Essays on corporate social responsibility : performance, risk, and value

This thesis is dedicated to the relationship between corporate social performance (CSP) and corporate financial performance (CFP). More specifically, it identifies and analyzes three distinct accounting- and finance-related topics from an investor’s perspective: The first paper tests whether the materiality of information on CSP plays a role in explaining earnings quality. Preliminary results show that firms performing well on material CSP have higher accruals quality. In contrast, material and non-material CSP is linked to higher real earnings management. Robustness checks show that industry regulation plays a major moderating role, whereas materiality of CSP is not necessarily a reliable indicator of higher earnings quality. The second paper investigates whether CSP is linked to the risk of facing litigation and whether CSP provides an insurance-like effect against litigation. The results show that an improvement in the measure of negative CSP of one standard deviation of an average sample firm reduces litigation risk from 3.1% to 2.4%. The losses in market value from litigation for a firm with low CSP are twice as high as the losses for a firm with high CSP. Implementing a trading strategy that is based on these findings yielded monthly positive alphas of 1.31%. The third paper examines whether CSP increases the prediction accuracy for credit risk in various machine learning algorithms. It finds that the prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e.g., random forests) outperforming traditional ones (e.g., linear regression). This finding is probably best explained by the ability of advanced machine learning algorithms to capture non-linearity and complex interaction effects in the data. Moreover, including information on CSP into firm credit risk prediction does not consistently increase prediction accuracy. All three papers converge on the relevance of CSP for the financial performance of firms. The first paper focuses on an accounting perspective on the relationship between CSP and CFP. The second paper introduces the market perspective. The third paper employs a rigorous predictive approach. The three papers have in common that they provide relevant and rigorous guidance on fundamental drivers of the CSP–CFP relationship to investors and analysts.

This thesis is dedicated to the relationship between corporate social performance (CSP) and corporate financial performance (CFP). More specifically, it identifies and analyzes three distinct accounting- and finance-related topics from an investor’s perspective: The first paper tests whether the materiality of information on CSP plays a role in explaining earnings quality. Preliminary results show that firms performing well on material CSP have higher accruals quality. In contrast, material and non-material CSP is linked to higher real earnings management. Robustness checks show that industry regulation plays a major moderating role, whereas materiality of CSP is not necessarily a reliable indicator of higher earnings quality. The second paper investigates whether CSP is linked to the risk of facing litigation and whether CSP provides an insurance-like effect against litigation. The results show that an improvement in the measure of negative CSP of one standard deviation of an average sample firm reduces litigation risk from 3.1% to 2.4%. The losses in market value from litigation for a firm with low CSP are twice as high as the losses for a firm with high CSP. Implementing a trading strategy that is based on these findings yielded monthly positive alphas of 1.31%. The third paper examines whether CSP increases the prediction accuracy for credit risk in various machine learning algorithms. It finds that the prediction accuracy varies considerably between algorithms, with advanced machine learning algorithms (e.g., random forests) outperforming traditional ones (e.g., linear regression). This finding is probably best explained by the ability of advanced machine learning algorithms to capture non-linearity and complex interaction effects in the data. Moreover, including information on CSP into firm credit risk prediction does not consistently increase prediction accuracy. All three papers converge on the relevance of CSP for the financial performance of firms. The first paper focuses on an accounting perspective on the relationship between CSP and CFP. The second paper introduces the market perspective. The third paper employs a rigorous predictive approach. The three papers have in common that they provide relevant and rigorous guidance on fundamental drivers of the CSP–CFP relationship to investors and analysts.