16) Alpha is the average of the return’s bias for a specific assets in
relation to a benchmark. The excess of return is also known as Jensen Measure Gastineau (1996, p.
162).
21 The base used in this work is 100.
22 “Chow breakpoint test”
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The estimated equations were simple regressions where the dependent variables were
the stock’s return for the periods and the independent variables were Dow Jones Industrial
Average Index return for the same period.
Two different results were expected after the Chow's breakpoint test :
1. Companies that presented a breakpoint in their alphas daily series after a CEO
change.
2. Companies where there was no breakpoint in their alphas daily return series after a
CEO change.
The results analysis were proceeded as follows: There are reasons to believe that in the
case of companies, which presented a breakpoint a change of CEO modified the course of their
stocks. There weren’t any reasons to believe that a CEO change was a relevant factor on the
stock performance of the companies that didn’t present a breakpoint. In this aspect there were
two possible results:
1. Companies with an alteration performance of their stocks;
2. Companies in which there was no change in the performance of their stocks.
Since there were two possible results, it was affirmed that the results had a binomial23,
distribution, thus, a binomial test may be estimated in other to verify if in the general context
of the big American corporations their CEOs do in fact deserve all the importance that has been
attributed to them in the last few years.
The second method, to test if the CEOs of large companies significantly influence the
return of their stocks, is a tendency test to verify the stock’s daily excess return and then, verify
if there was a significant change in the series’ tendency before and after the change of CEOs.
A comparison between the tendencies of the alphas series’ before and after the CEO
change will be carried through the T student test for a single sample. In this case the tendencies
of daily excess returns were provided by their average. The objective of the T student test is to
discover if the average of the differences between the average of the alphas series prior to the
CEO change and the average of the alphas series posterior to a CEO change equals zero24.
23 According to Bonfaire (2001, p. 2), a normal distribution is also known as distribution or
Bernoulli distribution.
24 Mathematicaly: H0: α1 - α2 = 0
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2.5. Mathematical Definitions
The definitions for minimum square line, Chow test, binomial distribution and binomial
test are listed below.
2.5.1. Minimum Square Lines
The minimum square line is like every line represented by a two variable equation (usually
x and y). According to Spielgel (1997, p. 372), the minimum square line best approximates or
adjusts the group of points (x1 ,y1), ... , (xn ,yn)25. Its equation is:
xy
βα
+=
(1.a)
By solving the following system, the α and β constants can be defined:
∑ ∑
+= xany
β
(1.b)
∑ ∑∑ +=
2
xnaxy
β
(1.c)
The α and β values in equations 1.b and 1.c are given by the follow equations:
()
( )
( )( )
∑∑∑∑
−= xy
xxy 2
α
(1.d)
( )
2
2∑∑ −x
xn
( )( )
∑∑ ∑
−= yxxy
n
β
(1.e)
( )
2
2∑∑ −xxn
2.5.2. Chow's breakpoint test
When there are doubts regarding the stability of estimated26 model’s coefficients the
Chow test should be applied, this test is frequently employed when it’s perceived or there are
reasons to believe that something relevant has occurred27 in the historical series in question28.
25 In this paper he group of points, x1 , x2 ... , xn will be the DJIA returns and the group of points y1 , y2 ...
, yn will be the companies stock return.
26 The estimated model in this work will be a simple regression.
27 The CEO change is the relevant factor of this paper.
28 In this paper, the series in question is the regression between the Companies Alphas and de DJIA....
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Suppose there is a series with N1 + N2 observations and K parameters that allows the
following model to be arranged:
ε
β
+
=xy
(2.a)
Now, suppose that it is known that a great change has occurred in the period (change of
CEO) and there are serious doubts that the model is the same for N1 observations and the last
N2 observations. To answer this question using the chow test its necessary to build a model for
the two series, one for the N1 observations and another one for the last N2 observations.
This model may be represented by the following regression:
1
11
1
ε
β
+
=x
y
(2.b)
22
22
εβ
+
=x
y
(2.c)
where:
x = Dow Jones Industrial Average daily returns
y = Stocks’ daily returns
If β1 = β2. In order to do so an UR (Unrestricted) model should be build.
2
1
2
1
0
0
x
x
y
y=
.
2
β
β
+
2
1
ε
ε
(2.d)
If the sum of the squared errors for model 2.b and 2.c are retained we can obtain:
()
()
knn
RSS
K
RSSRSS
UR
UR
R
2
21 −+
−
~
k
n
nFK 2
211
−+
(2.e)
Then, this model assumes an F distribution with a null equality hypothesis among the
coefficients.
