The fall of Spanish cajas : Lessons of ownership and governance for banks

Martín‐Oliver, Alfredo; Ruano, Sonia; Salas‐Fumás, Vicente

doi:10.1016/j.jfs.2017.02.004

Cited by 39 publications

(27 citation statements)

References 37 publications

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“…The fourth and final method consists of employing hard clustering algorithms (Ayadi et al ., 2011; Ayadi and De Groen, 2015; Roengpitya et al ., 2014, 2017; Hryckiewicz and Kozłowski, 2017; Martín‐Oliver et al ., 2017). For instance, Martín‐Oliver et al .…”

Section: Literature Review On Methods Used To Identify Banking Businementioning

confidence: 99%

“…For instance, Martín‐Oliver et al . (2017) employed hierarchical clustering with Ward's method on a sample of Spanish banks using different sub‐periods and found four business models (retail‐deposits, retail‐balanced, retail‐diversified, and retail‐market). The authors documented that small cooperative banks that migrated to more risky business models before the crisis suffered higher losses during the crisis than shareholder banks with the same business model.…”

Section: Literature Review On Methods Used To Identify Banking Businementioning

confidence: 99%

“…sets of firms that are active in a single sector and use similar strategies. (…) that reflect the long‐term choices of bank management with respect to assets, funding, capitalization and diversification’ (Mergaerts and Vander Vennet, 2016: 58). ‘A banking business model consists in a pattern of assets and liabilities adopted by one or several banks that differs from the pattern adopted by other banks, each with different combinations of expected return and risk’ (Martín‐Oliver et al ., 2017: 248).…”

Section: Definition Of ‘Banking Business Model’mentioning

confidence: 99%

“…Similarly, Martín‐Oliver et al . (2017) applied HC on six variables for a sample of Spanish banks and reported persistency levels of business model classification across consecutive periods (1999–2002 versus 2003–2007) that range from 10.4% (lowest) to 85.7% (highest). In our view, both studies raise some concerns regarding the usefulness and reliability of results that are obtained by applying hard classification methods to the identification of banking business models.…”

Section: Introductionmentioning

confidence: 99%

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Using clustering ensemble to identify banking business models

Marques

Alves

2020

Intell Sys Acc Fin Mgmt

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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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Section: Literature Review On Methods Used To Identify Banking Businementioning

confidence: 99%

Section: Literature Review On Methods Used To Identify Banking Businementioning

confidence: 99%

Section: Definition Of ‘Banking Business Model’mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using clustering ensemble to identify banking business models

Marques

Alves

2020

Intell Sys Acc Fin Mgmt

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“…Further empirical evidence highlights that different ownership types may affect bank risk-taking (Balla and Rose, 2019;Martín-Oliver et al, 2017), while no structural and persistent difference is found between publicly and privately-held banks (Akihgbe et al, 2017;Barry et al, 2011).…”

Section: Related Literaturementioning

confidence: 99%

The Risk Weighted Ownership Index: An Ex-Ante Measure of Banks’ Risk and Performance

2019

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Attributing ratings to the top-20 owners, we construct a Risk-Weighted Ownership index (RWO) to measure the profitability and risk-taking behaviour of the ownership structure at banks. Collecting data from 19 European countries plus the UK over the 2008-2017 period, preliminary results show strong evidence that RWO measures are significant in explaining bank performance and risk, at both an accounting and a market-based level. Overall, these results suggest that not only markets and regulators should look at bank's owners: instead, it is far more relevant to assess the contribution carried by top-owners to bank risk, both individually and collectively. JEL Classification: G21, G32period. First of all, we aggregate data of owner identity and percentages of ownership. Second, we gather the ratings released by Credit Rating Agencies (CRAs) -when available -or calculate it by referencing to either the Bank Z-Score or the Altman Z-Score (depending upon the owner's identity, whether financial or non-financial), based on the hypothesis that credit risk is correlated with equity risk (recently: Bai et al. 2017;Bekaert and Hoerova, 2016;Han et al., 2017). Where it was not possible to assign an idiosyncratic rating, we opted to attribute the rating of the country (systematic rating).On this dataset, we measure the RWO by assigning weighting coefficients pursuant to the Basel 1 framework, in relation to each owner's identity only, or Basel 2, by considering each owner's rating too. Then, we account for the effect of the single owner on the ownership structure by multiplying Basel coefficients by the percentage stake in the bank's capital. Hence, we can build two types of RWO index: one encompassing the sole identity of top-20 owners, another considering their "quality" too.Controlling for other corporate governance characteristics and regulatory environment, our preliminary results show strong evidence that RWO measures are relevant in explaining bank performance and risk, at both an accounting and a market-based level.The relevance of our findings is twofold. First, the RWO might be used as an early-warning indicator to signal when increasing the level of risk in bank decision-making is expected. This yields many remarkable implications in terms of stock market performance and volatility: also, considering the cross-correlation between financial markets. To policymakers, our results suggest that regulators, who already supervise banks' ownership structure, should not only look at whom owns the bank but, also, devote particular attention to analysing the risk appetite of potential owners, still within the context of the already-existing shareholding base.Our paper contributes to the emerging body of research by investigating the ways in which the ownership structure and specific categories of owners affect a bank's risk-taking. Compared to the extant literature, our paper is the first to propose a methodology to measure the risk-return profile of a bank's shareholding base as a driver of ex-ante risk-taking. In addition, we...

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Are bank risk disclosures informative? Evidence from debt markets

et al. 2020

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This study examines whether financial reporting with a specific focus on risk disclosures have a predictive (informative) effect on banks' credit ratings (BCRs) and, consequently, ascertains whether governance structures can moderate such an association. Using one of the largest bank‐level datasets collected from 12 Middle East and North African (MENA) countries over the 2006–2013 period to‐date, our findings are as follows. First, we find that risk disclosures have a predictive effect on BCRs. Second, we find that the relationship between risk disclosures and BCRs is contingent on the quality of governance structures. Specifically, we find that the informativeness of risk disclosures on BCRs is higher in banks with larger board size, greater independence, higher government ownership, and better Shariah supervisory board, but lower in banks with greater block ownership, higher foreign ownership and the presence of CEO duality. The central tenor of our findings remains unchanged after controlling for a number of firm‐ and country‐level factors, alternative risk disclosure measures, firm‐ and national‐level governance proxies, different types of banks, and potential endogeneities. The findings have important implications for investors, especially bondholders, standard‐setters, regulators, and central governments.

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The fall of Spanish cajas : Lessons of ownership and governance for banks

Cited by 39 publications

References 37 publications

Using clustering ensemble to identify banking business models

Using clustering ensemble to identify banking business models

The Risk Weighted Ownership Index: An Ex-Ante Measure of Banks’ Risk and Performance

Are bank risk disclosures informative? Evidence from debt markets

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