the effect of independent directors on the profitability of a firm. using 85 companies in the s&p...

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EFFECTIVENESS OF

INDEPENDENT DIRECTORS

Independent Directors, a necessity for companies? Cigdem Sahin (140499)

Edna Twumwaa Frimpong (140518)

Anthony Njau (140535)

2014

GROUP ACE DUISENBERG SCHOOL OF FINANCE

1/1/2014

1

Independent Directors, a necessity for companies?


CONTENTS

INTRODUCTORY .................................................................................................................... 2

LITERATURE REVIEW ........................................................................................................... 4

METHODOLOGY ..................................................................................................................... 7

DATA DESCRIPTION .............................................................................................................. 9

GRAPHICAL ANALYSIS ...................................................................................................... 10

REGRESSION RESULTS & ANALYSIS .............................................................................. 11

ESTIMATED GENERALIZED LEAST SQUARE ................................................................ 15

TESTING ................................................................................................................................. 16

CONCLUSION ........................................................................................................................ 18

REFERENCES ......................................................................................................................... 20

2



INTRODUCTORY

The performance of a company depends on

the decisions made within the Board of

Directors (‘Board’). The Board can be

defined in two different ways: i) the Board

as a body of the company and ii) the Board

as individuals. Decisions of the Board

consists of decision of the body or as

individuals. This depends on the

authorizations within the Board.

Alongside the authorization of the Board in

decision making, the Board has tasks such

as keeping the legal responsibility as the

financial oversight within the company.

Also, the Board members are ambassadors

of the company and have voluminous tasks

where they need to advice the Board (as a

body) and support the revenue strategies.

‘According to the PwC Survey, the most

sought after new director attribute is

industry experience (48%), followed by

financial expertise (41%) and operational

expertise (38%). Additionally, the demand

for familiarity with “new age” business

skills (information technology, business

globalization, and the influence of social

media) has increased as boards look to add

new directors, according to Business

Week.’1

There are different kind of board

compositions, such as the one-tier and the

two-tier board, where in the former the

executives as well as the Independent

Directors are in the same board. In the latter

the Independent Directors have a separate

board.2 This paper focuses on the presence

1 http://www.businessweek.com/articles/2013-05-23/corporate-

directors-get-older-hold-their-seats-longer 2 Carsten Jungmann, The Effectiveness of Corporate Governance in One-Tier and Two-Tier Board Systems, European Company

(and attendance) of the Independent

Directors within the Board, where no

distinction will be made between the board

compositions when referred to the ‘Board’.

Some agency theorists3 suggest that in order

to align in the best way with the agent-

principal interests an effective governance

system is a necessity. This inter alia

involves the appointment of an independent

board of directors where this board will

monitor the Board as it discharges its duties

in the best interest of shareholders. This

shows that the size of the Board and the

number of executive directors on the Board

are regarded as proxies for the Board when

it is measured against firm performance.4

Independent Directors are defined as

neutral or un-conflicted outsiders within

the Board of the company in order to

oversee its management and among other

things “mitigate managerial opportunism

and promote shareholder value”.5

Companies are facing the pressure of

hiring and having Independent Directors on

the board to improve the strategic decision

making and with that increasing the

profits as o f the traded stocks.

The presence of Independent Directors

within the Board raised in the 1950 from

20% till 75%.6 The importance of their

presence is explained by the change in

and Financial Law Review, January 2007, Volume 3, Issue 4, Pages 426–474 3 Demsetz and Lehn, 1985; Jensen and Meckling, 1976; Fama and

Jensen, 1983 4http://www.prres.net/papers/Roselina_Board_Size_Board_Com

position_And_Property_Firm.pdf 5 Markets.FT, http://markets.ft.com/research/Lexicon/Term?term=independent-

board, accessed on 26 September.

6 J.N. Gordon, The rise of Independent Directors in the United States, 1950-2005, of shareholder value and stock market prices.

http://markets.ft.com/research/Lexicon/Term?term=independent-board


3



interest of disclosing information which is

primarily crucial for shareholders and the

financial markets the companies are trading,

for instance: “Perform Group PLC

independent board directors issued a

document to shareholders urging them to

reject a £702 million final takeover bid from

Access Industries Group. The board believe

that the offer undervalues the company and

its potential future growth.”7

It is argued that the effectiveness of the

Independent Directors are built upon inter

alia that they have:

1) less commitment to the management and

its vision;

2) external performance (of the company)

focus; and

3) compliance will to law and regulations,

such as disclosures.8

Therefore, the aim of this paper is focused

on the performance of companies which are

affected when the Board also consists of

Independent Directors.

The research question is: “What is/are the

main characteristics of the Independent

Directors affecting the performance of

companies?”

To analyse this relation, the returns of

companies operating within the S&P500 is

chosen as the dependent variable. The

reason for this lies in the enormous increase

from the 1950 till the present.

