Corporate Financial Performance and M&As( private equity ...

20/10/2014

Corporate Financial Performance and M&As( private equity buyout): Empirical Evidences from the UK Dimitrios Simos

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Abstract

The initial aim of this thesis is to discover the factors which influence the financial

performance of the UK companies when a merger or an acquisition takes place,

specific an action that was performed in the form of a private equity buyout . In

specific, we decided to utilize ROE and ROA ratios, gross margin profit and total

sales as dependent variables. The control variables are the current assets, current

liabilities, long-term liabilities (loans), previous experience (months) of CEO in the

same NACE and the administration experience (months on board). We decided to use

panel regression analysis by collecting annual data from 223 companies during 1997

and 2008. The empirical evidences indicate that the corporate financial performance is

influenced by the current liabilities, the long-term liabilities, the total assets and the

current assets. Furthermore, no statistical results were found about the overall

previous experience of the board’s member, the previous experience of the board’s

member in the same nace code as the new firm that emerges from a private equity

buyout, with the financial performance of the new firm.

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Contents

Chapter 1 – Introduction............................................................................................. 3

Chapter 2 – Organization, administration and financial performance .................. 6

2.1 Historical evolution and significance of the Organization and the

Administration.......................................................................................................... 6

2.2 The Organization and the financial performance ........................................... 8

Chapter 3 – Literature Review ................................................................................... 9

Chapter 4 - Methodology and Research Aims ........................................................ 15

4.1 Introduction ...................................................................................................... 15

4.2 Theoretical Background .................................................................................. 15

4.2.1. Research Aims ........................................................................................... 15

4.2.2 Explanation of the Variables .................................................................... 16

4.2.3. Simple Regression Analysis ..................................................................... 18

4.2.4 Autocorrelation .......................................................................................... 19

4.2.5. Heteroscedasticity ..................................................................................... 19

4.2.6 Multicollinearity ........................................................................................ 19

4.3. The estimating models .................................................................................... 20

4.4 Problems in statistical analysis........................................................................ 21

4.5 Statistical Analysis............................................................................................ 22

Chapter 5 – Epilogue ................................................................................................. 29

5.1 Conclusions ....................................................................................................... 29

5.2 Implications....................................................................................................... 30

Βibliography ............................................................................................................... 31

Appendix ..................................................................................................................... 34

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Chapter 1 – Introduction

Since 1970 Private equity buyouts constitute a continuously growing method of

buyouts. Buyout actions, including all the possible categories and separations (MBO,

SBO, MBI, BIMBO, and IBO), primarily and in most of the cases are aiming at the

restructuring and/or replacement of the target team’s management simultaneously

with the removal of the previous owner. Finance from a private equity and a potential

contribution of debt by financial institutions have been used for that reason. Viral V.

Acharya, Oliver F. Gottschalg, Moritz Hahn and Connor Kehoe (2013) used data

from a consulting company and major investors in PE funds and came up with results

that enhance the view concerning positive outcomes coming from PE buyouts

specifically in target firms. They found that the claiming ownership of PE companies

has a positive impact on the operating performance of the portfolio companies,

relative to that of the sector. In particular, during PE ownership the deal margin

(EBITDA/Sales) increases by around 0.4% p.a. above the sector median and the deal

multiple (EBITDA/Enterprise Value) increases by around 1 (or 16%) above the sector

median. In addition to that Robert S. Harris, Tim Jenkinson, Steven N. Kaplan (2012)

present evidence from over 200 institutional investors on the performance of nearly

1400 U.S. private equity (buyout and venture capital ) and discovered that U.S.

buyout private equity fund performance has exceeded that of public markets for most

vintages for a long period of time. Statistically significant results have shown that

there was an outperformance versus the S&P 500 averages 20% to 27% over the life

of the fund and more than 3% per year. The previous results strengthen my view and

have motivated me to focus on private equity buyouts, as they potentially depict an

effective way to profitability with a lot of advantages.

However, the main interests of this thesis is to examine the contribution of board

members specific individual characteristics to the financial performance of the target

firm, after a private equity buyout. The current literature on how to compose effective

boards in terms of outcomes (Financial Performance, Return, Governance etc) is more

than rich. According to Jensen (1993), companies with oversized boards tend to

become less effective. Yermack (1996) addresses these arguments empirically using a

sample of U.S. firms and finds that, indeed, having small boards enhances a

company’s performance and influences positively the investor’s behavior and

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company’s value. Vafeas (1999) approximates the intensity of Board activity by the

Board meeting frequency (i.e. the number of meetings of Board of Directors each

fiscal year). Using a sample of 307 U.S. companies in 1990 – 1994, his results show

that firms with a lower number of Board meetings exhibit the highest price to book

value. Hayes et al. (2005) explored the interactions between the percentage of shares

held by the directors and firm performance. Using a sample of S&P 500 firms for the

periods 1997 and 1998, they report a significant positive relationship between the

percentage of shares held by independent directors serving on the finance &

investment committee ,as well as on the strategy committee (but not in the other

committees) and firm´s performance. Wiersema and Bantel (1992) focus on the

demographic characteristics of the Board and their influence on the firm´s strategic

decisions. The age of board members represents one of the demographic variables

chosen for the study. Using a sample of 100 firms in1983, they report a negative

relationship between the average age of board members and the changes in corporate

strategies. This result shows that younger boards are more tolerant to bear more risk

and are more likely to accept major changes in the process of decision-making in

comparison to older directors.

However, the previous literature depicts that the results regard mainly the overall

board composition (average size and age, inside or outside directors etc.) and not

specific characteristics of the individual participants in the board of directors (as

professional experience). The paper of Viral V. Acharya, Oliver F. Gottschalg, Moritz

Hahn and Connor Kehoe (2013) and particularly their research on section 7 about

human capital factors and the professional experience contributes to my initial

thought about the importance of specific professional experience in relative fields of

the firm. A study on the leading partner professional experience in the deal shows a

relevancy between the return of the deal and the specific professional skills. PE firms

are keen on matching their partners to the need of their deals and came up with results

that are consistent with the success of choosing people with skills (professional

experience) that are matching the circumstances and result in higher abnormal

performances.

The previous review can be applied in private equity buyout and financial

performance of target firms considering the professional skill of the new board of

directors.

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As it was mentioned before, we will focus on private equity buyouts in the UK over

the last few years. The goals of this case study are based on the effect of previous

professional experience of the board of directors’ members and the correlation with

the financial outcome of the firms (target firm) that they represent now.

