The Finnish Venture Capital Market Performance and Development

Aalto University

School of Science

Master’s Programme in Industrial Engineering and Management

Julia Jutila

The Finnish venture capital market performance and development

Master’s Thesis

Helsinki, April 5, 2021

Supervisor: Markku Maula, Professor

Thesis advisor: Matias Kaila, M.Sc., Suomen Teollisuussijoitus Oy (Tesi)

AALTO UNIVERSITY

SCHOOL OF SCIENCE

INDUSTRIAL ENGINEERING AND MANAGEMENT

Author: ABSTRACT OF MASTER’S THESIS Julia Jutila Title of the thesis: The Finnish venture capital market performance and development Number of pages: Language: 90 English Major: Major code: Strategy and Venturing SCI3050

Supervisor and instructor: Prof. Markku Maula

Thesis advisors: Matias Kaila, Director of Fund Investments at Tesi The Finnish venture capital (VC) market and startup ecosystem are booming. 2019 was a record year in terms of the amount of funding Finnish startups attracted and the amount fundraised by Finnish VC funds. However, research on the financial performance and development of the Finnish venture capital market is scarce. For limited partners (LPs) and general partners (GPs) the lack of data can cause inefficient capital allocation.

This study focuses on assessing the development and the financial performance of the Finnish venture capital market. The financial performance of the market is studied in terms of returns and their distribution, value development of companies after the initial investment and the effect of staging an investment on returns. The literature review provides background to the development of the Nordic VC market and benchmarks the Finnish VC market against other venture capital markets. The empirical analysis is done employing the Finnish Industry Investment (Tesi) database, with annual data from 2005 to 2019. The sample includes 483 initial investments and their follow-on investments, made by 33 Finnish VCs between 1997 and 2019.

The results show that the positive development of the Finnish VC market and startup ecosystem has translated to higher financial returns from the asset class. Investments by fund vintages 1997-2001 have a multiple on invested capital (MOIC) of 0,6, while investments made by fund vintages 2008-2013 have a MOIC of 1,7. Investments made between 2012-2019 show earlier and higher value development than investments made between 2005-2011, indicating that the returns of funds that are currently active in the market have the potential to outperform earlier fund vintages.

The results also show that VC returns are highly skewed and exhibit some degree of power-law abiding behaviour. Individual venture capital investments to startups are risky, but LPs can effectively reduce risk by diversifying their investments to several VC funds or through funds-of-funds vehicles.

VCs can manage the risk related to the highly skewed nature of returns by staging investments, and a majority of a VC fund’s capital is allocated through follow-on investments. However, negative value development of startups is visible later than upside value development and defensive follow-on investing may be the only means to continue the operations of a company. Defensive follow-on investing can lead to avoiding losses, and thus the escalation of commitments is a phenomenon to be taken seriously. Keywords: private equity, venture capital, financial performance, return distribution, value development, staging of investments

AALTO-YLIOPISTO

PERUSTIETEIDEN KORKEAKOULU

TUOTANTOTALOUS

Tekijä: DIPLOMITYÖN TIIVISTELMÄ Julia Jutila Työn nimi: Suomen venture capital markkinan tuotto ja kehitys Sivumäärä: Julkaisukieli: 90 Englanti Pääaine Pääaineen koodi: Strateginen johtaminen ja kasvuyrittäjyys SCI3050

Työn valvoja ja ohjaaja Prof. Markku Maula

Työn ohjaaja: Matias Kaila, Johtaja, Rahastosijoitukset, Tesi Suomen aikaisen vaiheen kasvuyritysten pääomasijoitusmarkkina (VC-markkina) ja startup-ekosysteemi kukoistavat. 2019 oli ennätysvuosi mitattaessa suomalaisten kasvuyritysten ja suomalaisten VC-sijoittajien keräämän rahoituksen määrää. Tästä huolimatta Suomen VC-markkinan tuotoista on vain rajoitetusti tutkimusta. Tutkimuksen puute voi aiheuttaa pääomarahastoihin sijoittavien institutionaalisten sijoittajien (LP-sijoittajien) ja aikaisen vaiheen kasvuyritysten pääomasijoittajien (GP-sijoittajien) pääomien epätehokasta allokoitumista.

Tämä tutkimus keskittyy Suomen VC-markkinan kehityksen ja taloudellisen performanssin tutkimiseen. Taloudellista performanssia tutkitaan tuottojen, tuottojakauman ja yritysten arvonkehityksen kautta. Lisäksi työssä tutkitaan sijoitusten porrastamisen vaikutuksia tuotto-odotukseen. Työ sisältää sekä kirjallisuuskatsauksen, että empiirisen tutkimuksen. Empiirinen tutkimus on tehty käyttäen Tesin tietokantaa, joka sisältää vuotuista kohdeyritysdataa vuosille 2005–2019. Otos sisältää 33:n suomalaisen VC-sijoittajan tekemää 483 ensisijoitusta, ja jatkosijoitukset näihin yrityksiin vuosina 1997–2019.

Tutkimuksen tulokset osoittavat, että Suomen VC-markkinan ja startup-ekosysteemin kehitys näkyy parempana tuotto-odotuksena VC-sijoittajille. Vuosikertojen 1997–2001 VC-sijoittajien rahakerroin (MOIC) on 0,6, kun vuosikertojen 2008–2013 MOIC on 1,7. Lisäksi vuosina 2012-2019 tehdyt sijoitukset osoittavat nopeampaa ja korkeampaa arvonkehitystä kuin vuosina 2005-2011 tehdyt sijoitukset, mikä indikoi että tällä hetkellä aktiivisilla VC-rahastoilla on mahdollisuus ylittää aikaisempien vuosikertojen tuotot.

Tulokset osoittavat myös, että VC-sijoitusten tuottojen jakauma on vinoutunut, ja tuotot osoittavat viitteitä potenssijakaumasta. Yksittäiset VC-sijoitukset kasvuyrityksiin sisältävät korkean riskin, mutta LP-sijoittajat voivat merkittävästi vaikuttaa riskiin sijoittamalla hajautetusti VC-rahastoihin tai sijoittamalla VC- markkinaan rahastojen rahastojen kautta.

VC-sijoittajat voivat vaikuttaa riskiin sijoittamalla osan rahastosta ensisijoituksina ja osan jatkosijoituksina. Valtaosa VC-rahastojen pääomasta sijoitetaankin jatkosijoituksina yrityksiin. Kasvuyritysten negatiivinen arvonkehitys kuitenkin näkyy myöhemmin kuin kasvuyritysten positiivinen arvonkehitys, ja jatkosijoitus saattaa joissakin tilanteissa olla ainoa keino yritykselle jatkaa toimintaansa. Jatkosijoitukset yrityksen toiminnan jatkamiseksi saattaa kuitenkin johtaa tappioiden välttämiseen, ja sitoutumisen eskalaatio onkin vakavasti otettava ilmiö VC-sijoittamisessa. Asiasanat: pääomasijoittaminen, kasvuyrityssijoittaminen, tuotto, tuottojakauma, arvonkehitys, jatkosijoitukset

Acknowledgements

First and foremost, I want to thank my supervisor and instructor, Professor Markku Maula, for the expertise and support he has offered me throughout the journey. Thank you for all the practical guidance, theoretical background, and technical expertise.

I want to give my heartfelt thanks to Matias Kaila from Tesi, for being my thesis advisor and guiding me throughout the process. I am especially thankful for the empathetic support and mentoring. I want to also express my gratitude to Petteri Laakso for helping me with any observations, questions or problems that arose especially while analysing the data. In addition, I would like to thank the rest of the Tesi team for enabling my thesis on this interesting subject. I enjoyed having the opportunity to connect with many of them during the journey.

Finally, I want to thank my friends, family, and partner, Tapio. During my years at Aalto University, I have been lucky to find a group of inspiring and empowering friends, and the peer support especially during the thesis process was invaluable.

Julia Jutila Helsinki, 25 April 2021

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Table of Contents

Acknowledgements List of Tables Abbreviations

1. Introduction ...... 1

1.1. Background ...... 1 1.2. Research objective and questions ...... 4 1.3. Research design methodology and scope ...... 7

2. Literature review ...... 9

2.1. Definition of venture capital and startups ...... 9 2.2. The development of the Nordic and Finnish private equity market ...... 10 2.2.1. Early stages of venture capital in the Nordics ...... 10 2.2.2. The first and second coming of venture capital in Finland ...... 11 2.2.3. The boom of the venture capital industry in Finland in the 1990’s and the dotcom bubble ...... 12 2.2.4. The modern startup and venture capital environment ...... 13 2.3. Finnish venture capital market performance ...... 14 2.3.1. Performance metrics ...... 14 2.3.2. Finnish venture capital and private equity performance ...... 17 2.3.3. Comparable venture capital market returns ...... 19 2.4. Return distribution analysis of venture returns ...... 22 2.4.1. General return distribution characteristics of venture capital ...... 22 2.4.2. Return distribution in venture capital from a fund perspective ...... 25 2.4.3. Power Law analysis in venture capital ...... 27 2.5. Multiple on invested capital (MOIC) development after investment ...... 31 2.6. First- and follow-on funding ...... 33 2.7. Summary of hypotheses ...... 34

3. Data and methods ...... 36

3.1. Data ...... 36 3.1.1. Original data ...... 36 3.1.2. Study scope ...... 37 3.1.3. Data collection and reporting practices at Tesi ...... 37 3.1.4. Data validity and quality ...... 38 3.1.5. Manually corrected data ...... 39 3.1.6. Data confidentiality...... 39

3.2. Variables ...... 40

4. Results ...... 42

4.1. Summary statistics ...... 42 4.2. Descriptive analytics ...... 44 4.2.1. Overall financial performance ...... 44 4.2.2. Investment characteristics ...... 46 4.2.3. Investments in Finland and abroad ...... 51 4.2.4. Fund characteristics ...... 53 4.3. Return distribution ...... 54 4.3.1. Methodology ...... 54 4.3.2. Return distribution for companies by investment year ...... 54 4.3.3. Return distribution for funds by fund vintage ...... 56 4.3.4. Risk profile of venture capital investments ...... 58 4.3.5. Power law analysis...... 59 4.4. Company MOIC development after investment ...... 62 4.4.1. Methodology ...... 62 4.4.1. MOIC development for investments by investment year...... 63 4.4.2. MOIC development for investments grouped by MOIC ...... 65 4.5. First- and follow-on funding ...... 67 4.5.1. Methodology ...... 67 4.5.2. Statistics for first and follow-on funding ...... 68 4.5.3. Regression model ...... 73 5. Discussion and conclusions ...... 78

5.1. Discussion of the results and implications ...... 78 5.2. Reliability and validity ...... 80 5.3. Limitations and future research ...... 82

6. References ...... 84

List of Figures

Figure 1: Domestic and foreign VC investments into Finnish companies 2010-2019, (FVCA, 2020a) ...... 2 Figure 2: Nordic venture capital performance 1998-2015 (Copenhagen Economics, 2018; EIF, 2017; Nordic Venture Network, 2016; eFront, 2018) ...... 20 Figure 3: US venture capital returns (Cambridge Associates, 2018). TVPI calculated as average TVPI for the fund vintage groups...... 21 Figure 4: European and US venture Investments by return range, investments from 1996-2015 for Europe (EIF - Prencipe, 2017) and 2009-2018 for US (Correlation Ventures, 2019) ...... 23 Figure 5: Percentage of total money invested and ending value of all investments for US venture investments 1969-1985 (Sahlman, 1990) ...... 24 Figure 6: US VC fund TVPI Spread 2000-2018 Spread (Cambridge Associates, 2019) ...... 26 Figure 7: Venture Capital Investment Vehicle Return Profiles. Fund data based on European VC investments between 1983-1998 from VentureXperts and direct investment data from US VC investments from 1987 to June 2000 from VentureOne (Weidig & Mathonet, 2004) ...... 27 Figure 8: Probability of Exceeding Benchmark Return by Portfolio Size (Neumann, 2017) ...... 31 Figure 9: Finnish VC performance 1997-2019 ...... 46 Figure 10: Average and median investment size for first- and follow-on investments ...... 47 Figure 11: Exited companies by MOIC and holding period ...... 49 Figure 12: Current portfolio companies 31.12.2019 by MOIC and holding period ...... 50 Figure 13: Investments by region and fund vintage ...... 53 Figure 14: Fund portfolio company count and size in euros ...... 54 Figure 15: Return distribution of companies ...... 55 Figure 16: Fund return distribution by vintage group ...... 57 Figure 17: Risk profile for venture capital investments for fund vintages 2008-2013 ...... 58 Figure 18: Cumulative distribution function (CDF) for MOIC ...... 60 Figure 19: Cumulative distribution function for logMOIC ...... 60 Figure 20: MOIC development for companies by investment year ...... 64 Figure 21: MOIC development for companies with investment year 2005-2019, by MOIC-group ...... 66 Figure 22: Percentage of companies collecting follow-on investments from the same investor.. 70 Figure 23: Average percentage of funding invested on investment round ...... 71 Figure 24: Quartiles, average and median of first round average investment percentage ...... 72 Figure 25: Average investment size indexed to investment round 1 ...... 73

List of Tables

Table 1: Research questions...... 7 Table 2: Tesi published figures of Finnish venture capital market performance aggregated by fund vintage (Talouselämä, 2018) ...... 17 Table 3: EIF venture capital portfolio performance by team location Finland for fund vintages 1997-2015 ...... 18 Table 4: Hypotheses ...... 35 Table 5: Distinct count of scope funds, management companies and investments ...... 37 Table 6: Variables for empirical analysis ...... 41 Table 7: Company level summary statistics for 31.12.2019 ...... 43 Table 8: Finnish VC performance 1997-2019 ...... 45 Table 9: Mean MOIC and holding period of exited companies ...... 49 Table 10: Performance of investments to Finland and abroad ...... 52 Table 11: Methodology for indexing MOIC to investment year ...... 63 Table 12: MOIC by investment year, Years after investment = 5 ...... 65 Table 13: Variable summary statistics and Pearson correlation coefficients ...... 75 Table 14: Regression model...... 76 Table 15 Variance Inflation Factors ...... 77 Table 16: Hypotheses results ...... 78

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Abbreviations

Aaltoes Aalto Entrepreneurship Society CDF Cumulative Distribution Function DPI Distribution to Paid-In EIF European Investment Fund FoF Fund of funds FVCA Finnish Venture Capital Association GDP Gross Domestic Product GP General Partner ILPA Institutional Limited Partners Association IRR Internal Rate of Return KPI Key Performance Indicator LP Limited Partner LTD Limited Company MOIC Multiple on Invested Capital NPV Net Present Value NVN Nordic Venture Network NVPI Nordic Venture Performance Index OTC Over-the-Counter Market PME Public Market Equivalent RVPI Residual Value to Paid-In Sitra Sitra, the Finnish Innovation Fund SME Small and medium-sized enterprises Tesi Finnish Industry Investment TVPI Total Value to Paid In VC Venture Capital

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1. Introduction

This thesis aims to understand the performance and development of the Finnish venture capital (VC) market by studying the unique data gathered by the Finnish Industry Investment (Tesi1). The thesis aims to provide a data-driven view on the market dynamics of the Finnish venture capital landscape by studying performance and return characteristics of the Finnish venture capital market. The thesis is aimed for Limited Partners (LPs) and General Partners (GPs) investing in venture capital in Finland. The thesis provides GPs a way to assess and benchmark their investing strategy and performance to other GPs. For LPs, the thesis offers insight into the risk and return characteristics of the Finnish venture capital market and potential metrics to evaluate in GPs when making investment decisions.

The thesis starts by analysing the key market performance indicators of the Finnish venture capital scene, giving novel insight to how the Finnish VC market operates. After giving an understanding of the Finnish VC market, analysis will be conducted on the risk profile and distribution of venture capital returns in Finland. The skewed nature of venture capital returns will then be analysed through studying the value development of companies. Finally, the study will analyse the effect of first- and follow-on funding on a company and fund level.

1.1. Background

The Finnish venture capital scene has had a strong positive trend during the past years. 2019 was a record fundraising and investment year for Finnish venture capital funds. In 2019, Finnish venture capital funds raised a total of €384 million and invested €158 million in companies in Finland and abroad (FVCA, 2020a)2. Compared to the figures of 2010, the amount fundraised by

1 Tesi is a Finnish state-owned investment company with €1.3 billion of investments under management (EoY 2019). Tesi invests in venture capital and private equity funds and directly in growth companies, operating as an active minority owner. Tesi’s mission is to develop Finland’s venture capital and private equity market and promote Finnish businesses and Finland’s economic growth. 2 The Finnish Venture Capital Association (FVCA) publishes a wide range of research, statistics and analysis of the Finnish venture capital landscape. See their website www.pääomasijoittajat.fi for more.

Finnish venture capital funds has increased 420% and the sum invested by Finnish VCs has increased by over 50%.