7 http://www.sbcnews.co.uk/sportsbook/2014/10/06/perform-

group-independent-directors-back-rejection-of-takeover-bid/ 8 J.N. Gordon, The rise of Independent Directors in the United States, 1950-2005, of shareholder value and stock market prices.

The following independent variables are chosen

as regressors:

1) the numbers of Independent Directors in the

board as a change in the independent/Board

directors ratio;

2) the compensation of the Independent

Directors;

3) the change in the number of board meetings;

4) the change in the average age of the

Independent Directors; and

5) the S&P500 returns’ of the years under

consideration.

These items will be explained in the next

chapter.

4

LITERATURE REVIEW

This chapter will analyse the effect of

Independent Directors on the profitability

of a listed company.9

Independent directors generally are

expected to have positive effects on

corporate performance, because of

essentially their roles. The analogy is that,

the presence of Independent Directors who

by default have no material or pecuniary

relationship with the company, its

promoters, its directors and its subsidiaries

will bring more accountability into the

workings of organizations and overall

objectivity to the oversight function of the

board.

Literature discussions

A brief survey of corporate governance

literatures reveals differences of opinion

among authors concerning how

Independent Directors should be defined.

Despite these varied opinions, in general

they agree about the role of the Independent

Directors relative to corporate governance.

Interestingly, among economists; the father

of economics, Adam Smith,10

noted that the

key to firm´s success is to deal with the

separation of ownership and control,

perhaps the introduction of independent

board members on listed companies.

However, other studies seem to suggest that

the presence of these Independent Directors

will rather decrease the performance of

managers.11

Rosenstein and Wyatt report

that the appointment of an additional

9 Profitability refers to the Earnings Per Share. 10 Smith, A., (1776) The Wealth of Nations. Edited by Edwin Cannan, 1904.

Reprint edition, 1937. New York, Modern Library. 11 Roman Horváth, Persida Spirollari, Do the board of directors´ characteristics influence firm´s performance? the U.S. evidence

Independent Director on boards composed

mostly of Independent Directors results in

an increase in firm´s value.12

This finding

supports the idea that Independent Directors

are chosen in accordance with the interest

of shareholders. Using a sample of 934

large U.S. companies over the period 1985

to 1995, Bhagat and Black also find that

firms react in situations of low profitability

by increasing the number of Independent

Directors in the Board.13

On the other hand, Peng investigates

whether the appointment of Independent

Directors in a given year is affected by the

prior poor performance of the firm and

prior firm´s size law, while others are

optional.14

Defining the Independent Director

Independent director means a person other

than an executive officer or employee of the

company or any other individual having a

relationship which in the opinion of the

issuer of Board of Directors will interfere

with the exercise of independent judgment

in carrying out the responsibilities of a

director. Therefore the following people

shall not qualify as Independent Directors:15

(A) a director who is, or at any time during

the past three years was, employed by the

company or by any parent or subsidiary of

the company;

12 Rosenstein, S., Wyatt, J. G. (1990), “Outside Directors, Board Independence and Shareholder Wealth.” Journal of Financial

Economics, Vol. 26, No. 2, pp.175–191. 13 Bhagat, S., Black, B. (2001), “The Non-Correlation between Board Independence and Long Term Firm Performance.” Journal

of Corporation Law, Vol. 27, No. 2, pp. 231–274. 14 Peng, M. (2004), “Outside Directors and Firm Performance during Institutional Transitions.” Strategic Management Journal,

Vol. 25, No. 5, pp. 453–471. 15 http://www.sec.gov/Archives/edgar/data/1120193/000119 312512159982/d308647ddef14a.htm

http://www.sec.gov/Archives/edgar/data/1120193/000119%20312512159982/d308647ddef14a.htm

http://www.sec.gov/Archives/edgar/data/1120193/000119%20312512159982/d308647ddef14a.htm

5



(B) a director who accepted or who has a

family member who accepted any

compensation from the company in excess

of $60,000 during any period of twelve

consecutive months within the three years

preceding the determination of

independence, other than the following:

(i) compensation for board or board

committee service;

(ii) compensation paid to a family member

who is an employee (other than an

executive officer) of the company; or

(iii) benefits under a tax-qualified

retirement plan, or non-discretionary

compensation provided.

In addition to the requirements contained in

this paragraph (B), audit committee

members are also subject to additional,

more stringent requirements under

NASDAQ Rule 4350(d).16

Ratio Independent Director to Board

Dashew on the importance of Independent

Directors concludes that having a majority

of Independent Directors on a board will

add a great value to the business which also

brings expertise and objectivity which

assures shareholders and other owners that

they have representatives that who look at

issues critically with no vested interest.17

Average age

There has been enough debate going on if

the average age of the board affects the

overall performance.18

Whiles certain

studies showed a positive correlation

between an older average age board and

16 https://www.sec.gov/rules/other/nasdaqllcf1a4_5 /nasdaqllcamendrules4000.pdf 17 http://www.lesliedashew.com/pdf/importance-of-independent-