At the end, it is important to mention that the dependent variables were the ROA and

ROE ratios, the gross profit and total sales. As control variables were utilized the

current assets, current liabilities, long-term liabilities (loans), previous experience

(months) of CEO in the same NACE and the administration experience (months on

board).

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Chapter 2 – Organization, administration and financial performance

The purpose of this chapter is to provide theoretical background in order to support

how the effective organization and administration within a firm is able to boost its

financial performance. In specific, if there is an efficient administrative and

organizational framework in a firm, it will enhance its financial performance. The

most common theory which describes better the financial success or the failure of a

firm is the managerial theory.

The managerial theory was created to explain the markets’ situation where there is

perfect competition and the businesses have changed their interest in increasing their

sizes rather than maximizing their profits (Mullins, 2013). The supporters of this

theory separate the managers from the business owners. In specific, they refer to firms

which are surrendered or merged. The business owners are not able to have daily

intervention into the mangers decisions and actions. Therefore, it is possible for

managers to engage in some form of takeover or merger in order to satisfy their

personal ambitions, instead of seeking company’s profit maximization.

In specific, the current reference is commonly known as the Agency Theory. In

modern business organizations, there is a clear distinction between shareholders and

owners (employees - representatives). Therefore, the representatives do not to aim of

maximizing shareholder wealth but increasing their salaries and bonuses through

mergers and acquisitions. It occurs because of the failure of the owners to control

effectively the performance of their executives. This problem usually occurs in large

enterprises. Hence, it was described below managerial techniques and administrative

ways in order to achieve a satisfied level of financial performance (Mullins, 2013).

2.1 Historical evolution and significance of the Organization and the

Administration

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The personnel management exists in enterprises for several years. Each worker should

have defined tasks which will faithfully be followed in order to achieve his objectives.

One of the founders of organization and administration management is the Taylor

Frederic who believed that the only way, to have higher wages and increased profits,

was the increasing productivity. In short, the purpose of the management (Taylor

1910) is to ensure simultaneously maximum welfare of employer and employee.

Taylor believed that wages’ rise and profits increase will be achieved through higher

levels of productivity (Buchanan, 2013).

In addition, Henri Fayol, a French engineer, considered the father of the

administration theory who paid more attention on the higher hierarchical levels of

organizational and administrative pyramid. Also, he analyzed the duties of Directors

(Buchanan, 2013).

A few years later, Luther Gulick was one of the founders of scientific administration

and he created the definition of the management functions, which proved over time as

the most authentic. His definition was the basis of the organization, staffing,

coordination and the budget (Buchanan, 2013).

Max Weber, a French sociologist, (1864-1920) pay special emphasis to the

administration as a social phenomenon. He argued that bureaucracy is the most

efficient instrument for exercising control over the workers. The key features are the

defined remits, strict rules and the application of hierarchical organization system

((Buchanan, 2013).

Henry Laurence Gantt (1861-1919), a mechanical engineer, argued the need of

cooperation and understanding between management and the employees of a

company. He pointed out the importance of training and the employee's participation

in administrative problems.

After the end of World War II, the organization and personnel administration began to

consider as the most important part of a company which cared about its prosperity and

progress. Thus, they companies started to recruit individuals who had the appropriate

experience and university educational knowledge (Buchanan, 2013). For instance, the

positions of higher hierarchical levels of a company were taken by highly educated

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personnel. Thus, the personnel administration was considered as an investment to a

company.

2.2 The Organization and the financial performance

Additionally, Smet et al (2007) examined the connection of organizational and

financial performance. They discovered that the effective organization of a firm aids

its financial performance. In specific, if the higher ranks employees are well-educated

and experienced, the company will be able to achieve high financial performance. The

financial performance is greater when the board of directors’ members are highly

educated and have plenty of years’ experience in the same NACE. However, it occurs

when the executives only for company’s goodwill. It means that there no agency

costs. It is very difficult to be achieved because the directors usually acts for their own

interests and they attempt to enhance their career. On the other hand, the hierarchica l

organization is the keystone of the firm. If the company has organization and

administration inefficiency, it is obvious that its financial performance will not be

maximum.

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Chapter 3 – Literature Review

At this chapter we attempt to gather the appropriate information from past researches

concerning of the impacts of joint ventures announcements on the companies’ total

performance. We have used academic papers from ScienceDirect webpage which

have been published after the millennium (2000). ScienceDirect is website operated

by the Anglo-Dutch publisher Elsevier containing (as of 2013) about 11 million

articles from 2,500 journals and 6,000 e-books, reference works, book series and

handbooks. This website is approved by each university across the globe.

Bouaziz and Triki (2012) attempted to discover the key role of Directors’ Board on

the financial performance of a company. He collected data from 2007 to 2010 for 26

companies listed on the Tunisian stock exchange market. They used Return on Equity

(ROE), Return on Assets (ROA) and sales as dependent variables which measures the

total financial performance. In addition, the control variables were the independence

of board members, the overlapping functions, the size of the audit committee, the

frequency of meeting, the gender diversity of the board and the level of firm’s debt.

Also, they used descriptive statistics and regression analysis in order to produce their

empirical results. The findings indicated that all bivariate and multivariate tests show

the existence of determinism of some features of the board on financial performance

is measured by ROA, ROE and sales.

In addition, Levi et al (2014) discovered if the gender of directors on board influence

the occurrence of mergers or acquisitions as well as the financial performance of a

company. Also, they attempted to discover if the gender of a company’s CEO has an

impact on the existence of a merger and the company’s financial performance. In

specific, they collected a dataset of 1500 companies listed at S&P 500 from 1997 to

2009. They used ROA, ROE and Tobin’s Q as dependent variables. Also, the control

variables were the board size, the fraction of independent directors, the fraction of mal

directors linked to female directors, the sales growth, the experience of a director on

the board of directors and the book leverage. They utilized panel regression analysis

and descriptive statistics. Their empirical results show that found another association

between gender diversity on corporate boards and the functioning of boards in the

economically important arena of M&As.

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Moreover, Molz (1988) examined the managerial domination of boards of directors

and their financial performance. He collected a data of 50 firms which were taken

from the 1983 Fortune 500 industrial list. As dependent variable used the ratio ROE

and ROA. The control variables were the joint chairman/CEO, the outside-dominated

nominating committee, the outside-dominated social responsibility committee, the

inside versus outside directors, the frequency of board meetings, the stockholdings of

inside directors and the stockholdings of outside directors. Also, he used correlation

analysis, analysis of variance (ANOVA) and the confirmatory factor analysis (CFA).

