Asymmetric Investment Responses to Firm-specific Forecast Errors
Julian Berner, Manuel Buchholz, Lena Tonzer
Abstract
This paper analyses how firm-specific forecast errors derived from survey data of German manufacturing firms over 2007–2011 affect firms’ investment propensity. Understanding how forecast errors affect firm investment behaviour is key to mitigate economic downturns during and after crisis periods in which forecast errors tend to increase. Our findings reveal a negative impact of absolute forecast errors on investment. Strikingly, asymmetries arise depending on the size and direction of the forecast error. The investment propensity declines if the realised situation is worse than expected. However, firms do not adjust investment if the realised situation is better than expected suggesting that the uncertainty component of the forecast error counteracts positive effects of unexpectedly favorable business conditions. Given that the fraction of firms making positive forecast errors is higher after the peak of the recent financial crisis, this mechanism can be one explanation behind staggered economic growth and slow recovery following crises.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
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.
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Structural Stability of the Research & Development Sector in European Economies Despite the Economic Crisis
Jutta Günther, Maria Kristalova, Udo Ludwig
Journal of Evolutionary Economics,
Nr. 5,
2019
Abstract
When an external shock such as the economic crisis in 2008/2009 occurs, the interconnectedness of sectors can be affected. This paper investigates whether the R&D sector experienced changes in its sectoral integration through the recession. Based on an input-output analysis, it can be shown that the linkages of the R&D sector with other sectors remain stable. In some countries, the inter-sectoral integration becomes even stronger. Policy makers can be encouraged to use public R&D spending as a means of fiscal policy against an economic crisis.
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26.06.2019 • 14/2019
Studie: Wie Finanzkrisen Menschen unzufriedener machen und wie sich das verhindern lässt
Finanzkrisen haben nicht nur starke Verwerfungen im ökonomischen System zur Folge: Sie beeinflussen auch direkt die Lebenszufriedenheit der Menschen. Am stärksten betroffen sind die Schwachen der Gesellschaft, auch wenn diese unter Umständen gar nicht selbst mit Aktien spekulieren. Das ist das Ergebnis einer neuen Studie der Martin-Luther-Universität Halle-Wittenberg (MLU) und des Leibniz-Instituts für Wirtschaftsforschung Halle (IWH). Diese negativen Folgen könnten die Kauflust der Menschen schmälern und die Wirkung der Krise sogar noch verstärken. Die Studie wurde kürzlich in der Fachzeitschrift „The B.E. Journal of Economic Analysis & Policy“ veröffentlicht.
Lena Tonzer
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On the Empirics of Reserve Requirements and Economic Growth
Jesús Crespo Cuaresma, Gregor von Schweinitz, Katharina Wendt
Journal of Macroeconomics,
June
2019
Abstract
Reserve requirements, as a tool of macroprudential policy, have been increasingly employed since the outbreak of the great financial crisis. We conduct an analysis of the effect of reserve requirements in tranquil and crisis times on long-run growth rates of GDP per capita and credit (%GDP) making use of Bayesian model averaging methods. Regulation has on average a negative effect on GDP in tranquil times, which is only partly offset by a positive (but not robust effect) in crisis times. Credit over GDP is positively affected by higher requirements in the longer run.
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Private Equity and Financial Fragility During the Crisis
Shai B. Bernstein, Josh Lerner, Filippo Mezzanotti
Review of Financial Studies,
Nr. 4,
2019
Abstract
Does private equity (PE) contribute to financial fragility during economic crises? The proliferation of poorly structured transactions during booms may increase the vulnerability of the economy to downturns. During the 2008 crisis, PE-backed companies decreased investments less than did their peers and experienced greater equity and debt inflows, higher asset growth, and increased market share. These effects are especially strong among financially constrained companies and those whose PE investors had more resources at the crisis onset. In a survey, PE firms report being active investors during the crisis and spending more time working with their portfolio companies.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
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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Banks Fearing the Drought? Liquidity Hoarding as a Response to Idiosyncratic Interbank Funding Dry-ups
Helge Littke, Matias Ossandon Busch
IWH Discussion Papers,
Nr. 12,
2018
Abstract
Since the global financial crisis, economic literature has highlighted banks’ inclination to bolster up their liquid asset positions once the aggregate interbank funding market experiences a dry-up. To this regard, we show that liquidity hoarding and its detrimental effects on credit can also be triggered by idiosyncratic, i.e. bankspecific, interbank funding shocks with implications for monetary policy. Combining a unique data set of the Brazilian banking sector with a novel identification strategy enables us to overcome previous limitations for studying this phenomenon as a bankspecific event. This strategy further helps us to analyse how disruptions in the bank headquarters’ interbank market can lead to liquidity and lending adjustments at the regional bank branch level. From the perspective of the policy maker, understanding this market-to-market spillover effect is important as local bank branch markets are characterised by market concentration and relationship lending.
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On the Empirics of Reserve Requirements and Economic Growth
Jesús Crespo Cuaresma, Gregor von Schweinitz, Katharina Wendt
Abstract
Reserve requirements, as a tool of macroprudential policy, have been increasingly employed since the outbreak of the great financial crisis. We conduct an analysis of the effect of reserve requirements in tranquil and crisis times on credit and GDP growth making use of Bayesian model averaging methods. In terms of credit growth, we can show that initial negative effects of higher reserve requirements (which are often reported in the literature) tend to be short-lived and turn positive in the longer run. In terms of GDP per capita growth, we find on average a negative but not robust effect of regulation in tranquil times, which is only partly offset by a positive but also not robust effect in crisis times.
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