contractors.pdf 18 http://www.businessweek.com/articles/2013-05-23/corporate-

directors-get-older-hold-their-seats-longer;

http://blogs.law.harvard.edu/corpgov/2013/09/09/taking-a-fresh-look-at-board-composition/

corporate performance, other studies are

also of the view that the younger generation

brings to the table fresher outlook and

innovations. However, the trend on the

market according to the Stuart Board Index

2012 shows that the number of new

directors has slowed to 291 of 5,184 total

director seats in 2012, a 27% decrease from

2002. At the same time, the average age of

directors (68), average board tenure (8.7

years), and mandatory retirement age (72-

75) have all risen. Currently, 73% of S&P

500 companies have existing mandatory

retirement age policies, but sometimes they

are waived. Only 4% of S&P 500 boards

specify director term limits, with the

majority setting the limits between 10 and

15 years.19

Board meetings

There have been inconclusive and

conflicting studies concerning the test for

the relationship between frequency of board

meetings and corporate performance.

Boards are generally encouraged to meet

often as it considered an important way of

improving the effectiveness of the Board

and an important channel through which

they obtain firm specific information and

able to perform their monitoring role.20

On

the other hand, frequent board meeting will

also culminate into excess use of

management time, travelling expenses,

administrative cost and directors' meeting

fees which may prove expensive in the long

run. This may also affect enterprise

activities within the firm as resources are

being channelled into less productive

activities.21

19 Cochran, P., Wartick, S., & Wood, R. 1984. The average age of

boards and financial performance, revisited. Quarterly Journal of

Business and Economics, 23(4): 57-63. 20 Source Ntim and Osei(2011); impact of corporate board

meetings on corporate performance in south Africa 21 Roman Horváth, Persida Spirollari (2009)“Do the board of directors´ characteristics influence firm´s performance? the u.s.

https://www.sec.gov/rules/other/nasdaqllcf1a4_5%20/nasdaqllcamendrules4000.pdf

https://www.sec.gov/rules/other/nasdaqllcf1a4_5%20/nasdaqllcamendrules4000.pdf

http://www.businessweek.com/articles/2013-05-23/corporate-directors-get-older-hold-their-seats-longer




6



Compensation

Compensations usually serve as a form of

motivation to induce the best in directors

including Independent Directors so they can

remain objective in the discharge of their

duties.

“In a 1992-1993 study involving 161 of the

250 largest US listed companies, Newman

and Wright found that CEO compensation

was greater in firms having remuneration

committees that included at least one

executive director or affiliated non-

executive director than in firms having

remuneration committees consisting solely

of independent non-executive directors,

after controlling for company size,

performance, share ownership and CEO

tenure. Another finding was that the link

between CEO compensation and corporate

performance was stronger when there were

no executive directors or affiliated non-

executive directors on the remuneration

committee (i.e. independent remuneration

committee), especially when corporate

performance was unfavourable.’22

evidence” 22

http://www.law.unimelb.edu.au/files/dmfile/IndependentDirectorsreport2.pdf

Returns of the market

Having discussed the characteristics of the

Independent Directors is still one

explanatory variable left to discuss, the

returns of the S&P500. This variable is

used as a control variable where it has

importance according to the Capital Asset

Pricing Model (‘CAPM’) stating that in an

efficient market the alpha has to equal

zero.23

If the alpha of the stocks of the

companies within the market differs from

zero, then this would state that the market is

inefficient meaning that the prices (growth

rates) are less reliable to the extent of

deviation. If the alpha is positive, it means

that the stocks would perform better than

the market in contrary to the situation when

the alpha is negative.

23 Jensen, M.C., ‘Risk, The Pricing of Capital Assets, and The

Evaluation of Investment Portfolios.”, The Journal of Business 42.2 (1969), p. 167-247. JSTOR. Web. 27 Sept. 2012.

7

METHODOLOGY

This paper analyses the relationship of

companies having Independent Directors

relative to the performance of the

companies. Before the data collection

began, converting the data from PDF into

Excel file was performed. The Earnings Per

Share of the companies is chosen, because it

gives, in general, a clear output how the

company performed in that particular year.

The dependent variable will be analysed

relative to the explanatory variables.

The variables are regressed by using a panel

data model, where 85 companies operating

within the S&P500 market in the period

2003-2013. The main feature of the panel

data model is that the random variable of

interest is double indexed. For these

companies, data is retrieved from the

Spencer Stuart Board index (SSBI) with

respect to the explanatory variables, except

for the S&P500 returns. The time data

consist of annualized outputs.

After observing descriptive summary

statistics through scatter plots and

histograms of the data, the arithmetic

growth rate of the data is taken. For the

number of Independent Directors, first the

ratio of the independent with relative to

non-Independent Directors is calculated,

and then the growth rate is calculated. All

data is transposed in the correct way24

into

Excel where after importing the Excel file

into Eviews, where Eviews recognized the

data as panel data model.

