Thesis - Final

Analysts’ Forecasting Performance In
Relation To Their Information Environment:
Evidence from Corporate Governance Attributes and UK Corporate
Governance Code 2010.
Author: Danial Adibi
Student ID: 130006436
Degree: BSc (Hons) Accounting and Finance
Supervisor: Dr Andrew Yim
Submitted: April 2016

1
Plagiarism Statement
Student Name: Danial Adibi
Student Number: 13006436
Degree: BSc (Hons) Accounting and Finance
Dissertation Title: Analysts’ Forecasting Performance In relation To Their Information
Environment: Evidence From Corporate Governance Attributes and UK Corporate Governance Code
2010.
Supervisor Name: Dr Andrew Yim
To be completed by the student:
“I certify that I have complied with the guidelines on plagiarism outlined in my Course Handbook in
the production of this dissertation and that it is my own, unaided work”
Signature:
Danial Adibi
Undergraduate School

2
CONTENTS TABLE
ACKNOWLEDGEMENT 5
ABSTRACT 6
1.INTRODUCTION 6
2.LITERATURE REVIEW AND HYPOTHESE 13
2.1. Role of the Analysts and the Information Quality 13
2.2. Forecast Quality and Board Attributes 15
2.2.1. Board Size 16
2.2.2. Board Composition 18
2.2.2. CEO Duality 21
2.3. Forecast Quality and Auditing 23
2.3.1. Auditors’ Reputation 23
2.3.2. Size of Audit Committee 24
2.3.3. Audit Committee Independence 26
3. RESEARCH DESIGN AND METHODOLOGY 28
3.1. Description of Data and Sample Selection 28
3.2. Research Models 31
3.2.1. Univariate Tests 31
3.2.2. Multivariate Tests 34
3.3. Control Variables 36
2.3.1. Firm Size 37
2.3.2. Financial Leverage 38
2.3.3. Loss 39
2.3.4. Lagged Level of the Loss Function 40

3
4. EMPIRICAL FINDINGS AND DISCUSSIONS 41
4.1. Univariate Results 41
4.1.1. Forecast Error 41
4.1.2. Forecast Bias 43
4.2. Multivariate Results 50
4.2.1. Robust Statistics 50
4.2.2. Forecast Error 51
4.2.3. Forecast Bias 63
5. CONCLUSIONS 68
REFERENCES 74
APPENDIX A LIST OF FIGURES 83
APPENDIX B LIST OF TABLES 85

4
LIST OF TABLES
TABLE 1 – Sample Constitution procedure 30
TABLE 2 – Descriptive Statistics 47
PANEL A: Descriptive statistics of FEs and FBs for every semester 47
PANEL B: Descriptive statistics of FEs and FBs 48
PANEL C: Descriptive statistics of FEs and FBs before and after the
introduction of the UKCGC
48
PANEL D: Descriptive statistics of FBs between 2007-2011 and
2012-2015
48
TABLE 3 49
PANEL A: Welch’s t-test on FEs 49
PANEL B: Welch’s t-test – difference in mean of FBs between 2007-
2011 and 2012-2015
49
PANEL C: Welch’s t-test on FBs 50
TABLE 4 – Correlations 53
TABLE 5 – OLS Regression Outputs 54
TABLE 6 – DPD Regression Outputs 55

5
ACKNOWLEDGEMENT
On the outset of this study, I would like to extend my sincere gratitude to Cass Business
School for accepting me, and letting me fulfil my dreams of being a student.
I would also like to take this opportunity to thank all my committee members for their
active contributions, guidance, and encouragements.
I am ineffably indebted to Dr. Andrew Yim, who expertly guided me throughout this
study. His enduring enthusiasm for Accounting and Finance and his kindness helped make this
study very enjoyable.
I am also extremely thankful and pay my gratitude to Dr. Lorenzo Trapani for his
continuous support and valuable guidance in completing this study.
I also acknowledge with a deep sense of reverence my appreciation towards friends,
especially Jayllea Wischhoff, Milahat Sara Khan, and Shazia Shafique, who spent valuable
hours of their time consulting and supporting me throughout the study.
Above all, I am indebted to my family, whose love and value grows with age. Finally,
I acknowledge my mother, Armita, who is a champion, and blessed me with a life of joy in the
hours when the library’s lights were off.
Any omission in this brief acknowledgement does not mean lack of gratitude.
Thank You.
Danial Adibi.

6
ABSTRACT
This study examines (1) the relationship between corporate governance and analysts’
forecasting performance and (2) the impact of the UK Corporate Governance Code (UKCGC) 2010 on
analysts’ forecasting performance. Using dynamic and static panel models, I experiment on 142
corporations from the Financial Times Stock Exchange (FTSE) All-Share Index, and find that both
models demonstrate the following on average: (1) there is an inverse relationship between board size
and analysts’ forecasts accuracy; (2) analysts’ forecasts are more accurate and optimistic when one of
the four largest audit firms performs the audit engagement; (3) there is a positive relationship between
the size of the audit committee and analysts’ accuracy; (4) Analysts become less accurate but more
optimistic as financial leverage increases. In addition, the dynamic models reveal that analysts on
average underreact to new information and that analysts in the UK are becoming more optimistic over
time. My results provide evidence that (1) Analysts’ accuracy has improved since the introduction of
the UKCGC 2010 while the results from forecast bias are inconclusive; (2) firm-specific characteristics
influences analysts’ performance; and (3) there is a strong relationship between the strength of
corporate governance mechanisms and analysts’ performance.
Keywords: Corporate Governance, UK Corporate Governance Code 2010, Firm-Specific
Characteristics, Analysts’ Accuracy, Analysts’ Bias.
1. Introduction
In corporate law, a corporation is defined as an “artificial” person. However, every
corporation primarily is, in fact, a structure. They are setup to meet the needs that were not
fulfilled by previous business structures. From the Darwinian Theory, it became apparent that
each development is stronger and more resilient than the previous ones, and it is more
scrutinised by outsiders. This, in a nutshell, describes the need for corporate governance.
Corporate governance theory is a relatively new phenomenon; however, its
development roots back to agency theory. Smith (1838) identified the potential problems with
separation of ownership. Almost a century later, Payne et al. (1933) showed that ownership

7
and control of corporations became separated. However, agency theory did not peak until the
publication of Jensen and Meckling (1976) and Fama and Jensen (1983). The theory is based
on a relationship where the principal delegates work for the agent. The relationship is wounded
because it is likely to be poisoned with opportunism or self-interest. The relationship is further
deteriorated as a result of information asymmetry. Agency theory outlines that corporate
governance mechanisms, especially the board of directors, is a significant design to ensure that
the issues relating to the principal-agent relationship are minimised.
Today, corporate governance has become a much more complex issue. However, its
objective remains the same: to achieve more transparency and accountability, and a desire to
boost investors’ confidence. Thus, corporate governance aims to improve the information
environment.
Businesses require investors’ funds to be able to implement their plans, stay ahead of
the competition, and survive. To persuade investors to invest their funds in their business, they
must instil investors with confidence that the company is being well managed and that the
company will continue to be financially sound in the foreseeable future.
To have this assurance, investors refer to the general purpose financial statements and
other information that the company might release. Investors expect the general purpose
financial statements to reveal a complete picture of the entity’s financial position. Thus,
investors will use the financial statements to decide upon their ideal investment opportunity.

8
However, recent high-profile corporate collapses such as Enron, Parmalat, Royal Bank
of Scotland, and Olympus Corporation have adversely impacted the confidence of the general
public.
Academics and practitioners reason that the lack of effective corporate governance
mechanisms is the main reason behind these corporate collapses; and that good corporate
governance can help to prevent or, at least, reduce the likelihood, of these events happening
again. Most advanced market economies have attempted to solve the problem of the lack of
corporate governance, although, it has not been perfectly solved. For example, in response to
corporate scandals, the Financial Reporting Council (FRC) in 2010 introduced the UKCGC. It
adopted measures that should help enhance the boards’ performance and awareness of its
strengths and weaknesses. As a result, effective corporate governance should improve the
information environment.
Economic theory suggests that product market competition is the primary driver of
economic efficiency. However, I am doubtful that it can entirely solve the problems associated
with corporate governance. For example, competitive markets may cut the rate of return on
capital; thus, it can reduce the amount of capital that managers can seize. However, it is
ineffective in preventing managers from seizing competitive returns after the capital is sunk.
The corporate governance mechanisms could act as a barrier in this scenario (Shleifer and
Vishny, 1996).

9
In addition to the general purpose financial statement, which is backwards looking in
nature, investors are likely to consider analysts’ reports to discover their investment
opportunities. These analysts play a significant role in today’s economy. They are involved in
the process of collecting, analysing, and presenting data in a comprehensible fashion to the
investment community. In a nutshell, they are an intermediary between companies and
investors (Chung and Jo, 1996).
The work of an analyst is based on the information environment that they operate in.
The objective of this study is to examine the relationship between corporate governance, the
introduction of the UKCGC 2010, and analysts’ forecasting performance.
I define corporate governance as a function of boards’ attributes, auditing attributes, as
well as firm-specific characteristics. In addition, I define analysts’ performance as a measure
of their accuracy and bias.
A large body of researchers examine the relationship between boards’ attributes and the
quality of financial reporting (Beasley, 1996; Klein, 2002; Abbott et al., 2004). The results
from these studies are trivial. They reveal that better-structured boards improve the financial
reporting quality. However, in this study, I test whether these attributes have an impact on
analysts’ performance. Some studies attempt to answer this question in the context of Initial
Public Offering (IPO) (Mnif, 2010; Ahmad‐Zaluki and Nordin Wan‐Hussin, 2010). Very few
studies attempt to provide directly empirical evidence of the relationship between corporate

10
governance and analysts’ forecasting performance. For example, Karamanou and Vafeas
(2005) investigated the relationship between boards’ attributes, audit committees, and financial
reporting disclosure. They provided evidence that smaller boards are associated with greater
forecast accuracy. Furthermore, Ajinkya et al. (2005) inspected the relationship between the
percentage of independent directors, leadership structure, and earnings forecast quality. They
found a positive relationship between the proportion of independent directors on the boards
and forecast accuracy.
However, these two primary studies are based on data from the US, ignored
environments where information asymmetry is at its peak, and where monitoring mechanisms
play a major role in the oversight of management. Beside, these two studies looked at different
aspects of corporate governance in isolation. For example, Karamanou and Vafeas (2005) did
not incorporate leadership structure in their models.
I attempt to extend prior literature by firstly looking at the overall picture of corporate
governance influencing analysts’ forecasts. More precisely, I will combine the main factors
that are a function of corporate governance into a single model. Secondly, I will be modelling
my data using dynamic panel models as well as static panel models. The use of dynamic panel
models allows me to analyse the impact of analysts’ previous period performance on their
current performance. Thirdly, I find no research at this time that investigates the direct
relationship between the introduction of the UKCGC 2010 and analysts' forecasting

11
performance. The UKCGC influences the information environment; thus, it directly impacts
the performance of analysts and warrants empirical examination. Lastly, I will be contributing
to the small, but slowly growing, body of literature that examines the relationship between
corporate governance and analysts’ forecasting performance in the UK setting. UK provides
an interesting setting as corporate governance is less regulated than in the US. The US favours
a “rules-based” approach to corporate governance. For example, US corporations are required
by law to establish boards with a majority of independent directors. On the other hand, the UK
follows a “comply or explain” approach; companies can choose whether to comply with any
of the provisions of the UKCGC. Thus, companies have no statutory obligations to comply
with the UKCGC. They are only required to provide an explanation in the event of non-
compliance.
FRC claims that this approach is much stronger than the “rules-based” approach. For
example, fully independent audit committees were a norm in the UK before the EU introduced
a statutory requirement for listed companies to have an audit committee with at least one
independent director. Therefore, this study could potentially be used to compare and contrast
the performance of “rules-based” approach and “principles-based” approach; guide the FRC,
other regulators, and decision makers on improving the UKCGC and corporate governance
mechanisms.