2.5.3. Binomial Distribution
A Binomial distribution may only assume two values: 0 and 1. Such values are known
as failure and success29.
29 In this paper “success” means a breakpoint in the series, which was caused by the CEO
change; and a “failure” means that there was no breakpoint in the series.
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Considering a sequence with N independent experiences30 E1, E2, ..., En, where each
experience can only assume two possible variables: failure or success, and the outcome of a
failure or a success in one of the Ei experiences does not effect the occurrence of others. This
sequence of results E1, E2, ..., En is called Bernoulli distribution.
Thus, the Bernoulli distribution may be written as:
() ( )
≠
==
=
=
=
=
1or 0 for x 0
(Failure)
0
x
if
p
-1
q
(Success) 1 x if p
Pr x
X
Xf
(3.a)
2.5.4. Proportion tests
The values of the null and alternative hypotheses must be defined before the proportion
test can be applied; they are denoted by H0 e H1 respectively.
As mentioned previously, the objective of this study was to test if the CEOs of large
American companies were in fact decisive in the market performance of their stock. The Chow
test was used to verify if the change of CEOs had an impact on each one of the companies
separately. With a unilateral proportion test it will be possible to define if the proportion of
companies that presented (or not) a breakpoint had an impact on the alphas for a specific H0.
The following proportions were adopted in this study:
• H0 = 90%,
• H1 = smaller than H0 respective proportions.
This means that it could be verified if a change of CEOs was not relevant for the stock
performance in 90% of the cases. The unilateral proportions test is mathematically
demonstrated by:
H0: p = p0
H1: p < p0
The sample space Z is calculated by:
30 There will be 30 experiments in this work (the results of the Chow test for each one of the companies
that compose the DJIA).
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n
q
p
pp
Z
0
0
0
−
−
=
(4.a)
The Z result is compared to the desired α level of significance. It can be concluded that:
• If Z < -Zα , rejects H0
• If Z > Zα , rejects H0
• If | Z | > -Zα/2 , rejects H0
2.5.5. T student test for a sample
The objective of the T student test, in this study, was to discover if the average of the
difference between alphas series average prior to a CEO change by the alphas series average
posterior to a CEO change are equal to zero.
In other words, the hypothesis tested was H0: αa – αp = 0
The t statistics is calculated by:
(5.a)
The level of significance31 in which H0 may be accepted or rejected is reveled after
the t statistic is calculated.
2.6 Methodology Restrictions
There are restrictions in the proposed methodology; the first, as mentioned earlier, was
the market efficiency assumption acknowledged in order to maintain consistency in this
methodology. None the less, this hypothesis is common in the financial industry, according to
Peters (1998, p.8) the market efficiency hypothesis has dominated the financial universe for at
least thirty years, thus, Peters (1996, p. 13) affirms that for the past three decades, all theory
and research on finance depends on this hypothesis.
Another restriction was the studied period after the CEO change, it was decided to
establish a six year period. This restriction was imposed by the nature of the market, for if the
period was longer there would be a larger number of companies whose CEOs had not assumed
31 For the t test: degree of freedom = n-1
( )
,
/nxs
x
t
µ
−
=
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their positions for a period superior to three years, on the other hand, a shorter period leads to
less data for statistical tests.
3. ANALYSIS OF THE HYPOTHESES TESTS
The results of the tests described earlier are presented and interpreted in this section.
3.1. The Chow breakpoint Test
As mentioned before, this study’s model can be represented by the regression y = α +
βx, thus, table 3 displays the alpha and beta coefficients, correlation and the determinant
coefficient of all studied series32.