The panel data model is one that follows a

given sample of individuals over time, and

thus provides multiple observations on each

individual. The panel data model is chosen

24 Same composition as the data of Problem Set III.

1) to control the individual heterogeneity;25

2) to be able to identify and measure effects

that are simply not detectable in pure cross-

section or pure time series; and

3) to have more informative data. Because

of the presence of the many different

individuals (companies operating in very

different sectors), the differences of the

parameters needs to be considered as a

necessity.

Because of this the classical cross section

linear regression model is not suitable.

Therefore, the model is a fixed effect

model. The key assumption at this model is

that there are individuals that are not the

results of random variation and that they do

not vary across time. The model is also

known as the ‘Least Squares Dummy

Variable Model (‘LSDVM’).26

To analyse

the data the least squares method is used

with the software program 8. The panel data

model with fixed effects:

yit = αi + λt + βXit +uit

The fixed model replaces the mean for a set

of constants that depend on individual

cross-sectional units, but are constant over

time. The yit depends on a set of 5

exogenous variables (Xit….X5it)=X’it that

are specific to the it-unit, but are constant

over time.

25 Differences between the individuals. 26 http://en.wikipedia.org/wiki/Panel_analysis.

8



The X’it β*X consist of the following

variables:

1. change in the ratio of Independent

Directors/Board;

2. change in the average age of

Independent Directors;

3. change in the average compensation of

Independent Directors;

4. change in the number of board meetings

attended of Independent Directors; and

5. the returns of the market.

The αi is a constant representing the effects

of those variables peculiar to the it-

individual. The error term, uit represent the

effects if the omitted variables that are

peculiar to the individual units and time

periods. Here, the assumption is that the

error term is not correlated with the Xit.

The hypothesis testing:

H0: The explanatory variables are not

significantly correlated with the Earnings

Per Share.27

H1: The explanatory variables are


Per Share28

27 The null hypothesis is tested for β=0. 28 The alternative hypothesis is set at β≠0.

9

DATA DESCRIPTION

The historical data to be used in this

research is freely available from Yahoo!

Finance for the annual returns of the

stock markets and the Spencer Stuart

Board Index. The focus is the period

2003 to 2013, comprising of at least 800

observations with ten years for the U.S.

The data are the annualized returns of the

selected companies trading on the

chosen market and their profits at end

of year (also changes during the years),

converted to the difference of the logged

returns. The focus is in analysing the

question through U.S. stock market, the

S&P 5002.

The selection is purely based on the

availability of data relating with respect to

Independent Directors representing the

American market.

It consists of annual returns and EPS of

the 85 companies stock listed at the

S&P500.

The time period selected is for the

S&P500 stock listed companies for each

company from 2003-2013.

The SSBI will be used to obtain

information about the same 85 companies

stock listed at the S&P500.

Table I Descriptive Statistics

AGE EPS S_P500 MEETING RATIO

Mean 0.016430 0.234879 0.455369 0.070283 0.008994

Median 0.015152 0.079377 0.623259 0.000000 0.000000

Maximum 0.423077 39.66667 7.816850 3.250000 0.555556

Minimum -1.000000 -1.000000 -6.671498 -0.705882 -0.333333

Std. Dev. 0.099294 1.704075 3.299149 0.398115 0.069153

Skewness -1.976263 16.90526 0.054025 2.141045 0.939380

Kurtosis 29.12430 361.3122 4.286155 12.24521 9.855697

Jarque-Bera 24724.42 4587549. 58.99959 3676.614 1789.615

Probability 0.000000 0.000000 0.000000 0.000000 0.000000

The table provides information on the 850 observations in the full sample including mean,

standard deviation, Skewness, Kurtosis, Jarque-Bera tests and its P-value.

Table II Descriptive Statistics of the Residual

0

10

20

30

40

50

60

70

80

-4 -3 -2 -1 0 1 2 3 4 5

Series: Standardized Residuals

Sample 2004 2013

Observations 850

Mean -1.46e-17

Median -0.121912

Maximum 4.871570

Minimum -4.642897

Std. Dev. 1.504244

Skewness 0.424730

Kurtosis 3.963568

Jarque-Bera 58.43912

Probability 0.000000

The descriptive statistics of the standardized residuals which shows the difference

between the observed values and the estimated values.

10

GRAPHICAL ANALYSIS

Table III The Full Sample of the Model

Method: Panel EGLS (Cross-section weights)

Date: 10/17/14 Time: 10:33

Sample: 2003 2013

Periods included: 10

Cross-sections included: 85

Total panel (balanced) observations: 850

Linear estimation after one-step weighting matrix

White cross-section standard errors & covariance (d.f. corrected)

Variable Coefficient Std. Error t-Statistic Prob.