12
By using a sample of 142 companies listed on the FTSE All-Share Index, I detect the
following: (1) analysts’ forecasts accuracy have improved since the introduction of the
UKCGC 2010 while the results from analysts’ bias are inconclusive; (2) firm-specific
characteristics influence analysts’ performance; and (3) there is a positive relationship between
the strength of corporate governance and analysts’ performance.
The remainder of the paper proceeds as follows: Section 2 reviews the prior literature,
and sets out the hypotheses; Section 3 presents the research design and methodology; results
and discussions are presented in Section 4; lastly, Section 5 concludes the study.

13
2. LITERATURE REVIEW AND HYPOTHESES
2.1. Role of the Analysts and the Information Quality
There is a consensus among practitioners and scholars that analysts play a major role
in the flow of information within the financial markets. Jensen and Meckling (1976) suggested
that the role of analysts is to monitor management and to provide relevant information to
providers of capital. They argued that analysts exist because of their ability to reduce agency
costs, although, they provided no empirical evidence to support their argument.
On the other hand, Moyer et al. (1989) built on Jensen and Meckling (1976) by
providing empirical evidence. They concluded that analysts play a major role in reducing the
cost of debt and equity by making the markets more informationally efficient.
Similarly, Chung and Jo (1996) found that analysts play a major role in motivating
managers. This could be as a result of managerial incentives being linked to surpassing
analysts’ forecasts. Thus, analysts’ forecasts could potentially minimise the agency cost
associated with the separation of ownership and control.
In addition, Givoly and Lakonishok (1979) found a significant change in the price of
stocks on disclosure of analysts’ revisions; thus, analysts’ forecasts revisions are valuable to
investors. However, they concluded that markets are slow to react to the revisions announced
by analysts; therefore, allowing an opportunity for abnormal returns to be earned.

14
Givoly and Fried (1982) expanded on the report by Givoly and Lakonishok (1979) by
concluding that analysts’ forecast errors are closely related to security price movements. Thus,
analysts’ predictions are a better substitute for market expectation than the predictions
generated by the time-series models.
Moreover, Brown and Rozeff (1978) delivered several powerful conclusions. Using
nonparametric statistics, they showed that (1) analysts’ forecasts are more accurate when using
Box and Jenkins modelling in comparison to martingale, and sub-martingale models and (2)
the Value Line Investment Survey consistently outperforms the Box and Jenkins model. Thus,
analysts’ forecasts are more accurate than time-series models. Furthermore, by assuming that
markets are rational, they concluded that analysts’ forecasts should be used in corporate finance
decision-making; for example, when estimating the cost of capital.
A vast number of studies indicate that the information environment affects the analysts’
forecast errors. For example, Kross et al. (1990) took The Wall Street Journal coverage as a
proxy for the information environment. Using ordinary least squares analysis, bootstrapping
techniques, controlling for timing advantage, and firm size, they found a significant
relationship between the increased coverage in The Wall Street Journal and analysts’
forecasting accuracy.
Furthermore, Lang et al. (2002) made a number of key conclusions: (1) firms that cross-
list on the US stock exchange tend to receive more analyst coverage and have greater forecast

15
accuracy; (2) firms with higher analyst coverage and higher forecast accuracy tend to have a
higher value; and (3) cross-listing enhances the firm value through a better information
environment. Therefore, analysts’ forecasts become more accurate as the information
environment improves.
In addition, Lang and Lundholm (1996) suggested that forecast errors reduces for firms
that have a greater information disclosure. They also documented a significantly positive
relationship between forecast dispersion and information asymmetry. Thus, the lower the
information asymmetry or, the better the information environment, the more accurate the
analysts’ forecasts.
Based on the evidence from prior studies, I expect that analysts’ forecast quality is a
function of the information environment.
2.2. Forecast Quality and Board Attributes
Alchian and Demsetz (1972) described a firm as a set of contracts among factors of
production, where each factor of production is motivated by self-centeredness. In such firms,
there exists a separation of ownership and control. In these firms, internal monitoring of
managers is essential. Fama (1980) loudly expressed that individual managers are likely to be
concerned with the performance of their subordinates and their supervisors since their
managerial product is likely to be a positive function of theirs. However, the question remains
about who disciplines the managers. According to the optimal allocation of resources in

16
Modern Portfolio Theory, security holders are too diversified across the securities of many
firms; therefore, individual security holders will have no interest in personally overseeing the
activities of the firm. Consequently, this is left to the board of directors (Fama, 1980).
In this study, I define corporate governance as a function of the board of directors’
attributes, the quality of auditors, and the characteristics of audit committees. Prior studies
define board of directors’ attributes as board size, board composition, and board leadership
structure.
2.2.1. Board Size
Various academic researchers from the field of psychology support the hypothesis that
large groups promote deindividuation among group members (Mullen, 1987). Mullen and
Copper (1994) found a statistically significant relationship between group size and
performance. Slater (1958) concluded that larger groups are too “hierarchical, centralised, and
disorganise[d]”. An explanation of these findings could be that larger groups become very
complex extremely fast. For example, in a group of three people, there are six potential
combinations of relationships. The combinations increase to 996, if the group size is increased
to seven people (Kephart, 1950).
Many finance scholars have cited the studies stated above to show that the size of the
board could have an impact on the efficiency of the board.

17
For example, Jensen (1993) examined the effectiveness of corporate internal control
since the second industrial revolution. The study concludes that the internal control systems
have failed to cope with the shifts in technological, political, and economic environments. One
of the reasons for this failure is “oversized boards”. Therefore, smaller boards are likely to be
more effective as it is easier for Chief Executive Officers (CEOs) to control them.
Similarly, Lipton and Lorsch (1992) attempted to analyse the factors that make it
difficult for boards to operate effectively. They identified that “lack of time and board size”
were amongst the factors that influenced the effectiveness of the board. They claimed that when
boards have more than 10 members, it becomes extremely challenging to express ideas and
opinions, particularly as information becomes complex in nature, and that time is limited. In
addition, they claimed that larger boards tend to lack cohesiveness.
Yermack (1996) used Tobin’s Q as a measure of market valuation and analysed 452
large US industrialised corporations between 1984 and 1991. He found an inverse relationship
between board size and their effectiveness. This relationship has a convex shape. Therefore,
boards became ineffective when firms grow from small to medium-sized boards.
In general, these researchers have found that larger boards initially arrange for some
key functionalities of the board. However, after a certain point, they face coordination and
communication problems. Therefore, as the board size passes its optimal point, the
effectiveness of the board and the performance of the firm declines.

18
Slater (1958) claimed that the optimal point for a board size is when the board members
feel free enough to express negative and positive feelings and take an aggressive approach to
solving problems even at the risk of irritating each other. However, he stated that board
members, at all times, should respect each other.
Lastly, Karamanou and Vafeas (2005) showed that firms with smaller boards tend to
have a more accurate management earnings forecast.
Thus, I predict that firms with smaller boards have a better information environment.
As a result, the first hypothesis is as follows:
𝐻1𝑎: Firms with a smaller board of directors have a more effective corporate
governance; thus, more accurate forecasts.
𝐻1𝑏: Analysts will issue a more optimistic forecast for firms with smaller boards.
2.2.2. Board Composition
Hart (1983) concluded that incentive schemes are adequate to align the shareholders’
and managers’ interests. According to this argument, supervising the board is a hopeless
activity and it could limit the process of optimising management.
However, numerous researchers on corporate governance suggest that independent
directors are an essential part of monitoring management. For example, Fama (1980) declared
that managers are the best group of individuals to monitor themselves. However, managers
might decide that they will be better off by colluding, rather than competing against each other.

19
As a result, it is essential to include independent directors to reduce the probability of collusive
behaviours by managers.
Moreover, Fama (1980) stated that most independent directors are leaders from the
corporate and academic community; therefore, they have an incentive to “develop reputations
as experts in decision control”. They are “disciplined by the market for their services which
prices them according to their performance as referees”.
In addition, Jensen (1993) argued that the CEO is often the chairman/woman of the
board of directors. The chairman/woman is responsible for hiring, firing, and compensating the
CEOs. Clearly, when the CEO acts as a chainman/woman, there exists a conflict of interest.
Thus, the inclusion of independent directors helps the board to perform their duties effectively.
Fredrickson et al. (1988) studied a model for CEO dismissal and reported that inside
directors are unlikely to take a position against the CEO of a company. The paper concluded
that the inclusion of independent directors could be one of the key solutions to this problem.
Mace (1971) followed a similar line of thought as Fredrickson et al. (1988). Through a
series of intensive field research interviews, he concluded that CEOs who attempt to surround
themselves with inside directors are only obeying the letter of the law and not the spirit of the
law. These CEOs are likely to dampen rather than encourage a questioning mind. In the end,
he concludes that independent directors positively contribute towards the well-functioning of
the board.

20
Weisbach (1988) provided evidence for the argument made by Mace (1971) and
Fredrickson et al. (1998). He concluded that the inclusion of independent directors increases
the probability of a CEO losing their job after a period of poor performance; therefore,
independent directors tend to enhance the value of a firm through their CEO replacements.
However, the study does not display similar remarks for insider-dominated boards.
More recently, researchers have shifted their focus toward the relationship between
board independence and quality of financial disclosures. For example, Beasley (1996) analysed
75 fraud and 75 no-fraud firms using a logistic regression analysis and reported that firms with
a higher percentage of independent directors have a lower statistically significant likelihood of
committing financial fraud.
Similarly, Uzun et al. (2004) analysed corporate wrongdoings between 1978 and 2001
to document that the probability of wrongdoings is less when independent directors dominate
the board.
Chen et al. (2006) found a similar result as Beasley (1996) and Uzun et al. (2004) in
China.
Moreover, Karamanou and Vafeas (2005) concluded that firms with a higher proportion
of independent directors tend to have a higher forecast quality.
Lastly, Ajinkya et al. (2005) reported that firms with a higher proportion of independent
directors have a more accurate forecast and a more conservative earnings forecast.

