In this paper we examine which business choices are more likely to increase the profitability and distance to distress of banks, and whether changing business model pays off. Our analysis is framed under the theoretical premises of the strategic groups theory and the agency theory.We find that the profitability and distance to distress increase with the use of customer deposits and equity, and decrease with size; also, relationship banks and banks with a retail focused business model tend to outperform those with other orientations and business models, respectively. Moreover, we document the heterogeneous impact of business model choices on performance by finding that income diversification only bears a positive impact on the distance to distress of banks highly focused on relationship banking, and size only bears a negative effect on the profitability of these banks as well; additionally, only banks with low relationship banking orientation significantly benefit from customer deposits. We also dedicate considerable effort to studying the effects of business model changes on profitability and find that shifts from the retail diversified funding model to either the retail focused or the large diversified model improve performance in the medium term. Finally, we find evidence that large diversified banks benefited from internal capital markets during the twin financial crisis in Europe (2008Europe ( -10, 2011) by tapping into low-cost funding from subsidiaries. Our results are robust to changes to our baseline model that account for endogeneity and persistency issues.

Summary The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.

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