Alpha 0.239982 0.028671 8.370298 0.0000

S-P500 0.004546 0.001841 2.468997 0.0138

AGE -0.278941 0.107638 -2.591466 0.0097

RATIO 0.288580 0.142618 2.023447 0.0434

MEETING -0.073780 0.026382 -2.796580 0.0053

Effects Specification

Cross-section fixed (dummy variables)

Weighted Statistics

R-squared 0.133841 Mean dependent var 0.496049

Adjusted R-squared 0.033681 S.D. dependent var 1.618224

S.E. of regression 1.588839 Sum squared resid 1921.075

F-statistic 1.336270 Durbin-Watson stat 2.218654

Prob(F-statistic) 0.026697

Unweighted Statistics

R-squared 0.098495 Mean dependent var 0.234879

Sum squared resid 2222.559 Durbin-Watson stat 2.242423

This table shows the output of the full sample of the regression.

Table IV Residual Covariance & Correlation Matrix

Covariance

Correlation AGE EPS S&P500 MEETING RATIO

AGE 0.009848

1.000000

EPS -0.006110 2.900456

-0.036152 1.000000

S_P500 0.007431 0.311156 10.87158

0.022710 0.055411 1.000000

MEETING 0.002015 -0.047449 0.052169 0.158309

0.051025 -0.070022 0.039766 1.000000

RATIO 0.000146 0.000296 -0.009555 -0.000371 0.004777

0.021331 0.002515 -0.041931 -0.013488 1.000000

First, the variances of the residuals across cross-sectional units range from 0.024312 to

-0.093792. It seems unlikely that these are all realizations of a single σ. The differences

among the variances suggest that there is cross-sectional heteroskedasticity. Second,

with respect to the correlation output, it is clear that there is no strong relationship between

the explanatory variables.

11

REGRESSION RESULTS & ANALYSIS

Starting point and the Haussmann Test

For running the regression there were a few

choices to make. First the Haussmann test

was conducted. The Haussmann test

pointed the use of random effects out, with

a cross-section random probability of 0.645

for the regression using the returns of the

EPS. However, the fixed effects model was

preferred due to an economic analysis of

the data set.29

One way Effects Model

As mentioned before, the panel data model

is fixed for the cross sectional data and not

for the time period. This is the so called, the

one way Effects Model.30

Individuals weighted

Because of the many different individuals

which have different characteristics such as

size, risk and revenues, the individuals are

weighted. At first sight, the outcomes of the

regression were improved. However, the

improved outcomes were not significant

yet. Therefore, more improvement was

necessary. Literature pointed out the

heteroskedasticity.

Homo- versus heteroskedasticity

There is evidence that heteroskedasticity

occurs in cross-sectional data.31

Cross-

sectional data consists of observations

which are all for the same time period (a

particular month, day or year), but are from

different entities. In this case there are a lot

of individuals who are all different. The

individuals are companies which operate in

29 Tom S. Clark and Drew A. Linzer, Should I use fixed or

random effects?, http://polmeth.wustl.edu/media/Paper/ClarkLinzerREFEMar2012.

pdf 30 Cameron_&_Trivedi_Microeconometrics, Chapter 2. 31 http://people.stfx.ca/tleo/econ370term2lec1.pdf.

very different sectors, differ from size,

riskiness and revenues. They are all

exposed to different factors which have

effect on their business performance.

Therefore, there is no reason to believe that

the companies should be treated the same.

Thus, the (homoskedasticity) assumption

that all the standards errors of the variables

have the same variance would not be

accurate.32

Here, the heteroskedasticity is of

high importance and solves this problem.

The heteroskedasticity occurs when these

differences are present the assumption the

treat the individual differently rises.

The consequence is that the different

observations’ errors have different

variances, for instance when Var(εi)=σi2.

These errors are called heteroskedasticity.

Heteroskedasticity take care of the biases of

the estimators meaning estimators where

heteroskedasticity is taken into

considerations are unbiased.

White cross-sectional weights

To test the heteroskedasticity first the

residual plots are analysed and then the

model is tested by the White Test. The plot

where the independent and dependent

variables are plotted shows that the

observations are not the same across each

value of independent variable. The White

Test is quite general and is designed to test

for heterodasticity of an unknown form, for

instance in cases that there is no knowledge

that the variance of the error if proportional.

In Eviews the option ‘White cross-

sectional’ is conducted which provided the

outcome that the first estimated standard

errors changed significantly compared to

32 http://people.stfx.ca/tleo/econ370term2lec1.pdf.

12



the White-cross-sectional. The outcomes of

the regression were for the most

explanatory variables significant, but not

for the market and the compensation.

Because the compensation had the highest

variance, this explanatory variable was

subtracted from the equation which leads to

the fact that the markets’ P-value became

significant as well.

In addition, the correction of the degrees of

freedom is taken into account. The degrees

of freedom describe the number of values in

the final calculation of a distribution that

are free to vary. Without the degrees of

freedom it is not possible to calculate or to

understand any underlying individual

variability. 33

The analysis concerns the model where the

explanatory variable compensation is not

taken into account, because if there is any

correlation between the compensation and

the EPS, then the relationship is more likely

not be a linear one. Therefore, it seems to

be more accurate that this explanatory

variable is lacking in the analysis.