21
From the rationalisation of these researchers, I expect firms that have a higher
proportion of independent directors also have a stronger corporate governance and a better
information environment.
As a result, the second hypothesis is as follows:
𝐻2𝑎: Firms with a higher proportion of independent directors have a more effective
corporate governance; thus, more accurate forecasts.
𝐻2𝑏: Analysts will issue a more optimistic forecast for firms with a higher proportion
of independent directors.
2.2.2. CEO Duality
The board of directors, appointed by the shareholders, have the authority to fire, direct,
and hire CEOs. However, as I noted earlier by citing Jensen (1993), the CEOs are usually the
chairman/woman of the board. Thus, there is a conflict of interest.
Patton and Baker (1987) stated that the chairman/woman decides on the agenda of the
meeting and on the information to support the agendas. Thus, when the CEO is also the
chairman/woman, the board tends to lose its efficiency and effectiveness.
Loebbecke et al. (1989) analysed the auditor’s experience with material irregularities.
They asked 165 audit partners, who have experience in material irregularities, to participate in
their research. They predicted that the likelihood of a fraud is much higher where a dominated
person influences decisions.

22
More recently, researchers have been focusing on the relationship between CEO duality
and the financial reporting process. For instance, Dechow et al. (1996) looked at firms that
have violated the US generally accepted accounting principles (GAAP) and found that the
probability of these violations is higher in firms where the CEO serves as the chairman/woman.
Carcello and Nagy (2004) reported a similar relationship.
Lastly, Beasley et al. (1999) looked at financial report frauds that have been identified
bySecurity Exchange Commission (SEC) between 1987 and 1997. They showed that the CEOs
were involved in 72% of the financial reporting frauds and that 66% of these CEOs were also
the chairman/woman of the board.
From Prior studies, I expect a negative relationship between CEO duality and the
quality of the information environment.
As a result, the third hypothesis is as follows:
𝐻3𝑎: Firms adopting a dual CEO structure have a less effective corporate governance;
thus, less accurate forecasts.
𝐻3𝑏: Analysts will issue a more conservative forecast for firms choosing a dual CEO
structure.

23
2.3. Forecast Quality and Auditing
2.3.1. Auditors’ Reputation
Auditors give an independent opinion on whether: (1) the financial statements give a
“true and fair view” of the company’s position and (2) they have been prepared in agreement
with applicable accounting standards. In summary, they aim to enhance the degrees of
confidence by reducing the level of uncertainty for users of financial statements (ICAEW,
2006). Thus, on balance, auditors contribute to the quality of the information environment.
Watts (1977) and Benston (1980) both argued that professional accounting services are in
demand because investors and other users of financial statements fear that those charged with
governance may not pursue outsiders’ interests.
Regulators tend to claim that audit quality is independent of the size of the audit firms;
therefore, when choosing an auditor, size should be an irrelevant factor (AICPA, 1980). Many
researchers are supportive of this argument. For instance, Arnett and Danos (1979) used
questionnaires and interviews to emphasise that size is not a determinant of success. They also
emphasised that if professionalism were maintained, then it would be unfair to distinguish
between Certified Public Accountants based on size.
Contrary to this view, DeAngelo (1981) found that audit firm size and audit quality are
positively related. He argues that large audit firms serve larger clients; therefore, they have a
lot “more to lose” because larger clients provide a more significant economic advantage to

24
audit firms. As a result of this collateral, it is best for large audit firms to provide a higher audit
quality.
Also, Craswell et al. (1995) found that the eight largest audit firms in Australia charge
a 30% premium in comparison to the other audit firms. They ration that this premium is a result
of better audit quality.
Clarkson (2000) used a sample from Toronto Stock Exchange to conclude that firms
that are audited by higher audit quality firms (the largest six auditors in Canada) are more likely
to have a lower forecast error. Research performed by Hartnett and Romcke (2000) and Cheng
and Firth (2000) yielded the same conclusion in Australia and Hong Kong, respectively.
Thus, I expect a positive relationship between the reputation of the auditor and the
As a result, the fourth hypothesis is as follows:
𝐻4𝑎: Firms audited by the big audit firms have a better information environment; thus,
more accurate forecasts.
𝐻4𝑏: Analysts will issue a more optimistic forecast for firms that are audited by large
audit firms.
2.3.2. Size of Audit Committee
Some studies focus on the impact of audit committees on organisations. For instance,
Wild (1996) looked at the quality of accounting earnings before and after the formation of audit

25
committees. He found that the market reaction was 20% greater after the formation of an audit
committee. Thus, an audit committee improves the quality of accounting earnings.
Moreover, McMullen (1996) analysed shareholder lawsuits alleging fraud, earnings
restatement, SEC enforcement actions, illegal acts, and auditor turnover as proxies for quality
of financial reporting. He found a statistically significant evidence that firms with an audit
committee are more likely to have a higher quality of financial reports. Dechow et al. (1996)
reached a similar conclusion.
A more recent study by Felo et al. (2003) showed that there is a significant positive
relationship between audit committee size and the financial reporting quality. However, their
results only hold in their univariate analysis and not in their multivariate analysis. Besides, Lin
et al. (2006) showed that there is a negative association between audit committee size and the
occurrences of earnings management.
Thus, I predict a positive relationship between the size of the audit committee and the
As a result, the fifth hypothesis is as follows:
𝐻5𝑎: Firms with a larger audit committee have a more effective corporate governance;
thus, more accurate forecasts.
𝐻5𝑏: Analysts will issue a more optimistic forecast for firms that have a larger audit
committee.

26
2.3.3. Audit Committee Independence
Klein (2002) showed that there is a non-linear negative relationship between audit
committee independence and earnings manipulations. However, her results are only significant
when an audit committee has less than a majority of independent directors.
Similarly, Bedard et al. (2004) showed that audit committee independence reduces the
likelihood of earnings management.
Abbott et al. (2004) examined 41 firms that issued fraudulent reports and 88 firms that
restated annual results. They found a significant negative relationship between the
independence of audit committees and financial reporting restatements.
In addition, Beasley (1996) documented that firms with a lower number of independent
directors on their audit committee are more likely to be involved in financial statement fraud.
Persons (2005) reached the same conclusion by showing that independent audit committees
positively contribute towards the financial reporting process and that the likelihood of fraud is
lower in audit committees that are only comprised of independent directors.
From these prior studies, I expect a positive relationship between the independence of
the audit committee and the quality of the information environment.
As a result, the sixth hypothesis is as follows:
𝐻6𝑎: Firms with a larger audit committee have a more effective corporate governance;
thus, more accurate forecasts.

27
𝐻6𝑏: Analysts will issue a more optimistic forecast for firms that have a higher
proportion of independent directors on their audit committee.
The remainder of this study will attempt to answer the following two questions:
𝑄1: Does sound corporate governance help to improve analysts’ forecasts?
𝑄2: Has the introduction of the UKCGC 2010 helped to improve analysts’
performance?

28
3. RESEARCH DESIGN AND METHODOLOGY
3.1. Description of Data and Sample Selection
To assess the impact of corporate governance and the UKCGC 2010 on analysts’
forecasting performance, this study focuses on corporations listed on the FTSE All-Share
Index. The index is an aggregate of FTSE 100 Index, FTSE 250 Index, and FTSE SmallCap
Index. Consequently, it captures 98% of the UK’s market capitalisation. The use of this index
allows for the control of firm characteristics such as liquidity.
I will be collecting data from the second half of 2005 (2005S2) to the first half of 2015
(2015S1). The 10-year period is consistent with other researchers that study corporate
governance. For example, Ahmad‐Zaluki and Nordin Wan‐Hussin (2010) looked at corporate
governance and earnings forecast accuracy in Malaysia between 1999 and 2006. Moreover, the
period allows for a comprehensive study of the UKCGC 2010.
I collected my data using the following three sources: (1) Bloomberg Terminal, (2)
Fame UK, and (3) hand-collected data.
Using Bloomberg Terminal, I observed 643 corporations that were listed on the FTSE
All-Share Index. Then, I gathered data on the actual and forecasted Adjusted Earnings per
Share (EPS+) for all corporations over the 10-year period. Where data was incomplete or,
unavailable, the corporations were deleted from the sample. After this round of data collection,
the sample size was reduced to 442 corporations.

29
From Bloomberg Terminal, data on firm size and leverage was collected. The total
value of assets was used as a proxy for firm size and the total debt to total asset ratio as a proxy
for leverage. These proxies are consistent with Mnif (2010) and Ahmad‐Zaluki and Nordin
Wan‐Hussin (2010). In addition, I gathered the data on CEO duality and audit committee
characteristics from Bloomberg Terminal. After this round of data collection, the sample size
shrunk to 237 corporations.
To gather data on auditors, I used Fame UK database. Data was available for all the
237 corporations that I collected from Bloomberg.
The data for board size and independent directors had to be hand collected. Although,
Bloomberg Terminal does release information on board size and independent directors, the
majority of the data set is incomplete for the purpose of this study. If the Bloomberg Terminals
were used to gather these two variables, then the sample size would have reduced to 97
corporations. This sample size is unsatisfactory when compared to prior studies. On the other
hand, Fame UK does not release historical data on board size or board independence.
Therefore, the only solution was to hand collect the information from the financial
statements of the remaining corporations.
After this round of data collection, the final sample size amounts to 142 corporations.
This sample size is satisfactory as studies by Jelic et al. (1998), Mnif (2010), and Ahmad‐

30
Zaluki and Nordin Wan‐Hussin (2010) on forecast accuracy used a sample size of 124, 117,
and 235 firms, respectively. Table 1 illustrates the procedure for sample constitution.
Table 1
Sample Constitution procedure
Sample Corporations
Initial corporations on FTSE All-Share Index
Corporations excluded because of lack of data on EPS+
Corporations excluded because of lack of data on asset size and leverage
Corporations excluded because of lack of data on auditors
Corporations excluded because of lack of data on board size, and independent
directors
643
201
205
0
95
Final Sample Size 142
The sample selection can be affected by a reporting bias that skews the availability of the
data. This indicates that observations of a particular kind are more likely to be reported. This
problem could arise when considering the availability of data for smaller corporations.
Financial statements for many of the smaller firms were not observable throughout the 10-
year period. Therefore, it is likely that the results are biased towards medium sized and larger
sized firms. Approximately 35% of the corporations in the sample are listed on the FTSE 100
Index.
In addition, to avoid or, reduce the likelihood, of inferences being infected by Type-I error
(false positive) and Type-II error (false negative), all statistical inferences will be tested at
1%, 5%, and 10%. These percentages reflect the probability that the findings in this study are
as a result of chance or error.

