This paper examines the effect of credit rating disagreements on merger and acquisition (M&A) decisions. We show that acquirers with split ratings prefer to use stock to finance their acquisitions. More importantly, we find that acquirers with split ratings experience lower announcement returns. Further analysis shows that overpayment by acquirers with split ratings is concentrated in acquirers with entrenched managers. Our results are robust to alternative identification strategies. Overall, our evidence indicates that credit rating disagreements are heavily priced in the M&A market.

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

We survey 149 leading academic researchers on bank capital regulation. The median (average) respondent prefers a 10% (15%) minimum non-risk-weighted equity-to-assets ratio, which is considerably higher than the current requirement. North Americans prefer a significantly higher equity-to-assets ratio than Europeans. We find substantial support for the new forms of regulation introduced in Basel III, such as liquidity requirements. Views are most dispersed regarding the use of hybrid assets and bail-inable debt in capital regulation. 70% of experts would support an additional market-based capital requirement. When investigating factors driving capital requirement preferences, we find that the typical expert believes a five percentage points increase in capital requirements would “probably decrease” both the likelihood and social cost of a crisis with “minimal to no change” to loan volumes and economic activity. The best predictor of capital requirement preference is how strongly an expert believes that higher capital requirements would increase the cost of bank lending.

We investigate how the demographic composition of the workforce along the sex, nationality, education, age and tenure dimensions affects job switches. Fitting duration models for workers’ job‐to‐job turnover rate that control for workplace fixed effects in a representative sample of large manufacturing plants in Germany during 1975–2016, we find that larger co‐worker similarity in all five dimensions substantially depresses job‐to‐job moves, whereas workplace diversity is of limited importance. In line with conventional wisdom, which has that birds of a feather flock together, our interpretation of the results is that workers prefer having co‐workers of their kind and place less value on diverse workplaces.

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.

We develop an ethical preference-based model that reproduces the average return and volatility spread between sin and non-sin stocks. Our investors do not necessarily boycott sin companies. Rather, they are open to invest in any company while trading off dividends against ethicalness. When dividends and ethicalness are complementary goods and investors are sufficiently risk averse, the model predicts that the dividend share of sin companies exhibits a positive relation with the future return and volatility spreads. An empirical analysis supports the model’s predictions. Taken together, our results point to the importance of ethical preferences for investors’ portfolio choices and asset prices.

We use rich microeconomic data on performance and choices of students at college entry to analyze interactions between the selection mechanism, eliciting college preferences through exams, and the allocation mechanism. We set up a framework in which success probabilities and student preferences are shown to be identified from data on their choices and their exam grades under exclusion restrictions and support conditions. The counterfactuals we consider balance the severity of congestion and the quality of the match between schools and students. Moving to deferred acceptance or inverting the timing of choices and exams are shown to increase welfare. Redistribution among students and among schools is also sizeable in all counterfactual experiments.

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.

In this paper, we use the panel of the first two waves of the Household Finance and Consumption Survey by the European Central Bank to study housing demand of European households and evaluate potential housing market regulations in the post-crisis era. We provide a comprehensive account of the housing decisions of European households between 2010 and 2014, and structurally estimate the housing preference of a simple life-cycle housing choice model. We then evaluate the effect of a tighter LTV/LTI regulation via counter-factual simulations. We find that those regulations limit homeownership and wealth accumulation, reduces housing consumption but may be welfare improving for the young households.

We develop a model that reproduces the return and volatility spread between sin and non-sin stocks, where investors trade off dividends with the ethical assessment of companies. We relax the assumption of boycott behaviour and investigate the role played by the dividend share of sin stocks on their return and volatility spread relative to non-sin stocks. We empirically show that the dividend share predicts a positive return and volatility spread. This pattern is reproduced by our model when dividends and ethicalness are complementary goods and investors are sufficiently risk averse.