Data description

The selection of FTSE 350 companies in this study is due to the degree of ambiguity

between UK companies, and the corporate governance environment. In the UK, the high

levels of freedom and the inadequate external discipline by the market for corporate

control, enables directors to have a high level of discretion over their corporate decisions.

One such decision is capital structure, which is the dependent variable in this study

(Florackis and Ozkan, 2009). The high levels of discretion incorporate disparity between

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decisions provides questions as to why companies have chosen a specific mix of debt and

equity.

The main source of data is secondary data from DataStream, a database that contains

published accounting data for listed companies. The database contains financial

statements that include the balance sheet, income statement and cashflow statement. In

addition, the database provides accounting ratios such as current ratio and debt divided by

equity. A second source of data is BoardEx; a ‘Relationship Capital Management’

database that contains data on board members for listed and non-listed companies across

the globe. The data is inconsistent for individuals, and is controlled to some extent by the

individual and their profile in the public domain (Schmidt, 2014).

The initial source of all of the data is company’s financial statements (through DataStream and Boardex). The combination of the databases presents some gaps in the

dataset. Complete and balanced data is often a challenge to find; data that is not

contained within the two databases is hand collected from the financial statements. The

annual reports for the year 2002 are rarely available; on average company websites only

contain financial statements for the five previous years. Therefore, the period for which

the financial statements are collected is limited to 2003-2012; data is collected up to the

end of the financial year end of 2012. In line with financial reporting regulations,

companies have nine months in which to prepare, and then release their financial

statements (in accordance with the FRC). Therefore, for those companies with a

December year end of 2013, the financial statements would not be released until the end

of September 2014.

In this study, those companies that were relatively new to the stock market and have

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deemed to be sufficient is 2 years. The majority of IPO’s occur with a simultaneous share offering to investors (London Stock Exchange, 2012). Additional capital is raised

through issuing new shares to new or existing shareholders (primary offering). Existing

shareholders can sell their shares to new or existing shareholders (secondary offering), or

companies can use a combination of both options. Baker and Wurgler (2002) study the

market timing effect on the issuance of debt and equity, IPO’s can result in short-term changes to a company’s capital structure. Companies without at least two years of financial statements between 2003-2012 are excluded from the sample; this is in line with

a previous study (Ozkan and Ozkan, 2004), who required data for a minimum of a six

year period out of a total of sixteen years. This condition will be imposed to ensure

changes in capital structure and corporate governance are not affected as a result of a new

listing on the LSE. An IPO is undertaken through the issue of equity; therefore, during

the first few years the capital structure may consist of a heavier weighting of equity at the

expense of debt, this is not a reflection of a company’s capital structure. Secondly, this

study is over a ten-year period to avoid short-term decisions being reflected in the results,

two years of data is not a true reflection of a company and the removal of these

companies ensures short-termism is avoided.

The UK is the focus of this study, which is further split into companies listed on the

FTSE 100 and FTSE 250, these combine to form the FTSE 350. The financial, banks,

insurance, and utilities companies are removed from the study, leaving a total of 231

companies for the UK sample. The reason for excluding financial, banks and insurance

companies is due to the different capital structures that exist because of the nature of the

industry they operate in. This is because the debt in financial companies cannot be

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utility companies is due to the high level of regulation in the industry, and is in line with a

previous study (Ozkan, 2001).

The final sample is composed of 231 UK listed companies8 across the period 2003-2012, which consists of approximately 2,200 observations and an unbalanced data set. The

companies in the sample are those that were listed on the FTSE350 as at 2012.

Therefore, the study allows the entrance and exist of companies during the sample, and

this results in an unbalanced dataset. Survivorship bias occurs when studies are

conducted on databases that have eliminated all companies that have ceased to exist

(often due to bankruptcy). The findings from such studies most likely will be upwardly

biased, since the surviving companies will look better than those that no longer exist. For

example, many mutual fund databases provide historical data about only those funds that

are currently in existence. As a result, funds that have ceased to exist due to closure or a

merger do not appear in the databases. Generally, funds that have ceased to exist have

lower returns relative to the surviving funds. Therefore, the analysis of a mutual fund

database with survivorship bias will overestimate the average mutual fund return because

the database only includes the better performing funds. Due to the difficult in collecting

information on delisted companies, who often have poor performance, we hope after

allowing firms to enter and exit the market freely the bias will be minimised.

In order to reduce the risk of outliers, the data is winsorised at the 1% level using a

STATA programme. The process of winsorising the data ensures that the data is not

distorted due to outliers in the data. In this study, dummy variables are not winsorised as

they are bound between 0 and 1. In relation to other adjustments, companies who have

nonsensical results, for example excessive gearing ratios and negative book values were

excluded from the analysis to avoid distorting the data due to errors in the database. An

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There are two alternative methods to winsorising. Firstly, there is the interquartile

method, which is a graphical approach that displays the distribution of data and indicates

which observations might be outliers. However, the most popular alternative to

winsorising is DFBETA (Baum, 2006). DFBETA is where a measure is found for each

observation in the dataset. The DFBETA for an observation is the difference between the

regression coefficient for each independent variable which is calculated for all of the data,

and the regression coefficient that is calculated with the observation deleted, scaled by the

standard error calculated with the observation deleted.