This research has contributed in two significant ways to an understanding of the

impact of board of directors on corporate performance. First, it offers a scale for

measuring the degree of managerial domination and pluralism in any Fortune 500

board of directors. Second, the use of this scale has weakened the argument that

managerial dominated boards are associated with superior financial performance.

Furthermore, Palmberg (2011) attempted to investigate the performance effect of

corporate board of directors. They collected data from 132 firms listed on the

Stockholm Stock Exchange during the 2005 to 2008 period. She used ROE and ROA

ratios in order to represent the financial performance. Also, she used descriptive

statistics, correlation analysis and panel regression analysis in order to estimate her

empirical findings. The results show that Swedish firms, on average, have inefficient

investment strategies, and firms invest in projects for which the marginal cost of

capital is higher than the marginal return. This estimated marginal q value agrees with

previous research for Sweden.

In addition, Amici et al (2013) examined the impact of mergers at the financial

performance in the banking sector. The collected data from 1999 to 2009 for the

United States and the European Union. They calculated ROE and ROA ratios in order

to estimate the financial performance before and after the merger. They used

descriptive statistics and regression analysis in order to estimate their empirical

results. The findings indicate that merger creates shareholder value when involving

non-banking financial partners and allowing banks to expand abroad, while

international strategic alliances tend to destroy shareholder value.

According to Beamish and Lupton (2009), the mergers and acquisitions help

companies to grant access to new markets, obtain knowledge, capabilities and other

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tangible and intangible resources. The researchers investigated the financial

performance in order to observe the directors board impact the last 25 years. In

addition, they used no empirical analysis, but they analyzed descriptively the total

performance, the knowledge management, the governance and the control. Also, they

focused on the internationalization and the cultural differences of joint ventures. In

specific, their research seems to be a literature review. At the end, they discovered

that the necessity of honesty, trust, and commitment for the success of the merger,

settling disputes by focusing on what is best for the merger rather than individual

partner objectives, and division of managerial responsibilities according to the

functional expertise of each partner.

Furthermore, Bowe et al (2014) examined the impact of merger announcements on

the equity share ownership structure in international joint ventures. They used a

pooled cross-section dataset from UK-based and foreign mergers which took place

from 1995 to 2000. Hence, they collected 442 observations. The dependent variable

was the performance of the company (ROA and ROE) and the control variables were

the firm growth options, the firm size, the firm profitability, the value chain location,

the foreign partner industry and the temporal considerations. Their empirical results

show that the performance of the company after the announcement of joint venture is

influenced positive by the firm growth options, the value chain location and the

temporal considerations.

In addition, Chang and Huang (2002) investigated the importance of corporate

multinationalism explaining the wealth impacts of mergers announcements in Taiwan.

The researchers collected a sample from January 1988 to October 1999 including each

merger or acquisition which took place in Taiwan. They used descriptive statistics

methodology and cross-sectional regression analysis in order to estimate their

empirical results. The evidences show that firms announcing international joint

ventures into countries with no prior operational activity experience significantly

favorable market response. In contrast, prior experience in international joint ventures

does not explain the cross-sectional difference of announcement period abnormal

returns.

Moreover, Cheng et al (1998) examined the impact of US-Chinese mergers on the

financial performance of a company. Therefore, they collected a dataset from 1973 to

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1993 including 103 mergers which took place during these period in the US and in

China. As dependent variable, they used ROE and ROA. Also, as control variables,

they utilized current ratio, debt ratio, total asset turnover ratio, directors board

experience, industry classification, prior experience in China, location of headquarters

and the date of the merger announcement. Additionally, the used cumulative

abnormal returns methodology and regression analysis in order to estimate their

empirical findings. In specific, they discovered none of these factors affect the size of

the financial performance before or after the occurrence of a merger.

Chung et al (1993) explored the effect of merger on the firms’ financial performance.

They gathered a dataset from January 1969 to December 1989. The da ta were public

announcements of agreement to form, approval of the U.S. government or the

partners’ governments, and formal establishment of the merger. Therefore, the sample

size included 230 international mergers formed by 173 US companies. In addition,

they used descriptive statistics methodology and regression analysis in order to

estimate their empirical findings. At the end, the researchers discovered that the

impacts of international mergers on financial performance. It also determines the

gains or losses affected by the mergers of cross-border alliances are related to the

economic status of the host countries and foreign partners.

Additionally, Elayan (1993) examined the effects mergers on the financial

performance real estate sector in the United States. He collected a dataset from

January 1972 to December 1989. Also, he used ROE and ROA ratio in order to

discover if the mergers influence the stock returns of real estate companies as well as

their financial performance. They utilized regression analysis and descriptive statistics

in order to produce their findings. At the end, they found out that synergy is a major

factor as a source of gain to companies involved in mergers. Real estate companies

have some unique institutional characteristics in the form of informational advantage

regarding local real estate’s markets or superior managerial and technical expertise

that causes real estate companies to achieve higher profits from merger relative to

non-real estate companies.

Moreover, Georgieva et al (2012) examined the effects of legislation, regulations and

culture on cross-border mergers. They collected a dataset from 1988 to 2006

including mergers from 105 countries across the globe. Hence, the collected 1101

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observation. Also, the dependent variable was the number of mergers which took

place in each country. As control variables, it was utilized the GDP, the GDP growth,

the rule of law, the US FDI as a portion of GDP, the total amount of imports and

exports as a portion of GDP, the Hofstede distance, the language, the religion, the

competiveness and the political instability. Also, the researchers used multiple

regression analysis in order to estimate their empirical results. The findings of current

research indicate that mergers are an intermediate (hybrid) organizational form

between market- like contracts among independent entities and firm-like deals such as

M&A. Also, suggest that the total volume of mergers with US partners increases if the

legal environment of the foreign country is weak and the US FDI are positively

related to the volume of cross-border mergers in foreign countries, suggesting that

FDI and cross-border contracting are complements. At the end, they found that

companies may enter mergers to overcome trade barriers, and/or that the impact of

cultural differences may be mitigated by the “openness” of the countries of domicile

of the participating firms.

In addition, Richards and Yang (2007) examined the factors which influence the

companies’ equity structure after the announcement of a merger. In specific, they

relied on environmental uncertainty, behavioral uncertainty faced by the foreign

partner, two components of national culture-power distance and uncertainty

avoidance, and the moderating effects of uncertainty avoidance on the two types of

uncertainty. The researchers collected a dataset of 543 observation from 1985 to 2004.