Table 3: Regression results
Regression
Α
Β
Correlation
R
2
AA
-0.000003
0.2784
0.5651
0.3193
AXP
-0.000087
0.3625
0.7324
0.5364
MO
-0.000079
0.1392
0.2618
0.0685
BA
0.000040
0.3616
0.6366
0.4053
CAT
-0.000461
0.4655
0.7330
0.5373
C
-0.000250
0.4172
0.7609
0.5789
KO
-0.000021
0.4115
0.4772
0.2277
DD
0.000368
0.2994
0.5552
0.3082
XOM
0.000321
0.2456
0.4421
0.1954
GE
0.000056
0.4289
0.7455
0.5557
GM
0.000143
0.3680
0.6754
0.4561
HPQ
0.000282
0.1534
0.4405
0.1940
HD
0.000028
0.2709
0.5877
0.3453
HON
0.000147
0.2754
0.6011
0.3613
IBM
0.000018
0.3003
0.5749
0.3305
INTC
0.000440
0.1571
0.4496
0.2021
JNJ
-0.000028
0.3097
0.4238
0.1796
JPM
0.000109
0.3177
0.7001
0.4901
MCD
-0.000080
0.2605
0.4041
0.1633
MRK
0.000427
0.1925
0.4397
0.1933
MSFT
0.000073
0.2742
0.5716
0.3267
PFE
0.000123
0.2659
0.4610
0.2125
PG
-0.000162
0.3327
0.5966
0.3559
SBC
0.000076
0.2009
0.4691
0.2201
MMM
-0.000085
0.4321
0.6183
0.3823
UTX
0.000202
0.2208
0.4678
0.2188
VZ
0.000032
0.2538
0.4523
0.2046
WMT
-0.000151
0.3213
0.5831
0.3400
DIS
0.000481
0.1994
0.4157
0.1728
32 The E-views 4.1 software was used to generate the regressions, alpha, beta, correlation
and determination coefficients.
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Table 4, presents the Chow test, its F statistics and their respective log likelihood ratio33.
Table 4: Results of the “Chow breakpoint test”
Chow Test
F-statistic
Log likelihood ratio
AA
62.3082
120.0437
AXP
28.2182
55.5512
MO
2.5625
5.1304
BA
0.4776
0.9587
CAT
8.8368
17.5829
C
14.7601
29.2768
KO
3.4032
6.8114
DD
2.7824
5.5693
XOM
0.9454
1.8947
GE
17.3469
34.3911
GM
1.0284
2.0624
HPQ
13.9516
27.7238
HD
9.4374
18.8049
HON
32.9363
64.6424
IBM
46.0045
89.3933
INTC
0.6585
1.3199
JNJ
23.7688
46.8784
JPM
9.0186
17.9777
MCD
0.4622
0.4605
MRK
11.1152
22.1272
MSFT
40.8481
79.7686
PFE
19.4854
38.5774
PG
3.9509
7.9021
SBC
10.9095
21.7206
MMM
67.5759
129.7712
UTX
9.0283
17.9956
VZ
22.9687
45.3256
WMT
0.1037
0.2080
DIS
3.2233
6.4500
It can be noticed from the F statistics results obtained through the Chow34 breakpoint test
that:
1. With a 95% of confidence level no CEO change caused a breakpoint. In other words, it
can be suggested, with a 95% level of confidence, that the CEOSs influence on the price
of stocks is not large enough to cause a tendency change on their stocks in comparison
to the DJIA index.
2. Only one company presented a breakpoint with a 90% level of confidence, thus, it can
be affirmed that a change of CEO in Wall Mart caused a breakpoint in its series.
33The Chow breakpoint tests were also estimated by the E-views 4.1 software.
34According to the results presented on table 3.1.2
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3. Only two CEO changes caused a breakpoint in their series with a 75% level of
confidence. Besides Wall Mart, it can be suggested that the Altria Group also suffered
a breakpoint with their CEO change at a 75% level of confidence.
3.1.1. Proportion test for the Chow tests results
Based on the individual results of the Chow's Breakpoint test, the conclusions may be
generalized in the form of a proportion test. The ideal would be a proportion test with a null
hypothesis at p = 1, however, given the binomial test deficiency for p values near one, it is
necessary to use smaller values for p. Hence, it was tested at which level of confidence there
will be no breakpoint in 90% of the cases35.
1. At a 95% level of confidence, the Chow test results didn’t present a breakpoint, thus,
with a 95,28% level of confidence it can be affirmed that in at least 90% of the cases
there were no breakpoint.
2. Only one company presented a breakpoint with a 90% level of confidence the Chow
test result, thus with 84.82% of confidence it can be affirmed that at least in 90% of
cases were be no breakpoint.
3.2. T student test for the stock’s alphas
As mentioned earlier, this study has used the T student test to discover if it is possible
to make the following declaration: The average of the difference between the average of the
alphas series prior to a CEO change and the average of the alphas series posterior to a CEO
change equals zero.
Table 5 summarizes the results of the stock’s average excess return for the periods
before and after, as well as, their differences36.