31
Lastly, the sample is winsorized at a 5% cut off point. This procedure should reduce the
impact of spurious outliers.
3.2. Research Models
3.2.1. Univariate Tests
The loss function is the main ingredient for testing the hypotheses stated in Section 2.
A loss function occurs when a forecast, 𝐹̂𝑡+ℎ, differs from the actual observation, 𝑌𝑡+ℎ;
therefore, the loss function would equate to ℇ 𝑡+ℎ = 𝑌𝑡+ℎ-𝐹̂𝑡+ℎ.
The statistical loss function can be defined in numerous ways. For example, Dreman
and Berry (1995) defined four different loss functions: (1) (Actual EPS –Forecast
EPS)/|(Actual EPS)|; (2) (Actual EPS –Forecast EPS)/|(Forecast EPS); (3) (Actual EPS –
Forecast EPS)/Standard deviation of trailing eight-quarter actual EPS; and (4) (Actual EPS –
Forecast EPS)/Standard deviation of trailing seven-quarter change in EPS.
However, the consensus amongst researchers is to use the absolute loss function as used
by Baldwin (1984), Fairfield et al. (1996), Dichev and Tang (2009), and Schröder and Yim
(2014).
I define the forecast error (FE) loss function as:
𝐹𝐸𝑖,𝑡 =
|𝑌𝑖,𝑡−𝐹̂ 𝑖,𝑡|
|𝐹̂ 𝑖,𝑡|
(1).
where, 𝐹𝐸𝑖,𝑡 is the forecast error for company i at time t, 𝑌𝑖,𝑡 is the actual earning of
company i at time t, and 𝐹̂𝑖,𝑡 is the forecast earning of company i at time t. Thus, the FE is the

32
difference between the absolute value of actual reported earnings by the corporations and the
analysts’ forecasts, deflated by the absolute value of the analysts’ forecasts.
Ideally, one would prefer to have an error term that is as close as to zero as possible.
I define the forecast bias (FB) as:
𝐹𝐵𝑖,𝑡 =
𝑌𝑖,𝑡−𝐹̂ 𝑖,𝑡
|𝐹̂ 𝑖,𝑡|
(2).
where 𝐹𝐵𝑖,𝑡 is the forecast bias for company i at time t. A positive coefficient on FB
represents conservatism while a negative coefficient represents optimism.
Descriptive statistics will be used to analyse the FEs and the FBs. In addition, two-
sample procedures are used to check whether the population mean of the FEs and the FBs have
improved since the introduction of the UKCGC 2010.
The sample will be divided into two sub-samples. One representing the period before
the introduction of the UKCGC 2010, represented as 𝜉, and another representing the period
after the introduction of the UKCGC 2010, represented as 𝜁.
If the UKCGC meets its objective of improving the information environment of
corporations, then it is reasonable to assume that the analysts’ performance will improve. As a
result, I will expect the volatility of analysts’ forecasts to reduce. Therefore, the population
variances of the two sub-samples are assumed to be unequal.
One could pre-test for equal variances before deciding whether to test for the difference
in mean with equal variances or to test for the difference in mean with unequal variances.

33
However, Zimmerman (2004) reported that this procedure would lead to a Type-I error in the
inferences. Thus, it is reasonable to assume that variances are unequal. As a result, Welch’s t-
test is conducted.
Welch’s t-test minimises Type-I error for unequal variance and unequal sample size
procedures; thus, making it an appropriate test for this study. The t-statistic is calculated as:
𝑡 =
𝜉̅−𝜁̅
√ 𝜏 𝑛
2
𝑛
+
𝜏 𝑚
2
𝑚
(3).
The Welch-Satterthwaite equation is then used to determine the degrees of freedom.
For 𝑘 sample variances 𝜏𝑖
2
(𝑖 = 1, … , 𝑘), where each variance has degrees of freedom
of 𝑣𝑖, a linear computation can be computed:
𝛾 = ∑ 𝜋𝑖.𝑘
𝑖=1 𝜏𝑖
2
; 𝑤ℎ𝑒𝑟𝑒 {𝜋𝑖 ∈ ℝ+} (4).
Typically the probability distribution of 𝜋𝑖 =
1
𝑣𝑖+1
cannot be expressed analytically.
Instead, it is approximated by another chi-squared distribution, in which the degrees of freedom
are computed as:
𝑣𝛾 ≈
(∑ 𝜋𝑖.𝑘
𝑖=1 𝜏𝑖
2
)
2
∑
(𝜋 𝑖.𝜏 𝑖
2)2
𝑣 𝑖
𝑘
𝑖=1
(5).
The hypotheses of this test are:
𝐻0: 𝜇 𝜉 = 𝜇 𝜁.
𝐻1: 𝜇 𝜉 ≠ 𝜇 𝜁.
Furthermore, a separate test is conducted where the FEs and the FBs for the first year
after the implementation of the UKCGC 2010 are removed. The rationale behind this test is

34
that there is likely to be a time lag associated with the adoption of the UKCGC, especially
given the “comply or explain” regulatory approach used in the UK. Therefore, the FEs and the
FBs in the first year are removed to prevent them from adversely impacting the results.
3.2.2. Multivariate Tests
Multivariate analysis will be conducted to identify factors that have an influence on
forecasting ability. The FEs and the FBs are regressed on variables defined in Section 2 and on
some additional control variables identified from prior studies. Below, I will outline the
regressions conducted and justify the rationale for the use of the control variables.
I will use Ordinary Least Squares (OLS) estimators to predict the following models:
𝐹𝐸𝑖,𝑡 = 𝛽0 + 𝛽1 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽3 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 + 𝛽4 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 +
𝛽5 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽6 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽7 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽8 𝐿𝐸𝑉𝑖,𝑡 + 𝛽9 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝑒𝑖,𝑡 (I).
𝐹𝐸𝑖,𝑡 = 𝛽0 + 𝛽1 𝑃𝑂𝑆𝑇𝑈𝐾𝐶𝐺𝐶𝑡 + 𝛽2 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽4 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 +
𝛽5 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 + 𝛽6 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽7 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽8 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽9 𝐿𝐸𝑉𝑖,𝑡 + 𝛽10 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝑒𝑖,𝑡
(II).
𝐹𝐵𝑖,𝑡 = 𝛽0 + 𝛽1 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽3 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 + 𝛽4 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 +
𝛽5 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽6 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽7 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽8 𝐿𝐸𝑉𝑖,𝑡 + 𝛽9 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝑒𝑖,𝑡 (III).
𝐹𝐵𝑖,𝑡 = 𝛽0 + 𝛽1 𝑃𝑂𝑆𝑇𝑈𝐾𝐶𝐺𝐶𝑡 + 𝛽2 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽4 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 +
𝛽5 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 + 𝛽6 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽7 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽8 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽9 𝐿𝐸𝑉𝑖,𝑡 + 𝛽10 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝑒𝑖,𝑡
(IV).

35
In addition, I will apply the Arellano-Bond estimators/Dynamic Panel Data (DPD) to
estimate the following models:
𝐹𝐸𝑖,𝑡 = 𝛽1 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽3 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 + 𝛽4 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 +
𝛽5 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽6 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽7 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽8 𝐿𝐸𝑉𝑖,𝑡 + 𝛽9 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝛽10 𝐹𝐸𝑖,𝑡−1 + 𝑒𝑖,𝑡 (V).
𝐹𝐸𝑖,𝑡 = 𝛽1 𝑃𝑂𝑆𝑇𝑈𝐾𝐶𝐺𝐶𝑡 + 𝛽2 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽4 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 +
𝛽5 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 + 𝛽6 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽7 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽8 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽9 𝐿𝐸𝑉𝑖,𝑡 + 𝛽10 𝐿𝑂𝑆𝑆𝑖,𝑡 +
𝛽11 𝐹𝐸𝑖,𝑡−1 + 𝑒𝑖,𝑡 (VI)
𝐹𝐵𝑖,𝑡 = 𝛽1 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽2 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽3 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 + 𝛽4 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 +
𝛽5 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽6 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽7 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽8 𝐿𝐸𝑉𝑖,𝑡 + 𝛽9 𝐿𝑂𝑆𝑆𝑖,𝑡 + 𝛽10 𝐹𝐸𝑖,𝑡−1 + 𝑒𝑖,𝑡 (VII)
𝐹𝐵𝑖,𝑡 = 𝛽1 𝑃𝑂𝑆𝑇𝑈𝐾𝐶𝐺𝐶𝑡 + 𝛽2 𝐵𝐷𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3 𝐼𝑁𝐸𝐷𝑖,𝑡 + 𝛽4 𝐶𝐸𝑂𝐷𝑈𝐴𝐿𝑖,𝑡 +
𝛽5 𝐴𝑈𝐷𝑄𝑈𝐴𝑖,𝑡 + 𝛽6 𝐴𝐶𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽7 𝐴𝐶𝐼𝑁𝐷𝑖,𝑡 + 𝛽8 𝐶𝑂𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽9 𝐿𝐸𝑉𝑖,𝑡 + 𝛽10 𝐿𝑂𝑆𝑆𝑖,𝑡 +
𝛽11 𝐹𝐵𝑖,𝑡−1 + 𝑒𝑖,𝑡 (VIII)
where:
FE = The absolute difference between the actual earnings and forecasted
earnings, deflated by the absolute value of the forecasted earnings.
FB = The difference between the actual earnings and forecasted earnings,
deflated by the absolute value of the forecasted earnings.
BDSIZE = The natural logarithm of the total number of directors on the board.
INED = Percentage of independent directors on the board.
CEODUAL = A dummy variable that equals 1 if the CEO is also the chairman of the
board, and 0 otherwise.
AUDQUA = A dummy variable that equals 1 if the auditors are one of big four audit
firms (PricewaterhouseCoopers, Deloitte, Ernst & Young, and KPMG), and
0 otherwise.

36
ACSIZE = The natural logarithm of the number of directors on the audit committee.
ACIND = The percentage of independent directors on the audit committee.
COSIZE = The natural logarithm of the total value of assets.
LEV = Percentage of total debt to total equity.
LOSS = A dummy variable that equals to 1 if the company reported a loss, and 0
otherwise.
POSTUKCGC = A dummy variable that equals to 1 if the forecast period is after the
introduction of the UKCGC 2010, and 0 otherwise.
FE_i,t-1 = A lagged variable of FE.
FB_i,t-1 = A lagged variable of FB.
e_it = The residuals.
Both Models will be estimated using fixed-effect regressions, which controls for
differences between individual analysts, companies, and seasonal differences in analysts’
accuracy (Agrawal et al., 2006; Capstaff et al., 1995).
All regression models will be tested for statistical robustness to ensure that the estimators
are unbiased and consistent. This should also confirm that the models are free from the
omitted variables bias. The estimators of regressions that omit relevant variables are biased
and inconsistent; the residuals of these regressions are also autocorrelated.
3.3. Control Variables
To ensure that the inferences in Section 4 are sound, the impact of confounding
variables should be reduced. These confounding variables could adversely affect the

37
relationship between the dependent variables and independent variables. Therefore, it is vital
that these variables are controlled. In order to isolate the effectiveness of corporate governance
on analysts’ performance, I find that prior studies control for the following variables: firm size,
leverage ratio, and whether the firm has made a profit or loss. In addition, I will justify the
inclusion of the lagged dependent variable.
2.3.1. Firm Size
Atiase (1980, 1985) analysed mispricing in securities and argued that the information
content required to identify mispricing is an increasing function of firm size.
Furthermore, Grant (1980) analysed the information difference in the content of
earnings announcement between over-the-counter (OTC) firms and firms on the New York
Stock Exchange (NYSE) and suggested that smaller firms have a relatively better information
environment than larger firms.
On the other hand, Bamber (1987) suggested that there is a significant negative
relationship between firm size and absolute value of unexpected earnings.
Similarly, Freeman (1987) made two important conclusions in regards to firm size and
the information environment: (1) securities of larger firms anticipate accounting earning earlier
than smaller firms and (2) abnormal returns associated with good and bad news is inversely
related to firm size. Thus, large firms have a better information environment.