2019 was a record year also in the amount of funding for Finnish startups, which raised a total of €293 million of funding. The growth in funding received by Finnish startups is driven by both the capability of Finnish investors being able to invest more, but also due to Finnish companies attracting more and larger investments from international VC investors. In 2019, Finnish startups received €180 million of investments from foreign VCs, which is a nearly 850% growth from the amount of investments by foreign VCs to Finnish companies in 2010. Figure 1 presents the sum of investments into Finnish companies in 2010-2019 by domestic and foreign VC investors.

Figure 1: Domestic and foreign VC investments into Finnish companies 2010-2019, (FVCA, 2020a) The average investment size of Finnish VCs has also increased in the last years for later stage venture and seed stage companies. While the average size of later stage investments by Finnish VCs was €0,6 million in 2015, it was €2,5 million EUR in 2019 (FVCA, 2020a). When including investments made by international investors, the average size of investment has risen even more, driven by large individual investments, in particular. In 2019 Finnish companies raised 14 funding rounds that were over 10 million €, many including renowned international investors, such as Wolt’s €110 million funding round that was led by ICONIQ Capital (Talouselämä, 2020).

The positive trend in the Finnish startup scene can also be perceived by the amount of new venture capital funds established in Finland and the amount of money at their disposal. The amount of funding raised by Finnish venture capital investors is the result of both existing GPs raising new, often larger, funds and new GPs being established in Finland. During recent years, the Finnish venture capital market has seen the entry of several new GPs in the venture capital scene, such as Maki, Evli Growth Partners, Nordic Ninja and Voima Ventures. Also, many of the existing GPs have raised even bigger follow-on funds, such as €130 million raised by Lifeline in November 2019 and €117 million raised by Inventure in 2017.

Despite the public interest towards the startup scene and the statistics available of the Finnish venture capital market in terms of startups receiving funding and new funds established, there is limited data published about the performance of venture capital funds in Finland. In addition, studying performance in venture capital requires data from a long time period, as VC-funds typically have an ex-ante determined lifetime of 10 years (Barrot, 2017).

Performance data of venture capital funded companies is traditionally only available to the LPs of the fund that invested in the company. The data of the fund’s financial performance and individual investments are also generally subject to confidentiality agreements that forbid the disclosure of the information. Data becomes typically available only if the parties involved3 in the transaction decide to publish the financial figures of the exit. Published exit data can be skewed both in the case of positive and negative liquidity events, thus drawing conclusions on market performance based on mere public data sources can provide results that do not represent actual market outcome. As an investor in a significant portion of funds in Finland, Tesi has aggregated years of data on the state of the Finnish private equity market, resulting in a database that provides a rare source for studying the Finnish venture capital market performance.

Tesi has an exceptional insight into the performance of the Finnish venture capital market. However, as the data is sensitive, Tesi has been cautious of releasing statistics based on the data. During recent years, Tesi has updated its strategy related to publishing statistics conducted from internal data acquired through fund investments. Tesi aims to develop and internationalise the

3 In addition, there are several databases that gather information from the general startup community. For example, Crunchbase has one of the largest databases on the venture capital industry and they encourage members of the community to submit (even proprietary) information

private equity industry in Finland and believes that one way to achieve this is by providing actors such as politicians and international investors up to date and accurate market data of the Finnish private equity market. Also, publishing performance data has become relevant only during the past years, as by time the amount of data acquired has grown big enough to allow for meaningful analysis, that does not violate any confidentiality agreements or expose any of the individual actors or events.

Tesi published the first release of performance-related metrics of the Finnish venture capital industry in April 2018. Tesi published that Finnish VC fund vintages 2002-2008 had a Total Value to Paid-In (TVPI) multiple of 0,9x, while investments in 2009-2015 had a TVPI of 1,3x as calculated by the year-end 2017 (Talouselämä, 2018). These results indicate that the Finnish venture capital market returns have improved significantly during the past 20 years, but that there is still room for further development.

As the Finnish venture capital market is developing and research on performance is scarce, visibility into the market dynamics of Finnish VC is limited. Having limited visibility to the market dynamics and performance affects especially LP and VC investors, but also other parties and factors relevant to the market, such as angel investors, startups and political decision making. For VCs, the lack of data means that the creation, execution, and enhancement of an investment strategy is based on personal or shared experiences and common beliefs as opposed to local market research and data. For LP’s, the lack of data makes efficient capital allocation difficult due to decisions made based on uncertain data and limited benchmarks. The difficulties are two-fold: it makes it difficult for LPs to optimize their capital allocation to venture capital as an asset class, and it makes it difficult for LPs to assess and compare VC funds effectively.

1.2. Research objective and questions

The objective of this study is to increase the transparency of Finnish VC by adding to the knowledge and understanding of the Finnish VC market. Specifically, the study aims to holistically assess and analyse the Finnish venture capital landscape by analysing research and deriving insight

from the Tesi internal database. The study aims to create value for LP and VC investors, by offering novel insight on the specificities of the Finnish VC market.

The study will analyse the core Key Performance Indicators (KPI’s) of the Finnish VC market. Core KPI’s include performance measures such as Multiple on Invested Capital (MOIC), Residual Value to Paid-In (RVPI) and Distribution to Paid-In (DPI). Other variables that will be analysed include the time from investment to exit, performance of investments to Finland and abroad, average fund size in € and count of portfolio companies. The core KPI’s will increase transparency in the market and offer tools for LPs and VCs to effectively benchmark practices. To give perspective on the numerical KPI’s, the figures of the Finnish VC market will be benchmarked against other comparable markets, mainly the Nordics and Europe. To deepen the analysis and better understand the changes happening in the Finnish VC market, the direction of change will also be studied. Therefore, the first research problem examined in this study is:

(1) How does the Finnish VC market perform in terms of Key Performance Indicators?

Key Performance Indicators include:

- MOIC, segmented by realized profits and unrealized profits, fund vintage and investment country (Finland or abroad) - Time to Exit of Investments - Average investment size by investment round - Fund portfolio size in € and company count

After building understanding on the Finnish VC market performance, analysis will be conducted on the distribution of Finnish VC returns. VC returns are generally known to be very skewed. A majority of companies return less than the sum of investments in the company and a small percentage of investments make up a large portion of total market returns (Prencipe, 2017). Otham (2019) even claims that “it is well known by investors that the return multiple of winning early- stage venture capital investments is consistent with a power-law distribution”. On the other hand, Prencipe (2017) is more cautious in the phrasing. Prencipe (2017) studied liquidity events and returns of about 3600 EIF-backed startups funded during the period of 1996-2015. The study concludes that the assumption of power law distributed returns in venture capital are partially correct in returns over 2.35x.

Understanding the VC return distribution is essential for both investors and LPs. For investors, the return distribution should affect portfolio construction and size. For Limited Partners, the return distribution relates and explains to the volatility and risk related to investing in the asset class. Thus, it is important to understand the way returns are distributed in Finland and assess whether returns follow a power law distribution. The second research question examined in this study is:

(2) How are Finnish VC investment returns distributed?

To understand the return distribution of VC investments more thoroughly, analysis will be conducted on the development of a company after investment. Around 60-70% of investments are made as follow-on investments4, but 60-70% of investments return less than the cost of the investment (Prencipe 2017, Othman 2019, Levine 2014). To understand how VCs distribute the capital to follow-on funding, the study will analyse the value development of companies after investment and the relationship of first and follow-on investments to investment returns. The third and fourth research questions examined in this study are:

(3) How does the MOIC of a company develop after investment? (4) How does company and fund level fund allocation to first and follow-on investments affect returns?

4 Percentage of € invested as follow-on investments, based on Tesi data.

Table 1: Research questions

No. Question

Finnish Venture Capital Market Key Performance Indicators

1 How does the Finnish VC market perform in terms of Key Performance Indicators?

Key Performance Indicators include:

Return distribution

2 How are Finnish VC investment returns distributed?

Value development and first and follow-on funding

4 How does the value and returns of a company develop after an investment?

5 How does company and fund level fund allocation to first and follow-on investments affect returns?

1.3. Research design methodology and scope

The research consists of two parts: the theoretical analysis and the quantitative analysis. The theoretical part is a literature review about the phenomena that are studied: performance metrics, return distribution, value development after investment and staging of funding. As research on the performance metrics is scarce the thesis will also include findings reported by different reputable sources, such as industry associations (e.g. the Finnish Venture Capital Association, FVCA), practitioner reports and newspaper articles. The literature review will give the reader a holistic overview of the Finnish venture capital landscape and provide a benchmark figures to other venture capital markets, mainly Europe and the US. The findings and conclusions of the literature review will be used as a source to answer the research questions, and as the basis to form the hypotheses for the empirical analysis.

After the literature review, quantitative data analysis on the Tesi data will be conducted to test the hypotheses. The data consists of venture capital investments to 483 companies made between 1997 and 2019, by Finnish VCs to companies in Finland and abroad. The Tesi data does not cover the full spectrum of VC events in Finland, but it is estimated to operate as a close proxy. The limitations of the data and results will be discussed further in the study.

The scope of the study is the Finnish VC market. The empirical analysis is made by analysing investments made by Finnish VCs, but the literature review draws from research from other geographical areas. Analysis of other markets is required to get insight for formulating the hypotheses, as there is limited research from the Finnish VC market. In addition, analysis from other markets is used for benchmarking the results of the empirical analysis, as stand-alone figures from the Finnish VC market would bring little insight.

2. Literature review

2.1. Definition of venture capital and startups

Venture capital is a form of private equity. As stated by Invest Europe “Private equity is the provision of equity capital by financial investors – over the medium or long term to non-quoted companies with high growth potential” (Invest Europe, 2007) The two typical classes of private equity are venture capital and buyout. However, it is important to note that not all sources consider venture capital to be a subclass of private equity. Some sources use the term private equity in a manner which does not include the asset class of venture capital (Bertoni, et al., 2011).

VC is typically distinguished from other private equity activities such as buyout by the stage of the companies that the investors are acquiring a stake in. Venture capital funds typically invest in earlier stage companies, such as companies still at initial concept (seed investing), companies in the phase of starting business and marketing (startup investing) or companies aiming to quickly expand their operations and grow their revenue by scaling (later stage venture). It is also typical that later stage venture capital investors invest in companies that have been earlier financed by other venture capital investors (Eurostat, 2020). Especially the division between later-stage venture and other means of private equity is not always clear, as they can slightly overlap with each other (Metrick & Yasuda, 2011).

Venture capital is defined in this study by the five main characteristics as described in the book Venture Capital and the Finance of Innovation (Metrick & Yasuda, 2010):

1) A VC is a financial intermediary, meaning that it takes the investors capital and invests it directly in portfolio companies 2) A VC invests only in private companies. This means that once the investments are made, the companies cannot be immediately traded on public exchange 3) A VC takes an active role in monitoring and helping the companies in its portfolio 4) A VC’s primary goal is to maximize its financial return by exiting investment through a sale or an initial public offering (IPO) 5) A VC invests to fund the internal growth of companies

This study focuses on the VC industry of Finland. In this study, all funds that operate mainly in Finland are classified as Finnish VC funds, even if they invest also outside of Finland. The FVCA member list has been used as an additional resource in classifying Finnish VC funds.

Startups are defined in this study as FVCA characterizes them on its website:

“Startups usually strive for aggressive growth. While building their first product and searching for the product-market-fit, they tend not to be profitable – and often times need external funding to finance their operations. Venture capitalists make minority investments into these companies. They aim at increasing the value of startups by working with them actively, and eventually look for exit opportunities through acquisition or IPO”. (FVCA, 2020b)

In this study, all companies that have received investments from venture capital funds are classified as startups.

2.2. The development of the Nordic and Finnish private equity market

2.2.1. Early stages of venture capital in the Nordics

Venture capital as an asset class has become established only during the late 2000s. Venture capital as an industry was born from the need of entrepreneurial companies needing financing but being incapable of providing the required collateral and safety that bank lenders required. The first modern venture capital company can be traced to the US in 1946, when the American Research and Development Corporation (ARD) began operations (Florida & Kenney, 1988). ARD operated as a publicly traded corporation while most current funds operate as limited partnerships.

The Nordic Venture capital industry was born significantly after that of the US, during the late 1970’s and early 1980’s. The first venture capital fund in the Nordics was Företagskapital established in Sweden in 1973. The public sector was also collaborating on the development of the industry, and many of the earliest venture capital companies established in the Nordics were semi- private, as stated in the Hyytinen & Pajarinen (2001) study “i.e. based on co-operation between the government and private sector”. During the 1970’s and 1980’s the development of the Finnish

venture capital landscape was slower than that of the other Nordic countries (Hyytinen & Pajarinen, 2001).

In the beginning, the Nordic venture capital industry was driven by strong macro-economic circumstances combined with rising stock-prices. The positively started development took a long- lasting setback in the mid and late 1980’s, when the flows of risk capital decreased significantly. This led to stagnation in the Nordic venture capital landscape and “when compared to the US and to many other European countries, the Nordic venture capital industry remained – despite the strong start – underdeveloped the entire 1980s and, --, much of the early 1990s” (Hyytinen & Pajarinen, 2001).

2.2.2. The first and second coming of venture capital in Finland

The Finnish Venture Capital market dates back to 1967 when the Finnish government established both the venture capital firm Sponsor and the Finnish National Fund for Research and Development (Sitra) (Luukkonen, 2006). Sponsor was established as a single Limited Company (Ltd.), and the Bank of Finland subscribed for 60% of Sponsors shares. Instead of having a pure capitalistic goal, Sponsor was established to develop Finland’s financial system. Sponsor was to holistically support prospective small and medium-sized enterprises (SME’s), by providing capital for growth, support for management and help in innovating product ideas (Seppä, 2000).

The early Finnish venture capital industry was closely governed and influenced by the Ministry of Finance. For example, private equity and venture capital companies could apply for a Lex Sponsor5 status, if the company was considered to be important as an entity operating to develop and enhance investments of the industry. The Lex Sponsor -status made dividend income tax free. Seppä (2000) described the Lex Sponsor status as “a double-edged sword, whereby the objective of fostering growth and creating new jobs was attached to the leading venture capital companies ‘by definition’”, which emphasized the “social worker image” of the venture capital industry.

The private venture capital landscape saw its first boom in the 1980s, due to a well-developing economy and rising stock prices, combined with the creation of the over-the-counter (OTC) market

5 The Lex Sponsor was added to the Finnish company tax code on 3 November 1978

in Finland in 1984. Between 1984 and 1986, 23 new venture capital companies were formed in Finland, of which a majority were privately owned. However, this early boom was soon followed by a collapse, when a shake-out period began in Sweden during the mid-1980s, that was followed by the other Nordic countries in the late 1980s. During this time, many of the early venture capital companies left the market and the public opinion on VC in Finland turned from positive to negative. (Seppä, 2000)

2.2.3. The boom of the venture capital industry in Finland in the 1990’s and the dotcom bubble

In the 1990s, the venture capital industry had developed in Finland from governmental activity first, to publicly held Limited company (LTD) structures and then to firms structured as Limited Partnerships (Seppä, 2000). Despite the shift from public to private, the role of public players was still key in revitalizing the venture capital industry in Finland (Hyytinen & Pajarinen, 2001). For example, in 1995 the Finnish Industry Investment Ltd (Tesi), was established to spur entrepreneurial venture capitalism and develop the matching of investment capital to business ventures through their fund-of-funds (FoF) vehicle (Seppä, 2000).

The ratio of private equity6 raised to GDP increased especially in the mid- and late 1990s. While before 1995 the annual amount of private equity funds raised in Finland was under 63 million € per annum, in 1999 655 million € was raised. The high amount of funding raised soon also translated to a substantial growth of annual investments made, both in terms of number of companies and total funding during the 1990s (Luukkonen, 2006).

The period from 1998 to 2001 is known as the “dotcom bubble”. It saw a rapid increase in valuations and capital volumes, before they dropped dramatically (Valliere & Peterson, 2004). The dotcom crash also affected the venture capital industry in Finland. Total venture capital investments in Finland were at a record high of 175 m€ in 2001 (FVCA, 2015). Venture investments in Finland declined to 97 million € in 2002 and 71 million € in 2003 (FVCA, 2015). The decline in total venture capital investments by Finnish VC was due to both a lower number of

6 Including also other activities than venture capital, i.e. buyout

annual investments made (290 in 2001 and 240 in 2004), as well as a decline in average investment size (0.6 million € in 2001, 0.3 million € in 2004) (FVCA, 2015). Some of the funds lost large sums in the volatile market environment and several GPs that operated in the market were not able to collect further venture capital funds after the turbulent times (Kaila, M, personal communication, Feb 5, 2020).

2.2.4. The modern startup and venture capital environment

After 2001 and the dotcom boom, the Finnish venture capital saw a second, albeit much smaller boom in 2004-2008. The crash that followed the boom resulted in a significant reduction of activity in the Finnish venture capital market for the years 2009-2012 (Parviainen, 2017).

