Too Dispersed to Monitor? Ownership Dispersion, Monitoring, and the Prediction of Bank Distress

Auvray, Tristan; Brossard, Olivier

doi:10.1111/j.1538-4616.2012.00506.x

Cited by 18 publications

(11 citation statements)

References 68 publications

(63 reference statements)

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“…There are good theoretical reasons to suspect that corporate strategies are influenced by ownership concentration and investor characteristics, particularly in the case of long-term decisions such as R&D investment. For example, large blockholders have to monitor and influence managerial decisions because they have large stakes in firms' capital (Holmström &Tirole, 1993, Auvray andBrossard, 2012). Investors' country of origin may produce specific monitoring behaviours because of the different national legal systems governing asset management practices (Dupuy et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

“…Many authors have recognized that the involvement of large shareholders in monitoring and influencing managers has the potential to reduce agency problems (Demsetz & Lehn, 1985;Shleifer & Vischny, 1986;Holmström & Tirole, 1993;Hart, 1995;Maug, 1998, Auvray andBrossard, 2012). In fact, too dispersed shareholders will have no incentives to engage in monitoring because monitoring is costly and small shareholders may hope that others will engage in this activity.…”

Section: Type Of Investors Monitoring and Influencingmentioning

confidence: 99%

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Ownership structures and R&D in Europe: the good institutional investors, the bad and ugly impatient shareholders

Brossard

Lavigne

Sakinç

2013

Industrial and Corporate Change

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This study examines the relationship between ownership structures in large European companies and their innovative activity in terms of R&D spending. The analysis is performed on a sample of 324 large innovative companies over 8 years. Contrary to the view that institutional investors can have a negative influence on R&D spending, we report a positive impact of these investors. Our study also tests the impact of 'impatient' institutional investors and provides evidence of their negative influence on R&D spending.

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Section: Resultsmentioning

confidence: 99%

Section: Type Of Investors Monitoring and Influencingmentioning

confidence: 99%

Ownership structures and R&D in Europe: the good institutional investors, the bad and ugly impatient shareholders

Brossard

Lavigne

Sakinç

2013

Industrial and Corporate Change

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“…Nobel laureate Robert Engel 2011finds that there is a strong link between falling equity prices and bankruptcy and uses a Merton style model to effectively measure systemic risk. Auvray & Brossard (2012) find that the model can be a good lead indicator of bank distress, due to its market component. We link the asset fluctuations generated by Merton KMV to credit ratings, thus creating an original, innovative and dynamic ratcheting rating system, allowing the rating to fluctuate with market conditions.…”

Section: Methodsmentioning

confidence: 87%

A dynamic credit ratings model

Allen

Powell

Singh

2013

Piantadosi, J., Anderssen, R.S. And Boland J. (Eds) MODSIM2013, 20th International Congress on Modelling and Simulation

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The Global Financial Crisis (GFC) provided overwhelming evidence of the problems caused by inadequate credit ratings. Losses and problem loans experienced by banks over this period were staggering. Yet many of the securitized sub-prime parcels which were widely seen as an underlying cause of the GFC, as well as corporate obligors who experienced severe difficulties during the GFC, retained extremely strong external credit ratings. They may have had low perceived risk at the time of rating, but as circumstances changed, the ratings stayed static and became far removed from the underlying risk. A key problem is that the external credit ratings do not fluctuate with changing economic circumstances. Whilst there are models which measure changing default risk, they are not linked to credit ratings and it is often the rating itself, not the underlying risk that drives behavior, such as the purchase of securitized parcels, the pricing of credit risk, and the allocation of capital for credit risk, which under the Basel standardized model for corporates is based on the rating itself. This problem is exacerbated by the fact that these ratings carry descriptors such as "extremely strong capacity". This descriptor may no longer be appropriate for the rated company if the market turns dramatically, yet the rating and descriptor remain unchanged. To overcome this problem, this paper shows how an innovative fluctuating credit ratings model can be generated by linking the Merton structural credit model to a credit ratings framework. The Merton model measures fluctuations in daily asset values and, using a combination of these fluctuating asset values and the capital structure of a company, it measures Distance to Default (DD) and the Probability of Default (PD) associated with each DD. Under the Merton structural model, default occurs when the firm's debt exceeds asset values. Thus as fluctuations in asset values become more volatile, DD also becomes more volatile and PD increases. External raters such as Moody's provide PD's associated with each rating. Thus by using the Merton model, we are able to generate PDs which fluctuate over time and link these PD's to credit ratings. Therefore, as our PD's fluctuate, so do the credit ratings. To illustrate our approach, we apply this model to a French motor vehicle company (Renault) which experienced severe distress during the GFC. We compare the Moody's rating changes that took place for Renault over the 2006-2009 period, which captures the events leading up to and during the GFC. Over this period, only three Moody's external ratings changes took place and throughout this period, Renault stayed in the Moody's 'moderate' risk band. Based on this, an investor would likely assume the company was in reasonable financial health, and a bank would not be required to change its capital allocation for this company if it was a borrower. Yet during this period, the company experienced such severe financial problems that it had to be bailed out by the French Government. Our model, on the other ha...

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“…Probit models are frequently used to determine the factors influencing failures of traditional banks (e.g., Cole and Gunther, 1998;Reynaud, 2010;Kerstein and Kozberg, 2013). Other regression techniques comprise logit models (e.g., Thomson, 1991;Arena, 2008;Cole and White, 2012;Jin et al, 2011), the linear probability model (e.g., Poghosyan andČihak, 2011), and survival models 3 (e.g., Wheelock and Wilson, 2000;Arena, 2008;Auvray and Brossard, 2012).…”

Section: Methodsmentioning

confidence: 99%

Explaining Failures of Microfinance Institutions

2013

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We empirically study the determinants of failures of microfinance institutions based on the CAMELS rating components and microfinance-specific measures by applying probit regression techniques. Our findings confirm the capital adequacy (C), the asset quality (A), the management capability (M), the earnings (E), and the sensitivity to market risk (S) as explaining factors of failures of microfinance institutions. Regarding microfinance-specific effects, there is a positive influence of the percentage of female borrowers on the likelihood of failure. Moreover, we find evidence that regulation, the presence of donations, and the rapid growth of an MFI affect the probability of failure.

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Too Dispersed to Monitor? Ownership Dispersion, Monitoring, and the Prediction of Bank Distress

Cited by 18 publications

References 68 publications

Ownership structures and R&D in Europe: the good institutional investors, the bad and ugly impatient shareholders

Ownership structures and R&D in Europe: the good institutional investors, the bad and ugly impatient shareholders

A dynamic credit ratings model

Explaining Failures of Microfinance Institutions

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