The dataset covered 31 different countries. Also, they utilized descriptive statistics,

pairwise correlation analysis and regression analysis. The empirical evidences show

that provide support for the national culture perspective. Culture is related to the

multinational partner's equity ownership. At the end, their study discovered that

culture makes a difference with regard to MNE equity ownership. Specifically, low

uncertainty avoidance and high power distance levels of the MNE home country are

significantly related to MNE equity ownership.

In addition, Merchant (2012) examined the impacts of mergers on stock-market

performance in three country groups. The researcher collected data from 1986 to

1999. The country groups were categorized as developed countries, newly

industrialized countries and emerging countries. Also, he utilized descriptive

statistics, MANOVA analysis and regression analysis in order to produce his

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empirical results. In specific, he discovered highlight systematic differences in merger

characteristics across three host-country groups: (i) developed countries, (ii) newly

industrialized countries, and (iii) developing countries. Importantly, his study

proposes that the stock-market performance of American firms whose mergers are

located in different host-country contexts can be better understood if mergers are

viewed within relatively homogeneous host-country groups than at coarser levels of

locational aggregation. At the end, some merger characteristics consistently influence

firms’ shareholder value (albeit sometimes in opposite directions) whereas the

valuation impact of other characteristics depends upon a particular host-country

group.

At the end, Priya and Nimalathasan (2013) attempted to discover the board of

directors’ features which influence the financial performance of companies in Sri

Lanka. They collected a dataset of 141 observations from 2008 to 2012. They used

ROE and ROA ratios as dependent variables. Also, the control variables were number

of regular meetings held by the board of directors, the average age of board of

directors’ members, the number of women in board, the inside directors, the CEO

duality (both chairman and CEO), the board composition and the board size.

Additionally, they used descriptive statistics, correlation analysis and panel regression

analysis in order to estimate their empirical results. At the end, the empirical findings

revealed that there is a significant relationship that exists between board of directors’

characteristics and financial performance. The suggested Number of Women in Board

(NWB) and Inside Directors (ISD) are significantly correlated with Return on Asset

(ROA) at 5 percent level of significance. NWB and ISD are significantly correlated

with Return on Equity (ROE) at 5 percent level of significance. At the same time

CEO Duality (CEO DUAL) is significantly correlated with ROE at 1 percent level of

significance. Finally the rest of other variables are not correlated. The results add

insight on the relation between monitoring mechanisms and financial performance of

companies in an emerging market.

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Chapter 4 - Methodology and Research Aims

4.1 Introduction

The current statistical analysis attempts to investigate the factors which influence the

profitability of UK companies before and after the acquisition or merger which took

place. In specific, the merger or the acquisition occurred in 2002. We collected data

from 222 companies in the UK and we used the total turnover, the gross profit, the

ROE ratio and ROA ratio as dependent variables. The data was collected from the

ThomsonOne database where we discovered 222 pre-buyout firms and 221 post-

buyout organizations that belong to the sample of management buyout transaction in

the UK from 2003 to 2008. Also, the accounting and the financial information was

gathered from Orbis and Amadeus database. Furthermore, it is important to mention

that the required information about the mergers or the acquisition was collected from

Zephyr database. Additionally, as control variables, we used the total assets, the

current liabilities, the current assets, the long-term liabilities (loans), the previous

experience of CEO in the same NACE (sector experience) and the experience of CEO

on board (administration experience). The data was calculated by using the mean of

each variable. In specific, we calculated the mean of each variable from 1997 to 2002

in order to investigate the era before acquisition or merger. Also, we computed the

mean of each variable from 2003 to 2008 in order to examine the period after the

acquisition or merger. Therefore, we execute four regression analysis for each period

in order to examine the impact of each factor on the dependent variable. At the end, it

is important to mention that we used the STATA program in order to compute our

empirical results.

4.2 Theoretical Background

Before displaying the empirical analysis, it is important to mention the theoretical

methodology that we use and to present the research aims.

4.2.1. Research Aims

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The aims of this empirical research is to discover the factors which influence the

profitability of UK companies before and after an acquisition or merger which took

place in 2002. In specific, we assume that the profitability of a company (measured by

ROE, ROA, gross profit and total sales) is influenced by total assets, current assets,

current liabilities, long-term liabilities (loans), previous experience (months) of CEO

in the same NACE and the administration experience (months on board).

The research hypotheses are presented below:

Hypothesis 1

Ho: There is not statistically significance between the ROA/ROE/gross profit/total

sales, as dependent variables, and total assets, current assets, current liabilities, long-

term liabilities (loans), previous experience (months) of CEO in the same NACE and

the administration experience (months on board), as control variables.

Ha: There is statistically significance between the ROA/ROE/gross profit/total sales,

as dependent variables, and total assets, current assets, current liabilities, long-term

liabilities (loans), previous experience (months) of CEO in the same NACE and the

administration experience (months on board), as control variables.

4.2.2 Explanation of the Variables

During this part, we describe the nature of the dependent and control variables.

As dependent variables were used the ROE ratio, the ROA ratio, the gross margin and

total turnover.

In specific, the return on earnings (ROE) is a ratio which measures corporation’s

profitability. The calculation formula is:

𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐸𝑞𝑢𝑖𝑡𝑦=𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒

𝑆𝑕𝑎𝑟𝑒𝑕𝑜𝑙𝑑𝑒𝑟′𝑠 𝐸𝑞𝑢𝑖𝑡𝑦

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In addition, the return on assets (ROA) is a ratio which gives an idea as to how

efficient management is at using its assets to generate earnings (Damodaran, 2011).

The calculation formula is:

𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐴𝑠𝑠𝑒𝑡𝑠 =𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

Additionally, the gross margin was calculated by deducting the cost of goods from the

total sales. The calculation formula is:

𝐺𝑟𝑜𝑠𝑠 𝑀𝑎𝑟𝑔𝑖𝑛 = 𝑆𝑎𝑙𝑒𝑠 − 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑔𝑜𝑜𝑑𝑠

At the end, we used the total sales as the fourth dependent variable in order to

estimate the profitability (Damodaran, 2011).

On the other hand, we present the nature of the control variables.

In specific, we used the total assets which show every fixed or current asset which

belongs to a company. In addition, the current assets indicate each asset which is not

stable during the calendar year. Also, we used the long-term liabilities which show the

liabilities of a firm which do not change easily. Long-term loans belong to this

category. Also, we utilized the current liabilities which express the liabilities of a firm

in a calendar year. This size changes easily in the short-run.

Additionally, we used as control variable the CEO’s experience in the same NACE

and the total administrative experience on Board of Directors.

