This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

Theoretically, bank‘s loan monitoring activity hinges critically on its capitalisation. To proxy for monitoring intensity, we use changes in borrowers‘ investment following loan covenant violations, when creditors can intervene in the governance of the firm. Exploiting granular bank-firm relationships observed in the syndicated loan market, we document substantial heterogeneity in monitoring across banks and through time. Better capitalised banks are more lenient monitors that intervene less with covenant violators. Importantly, this hands-off approach is associated with improved borrowers‘ performance. Beyond enhancing financial resilience, regulation that requires banks to hold more capital may thus also mitigate the tightening of credit terms when firms experience shocks.

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

We isolate the direct bank-to-sovereign distress channel within the eurozone’s sovereign-bank-loop by exploiting the global, non-eurozone related variation in stock prices. We instrument banking sector stock returns in the eurozone with exposure-weighted stock market returns from non-eurozone countries and take further precautions to remove any eurozone crisis-related variation. We find that the transmission of instrumented bank distress, while economically relevant, is significantly smaller than the corresponding coefficient in the unadjusted OLS framework, confirming concerns on reverse causality and omitted variables in previous studies. Furthermore, we show that the spillover of bank distress is significantly stronger for countries with poorer macroeconomic performances, weaker financial sectors and financial regulation and during times of elevated political uncertainty.

• Signs of a slowing world economy are piling up: Since the beginning of the year purchasing manager indices have been declining globally, in summer higher US interest rates led investors to withdraw capital from emerging markets, and as a consequence, capital costs rose and currencies depreciated in many emerging markets economies. In October stock prices decreased markedly worldwide including the US, despite the strong upswing in this country. • As a consequence of the turmoil on financial markets, monetary conditions in many emerging economies are no longer favorable. What ultimately counts for the prospects of the global economy, is, however, the performance of the US, the Euro area, China and Japan. The upswing in the US appears stable enough to continue well into 2019. While at present the rest of the group appears to lose momentum, there is a good chance that production in each of these economies will still expand at rates that are close to their potential growth. Further protectionist rounds are the most important risk to this scenario. • In the first half of the current year the euro area economy expanded at a markedly slower rate than in 2017, about 0.4% per quarter, but still substantial. Rising risk premia on Italian assets will probably force banks in this large country to tighten credit conditions, and a slowing world trade will dent export growth. All in all, we expect GDP growth in the euro area to go down from 2.4% in 2017 to 2.0% in 2018 and to 1.7% in 2019. • Employment continues to expand and vacancy rates are at present higher than in 2017. As a consequence, wages rise more quickly: nominal compensations per employee started accelerating early in 2017, and negotiated wages followed at the beginning of 2018. Wage inflation of slightly below 2.5% and healthy growth in employment raise real labor incomes markedly. • Our forecasts are based on the assumption that rating agencies will continue as-signing investment grade to the Italian government debt, and that the Italian government and the European Commission will find a compromise about the draft budget of the country in the coming weeks. Another assumption is that the UK will not exit the EU in an unorderly way in March 2019.

We investigate the (unintended) effects of bank executive compensation regulation. Capping the share of variable compensation spurred average turnover rates driven by CEOs at poorly performing banks. Other than that, banks‘ responses to raise fixed compensation sufficed to retain the vast majority of non-CEO executives and those at well performing banks. We fail to find evidence that banks with executives that are more affected by the bonus cap became less risky. In fact, numerous results indicate an increase of risk, even in its systemic dimension according to selected measures. The return component of bank performance appears to be unaffected by the bonus cap. Risk hikes are consistent with an insurance effect associated with raised the increase in fixed compensation of executives. The ability of the policy to enhance financial stability is therefore doubtful.

The paper investigates whether diversification/focus across assets, industries and borrowers affects bank performance when banks’ abilities (screening and monitoring) are considered. The initial results show that diversification (focus) at the asset, industry and borrower levels is expected to decrease (increase) returns. However, once banks’ screening and monitoring abilities are controlled for, the effect of diversification/focus either gets weaker or disappears. Further, in some cases, these abilities enhance banks’ long-run performance, but in others they prove to be costly, at least, in the short run. Thus, the level of monitoring and screening abilities should be taken into consideration in understanding, planning and implementing diversification/focus strategies.

A number of recent studies compare the performance of Islamic and conventional banks with the use of individual financial ratios or efficiency frontier techniques. The present study extends this strand of the literature, by comparing Islamic banks, conventional banks, and banks with an Islamic window with the use of a bank overall financial strength index. This index is developed with a multicriteria methodology that allows us to aggregate various criteria capturing bank capital strength, asset quality, earnings, liquidity, and management quality in controlling expenses. We find that banks differ significantly in terms of individual financial ratios; however, the difference of the overall financial strength between Islamic and conventional banks is not statistically significant. This finding is confirmed with both univariate comparisons and in multivariate regression estimations. When we look at the bank financial strength within regions, we find that conventional banks outperform both the Islamic banks and the banks with Islamic window in the case of Asia and the Gulf Cooperation Council; however, Islamic banks perform better in the MENA and Senegal region. Second stage regressions also reveal that the bank overall financial strength index is influenced by various country-specific attributes. These include control of corruption, government effectiveness, and operation in one of the seven countries that are expected to drive the next big wave in Islamic finance.

This paper investigates the relationship between local banking structures and SMEs’ access to debt and performance. Using a unique dataset on bank branch locations in Poland and firm-, county-, and bank-level data, we conclude that a strong position for local cooperative banks facilitates access to bank financing, lowers financial costs, boosts investments, and favours growth for SMEs. Moreover, counties in which cooperative banks hold a strong position are characterized by a more rapid pace of new firm creation. The opposite effects appear in the majority of cases for local banking markets dominated by foreign-owned banks. Consequently, our findings are important from a policy perspective because they show that foreign bank entry and industry consolidation may raise valid concerns for SME prospects in emerging economies.