How large language models incorporate venture capital into investor portfolios

The Crowdfunding Effects on Venture Capital Investments

Venture Capital Optimal Investment Portfolio Strategies Selection in Diffusion - Type Financial Systems in Global Capital Markets with Nonlinearities

This paper examines the long-run relationship between venture capital investment and per capita economic growth in the 19 European Economic Area (EEA) countries for the period 1989-2014. We use three different indicators of venture capital (VC) investment, namely VC at early stage investment, VC at later stage investment, and VC total investment. Using cointegration technique, the study warrants the support of long-run relationship between VC investment and per capita economic growth in a few cases, typically with reference to a particular VC indicator and per capita economic growth we use. Using vector auto-regressive (VAR) model for testing the Granger causalities, the study acknowledges mixed evidence of the relationship between the venture capital investment and per capita economic growth in the selected EEA countries, both by individual country and at the panel setting. In some instances, venture capital investment leads to per capita economic growth, lending support to supply-leading hypothesis (SLH) of VC investment-growth nexus. In other instances, it is the per capita economic growth that regulates the level of venture capital investment, lending support to demand-following hypothesis (DFH) of VC investment-growth nexus. There are also circumstances, where venture capital investment and per capita economic growth are mutually interdependent. That is the situation where both are self-reinforcing and offer support to feedback hypothesis (FBH) of VC investment-growth nexus. In addition, there are also instances, where the venture capital investment and per capita economic growth are independent of each other. This is the situation where both are neutral and offer support to neutrality hypothesis (NLH) of VC investment-growth nexus. To summarize, Granger causality results vary from country to country within the EEA countries, depending upon the type of venture capital investment and per capita economic growth that we use in a particular empirical exploration process. The policy implication of this study is that the economic policies should recognize the differences in the venture capital investment and per capita economic growth in order to maintain sustainable development in these EEA countries.

The article examines the impact of information and communication technologies (ICT), innovation, and formal and informal institutions on venture capital (VC) investment. The analysis is based on 28-year data spanning 1990-2017 from 19 European and 13 Asia-Pacific countries using generalized two-stage least square instrumental variable technique. After controlling for endogeneity, the results show that ICT, innovation, and informal institutions hold a strong impact on VC investment. ICT and innovation exert a positive and significant influence on VC investment whereas formal institutions exert a positive yet insignificant effect on VC investment. Among the informal institutions, power distance and individualism exert significant and positive influence whereas uncertainty avoidance has significant and negative influence on VC investment. The interaction analysis demonstrates that the association between ICT and VC is strong when institutional quality is high. Moreover, the impact of innovation on VC is pronounced in highly digitized and highly uncertainty-tolerant environments. Explanation of VC capital investment also vary with geography as the effects of trend, ICT and uncertainty avoidance on VC investment are noticeable in the Asia-Pacific region whereas power distance is prominent in the European region. The article makes important contributions to the literature of VC by revealing novel interactions between formal and informal institutions, ICT and innovation depicted in a conceptual model. The study also brings in important highlights to the policy debate on VC development by showing how exactly VC investments are tangled with the different dimensions of institutional and technological environment.

Indian economy witnessed high inflow of capital for start-ups in current fiscal year through venture capital (VC) investment. From different Indian VC deals, it is evident that VC investors prefer to invest jointly. In other words, joint investment or co-investment or syndication is a common trend in Indian VC industry. VCs adopt this strategy to minimise their future uncertainties as a part of the control mechanism. In this study, an attempt is made to find out different determinants of this syndication strategy. The samples taken in this study are retrieved from Venture Intelligence database for the period 2005–2014. The data are analysed through linear regression and binomial logistic regression. Two empirical models have been developed. The derived models validate different control variables and deal with specific characteristics to comprehend the rationale of syndication mechanism. The findings of the study indicate that the past experience and the number of industry exposure of a VC in IT and ITES industry are the major predictors for a syndication decision. Subsequently, the precautionary investment attributes like number of investment round, stage funding, etc. draw the interest of potential co-investors in a syndicated deal. Syndication mechanism benefits the VC investors through sharing of risk of investment in a start-up and preparing them for a successful exit. Extant literature supports the results as Indian VC investors prefer to share the risk profile of a start-up business and adopt different risk diversion mechanisms to attract co-investors in the deal. Furthermore, the joint investment by investors drag more funding amount and also create more human capital for efficient management of the investment in VC-backed portfolio.

We examine the impact of crowdfunding on venture capital (VC) investments in the presence of competition among VC firms. Our economic model comprises a startup, a crowdfunding platform, and two VC firms, each with its own perception of the startup’s potential. The startup seeks equity funding from the VC firms and decides on the size of its equity offer. If the VC firms decline to invest, the startup pivots to crowdfunding. Following the crowdfunding campaign, all firms update their beliefs about the startup’s likelihood of success based on the crowdfunding outcome, prompting the VC firms to revisit their investment decisions, now with a reduced equity offer if the crowdfunding outcome was positive. This study provides theoretical underpinnings at the firm level for the observed aggregate-level empirical relationships, both positive and negative, between crowdfunding and VC investments. Specifically, based on our model, the positive relationship, where increased crowdfunding activity leads to a rise in subsequent VC investments, is attributed to startups that fail to secure VC funding in the absence of a crowdfunding platform but succeed in attracting VC attention after a successful crowdfunding campaign. In contrast, the negative relationship is attributed to highly valued startups that could have secured VC funding without crowdfunding, but with crowdfunding, VC firms choose to defer their investment decision until after the crowdfunding campaign’s result is known; on the one hand, a successful crowdfunding outcome lowers their post-crowdfunding VC investment demand, and on the other hand, an unsuccessful outcome deters potential VC investors. Besides these relationships, we also identify another dynamic, where the option of accessing crowdfunding raises VC investment, even if the startup does not actually launch a crowdfunding campaign. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295] and the National Natural Science Foundation of China [Grant 71901135]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.00994 .