At the end, we should justify the selection of this variables. In specific, there are

plenty researchers who use ROE or ROA ratios in order to express the financial

profitability of a company. For instance, Bouaziz and Triki (2012) examined the key

role of Directors’ Board on the financial performance of a company in Tunisia. The

control variables were the independence of board members, the overlapping functions,

the size of the audit committee, the frequency of meeting, the gender diversity of the

board and the level of firm’s debt. Additionally, Levi et al (2014) discovered if the

gender of directors on board influence the occurrence of mergers or acquisitions as

well as the financial performance of a company. Also, they attempted to discover if

the gender and the experience of a company’s CEO has an impact on the existence of

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a merger and the company’s financial performance. The control variables were the

board size, the fraction of independent directors, the fraction of mal directors linked to

female directors, the sales growth, the experience of a director on the board of

directors and the book leverage.

Moreover, Priya and Nimalathasan (2013) attempted to discover the board of

directors’ features which influence the financial performance of companies in Sri

Lanka. The control variables were number of regular meetings held by the board of

directors, the average age of board of directors’ members, the number of women in

board, the inside directors, the CEO duality (both chairman and CEO), the board

composition and the board size.

Therefore, we are able to support that the majority of researchers use ROE and ROA

ratio in order to estimate financial performance. Also, there are limited academics,

such as Bouaziz and Triki (2012), who use total sales as a dependent variable in order

to estimate the financial performance. Additionally, we decided to utilize initiatively

gross margin in order to examine if the regression analyze will produce the same

results as the previous financial performance variables.

4.2.3. Simple Regression Analysis

The regression analysis is a statistical method in order to explore linear linkages

between variables. The researcher often searchers for causal impacts of control

variables on the dependent variable. For instance, how the total income influences the

total consumption in an economy. In addition, the researcher collects a dataset on a

range of his interest and executes regression to estimate the causal effect. Also, the

explorer evaluates the “statistical significance” of the estimated linkages and the

degree of confidence that the pragmatic linkage is close to the estimated linkage.

(Verbeek, 2009)

A common linear regression model is presented such as,

Υi = β0 + β1Χi + εi,

Where Y is the dependent variable, βο is the constant of the model, β1 is the

coefficient of the independent variable X, X is the independent variable and ε is the

error term (Halkos,2006)

19

4.2.4 Autocorrelation

The term “autocorrelation” is related with the correlation of lagged value and the

current value of a variable. This phenomenon is common in time series analysis.

Autocorrelation is named usually as “lagged correlation” or “serial correlation”. The

positive autocorrelation may be characterized as a specific form of “persistence”. It

means that there is a trend for a system to constant from one observation to the next

(Halkos,2006).

4.2.5. Heteroscedasticity

The heteroscedasticity phenomenon takes place when the variation of the errors are

not similar across observations i.e. variances are not stable.

There are two types of heteroscedasticity:

1. Unconditional Heteroscedasticity: When variation does not systematically raises

or fall with fluctuations in the value of control variable. This phenomenon violates the

assumption 4 of Gauss-Markov Theorem but does not create any significant problems

at the regression analysis.

2. Conditional Heteroscedasticity: It takes place when error variance changes with

the value of control variable and it is more problematic.

4.2.6 Multicollinearity

Multicollinearity is a statistical phenomenon which takes place if there are strong

correlation between the control variable. The basic problem in regression analysis is

that the control variables seem to be statistically significant, but in fact they are not.

Multicollinearity exists when there are linear correlation relationships in the model.

In addition, multicollinearity is able to be detected by using the Variance Inflation

Factor (VIF) or the Tolerance indicator. (Katos,2006)

Tolerance indicator = 1 –R2, where R2 is the goodness of fit

20

𝑽𝑰𝑭 =𝟏

𝑻𝒐𝒍𝒆𝒓𝒂𝒏𝒄𝒆 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓

4.3. The estimating models

As we have already mention, we decided to utilize four econometric models in order

to examine the factors which influence the profitability of the UK companies

before/after the merger or acquisition. Before displaying the mathematic formula of

the models, it is important to mention the reasons where we decided to use the

logarithmic models. The logarithmic transformation of the variables is very common

in regression analysis. We used logarithms in order to handle a situation where a non-

linear linkage exists between dependent and control variables (Verbeek, 2009). Thus,

we assure more the stability (fit) of our models. Additionally, we should mention that

a logarithmic transformation of the variables changes also the interpretations of the

results. In specific, each variable (dependent or control) was transformed into

percentages. Hence, an increase of a control variable by 1% will have a similar effect

on the dependent variable (depends on the positive or negative impact of control

variable).

The mathematic expression of each model is provided below:

Model 1

We decided to use the natural logarithm of total sales as the dependent variable.

𝑙𝑜𝑔𝑠𝑎𝑙𝑒𝑠 = 𝐶0 + 𝐶1 ∗ 𝑙𝑜𝑔𝑇𝐴+ 𝐶2 ∗ 𝑙𝑜𝑔𝐶𝐴+ 𝐶3 ∗ 𝑙𝑜𝑔𝐶𝐿 + 𝐶4 ∗ 𝑙𝑜𝑔𝐿𝐿+ 𝐶5

∗ 𝑙𝑜𝑔𝑆𝐸𝑥𝑝 + 𝐶6 ∗ 𝑙𝑜𝑔𝐴𝑑𝑚𝐸𝑥𝑝 + 𝑢

Model 2

We utilized the ROE ratio as a dependent variable in order to measure profitability.

𝑅𝑂𝐸 = 𝐶0 + 𝐶1 ∗ 𝑙𝑜𝑔𝑇𝐴+ 𝐶2 ∗ 𝑙𝑜𝑔𝐶𝐴+ 𝐶3 ∗ 𝑙𝑜𝑔𝐶𝐿+ 𝐶4 ∗ 𝑙𝑜𝑔𝐿𝐿 + 𝐶5 ∗ 𝑙𝑜𝑔𝑆𝐸𝑥𝑝

+ 𝐶6 ∗ 𝑙𝑜𝑔𝐴𝑑𝑚𝐸𝑥𝑝 + 𝑢

21

Model 3

We decided to utilize the natural logarithm of gross profit as a dependent variable.

𝑙𝑜𝑔𝑟𝑜𝑠𝑠 = 𝐶0 + 𝐶1 ∗ 𝑙𝑜𝑔𝑇𝐴+ 𝐶2 ∗ 𝑙𝑜𝑔𝐶𝐴+ 𝐶3 ∗ 𝑙𝑜𝑔𝐶𝐿+ 𝐶4 ∗ 𝑙𝑜𝑔𝐿𝐿+ 𝐶5

∗ 𝑙𝑜𝑔𝑆𝐸𝑥𝑝 + 𝐶6 ∗ 𝑙𝑜𝑔𝐴𝑑𝑚𝐸𝑥𝑝 + 𝑢

Model 4

We used the ROA ratio as a dependent variable.

