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The purpose of this paper is to determine a financial institution's optimal hedging degree under consideration of costly earnings volatility induced by fair value accounted derivatives. The discussion on the adoption of fair value accounting in the financial industry has been rather controversial in recent years. Under this accounting regime, the change in market values of specific assets must be considered as profit or loss. Critics argue that fair value accounting induces higher earnings volatility compared to historical cost accounting and, therefore, may initiate a downward spiral during recessions. Thus, increased earnings volatility induces costs, which can be explained by disappointed capital market expectations. Consequently, in general, a lowering of earnings volatility will be rewarded. Consistent with this theoretical finding, empirical research provides strong evidence that companies pursue income smoothing to reduce earnings volatility. In contrast to industrial corporations, financial institutions may easily reduce their earnings volatility by engaging in additional hedging activities. However, more intense hedging usually reduces expected profits.

Based on a research project initiated by a large German bank, this study quantitatively models the trade-off between the (utility of) costs of earnings volatility and the reduction of profit potential through additional hedging.

By conducting sensitivity analyses and simulations of the crucial factors of the trade-off, we examine relevant causal relationships to obtain first indications about the economic benefits of income smoothing.

To the best of our knowledge, we are the first to develop an optimization model that supports decision-making by attempting to determine an optimal (additional) hedging degree considering the costs induced by earnings volatility.

This paper aims to improve decision making in credit portfolio management through analytical data-mining methods, which should be used as data availability and data quality of credit portfolios increase due to (semi-)automated credit decisions, improved data warehouses and heightened information needs of portfolio management.

To contribute to this fact, this paper elaborates credit portfolio analysis based on cluster analysis. This statistical method, so far mainly used in other disciplines, is able to determine “hidden” patterns within a data set by examining data similarities.

Based on several real-world credit portfolio data sets provided by a financial institution, the authors find that cluster analysis is a suitable method to determine numerous multivariate contract specifications implying high or, respectively, low profit potential.

Nevertheless, cluster analysis is a statistical method with multiple possible settings that have to be adjusted manually. Thus, various different results are possible, and as cluster analysis is an application of unsupervised learning, a validation of the results is hardly possible.

By applying this approach in credit portfolio management, companies are able to utilize the information gained when making future credit portfolio decisions and, consequently, increase their profit.

The paper at hand provides a unique structured approach on how to perform a multivariate cluster analysis of a credit portfolio by considering risk and return simultaneously. In this context, this procedure serves as a guidance on how to conduct a cluster analysis of a credit portfolio including advices for the settings of the analysis.