38
Capstaff et al. (1995) used a fixed effect model to conclude that forecast errors in the
UK are generally smaller for larger firms over a short horizon; thus, they suggested that larger
firms have a better information environment.
Furthermore, a significant number of studies looking at IPOs and management forecast
errors reported a negative correlation between forecast errors and firm size (Firth and Smith,
1992; Pedwell et al., 1994).
In addition, Beckers et al. (2004) examined bias in European analyst’s earnings
forecasts and found an inverse relationship between firm size and forecast errors.
However, Bhaskar and Morris (1984) looked at the accuracy of brokers in the UK and
found that the coefficient of firm size when regressed on forecast error is statistically
insignificant. Ferris and Hayes (1977) reached a similar remark.
Despite the mixed evidence from prior studies, I expect a positive relationship between
firm size and analysts’ performance.
2.3.2. Financial Leverage
Modigliani and Miller (1958) proposed that higher leverage increases the rate of return
on equity in a world with or without taxes because of the additional risk accepted by the
investors. This proposition is supported by empirical evidence. For example, Mandelker and
Rhee (1984) showed that operating and financial leverage has significant explanatory power to
describe the changes in systematic risk.

39
In addition, Hamada (1972) concluded that 21% to 24% of the systematic risk of
common stocks can be explained by the added financial risk accepted through the use of debt
and preferred stock.
Systematic risks are the result of uncertainty inherent in the markets. This type of
uncertainty could adversely impact the analysts’ information environment; thus, it could
adversely affect their forecasting ability.
Eddy and Seifert (1992) showed that there is a significant interaction between leverage
and variability in earnings causing the forecast errors. Dhaliwal et al. (1991) and Francis et al.
(1998) reached the same conclusion.
From the evidence provided by prior studies, I expect to observe a negative relationship
between financial leverage and analysts’ performance.
2.3.3. Loss
Hayn (1995) documented that the information content of losses is not as informative as
the profits about the firm’s prospects.
In addition, Burgstahler and Dichev (1997) provided evidence that firms would manage
earnings to avoid reporting a loss.
Building on the researchers above, Das and Somnath (1998) analysed analysts’ bias
and accuracy by looking at firms that report a profit or loss. They found that analysts are

40
systematically more biased towards firms that report a loss in comparison to non-loss making
firms. They also reached the same conclusion in regards to analysts’ forecast accuracy.
Similarly, Brown (2001) showed analysts’ performance is influenced by whether a
corporation reports a profit or loss.
In addition, Agrawal et al. (2006) assessed the impact of Regulation Fair Disclosure
(Reg FD) on the dispersion and accuracy of sell-side analysts’ forecasts and found a significant
positive relationship between forecast errors and loss-making firms.
Thus, I predict that the FEs will be substantially larger for firms that report a loss. I also
expect analysts to be more conservative when a company reports a loss.
2.3.4. Lagged Level of the Loss Function
Many studies analysed whether analysts underreact or overreact to new information;
however, the evidence from these studies are not conclusive. For instance, some researchers
showed that analysts underreact to new information (Abarbanell, 1991; Abarbanell and
Bernard 1992). On the other hand, DeBondt and Thaler (1990) provided evidence that analysts
systematically overreact to new information. However, an important study by Ali et al. (1992)
documented a positive serial correlation in analysts’ forecast errors. They concluded that
analysts underestimate the persistence of past errors in predicting future earnings and that
analysts on average set overly optimistic estimates of the next period’s earnings.
Thus, I believe that it is essential to control for previous FEs and FBs.

41
4. EMPIRICAL FINDINGS AND DISCUSSIONS
4.1. Univariate Results
4.1.1. Forecast Error
Appendix A - Graph 1 shows a plot of the mean of the FEs filtered by time. The figures
reported by Graph 1 are statistically significant; although, the periods 2008S2, 2013S2, and
2015S1 are only marginally significant. The full details of these results are shown in Table 2 –
Panel A.
By observing graph 1 only and ignoring the peaks of 2006S2 and 2008S2, the sample
is relatively stable. As Graph 1 presents, the largest error has occurred in 2008S2. The error in
this period almost reached 1.3. This result may not be such a surprise as 2008S2 was associated
with the financial crisis. The financial crisis could have adversely impacted the information
environment of analysts; thus, reducing their accuracy. The graph shows that the mean forecast
errors have fallen since 2010S1; reflecting the improvement in the general economy and
analysts’ information environment. The errors reached as low as 0.28 in 2012S2.
Table 2 – Panel B presents descriptive statistics for the sample. The average forecast
error over the 10-year period is 0.5658. The mean is statistically significant at 1% (p<0.01).
On the other hand, the median, which is free from outliers, is 0.1667. The median is statistically
significant at 1% (p<0.01). Moreover, the volatility over the period, measured by standard
deviation, is 1.7108.

42
It is very tough to draw a conclusion based on the descriptive statistics over the 10-year
period alone. Instead, the data is split into two sub-samples as described in Section 3.2.1.
The descriptive statistics, presented in Table 2 – Panel C, shows that the mean of the
FEs before the introduction of the UKCGC 2010 is 0.6542, whereas the mean of the FEs after
the introduction of the UKCGC 2010 is 0.4774. Both results are statistically significant at 1%
(p<0.01). The results show that the mean of the FEs has reduced by approximately 27% since
the introduction of the UKCGC in 2010. The median and the standard deviation follow the
same trend and conclusion. The median and the standard deviation before and after the
introduction of the UKCGC in 2010 have fallen by approximately 26% and 4%, respectively.
Therefore, at face value, it is reasonable to conclude that the UKCGC 2010 has had a positive
impact on analysts’ forecasting accuracy.
Welch’s t-test was conducted to decide whether statistically there is enough evidence
to show that the mean of the FEs before the introduction of the UKCGC 2010 is any different
to the mean of the FEs after the introduction of the UKCGC 2010. The result of this test is
presented in Table 3 – Panel A. The outcome of this test is statistically significant at 1%
(p<0.01). Therefore, there is enough evidence to support statistically the hypothesis that the
means of the FEs before and after the introduction of the UKCGC 2010 are not the same. A
one-tailed test provided statistical evidence that the mean of the FEs after the introduction of

43
the UKCGC 2010 is lower than the mean of the FEs before the introduction of the UKCGC
2010. Thus, analysts’ mean errors have reduced since the introduction of the UKCGC 2010.
Welch’s t-test was repeated after removing the FEs from June 2010 – June 2011. The
rationale behind this test is that the implementation of the UKCGC 2010 may be associated
with a time lag (due to the “comply or explain” approach) that could adversely impact the
results. The outcome of this test was significant at 5% (p<0.05). Thus, after controlling for
time lag, there is enough evidence to show that the introduction of the UKCGC 2010 has
positively influenced analysts’ accuracy.
In summary, the univariate tests on the FEs show that analysts’ forecast errors on
average have reduced since the introduction of the UKCGC 2010.
4.1.2. Forecast Bias
Appendix A - Graph 2 illustrates a plot of the mean of the FBs filtered by time. A
positive coefficient hints at conservatism and a negative coefficient hints at optimism. As the
graph shows, analysts have been conservative in the periods surrounding the financial crisis
(2007S2-2008S2). The highest average optimism is experienced in 2009S1. This result must
be analysed with care. A primary reason for this optimism could be due to analysts’ forecasts
being greater than the actual earnings for that period. A lot of corporations during this period
experienced a fall in earnings; given that prior literature states that analysts underreact to new
information, then it is reasonable to assume that analysts’ forecasts were overstated. Thus,

44
leading to the negative coefficient in this period. On the other hand, the highest average
conservatism is experienced in 2013S2. A reverse explanation of 2009S1’s optimism could be
applied to 2013S2’s conservatism. In 13 out of the 20 periods, analysts have posted a
conservative forecast. Therefore, by considering Graph 2 only, I conclude that analysts have
been conservative throughout the sample period. The mean of every period, except 2007S1, is
statistically insignificant. However, this may not be a concern because the sample size is
relatively small to make any clear statistical conclusion. Therefore, the statistical significance
of the data is ignored for the purpose of this particular analysis. Table 2 – Panel A represents
the full details of these results.
Also, Table 2 – Panel B shows the descriptive statistics of the overall sample. The mean
forecast bias is positive (0.0241) and is statistically insignificant at 10% (p=0.2669). The mode
of forecasts bias is 0% and is statistically significant at 1% (p<0.01). In addition, the volatility
of forecast bias, measured by standard deviation, is 1.1567. The results from the descriptive
statistics provides enough evidence to show that analyst in the UK have been neutral and
possibly, slightly conservative over the 10-year period.
This conclusion is in contrast to the evidence documented by Francis and Philbrick
(1993), Kang et al. (1994), and Dreman and Berry (1995), who all reported that analysts
produce upwardly biased forecasts. However, the results are consistent with Abarbanell (1991),
Abarbanell and Bernard (1992), Elliott et al. (1995), and Teoh and Wong (1997).

45
Although the consensus is that analysts are optimistic, there could be several reasons
as to why my result contradict prior studies. Firstly, the majority of the data points in this study
are surrounded by the financial crisis of 2007-2008. Thus, it is not surprising to see that
analysts’ forecasts are hinting at conservatism because of the uncertainty surrounding the
markets. To analyse this phenomenon, I have calculated the mean of the sample from 2007S1
to 2011S2 and from 2012S1 to 2015S1. The first period represents the time surrounding the
financial crisis and the second period represents the time after the financial crisis.
The mean of the FBs surrounding the financial crisis is 0.0271 and the mean of the FBs
after the financial crisis is 0.0253. The full descriptive statistics is provided in Table 2 – Panel
D. I then tested for the difference in mean between these two periods and found that the results
are statistically insignificant, as shown by Table 3 – Panel B. From this analysis, I conclude
that analysts in the UK are conservative regardless of the market condition. Thus, on average,
analysts’ conservatism persists. However, this result should be analysed with caution because
the sample size for this particular test may be inadequate.
Moreover, country-level differences could be the reason for this contradictory result.
The majority of the studies reporting optimism are based in the US. However, it could be that
analysts in the UK are more conservative because of differences in practice and culture. For
example, Capstaff et al. (2001) stated that there is a considerable difference between the US
and Europe in their individual security markets and accounting practices.

46
Table 2 – Panel C shows the descriptive statistics of the FBs before and after the
introduction of the UKCGC 2010. The mean of the FBs before the introduction of the UKCGC
2010 is 0.0163. The mean of FBs after the introduction of the UKCGC 2010 has increased by
almost 96% to 0.0319. Table 3 -Panel C shows Welch’s t-test for the difference in mean of the
FBs before and after the introduction of the UKCGC 2010. The results show that there is a
statistically insignificant difference in the means of these two sub-samples.
Lastly, the test was repeated to take into account of the time lag effect. Although the p-
value reduced in favour of the alternative hypothesis; however, the result, presented in Table 3
– Panel C, remained statically insignificant. Thus, even after controlling for the time lag effect,
the analyst’s forecasts’ bias on average have not changed since the introduction of the UKCGC
2010.
In summary, I conclude that analysts are neutral, slightly conservative, and that the
UKCCG 2010 has had no impact on analysts’ forecasts bias.