After 2010, the venture capital landscape has seen significant development in terms of Finnish companies raising venture funding from domestic and foreign investors, Finnish VCs raising funding, and Finnish startups having successful exits. Since 2010, Finland has built a thriving startup ecosystem. In 2018, Finnish startups raised 0,096% of GDP in VC investments, which was more than any other European country and twice the European average (FVCA, 2019) and the Greater Helsinki Region was ranked as the number 4 Emerging Ecosystem in the Global Startup Ecosystem Report 2020 by Startup Genome (Startup Genome, 2020).

The thriving startup ecosystem that Finland now has is often attributed to the fall of Nokia and the birth of the entrepreneurial movement in the newly established Aalto University7 (Mitzer, 2015; Järvilehto, 2019). The Aalto Entrepreneurship Society (Aaltoes) was established in 2009, by students from Aalto University. It began as a grassroots movement, promoting entrepreneurship to students, and has had a key role in building the vibrant startup ecosystem there now is in the Helsinki region and in Finland (Vimma, 2018). For example, one of the many successes that started from the grassroots movement at Aalto University, is Slush. Aaltoes built Slush to be one of the leading startup events globally, attracting many of the world’s famous VCs to come and visit Finland.

7 Aalto University was established on 1 January 2010, when the Helsinki School of Economics, Helsinki University of Technology and the University of Art and Design Helsinki were merged.

The development of the Finnish startup ecosystem has been called “The Finnish Miracle” (Järvilehto, 2019). For an ecosystem to thrive, it needs access to entrepreneurially minded people, talent and capital and sufficient proximity to the customers and markets it is trying to serve and close links between the key actors of the ecosystem to enable fast development cycles (Järvilehto, 2019). These factors necessary for the ecosystem can be attributed to the “hypercollaborative” environment in Finland, where “ideas and technologies are discussed and developed freely, some of them ultimately manifesting as thriving startups” (Järvilehto, 2019).

2.3. Finnish venture capital market performance

2.3.1. Performance metrics

Venture capital market performance can be calculated in several ways. A common way of evaluating the performance of venture capital, is by studying the Multiple on Invested Capital (MOIC) or Total Value to Paid-In (TVPI). Other potential ways to assess venture capital performance include Public Market Equivalent (PME) or Internal Rate of Return (IRR). In the literature review, TVPI is the most common metric used, but in the empirical analysis of this study MOIC is used as the performance metric. MOIC is used as the performance metric, as the dataset used for analysis includes only company-level data (discussed further in Chapter 3. Empirical data and methods) and not fund level fees or expenses.

Difference of MOIC and TVPI

The Institutional Limited Partners Association (ILPA) defines TVPI as “The ratio of the current value of remaining investments within a fund, plus the total value of all distributions to date, relative to the total amount of capital paid into the fund to date” and MOIC8 as “Calculation performed by adding the reported value and the distributions received and subsequently dividing that amount by the total capital contributed” (ILPA, 2021). TVPI differs from MOIC, as MOIC is a gross metric, meaning that it is calculated before fees and carry, while TVPI is calculated as net of fees. In academia, the term MOIC is sometimes referred to as the term TVPI (Brown, et al.,

8 Referred to as ‘Investment Multiple’ by ILPA

2020), but for the purpose of this study, the metric will be called TVPI if it refers to fund level data that is net of fees and MOIC if it is gross of fees.

Multiple on Invested Capital (MOIC)

MOIC can be broken down to Distributed Value to Paid In (DPI) and Residual Value to Paid In (RVPI)9.

In this study, MOIC for company i or fund i at year t  [t(0), …., T]]:

푐푢푟푟푒푛푡 푣푎푙푢푎푡푖표푛(푖,푡) + 푟푒푎푙푖푧푎푡푖표푛푠(푖,푡) MOIC(i,t) = 푖푛푣푒푠푡푚푒푛푡푠(푖,푡)

Where t(0) is the investment year and T(i) the current point in time.

For company level returns: Current valuation (i,t) is the valuation of the startup shares and other financial instruments held by the VC fund at end-of-year t, realizations (i,t) is the total amount of cash realized from a sale or liquidation event until end-of year t, and investments (i,t) is the total sum invested in the company i until end-of-year t.

For fund level returns: Current valuation (i,t) is the valuation of all startup shares and other financial instruments held by the VC fund i at end-of-year t, realizations (i,t) is the total amount of cash realized for fund i from all sales or liquidation events until end-of year t, and investments (i,t) is the total sum invested by the fund i in all companies until end-of-year t.

For market level returns: Current valuation (i,t) is the valuation of all startup shares and other financial instruments held by all VC funds i at end-of-year t, realizations (i,t) is the total amount of cash realized from all startups for all funds i from all sales or liquidation events until end-of year t, and investments (i,t) is the total sum invested by all funds i in all companies until end-of- year t.

Realization multiple: Distributed to Paid-In (DPI) 10

DPI represents the realization multiple. DPI is the ratio of money paid back to investors to total invested money. As DPI takes into consideration only value that has been realized, the percentage

9 MOIC = RVPI + DPI 10 In this study, DPI and RVPI are gross metrics

of DPI of total fund or company value increases over time. DPI for company i or fund i at year t  [t(0), …., T]]:

푟푒푎푙푖푧푎푡푖표푛푠(푖,푡) DPI(i,t) = 푖푛푣푒푠푡푚푒푛푡푠(푖,푡)

Multiple for unrealized value: Residual Value to Paid-In (RVPI)

RVPI represents the unrealized value of a company or fund portfolio. As company valuations are estimates done by portfolio fund managers, RVPI is an indicator of potential outcomes. As a fund or company ages, the percentage of unrealized returns (RVPI) decreases, until TVPI = DPI and RVPI = 0. RVPI for company i or fund i at year t  [t(0), …., T]]:

푐푢푟푟푒푛푡 푣푎푙푢푎푡푖표푛 (푖,푡) RVPI(i,t) = 푖푛푣푒푠푡푚푒푛푡푠(푖,푡)

Other metrics used in studying venture capital performance: IRR and PME

IRR is a metric used to estimate the profitability of investments. The main difference of IRR and money multiple metrics such as MOIC is that IRR considers the time-value for money. IRR is calculated by setting the net-present value (NPV) of all cash flows to 0 in a discounted cash flow analysis. The IRR is a potential metric for studying fund performance, although it has some drawbacks, especially when calculating company level data. The IRR captures the time value of money but engrained in it is the unrealistic assumption that after liquidation events the money could be reinvested at the IRR rate. In addition, the IRR is skewed to value large quick exits even disregarding the performance of latter investments (Ang & Sorensen, 2012). From a LP perspective, the IRR is a relevant metric to study when considering asset allocation across different asset classes. This is because money multiple metrics do not take into account the long time period that capital is tied up for in venture capital investments.

Differing from IRR and TVPI being absolute performance measures, PME is used to measure performance relative to the market. However, as the asset class is illiquid in nature and the timing of cash flows is irregular, comparing private equity returns to public market returns may not be sensible (Preqin, 2015). Similarly to IRR, this measure of performance is a very relevant metric

especially to LPs, as that way they can evaluate their total profit and risk profile allocation across asset classes.

2.3.2. Finnish venture capital and private equity performance

First Finnish venture capital market level data was first published in 2018 by Tesi. The data for the analysis was compiled of 22 venture capital funds that Tesi had invested in, which were established in Finland between the years 2002 and 2015. Table 2 presents the published results.

Venture capital funds established between 2002-2008 had a TVPI of 0.9. The total TVPI being under 1, indicates that on average these funds are not profitable and are not able to return the invested money back to Limited Partners. Funds established in 2009-2015 had an improvement in profitability, with a TVPI of 1,3 (Talouselämä, 2018).

A majority of the profits were still unrealized when the analysis was published. Of funds established in 2002-2008, 56% of the profit was unrealized value, while for fund vintages 2009- 2015 85% of profits was unrealized value. The high percentage of unrealized profits for fund vintages 2009-2015 make the profitability figures speculative, as actual outcome will only be seen once all the funds have exited their positions. Tesi published these figures at the FVCA Spring Seminar and they were reported by the Talouselämä newspaper.

Table 2: Tesi published figures of Finnish venture capital market performance aggregated by fund vintage (Talouselämä, 2018)

Fund vintage 2002-2008 2009-2015

DPI 0.4 0.2 RVPI 0.5 1.1 TVPI 0.9 1.3

In 2017, the European Investment Fund 11 (EIF) published figures on the performance of investments made using EIF Own Resources. This may not be a fully representative set of venture capital funds in Europe, as EIF Own Resources makes investments that are “driven by co-

11 EIF is a European Union agency for the provision of finance to SMEs. EIF mainly invests in venture capital funds, acting as their intermediaries, to provide financing to high-tech SMEs in their early and growth phases.

investment obligations alongside investments made on behalf of third parties, many of which are targeting different policy objectives” (EIF, 2017). The data consists of only 9 funds located in Finland. The pooled TVPI of the funds included in the study is 0.68, which means that this set of funds were clearly unprofitable and not able to return the invested capital.

Table 3: EIF venture capital portfolio performance by team location Finland for fund vintages 1997-2015

Fund vintage 1997-2015 Pooled Upper Quartile Median Lower Quartile

DPI 0,56 0,59 0,12 0,01 RVPI 0,12 0,46 0,68 0,32 TVPI 0,68 1,05 0,8 0,33

The amount of Finnish funds EIF has invested in during the period of the study is very small, so selection bias caused by a small sample size must be taken into consideration. In addition, as commented in the Talouselämä newspaper by Matias Kaila (Director of Fund Investments at Tesi) the sample represented in the data published by EIF is not representative of the current Finnish venture capital landscape. He elaborates, that the EIF data is a very small sample of early 2000 funds, of which many had troubling times after the dotcom bubble (Talouselämä, 2018).

It also must be noted, that EIF invests according to its policy objectives. The two main statutory objectives include (EIF, 2020):

- fostering EU objectives, notably in the field of entrepreneurship, growth, innovation, research and development, employment, and regional development - generating an appropriate return for our shareholders, through a commercial pricing policy and a balance of fee and risk-based income.

These objectives may differ from the investment thesis and rationale of privately managed funds, especially regarding the clause of fostering EU objectives. Thus, the portfolio of investments made by the EIF Own Resources is susceptible to sample bias and not representing accurately the general market environment, especially in the context of smaller markets where their count of investments is small, or the investments are timewise not well diversified.

2.3.3. Comparable venture capital market returns

Reference benchmarks for the Finnish venture capital market performance will include mainly the performance of the Nordic VC and European VC. Figure 2 (see below) presents the findings of benchmark TVPI figures for Nordic and European VC.

The Nordic venture capital associations 12 DVCA, FVCA, NVCA and SVCA published in December 2018 that the TVPI13 for Nordic VC for the timeframes 2002-2008 and 2009-2015 were 1.1x and 1.4x, respectively (Copenhagen Economics, 2018). The study was conducted by Copenhagen Economics, and gathered the data from the national venture capital associations and public databases.

The Nordic Venture Network (NVN) published a study on Nordic venture capital performance in 2016, where they defined a set of metrics called the Nordic Venture Performance Index (NVPI). In the study “NVPI – Nordic Venture Performance Index” (Nordic Venture Network, 2016) they calculated the performance of 8 venture capital firms that were a member of NVN between 2002- 2016. These 8 venture capital firms had in total 32 funds and 609 portfolio companies. Although 8 firms might seem like a small sample, the dataset is relatively large, due to NVN members including fund investors such as Tesi and several of the largest venture capital firms in the Nordics such as Creandum and Industrifonden. However, the sample of 8 venture capital firms has not been further elaborated on, and thus several important aspects of the sample cannot be determined, such as sample division by geography, stage, or fund size.

During 2002-2005 the Nordic Venture Performance Index (NVPI) TVPI was around 1, after which it saw a significant rise in 2006 to 2,3 and in 2007 to 2,5. After that, the TVPI has been declining on average 6% per annum to 2015. The average TVPI 2002-2008 was 1,7 and between 2009-2015 1.5. The NVPI should be analysed mainly by the trends that the data reveals rather than looking at

12 DVCA = Danish Venture Capital Association, FVCA = Finnish Venture Capital Association, NVCA = Norwegian Venture Capital Association and SVCA = Swedish Venture Capital Association 13 Based on different data sources: Nordics is calculated as a simple average of the four countries. Data for Denmark is based on the public fund “Vækstfonden” and hereby only include a subset of Danish funds. Data for Finland is based on Tesi data. Data for Norway is based on Preqin and data for Sweden is based on EIF data. The analysis does not have time series data for Norway and Sweden, and it is assumed that the growth of TVPI in Norway and Sweden follows that of Finland and Denmark. The figures reported here are taken straight from the report, without any additional calculations being made.

individual years, as the figures are calculated as a two-year moving average to preserve the confidentiality of the investors.

EIF-published14 figures European venture capital TVPI appears to have significant growth for more recent fund vintages, with a TVPI of 0,8 for fund vintages 1998-2002 and a TVPI of 1,2 for fund vintages 2009-2015 (EIF, 2017). eFront-published figures for Western Europe an VC show a pooled TVPI of 1.3 for fund vintages 1998-2015 (eFront, 2018).

Figure 2: Nordic venture capital performance 1998-2015 (Copenhagen Economics, 2018; EIF, 2017; Nordic Venture Network, 2016; eFront, 2018)15

When comparing European venture capital returns to US venture capital returns, it is evident that European venture capital returns have been underperforming in the past. In a study published by Cambridge Associates (2018) the average TVPI for US fund vintages 2002-2008 was 1,6, while the average TVPI for fund vintages 2009-2015 was 1,8 (see Figure 3). The report by the Nordic venture capital associations presents similar figures: a TVPI of 1,7 for US fund vintages 2002-

14 The EIF-published figures are from the same source data as used in Chapter 2.3.2. and thus are subjective to the same caveats and policy objectives as discussed earlier. 15 NVN TVPI is calculated as an average of the TVPI’s for the fund vintages 2002-2008 and 2009-2015. In the study the TVPI for each fund vintage is calculated as a two-year moving average due to confidentiality reasons. EIF notes that these figures “shall be read in the context of such policy oriented [the EU objectives] approach”. These were discussed in more detail in chapter 2.3.2. eFront data based on calculations made in the currency EUR.

2008 and 1,9 for fund vintages 2009-2015 (Copenhagen Economics, 2018). The report by Nordic venture capital associations also concludes that Nordic and European VC is trailing in profits to the US, due to the US market being more mature and thus having more structures that contribute to success. Nordic VC is still missing larger later-stage venture funds with repeated cycles of success. This market white space is caused by on average smaller fund sizes and investment sizes, which forces prosperous companies to search for funding internationally – leaving a profit potential unused by Nordic VC’s (Copenhagen Economics, 2018).

Figure 3: US venture capital returns (Cambridge Associates, 2018). TVPI calculated as average TVPI for the fund vintage groups.

Despite European venture capital performance lagging behind US venture returns, this difference is reduced when accounting for the vintage years of the companies and the development stage of the market. A study by Axelson & Martinovic (2015) finds that experienced entrepreneurs and venture capitalists are determinants to higher profits. The study finds that as the European venture capital landscape is younger than that of the US, it is still lacking the same mass of serial entrepreneurs and experienced venture capitalists operating in the market: with the share of entrepreneurs with previous entrepreneurial experience in the US between 1995 and 2015 being around 35% in the, but only 15% in Europe. The study concludes that there is “no difference in the likelihood of IPOs between European and US deals from the same vintage year” but that “European trade sales [European company sold] are less likely and less profitable than US trade sales” (Axelson & Martinovic, 2015).

Hence, as the Finnish market has in the past developed more slowly than that of the European, we expect profits to be trailing behind European VC for older fund vintages. The potential gap in returns is expected to have decreased as the Finnish VC and startup ecosystems have developed and returns of recent fund vintages are estimated to be on par with European returns.

H1: Finnish venture capital performance trails European venture capital performance for older fund vintages (fund vintages < 2002) but has significantly developed after that to be on par with European venture capital performance.

2.4. Return distribution analysis of venture returns

2.4.1. General return distribution characteristics of venture capital

Venture Capital is generally known to be an industry where a few successful investments contribute most of the market returns. In industry lingo, venture capital is often described in ways such as “hitting for the home run” or “profits follow a power law distribution”. The point is further amplified by the stories told about the profits made by early investors in successful companies such as Facebook, Uber or Robinhood.

For example, one of the top VC investments ever made, was when Sequoia backed WhatsApp in 2011 and 2013 with $8M and $52M respectively. These investments were made with valuations of $78.4M in 2011 and $1.5B in 2013, resulting in a roughly 18% ownership of WhatsApp. WhatsApp was acquired by Facebook in 2014 for $22B. This meant a return of over for $3B and 50x money multiple for Sequoia, and in addition, it was the only venture investor of the company (Crunchbase, 2019). However, these “home runs”, are rare. According to a Crunchbase study in 2018, only 0.91% of companies raising seed funding in the US between 2008 and 2010 had reached a valuation over $1B before August 2018 (Crunchbase, 2018).

