Firm Social Networks, Trust, and Security Issuances
Ming Fang, Iftekhar Hasan, Zenu Sharma, An Yan
European Journal of Finance,
Nr. 4,
2022
Abstract
We observe that public firms are more likely to issue seasoned stocks rather than bonds when theirs boards are more socially-connected. These connected issuers experience better announcement-period stock returns and attract more institutional investors. This social-connection effect is stronger for firms with severe information asymmetry, higher risk of being undersubscribed, and more visible to investors. Our conjecture is this social-network effect is driven by trust in issuing firms. Given stocks are more sensitive to trust, these trusted firms are more likely to issue stocks than bonds. Trustworthiness plays an important role in firms’ security issuances in capital markets.
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Going Public and the Internal Organization of the Firm
Daniel Bias, Benjamin Lochner, Stefan Obernberger, Merih Sevilir
SSRN Working Paper,
May
2022
Abstract
We examine how firms adapt their organization when they go public. To conform with the requirements of public capital markets, we expect IPO firms to become more organized, making the firm more accountable and its human capital more easily replaceable. We find that IPO firms transform into a more hierarchical organization with smaller departments. Managerial oversight increases. Organizational functions dedicated to accounting, finance, information and communication, and human resources become much more prominent. Employee turnover is sizeable and directly related to changes in hierarchical layers. New hires are better educated, but younger and less experienced than incumbents, which reflects the staffing needs of a more hierarchical organization. Wage inequality increases as firms become more hierarchical. Overall, going public is associated with a comprehensive transformation of the firm's organization which becomes geared towards efficiently operating a public firm.
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Economic Sentiment: Disentangling Private Information from Public Knowledge
Katja Heinisch, Axel Lindner
IWH Discussion Papers,
Nr. 15,
2021
Abstract
This paper addresses a general problem with the use of surveys as source of information about the state of an economy: Answers to surveys are highly dependent on information that is publicly available, while only additional information that is not already publicly known has the potential to improve a professional forecast. We propose a simple procedure to disentangle the private information of agents from knowledge that is already publicly known for surveys that ask for general as well as for private prospects. Our results reveal the potential of our proposed technique for the usage of European Commissions‘ consumer surveys for economic forecasting for Germany.
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Executive Equity Risk-Taking Incentives and Firms’ Choice of Debt Structure
Iftekhar Hasan, Walid Saffar, Yangyang Chen, Leon Zolotoy
Journal of Banking and Finance,
December
2021
Abstract
We examine how executive equity risk-taking incentives affect firms’ choice of debt structure. Using a longitudinal sample of U.S. firms, we document that when executive compensation is more sensitive to stock volatility (i.e., has higher vega), firms reduce their reliance on bank debt financing. We utilize the passage of the Financial Accounting Standard (FAS) 123R option-expensing regulation as an exogenous shock to management option compensation to account for potential endogeneity. In cross-sectional analyses, we find that the documented effect of vega is amplified among firms with higher growth opportunities and more opaque financial information; we also find vega's effect is mitigated in firms with limited abilities to tap into public debt market. Supplemental analyses suggest that firms with higher vega face more stringent bank loan covenants. We conclude that, by encouraging risk-taking, higher vega reduces firms’ reliance on bank debt financing in order to avoid more stringent bank monitoring.
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Corporate Loan Spreads and Economic Activity
Anthony Saunders, Alessandro Spina, Sascha Steffen, Daniel Streitz
SSRN Working Paper,
2021
Abstract
We use secondary corporate loan-market prices to construct a novel loan-market-based credit spread. This measure has considerable predictive power for economic activity across macroeconomic outcomes in both the U.S. and Europe and captures unique information not contained in public market credit spreads. Loan-market borrowers are compositionally different and particularly sensitive to supply-side frictions as well as financial frictions that emanate from their own balance sheets. This evidence highlights the joint role of financial intermediary and borrower balance-sheet frictions in understanding macroeconomic developments and enriches our understanding of which type of financial frictions matter for the economy.
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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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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Empirical Economics,
Nr. 1,
2020
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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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,
Nr. 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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Early-Stage Business Formation: An Analysis of Applications for Employer Identification Numbers
Kimberly Bayard, Emin Dinlersoz, Timothy Dunne, John Haltiwanger, Javier Miranda, John Stevens
NBER Working Paper,
Nr. 24364,
2018
Abstract
This paper reports on the development and analysis of a newly constructed dataset on the early stages of business formation. The data are based on applications for Employer Identification Numbers (EINs) submitted in the United States, known as IRS Form SS-4 filings. The goal of the research is to develop high-frequency indicators of business formation at the national, state, and local levels. The analysis indicates that EIN applications provide forward-looking and very timely information on business formation. The signal of business formation provided by counts of applications is improved by using the characteristics of the applications to model the likelihood that applicants become employer businesses. The results also suggest that EIN applications are related to economic activity at the local level. For example, application activity is higher in counties that experienced higher employment growth since the end of the Great Recession, and application counts grew more rapidly in counties engaged in shale oil and gas extraction. Finally, the paper provides a description of new public-use dataset, the “Business Formation Statistics (BFS),” that contains new data series on business applications and formation. The initial release of the BFS shows that the number of business applications in the 3rd quarter of 2017 that have relatively high likelihood of becoming job creators is still far below pre-Great Recession levels.
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