𝑅𝑂𝐴 = 𝐶0 + 𝐶1 ∗ 𝑙𝑜𝑔𝑇𝐴+ 𝐶2 ∗ 𝑙𝑜𝑔𝐶𝐴+ 𝐶3 ∗ 𝑙𝑜𝑔𝐶𝐿+ 𝐶4 ∗ 𝑙𝑜𝑔𝐿𝐿 + 𝐶5 ∗ 𝑙𝑜𝑔𝑆𝐸𝑥𝑝

+ 𝐶6 ∗ 𝑙𝑜𝑔𝐴𝑑𝑚𝐸𝑥𝑝 + 𝑢

Where,

logsales = natural logarithm of sales

logross =natural logarithm of gross profit

ROE = Return on Earnings ratio

ROA = Return on Assets ratio

logTA = natural logarithm of total assets

LogCA = natural logarithm of current assets

LogCL = natural logarithm of current liabilities

logLL = natural logarithm of long-term liabilities

logSexp = natural logarithm of experience in the same NACE

logAdmExp = natural logarithm of administrative experience on board

C0 = the constant

u = the error term

4.4 Problems in statistical analysis

22

According to Gauss-Markov theorem (Plackett, 1950), we should check for

heteroskedasticity, multicollinearity and autocorrelation problems in the regressions.

The check of collinearity problems between the independent variables was made by

using the VIF – collinearity diagnostics. The detection of autocorrelation was taken

place by using the Breusch-Godfrey Serial Correlation LM test. Also, the

heteroscedasticity was detected by using White heteroscedasticity test.

The residuals heteroskedasticity problems were corrected the White methodology for

coefficient covariance matrix (White heteroskedasticity-consistent standard errors &

covariance). Also, the autocorrelation problems were eliminated by adding a first

degree autoregressive model (AR1) in the initial regression model.

4.5 Statistical Analysis

At this part of the statistical analysis we presents the empirical results of each model.

The table below displays the evidences of first model between the two eras.

The table displays the results for the first model, using total assets, current assets,

current liabilities, long-term liabilities, NACE experience and Board Directors

experience as control variables. The significance level (*) is equal to 0.05 (2-sided)

The values of t-statistics are presented in parentheses, as well as the coefficient

values (no brackets). Dependent variable is sales. There are two groups (before

23

and after M&A).

Variables Before 2002 After 2002

Total Assets 0.210

(1.429)

0.376

(3.089)

Current Assets -0.020

(-0.692)

0.047

(1.109)

Current Liabilities 0.778*

(6.397)

0.464*

(4.723)

Long-term Liabilities -0.093*

(-2.155)

-0.089

(-1.795)

NACE experience 0.043

(0.582)

-0.072

(-1.109)

Board of Directors

Experience

-0.034

(-0.659)

0.029

(0.296)

C0 0.863*

(3.309)

1.173*

(4.017)

Observations 222 222

R-squared 0.682 0.618

Adjusted R-squared 0.667 0.592

F-statistics 49.26 24.49

*statistically significant at a=0.05, (t-statistic in parenthesis)

The results show that the total sales are influenced statistically significant by the

current liabilities and the long-term liabilities. In specific, the current liabilities have a

positive impact (0.778) on the total turnover. In specific, if the current liabilities

increase by 1%, the total sales will raise by 0.78%. On the other hand, there is a

negative effect (-0.093) between the long-term liabilities and the total sales. In

specific, if the long-term liabilities increase by 1% then the total turnover will

decrease by 0.093%. In addition, the other control variables do not influence the total

sales. The results above are for the period before the merge or the acquisition take

place. Also, the R-Squared value is satisfied (0.682) which shows how successful is

the fit of the model.

Moreover, at the period after the occurrence of merger or acquisition, only the current

liabilities influence positive (0.464) the total sales. In specific, if the current liabilities

24

increase by 1% then the total sales will raise by 0.464%. The impact seems to be

lower than the pre-merger/acquisition period. The R-squared value is satisfying at

61.8%. Also, none other control variable is statistically significant and therefore they

do not influence the total sales (company’s profitability).

The table present the findings for the second model, using total assets, current

assets, current liabilities, long-term liabilities, NACE experience and Board

Directors experience as control variables. The significance level (*) is equal to

0.05 (2-sided) The values of t-statistics are presented in parentheses, as well as the

coefficient values (no brackets). Dependent variable is ROE ratio. There are two

groups (before and after M&A).


Total Assets -3.660*

(-5.690)

-1.321*

(-2.157)

Current Assets 0.141

(0.988)

-0.233

(-1.169)

Current Liabilities 2.638*

(4.878)

0.793

(1.589)

Long-term Liabilities 0.880*

(4.734)

0.788*

(3.173)

NACE experience -0.220

(0.274)

-0.203

(-0.659)

Board of Directors

Experience

0.175

(-0.644)

-0.194

(-0.393)

C0 0.836

(0.727)

-0.110

(-0.081)


R-squared 0.200 0.132




Additionally, the findings indicate that the ROE ratio is influenced statistically

significant by the total assets and the long-term liabilities. In specific, the total assets

have a negative effect (-3.660) on the ROE ratio. In specific, if the total assets

25

increase by 1%, the ROE ratio will decrease by 3.66%. On the other hand, there is a

positive impact (0.880) between the long-term liabilities and the ROE ratio. In

specific, if the long-term liabilities increase by 1% then the ROE ratio will raise by

0.88%. In addition, the other control variables do not influence the ROE ratio. The

empirical evidences above study the period before the occurrence of merge or

acquisition.

Furthermore, at the period after 2002, the total assets still influence negative (-1.321)

the ROE ratio. In specific, if the total assets increase by 1% then the ROE ratio will

fall by 1.32%. Also, the long-term liabilities have a positive impact (0.788) on the

ROE ratio. In specific, if the long-term liabilities increase by 1% then the ROE ratio

will raise by 0.79%. The impact of both control variable seems to be lower than the

pre-merger/acquisition period. Also, none other control variable is statistically

significant and hence they do not influence the ROE ratio (company’s profitability).

The table displays the results for the third model, using total assets, current assets,

current liabilities, long-term liabilities, NACE experience and Board Directors

experience as control variables. The significance level (*) is equal to 0.05 (2-sided)

The values of t-statistics are presented in parentheses, as well as the coefficient

values (no brackets). Dependent variable is gross margin. There are two groups

(before and after M&A).