Precision of the estimators

ΑLPHA

The estimated α of the market is 0.239982

and has a standard error of 0.028671. The

standard error is very small meaning that

the estimated α is precise.

S&P500

The estimated β of the market is 0.004546



the estimated β is precise.

33 Shanta Pandey and Charlotte Lyn Bright, What Are Degrees of Freedom?

AVERAGE AGE

The estimated β of the average age of the

Independent Directors is -0.278941 and has

a standard error of 0.107638. The standard

error is small meaning that the estimated β

is precise, but not significant meaning it can

vary.

RATIO INDEPENDENT/BOARD

The estimated β of the ratio independent to

Board is 0.288580 and has a standard error

of 0.142618. The standard error is not small

meaning that the estimated β is not close to

being precise meaning that the estimated β

can vary.

MEETINGS

The estimated β of the market is -0.073780



the estimated β is precise.

Graph I shows the movement of the β of the

independent variables across the years.

Graph I Evolution β

The graph shows that the betas have

changed across the years. The most stable

beta is that of the market. However, it

seems that the average age has a movement

towards the beginning of 2005. Together

with the ratio independent/Board they are

-2.000

-1.000

0.000

1.000

2.000

3.000

4.000

2005 2006 2007 2008 2009 2010 2011 2012 2013

RATIO AGEMEETING SP500

Years 2004 - 2013

Ch

an

ge

in

Be

tas (

Ela

sti

cit

y)

13



more negatively correlated in the last few

years. This could be interesting for

(economic) researches to examine what the

reasons for these changes could be. From

the literature review it is clear that the ratio

independent/Board and the average age of

Independent Director are from high

importance.

R-squared

The interpretation of the given R2 and the

F-statistics (discussed after this paragraph)

is that they describe the explanatory power

of the entire specification, including the

estimated fixed effects.

The output of the full sample with respect

to the R2 and the adjusted R

2 is very low

which would lead to the conclusion that

there would be a bad fit of the model. The

adjusted R2 shows what the variability is of

the R2. When there are more explanatory

variables the adjusted R2 will be much

lower than the given R2. In the full sample

the adjusted R2 (0.033681) differs a lot

from the given R2

(0.133841).

However, looking at the R2 across years,

the R2 and the adjusted R

2 are both close to

1 meaning that there is actually a good fit.

Remarkable is that there are no real outliers

and the full sample’ R2 is not significant.

The R2 and the adjusted R

2 across the years

are plot in the graph below. This may be

due to the fact that in some years there was

no change in the explanatory variables

which differs across years.

Graph II Evolution R2

F-statistic

The F-statistic, also known as an F-value, is

a random variable that has an F distribution.

The F-statistics represent the value of an F-

statistic having a cumulative probability of

(1-α). Here, the cumulative probability of

0.95 is taken. The F-statistics have to be

examined to p<0.05. The F-statistics of the

model is set at a value of 1.336270 having a

P-value of 0.026697 leading to the

conclusion that the model is significant.

The per year F-statistics shows that all F-

statistics are set at a P-value of 0.0000

meaning that the model per year is

significant. The values of the F-statistics

vary across the years which can explain the

difference of the P-value of the full sample.

Residuals Sums of Squares

The residuals sums of squares are used to

measure the variance in the data set that is

not explained by the regression model. It

measures the amount of error remaining

between the regression function and the

data set. A smaller residual sum of squares

figure represents a regression which

explains a greater amount of data.34

The

sum squared resid of the full sample is set

at 1921.075 which are not explained by the

34 http://www2.gsu.edu/~dscthw/8110/SumsofSquares.pdf

0,8

0,85

0,9

0,95

1

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

R^2

Adjusted R^2

14



regression.

The output of the sum squared resid across

the years varies a lot where in two years the

change is very high (249.9144 and

670.6307). The output in all the other years

varies from 15-60. The differences between

these years could be the reason that the

output of the full sample is very high.

Fixed Effects

The Fixed Effects of the 85 companies

varies a lot from each other. When the

Fixed Effects of the full sample are plotted,

it is clear that most of the Fixed Effects are

negative. Looking at the frequency, about

62 individuals of the 85 have negative

Fixed Effects. The other individuals have a

higher positive value with respect to the

individuals having a negative Fixed Effect.

Graph III Distribution Fixed Effects

15

ESTIMATED GENERALIZED LEAST SQUARE The Ordinary Least Square (‘OLS’) needs

to satisfy some assumptions. In contrary to

these assumptions the Generalized Least

Square (‘GLS’) does not satisfy them. The

GLS produces an optimal unbiased35

estimator of β for situations with

heterogeneous variance.

The OLS consists of five assumptions:

1) the mean of the error is set at zero;

The mean of the error term in the full

sample is set at 1.57E-16 which is very

close to zero.

2) homoskedasticity; the standard

deviation of the error term is set at

0.986122 which is quite high meaning

that there is a high variability. The

residual plot is observed and it seems

that the error term is not reverting to the

mean, see Graph IV.

