Equity Crowdfunding: High-quality or Low-quality Entrepreneurs?
Entrepreneurship Theory and Practice,
Equity crowdfunding (ECF) has potential benefits that might be attractive to high-quality entrepreneurs, including fast access to a large pool of investors and obtaining feedback from the market. However, there are potential costs associated with ECF due to early public disclosure of entrepreneurial activities, communication costs with large pools of investors, and equity dilution that could discourage future equity investors; these costs suggest that ECF attracts low-quality entrepreneurs. In this paper, we hypothesize that entrepreneurs tied to more risky banks are more likely to be low-quality entrepreneurs and thus are more likely to use ECF. A large sample of ECF campaigns in Germany shows strong evidence that connections to distressed banks push entrepreneurs to use ECF. We find some evidence, albeit less robust, that entrepreneurs who can access other forms of equity are less likely to use ECF. Finally, the data indicate that entrepreneurs who access ECF are more likely to fail.
Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
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.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
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.
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Reports des European Forecasting Network (EFN)
Reports des European Forecasting Network (EFN) Das European Forecasting Network...