The distribution of profits in venture capital is very skewed. A small number of exits make a large sum of the total profits in the industry. As can be seen in

Figure 4, roughly two thirds of EIF’s exits in venture capital return a TVPI multiple under 1, while only 4% of exits return a multiple over 5x (Prencipe, 2017). A US VC preforms better than

European VC and is also reflected in the return distribution. In an analysis by Correlation Ventures (2019), it was calculated that 7% of venture investments returned 5-10x and 5% of investments over 10x.

H2: Venture capital investments in Finland have a skewed return distribution with a similar percentage of companies in each return class as in the benchmark studies

Figure 4: European and US venture Investments by return range, investments from 1996-2015 for Europe (EIF - Prencipe, 2017) and 2009-2018 for US (Correlation Ventures, 2019)16

Similar results were reported in an earlier study by Sahlman (1990). The study was conducted based on data gathered from a Venture Economics survey that had a total of 383 individual investments made by 13 VCs between 1969 and 1985. Results indicate a similarly skewed return distribution, as can be seen in Figure 5. More than one-third of total money invested resulted in an

16 Source 1 – EIF includes 2065 early-stage VC investments made between 1996 and 2015 by EIF backed VC funds. All exit types, such as write-offs, liquidations and successful sales are included. EIF included a return class of “At cost” in their study, which included all exits with a TVPI between 0.8

Source 2 – Correlation Ventures “CV” is a modern venture capital company using predictive analytics in making investment decisions. They are an active and relied on source for statistics and insights about the venture capital industry. The data is from Dow Jones VentureSource and other primary and secondary sources, totaling $20.5B invested and 27 878 financing events. Figure 4 shows the % of financings in U.S. venture-funded companies exiting or going out of business between 2009 and 2018 (Correlation Ventures, 2019)

absolute loss, meaning a TVPI under 1. On the other hand, these losses are countered with a small share of companies that resulted in a big pay-off. 6,8% of invested money returned a total of 49.4% of all returns in the market (Sahlman, 1990). The total TVPI for the investments included in the study result in a TVPI of 4.3. This significantly higher TVPI than has been reported in later studies is possibly due to the vintages included in the study, since venture capital returns have fluctuated over time.

Figure 5: Percentage of total money invested and ending value of all investments for US venture investments 1969-1985 (Sahlman, 1990)

Cochrane published one of the most comprehensive studies on the risk and returns of venture capital in 2005 (Cochrane, 2005). Cochrane’s findings indicate that an individual venture-funded companies’ returns follow well a continuously compounded geometric log return of 15% and a standard deviation of 89% when corrected for selection bias. Cochrane studies performance as annualized return, calculating returns from each financing round until IPO, acquisition or write- off. He argues that as venture capital is an illiquid market in nature, performance cannot be estimated by follow-on or further funding rounds as they are still unrealized value. The method of the study potentially reflects more accurately the overall returns of the venture capital market than calculating performance based on unrealized valuations but does not potentially reflect the returns of investors participating on several or different funding rounds of a company. Cochrane does not imply how he handles intermediate funding rounds in his study. The study is based on the

VentureOne17 database and covers a total of 7,765 companies and funding round events between 1987 and 2000.

2.4.2. Return distribution in venture capital from a fund perspective

Individual firm returns in the venture capital market clearly follow a non-normal distribution, where overall market returns are heavily weighed by few outcomes. However, individual firm returns in the venture capital market do not provide VC investors and Limited Partners enough information on portfolio construction and capital allocation.

The spread between profits made by the upper and lower quartile funds is also significant, as can be seen in Figure 4. In a study made by Cambridge Associates in 2019 covering the records of 1544 venture capital funds formed between 1995 and 2018. US fund vintages established between 2000 and 2015 had an average TVPI of 1,8, the upper quartile of funds had a TVPI of 2,1 and the lower quartile of funds had a TVPI of 1,118 (see Figure 6) (Cambridge Associates, 2019). The spread between the upper and lower quartile funds was on average 2,1. The skewed distribution of venture capital fund profits is also visible in the figures of industry pooled return and median return. The median return is consistently below the pooled return, due to a small portion of funds returning a majority of the profits. Between 2000 and 2015, the pooled return19 of US venture capital funds was 1,9 while the median return was notably lower, being 1,4.

17 Cochrane acknowledges that the database may not be free of the survival bias of funding events, although it self- reports to have covered approximately 98% of financing rounds during the period that data was gathered from. At the time of the study, VentureOne gathered data from any funding rounds that had involved in them an investor of over $20M of assets under management, and then backtracked the information for earlier funding rounds of the company in question. This and other reasons may result in selection bias in the data, as described and studied more thoroughly Gompers & Lerner (2000) and Kaplan et al. (2002) 18 Average TVPI for funds and upper and lower quartile calculates as the average of the arithmetic mean for each fund vintage year. 19 Pooled returns represent the net return calculated on the aggregate of all cash flows and market values as reported by individual fund managers in their quarterly and annual audited financial reports. These returns are net of management fees, expenses and performance fees that take the form of carried interest.

Figure 6:20 US VC fund TVPI Spread 2000-2018 Spread (Cambridge Associates, 2019)

Weidig and Mathonet (2004) argue, that individual venture capital investments are risky assets, but investments widely diversified into the market are not necessarily so. According to the study, Limited Partner investors can practically eliminate the risk of returns being under the invested capital, when investing in venture capital through vehicles such as fund-of-funds (see Figure 7). Their conclusions rely on an analysis made based 5000 direct investments and return data of roughly 750 US VC funds and 300 European funds, with 50000 simulated fund-of-funds. The data covers transactions from the period of 1983 to 1998 for fund data and 1987 to June 2000 for direct investment data. Only funds that have been fully realized have been included in the study.

20 Cambridge Associates (CA) is a privately held investment firm in the United States, with almost $400B of assets under advisement and $30B of assets under management. At the time of the study, CA claimed to have a database covering. The data is from annual fund financial statements provided to CA as a LP of the funds and combined with other data sources such as data provided by Thomson Reuters and the Institutional Limited Partners Association (ILPA). Figure 4 includes data compiled from 1544 US venture capital funds formed between 1995 and 2018. According to CA research, most funds take a minimum of six years to settle in their final quartile, and thus performance metrics of more recent vintage years are not as reliable as older vintages.

Figure 7: Venture Capital Investment Vehicle Return Profiles. Fund data based on European VC investments between 1983-1998 from VentureXperts and direct investment data from US VC investments from 1987 to June 2000 from VentureOne (Weidig & Mathonet, 2004)21

The conclusions are evident: there is ample study from other asset classes on how diversifying the investment portfolio affects risk and return, mainly from publicly traded stocks. A venture capital portfolio has a combination of firms, typically from different industries. The firm-specific risk of the portfolio can thus be minimized by having a large portfolio. However, it is to be noted, that diversification does not reduce the systematic risk of the portfolio (Berk & DeMarzo, 2014). In addition, some studies argue that maintaining a more specialized portfolio can be especially useful in the case of venture capital, where access to networks, information and deal flow from other actors is crucial (Bygrave, 1988).

2.4.3. Power Law analysis in venture capital

Venture capital is often stated to follow a power law. As Peter Thiel has famously said in his book Zero to One: Notes on Start Ups, or How to Build the Future “we don’t live in a normal world; we

21 Weidig & Mathonet (2004) have compiled the data from Cochrane (2003) and the VentureXperts. The study notes, that reliable data for European VC investments was not available for direct investments. See Weidig & Mathonet (2004) and Cochrane (2003) for more information. Figure 7 is a modified chart from the chart published in the Weidig & Mathonet (2004) study.

live under a power law”, referencing not only to phenomena in general, but especially to profits in venture capital (Thiel & Master, 2014). Many other top VC investors have said the same thing. Marc Andreessen has explained as such “The key characteristics of venture capital is that returns are a power-law distribution --- [from 200 fundable startups in the US each year] about 15 of those will generate 95% of all the economic returns. Even the top VCs write off half their deals” (Griffin & Andreessen, 2014).

Mathematically, a phenomenon follows a power-law distribution if

P(x) = Cx-α

Where C>0 is a normalized constant, x is the variable and α is the scale parameter22. In a power- law distribution, the smaller the α the heavier the tail of the distribution curve is. Due to the extreme right skewness of the power law distribution it has several interesting characteristics. When 1< α<2 all the moments of the distribution are infinite, including the mean. When 2< α<3 the first moment is finite, but the second moment and higher moments are infinite, meaning that extreme events dominate the summary statistics. When a moment does not exist (is infinite), it means that the sample estimate grows with sample size n. A power law distributed statistic forms a linear plot when plotted on a log-log chart, although this is not a sufficient condition to determine a power law distribution. Clauset et al. (2009) propose a procedure for testing for a power-law fit in a continuous data set. The procedure is based on testing the Kolmogorov-Smirnov distance measurement of the data and theoretical model, after which if a meaningful p-value23 is obtained, other statistical distribution should be ruled out.

Despite the deeply ingrained thinking of venture capitalists that profits in venture capital follow a power law, not much scientific study has been published on the phenomena. However, for VC investors and LP investors alike, understanding the market dynamics especially on profit distribution would allow for better decision making on how to invest and hedge risk. According to a study by Scherer (1996) “the potential variability of economic outcomes with Pareto-Levy distributions is so great that large portfolio draws from year to year can have consequences for the

22 See for example u (2005) on more theory for power law distributions 23 The p-value is defined as the fraction of the synthetic distances that are larger than the empirical distance. The p- value is meaningful if it is close to 1, meaning that the difference between the empirical data and the model cannot be attributed to statistical fluctuations alone. (Clauset, et al., 2009)

macroeconomy”. As multiplicative processes, network and winner takes it all -characteristics24 are well known to be present in venture capital, the presence of Paretian returns certainly requires examination.

Scherer (1996) studies the distribution of returns from innovation, especially to see whether they conform to a Paretian distribution. In his study, he examines the royalties from U.S. university patent portfolios, the quasi-rents from marketed pharmaceutical entities and the returns of two- samples of high-technology venture startups. He concludes that the distribution is closer to log normality than to Paretian. All samples included in his study are relevant for the purpose of this study, as patents are closely linked to startups and innovation (especially VCs investing in deeptech), medtech is a field of venture capital activity, and samples of high-technology venture startups are at the core of the study.

In the EIF study “The European venture capital landscape: an EIF perspective, Volume 3: Liquidity events and returns of EIF-backed investments” (2017) analysis on whether returns follow a power-law distribution is also studied. The study follows the procedure of Clauset et al. (2009), and the method delivers an estimated scaling parameter of α=2.45 (±0.09), with a p-value of 0.915, thus not being able to rule out a Paretian distribution at most typical confidence levels. However, they point out, that the sample size is not consistent with the Virkar and Clauset (2014) rule of thumb ntail>300, and thus the results cannot be comfortably relied on.

The effects of power law distributed profits in venture capital effect especially portfolio building and hedging risk. If the industry were to conform to a power law, ceteris paribus, α<2, the average return of the portfolio would grow by each added company, with an infinite number of picks resulting in an infinite return. Although there is some evidence showing individual venture returns behaving in a Paretian manner, according to the mathematical properties of power laws, this return should be visible also in a combination of venture funded companies e.g. in fund-of-fund returns. However, the study by Weidig and Mathonet (2004), see Figure 7, presents contradicting findings.

24 If none of these characteristics were evident in venture capital, the basis to conduct analysis on Paretian returns would be weak. Multiplicative processes and network effects are especially evident in many sectors of ICT, of which companies such as Facebook, Google and Amazon and Apple are good examples. These companies combined with Microsoft and Netflix together were worth slightly over 25% of the S&P 500 by market cap in April 2020. All of these companies had raised venture capital funding.

Neuman (2017) calculates a mathematical model on how portfolio size affects the probability of returning a certain multiple. The profit distribution he uses in his analysis is the following:

25 with x0=1 and α=1.98 . This means that 2/3 of the returns of individual companies are < 1 and the rest of companies follow a power law distribution. Neumann does a Monte Carlo simulation to drive through and calculate the probabilities of multiples 1 to 15 on several portfolio sizes (return multiples 1 to 4x below in Figure 8). The results show that as the portfolio size grows the probability of reaching a certain multiple similarly grows. For example, if one were to make a million investments, all the portfolios would reach a return of 4x. However, this is not applicable to real life, as constructing a portfolio of that size would be virtually impossible, already because the pool of potential high-growth startups is not as numerous. Thus, the most interest is in the results regarding much smaller portfolios. In his analysis, the probability of reaching higher multiples quickly decelerates as the portfolio size grows. For example, the % of portfolios that equal or exceed 3x, is 19% if the portfolio size is 20. If the portfolio grows to 50 or 100 the probabilities respectively are 25% and 30%. Even investing in 1000 companies would give you only a 52% chance of reaching a 3x return.

25 The α is calculated as average of 16 different datasets and sources related to venture, including different stages of venture, TVPI and other performance measures as well as revenue growth of startups. More details from the source (Neumann, 2015).

Figure 8: Probability of Exceeding Benchmark Return by Portfolio Size (Neumann, 2017)

Simplifying venture returns to following a Paretian distribution gravely leaves out some of the important characteristics in venture capital that affect success, such as networks and the ability of a VC to support and influence the growth of a company. With extensive background research and first-hand experience of venture capital, Peter Thiel has summarized his thoughts on how power law distribution characteristics in the industry should be interpreted by venture capitalists. In his opinion, a standard venture capital fund should invest in 7-8 companies that have the potential of a 10x return, for investors to have the chance and time to bet on successful technology companies. He argues, that basically the logic would also work for a fund betting on 100 companies of which each would have the possibility to generate a 100x return, but he insists that realistically that would look “less like investing and more like buying lottery tickets” (Thiel & Master, 2014).

H3: Venture capital investment returns follow a power law distribution

2.5. Multiple on invested capital (MOIC) development after investment

The skewed nature of returns indicates that an investor, does not ex-ante know which ideas and companies will work, or else unprofitable investments would not be a majority of all venture

capital investments made (Kerr, et al., 2014). In this thesis we study the MOIC development data after investment for two reasons: first, the MOIC development of investments gives us additional insight to the development of companies and thus adds on the understanding of how the return distribution is realized and second, we want to understand potential timeseries changes happening in the return distribution of investments as it can indicate the direction of development in VC returns.

For companies that have not been exited, MOIC generally follows the valuation development of a company. MOIC is a function of unrealized and realized value of a company (MOIC = (Current valuation + Realizations) / Investments) and for non-exited companies, realizations are most often 0 until the exit. Circumstances where the MOIC development does not necessarily follow the valuation development of a company can include instances such as: the investor has sold a part of their shares, the investor has invested on multiple different rounds with different valuations, or the investor has used contractual ways such as a convertible note with a valuation cap. The reported valuation of an investment is typically the valuation of the latest funding round, and in between rounds it is usually adjusted only in case of highly meaningful developments in the company. As research on the MOIC-development after investment is scarce, the phenomena will be analysed by studying the valuation development of companies from one stage to another.

As a company successfully evolves to raise additional funding rounds to scale the growth, the valuation typically grows. Pitchbook reports that European start-up companies had a median early- stage VC valuation step-up multiple of 1,6x in 2019 and median late-stage VC pre-money valuation step-up multiple of 1,4x (PitchBook, 2020). The valuation step-up multiple is the change of valuation between two funding rounds. The median age of companies from founding to raise an angel or seed round was 1,8 years in 2019, from founding to raise an early-stage venture capital round was 3,3 years and from founding to raise a late-stage funding round was 7,9 years (PitchBook, 2020). The datapoints have inherent survivorship bias, as the data is presented for only the companies that have raised the funding round in question, not for all companies raising initial angel or venture capital funding.

As there is no research on the valuation development of venture funded companies, no hypothesis will be formed about the phenomena. The MOIC development will be studied to increase the

understanding about the state and development of the Nordic venture capital landscape, and to bring additional insight to the analysis of return distributions and first- and follow-on funding.

2.6. First- and follow-on funding

When an investor invests in a company, it is usually specified in the investment agreement that the investor has a right for pro rata investments (pre-emptive rights) on further funding rounds. Staged financing is a commonly used tool in venture capital. It offers investors the opportunity to gather information, monitor the progress of a company and to control the investment risk by offering the opportunity to either invest more or cut losses (Wang & Zhou, 2004; Gompers, 1995; Tennert, et al., 2018).

Follow-on investments can be used in defensive or offensive situations. A defensive situation refers to a situation, where the investor asses the company to have potential, but the company is having difficulties raising further funding. A defensive situation can be the result of the company not achieving the forecasts presented during the initial investment, such as sales figures, progress of research and development or changed market conditions. In a defensive situation, the initial investor may be the only source of capital available for the company to continue operations.