3) error terms are uncorrelated; this

assumption refers to the assumption that

the error terms are uncorrelated or that

the covariance of the errors are zero.

This can be tested with the Durbin-

Watson test which is always between 0

and 4. A value of 2 means that there is

no autocorrelation in the sample. Values

approaching 0 indicate positive

autocorrelation and values toward 4

indicate negative autocorrelation.36

The

full sample gives an output of 2.218654

which is very close to 2 meaning that it

is very likely there is no autocorrelation.

4) independent variables are non-

stochastic; this means that they are not

random and that they are stationary;

they don’t follow a trend.

35 Bias of an estimator is the difference between the estimator's

expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called

unbiased. 36 http://www.investopedia.com/terms/d/durbin-watson-statistic.asp

5) random sample of n-observations; and

In this case the Jarque Bera test37

tells if

the error term is normally distributed.38

The P-value of the Jarque-Bera is set at

0.00000 meaning that the distribution is

not a Gaussian bell.39

The requirements

are also set in the footnote satisfying the

Jarque-Bera test results.

6) linear in parameters; this assumption

can be tested with the Ramsey test.40

The test shows that if non-linear

combinations of the explanatory

variables have any power in explaining

the response variable, the model is mis-

specified meaning the linearity is not

the right movement explaining it.

Graph IV Distribution Error Term

0.0

0.2

0.4

0.6

0.8

1.0

1.2

-2 -1 0 1 2 3 4 5 6 7 8 9

De

nsit

y

RESID

37http://davegiles.blogspot.nl/2014/02/some-things-you-should-

know-about.html 38 http://www.stat.yale.edu/Courses/1997-98/101/normal.htm. 39 A Gaussian bell has it mean set at zero, all normal density

curves satisfy the following property which is often referred to as the Empirical Rule for the standard deviations (68-95-99.7%

rule); the Skewness states the symmetry of the observations

meaning the observations left of the mean should be equal to the observations right of the mean. The kurtosis should be set at 3. 40

http://homepages.uel.ac.uk/D.A.C.Boyd/FE3003%20Ramsey%20Reset%20Test.doc

Table VII Descriptive Statistics

Error Term

Mean 1.57E-16

Std. Dev. 0.986122

Skewness 5.565723

Kurtosis 43.12919

Jarque-Bera 12284.35

Probability 0.000000



16

TESTING

In this chapter the null hypothesis is tested

for the full sample which would be more

accurate since the output of one year or two

to three years would not be accurate

meaning that the results of the testing are

not reliable. Taking the full sample gives a

better overview if the explanatory variables

have indeed explanatory powers.

P-value

Small P-values suggest that the null

hypothesis is unlikely to be true. The

smaller it is, the more convincing is the

rejection of the null hypothesis. In the

testing part, the P-values are also tested for

the statistically highly significant P<0.001

which provides the chance of one in a

thousand of being wrong.41

One-sided versus two-sided test

The one-sided test is a hypothesis test in

which the values for which rejecting the

null hypothesis are located entirely in one

tail of the probability distribution.42

Thus,

the one tailed test refers either to the left tail

or the right tail of the distribution. The two-

sided test the case is when the alternative

hypothesis is set at β≠0. It measures both

tails of the distribution. In this paper the

alternative hypothesis is set for when β≠0,

and therefore the two-sided test is

conducted.

41 www.statsdirect.com/help/default.htm#basics/p_values.htm 42 www.stats.gla.ac.uk/steps/glossary/hypothesis_testing.html#cr

The null and the alternative hypothesis were

formulated as following:



Per Share.



Per Share.

Expectations & sample results

Based on the literature review the

expectations were that the independent

variables have indeed explanatory power.

The question is if the explanatory power of

the independent variables is linear. For the

independent variable compensation it is

clear that if there is a relationship, it is not a

linearity one. For the other independent

variable it seems that there is a high chance

that their relationship with the dependent

variable is a linear one.

Null hypothesis challenged

S&P500

The T-statistics of the estimated β has at a

confidence level of 1% a value of 2.468997

which is <2.57, leading to not rejecting the

null hypothesis.



which is >1.96, leading to rejecting the null

hypothesis.


confidence level of 10% a value of

2.468997 which is >1.64, leading to

rejecting the null hypothesis.

The P-value is set at a value of 0.0138

17



which is p<0.10 and p<0.05; leading to

rejecting and for the p>0.001 and p>0.01

not rejecting the null hypothesis.

AVERAGE AGE



2.59146643

which is >2.57, leading to




2.59146644





2.59146645




which is p<0.01; p<0.05 and p<0.10

leading to rejecting the null hypothesis. For

the p>0.001 the null hypothesis is not

rejected.

RATIO INDEPENDENT/BOARD



which is <2.57, leading to not rejecting the

null hypothesis.



which is >1.96, leading to rejecting the null

hypothesis.



