Joel Suss, David Bholat, Alex Gillespie and Tom Reader
‘Bad cultures’ at banks are often blamed for scandals and crises, from the global financial crisis to the mis-selling of payment protection insurance (PPI) in the UK. Yet surprisingly little research has tested this claim. This is because quantifying culture is difficult to do. Our working paper gives it a go. Leveraging unique access to data available to regulators, we diagnose the cultural health of the UK banking sector. We find that banks with organisational cultures two standard deviations below the sector average are associated with a 50% increased risk of failure.
Marcus Buckmann, Paula Gallego Marquez, Mariana Gimpelewicz and Sujit Kapadia
Bank failures are very costly for society. Following the 2007/2008 global financial crisis, international regulators introduced a package of new banking regulations, known as Basel III. This includes a wider range of capital and liquidity requirements to protect banks from different risks. But could the additional complexity be unnecessary or even increase risks, as some have argued? In a recent staff working paper, we assess the value of multiple regulatory requirements by examining how different combinations of metrics might have helped prior to the 2007/2008 crisis in gauging banks that subsequently failed. Our results generally support the case for a small portfolio of different regulatory metrics: having belts and braces (or suspenders) can strengthen the resilience of the banking system.
Fraser Drew, David Humphry, Michael Straughan and Eleanor Watson
For most of us buying insurance nowadays, price comparison websites offer plenty of choice. But how much competition in insurance markets is there? There are very few studies that address this question (see here for a summary), unlike for banking where there is a wide literature. We take an exploratory approach to address the question, applying benchmarks used in competition research to a unique set of reporting data across multiple UK insurance regulatory regimes, with the hope of stimulating further work. We find competition generally works well in UK life and non-life insurance markets, despite increases in life market concentration over the past 25 years. However, competition regulators have found practices in specific markets that harm consumers.
What was the root cause of the financial crisis? Ask any economist or banker and undoubtedly they will at some point mention leverage (see e.g. here, here and here). Yet when a capital requirement based on leverage — the leverage ratio requirement — was introduced, fierce criticism followed (see e.g. here and here). Drawing on the insights from a working paper, and thinking about the main criticism — that a leverage ratio requirement could cause excessive risk-taking — this seems not to have been the case.
Zahid Amadxarif,Paula Gallego Marquez and Nic Garbarino
“We’ve done a lot to lower prudential barriers to entry into the banking sector […] but have we done enough to lower the equivalent barriers to growth?” asked PRA CEO Sam Woods in a recent speech. To make regulation proportionate, policymakers adapt regulatory requirements to the risks posed by each firm. But regulators face a trade-off between addressing systemic risks in a proportionate way and limiting regulatory complexity. New thresholds can create complexity and cliff-edge effects that can discourage healthy firms from growing. We identify regulatory thresholds for UK banks and building societies using textual analysis on a new dataset that contains the universe of prudential rules.
The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.
The financial crisis exposed banks’ vulnerability to a type of risk associated with derivatives: credit valuation adjustment (CVA) risk. Despite being a major driver of losses – around $43 billion across 10 banks according to one estimate – there had been no capital requirement to cushion banks against these losses. New rules in 2014 changed this.
Qun Harris, Analise Mercieca, Emma Soane and Misa Tanaka.
The bonus regulations were introduced based on the consensus amongst financial regulators that compensation practices were a contributing factor to the 2008-9 financial crisis. But little is known about how they affect behaviour in practice. So we conducted a lab experiment to examine how different bonus structures affect individuals’ risk and effort choices. We find that restrictions on bonuses, such as a bonus cap, can incentivise people to take less risk. But their risk-mitigating effects weaken or disappear once bonus payment is made conditional on hitting a high performance target. We also find some evidence that bonus cap discourages effort to search for better projects.
Sebastian de-Ramon, Bill Francis and Michael Straughan.
There is a debate in the regulatory and academic community about whether competition is good or bad for bank stability, particularly following the financial crisis (see Chapter 6 of the Independent Commission on Banking final report). The debate tends to be seen as a head-to-head argument between two camps: those that see competition as bad for stability (competition-fragility) versus those that see competition as good (competition-stability). In new research, we look at how competition affects the stability of banks in the UK. We find that competition affects less stable firms differently than more stable firms and that focussing on what happens to the average firm may not be sufficient.