An offensive situation refers to a situation, where the company has developed positively and shows strong future potential. In this case, the company often has interest from multiple investors, and the initial investors may want to use their pre-emptive right to defend their position on an up- round. Due to the skewed nature of venture capital returns, it is crucial for venture capital investors to maximize the returns of successful investments. Offensively follow-on investing in high potential portfolio companies is a tool to capitalize on the wins. A special type of fund that invests only in the later stages of initial investments made by the same General Partner is called an “Opportunity Fund”.

In a defensive situation, the investor must decide whether to cut losses or continue funding the company. Prior research establishes, that escalation of commitment is a common way of acting when facing adverse losses (Staw, 1981; Novemsky & Kahneman, 2005; Sleesman, et al., 2018).

As Staw (1981) states “The crucial issue is whether there is a tendency to escalate commitment above and beyond what would be warranted by the “objective” facts of the situations – the answer to this question must be a qualified Yes.” This dynamic is in play also in venture capital.

The advantages of staging investments, such as additional information and flexibility, are only realized if the investor is effective at terminating unsuccessful investments based on updated information (Gompers, 1995). A study by Guler (2007)26 argues that the more rounds (count of investment rounds) an investor has invested in, the less likely they become to terminate the investment27, despite evidence of declining returns with the number of financing rounds. Guler (2007) concludes, that the reason for investment decision-making not mirroring returns may be a result of intraorganizational politics, contractual pressures from co-investment partners or investment norms in the industry that may be penalized by syndication networks. The results are supported in the study by Devigne et al. (2016)28 which concludes that international investors are less likely to escalate commitments than domestic investors when a startup is not exhibiting potential, due to lower social and emotional involvement with the project and the local economic and social environment. However, even if some investors may have a tendency to escalate commitments to companies that are not performing to expectations, it is expected that these sums would be smaller in size than follow-on investments to well-performing companies. Gompers (1995) found that companies leading to IPO (i.e. companies with better returns) received in total more financing from the same investors.

H4: Better performing companies receive a larger percentage of investments as follow-on investments than companies that are unsuccessful

2.7. Summary of hypotheses

The hypotheses that are to be examined in this study have been formed by the findings of the literature review. If a phenomenon has been studied to a limited extent, the hypothesis has been

26 Guler (2007) qualitative and quantitative study based on US venture capital investments made between 1989-2004. 27 Investments need to be terminated latest at fund closing, typically after 10 (+1 or 2) years 28 Devigne et al. (2016) is based on analysis of 1618 venture capital investments in 684 European technology companies by 1060 VC firms, with the first investment made during 1994-2004.

formed by studying similar or related phenomena. The hypotheses aim to provide answers to the research questions of this study, i.e. to evaluate the performance and development of the Finnish venture capital landscape, mainly from the perspective of returns. The hypotheses are presented in Table 4.

Table 4: Hypotheses

No. Hypotheses H1 Hypothesis 1: Finnish venture capital performance trails European venture capital performance for older fund vintages (fund vintages < 2002) but has significantly developed after that to be on par with European venture capital performance.

H2 Hypothesis 2: Venture capital investments in Finland have a skewed return distribution with a similar percentage of companies in each return class as in the benchmark studies

H3 Hypothesis 3: Venture capital investment returns follow a power law distribution

H4 Hypothesis 4: Better performing companies receive a larger percentage of investments as follow-on investments than companies that are unsuccessful

3. Data and methods

3.1. Data

The data used for the empirical study is proprietary fund transaction level data collected by Tesi. As a Limited Partner of numerous Finnish funds, Tesi has the right to obtain transaction level data from all investees.

3.1.1. Original data

The dataset used for the study is a combined dataset of two different datasets. The two main datasets consist of year-end Tesi investee portfolio company data for years 2005-2019. The two datasets originally had slight differences in variables and reporting measures, so they have been synchronized and combined, resulting in the dataset of the study.

Dataset 1: Investee portfolio company level data at year end 2005-2018

The dataset is formatted as such, that there is one row for each unique company and investor combination, for each year 2005-2018 (‘Date of record’). The dataset shows the cumulative situation for each company, at each ‘Date of record’.

Example of dataset 1:

First investment [etc. Other Company name Investor Exit date Investment sum Date of record date variables] Example company Investor A fund II 31.3.2002 - 1 000 000,00 € 31.12.2005 Example company Investor A fund II 31.3.2002 - 1 200 000,00 € 31.12.2006 Example company Investor A fund II 31.3.2002 31.6.2007 1 200 000,00 € 31.12.2007 Example company Investor A fund II 31.3.2002 31.6.2007 1 200 000,00 € [year end 2008-2018]

Dataset 2: Investee portfolio company level data at year end 2019

The dataset 2 has the situation at a company level for the year end 2019.

Example of dataset 2:

First investment [etc. Other Company name Investor Exit date Investment sum Date of record date variables] Example company Investor A fund II 31.3.2002 31.6.2007 1 200 000,00 € 31.12.2019

3.1.2. Study scope

The original datasets have been filtered to the scope of the study. The filtering conditions for the data were to include only Finnish investors and only venture capital funds. Finnish investors include all funds that originate from Finland and mainly operate in Finland. Venture capital funds have been defined according to FVCA guidelines, that were discussed in Chapter 2.1. Due to these limitations, funds that invest primarily through other instruments than equity, such as loan vehicles, or funds with a regional development agenda are not a part of the scope.

During the years 2005-2019, Tesi has invested in 33 funds, which are included as part of the study. These 33 funds are operated by 16 different management companies and have made 483 investments. The investments have been made to companies both in Finland and abroad, and all the investments made by Finnish venture capital funds are analysed as a part of the study.

Table 5: Distinct count of scope funds, management companies and investments

Distinct count of Distinct count of Fund vintage Distinct count of funds management companies investments

All fund vintages 33 16 483

3.1.3. Data collection and reporting practices at Tesi

The data used in the study is obtained from obligatory reporting from funds. All recent funds report quarterly, but Tesi still has some older funds in its portfolio (typically fund vintages that are 10 years or older), that report only twice a year. All funds must report according to general industry guidelines defined by Invest Europe. Tesi records the company level data as the General Partners have reported it.

Before 2006, Tesi used Microsoft Excel to track their portfolio of funds and the funds’ portfolio companies. Since 2006 Tesi has used FrontInvest by eFront to track their portfolio. Reports from General Partners are manually entered to the system. Until 2018 Tesi internally entered the report data to FrontInvest twice a year (situation for end of June and end of December). During 2018, they shifted to use an external partner to handle manual data input from GP reports. From the beginning of 2019, data has been entered for the situation at the end of each quarter.

3.1.4. Data validity and quality

Potential inaccuracies may occur in the data due to several reasons:

Issues or mistakes in manual handling of data

Data is manually transferred from fund reports to the system in use. Manual handling of data is susceptible to human errors, which is often hard to discount for in large masses of data.

Tesi aims to prevent these issues by reconciliation, checking and correcting data both at a fund level and company level. Fund level data is checked for each fund, and thus represents the situation at a fund level accurately. Company level data is checked for key value drivers and valuation, especially for the investments that most affect the fund level financials. Thus, it is more probable that manual data entry issues occur at a company level, but they are expected to be minor in nature.

Changes in reporting practices in 2005/2006 and 2018/2019

In 2006, Tesi shifted to use the FrontInvest by eFront program, after using Microsoft Excel as their reporting and data handling tool. Similarly, Tesi’s reporting practices changed in 2018, when they changed to quarterly inputting data for all investee’s portfolio companies. A change in reporting practices can create discontinuation and synchronization issues in the data.

Due to the change in reporting practices 2005/2006, 2005 is the first year for annual data. 31.12.2005 is the first date of record in the data, and it shows the cumulative situation for each company at the year end of 2005. This means, that there is no annual data for investments made before 2005. The first datapoint being the cumulative situation at year-end 2005, means that the specificity of the data does not allow for studying funding rounds or annual development of companies prior to investments made in 2005.

As Tesi changed to an external partner (eFront Insights) in 2018 for logging fund reports to, they were also able to increase the quality of the data. From 1.1.2019, data has been logged quarterly for each fund and company. In the transition, also information and specificity of portfolio

companies was increased, such as logging investment and exit dates according to the specific date, not the quarter of the event.

Of the 483 companies in the study, 221 companies are logged in both datasets (Dataset 1. 2005- 2018 and Dataset 2. 2019), while 262 companies are logged only in the 2005-2018 dataset. However, these 262 companies are all older investments, with the earliest investment year being 1997 and the latest being 2010.

3.1.5. Manually corrected data

The dataset has been manually checked and corrected for several variables. Corrections include both correcting minor reporting issues, such as clearly invalid variable inputs, as well as some systematic issues, such as the differentiation of fund and company names between the two original datasets. Systematic checks have been done on both a company and fund level, to ensure that the data has no significant issues.

For company level variables checks and corrections include synchronizing and checking company names, holding periods, company country and combining data of a company before and after an exit event. Additional scrutiny has been used in checking all over MOIC = 3x company returns, as this group of companies is the smallest subset, but has the most impact on results. For fund level variables, checks and corrections include checking and correcting fund and management company names, fund vintages and fund size29.

3.1.6. Data confidentiality

For confidentiality purposes, the original data is not available to the public. If data is required for research purposes, the researcher may be in contact with Tesi, who can at their discretion allow for additional research to be conducted based on the data. The dataset used in this study has been frozen and saved on Tesi’s servers. All methods for combining, manipulating, and synchronizing

29 Fund size determined from Tesi internal database and Kaupparekisteri-reports

the data have been carefully documented and all changes have been logged, to allow for potential backtracking of analysis and results.

For confidentiality purposes, this research also anonymizes all individual data. Thus, all viewpoints and subsets for analysis have been chosen in a manner that allows for full anonymity of all funds and companies in the dataset. In addition, in all summary statistics any values indicating an individual event have been fully subtracted from the study.

3.2. Variables

The dataset has an individual row for each investment 2005-2019, with data including variables related to performance, the investor, and other characteristics of the investment (see Table 6). An individual investment is the unique combination of an Investor and a Company (variable: ‘Company & Investor’). Thus, if there are several investments into the same company by different investors, these are recorded as multiple different investments. Similarly, if multiple investments are made to the same company by different funds of the same management company, these instances are recorded as multiple different investments. In this thesis, “company” refers to each unique investment (unique ‘Company & Investor’ combination), if not otherwise specified as “distinct company”.

Table 6: Variables for empirical analysis

Variable name Variable explanation Variable specification (acronym) Company name Company name Investor Fund name First Investment Date First investment date Inv currency Investment currency For some companies reported at the specificity level of a Exit date Exit date quarter. For exit years >2010, typically reported actual date. Manually corrected country for companies that have Country Company origin or main operating location changed the location of the company due to an exit event Investments Total sum invested at date of record (EUR) Realizations Cumulative realizations at date of record (EUR) Current Cost Current cost at date of record (EUR) Current Valuation Valuation at date of record (EUR) MOIC Multiple on Invested Capital = (Realizations+'Current Valuation')/Investments RVPI Residual Value to Paid In ='Current Valuation'/Investments DPI Distributed Value to Paid In = Realizations/Investments Management company Fund management company Fund vintage Fund vintage (year of first investment) Date of record Time in point Year end 2005-2019 ID Unique row identifier Number Manually corrected rows include rows with clear Manually corrected Indicator for rows that have been manually adjusted reporting errors Investment year Year of initial investment 2000-2019 for companies that have been exited, empty Exit year Year of exit for companies that have not been exited Exited Dummy variable for exit year Exited = 1 if company has been exited Indicator for row of last point in time for specific Binary 0/1, for older investments 2018, for more recent Last year of data investment investments 2019 Fund size Fund size (EUR) Investor fund size Also referred to as "Size of funding round". If several Invested sum Sum invested (EUR) funding rounds are made during the same year, these are calculated as one Only for date of record entries when an investment was Investment round Investment round number made Company & Investor Individual identifier for each investment Referred to as “company” or “investment” Holding time = Exit date - First investment date. Empty Holding period Holding time of company in years for companies that have not been exited pct_IR1 = IR1 / Investments Investment round 1 (IR1) Amount (EUR) invested on first investment round Both variables only for investment year >= 2005 and for 'Last year of data' = 1 Percentage invested on first investment round of total Both variables for funding rounds 2-6 and a combined Percentage IR1 (pct_IR1) investments to the company by the investor variable for funding rounds 7-12 Years from investment Years from initial investment Years from investment 1 = Year of initial investment

Finland Dummy variable for country = Finland Year 1 = Year of initial investment

IR1 relative to investor IR 1 size divided by average IR1 size of the Investor

4. Results

4.1. Summary statistics

The data covers in total 483 unique company and investor combinations (variable “company & investor”, referred to as “companies” or “investments”). The investments in the study have been made into 433 distinct companies, by 33 distinct investors. The 33 funds (variable “investor”) in this study are from 16 management companies30. The investments and fund vintages both range from 1997 to 2019 and investments have been made to companies in 20 countries.

The company level statistics summary (see Table 7) shows the data for ‘Last year of data’ = 1, meaning the situation at year-end 2018 or 2019 depending on the company. It shows the values as cumulative values for all other variables, than those where a specific investment round is specified.

On average a total of 2,1 million € is invested to the companies in the dataset. The average return (variable “MOIC”) for a company is 1,2 of which half of the value has been realized and half is from current valuation. The average holding period has been calculated only for exited companies, and in the study 320 companies have been exited. The average holding period for companies is 6 years.

The number of observations for ‘Invested sum’ on ‘Investment round’ (variables IR1 – IR7-12) is lower than the number of the observations for the full dataset, as these figures have been calculated for only companies with ‘Investment year’ > 2005. The figures presented in the summary statistics table do not represent the variable accurately, as it includes also very recent investments, that have not yet had the chance to develop and raise further funding.

The sum invested on later funding rounds (variables IR1 – IR7-12) and average percentage invested on later funding rounds (variables pct_IR1 – pct_IR7-12) declines significantly. This is a result of not all companies raising further funding rounds. If a company has not received funding on a specific funding round, the value for the variables in question is 0. These figures are studied

30 Management companies generally aim to collect a new fund after the previous fund has been invested. For example: the general partners of fund ABC have invested 100% of “Fund ABC I”. After this they typically aim to fundraise a new fund, named for example “Fund ABC II”. These are managed by the same general partners (the same “management company”).

in more detail in chapter 4.5. In chapter 4.5 also the Pearson correlation coefficients will be provided to the variables analyzed in the regression model.

Table 7: Company level summary statistics for 31.12.2019

Variable Obs Distinct company & investor 483 483 company name 483 433 investor 483 33 management company 483 16 country 483 20

Variable Obs Mean Std. Dev. Min Max p25 median p75 investments 483 2 133 847 2 229 524 [anonymized] [anonymized] 590 000 1 357 520 3 082 997 realizations 483 1 129 002 4 103 186 [anonymized] [anonymized] 0,00 0,00 379 921 current cost 483 804 137 1 665 744 [anonymized] [anonymized] 0,00 0,00 749 990 current valuation 483 1 411 906 4 511 151 [anonymized] [anonymized] 0,00 0,00 643 184 MOIC 483 1,17 2,57 [anonymized] [anonymized] 0,00 0,71 1,33 RVPI 483 0,61 1,66 [anonymized] [anonymized] 0,00 0,00 0,45 DPI 483 0,56 1,83 [anonymized] [anonymized] 0,00 0,00 1,00 fund vintage 483 2006 7,19 1997 2019 2 000 2 004 2 012 manually corrected 483 0,04 0,20 0,00 1,00 0,00 0,00 0,00 inv year 483 2007 7,12 1997 2019 2001 2005 2014 exit year 320 2009 5,20 2000 2019 2005 2009 2014 last year of data 483 1,00 0,00 1,00 1,00 1,00 1,00 1,00 fund size 483 54 400 000 34 300 000 [anonymized] [anonymized] 28 800 000 56 800 000 69 600 000 holding period 320 6,0 3,4 [anonymized] [anonymized] 3,50 5,50 7,94 IR1 size relative to investor 253 0,99 0,69 0 4,19 0,49 0,85 1,25 IR1 253 997 163 879 747 [anonymized] [anonymized] 350 000 750 000 1 440 000 IR2 253 532 276 681 838 [anonymized] [anonymized] 0,00 254 448 800 000 IR3 253 329 788 564 184 [anonymized] [anonymized] 0,00 0,00 500 000 IR4 253 228 496 463 691 [anonymized] [anonymized] 0,00 0,00 270 727 IR5 253 100 050 271 648 [anonymized] [anonymized] 0,00 0,00 0,00 IR6 253 64 231 241 432 [anonymized] [anonymized] 0,00 0,00 0,00 IR712 253 87 515 551 528 [anonymized] [anonymized] 0,00 0,00 0,00 pct IR1 253 0,59 0,33 0,00 1,00 0,29 0,54 1,00 pct IR2 253 0,21 0,21 0,00 1,23 0,00 0,18 0,31 pct IR3 253 0,10 0,14 0,00 0,74 0,00 0,00 0,17 pct IR4 253 0,06 0,10 0,00 0,56 0,00 0,00 0,10 pct IR5 253 0,02 0,05 0,00 0,29 0,00 0,00 0,00 pct IR6 253 0,01 0,04 0,00 0,38 0,00 0,00 0,00 pct IR712 253 0,01 0,06 0,00 0,48 0,00 0,00 0,00 finland 483 0,73 0,45 0,00 1,00 0,00 1,00 1,00

4.2. Descriptive analytics

4.2.1. Overall financial performance

Overall financial performance of the Finnish venture capital market is defined by studying the variable MOIC. As the analysis includes also recent fund vintages, also the relationship between realized value (DPI) and unrealized value (RVPI) 31 are studied.