47
Table 2
Descriptive Statistics
Notes: The tables show descriptive statistics of FEs and FBs. 𝐹𝐸𝑖,𝑡 =
|𝑌𝑖,𝑡−𝐹̂𝑖,𝑡|
|𝐹̂𝑖,𝑡|
and 𝐹𝐵𝑖,𝑡 =
𝑌𝑖,𝑡−𝐹̂𝑖,𝑡
|𝐹̂𝑖,𝑡|
; where, 𝐹𝐸𝑖,𝑡 is
the forecast error for company i at time t, 𝑌𝑖,𝑡 is the actual earning of company i at time t, and 𝐹̂𝑖,𝑡 is the forecast earning of
company i at time t. Within the tables, SD denotes sample standard deviation, and N denotes the number of observations. In
addition, Pre-UKCGCG represent data up to 2010S2, and Post-UKCGC represent data beyond 2010S2. The medians in Panels
C and D were not tested for statisticals significance.
*** Denotes statistical significance at 1% level in two-tailed tests.
** Denotes statistical significance at 5% level in two-tailed tests.
* Denotes statistical significance at 10% level in two-tailed test.
~ Marginally statistically significant at 10% level in two-tailed test.
Panel A: Descriptive statistics of FEs, and FBs for every semester
FEs FBs
Time Mean SD N test-statistics Mean SD N test-statistics
2005S2 0.5152 0.9078 142 6.7628 *** -0.1180 1.0380 142 -1.3551
2006S1 0.5290 1.6792 142 3.6561 *** -0.0314 1.0608 142 -1.3259
2006S2 0.9028 2.3072 142 2.6609 *** 0.1833 1.6398 142 -0.8577
2007S1 0.6647 1.9312 142 3.1789 *** 0.1007 1.2400 142 -1.1343
2007S2 0.5783 1.0409 142 5.8979 *** 0.1192 1.1857 142 -1.1862
2008S1 0.5624 1.8995 142 3.2320 *** -0.0109 1.2223 142 -1.1507
2008S2 1.1285 2.5072 142 2.4486 ~ 0.0178 1.8198 142 -0.7729
2009S1 0.6543 1.6709 142 3.6743 *** -0.2420 1.2269 142 -1.1464
2009S2 0.7013 1.8671 142 3.2882 *** 0.1095 1.4121 142 -0.9961
2010S1 0.3058 0.5300 142 11.5845 *** 0.0347 0.6114 142 -2.3006
2010S2 0.4618 1.6100 142 3.8132 *** 0.0522 0.9104 142 -1.5449
2011S1 0.2546 0.3828 142 16.0367 *** -0.0133 0.4601 142 -3.0572
2011S2 0.5473 2.2226 142 2.7622 *** 0.1034 1.1315 142 -1.2431
2012S1 0.2836 0.4098 142 14.9802 *** -0.0598 0.4953 142 -2.8396
2012S2 0.5407 1.8177 142 3.3774 *** 0.1553 1.1983 142 -1.1738
2013S1 0.3550 0.8002 142 7.6719 *** -0.0934 0.8709 142 -1.6150
2013S2 0.8041 2.6005 142 2.3608 ~ 0.3011 1.5523 142 -0.9061
2014S1 0.3646 1.1124 142 5.5191 *** -0.0778 0.7854 142 -1.7908
2014S2 0.4581 1.4544 142 4.2211 *** -0.0501 0.8352 142 -1.6841
2015S1 0.7040 2.4269 142 2.5296 ~ 0.0016 1.2873 142 -1.0927

48
Panel B: Descriptive statistics of FEs and FBs
Variables FE FB
Mean 0.5658 *** 0.0241
Median 0.1667 *** -0.0141 ***
SD 1.7108 1.1567
Observations 2840 2840
Panel C: Descriptive statistics of FEs and FBs before and after the introduction of the UKCGC
FEs FBs
Variables Pre-UKCGC Post-UKCGC Pre-UKCGC Post-UKCGC
Mean 0.6542 *** 0.4774 *** 0.0163 0.0319
Median 0.1988 0.1467 -0.0178 -0.0123
SD 1.7465 1.6704 1.2865 1.0108
Observations 1420 1420 1420 1420
Panel D: Descriptive statistics of FBs between 2007-2011 and 2012-2015
Variables 2007-2011 2012-2015
Mean 0.0271 * 0.0253
Median 0.0000 -0.0230
SD 1.1814 1.0627

49
Table 3
Welch’s t-test
Notes: Welch’s t-test is conducted to analyse the difference in mean between two-samples assuming unequal
variances and unequal sample size. Pre-UKCGC represent data up to 2010S2, and Post-UKCGC represent data beyond
2010S2. Under the null hypothesis, the means of the two-subsamples are equal.
*** Denotes statistical significance at 1% level.
** Denotes statistical significance at 5% level.
* Denotes statistical significance at 10% level.
Panel A: Welch’s t-test on FEs
Including first year effect Removing first year effect
Variables Pre-
UKCGC
Post-
UKCGC
Difference in
mean
Pre-
UKCGC
Post-
UKCGC
Difference in
mean
Mean 0.6542 0.4774 0.6542 0.5015
Observations 1420 1420 1420 994
Variance 3.0501 2.7903 3.0501 2.8886
t-test 2.7575 2.1490
p-value (two-
tailed)
0.0059 *** 0.0317 **
p-value (one-
tailed)
0.0029 *** 0.0159 **
Panel B: Welch’s t-test – difference in mean of FBs between 2007-2011 and 2012-2015
Variables 2007-2011 2012-2015
Mean 0.0271 0.0253
Variance 1.3958 1.1281
t-test 0.0397
p-value (two-tailed) 0.9683

50
Panel C: Welch’s t-test on FBs
Including first year effect Removing first year effect
Variables Pre-
UKCGC
Post-
UKCGC
Difference
in mean
Pre-
UKCGC
Post-
UKCGC
Difference
in mean
Mean 0.0163 0.0319 0.0163 0.0253
Observations 1420 1420 1420 994
Variance 1.6552 1.0216 1.6552 2.8886
t-test 0.3606 -7.6035
p-value (two-
tailed)
0.7189 0.9928
p-value (one-
tailed)
0.3594 0.4964
4.2. Multivariate Results
4.2.1. Robust Statistics
Table 4 represents the Pearson product-moment correlation. All the correlations are
lower than 0.8; thus there is no sign of multicollinearity (Kennedy, 2003).
Moreover, I find that the residuals of the OLS models are cross-sectionally dependent.
On the other hand, the residuals of the DPD models are free from cross-section dependency.
The results of the cross-section dependency test for the OLS models and the DPD models are
discussed in details in Appendix B – Table 1 and Table 2, respectively.
Furthermore, the F-statistics and the Durbin-Watson statistics reveal that the OLS
models are better than a ‘constant only’ model and that the residuals are not autocorrelated.
The results of these tests are presented in Table 5.

51
Lastly, the J-statistics provided enough evidence that the DPD models are
asymptotically chi-squared distributed; thus, the models are correctly specified.
In summary, the results from these misspecification tests show that the adoption of OLS
and Arellano-Bond estimators appears to be adequate.
4.2.2. Forecast Error
Table 5 and Table 6 present the results of the OLS regressions and the DPD regressions,
respectively. Results from Model I shows that the coefficient of the constant ( 𝜷 𝟎), which
represents the average, is negative (-2.109631) and is statistically insignificant at 10%
(p=0.3565).
The coefficient of board size (BDSIZE) is positive (1.233102) and is statistically
significant at 1% (p<0.01). Thus, there is a negative relationship between analysts’ forecasts
accuracy and board size. This result agrees with my expectation and with prior studies that
indicate smaller boards on average are more effective than larger boards (Jensen, 1993; Lipton
and Lorsch, 1992; Slater 1958). The result is also consistent with studies that suggest analysts’
forecasts are more accurate when boards are smaller (Karamanou and Vafeas, 2005). This
result could be due to the simplicity of the relationship between board members when the board
is relatively smaller (Kephart, 1951). Another possible explanation for this result could be
associated with accountability. Smaller boards can be more effective because their members
are more likely to be held accountable for their actions in comparison to members of a larger

52
board. It will be more challenging for members of a smaller board to avoid possible future
blame; therefore, they are likely to be more efficient and effective at devising and implementing
plans. This result provides enough evidence in favour of 𝐻1𝑎.
The coefficient of board independence (INED) is positive (0.099604). However, the
coefficient is statistically insignificant at 10% (p=0.8838). Therefore, 𝐻2𝑎 is not supported.
Board independence on average has no impact on the accuracy of analysts. This result is in
contradiction with my expectation and the results found by Karamanou and Vafeas (2005). A
possible explanation for this is that incentive schemes are likely to discipline managers;
therefore, independent directors add no value to the information environment (Hart, 1983).
Furthermore, Fama (1980) suggested that managers are the best set of groups to monitor
themselves. Thus, it could be true that the contribution of independent directors is minimal and
insignificant. Moreover, a study by Acharya (2009) showed that independent directors only
spend 20 days per year with their respective corporations; and the majority of those 20 days
are spent in formal boards and committee meetings. It is not a great surprise that they add no
value to the information environment.
The coefficient of CEO duality (CEODUAL) is positive (0.032540) and is statistically
insignificant at 10% (p=0.9405). Thus, the leadership structure of the board on average has no
impact on analysts’ forecast accuracy. Therefore, 𝐻3𝑎 is not supported. This result is consistent
with Mnif (2010).

53
Correlation POSTUKCGC BDSIZE INED CEODUAL AUDQUA ACSIZE ACINED COSIZE LEV LOSS
POSTUKCGC 1
BDSIZE -0.0240 1
INED 0.1866 *** 0.0616 ** 1
CEODUAL -0.0511 ** -0.1035 *** -0.0981 *** 1
AUDQUA 0.0116 -0.0642 *** 0.0883 *** 0.0234 ** 1
ACSIZE 0.0898 *** 0.4251 *** 0.2416 *** -0.0677 *** -0.0717 *** 1
ACINED -0.0022 -0.0210 0.4569 *** -0.0558 ** -0.0338 0.0382 ~ 1
COSIZE 0.0458 * 0.6363 *** 0.3605 *** -0.1073 *** 0.0657 *** 0.3348 *** 0.1207 *** 1
LEV -0.1047 *** -0.0046 -0.0082 0.0622 ** 0.0242 -0.0230 -0.0593 ** 0.1298 *** 1
LOSS -0.0183 0.0150 0.0085 0.0047 0.0026 -0.0012 0.0318 0.0282 0.0226 1
Table 4
Correlations
Notes: This table shows the bivariate Pearson correlation between independent variables. POSTUKCGC is a dummy variable that equal 1 if the observations fall after 2010S2, and 0
otherwise; BDSIZE is the natural log of board size; INED is the percentage of independent directors on the board; CEODUAL is a dummy variable that equal 1 if the CEO acts as the
chairman/woman, and 0 otherwise; AUDQUA is a dummy variable that equals 1 if audit engagement is performed by one of the big 4 audit firms, and 0 otherwise; ACSIZE is the natural log
of audit committee size; ACINED is the percentage of independent directors on the audit committee; COSIZE is the natural log of total assets; LEV is the percentage of total debt to total
equity; and LOSS is a dummy variable that equals 1 if the company has reported a loss, and 0 otherwise.