Total Assets 0.594*

(5.533)

0.773*

(6.402)

Current Assets -0.019

(-0.428)

-0.091*

(-2.409)

Current Liabilities 0.162 0.108

26

(1.728) (1.101)

Long-term Liabilities -0.032

(-1.211)

-0.015

(-0.344)


(-0.501)

-0.017

(-0.295)

Board of Directors

Experience

0.049

(0.391)

-0.090

(-1.015)

C0 0.963*

(4.566)

0.651*

(2.564)


R-squared 0.625 0.696




Moreover, the empirical results express that the gross profit is influenced statistically

significant by the total assets. In specific, the total assets have a positive impact

(0.594) on the gross profit. In specific, if the total assets increase by 1%, the gross

profit will raise by 0.59%. In addition, the other control variables do not influence the

gross profit. The results above are for the period before the occurrence of the merge or

the acquisition. Also, the R-Squared value is satisfied (0.625) which shows how

successful is the fit of the model.

Additionally, at the period after the existence of merger or acquisition, the total assets

and the current assets influence statistically significant the gross profit. The impact of

total assets is positive and the effect of current assets is negative. In specific, if the

total assets increase by 1% then the gross profit will rise by 0.77%. The impact seems

to be higher than the pre-merger/acquisition period. Also, if the current assets increase

by 1% then the gross profit will fall by 0.09%. The R-squared value is satisfying at

69.6%. Also, none other control variable is statistically significant and therefore they

do not influence the gross profit.

The table present the findings for the fourth model, using total assets, current

assets, current liabilities, long-term liabilities, NACE experience and Board

Directors experience as control variables. The significance level (*) is equal to

27

0.05 (2-sided) The values of t-statistics are presented in parentheses, as well as the

coefficient values (no brackets). Dependent variable is ROA ratio. There are two

groups (before and after M&A).


Total Assets 0.018

(0.383)

1.652*

(3.126)

Current Assets 0.002

(0.219)

-0.118

(-0.703)

Current Liabilities -0.040

(-0.997)

-1.149*

(-2.688)

Long-term Liabilities -0.028*

(-2.201)

-0.711*

(-3.743)


(-0.711)

0.216

(0.798)

Board of Directors

Experience

0.026

(0.531)

0.385

(0.987)

C0 0.242*

(2.823)

-2.224

(-1.875)


R-squared 0.095 0.227




The empirical evidences above show that the long-term liabilities influence

statistically significant the ROA ratio in the period before 2002. The impact is slightly

negative (-0.028). In specific, if the long-term liabilities increase by 1% the ROA ratio

will fall by 0.03%. Also, none other control variable is statistically significant which

means that they do not influence the ROA ration. Additionally, during the era after

2002, the total assets, the current liabilities and the long-term liabilities have a

statistically significant impact on the ROA ratio. The impact of total assets seems to

be positive and the effect of current and long-term liabilities seems to be negative. In

specific, if the total assets increase by 1%, the ROA ratio will raise by 1.65%. Also, if

the current liabilities raise by 1%, the ROA ratio will decrease by 1.15%. In addition,

28

if the long-term liabilities increase by 1%, the ROA ratio will fall by 0.71%. At the

end, none other independent variable seems to play a statistically significant role on

the ROA ratio.

29

Chapter 5 – Epilogue

The last section of the thesis includes a laconic analysis of the empirical results. In

specific, we present the evidences of our empirical examinations. In addition, we

propose some implications about potential researches concerning of the factors which

influence the financial performance of a company after a merger or acquisition took

place.

5.1 Conclusions

The current empirical investigation attempted to discover the factors which influence

the financial performance of UK companies before and after the acquisition or merger

which took place in the form of a private equity buyout. We used regression analysis

in order to produce our empirical results. The dependent variables were the ROA and

ROE ratios, the gross profit and total sales. As control variables were utilized the

current assets, current liabilities, long-term liabilities (loans), previous experience

(months) of CEO in the same NACE and the administration experience (months on

board). In specific, the current liabilities influence positively the total turnover. On the

other hand, there is a negative effect between the long-term liabilities and the total

sales. The results above are valid for the pre-merger era. Moreover, at the period after

the occurrence of merger or acquisition, only the current liabilities influence positive

the total sales. The impact seems to be lower than the pre-merger/acquisition period.

Additionally, the findings indicate that the ROE ratio is influenced negative by the

total assets and the long-term liabilities. On the other hand, there is a positive impact

between the long-term liabilities and the ROE ratio. These findings are valid for the

pre-merger period. Furthermore, at the period after 2002, the total assets still

influence negative the ROE ratio. Also, the long-term liabilities have a positive

impact on the ROE ratio. The impact of both control variable seems to be lower than

the pre-merger/acquisition period.

Moreover, the empirical results express that the gross profit is influenced positively

by the total assets. In addition, the other control variables do not influence the gross

profit. The results above are for the period before the occurrence of the merge or the

acquisition. Additionally, at the period after the existence of merger or acquisition, the

30

total assets and the current assets influence the gross profit. The impact of total asse ts

is positive and the effect of current assets is negative. The impact seems to be higher

than the pre-merger/acquisition period.

At the end, the empirical evidences above show that the long-term liabilities influence

slightly negative the ROA ratio in the period before 2002. Also, none other control

variable is statistically significant which means that they do not influence the ROA

ration. Additionally, during the era after 2002, the total assets, the current liabilities

and the long-term liabilities have an impact on the ROA ratio. The effect of total

assets seems to be positive and the effect of current and long-term liabilities seems to

be negative.

5.2 Implications

The current empirical analysis attempted to discover the factors which influence the

financial performance of a company in the UK after the occurrence of a private equity

buyout . However, these findings are valid only in the UK. It means that it is not

known if other European countries have the same features. Therefore, the potential

researchers should examine if a same empirical analysis is applicable in the US, the

Eurozone, the Russian Federation or Japan. When there are empirical results, it will

be possible to make a cross-country comparison.