MOIC = (Realizations + Current Valuation) / Investments32 DPI = Realizations / Investments RVPI = ‘Current Valuation’ / Investments)

The return multiples are calculated for market level returns, fund level average returns and company level average returns. The key difference between the metrics is, that the market level returns are calculated as actual total investments and returns while company and fund level returns are calculated as average returns, without any weighting for investment or fund size.

Market level Market level analysis represents a market index or general market performance. Calculated as a sum of all investments, returns and current valuation.

For example: Market level MOIC = (Sum of all realizations + Sum of all current valuations) / Sum of all investments

Fund and company level average returns Fund and company level returns are calculated as an average of fund or company returns, without weighting for fund or investment size.

31 The return multiples MOIC, RVPI and DPI are discussed in more detail in Chapter 2.3.1. Performance metrics 32 Also, MOIC = RVPI + DPI.

For example: Fund level average MOIC = Average of MOIC for fund returns33 For example: Company level average MOIC = Average of MOIC for company level returns

Table 8 displays the MOIC, RVPI and DPI for Finnish VC 1997-2019. The returns are grouped by 4 fund vintage groups: fund vintages 1997-2001, 2002-2007, 2008-2013 and 2014-2019. The first vintage group covers a period of 5 years, and the other fund vintage groups cover a period of 6 years.

Table 8: Finnish VC performance 1997-2019

Finnish VC Market level returns Fund level average returns Company level average returns performance MOIC RVPI DPI MOIC RVPI DPI COUNT MOIC RVPI DPI COUNT All vintages 1,19 0,66 0,53 1,18 0,64 0,54 33 1,17 0,56 0,61 483 1997-2001 0,64 0,00 0,64 0,88 0,00 0,88 12 0,86 0,00 0,86 230 2002-2007 1,02 0,33 0,69 0,69 0,20 0,49 5 0,52 0,13 0,39 41 2008-2013 1,71 1,10 0,60 1,76 1,20 0,56 9 1,97 1,33 0,64 99 2014-2019 1,66 1,57 0,10 1,37 1,33 0,04 8 1,31 1,18 0,13 113

Figure 9 shows that market level and fund level average returns have improved for each fund vintage group until the fund vintage group 2014-2019. The development from fund vintage group 2008-2013 to 2014-2019 is on a market level stable, but slightly declining for average company level and fund level returns. However, this is a result of how the figures have been calculated, not an indication that performance is declining. The reported valuation of a company is typically kept as the initial investment valuation if no significant developments or external investor have invested in the company, so for recent investments the MOIC is often 134. Thus, the fund vintages 2014- 2019 have a larger portion of MOIC = 1 companies, and as the company-level and fund-level performance is calculated as an average of the MOIC for companies and funds, this pushes the average downwards. The market level TVPI is not calculated as an average, but as the sum of all

33 As discussed in chapters 3.2.1 Performance metrics and 3.1. Data, fund level returns do not include management fees or other expenses. Thus, the metric used to describe performance is MOIC, and not TVPI. 34 Based on discussions with industry experts. Furthermore, the performance of recent fund vintages cannot be accurately measured or estimated, as during the first four to five years there are no clear differences in financial metrics between ultimately ‘good’ or ‘bad’ funds (Mathonet & Meyer, 2007).

‘current valuations’ and ‘realizations’ divided by ‘investments’ and is thus not affected as much by the phenomena.

The indicative nature for the performance of fund vintages 2014-2019 is also visible by studying the relation of realized profits to unrealized profits. On a market level basis, under 6% of total TVPI has been realized at the date of record 31.12.2019 for the fund vintages 2014-201935. In addition, most of the funds have an ongoing investment period and almost all investments are still active portfolio companies.)

Figure 9: Finnish VC performance 1997-2019

4.2.2. Investment characteristics

4.2.2.1. Investment size and funding rounds

Investment size has been calculated for all companies with data on investment size, which means all initial investments made in or after the year 2005. An investment round is considered as a change in cumulative investments made in a company by the same investor, which is larger than

35 6% not including that investments are still to be made from the fund vintages 2014-2019

10 000 € 36 in size. The data does not record any changes made during the period of a year, so if the company has received several rounds of funding during the same year, these are calculated as one investment round.

Figure 10 shows the average and median investment size for each unique ‘Company & Investor’ combination, segmented by funding round. As the data represents only the first- and follow-on investments made by an investor to the same company, it does not represent or indicate total funding round size. The total sample size for the data is 251 companies and 757 investment rounds.

Figure 10: Average and median investment size for first- and follow-on investments The average size of the investment declines until investment round 5, after which it slightly grows. The count of investments declines throughout the data, as not all companies collect further investments from the same investor. The count of investments grows for funding rounds 7-12 since the variable ‘IR7-12’ is a sum of all investments made during funding rounds 7-12. The median investment size constantly trails the average size of investments, due to skewness of the variable ‘Investment sum’. In Chapter 4.5. the data will be studied further to analyse the average investment size indexed to initial investment size. In addition, analysis will be conducted on the dynamics and effect of first- and follow-on funding on a company and fund level.

36 10 000 € has been chosen as a threshold for investment size, to reduce noise in the data. There are some smaller changes in the data, but these instances largely seem like reporting and reconciliation errors.

4.2.2.2. Time to exit

Of the 483 companies in the study, 320 companies (66%) have been exited, while 163 companies are still current portfolio companies of the investors. Figure 11 shows, that a majority of companies have been exited before they have been 6 years in the portfolio. For MOIC <= 1x companies and 1x <= MOIC <= 3x companies, 50% of exits happen between 2 to 6 years from the initial investment.

Despite a fund lifecycle typically being 10 years, there seems to regularly be companies that exceed the 10-year mark, especially in the group of companies with MOIC <= 1x. This indicates that it is difficult for fund managers to effectively exit these companies. For MOIC <= 1x, 1x < MOIC <= 3x and MOIC >3x the 90th percentile of holding time is 11,0 years; 9,0 years and 8,4 years, respectively.

For exited companies with MOIC > 3x, the most companies have been exited in less than 2 years after the initial investment. The number of observations is significantly lower for MOIC > 3x companies, which causes chance variation in the data. In addition, the group of MOIC > 3x companies includes a case of several investors investing in the same company which was exited in less than 2 years for all investors. Thus, the distribution of holding period would look different if analysis were made based on distinct companies, not based on unique company and investor (variable ‘Company & Investor’) combinations.

Figure 11: Exited companies by MOIC and holding period The variable holding period has a mean of 6,00 with a standard deviation of 3,44. Table 9 shows the mean holding period for companies grouped by MOIC. The chart also includes the pairwise correlations of holding period and MOIC, calculated both for all exited companies and companies by their MOIC-group.

Table 9: Mean MOIC and holding period of exited companies

Pairwise correlation Mean holding Count of holding time and period (years) MOIC All companies 483 All exited companies 320 6,00 -0,086 MOIC <= 1x 234 7,32 0,168*** 1x < MOIC <=3x 60 5,71 0,023 MOIC >3x 26 4,70 -0,071

*** p<0,01, ** p<0,05, * p<0,1

The declining mean holding period for better performing companies indicates that on average a higher performing company is exited earlier than an unsuccessful investment (MOIC <= 1x). This observation is not supported by calculating pairwise correlation coefficients, as there is no statistically significant correlation when analysing the full dataset of 320 companies.

For the subgroup of companies exited that have MOIC <= 1x, there is statistically significant positive correlation (0,168), with a p-value under 0,01. This would mean, that the longer a MOIC <= 1x performing company is in the portfolio, the higher its exit value will be, although still a maximum of MOIC = 1. This may indicate that clearly unsuccessful investments are exited earlier, than companies that may still have potential to return the initial investment.

At the date of record 31.12.201937, there were 163 companies still in the portfolios of the investors. A majority of the current portfolio companies are recent investments, as 56% of the current portfolio companies have received the initial investment during the past four years from the date of record 31.12.2019. Figure 12 shows the MOIC of the current portfolio companies grouped by their holding period.

Figure 12: Current portfolio companies 31.12.2019 by MOIC and holding period

For the investments made during the past two years, 73% of the companies still have a MOIC <= 1x. The average MOIC for these companies is 0,95x, hence nearly all the companies in this MOIC- group have a MOIC = 1. As discussed earlier, this is expected, as if no significant developments

37 Date of record 31.12.2019 refers to the “Current” moment

have occurred or external investors invested in the company, the reported valuation is typically kept as the initial investment valuation.

The investments made between 2-4 years (31.12.2015-31.12.2017) before the current point in time, indicate strong potential for these companies. 84% of these companies have developed so that the MOIC is over 1x. For investments made 4-8 years ago, the percentage of unsuccessful investments (MOIC <= 1x) and very successful investments (MOIC > 3x) has grown compared to earlier years. Investments made 8-10 years ago do not indicate strong potential, but investments made over 10 years ago indicate that some highly potential investments are possibly waiting for the right exit opportunity. MOIC development of a company is studied in further detail in Chapter 4.4.

4.2.3. Investments in Finland and abroad

4.2.3.1. Performance comparison of investments to Finland and abroad

The investors in the study have made a total of 131 investments abroad, representing 27% of all investments made. Table 10 shows the market level realized and unrealized returns for investments made to companies in Finland and companies abroad. For market level MOIC is 7% lower for investments made abroad, compared to investments made into Finnish companies. The lower MOIC is affected especially by the weak performance of investments made abroad by fund vintages 2001 and earlier. For more recent fund vintages, the difference in performance between investments to Finland and abroad has been decreasing, and for fund vintages 2008-2013, investments made abroad performed 8% better than investments made in Finland. For the youngest fund vintage group, the data is indicative.

Table 10: Performance of investments to Finland and abroad

Market level Investments in Finland Investments abroad returns Fund vintage MOIC RVPI DPI COUNT MOIC RVPI DPI COUNT All vintages 1,22 0,62 0,60 352 1,13 0,74 0,39 131 1997-2001 0,76 0,00 0,76 175 0,42 0,00 0,42 55 2002-2007 0,59 0,01 0,58 35 2,95 1,75 1,20 6 2008-2013 1,67 1,03 0,64 68 1,79 1,27 0,53 31 2014-2019 1,71 1,56 0,14 74 1,59 1,58 0,01 39

4.2.3.2. Percentage of investments to Finland and abroad

The sum of investments made to Finnish companies has ranged from 64% to 69%, and the count of investments has ranged from 65% to 76% excluding the vintage group 2002-2007 due to a very small sample size (see Figure 13). According to the data, there is no clear intention of Finnish funds trying to increase the amount of investments abroad as the levels of investments to Finland have stayed relatively stable for fund vintages 2008-2013 and 2014-2019. The figures for fund vintages 2014-2019 are still indicative as the funds are still in the phase of investing, so conclusive assumptions cannot be made.

Fund vintages 2002-2007 have invested significantly less abroad than other fund vintages. The dotcom bubble may have a role in this. As the dotcom bubble was a difficult environment to navigate for fund managers due to the volatile market conditions, it may be that after the turbulent times, fund managers preferred to invest in the market they were the most familiar with, meaning Finland.

Figure 13: Investments by region and fund vintage

4.2.4. Fund characteristics

The median count of portfolio companies is analysed for fund vintages 1997-2007 and 2008-2014. The latter vintage group is capped at the fund vintage 2014 to ensure result validity. Fund vintages 2015 onwards, were still in the process of making initial investments, and thus the current portfolio size would not reflect the actual portfolio size.

The median company count is 12 for both older and younger fund vintages, but the later fund vintages have less spread in the company count (see Figure 14). The younger fund vintages have on average smaller portfolios than the older fund vintages, as the average of company count has decreased from 16 to 14. This is reflected especially in the upper quartile of portfolio count. While for fund vintages 1997-2007 the upper quartile for company count is 21, for fund vintages 2008- 2014 the upper quartile is 16. The average fund size has grown 76% from 36 million EUR to 63 million EUR from fund vintages 1997-2007 to fund vintages 2008-2019. The growth in fund size is in align with the industry statistics of Finnish VCs fundraising larger funds during the last 10 years (FVCA, 2020a).

Figure 14: Fund portfolio company count and size in euros

4.3. Return distribution

4.3.1. Methodology

The MOIC distribution has been calculated for companies based on their exit MOIC, if the company has been exited, and the MOIC at ‘Date of record’ = 31.12.209 if the company has not been exited. The MOIC distribution is referred to as the “Return distribution”. As the analysis includes unrealized profits, the figures may not accurately represent the actual returns once all profits for the funds and companies are realized.

4.3.2. Return distribution for companies by investment year

For companies with the initial investment made in 1997-2007, the total sum invested is 500 million EUR, of which 310 million EUR has been returned to investors and 0,6 million euros is unrealized value. The market level MOIC for investments made 1997-2007 is 0,63, of which under 1% is still unrealized profits. Thus, this subset of companies accurately represents actual returns.

66% of the investments made in 1997-2007 returned less than half of the total money invested, and 9% of investments returned 0,5-1x the investment. 15% of the investments returned between 1-2x, 7% between 2-5x and 3% of investments returned over 5x. The return distribution is presented in Figure 15.

For companies with the initial investment made in 2008-2019, the total sum invested is 530 million EUR, of which 236 million EUR has been returned to investors and 681 million EUR is still unrealized value. The market level MOIC for this subset of companies is 1,73, of which 74% is still unrealized value. The return distribution for these companies is highly indicative, as a majority of the value is still unrealized value.

For the investments made in 2008-2019, 26% of companies have returned or are valued at under 0,5x and 31% of companies have returned or are valued at 0,5-1x. 25% of companies have returned or are valued at 1-2x, 15% between 2-5x and 4% over 5x.

Figure 15: Return distribution of companies The EIF data (Prencipe, 2017) for 2065 early-stage investments made between 1996-2015 by EIF backed VC funds shows that 78% of the companies returned under 1,2x, 17% of companies 1,2- 5x and 4% of companies over 5x. Compared to this datapoint the investments made between 1997- 2007 perform on par with the European venture capital landscape and the investments made by Finnish VC’s between 2008-2019, are outperforming the EIF investments. This is especially due

to the lower percentage of companies that have a MOIC <= 1x. While nearly 80% of the EIF investments returned under 1,2x, the percentage of companies under 1x for investments made by Finnish VC’s is only 56%. This has translated to a significantly higher portion of companies that are performing relatively well, meaning companies that have returned or are valued at 1-5x. The percentage of companies exited or valued at over 5x is 4% in both the EIF data for investments made in 1996-2015 and the Tesi data for investments made 2008-2019. Thus Hypothesis 2: Venture capital investments in Finland have a skewed return distribution with a similar percentage of companies in each return class as in the benchmark studies, is supported.

4.3.3. Return distribution for funds by fund vintage

Figure 16 displays the return distribution for fund vintages 1997-2007 and fund vintages 2008- 2013. The return for fund vintages 2014-2019 has not been presented, as these funds have on average invested only 34% of the total fund size, while fund vintages 2008-2013 have invested on average 80% of the total fund size. The percentage of unrealized returns is 5% for fund vintages 1997-2007 and 65% for fund vintages 2008-2013.

Figure 16 shows the development of returns on a fund level for fund vintages. The improving company level returns have translated to better performing funds for later fund vintages. While for fund vintages 1997-2007, 71% of funds returned less than or equal to 1x, this has declined to only 25% of fund vintages 2008-2013 performing at a level of under 1x returns.

Figure 16: Fund return distribution by vintage group The performance of Finnish venture capital funds is below the performance of European venture capital funds for fund vintages 1997-2007, but the performance of Finnish venture capital funds has evidently developed for fund vintages 2008-2013. The EIF study published in 2017 presents the figures for average TVPI by fund vintage for the years 1997-2015. The average TVPI for all EIF investee fund vintages 1997-2007 was 0,92 and 1,27 for fund vintages 2008-201338 (EIF, 2017). The average MOIC for Finnish venture capital funds for fund vintages 1997-2007 is 0,82 and 1,76 for fund vintages 2008-2013. The returns are not comparable as such, since the EIF measures TVPI, which is net of fees, while the analysis on Tesi data is calculated without accounting for fees or other costs related to the fund’s operations. However, the Tesi-published figures in 2018 showed a TVPI of 1,3 for fund vintages 2009-2015 (Talouselämä, 2018), which indicates on-par performance with European venture capital. However, as the data for EIF- published figures is calculated as an average of different fund vintages and not as a pooled TVPI for the different fund-vintages, and the studies do not have an equal amount of years for the fund vintages to develop, a conclusive statement cannot be made. Thus Hypothesis 1: Finnish venture capital performance trails European venture capital performance for older fund vintages (fund

38 The average TVPI was not presented for those fund vintages that had a limited count of observations. The average TVPI has been calculated as the average of ‘Average TVPI’ for the fund vintages included in the group. The analysis includes both exited and current portfolio companies.

vintages < 2002) but has significantly developed after that to be on par with European venture capital performance, is partially supported.