54
Model I Model II Model III Model IV
Pred. Coef. t Coef. t Pred. Coef. t Coef. t
β0 (+) -2.1096 -0.9223 -2.2221 -0.9948 (+) -0.2006 -0.1394 -2.2221 -0.8563
POSTUKCGC (-) -0.0209 -0.1923 (-) -0.0209 -0.1428 **
BDSIZE (+) 1.2331 2.6785 *** 1.4308 3.0906 *** (+) 0.0048 0.0164 1.4308 1.9986
INED (-) 0.0996 0.1462 0.0366 0.0553 (-) -0.1620 -0.3780 0.0366 0.0648
CEODUAL (+) 0.0325 0.0746 0.0338 0.0764 (+) 0.1205 0.4391 0.0338 0.1564
AUDQUA (-) -1.3775 -2.5802 *** -1.4510 -2.6896 *** (-) -0.7917 -2.3562 -1.4510 -1.0178
ACSIZE (-) -0.5824 -1.9045 ** -0.6264 -2.0246 ** (-) -0.0200 -0.1037 ** -0.6264 -2.1310 **
ACINED (-) -0.1774 -0.1770 -0.0854 -0.0843 (-) -0.3248 -0.5146 -0.0854 -0.1897
COSIZE (-) 0.2178 0.8897 0.1858 0.7829 (-) 0.2056 1.3350 0.1858 0.5530
LEV (+) 1.3938 1.9731 ** 1.4979 2.1518 ** (+) -0.8972 -2.0180 ** 1.4979 1.9840 **
LOSS (+) 0.1622 0.8511 0.1511 0.7839 (+) -0.0111 -0.0926 0.1511 0.7088
N 140 140 140 140
F 2.3576 *** 2.1092 *** 2.1176 *** 2.1092 ***
Adj. R-squared 0.1191 0.0898 0.1002 0.0898
Table 5
OLS Regression Outputs
Notes: Models I, II, III, and IV are estimated using OLS estimators. The dependent variable in Model I, and II is forecast errors; measured by the absolute difference in the actual earnings
and forecasted earnings, deflated by absolute value of forecasted earnings. On the other hand, the dependent variable in Model III, and IV is forecast bias; measured by the difference in the
actual earnings and forecasted earnings, deflated by absolute value of forecasted earnings.

55
Model V Model VI Model VII Model VIII
Pred. Coef. t Coef. t Pred. Coef. t Coef. t
FE(-1) (?) -0.1223 -2148.3810 *** -0.1253 -4194.5470 ***
FB(-1) (?) -0.2923 -7740.5190 *** -0.3040 -5337.8790 ***
POSTUKCGC (-) -0.2928 -75.6590 *** (?) -0.7640 -198.3656 ***
BDSIZE (+) 11.2712 1367.0600 *** 10.8814 1873.4310 *** (-) 2.3765 246.2567 *** 1.8955 228.7119 ***
INED (-) 2.3521 116.9714 *** 3.2143 226.5443 *** (-) -1.7130 -213.5987 *** -1.2377 -177.3096 ***
CEODUAL (+) 0.5181 36.3075 *** 0.5774 39.8436 *** (+) 1.0816 125.6108 *** 1.0319 164.2673 ***
AUDQUA (-) -14.7999 -2867.8640 *** -14.8818 -2275.4080 *** (-) -2.4439 -640.4180 *** -1.9580 -180.4493 ***
ACSIZE (-) -5.6434 -710.5708 *** -5.3663 -1236.5090 *** (-) -0.6978 -182.1949 *** -0.4397 -182.1948 ***
ACINED (-) -1.5997 -690.7427 *** -2.2666 -698.7836 *** (-) -5.8321 -1213.4830 *** -6.9361 -1951.0450 ***
COSIZE (-) -1.2245 -257.0554 *** -0.8796 -239.3638 *** ? -0.7505 -301.5139 *** -0.5698 -158.8853 ***
LEV (+) -6.7199 -909.8510 *** -7.3654 -1181.1720 *** ? -6.2230 -463.1115 *** -7.6028 -1504.2000 ***
LOSS (+) 0.5136 288.8963 *** 0.4962 343.1342 *** (-) 0.6891 564.6827 *** 0.6861 295.0672 ***
J 102.3538 126.3619 134.2102 100.0865
Table 6
DPD Regression Outputs
Notes: Models V, VI, VII, and VIII are estimated using Arellano-Bond estimators. The dependent variable in Model V, and VI is forecast errors; measured by the absolute difference in
the actual earnings and forecasted earnings, deflated by absolute value of forecasted earnings. On the other hand, the dependent variable in Model VII, and VIII is forecast bias; measured by
the difference in the actual earnings and forecasted earnings, deflated by absolute value of forecasted earnings.

56
The coefficient of auditor quality (AUDQUA) is negative (-1.377541) and is
statistically significant at 1% (p<0.01). This result is consistent with my prediction and with
other studies that document there is a positive relationship between auditors’ reputation and
the quality of the information environment (Watts, 1977; Benston, 1980; DeAngelo, 1981).
This improvement in the information environment is likely to be the result of reputable auditors
giving an independent and professional verification in regards to the financial statements
showing a “true and fair view” of the entity’s financial position. I conclude that analyst’
forecast errors reduce when the audit engagement is carried out by one of the four largest audit
firms. This conclusion is consistent with Clarkson (2000), Hartnett and Romcke (2000), and
Cheng and Firth (2000). Therefore, there is enough evidence to support 𝐻4𝑎.
Similarly, the coefficient on the size of audit committee (ACSIZE) is negative (-
0.582357) and is statistically insignificant at 5% (p=0.0570). However, the p-value is too
marginal to reject the hypothesis that larger audit committees are associated with lower forecast
errors. Thus, there is enough evidence to support 𝐻5𝑎. The size of audit committee can have a
positive impact on the entity’s information environment (Wild, 1996; McMullen, 1996; Felo
at el., 2003). This result could be because an audit committee is likely to influence the internal
control systems positively and that they work towards clarifying the roles and responsibilities
of the board of directors. Consequently, aligned with my expectation, I conclude that analysts’
forecasts are more accurate when audit committees are larger.

57
Moreover, the coefficient on the independence of audit committee (ACINED) is
negative (-0.177444) and is statistically insignificant at 10% (p=0.8596). Thus, I find no
significant relationship between analysts’ errors and the independence of audit committee.
Thus, 𝐻6𝑎 is not supported. However, this result must be analysed with care. The majority of
the corporations have a 100% independent audit committee; thus, it may be reasonable that no
statistically significant relationship between the independence of audit committees and
analysts’ performance is found. A similar conclusion was reached by Klein (2002); her results
on audit committee independence were only significant for less than a majority of independent
directors.
Consistent with Bhaskar and Morris (1984) the coefficient on company size (COSIZE)
is insignificant at 10% (p=0.3738). In addition, there is no significant relationship at 10%
(p=0.3948) between analysts’ forecast errors and whether the company has reported a profit or
loss (LOSS). On the other hand, the coefficient of leverage (LEV) is positive (1.393814) and
is statistically significant at 5% (p<0.05). This result is consistent with my expectation and with
Eddy and Seifert (1992), which suggest that leverage negatively impacts the information
environment of analysts; thus, their forecast accuracy.
Model II analyses the impact of the UKCGC on forecast accuracy. The coefficient on
the dummy variable POSTUKCGC is negative (-0.020927) and is statistically insignificant at

58
10% (p=0.8475). Therefore, using the static model the UKCGC 2010 on average has no impact
on analysts’ forecast errors.
Furthermore, consistent with my expectations and Model I, the coefficients on board
size (BDSIZE), quality of auditors (AUDQUA), and the size of audit committee (ACSIZE)
are all negative and statistically significant. Moreover, the coefficient of financial leverage
(LEV) remains positive and statistically significant. Therefore, these four factors continue to
impact analysts’ forecast errors even after the introduction of the UKCGC 2010. By comparing
Model I to Model II, the p-values of all these four factors have reduced. Therefore, these
variables have become slightly more statistically significant after the introduction of the
UKCGC 2010. In addition to this, the magnitude of the coefficients have increased in Model
II. Therefore, after the introduction of the UKCGC 2010, these factors contribute more towards
improving analysts’ accuracy.
Finally, consistent with Model I, the coefficients of all other variables in Model II
remain statistically insignificant. They have no explanatory power.
Model V and VI are dynamic in nature. The results from Model V show that the
coefficient on board size (BDSIZE) is positive (11.27119) and is statistically significant at 1%
(p<0.01). Thus, consistent with Model I and II, there is an inverse relationship between board
size and analysts’ forecast errors. Therefore, there is enough evidence in favour of 𝐻1𝑎.

59
The coefficient on the percentage of independent directors (INED) is positive
(2.352090) and is statistically significant at 1% (p<0.01). Thus, there is enough evidence in
favour of 𝐻2𝑎 . As the percentage of independent director’s increases, analysts’ accuracy
deteriorates. This contradicts my expectation and prior literature that suggest independent
directors should improve the information environment of corporations (Mace, 1971; Beasley,
1996; Fredrickson et al., 1998). In addition to the possible explanations that were given to
justify the insignificance of independent directors in Model I, a possible explanation for this
contradictory result could be that independent directors may become hesitant to challenge their
respective boards with time. For example, Fama (1980) stated independent directors have an
incentive to “develop reputations as experts in decision control”. However, it could be possible
that once this reputation has been built, in order to protect it, independent directors become
conservative in challenging the board; thus, neutralising or worsening their initial positive
impact.
significant at 1% (p<0.01). Thus, there is enough evidence to support 𝐻3𝑎. Analysts’ forecast
errors increase when the CEO is also the Chairman/woman of the board. This is consistent with
studies by Dechow et al. (1996) and Beasley et al. (1999).
In addition, the coefficient on auditors’ quality (AUDQUA) is negative (-14.79992)
and is statistically significant at 1% (p<0.01). There is enough evidence in favour of 𝐻4𝑎. Thus,

