Dynamic Equity Slope
Matthijs Breugem, Stefano Colonnello, Roberto Marfè, Francesca Zucchi
University of Venice Ca' Foscari Department of Economics Working Papers,
No. 21,
2020
Abstract
The term structure of equity and its cyclicality are key to understand the risks drivingequilibrium asset prices. We propose a general equilibrium model that jointly explainsfour important features of the term structure of equity: (i) a negative unconditionalterm premium, (ii) countercyclical term premia, (iii) procyclical equity yields, and (iv)premia to value and growth claims respectively increasing and decreasing with thehorizon. The economic mechanism hinges on the interaction between heteroskedasticlong-run growth — which helps price long-term cash flows and leads to countercyclicalrisk premia — and homoskedastic short-term shocks in the presence of limited marketparticipation — which produce sizeable risk premia to short-term cash flows. The slopedynamics hold irrespective of the sign of its unconditional average. We provide empirical support to our model assumptions and predictions.
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Corona Shutdown and Bankruptcy Risk
Oliver Holtemöller, Yaz Gulnur Muradoglu
IWH Online,
No. 3,
2020
Abstract
This paper investigates the consequences of shutdowns during the Corona crisis on the risk of bankruptcy for firms in Germany and United Kingdom. We use financial statements from the period 2014 to 2018 to predict how pervasive risk of bankruptcy becomes for micro, small, medium, and large firms due to shutdown measures. We estimate distress for firms using their capacity to service their debt. Our results indicate that under three months of shutdown almost all firms in shutdown industries face high risk of bankruptcy. In Germany, about 99% of firms in shutdown industries and in the UK about 98% of firms in shutdown industries are predicted to be under distress. The furlough schemes reduce the risk of bankruptcy only marginally to 97% of firms in shutdown industries in Germany and 95% of firms in shutdown industries in the United Kingdom in case of a three-month shutdown. In sectors that are not shutdown under conservative estimates of contagion of sales losses, our results indicate considerable risk of widespread bankruptcies ranging from 76% of firms in Germany to 69% of firms in the United Kingdom. These early findings suggest that the impact of corona crisis on corporate sector via shutdowns can be severe and subsequent policy should be designed accordingly.
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Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Gonul Colak, Mengchuan Fu, Iftekhar Hasan
Pacific-Basin Finance Journal,
June
2020
Abstract
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
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The Creation and Evolution of Entrepreneurial Public Markets
Shai B. Bernstein, Abhishek Dev, Josh Lerner
Journal of Financial Economics,
No. 2,
2020
Abstract
This paper explores the creation and evolution of new stock exchanges around the world geared toward entrepreneurial companies, known as second-tier exchanges. Using hand-collected novel data, we show the proliferation of these exchanges in many countries, their significant volume of Initial Public Offerings (IPOs), and lower listing requirements. Shareholder protection strongly predicted exchange success, even in countries with high levels of venture capital activity, patenting, and financial market development. Better shareholder protection allowed younger, less-profitable, but faster-growing, companies to raise more capital. These results highlight the importance of institutions in enabling the provision of entrepreneurial capital to young companies.
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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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