4.3.4. Risk profile of venture capital investments

From a Limited Partner perspective, the risk profile of an asset class is a key aspect of asset distribution decision-making. Figure 17 shows the risk profile of venture capital investments for fund-vintages 2008-2013. The fund vintages 2008-2013 have been chosen for the analysis, as it is the latest group of fund vintages that can be relatively reliably studied in terms of performance. There are in total 8 funds in the vintage group, and they have made in total investments to 99 companies. The market level MOIC for the fund vintages 2008-2013 is 1,71 of which 65% of returns have been realized.

Figure 17: Risk profile for venture capital investments for fund vintages 2008-2013 The analysis provides initial verification to what Weidig and Mahonet (2014) argue. Individual venture capital investments are risky assets, but investments diversified into the market are not necessarily so. From a Limited Partner perspective this means, that investing in a Fund-of-Funds vehicle, is significantly less risky, than investing straight into startups or a small amount of funds.

4.3.5. Power law analysis

It has been established, that the return distribution of venture capital investments does not follow a normal distribution but is significantly skewed. In the power law analysis, companies with the investment year 2015 or earlier will be studied, to allow for a minimum of 4 years of value development for a company. The returns will be analysed by the final MOIC, meaning the MOIC at exit or at 31.12.2019 if the company has not been exited.

The scope includes 388 investments, with a mean MOIC of 1,16 and a standard deviation of 2,86. The skewness of the variable is 7,15 and the kurtosis 69,33. The skewness and kurtosis are clearly visible from the cumulative distribution function displayed in Figure 18. For a distribution to be consistent with a power law distribution, a log-log cumulative distribution function approximates a linear function with slope alpha. Figure 19 shows the cumulative distribution function for the variable logMOIC. ‘logMOIC’ has been generated by taking the logarithm of MOIC for all companies with MOIC >=1. The log-log cumulative distribution function of MOIC for this subset of companies does not approximate a straight line, but when analysing a smaller subset of the tail the log-log plot approximates a linear function. Through this empirical testing it is concluded, that for returns with MOIC >=3x, the log-log plot of the CDF distribution approximates a linear function, and the values appear to exhibit power law behaviour. This function is not presented in the thesis, as it makes individual events susceptible to loss of anonymity.

MOIC

Figure 18: Cumulative distribution function (CDF) for MOIC39

Figure 19: Cumulative distribution function for logMOIC40

Thus, the return distribution will be tested for a power law fit, for MOIC multiples over 3x. For testing whether the distribution of MOIC follows a power law fit, the simple test for power-law behavior by Urzua et al. (2020) will be used. The Urzua (2020) test provides the test result ‘PWL’41

39 The values of the x-axis have been hidden to preserve the confidentiality of the data 40 The values of the x-axis have been hidden to preserve the confidentiality of the data 41 The calculation and methodology for PWL can be studied in more detail from Urzua, et. al (2020)

and the p-value for the test. For the test, the lower limit of the variable is defined, at a point which is estimated as the lower bound of power law behavior. This is MOIC = 3 in the sample. The power law test returns PWL= 0,780 with a p-value of 0,677. Given the p-value, it indicates that there is good evidence that the return distribution follows a power law distribution, as the null hypothesis would not be rejected at typical significance levels.

Virkar and Clauset (2014) argue, that a large p does not necessarily imply the correctness of a power law distribution in the data, as it may be that other distributions would fit the data better, or that the sample of observations is too small to distinguish a power law fit from empirical data. To confidently state that returns would follow a power law distribution, it would need to be determined, that other distributions do not fit the data better, such as a lognormal distributions or exponential distributions. However, Virkar and Clauset (2014) present that as a “rule-of-thumb” the size of the sample should be larger than 300 observations to distinguish a power law distribution from a lognormal distribution, which is not possible from the dataset used for the analysis.

Thus, Hypothesis 3 does not receive conclusive support from the analyses. Venture capital investments follow a highly skewed distribution, that exhibits some elements of a power law distribution in companies with MOIC >3x, but this cannot be comfortably determined from the small sample size.

4.4. Company MOIC development after investment

4.4.1. Methodology

Company level MOIC development was calculated by creating a new variable ‘Years from investment’ that indicates the years from investment. The MOIC is then indexed to the variable ‘Years from investment’ instead of the ‘Date of record’, with the year of the initial investment being ‘Years from investment’ = 1. As the analysis requires data on the annual change of MOIC, the analysis cannot be calculated for investments made before 2005. For the investment years 2005-2019, there are a total of 253 companies. These 253 companies have a total of 1250 instances of MOIC data for ‘Years from investment’ 1-10. An individual company has on average 5,1 instances of MOIC data. The MOIC for the variable ‘Years from investment’ has been calculated as the average MOIC of the companies, and the quartiles for MOIC have been calculated as exclusive quartiles.

The MOIC development is measured for each distinct ‘Company & Investor’ and it does not include data of external investor participation. accounts. Despite the data not including information of external investor participating, the valuation at year-end accounts for also changes in valuation due to new investors investing in a company.

The MOIC is calculated for the companies with data for the variable ‘Years from investment year’. If a company is exited, the MOIC is not calculated for years after the exit year. Similarly, if the ‘Investment year’ + ‘Years from investment’ variable - 1 is greater than 2019 (last year for data in the study) the MOIC is not included for any further years.

The case of exit year is demonstrated with Example company 1 in Table 11. The company raised its initial investment in 2009, for which the MOIC was 1. The company then developed and was exited at 3x in 2011. Thus, the MOIC-values are calculated for Year 1 (‘Investment year’), Year 2 (‘Investment year’ +1) and Year 3 (‘Investment year’ +2). For Year 4, the MOIC is left empty, as (‘Exit year’ – ‘Investment year’ +1) < 4 (‘Years from investment’ = 4).

The case of a company still in the portfolio at the end of year 2019 is demonstrated by Example company 2. The company has MOIC data for years 2016, 2017, 2018 and 2019. Thus, there is MOIC data only for ‘Years from investment’ 1-4.

Table 11: Methodology for indexing MOIC to investment year

MOIC - Years from investment year Investment year Exit year 1 2 3 4 5 [etc] Example company 1 2009 2011 1 2 0,5 Example company 2 2016 N/A 1 1,1 1,5 3

For the analysis on how companies have developed by MOIC, the companies have been grouped by their MOIC at 31.12.2019 or at exit year. Thus, ‘Example company 1’ would be classified in the group MOIC <= 1 and ‘Example company 2’ in the group 1 < MOIC <= 3.

4.4.1. MOIC development for investments by investment year

Figure 20 demonstrates the MOIC development for companies with investment year 2005-2019, and the subgroups 2005-2011 and 2012-2019. The MOIC development for investments 2005-2019 shows that the majority of companies (quartiles 1-3) see no MOIC development for the first two years after investment. On year 3, the upper quartile of companies starts seeing MOIC development, but the upper quartile of MOIC starts growing more significantly only on Year 6. The downside potential is visible slightly later than the upside potential. For the investments made between 2015-2019, the lower quartile of MOIC decreases below the initial MOIC on Year 4. The median MOIC stays at 1 until year 8. This is largely due to companies being valued at MOIC=1, if no significant positive or negative developments happen in the company.

Figure 20: MOIC development for companies by investment year Figure 20 also shows the development of the venture capital market from investments made in 2005-2011 to investments made 2012-2019. The lower quartile, median and upper quartile of MOIC is higher for investments made 2012-2019 compared to investments made 2005-2019, for all ‘Years form investment’. The positive development is visible both in the lower quartile and upper quartile of MOIC. For the investments made 2012 to 2019, the lower quartile of MOIC is consistently higher and never drops below MOIC = 0,5. This could indicate that investors are able to return a majority of the invested sum for investments that are non-successful (MOIC < 1x). On a fund level this would mean that Limited Partners would have a smaller risk of losing the whole investment, even if the fund is not able to find a highly successful company (MOIC > 5x) to their portfolio.

For investments made between 2012-2019, the upper quartile of MOIC starts developing significantly quicker than for investments made 2005-2011.Table 12 shows the situation for ‘Years after investment’ =5. For investments made between 2012-2019, the upper quartile of MOIC is 60% higher than the upper quartile of MOIC for investments made between 2005-2011. The data indicates that more recent investments have significantly more potential than earlier investments and that more recent investments have started showing upside potential sooner after the investment.

Table 12: MOIC by investment year, Years after investment = 5

MOIC for ‘Years after 2005-2019 2005-2011 2012-2019 investment’ = 5

Lower quartile 0,3 0,2 0,5

Median 1,0 1,0 1,0

Upper quartile 1,4 1,1 1,8

Count of companies 253 82 171

4.4.2. MOIC development for investments grouped by MOIC

Positive MOIC development is visible sooner after the investment than negative MOIC development, as can be seen from Figure 21. The MOIC-group 1 refers to the companies with final MOIC <= 1x, MOIC-group 2 to the companies with final MOIC > 1 and MOIC <=3, and MOIC- group 3 to the companies with final MOIC > 3x 42.

42 MOIC group calculated by the exit MOIC or MOIC at 31.12.2019 if the company has not been exited. This MOIC is called the “Final MOIC”.

Figure 21: MOIC development for companies with investment year 2005-2019, by MOIC-group

For the MOIC-group 1 companies, the median final MOIC is 0,26 and the average final MOIC is 0,47. The lower quartile of companies has a declining MOIC from Year 3 to Year 5, after which the MOIC stays under 0,05 for years 6-10. The median MOIC is 1 until Year 4, after which it starts declining until Year 9. The upper quartile of MOIC never develops past MOIC = 1, but stays at the 1x level until year 8 after which it declines significantly. The 90th percentile of MOIC stays mainly between the range of 1,1 and 1,4. The average MOIC declines linearly from year 3 onwards.

The average final MOIC is lower than the average MOIC of ‘Years from investment’, until Year 9. This indicates discrepancy between the reported MOIC to actual exit MOIC, as the average holding period for MOIC <= 1x companies is 7,32 (see chapter 4.2.2.2. Time to exit). The increasing median and upper quartile MOIC on Year 10 are a result of exits43 of many MOIC = 0 companies on Year 9, which are thus not in the statistics anymore for Year 10.

For companies in the MOIC-group 2, the interquartile range of MOIC mainly stays between 1 and 2 for all ‘Years after investment’. Despite this, the median value of MOIC develops linearly from Year 2 to Year 10. While in Year 2 the median value of MOIC is 1, the median value of MOIC is 1,6 on Year 10. For this group of companies, the median holding period for exited companies was 5,71.

Similarly, to the other MOIC-groups, MOIC-group 3 companies see little MOIC development during Year 1 and Year 2. During years 3 to 4 the median MOIC stays a 1,4 but the upper quartile starts developing significantly, growing to MOIC = 3,5 on Year 4. The median MOIC increases for almost all years, reaching 4,6 on Year 10. Despite the median MOIC growing until Year 10, the average MOIC peaks at Year 7, after which it starts declining. The mean holding period for companies exited with a MOIC of over 3 is 4,70 years. As both the median and average MOIC continue to grow past this, exiting the companies at a slightly more mature state may result in better returns. However, as the average MOIC declines in the data after Year 7, it indicates that the investors cannot trust only in holding a highly potential company in the portfolio as long as possible. The investor needs to carefully assess the best timing for an exit, keeping in mind both the fund lifecycle and the exit opportunities available at a current point in time and in the future.

4.5. First- and follow-on funding

4.5.1. Methodology

First and follow-on funding as an investment strategy has been analyzed by descriptive analytics and regression modeling.

43 An exit can also mean a write-off of the company.

The data for the analysis on first- and follow-on funding has been calculated from the ‘Company level’ -data. The initial investment is represented by the variable ‘Investment round 1’ (IR1). The following investment rounds have been calculated as a change of over 10 000 EUR in the sum of investments in the company. The specificity of the model is limited by the original data displaying the situation for every company at a year-end level, not at an investment round level. Hence, if the company has raised multiple investment rounds during the same year, these are grouped as one investment round in the data. As previously, a company refers to the variable ‘Investor & Company’, meaning the unique combination of ‘Investor’ and ‘Company’.

The analysis for MOIC subgroups has been calculated by the MOIC at ‘Date of record’ = 31.12.2019, which is also the exit MOIC if the company has been exited. The analysis has been made for companies with investment year44 2005-2015. The lower limit for companies’ investment year is defined by the requirement of annual data, which the dataset does not have for years before 2005. The upper limit of 2015 for companies is to allow for sufficient data on possible further funding to the companies.

The analysis for first- and follow-on funding also includes a new variable ‘IR1 size relative to investor’. This variable measures the investment round 1 size in €, relative to the average size investment the investor makes on investment round 1. ‘IR1 size relative to investor’ is calculated for each company by dividing the Investment round 1 (EUR) by the average Investment round 1 (EUR) of the investor.

4.5.2. Statistics for first and follow-on funding

56% of companies raise more than 3 subsequent investment rounds from the same investor. Figure 22 displays the percentage of companies collecting further investment rounds from the same investor, grouped by MOIC. The percentage of companies raising funding rounds from the same investor has been calculated by dividing the count of investments for each investment round by the count of companies raising the first investment round. All companies have raised at least one

44 Investment year refers to the year of the initial investment. All further investment rounds are included in the analysis for the companies with the initial investment year 2005-2015.

investment round, so the percentage of companies raising investment round 1 is 100%. It then declines as not all companies receive follow-on investments from the same investor. The MOIC- group45 is defined by the MOIC at date of record 31.12.2019, and this is the MOIC that is referred to in the analysis. As the latest investment year in the study is 2015, it may be that some companies will still develop to collect further investments from the same investor. This would affect the actual percentage, but the effect on the differences between MOIC-groups is estimated to be small, as it affects companies of all MOIC-groups relatively similarly.

For investment rounds 2-4 there are only slight differences in the percentage of companies collecting further investment rounds when analyzing by the different MOIC-groups. For investment round 5 and onwards, the percentage of companies raising further investment rounds from the same investor declines more steeply for the group of companies with MOIC <= 1x than for the companies that have a MOIC > 1x.

The data in the analysis does not allow for studying the actual percentage of companies raising follow-on funding rounds, as it does not include external investors. Especially when companies develop positively and show high potential, they typically raise larger funding rounds. Larger funding rounds often require the participation of new, later stage investors, for the company to get access to larger investment sums. The initial investors may either be a part of these funding rounds or not.

45 MOIC-groups refers to the following groups: companies with MOIC < 1x (MOIC-group 1), companies with MOIC > 1x and MOIC <= 3x (MOIC-group 2) and companies with MOIC >3x (MOIC-group 3)

Figure 22: Percentage of companies collecting follow-on investments from the same investor

The average percentage of money invested on ‘Investment round 1’ is 46% of total investments made to a company by the same investor. The differences between the different MOIC-groups are relatively small. For all MOIC-groups, 41% to 49% of funding is on average invested on the first investment round. Figure 23 shows the average percentage of funding invested on investment rounds 1-6 and investment round 7 onwards. The average percentage of funding by investment round is calculated for only the companies that have received funding on the investment round. The percentage of funding invested after the initial investment round declines until investment round 5, after which it slightly rises. On investment round 7 and later, on average 15% has been invested for MOIC-group 1 companies, 20% for MOIC-group 2 and 12% for MOIC-group 3. These figures are indicative, as the amount of observations for these investment rounds is small.

Figure 23: Average percentage of funding invested on investment round

Figure 24 shows the interquartile range, average and median for the average percentage of funding invested on the first investment. The average percentage of funding invested on the first investment round describes how much an investor has invested in a company after the initial investment. The quartiles and median for first round average investment percentage have been calculated as exclusive quartiles for the average percentage of first round investments for the subset. The first round investment percentage for any company has been calculated by dividing the sum invested on the first investment round by the sum of all investment in the company (‘IR1’ / ‘Investments’), by the same investor. For companies where the investor has invested only on the first round, the percentage invested on funding round 1 is 100%, and if the investor does additional investments in the company, the percentage invested on investment round 1 declines.

The median of average first round investment percentage is 38%. For MOIC <= 1x, 1x < MOIC <= 3x and MOIC > 3x companies the median average investment percentage for investment round 1 is respectively 41%, 37% and 30%. The chart displays that a majority of companies with MOIC < 1x get over 50% of total funding on later investment rounds than investment round 1. However, the chart also displays, that on average better performing companies receive a larger percentage of funding on later funding rounds than unsuccessful investments (MOIC < 1x).