60
consistent with my expectation and Model I, analysts’ forecasts are more accurate on average
when the audit engagement is carried out by one of the four largest audit firms. An interesting
observation is the difference in the magnitude of the coefficient of AUDQUA in Model V in
comparison to Model I. In absolute terms, the coefficient in Model V is approximately 11x
larger than the coefficient in Model I. Thus, in the dynamic model auditors’ reputation has a
more significant impact on the information environment and analysts’ accuracy.
The coefficient on the size of the audit committee (ACSIZE) is negative (-5.643374)
and is statistically significant at 1% (p<0.01). This provides evidence in favour of 𝐻5𝑎 .
Therefore, consistent with Model I, there is a negative relationship between the size of an audit
committee and analysts’ forecast errors.
The coefficient on the independence of audit committee (ACINED) is negative (-
1.599677) and is statistically significant at 1% (p<0.01). Thus, there is enough evidence to
support 𝐻6𝑎. This is consistent with prior literature that suggests there is a positive relationship
between the independence of the audit committee and the quality of the information
environment (Beasley, 1996; Persons, 2006).
In regards to the control variables, the coefficient on company size (COSIZE) is
negative (-1.224536) and it is statistically significant at 1% (p<0.01). This is consistent with
my expectation that larger firms have a better information environment; thus, higher forecast
accuracy. A similar conclusion was reached by Capstaff et al. (1999), Firth and Smith (1992),

61
and Pedwell et al. (1994). One possible explanation for this result could be that larger firms
are required to make a greater amount of disclosure (Firth, 1980; Schipper, 1981). This should
help reduce information asymmetry and allow analysts to make more accurate predictions
(Lang and Lundholm, 1996).
In addition, the coefficient on leverage (LEV) is negative (-6.719866) and is
statistically significant at 1% (p<0.01). This result indicates that analysts’ forecast errors on
average improves as the percentage of leverage increases. This contradicts my expectation, the
result from Model I, and prior studies that suggest higher leverage should have an adverse
impact on analysts’ accuracy (Eddy and Seifert, 1992; Dhaliwal et al., 1991; Francis et al.,
1998). A possible explanation for my result could be given by the trade-off theory of capital
structure, which states that the marginal benefit of a further increase in debt decreases, as debt
increases. More precisely, the theory states that there is a trade-off between interest tax shield
and bankruptcy. Therefore, analysts may use leverage to gain information about the prospects
of the company; thus, achieving a sounder forecast. Another explanation could be that debt
helps to discipline the management team of an organisation because they have to satisfy interest
payments. This disciplinary action could subsequently lead to a greater level of disclosure and
better information environment; thus, making analysts’ forecasts more accurate.
The third control variable in this model is a dummy variable that looks at whether the
company has recorded a profit or loss (LOSS). The coefficient on this dummy variable is

62
positive (0.513587) and is statistically significant at 1% (p<0.01). I conclude that there is a
positive relationship between a loss-making firm and analysts’ forecast errors. This is
consistent with the findings of Das and Somnath (1998), Brown (2001), and Agrawal et al.
(2006).
Lastly, the lagged forecast error (FE(-1)) is negative (-0.122340) and is statistically
significant at 1% (p<0.01). Thus, when the forecast errors in the previous period is larger, the
forecasts errors in the current period tends to be smaller. This could be as a result of analysts
underreacting to new information (Abarbanell, 1991; Abarbanell and Bernard 1992). They use
previous period information to make a sounder prediction in the current period.
Model VI looks at the impact of the UKCGC 2010 using a dynamic model. The
coefficient on the dummy variable POSTUKCGC is negative (-0.292849) and is statistically
significant at 1% (p<0.01). Therefore, analysts’ forecast errors have improved since the
introduction of the UKCGC 2010.
Consistent with Model V, the coefficients of board size (BDSIZE), the independence
the of board (INED), CEO duality (CEODUAL), auditors’ quality (AUDQUA), size of audit
committee (ACSIZE), the independence of the audit committee (ACINED), company size
(COSIZE), percentage of leverage (LEV), and the dummy variable LOSS are statistically
significant. Thus, these variables continue to play a major role in analysts’ forecast accuracy.

63
4.2.3. Forecast Bias
The constant (𝜷 𝟎) in Model III is negative (-0.200621) and is statistically insignificant
at 10% (p=0.8892).
The coefficient of board size (BDSIZE) is positive (0.004763) and is statistically
insignificant at 10% (p=0.9869). Thus, there is not enough evidence in favour of 𝐻1𝑏 .
Therefore, board size has no impact on forecast bias.
In addition, the coefficients of independent directors (INED) and CEO duality
(CEODUAL) are statistically insignificant. Therefore, they have no explanatory power. Thus,
there is not enough evidence in favour of 𝐻2𝑏 and 𝐻3𝑏.
The coefficient on auditors’ quality (AUDQUA) is negative (-0.791717) and
statistically significant at 5% (p<0.05). Thus, there is enough evidence to support 𝐻4𝑏. These
results indicate that analysts are more likely to set optimistic forecasts when the audit
engagement is carried out by one of the four largest audit firms in the UK. This could because
analysts have more confidence in the audit procedures performed by the reputable audit firms.
The coefficients of audit committee size (ACSIZE) and the percentage of independent
directors on audit committee (ACINED) are statistically insignificant. Thus, there is not
enough evidence in favour of 𝐻5𝑏 and 𝐻6𝑏.
In regards to the control variables, the size of the company (COSIZE) and whether the
company has made a profit or loss (LOSS) are statistically insignificant; thus, they have no

64
impact on analysts’ forecast bias. On the other hand, the coefficient of leverage (LEV) is
negative (-0.897170) and statistically significant at 5% (p<0.05). In addition to the
explanations provided in Model IV that justified leverage can positively impact the information
environment, another possible explanation could be that a positive signal is sent to the market
when a corporation takes up more debt. This positive message could be because the corporation
is confident that they are going to be profitable in the future to satisfy the interest payments
and the principal of their debts; they can pass on this high confidence to other participants in
the market, including the analysts. This explanation is consistent with the pecking order theory.
Taking Model IV into consideration, the coefficient on the dummy variable
POSTUKCGC is negative (-0.041462) and is significantly insignificant at 10% (p=0.5424).
Thus, the introduction of the UKCGC 2010 has had no impact on analysts’ forecast bias.
The coefficient on leverage (LEV) remains negative (-1.053036) after the introduction
of the UKCGC 2010 and is marginally significant at 10% (p=0.0997). The magnitude of this
variable has increased in comparison to Model III; thus, leverage helps to explain more of
analysts’ forecast bias since the introduction of the UKCGC 2010.
In addition, the coefficient of auditors’ quality (AUDQUA) remains negative (-
0.758231) and is statistically significant at 5% (p<0.05). Thus, the reputation of an auditor
continues to explain analysts’ forecast bias.

65
All other variables in Model IV are statistically insignificant and they have no
explanatory power.
In Model VII, the coefficient of board size (BDSIZE) is positive (2.376511) and is
statistically significant at 1% (p<0.01). This provides enough evidence in favour of 𝐻1𝑏 .
Therefore, as the size of the board increases, analysts become more conservative.
The coefficient on the independence of the board (INED) is negative (-1.713000) and
is statistically significant at 1% (p<0.01). Thus, there is enough evidence to support 𝐻2𝑏.
Analysts become more optimistic as the parentage of independent directors increases. This is
consistent with my expectation and with prior studies that suggest independent directors should
improve the information environment of corporations (Mace, 1971; Beasley, 1996;
Fredrickson et al., 1998).
significant at 1% (p<0.01). Thus, there is enough evidence to support 𝐻3𝑏. Analysts on average
are more conservative when the CEO is also the chairman/woman of the board. This is
consistent with my expectation and prior studies that suggest CEO duality adversely impacts
the information environment (Loebbecke et al., 1989; Dechow et al., 1996).
The coefficient of auditors’ quality (AUDQUA) is negative (-2.443909) and is
statistically significant at 1% (p<0.01). This indicates that analysts on average are more
optimistic when one of the big four carries out the audit engagement. This result is consistent

66
with Model III and supports 𝐻4𝑏. In addition, in the dynamic model, the magnitude of auditors’
quality is approximately 5x greater than the magnitude in the static model.
The coefficient on the size of audit committee (ACSIZE) is negative (-0.697784) and
is statistically significant at 1% (p<0.01). Thus, there is enough evidence in favour of 𝐻5𝑏.
Therefore, analysts’ forecasts are more optimistic when the audit committee is larger.
The coefficient on the independence of audit committee (ACINED) is negative (-
5.832092) and is statistically significant at 1% (p<0.01). Thus, there is enough evidence to
support 𝐻6𝑏. Analysts are more optimistic when the percentage of independent directors on the
audit committee increases.
In regards to the control variables, the size of the company (COSIZE) is negative (-
0.750499) and statistically significant at 1% (p<0.01). Therefore, analysts’ forecasts are more
optimistic for larger corporations. This may be due to larger firms being scrutinised more by
regulators and the general public (Scherer, 1980; Cooper et al., 1986). As a result of this
increased scrutiny, analysts feel more optimistic about the corporation’s information
environment.
The coefficient of leverage (LEV) is negative (-6.223047) and is statistically significant
at 1% (p<0.01). Thus, consistent with Model IV, analysts on average issue a more optimistic
forecast when the percentage of leverage increases.

67
Moreover, the dummy variable LOSS is positive (0.689143) and is statistically
significant at 1% (p<0.01). Therefore, in line with my expectation and prior literature analysts’
are more conservative when the corporation reports a loss (Agrawal et al., 2006).
The lagged forecast bias variable (FB(-1)) is negative (-0.292278) and is statistically
significant at 1% (p<0.01). This indicates that, on average, analysts are becoming more
optimistic than the previous period. The recent economic recovery has improved the external
environment of corporations, which could be the cause for such optimism.
Lastly, considering Model VIII, the dummy variable POSTUKCGC is negative (-
0.770405) and is statistically significant at 1% (p<0.01). Thus, analysts have become more
optimistic since the introduction of the UKCGC 2010. The conclusions about the other
variables after the introduction of the UKCGC 2010 is the same as Model VII.

68
5. CONCLUSIONS
Businesses are always looking for investors’ funds to stay ahead of the competition and
survive. On the other hand, investors look at the general purpose financial statements and
analysts’ forecasts to discover an investment opportunity. However, recent high-profile
corporate scandals have adversely impacted the confidence of the general public. This has put
corporations, regulators, and governments under immense pressure to improve corporate
governance and the information environment. Prior literature suggests that analysts’
performance is a positive function of their information environment (Lang and Lundholm,
1996).
This study examines a sample of 142 corporations from the FTSE All-Share Index to
establish whether corporate governance attributes have an impact on the analysts’ performance.
Specifically, it assesses the impact of corporate governance attributes on forecast accuracy and
forecast bias. In addition, it empirically studies the impact of the UKCGC 2010 on forecast
accuracy and forecast bias. More precisely, it investigates whether analysts’ forecasting
performance have improved since the introduction of the UKCGC 2010 as a result of an
increase in quality and transparency of financial operations. In order to gain additional insights,
this study goes a step further to analyse matters using dynamic models as well as static models.
The univariate tests show that the volatility and average forecast errors have fallen since
the introduction of the UKCGC 2010. Welch’s two-tailed test show that the average forecast

Thesis - Final

Recommended

Recommended

More Related Content

What's hot

What's hot (18)

Similar to Thesis - Final

Similar to Thesis - Final (20)

Thesis - Final