Age and High-Growth Entrepreneurship
Pierre Azoulay, Benjamin Jones, J. Daniel Kim, Javier Miranda
American Economic Review: Insights,
No. 1,
2020
Abstract
Many observers, and many investors, believe that young people are especially likely to produce the most successful new firms. Integrating administrative data on firms, workers, and owners, we study start-ups systematically in the United States and find that successful entrepreneurs are middle-aged, not young. The mean age at founding for the 1-in-1,000 fastest growing new ventures is 45.0. The findings are similar when considering high-technology sectors, entrepreneurial hubs, and successful firm exits. Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs.
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Comparing Financial Transparency between For-profit and Nonprofit Suppliers of Public Goods: Evidence from Microfinance
John W. Goodell, Abhinav Goyal, Iftekhar Hasan
Journal of International Financial Markets, Institutions and Money,
January
2020
Abstract
Previous research finds market financing is favored over relationship financing in environments of better governance, since the transaction costs to investors of vetting asymmetric information are thereby reduced. For industries supplying public goods, for-profits rely on market financing, while nonprofits rely on relationships with donors. This suggests that for-profits will be more inclined than nonprofits to improve financial transparency. We examine the impact of for-profit versus nonprofit status on the financial transparency of firms engaged with supplying public goods. There are relatively few industries that have large number of both for-profit and nonprofit firms across countries. However, the microfinance industry provides the opportunity of a large number of both for-profit and nonprofit firms in relatively equal numbers, across a wide array of countries. Consistent with our prediction, we find that financial transparency is positively associated with a for-profit status. Results will be of broad interest both to scholars interested in the roles of transparency and transaction costs on market versus relational financing; as well as to policy makers interested in the impact of for-profit on the supply of public goods, and on the microfinance industry in particular.
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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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Trade, Misallocation, and Capital Market Integration
Laszlo Tetenyi
IWH-CompNet Discussion Papers,
No. 8,
2019
Abstract
I study how cross-country capital market integration affects the gains from trade in a model with financial frictions and heterogeneous, forward-looking firms. The model predicts that misallocation among exporters increases as trade barriers fall, even as misallocation decreases in the aggregate. The reason is that financially constrained productive exporters increase their production only marginally, while unproductive exporters survive for longer and increase their size. Allowing capital inflows magnifies misallocation, because unproductive firms expand even more, leading to a decline in aggregate productivity. Nevertheless, under integrated capital markets, access to cheaper capital dominates the adverse effect on productivity, leading to higher output, consumption and welfare than under closed capital markets. Applied to the period of European integration between 1992 and 2008, I find that underdeveloped sectors experiencing higher export exposure had more misallocation of capital and a higher share of unproductive firms, thus the data is consistent with the model’s predictions. A key implication of the model is that TFP is a poor proxy for consumption growth after trade liberalisation.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
No. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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02.10.2019 • 20/2019
Joint Economic Forecast Autumn 2019: Economy Cools Further – Industry in Recession
Berlin, October 2, 2019 – Germany’s leading economics research institutes have revised their economic forecast for Germany significantly downward. Whereas in the spring they still expected gross domestic product (GDP) to grow by 0.8% in 2019, they now expect GDP growth to be only 0.5%. Reasons for the poor performance are the falling worldwide demand for capital goods – in the exporting of which the Germany economy is specialised – as well as political uncertainty and structural changes in the automotive industry. By contrast, monetary policy is shoring up macroeconomic expansion. For the coming year, the economic researchers have also reduced their forecast of GDP growth to 1.1%, having predicted 1.8% in the spring.
Oliver Holtemöller
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Pricing Sin Stocks: Ethical Preference vs. Risk Aversion
Stefano Colonnello, Giuliano Curatola, Alessandro Gioffré
European Economic Review,
2019
Abstract
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.
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Predicting Free-riding in a Public Goods Game – Analysis of Content and Dynamic Facial Expressions in Face-to-Face Communication
Dmitri Bershadskyy, Ehsan Othman, Frerk Saxen
IWH Discussion Papers,
No. 9,
2019
Abstract
This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.
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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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Accounting Quality in Banking: The Role of Regulatory Interventions
Manthos D. Delis, Iftekhar Hasan, Maria Iosifidi, Lingxiang Li
Journal of Banking and Finance,
2018
Abstract
Using the full sample of U.S. banks and hand-collected data on enforcement actions over 2000–2014, we analyze the role of these interventions in promoting several aspects of accounting quality. We find that enforcement actions issued for both risk-related and accounting-related reasons lead to significant improvements in accounting quality. This improvement is consistently found for earnings smoothing, big-bath accounting, timely recognition of future loan losses, the association of loan loss provisions with future loan charge offs, loss avoidance, and cash flow predictability and earnings persistence. Most of the effects are somewhat more potent in the crisis period and survive in several sensitivity tests. Our findings highlight the imperative role of regulatory interventions in promoting bank accounting quality.
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