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This paper aims to analyze the effects of financial constraints and the financial crisis on the financing and investment policies of newly founded firms. Thereby, the analysis adds important new insights on a crucial segment of the economy. We make use of a large and comprehensive data set of French firms founded in the years 2004-2006, i.e. well before the financial crisis. Our panel data analysis shows that the global financial crisis imposed a shock (mostly demand-driven) on the financing as well as on the investments of these firms. Moreover, we find that financially constrained firms use less external debt financing and invest smaller amounts. They also rely on less trade credit. With regard to bank financing, newly founded firms which are more financially constrained accumulate less bank debt and repay initial bank debt slower than their non-financially constraint counterparts. Finally, we find that financially constrained firms are affected to a smaller degree by the financial crisis than their less financially constrained counterparts.
Broad, long-term financial and economic datasets are a scarce resource, in particular in the European context. In this paper, we present an approach for an extensible, i.e. adaptable to future changes in technologies and sources, data model that may constitute a basis for digitized and structured long- term, historical datasets. The data model covers specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence achieving extensibility. Furthermore, based on historical German company and stock market data, we discuss a relational implementation of this approach.
We analyze the impact of decreases in available lending resources on quantitative and qualita- tive dimensions of firms’ patenting activities. We thereby make use of the European Banking Authority?s capital exercise to carve out the causal effect of bank lending on firm innovation. In order to do so we combine various datasets to derive information on firms’ financials, their patenting behaviors, as well as their relationships with their lenders. Building on this self- generated dataset, we provide support for the “less finance, less innovation” view. At the same time, we show that lower available financial resources for firms lead to improvement in the qualitative dimensions of their patents. Hence, we carve out a “less finance, less but better innovation” pattern.
We investigate the differential effect of the COVID-19 shock to the stock market shock on the share prices of firms with different levels of ESG (Environmental, Social and Governance) scores. Thereby, we analyse whether and to what extent better ESG ratings provided insurance for investors in the stocks of those firms during this shock. We focus our analysis on the European market in which ESG investment plays a particularly important role. Using a broad sample of listed firms we provide mixed evidence. On the one hand, we show that immediately after the start of the shock firms with a higher ESG score outperformed their peers. On the other hand, this effect faded less than six weeks later. Given the quick recovery of the market our finding supports the idea that ESG stocks provide limited insurance in severe crises.
Spillovers of PE investments
(2022)
In this paper, we investigate a primary potential impact of leveraged buyout (LBOs) transactions: the effects of LBOs on the peers of the LBO target in the same industry. Using a data sample based on US LBO transactions between 1985 and 2016, we investigate the impact of the peer firms in the aftermath of the transaction, relative to non-peer firms. To account for potential endogeneity concerns, we employ a network-based instrumental variable approach. Based on this analysis, we find support for the proposition that LBOs do indeed matter for peer firms’ performance and corporate strategy relative to non-peer firms. Our study supports a learning factor hypothesis: peers gain by learning from the LBO target to improve their operational performance. Conversely, we find no evidence to support the conjecture that peers lose due to the increased competitiveness of the LBO target firm.
ChatGPT, der Prototyp eines Chatbot, von dem amerikanischen Unternehmen OpenAI entwickelt, ist im Augenblick in aller Munde. Gefragt wird auch: Stellt diese Software eine Herausforderung für den Bildungsbereich dar, werden künftig damit Haus- und Abschlussarbeiten erstellt? Prof. Uwe Walz, Professor für VWL, insbesondere Industrieökonomie an der Goethe-Universität, hat den Chatbot bereits im laufenden Wintersemester mit Studierenden analysiert.