Venture Capital and Clean Technology Investment and Innovation

Skin in the Game: An Analysis of Venture Capital Investment in Dermatology from 2002 to 2021

Research Summary Government involvement plays a significant role in fostering entrepreneurship. We examine how government involvement in venture capital (VC) investments shapes the decoupling between risk‐taking and investment returns. We distinguish government involvement through state ownership (GVC) and personal political connections (connected VC). We theorize that government involvement through GVC is associated with the downside risk–return paradox, that is, concurrent higher risk‐taking and lower‐than‐expected returns. In contrast, government involvement through connected VCs is linked to the upside risk–return paradox, that is, concurrent lower risk‐taking and higher‐than‐expected returns. Our theoretical predictions receive general support from analyses using longitudinal data from VC firms in China. Our study sheds light on the heterogeneity in the decoupling between risk and return and the underlying mechanisms through which governments influence entrepreneurship. Managerial Summary Governments often seek to promote entrepreneurship; yet, their involvement in start‐ups tends to deviate from the conventional wisdom of a positive risk–return association. Government‐owned venture capital (VC) firms tend to take greater risks and invest in earlier‐stage start‐ups, but yield lower returns from their investments. Conversely, VC firms that have personal ties to government officials tend to take lower risks and prefer later‐stage start‐ups, yet achieve higher returns. These intriguing findings reveal the complexities of government participation in entrepreneurial activities and offer valuable insights for policy formulation. In addition, our findings highlight the importance for entrepreneurs to recognize diverse goals and resources in government involvement. Considering this when seeking external financing is helpful in positioning start‐ups for growth and success.

Bank Entry Into the Venture Capital Industry

This study investigates the role of large language models (LLMs) in retail investors (RIs) decision-making processes from the perspective of the Theory of Planned Behaviour (TPB). It explores whether LLMs can replace or change the role of financial experts and whether introducing LLMs may lead to more infromed RIs’ decisions. Qualitative interviews were conducted with experienced RIs (n = 8). Secondary data were gathered from YouTube recordings (n = 44). Thematic analysis and Retrieval-Augmented Generation (RAG) methodology was used for data extraction and analysis of the scripts. The findings indicate that while LLMs have the potential to enhance accessibility to expert opinions and provide more informed investment decisions, they are unlikely to replace human experts. RIs show a preference for combining LLM insights with human expertise, recognising the limitations of LLMs in managing complex and nuanced investment information. The study highlights the usefulness of the TPB as a framework for the exploration of the topic. It also introduces a novel research method - advanced data extraction techniques on a vast unstructured dataset.?Results contribute to the understanding of LLMs potential in supporting RIs and confirms the usefulness on the AESTIMA tool for data extraction and analysis.

Purpose – The purpose of this paper is to empirically demonstrate that drivers of venture capital (VC) investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist across industries within each of these sectors. Design/methodology/approach – The basic hypothesis is that “not only different stages of VC investments have different drivers, but VC investments in different sectors of the economy are also driven by different drivers.” The paper tests this hypothesis using a Poterba (1989) type supply and demand framework in the multivariate time-series regression analysis. Findings – This paper empirically demonstrates that drivers of VC investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist by stages of investment and across industries within each of these sectors. In particular, the paper finds that the importance of the number of VC-led initial public offering (IPO) transactions as the main driver of VC investment decreases with the level of technology involved in the sector. IPO transactions are particularly important in software, networking and equipment, and business products and services industries. In contrast to earlier literature, however, the paper do not find a more pronounced effect of IPOs for seed and late stages of VC investments. Similarly, the positive impact Sarbanes-Oxley Act of 2002 – which mainly impacts public companies – also intensifies with a decrease in the level of technology involved in the sector, and the paper do not find a negative impact. The Act is important particularly for VC investments in medium- and low-tech sectors and in early or expansion stages. Originality/value – In analyzing the determinants of VC in a supply and demand framework as in Poterba (1989), the paper differentiates between different sectors (17 industries) and stages of VC (four stages: seed, early, expansion, late). Such level of differentiation is novel and allows more refined and better targeted public policy measures.

Venture capital investment in university spin-offs: Evidence from an emerging economy

We study the investment patterns of different types of venture capital (VC) investors in Europe: independent VC, corporate VC, bank-affiliated VC and governmental VC. We rely on a unique dataset that covers 1663 first VC investments made by 846 investors in 737 young high-tech entrepreneurial ventures in seven European countries. We compare the relative specialization indices of the different VC investor types across several dimensions that characterize investee companies: industry, age, size, stage of development, distance from the investor and country. Our findings indicate that VC investor types in Europe differ substantially in their investment patterns when compared to one another and that, in terms of investment patterns, governmental VC investors appear to be the most distinct type of VC investor. The investment patterns of different VC investors are stable over time and similar across different European countries. Finally, the investment patterns of the different VC investor types in Europe are significantly different from those observed in the USA.

Innovation in Health Care: Time for a Gut Check