Figure 24: Quartiles, average and median of first round average investment percentage

This is also reflected when studying the average investment size invested on an investment round. Figure 25 shows the average investment size for different investment rounds, where the investments have been indexed to the size of the initial investment. The average investment size has been calculated for those instances, where a company has received funding from the initial investor on the investment round in question. Contrary to the earlier graphs, the last investment round depicts the average investment size for round 7, not for rounds 7-12.

For the MOIC < 1x companies that have raised funding on investment rounds 2-4, the average investment size on investment rounds 2 and 3 has been larger in size than the initial investment, and only the fourth and following investment rounds show the average investment size declining under the initial investment. The average investment size for MOIC-groups 2 and 3 is higher than the initial investment for investment rounds 2-4, after which there is more fluctuation in the average investment size.

Although unsuccessful investments receive funding on a similar number of different investment rounds than successful investments, the average size of follow-on investments is smaller for unsuccessful companies. As external investor participation is not studied, it may be that for MOIC <= 1x companies, the ticket size of the initial investors makes a large share of total funding raised

by a company. On the other hand, for companies that are successful and show strong potential, the amount of money available is often more than the investment round size, and thus the initial investors may not be able to invest more than a pro-rata basis allows them to 46.

Figure 25: Average investment size indexed to investment round 1

4.5.3. Regression model

The initial analysis corroborates the findings of Gompers (1995): the advantages of staging investments is only realized if the investor is effective at terminating unsuccessful investments based on new information. To understand the phenomena deeper and test the Hypothesis 4, regression analysis will be conducted on the dependency of MOIC and percentage first round funding (pct_IR1).

The objective is to find out whether the percentage invested in a company on its first funding round affects performance. The dependent variable is MOIC, as it represents the performance of an investment. The independent variables are holding time, IR1 relative to investor, fund size, country (Finland or abroad) and investment year. All variables used in the model are summarized in Table 13. The Ordinary Least Squares (OLS) regression model shows that the percentage invested on

46 Based on discussions with industry experts

investment round 1 does not have a statistically significant correlation with MOIC. Thus, it cannot be concluded that better performing companies would receive more follow-on investments and Hypothesis 4 is rejected. The OLS regression results are presented in Table 14. The models have been calculated with companies that have ‘Investment year’ 2005-2015 and with robust standard errors to allow for some heteroscedasticity in the variable MOIC, as it is not normally distributed. Model 1 is calculated with the variable pct_IR1 and Model 2 with the variable pct_IR4-12. Pct_IR4-12 indicates the amount of funding a company has received on rounds 4-12, as a percentage of total funding the company has received. It is studied, as it provides an angle to the amount of funding a company has received on later funding rounds. A high pct_IR4-12 would indicate that the investments to the company have been weighted towards funding round 4 and later funding rounds.

As investment year, exit year and fund vintage have strong correlations which each other, only investment year was chosen to the regression model as an independent variable. As has been studied in Chapter 4.2.1. investments that have been made later perform better than earlier investments. Thus, the investment years have been included as dummy variables in the regression model. The regression model shows that investment years 2009, 2012, 2014 and 2015 correlate positively with MOIC, with a p-value under 0,01. It validates the earlier observation, that the Finnish venture capital market performance has improved during more recent years.

The variable ‘IR 1 relative to investor’ is studied to assess whether there is indication, that an investor invests larger initial investments to companies that eventually perform well. The variable ‘IR1 relative to investor’ measures the size of the initial investment compared to the average size of an initial investment by the investor47. As ‘IR 1 relative to investor’ has significant correlation with pct_IR1 and pct_IR4, the multicollinearity is tested by studying the Variance Inflation Factors (see Table 15). The VIF is under 10 for all variables, so multicollinearity should not be a significant issue in the models.

47 ‘IR 1 relative to investor’ is calculated by dividing the size of the initial investment to a company with the size of the average initial investment made by the investor. Thus, a value over 1 would implicate a larger than average initial investment made by the investor, and vice versa.

Fund size48 and Finland are independent variables that have been included to add specificity to the model. They have no significant correlation with any of the other variables, and do not offer explanations for performance in the regression models.

Table 13: Variable summary statistics and Pearson correlation coefficients

Variables Obs Mean Std. Dev. 1 2 3 4 5 6 7 8 9 10 1 MOIC 156 1,6 4,0 1,000 2 investment year 156 2 011 3 0,163** 1,000 3 exit year 156 1 098 1 006 -0,143* -0,382*** 1,000 4 fund vintage 156 2 009 3,8 0,200** 0,894*** -0,412*** 1,000 5 fund size 156 48 700 000 28 100 000 -0,023 0,114 -0,024 0,048 1,000 6 finland 156 0,71 0,46 0,032 -0,064 0,030 0,050 -0,197** 1,000 7 holding time 156 6,19 2,86 -0,027 -0,354*** -0,428*** -0,200** -0,133* 0,066 1,000 8 pct_IR1 156 0,46 0,31 -0,024 0,072 0,382*** -0,013 -0,201** 0,028 -0,381*** 1,000 IR1 size relative to 9 156 0,99 0,68 0,095 -0,012 0,026 -0,018 -0,012 0,043 investor 0,011 0,290*** 1,000 10 pct_IR4-12 156 0,17 0,20 -0,009 -0,058 -0,333*** 0,087 0,189** -0,014 0,435*** -0,679*** -0,164** 1,000 *** p<0.01, ** p<0.05, * p<0.1

48 Fund size is often studied by transforming the variable using a natural logarithm. This was also tested, but the regression model did not offer any additional benefits to the models presented in the study, and is thus not presented.

Table 14: Regression model

Model 1 Model 2 Robust Robust Dependent variable: Beta Beta Standard Standard MOIC Coefficient Coefficient Error Error pct_IR1 -1,166 0,791 pct_IR4-12 0,137 2,065 holding time -0,028 0,147 0,027 0,186 IR1 relative to investor 0,579 0,836 0,431 0,925 fund size 0,000 0,000 0,000 0,000 country finland 0,230 0,607 0,254 0,618 investment year 2006 -0,187 0,513 -0,403 0,620 2007 -0,327 0,577 -0,333 0,540 2008 0,783 0,784 0,659 0,706 2009 1,406** 0,697 1,271* 0,697 2010 0,884 0,539 0,876 0,556 2011 0,571 0,529 0,547 0,459 2012 3,729** 1,802 3,618** 1,783 2013 2,566 1,975 2,689 2,142 2014 1,419*** 0,496 1,364*** 0,455 2015 1,177** 0,535 1,135** 0,550

Constant 0,612 1,225 -0,241 0,851

Observations 156 156

F-statistic 2,74 3,07 R-squared 0,097 0,092 Root of the MSE 3,959 3,971

*** p<0,01; ** p<0,05; * p<0,1

Table 15 Variance Inflation Factors

Variable Model 1 Model 2

pct_IR1 1,53 pct_IR4-12 1,45 holding time 1,85 1,84 IR1 relative to investor 1,21 1,12 fund size 1,36 1,35 country finland 1,08 1,08 investment year 2006 1,82 1,79 2007 1,81 1,81 2008 2,14 2,15 2009 2,23 2,21 2010 1,61 1,63 2011 1,42 1,43 2012 2,07 2,06 2013 1,94 1,94 2014 2,62 2,62 2015 1,87 1,87

Mean VIF 1,77 1,76

5. Discussion and conclusions

5.1. Discussion of the results and implications

The aim of this study was to understand the development and performance of the Finnish venture capital market. As there are very limited prior published studies about the Finnish venture capital landscape’s performance, this study is hopefully able to shed some light on the return dynamics of the market. The study was made with the help of Tesi, and by using their extensive and unique dataset as a basis for analysis. The research questions were formulated so, that they would provide as much as possible meaningful insight to the VCs and LPs operating in the market.

The research questions focused on three interlinked phenomena: the Finnish venture capital market performance, the distribution of returns, and the value development and first- and follow-on funding of companies. The theoretical part of the study was the literature review. The literature review was used as the basis for the hypotheses, and as support in answering the research questions. Table 16 shows the hypotheses results.

Table 16: Hypotheses results

No. Hypotheses Result

H1 Hypothesis 1: Finnish venture capital performance trails European Partially supported venture capital performance for older fund vintages (fund vintages < 2002) but has significantly developed after that to be on par with European venture capital performance.

H2 Hypothesis 2: Venture capital investments in Finland have a skewed Supported return distribution with a similar percentage of companies in each return class as in the benchmark studies

H3 Hypothesis 3: Venture capital investment returns follow a power law Partially supported distribution

H4 Hypothesis 4: Better performing companies receive a larger percentage of Not supported investments as follow-on investments than companies that are unsuccessful

The literature review highlighted that venture capital is a risky asset class trying to predict the unknown future, and that these characteristics play a key role in how the market develops and how

actors operate in the market (i.e. Sahlman 1990; Cochrane 2005; Parviainen 2017; Prencipe 2017). The nature of the asset class has had its role to play in how the Finnish venture capital market has developed, it explains the skewed return distribution, and it explains why most companies never see any significant value development.

The research question 1 was to assess and determine the performance and development of the Finnish venture capital industry. The literature review analyzed the history and development of the Finnish venture capital industry. It concluded that the VC market in Finland is young, and has been behind the development of Nordic, European and US VC until the 2010s (Seppä 2000, Hyytinen & Pajarinen 2001). However, during the past 5 to 10 years, there has been significant development in the Finnish venture capital landscape, both in terms of the positive mindset towards VC and entrepreneurship, and in figures representing VC activity (FVCA 2020, Järvilehto 2019). This development has also translated to indicators that represent performance: returns on a fund level, market level and company level have notably improved since the beginning of the 2000s. Furthermore, the analysis on MOIC development after investment, shows that investments made between 2012-2019 have developed very positively. 80% of these companies are still in the portfolios of VC investors, indicating positive improvement in the returns of current investors.

As the Finnish venture capital industry has developed, a decline can be seen in the amount of unsuccessful (MOIC <= 1x) investments – meaning that more companies are able to grow their business in a way that increases the value of the company. As the percentage of MOIC <= 1x companies has declined, also the risk profile of fund investments has decreased. While 71% of fund vintages 1997-2007 had a MOIC <= 1x, for fund vintages 2008-2013 only 25% are performing at a level of MOIC <= 1x. For LP’s, this decreases the risk of losing the whole investment. Furthermore, LPs can effectively reduce the risks of the asset class, by investing widely into the market, through a portfolio of several VC funds or by investing in a FoF vehicle (Weidig & Mahonet 2004). The analysis of research question 2 concluded, that venture capital returns are highly skewed on a company level, and even exhibit some degree of power law abiding behavior.

Research questions 3 and 4 focused on the value development of a company after an investment, and the effect of first- and follow-on funding to company level returns. The two questions are closely related to each other, since a common perception is, that follow-on investments are tied to

value development. Research indicates that staging investments has several advantages, as optimally it enables the investor to control and capitalize on the risk related to the investment (Wang & Zhou, 2004; Gompers, 1995; Tennert, et al., 2018). However, follow-on investing to only successful companies is not easy. As everyone else, also investors are prone to behaviors such as escalating commitments and evading losses (Staw 1981, Novemsky & Kahneman 2005, Devigne 2016).

The difficulty of whether to follow-on invest or cut losses is complicated, as at the time of a potential follow-on investment, the investor may not have any additional information that either supports or renounces the original investment decision. The median MOIC for companies, with a final MOIC over 3x, is 1,4 on the fourth year from the investment year, meaning that half the companies have not yet seen nearly any development in the valuation during the first 3-4 years. Thus for an investor with a company that has a MOIC 1 on Year 4 in their portfolio, there may still be a valid hope that the company develops to be succesfull. However, as the return distribution of MOIC depicted, most companies do not. This thesis does not provide indication to how the investor should decide between continuing to fund and cutting losses, as each company and situation is undoubtedly unique. However, the analysis on the value development of companies after investment offers perspective to the dynamics behind that choice, and the analysis on first- and follow-on investments highlights, that escalation of commitment is a phenomenon to be taken seriously.

5.2. Reliability and validity

The reliability of a research refers to the extent the results are consistent when repeating the study. (Heale & Twycross, 2015). In the case of this study, the dependent variable MOIC operated as a proxy for financial performance. MOIC is a generally accepted measure for performance, albeit not being an unambiguous measure, due to lacking the component of time, and expenses related to the performance (namely, investor management fee and carry), but can still be considered a reliable metric for performance.

The independent variables used in the different analysis, were based on the literature review and common industry practice. However, for some of the analysis, such as MOIC development after investment and first- and follow-on funding effect on performance, limited to no prior relevant research is available. As these analyses cannot be reflected on through comparison to other research, the confidence levels on reliability are lower. On the other hand, the dataset used as the bases for the study is reliable, as it consists of actual reported figures, as opposed to generalized market databases with unknown sources for the data.

The validity of an analysis has two parts: internal validity and external validity. Internal validity refers to the legitimacy of the results, due to the way the data was recorded, and analysis was conducted (Lakshmi & Mohidbeen, 2013). Internal validity also captures the extent to which the study is able to assess, the extent to which the dependent variables were affected by the independent variables as opposed to other factors. In the case of this study, the internal validity is mainly relevant to the regression model on first- and follow-on funding. As the phenomenon had not been studied previously, there was no established guidelines on assessing independent and control variables. In addition, the analysis included only a set of general basic diagnostic tests, and a larger set of diagnostic tests would have improved the reliability of the regression models.

External validity measures the generalizability of the results, meaning the extent to which the results are transferable to other group of interest (Lakshmi & Mohidbeen, 2013). Concerns of external validity, related to sample bias, generalizability of environment and of the time period, are all of some degree of concern in the study.

Possible sample bias of the study could be due to two reasons: Tesi might not have invested in a representative sample of Finnish VC’s, and the scoping of VCs from the full Tesi dataset might have included biases. However, the large sample size offers confidence that the first concerns is not playing a major effect on the results of the study. The second concern is estimated to be minor, as fund selection was done according to the clear principles and guidelines outlined in the study.

The generalizability of the results to other environments or time-periods is also a concern. The empirical analysis of the study was scoped to include only Finnish VCs, and to include all fund vintages of which there was data available. The literature review covered a wide variety of research from other venture capital markets, such as the Nordics, Europe and US, and also a variety of time-

periods that have been analysed. Although many of the trends observed in the empirical analysis seem to be similar as experienced in other markets, the results cannot be comfortably generalized across markets. Each market has at every point in time unique factors affecting it, and as both the literature review and empirical analysis show, the market shows notable change and development, even in short cycles. Although, the values and results cannot be generalized comfortably from one market to another, the underlying phenomena behind the results can be more comfortably generalized across markets, especially when generalizing to markets similar to Finland. The validity of the results in terms of the time-period is also of concern, due to the market developing fast, but research requiring minimum 5-10 years from the investment year to allow for meaningful analysis. This is discussed further in the limitations of the research.

5.3. Limitations and future research

The most critical limitation of the study is that analyses has been based on a dataset that includes company level annual data. All analyses have been done by aggregating and segmenting this data, and thus, fund-level and market level data does not include any fees or expenses. This limitation also builds a reasoning for further research. Further reseach could be done from the basis of fund level data, that includes all fees and expenses and potentially the full cashflow from LP to fund. This would allow for studying the TVPI instead of MOIC, and thus the phenomena better from an LP’s point of view.

As the study has shown, the venture capital landscape in Finland is not steady, but developing fast. This presents a second limitation of the study. The study does not reflect accurately the state of the Finnish venture capital market at the time of publishment, as a lot has happened since then. The market has seen several large exits and new venture capital funding rounds raised since the end of 2019, and the new events can dramatically affect the results of this study. In addition, the results of especially fund-level analysis cannot be forecasted to funds that have been raised during recent years. Funds typically have a lifecycle of 10 years, and this poses limitations to the results. Thus, many of the fund-level analysis have been capped at fund vintage 2013 or 2015, to allow for some indicative development to be visible. This means, that the performance and investment strategy of

funds raised during the recent years may look very different to the funds that have been analyzed in this study.

Furthermore, as the venture capital market develops and more companies exit or collect venture funding, the count of observations grows. In this study, the analysis has been taken to a level that is as specific as possible to provide meaningful results, but still preserves the confidentiality of any individual company, fund, or event. As the count of observations grows, more meaningful study can be conducted especially on analyses that require a large number of observations, i.e. the power law analysis.

This study focused on a VC- and LP-investor point of view, but to develop the market forward, analysis on venture capital funding is also required from a startup’s point of view. To conduct such analysis, data from an investor’s perspective needs to be combined with general market data. This would offer a way to analyze the functionality of the venture capital market, as phenomena such as funding gaps could be perceived. Research on venture funding from a startup’s perspective could potentially reveal issues or areas, that require the interest and need action from the public sector and politicians.

As a conclusion, many aspects of how the local Finnish venture capital market operates, and how it potentially differs from other markets, are still unknown. Transparent and open analysis are required to educate the actors involved in the venture capital market, and to drive development forward.

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