2.023447 which is p>1.64, leading to



43 Absolute value is taken into account. 44 Absolute value is taken into account. 45 Absolute value is taken into account.

which is p<0.10 and p<0.05; leading to

rejecting and for the p>0.001 and p>0.01

not rejecting the null hypothesis.

MEETINGS



2.79658046





2.79658047





2.79658048




which is p<0.01;<0.05 and < 0.10 leading

to rejecting the null hypothesis. For the

p>0.001 the null hypothesis is not rejected.

Overall testing result

The results shows that the P-value is lower

in case when p<0.10 and p<0.05 (and in

some cases p<0.01 and <0.001). This means

that the distributions have very small

overlapping areas. Based on the almost all

the rejecting of the null hypothesis, here we

state that we support the claim of the

alternative hypothesis (which was that

β≠0).

46 Absolute value is taken into account. 47 Absolute value is taken into account. 48 Absolute value is taken into account.

18



CONCLUSION

To examine how characteristics of

Independent Directors affect firm

performances, 85 companies operating in

the S&P500 are examined where the firm’s

performance is measured using the Earnings

Per Share which is the dependent variable.

The change in average age, change in

average Board meetings attending, the

returns of the S&P 500 and the ratio of

Independent Directors/Board are taken as

the explanatory variables relative to the

dependent variable. These independent

variables are chosen, because they are

pointers of best practices/rules within the

Corporate Governance.

The research question to be answered was:

“What is/are the main characteristics of the

Independent Directors affecting the

performance of companies?”

To answer the research question, the

following formulated hypotheses will be

tested:



Per Share49



Per Share:

Expectations

The expectation of the testing was that

there would be a positive relationship

between the firm performance indicator

(EPS) and the characteristics of the

Independent Directors within the Board as

well as the market returns. The

When β=0

implications of these research results can

be useful to understand the underlying

motives of the corporate governance of

public companies and adds value as an

empirical research in respect to the

volatility of the stock markets and the

control of the management decisions,

such as hiring and engaging diverse

directors which impact performances and

with that the profits of companies.

Results of testing

From the testing it has been clear that the

average age has a significant influence on

the performance of firms as a corporate

governance indicator. At the confidence

levels of, 0.01, 0.05 and 0.10, the null

hypothesis is rejected, however at a

confidence level of 0.001, the null

hypothesis is not rejected (one out of

thousand marginal error) which provides a

strong indication that there is indeed a very

strong relationship between change in

average age and the profitability of a firm

or company. Also, this affirms what is

argued in the literature review.

RATIO INDEPENDENT DIRECTORS/

BOARD

Interestingly in this case, is the strong

correlation between the Earnings Per Share

and this explanatory variable. At a

confidence levels of 0.10 and 0.05, the null

hypothesis is rejected. This does not apply

for the stricter confidence levels of 0.001

and 0.01, where the null hypothesis is not

rejected indicating that there is not a highly

significantly correlation between the ratio

of Independent Directors/Board and the

profitability of the company. This affirms

the notion that even though Independent

Directors bring objectivity and monitoring

roles of other executives, their activities

may sometimes hamper the general

19



performance of CEOs and other Executives

they seek to monitor.

RETURNS OF THE MARKET

Comparable to the results of the ratio of

Independent Directors/Board, this

explanatory variable is also significantly

correlated with the performance indicator at

only 0.05 and 0.10 confidence level. This

does not apply for the stricter confidence

levels of 0.001 and 0.01, where the null

hypothesis is not rejected leading to the

conclusion that this explanatory variable is

not highly significantly correlated with the

EPS.

AVERAGE MEETINGS

Interestingly this indicator has proven to

have a very strong and significant impact

and correlates positively with profitability.

At the confidence levels of, 0.01, 0.5 and

0.1 the null hypothesis was not rejected

implying that the profitability is

significantly dependant on the meetings of

board members,(even though it is rejected

at 0.001, the error margin is very strict and

very minute). This also seems to be confirm

the literature where is stated by Ntim and

Osei in their research on the impact of

corporate board meetings on corporate

performance” concluded that “there exists a

statistically significant and positive

association between the frequency of

corporate board meetings and corporate

performance, implying that boards that

meet more frequently tend to generate

higher financial performance50

.

50 Ntim and OSEI, impact of corporate meetings on corporate performance.

Overall Conclusion & Recommendations

Based on the findings the conclusion is that,

to improve on profitability, companies

should concentrate a little bit more on

characteristics and activities of the Board

such as the average age and the meetings

per year attended by the Independent

Directors. For example, they should

consider increasing the youthfulness of the

Board or reducing the average age of board

members since the average age of Board is

highly significantly correlated to

profitability of firms. Also, the Board

should also consider attending Board

meetings more frequently since frequent

meetings leads to, in general, higher firm

performance.

For the market and the number of

Independent Directors in the Board, is that

they are also significant correlated, but not

highly significant. This variable should

taken into account as well and care should

be taken in not increasing and decreasing

the numbers beyond a certain reasonable

number. For the market, it can be said that

companies also have firm-specific risks and

profitability.

20



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