Venture Capital Communities

Although venture capitalists (VCs) can choose from thousands of potential syndicate partners, many co-syndicate with small groups of preferred partners. We term these groups “VC communities.” We apply computational methods from the physical sciences to 3 decades of syndication data to identify these communities. We find that communities comprise VCs that are similar in age, connectedness, and functional style but undifferentiated in spatial location. Machine-learning tools classify communities into 3 groups roughly ordered by their age and reach. Community VC financing is associated with faster maturation and greater innovation, especially for early-stage firms without an innovation history.

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Venture Capital and Valuation of Innovative Start-ups: the Business Case of Mosaicoon

International Journal of Advances in Management and Economics ◽

10.31270/ijame/v09/i02/2020/1 ◽

2020 ◽

Vol 9 (2) ◽

pp. 01-15

Author(s):

Antonio Del Pozzo ◽

◽

Salvatore Loprevite ◽

Domenico Nicolò ◽

◽

...

Keyword(s):

Venture Capital ◽

Performance Indicators ◽

Early Stage ◽

Business Case ◽

Cash Flows ◽

Venture Capitalists ◽

Expected Return ◽

Continuous Growth ◽

Start Up ◽

Start Ups

This article analyzes the decline of one of the best well-known and promising European start-ups: Mosaic on L. t. d. The business case is emblematic of many bankruptcies caused by strategies focusing on the expectations of continuous growth of economic capital and based on unconventional performance indicators, without considering the economic-financial results and self-financing. The expectations of return on capital are extremely high and this forces one to undertake risky growth paths with very high expected return rates. This also happens in the absence of an advanced and effective capital market. Venture capitalists, even when they are public, cannot compensate for these excesses. The analysis of the case contributes to the debate on the complex topic of assessing the potentiality of start-ups and it provides useful suggestions to operators (venture capitalists, business angels, start uppers, investors, etc.) for greater prudence in considering the non-financial performance indicators. Start-ups do not produce economic results in the early stage, so they may also be valued by using non-financial metrics. However, unconventional indicators cannot be the only parameters for evaluating. When the firm is a start-up without meaningful financial information, it is more appropriate to refer to a reliable business plan drawn up on rigorous estimates of expected incomes and cash flows. Keywords: Venture capital, Start-up, Default, Performance indicators, Business case.

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Machine Learning and data mining tools applied for databases of low number of records

Advanced Engineering Research ◽

10.23947/2687-1653-2021-21-4-346-363 ◽

2022 ◽

Vol 21 (4) ◽

pp. 346-363

Author(s):

Hubert Anysz

Keyword(s):

Machine Learning ◽

Data Mining ◽

Computational Methods ◽

Large Datasets ◽

Learning Tools ◽

Data Preparation ◽

Preparation Methods ◽

Use Of Data ◽

Small Set ◽

Mining Tools

The use of data mining and machine learning tools is becoming increasingly common. Their usefulness is mainly noticeable in the case of large datasets, when information to be found or new relationships are extracted from information noise. The development of these tools means that datasets with much fewer records are being explored, usually associated with specific phenomena. This specificity most often causes the impossibility of increasing the number of cases, and that can facilitate the search for dependences in the phenomena under study. The paper discusses the features of applying the selected tools to a small set of data. Attempts have been made to present methods of data preparation, methods for calculating the performance of tools, taking into account the specifics of databases with a small number of records. The techniques selected by the author are proposed, which helped to break the deadlock in calculations, i.e., to get results much worse than expected. The need to apply methods to improve the accuracy of forecasts and the accuracy of classification was caused by a small amount of analysed data. This paper is not a review of popular methods of machine learning and data mining; nevertheless, the collected and presented material will help the reader to shorten the path to obtaining satisfactory results when using the described computational methods

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Which criteria matter most in the evaluation of venture capital investments?

Journal of Small Business and Enterprise Development ◽

10.1108/jsbed-10-2013-0165 ◽

2014 ◽

Vol 21 (3) ◽

pp. 505-527 ◽

Cited By ~ 10

Author(s):

José Carlos Nunes ◽

Elisabete Gomes Santana Félix ◽

Cesaltina Pacheco Pires

Keyword(s):

Venture Capital ◽

Early Stage ◽

Management Team ◽

Venture Capitalists ◽

Capital Investments ◽

Share Capital ◽

Content Type ◽

The Impact ◽

Venture Capital Investments ◽

Public Share

Purpose – The purpose of this paper is to identify the importance assigned to the various criteria used by the Portuguese venture capitalists (VCs) to evaluate and select early stage venture capital (VC) projects. Design/methodology/approach – The data were collected through a questionnaire answered by 20 Portuguese VCs. The authors use descriptive statistics techniques and non-parametric tests to identify the most valued criteria and test differences in the importance assigned to the criteria of several types of VCs and investments. Findings – The study reveals that personality and experience of the entrepreneur and of the management team are the most valued groups of criteria. VCs with a majority of private share capital value more the personality of the entrepreneur and management team than the companies with a majority of public share capital. Additionally, the VCs that did not yet internationalize consider the personality of the entrepreneur and management team and the financial aspects, to be more important than the VCs that have already expanded abroad. Originality/value – It provides evidence on the VCs behavior in a small VC market. Since most of the existing literature on this area refer to large VC markets, the present study is important to investigate whether the conclusions reached by the previous studies can be extended to a small VC market. Also, this study is a contribution to the literature on the internationalization of VCs and it is the first study that explores the impact of the VCs being internationalized on the value given to the various selection criteria of early stage VC projects.

