Browsing by Subject "Kosten für Eigenkapital"

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    Disclosure of executive remuneration in large banks
    (2008) Schott, Max; Burghof, Hans-Peter
    The first part of this thesis analyzes and quantifies the magnitude of executive remuneration disclosure of the world?s 245 largest exchange-listed banks, using annual reports as the primary source of information, and the diverse disclosure rules in the 31 countries in which these banks are domiciled. Descriptive statistics suggests that banks located in common law countries have higher (more than three times in terms of the quantifying proxy) disclosure compared to banks in civil law countries. Banks in common law countries generally surpass country-level disclosure requirements, whereas those in countries with civil law tradition fall short. Using factor analysis and regression analysis, the evidence supports a statistically stronger relationship of disclosure quality with systemic determinants like law paradigm and type of financial system than with market size effects. The second part addresses the question whether better disclosure of executive remuneration explains lower risk premiums demanded by investors and/or better stock performance. The evidence suggests that better disclosure significantly corresponds with and supports higher Sharpe ratios and higher Tobin?s qs. With respect to price-earnings ratios, lower risk premiums - and thus higher price-earnings ratios - can be supported by better disclosure if a special gauging of the outlier is applied. The third part hypothesizes that better disclosure supports higher abnormal stock returns controlled for the risk prices of volatility, size, book-to-market ratio and momentum. The evidence suggests that an investment strategy that buys the stocks of the half of the banks with above-average disclosure of executive remuneration and sells short the stocks of the half of the banks with below-average disclosure generates a (striking) 8.4% annually mean abnormal return between January 1995 and December 2006. The result is robust to varied portfolio compositions and different model specifications.