31

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34

Appendix

Dependent Variable: LOGSALESPRO

Method: Least Squares

Date: 09/16/14 Time: 23:09

Sample (adjusted): 2 222

Included observations: 145 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

LOGTAPRO 0.209995 0.146862 1.429878 0.1550

LOGCAPRO -0.020305 0.029355 -0.691696 0.4903

LOGCLPRO 0.777997 0.121629 6.396465 0.0000

LOGLLPRO -0.092639 0.042992 -2.154821 0.0329

LOGEXPNACEPRO 0.042713 0.073402 0.581902 0.5616

LOGEXPRO -0.093993 0.142446 -0.659849 0.5105

C 0.863206 0.261268 3.303914 0.0012

R-squared 0.681719 Mean dependent var 4.325392

Adjusted R-squared 0.667881 S.D. dependent var 0.421276

S.E. of regression 0.242780 Akaike info criterion 0.053752

Sum squared resid 8.134031 Schwarz criterion 0.197456

Log likelihood 3.103004 Hannan-Quinn criter. 0.112144

F-statistic 49.26327 Durbin -Watson stat 2.085479

Prob(F-statistic) 0.000000

Dependent Variable: LOGSALESAFTER


Date: 09/16/14 Time: 23:41




LOGTAFTER 0.376054 0.121738 3.089036 0.0027

LOGCAFTER 0.046671 0.042087 1.108914 0.2704

LOGCLAFTER 0.464068 0.098252 4.723262 0.0000

LOGLLAFTER -0.088829 0.049499 -1.794554 0.0760

LOGEXPNACEAFTER -0.072109 0.065026 -1.108925 0.2704

LOGEXPAFTER 0.029413 0.099510 0.295580 0.7682

C 1.173402 0.292132 4.016684 0.0001



35



Log likelihood -5.181621 Hannan-Quinn criter. 0.323288



Dependent Variable: LOGROEPRO


Date: 09/16/14 Time: 23:31




LOGCAPRO 0.140681 0.142377 0.988088 0.3247

LOGCLPRO 2.637859 0.540681 4.878771 0.0000

LOGEXPNACEPRO -0.219812 0.341310 -0.644026 0.5205

LOGEXPRO 0.175439 0.641111 0.273648 0.7847

LOGLLPRO 0.880000 0.185856 4.734846 0.0000

LOGTAPRO -3.660160 0.643801 -5.685232 0.0000

C 0.835891 1.149183 0.727379 0.4681

R-squared 0.199732 Mean dependent var -1.237500







Dependent Variable: LOGROEAFTER


Date: 09/16/14 Time: 23:41




LOGTAFTER -1.320688 0.612261 -2.157068 0.0338

LOGCAFTER -0.233248 0.199498 -1.169174 0.2456

LOGCLAFTER 0.792565 0.498500 1.589900 0.1156

LOGLLAFTER 0.788466 0.248519 3.172660 0.0021


LOGEXPAFTER -0.193541 0.493087 -0.392510 0.6957

C -0.109740 1.363167 -0.080504 0.9360

R-squared 0.131670 Mean dependent var -0.911337






36


Dependent Variable: LOGROSSPRO


Date: 09/16/14 Time: 23:36



White heteroskedasticity-consistent standard errors & covariance


LOGTAPRO 0.593811 0.107309 5.533641 0.0000

LOGCAPRO -0.018791 0.043930 -0.427748 0.6694

LOGCLPRO 0.162364 0.093988 1.727499 0.0859

LOGLLPRO -0.031897 0.026344 -1.210779 0.2277

LOGEXPNACEPRO -0.033785 0.067210 -0.502673 0.6159

LOGEXPRO 0.049165 0.125752 0.390972 0.6963

C 0.963014 0.210879 4.566658 0.0000





Log likelihood 6.939728 Hannan-Quinn criter. 0.052459


Prob(F-statistic) 0.000000 Wald F-statistic 48.32028

Prob(Wald F-statistic) 0.000000

Dependent Variable: LOGROSSAFTER


Date: 09/16/14 Time: 23:38




LOGTAFTER 0.773260 0.120776 6.402433 0.0000

LOGCAFTER -0.090971 0.037751 -2.409760 0.0178

LOGCLAFTER 0.108348 0.098470 1.100307 0.2739

LOGLLAFTER -0.015219 0.044293 -0.343602 0.7319


LOGEXPAFTER -0.089881 0.088601 -1.014452 0.3129

C 0.651089 0.253916 2.564191 0.0119 R-squared 0.696406 Mean dependent var 4.031909

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Adjusted R-squared 0.677818 S.D. dependent var 0.416423 S.E. of regression 0.236366 Akaike info criterion 0.017472

Sum squared resid 5.475163 Schwarz criterion 0.194403 Log likelihood 6.082716 Hannan-Quinn criter. 0.089168 F-statistic 37.46654 Durbin-Watson stat 2.563017


Dependent Variable: ROAPRO

Method: Least Squares Date: 09/16/14 Time: 23:44 Sample (adjusted): 1 222

Included observations: 176 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LOGTAPRO 0.017999 0.046942 0.383436 0.7019

LOGCAPRO 0.002278 0.010450 0.217967 0.8277 LOGCLPRO -0.040133 0.040269 -0.996626 0.3204 LOGLLPRO -0.028075 0.012754 -2.201179 0.0291

LOGEXPNACEPRO -0.018477 0.026008 -0.710424 0.4784 LOGEXPRO 0.026085 0.049150 0.530727 0.5963

C 0.242378 0.085848 2.823325 0.0053


Adjusted R-squared 0.063162 S.D. dependent var 0.092590 S.E. of regression 0.089618 Akaike info criterion -1.947562

Sum squared resid 1.357303 Schwarz criterion -1.821463 Log likelihood 178.3854 Hannan-Quinn criter. -1.896417 F-statistic 2.966441 Durbin-Watson stat 2.149535


Dependent Variable: LOGROAFTER Method: Least Squares

Date: 09/16/14 Time: 23:46 Sample (adjusted): 1 222 Included observations: 76 after adjustments

Variable Coefficient Std. Error t-Statistic Prob. LOGTAFTER 1.652093 0.528441 3.126356 0.0026

LOGCAFTER -0.117929 0.167684 -0.703279 0.4842

LOGCLAFTER -1.149494 0.427601 -2.688240 0.0090 LOGLLAFTER -0.711016 0.189968 -3.742823 0.0004

LOGEXPNACEAFTE

R 0.215532 0.270107 0.797951 0.4276 LOGEXPAFTER 0.384857 0.389768 0.987399 0.3269

C -2.223779 1.186052 -1.874942 0.0650

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R-squared 0.226781 Mean dependent var -2.044749 Adjusted R-squared 0.159545 S.D. dependent var 0.978870

S.E. of regression 0.897392 Akaike info criterion 2.708936 Sum squared resid 55.56656 Schwarz criterion 2.923609 Log likelihood -95.93956 Hannan-Quinn criter. 2.794730

F-statistic 3.372893 Durbin-Watson stat 1.129042 Prob(F-statistic) 0.005649

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