Data sources

In this thesis, secondary data is used to answer the two research questions. The data has been collected from two main sources: bank’s annual reports and Datastream database. Data is collected as follows:

CDS and derivatives data: Consistent with many prior empirical studies on derivatives use, the data on CDS is hand-collected from banks’ annual reports (e.g., Allayannis and Ofek, 2001; Rajgopal and Shevlin, 2002; Supanvanij and Strauss, 2010). CDS users are identified by searching the annual reports for key words like credit default swap, default contract, single-name default swaps, default swap, and CDS. The details about the notional value of CDS contracts are collected from banks’ balances sheet and the additional disclosure in the notes.

Derivatives disclosure practice of European banks is in fact diverse. For example, the UK banks provide a common reporting framework and more detailed information compared with

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other European countries. This makes the data collection of CDS and other derivatives labour and time intensive. Some annual reports only disclose the total value of credit derivatives contracts without classifying the figures into different credit derivatives categories. However, they clearly explain that most of their credit derivatives positions are in the form of CDS.

Remuneration data: Unlike US firms, compensation data for European firms is not readily

available in machine-readable form, and there is no database which provides a complete classification of compensation data for European firms (Conyon et al., 2011; Renneboog and Zhao, 2011). Therefore, compensation data requires hand-collection using banks’ annual reports. Empirical studies point out that assembling data on executive compensation across European countries is difficult due to the different country governance structures, legal and accounting systems and alternative ways of measuring executive compensation (e.g., Abowd and Bognanno, 1993; Carpenter and Yermack, 1999). Data is collected for the following components of CEO compensation:

1) Stock option data: Consistent with previous studies, the value of stock option compensation is calculated using the Black-Scholes (1973) option valuation model. For this purpose, the following details about stock option compensation are obtained from bank’s annual reports:

 Total number of options at the end of the year.

 Exercise price.

 Time to expiration (time-to-maturity).

 Price of the underlying stock

These key parameters for Black-Scholes option pricing model are normally disclosed in details in the directors’ remuneration reports. The Black-Scholes formula has become standard practice in executive compensation literature to estimate the value of executive

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options (Conyon and Murphy, 2000; Coles et al. 2006). Appendix A describes the option valuation model in more details.

2) Cash compensation: The total annual cash compensation (annual salary and annual cash bonus) data is obtained from bank’s annual reports. Salary and cash bonuses represent an important and common proportion of executive compensation (Balsam, 2001). It is common for empirical studies to use the value of executive cash pay as the measure for risk aversion (Coles, et al., 2006).

3) Stock Grants: The bank’s annual report is also used to collect data on executive stock compensation (restricted stock and long term incentive plan). To calculate the value of executive stock compensation the total number of stock holding is multiplied by closing stock price. Executive ownership as a percentage of stock holding at the end of the year is also included.

CEO characteristics: Data on CEO characteristics (i.e., age and tenure) is hand-collated

from bank annual reports.

Recently, more detailed information about managerial compensation has been made available in annual reports, especially after adopting the Greenbury Report (1995), Hampel Report (1998) and Directors’ Remuneration Report Regulations (2002). Furthermore, in a 2004 report, the European Union (EU) Commission formally recommended that all listed companies in the EU report details on individual executives’ compensation packages (Conyon, et al., 2013).30

The reason for choosing annual reports as a main source for the key variables in this thesis is because banks’ annual reports are considered a more dominant, reliable, and significant source of information (Cowton, 1998). Moreover, both CDS and compensation data disclosed

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Hand-collecting data of European CEO Compensation data is both labour and time-intensive This is because compensation data is not reported in the same tabular form across different European companies, making data collection more difficult (Conyons et al., 2011).

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in annual reports is edited and complies with the reporting requirements (Bartram et al., 2011; Conyon et al., 2011).

The second source of data used in this thesis is the Datastream database. Datastream was used to collect data on banks’ characteristics and control variables. Data on banks’ book value of debt and book value of assets are used to estimate banks’ leverage. Furthermore, Datastream is used to obtain data on market-to-book value of assets and total sales to measure growth opportunities and bank size respectively.

Datastream is also used to gather data on the number of geographical segments to measure diversification. There are different inputs used to measure vega, delta, and beta are obtained from Datastream, such as the volatility of the bank’s stock return, the volatility of the market’s return index, and risk-free rate.

Datastream covers a large number of stock markets and financial data for companies. In addition, it is a major source of data for global stock markets and empirical research for European companies. If accounting data is missing in Datastream, the company annual report is used. Datastream has also been used as a source in previous studies (e.g., Bartram et al., 2011; Renneboog and Zhao, 2011). All figures have been translated into the pound sterling currency using the Datastream exchange rate at the annual report date. Data was analysed using the Statistics Data Analysis (STATA) 10.