The future of venture capital decision making : the impact of quantitative sourcing and machine learning on the VC Investment process

Investing in early-stage startups is a difficult endeavor. Venture Capitalists use heuristics and base their decisions on past experiences, which can lead to biases. Recently, Venture Capitalists are increasingly using artificial intelligence and quantitative sourcing to support their investment process, while others still rely on traditional investment mechanisms. This research investigates the usage and impact of artificial intelligence and machine learning throughout the venture investment cycle to make investment decisions. This dissertation is an exploratory study that employs a qualitative research approach in the form of semi-structured interviews with ten European Venture Capitalists. The results show that Venture Capitalists utilize machine learning and web scraper tools, particularly during the deal origination, firm-specific screening, and general screening stages of the investment process, to solve the identification and selection challenges. As a result, investment processes become more efficient and less biased, allowing for more time to be spent advising and mentoring portfolio startups. It adds to the existing literature on how artificial intelligence and data can augment existing investment mechanisms during the venture capital decision-making process.Investir em startups na sua fase inicial exige um elevado empenho. Os investidores de capital de risco baseiam as suas decisões em pesquisa e experiências passadas, o que pode levar a enviesamentos. Embora muitos investidores de capital de risco ainda utilizem mecanismos de investimento tradicionais, tem havido um aumento na utilização de inteligência artificial e sourcing quantitativo para apoiar o processo de investimento. Esta investigação estuda a utilização e impacto da inteligência artificial e de machine learning ao longo do ciclo de investimento de risco para tomar decisões de investimento. Esta dissertação é um estudo empírico que utiliza uma abordagem de investigação qualitativa sob a forma de entrevistas semi-estruturadas com dez empresas de investimento de capital de risco europeias. Os resultados mostram que os investidores de capital de risco utilizam machine learning e ferramentas de recolha de dados na web, em particular durante o início da oportunidade de negócio, a seleção específica da empresa, e fases gerais de análise do processo de investimento, para resolver os desafios de identificação e seleção. Consequentemente, os processos de investimento tornam-se mais eficientes e menos tendenciosos, permitindo que se utilize mais tempo a aconselhar e a orientar as empresas do portfolio. Este estudo complementa a literatura existente relativamente a como a inteligência artificial e os dados podem elevar os mecanismos de investimento existentes durante o processo de tomada de decisão de capital de risco

Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

Suomen Teollisuussijoitus Oy:n (Tesi) arviointi

Arvioinnin kohteena on ollut työ- ja elinkeinoministeriön omistajaohjauksessa toimiva Suomen Teollisuussijoitus Oy:n (Tesi). Tesi on vuonna 1995 perustettu valtion omistama pääomasijoitusyhtiö, joka tekee vähemmistösijoituksia rahastoihin ja kohdeyrityksiin samoin ehdoin yksityisten sijoittajien kanssa. Arviointi tarkastelee Tesin toiminnan tehokkuutta, vaikuttavuutta ja yhteistyötä osana julkisen yritysrahoituksen kokonaisuutta pääomasijoitusmarkkinoilla vuosina 2015–2022. Pitkäjänteisen valtiollisen sijoittajan merkitys on korostunut epävarmoissa markkinatilanteissa ja markkinahäiriöissä. Viime vuosina erityisesti ilmastokriisi, koronapandemia ja Ukrainan sota ovat korostaneet Tesin roolia yhteiskunnallisten haasteiden ratkaisemisessa ja akuuttien markkinahäiriöiden tasaajana. Tesin toiminta muun muassa koronapandemian yhteydessä on laajasti koettu onnistuneena ja tärkeänä. Tesin rooli suomalaisessa pääomasijoitusmarkkinassa on merkittävä ja sen toiminta koetaan laadukkaana ja asiantuntevana. Kriittiset näkemykset liittyvät lähinnä Tesin koettuun portinvartijarooliin uusien rahastojen perustamisessa, sekä jossain määrin suoriin sijoituksiin. Tesillä on myös suuri merkitys tiedon tuottajana, sparraajana ja verkostojen rakentajana. Arviointi sisältää suosituksia sekä Tesin toiminnan kehittämiseksi että koko julkisen pääomasijoitustoiminnan vaikuttavuuden parantamiseksi