1,721,013 research outputs found
The Basel II reform and the provision of finance for SMEs: an analysis for a sample of Italian companies
No full textGreening the future: how venture capital nurtures cleantech companies’ growth in Europe
Full text linkThe paper examines the impact of venture capital (VC) financing on the growth of cleantech companies, using newly available data and a more comprehensive perspective on the sector. The analysis is conducted on a sample of 24,538 European cleantech companies identified using machine learning techniques, 401 of which received a first VC investment between 1988 and 2023. To adequately control for selection bias, we applied a matching procedure that allowed the creation of two control groups: one consisting of non-VC-backed cleantech companies and another of non-cleantech VC-backed companies. Our analysis reveals that, in terms of selection, investors discount the inherent risk of the sector by investing in fast-growing cleantech companies. Regarding the impact of VC on invested cleantech companies, when the differences at selection are neutralised, our results suggest that VC effectively supports the growth of invested cleantech companies, with a more pronounced effect in the short term
The identity of Social Impact Venture Capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining
No full textInvestment strategies of bank-affiliated and independent venture capitalists: a focus on innovation in the fintech sector in the wake of the global financial crisis
Full text linkWe investigate how bank-affiliated VCs (BVCs) change their investment strategy in fintech startups relative to independent VCs (IVCs) after the global financial crisis (GFC). To this end, we use the concept of mimetic isomorphism as a theoretical lens. We measure the innovation level of invested ventures by resorting to patent and patent quality data and several proxies deriving from text mining and semantic network analysis. We look at the selection dynamics of VCs based on the innovation level of their target ventures. We analyze data on VC investments in 6711 fintech ventures worldwide from 1995 to 2019. Our findings show that BVCs have changed, compared to IVCs, their patterns of investments after the exogenous shock provided by the GFC. While BVCs selected less innovative ventures compared to IVCs before the crisis, they aligned with IVCs by choosing more innovative ventures after the crisis
Lenders’ selection capabilities, patent quality and the outcome of patent backed loans
Full text linkIn this paper, we investigate the phenomenon of patent collateralization by empirically focusing on the factors that affect the outcome of the collateralization process. In particular, we want to examine to what extent patent quality, lenders’ characteristics, as well as lenders’ selection capabilities (i.e. in identifying high-quality patents) affect the likelihood of observing a security interest release. We identify the patents recorded in security agreements and their release from the USPTO Patent Assignment database. The final dataset is made up of a total of 8818 security interest agreement records, involving 133,110 patents pledged as collateral for debt between 2007 and 2010. We find evidence that a security interest is more likely to be released for patents with a higher technical merit and when the lenders are more experienced and are specialty finance companies. When considering other types of lenders (i.e. banks in particular) or less experienced lenders, the positive association between the security interest release and the technical merit of the pledged patent is lower. The evidence suggests that IP-backed loans represent an effective financial channel for those firms that control valuable intangible assets and that experience and specialization allow lenders to develop better selection capabilities
Entrepreneurial Spirits in Women and Men. The role of digital skills and financial literacy.
No full textIn this paper, we investigate the attitudes to
entrepreneurship of Italian households, focusing on the
importance of financial literacy and digital skills as
potentially relevant factors shaping entrepreneurship.
We put the gender focus to our analysis to detect whether, and to what extent, women and men differ in their
propensity to run a business. We carry out our research
by using the Bank of Italy SHIW dataset for the years
2008 and 2010. Our findings suggest a strong heterogeneity between men and women in the role played by
financial literacy and digital skills. Results show a positive and significant correlation between financial literacy and the probability of being an entrepreneur but
only for men. We also find that digitally skilled male
respondents are much more likely to be entrepreneurs
Regional and spatial issues in the financing of small and medium size enterprises and new ventures.
No full textThis editorial introduces the papers addressing regional and spatial aspects relating to the demand for, and the supply of,
finance for small and medium-sized enterprises (SMEs) and start-ups. Reflecting the breadth of financial instruments that
are potentially available to SMEs and new ventures (e.g., business angel, bank credit and credit card financing), this special
issue offers a combination of up-to-date studies that integrate the regional and spatial perspectives into the debate on
SMEs and start-up financing. Overall, the papers contribute to an understanding of the mechanisms by
The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining
Full text linkImpact investing is gaining momentum as an investment practice that optimizes both financial and social outcomes. However, the market is still in its emerging stage, and there is ambiguity regarding the definition of players and practices. In this paper, we adopt an investor identity perspective and use a linguistic approach to explore how social impact venture capitalists (SIVCs) communicate their identities and actions to their external stakeholders. Through a text mining analysis of the websites of 195 investors worldwide, our results reveal four types of investors who differ in terms of their social linguistic positioning and linguistic distinctiveness. Finally, by training a tree boosting machine learning model, we assess the extent to which the use of different linguistic styles is associated with website traffic
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