Table 5: Alphas average prior and posterior to the CEO change and their differences
Current Alphas
CEO
Previous Alphas
CEO
Differences
AA
0.0005
0.0009
0.0004
AXP
0.0005
0.0007
0.0002
MO
0.0002
0.0011
0.0010
BA
0.0012
-0.0004
-0.0016
CAT
0.0007
0.0010
0.0004
C
0.0006
0.0006
0.0000
KO
-0.0022
0.0003
0.0025
DD
-0.0001
0.0000
0.0001
35 p = 0.9
36 The alphas series, their average and their differences were calculated by the Microsoft Excel version
10.0.3506.0.
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XOM
-0.0001
0.0007
0.0008
GE
-0.0001
0.0006
0.0007
GM
0.0000
-0.0006
-0.0006
HPQ
0.0002
0.0003
0.0001
HD
0.0000
0.0005
0.0005
HON
0.0001
0.0002
0.0001
IBM
0.0000
0.0004
0.0004
INTC
0.0008
0.0010
0.0002
JNJ
0.0000
0.0005
0.0005
JPM
0.0004
0.0000
-0.0004
MCD
0.0009
0.0002
-0.0007
MRK
0.0008
-0.0002
-0.0011
MSFT
0.0006
0.0003
-0.0003
PFE
-0.0001
0.0007
0.0008
PG
0.0003
0.0015
0.0012
SBC
0.0005
0.0023
0.0018
MMM
0.0007
0.0004
-0.0003
UTX
0.0010
0.0010
0.0000
VZ
0.0002
0.0001
-0.0001
WMT
0.0003
0.0018
0.0015
DIS
0.0013
-0.0001
-0.0014
Table 6 presents the characteristics of the series displayed on table 3.2.1, which were
necessary to continue with the T student test. Table 3.2.2 also shows the T student (t statistic)
result for the formulated hypothesis in its last line.
H0: αa – αp = 0
Table 6 : The T Student test results for the differences between the alphas average
T student test for a sample
Sample’s average
0.000230537
H0 estimated average
0
Sample’s standard deviation
0.000892076
Sample size
29
Degree of Freedom
28
T statistic
1.391676012
With 90% level of confidence, the results of the T statistic introduced on table 3.2.2 has
permitted state that: there weren’t indications that the CEOs of the companies that compose the
DJIA may cause changes in their alphas’ averages. In other words, with a 90% level of
confidence it can be affirmed that the influence of a CEO on the their stocks’ prices was not
enough to cause a distortion on their returns’ tendencies.
3.3. Complementary tests for the series’ volatility
Complementary tests were estimated to analyze the series’ standard deviation since
previous tests do not indicate any evidence that a change of CEO caused alterations in the
alphas’ average.
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It has been tested that the series’ tendencies do not change, if we could state that the
series’ volatility didn’t alter either, we could probably affirm that: the CEO change did not
motivate any variation in the series’ main characteristic.
3.3.1. T Student test for the Standard deviation averages
The inference, previously applied to the t statistic for the alphas’ averages, was
employed here, however, this time, the series’ standard deviation was analyzed instead of the
average excess of returns.
Table 7 sums up the Standard deviation results and the differences37 of the standard
deviation of the excess of return series for the prior and posterior periods.
Table 7: Alphas’ averages before and after the CEO change and their differences
Current CEO
standard
Deviation
Previous CEO
standard
Differences
AA
0.0173
0.0249
0.0076
AXP
0.0154
0.0212
0.0058
MO
0.0202
0.0252
0.0050
BA
0.0104
0.0185
0.0082
CAT
0.0117
0.0139
0.0021
C
0.0105
0.0182
0.0077
KO
0.0118
0.0140
0.0022
DD
0.0199
0.0156
-0.0042
XOM
0.0100
0.0141
0.0041
GE
0.0129
0.0165
0.0036
GM
0.0112
0.0190
0.0078
HPQ
0.0369
0.0221
-0.0148
HD
0.0176
0.0251
0.0075
HON
0.0200
0.0248
0.0047
IBM
0.0133
0.0231
0.0098
INTC
0.0326
0.0218
-0.0108
JNJ
0.0130
0.0181
0.0051
JPM
0.0210
0.0217
0.0007
MCD
0.0117
0.0187
0.0071
MRK
0.0129
0.0155
0.0026
MSFT
0.0182
0.0251
0.0069
PFE
0.0167
0.0225
0.0058
PG
0.0179
0.0200
0.0022
SBC
0.0126
0.0337
0.0211
MMM
0.0108
0.0173
0.0066
UTX
0.0113
0.0140
0.0027
VZ
0.0164
0.0218
0.0054
WMT
0.0207
0.0176
-0.0031
DIS
0.0158
0.0194
0.0037
37 The alphas series, their Standard deviation and their differences were calculated by the Microsoft
Excel version 10.0.3506.0
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Table 8 presents the series’ necessary characteristics (displayed on table 3.2.1.) to
estimate the T student test. The result of the T student (t statistic) test for the formulated
hypothesis is exposed on the last line of table 3.2.2.