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Exit market liquidity and venture capitalists’ investment behaviour: Evidence from Australia, Canada and the United Kingdom

Corporate Ownership and Control ◽

10.22495/cocv8i1c8p1 ◽

2010 ◽

Vol 8 (1) ◽

pp. 743-757

Author(s):

Shrimal Perera ◽

Tabita Bertsch ◽

Jayasinghe Wickremanayake

Keyword(s):

United Kingdom ◽

Venture Capital ◽

Early Stage ◽

Market Liquidity ◽

Venture Capitalists ◽

The United Kingdom ◽

Capital Funding ◽

Exogenous Factors ◽

Start Ups ◽

Investment Behaviour

This study investigates the effect of exit market liquidity on venture capitalists’ (VCs’) investment behaviour. The sample consists of 4,758 investment rounds disbursed by venture capital funds in three selected common law-based OECD countries (Australia, Canada and the United Kingdom) during 1990-2005. The results indicate that investments in early-stage projects by VCs are not related to exit market liquidity conditions after controlling for exogenous factors. Empirical results, however, show that exit market liquidity is positively associated with VCs’ investments in new projects (as opposed to follow-on projects). Put differently, new firms (including start-ups) are more likely to obtain venture capital funding during times of liquid exit market conditions. Arguably, these findings highlight the importance of ‘timing’ of new project launch

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A Comparative Study of Different Machine Learning Tools

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i4.184190 ◽

2019 ◽

Vol 7 (4) ◽

pp. 184-190

Author(s):

Himani Maheshwari ◽

Pooja Goswami ◽

Isha Rana

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Learning Tools

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Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

International Journal of Computational Physics Series ◽

10.29167/a1i1p236-247 ◽

2018 ◽

Vol 1 (1) ◽

pp. 236-247

Author(s):

Divya Srivastava ◽

Rajitha B. ◽

Suneeta Agarwal

Keyword(s):

Machine Learning ◽

Image Processing ◽

Agricultural Production ◽

Bacterial Blight ◽

Early Stage ◽

Second Phase ◽

Computationally Efficient ◽

Stage Of Disease ◽

Accurate Detection ◽

Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

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How Are Venture Capitalists Rewarded? The Economics of Venture Capital Partnerships

SSRN Electronic Journal ◽

10.2139/ssrn.1571663 ◽

2010 ◽

Cited By ~ 2

Author(s):

Murillo Campello ◽

Rafael Matta

Keyword(s):

Venture Capital ◽

Venture Capitalists

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A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

Current Pharmaceutical Design ◽

10.2174/1381612826666200310122324 ◽

2020 ◽

Vol 26 (26) ◽

pp. 3049-3058

Author(s):

Ting Liu ◽

Hua Tang

Keyword(s):

Machine Learning ◽

Computational Methods ◽

Future Development ◽

Malaria Parasite ◽

Mitochondrial Proteins ◽

Learning Methods ◽

Machine Learning Methods ◽

Effective Drugs ◽

Construction Strategies ◽

Day By Day

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

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Collaboration Among Silicon Valley Venture Capitalists

The Oxford Handbook of Entrepreneurship and Collaboration ◽

10.1093/oxfordhb/9780190633899.013.38 ◽

2019 ◽

pp. 200-219

Author(s):

Lyda Bigelow ◽

Jennifer Kuan ◽

Kyle Mayer

Keyword(s):

Venture Capital ◽

Regulatory Focus ◽

Regional Differences ◽

Silicon Valley ◽

Psychological Effects ◽

Venture Capitalists ◽

Regulatory Focus Theory ◽

Industry Clusters ◽

Entrepreneurial Ventures ◽

Point Of Entry

Regional differences among industry clusters have long been a puzzle, especially when performance differences are significant. This chapter examines the case of venture capital investing, in which Silicon Valley differs from the rest of the world despite attempts to imitate its model. The point of entry in this chapter is the contract between venture capitalist and entrepreneur. Although such contracts have been analyzed in other research, this chapter argues that the psychological effects of different contract styles are of primary importance to innovative outcomes of entrepreneurial ventures. Thus, it argues that regulatory focus theory, which considers the psychological effects of contracting, is essential to understanding differences in practice and outcomes in venture capital clusters.

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