H0: σa – σp = 0
Table 8: T Student test results for the Alphas’ Standard deviation differences
Student test for a sample
Sample’s average
0.0039
H0 estimated average
0
Sample’s standard Deviation
0.006403285
Sample Size
29
Degree of Freedom
28
T Statistic
3.277377404
With a 99.75% level of confidence the results on table 3.3.2 permits state that: there
aren’t evidences to suggest that a change of CEO in the companies that compose the DJIA may
cause changes in their stocks’ alphas standard deviation. In other words, it is possible to affirm
with a 99.75% level of confidence that the CEOSs influence on their stocks’ price is not enough
to cause a significant alteration in their companies’ stocks standard deviation.
3.3.2. F Test
The CEOSs influence on the stock’s tendencies were tested in two ways; first through
the Chow Test, then, with the T test, nothing more reasonable than presenting a second test for
the series’ volatility. The result of the F test, for the standard deviations, is 0.876.
The F test confirms the result of the previous test: With 99.75% level of confidence the
F test enables the following statement : the CEOSs influence in their stocks’ prices were not
enough to cause significant changes in their companies’ stocks standard deviation.
4. CONCLUSIONS
The developed study demonstrated that, completely assume that the CEOSs of the main
American companies were a determinant factor in the success of these corporations is a widely
committed mistake.
After an impartial study (free from any evaluation or subjective examination) on the
Chief Executive Officers’ influence on the stock performance of the thirty main public
companies in the United States, it was concluded, based on strong statistical evidences and
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quantitative methods of analysis on the time series related to the companies’38 stocks
performance that: The CEOSs of the companies that compose the Dow Jones Industrial
Average index, for a period of six years, were not capable of significantly transforming these
corporations as to observe their influence on the stocks’ prices.
To explain this phenomenon few assumptions can be made: It’s reasonable to assume
that professionals with enough ability and experience to reach the position of a CEO in one of
these large companies, when faced with the necessary data to make a decision, they would
respond at the same manner or at least in a similar way. Another assumption was that, because
these companies are large physically and financially and considered global leaders in their
fields, the CEOSs have their roles minimized for their performance. Even though it was not
this study’s scope to find the reasons, it’s possible that there are many other factors more
relevant than leadership to the companies’ performance. These assumptions are not mutually
excluding, hence, one or more can be accepted as true.
Scientific methods suggest that common sense influenced by empirical conclusions
often mentioned in works, articles, newspapers, magazines, network transmissions and among
others, is in great part attributing untruthful and unjust credit to CEOSs. The common sense
created a tradition where CEOSs were believed to be a great differential; hence the myth of
their importance emerged. Their extraordinary salaries and space in the media and academic
world are justified through the myth of their essential importance to the companies’ results.
It was interesting to finally observe how this study had more general results to find
endorsement in the work of contemporary philosophers such as Theodor Adorno and Martin
Heidegger. These authors clearly criticize the contemporary world from the human perspective,
describing men as being rapidly absorbed by the technical autonomous process, procedures
these, which continually reduce the intervention and transformation power of human actions in
general39.
Future analyses on the CEOSs importance to their companies’ performance should be
more rigorous and careful, for the results presented in this study contradict common sense;
38 The total number of tests were: twenty-nine Chow tests, two T test, one F test and one
proportion test.
39 Cf. as for this aspect, Martin Heidegger, Voträge und Aufsätze, Theodor Adorno and
Max Horkheimer, Dialética do esclarecimento and Marco Casanova, Nada a caminho.
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another suggestion is that from this new perspective, some previous works should be
reevaluated, for they attribute the companies’ good or bad results to the CEOSs leadership
capacity.
In conclusion, this study opens the path for innumerous exploratory researches. This
study can be continued in different manners, that is, by applying the methodology developed
in this paper to smaller American companies or different economies.
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Brealey, R., & Myers, R. (2003). Principles of corporate finance. New York: McGraw Hill.
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Casanova, M. (2005). Nada a Caminho: Impessoalidade, Niilismo e Técnica a partir do
Pensamento de Martin Heidegger. Rio de Janeiro: Forense Universitária.
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