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Cloud of Science. 2015. Volume 2. Issue 2 http:/ / cloudofscience.ru ISSN 2409-031X
Analysis of Determinants of Russian Private Equity and VC Industry Based on Data Sample
A. Semenov1,2, O. Sokolova1
1National Research University - Higher School of Economics, Department of Finances, 20, Myasnitskaya str., Moscow, 101000
2Moscow Technological Institute, 38A, Leninckiy pr., Moscow, 119334
e-mail: semenov.venture@mail.ru
Abstract. Currently, the venture capital becomes more and more advanced and effective source of the innovation project financing, connected with a high-risk level. In the developed countries, it plays a key role in transforming innovation projects into successful businesses and creating prosperity of the modern economy. Actually, in Russia there are many necessary preconditions for creation of the effective venture investment system: the network of the public institutes for innovation financing operates; there is a significant number of the small and medium-sized enterprises, capable to sell production with good market potential. Now we can speak about Russian VC system as more than 500 deals was made during four last years. It was the time of VC expansion in Russia. Nevertheless, the story is very recent, so to understand the key driving factors, we should see the factors, which determine the size of investment rounds.
This paper studies the influence of various factors on the venture industry development mostly on the example of the IT-sector in Russia. The choice of the sector is based on the fact, that this segment is the main driver of the venture capital market growth in Russia, and the necessary set of data exists, as the certain amount of deals were publicly disclosed. Nevertheless, some examples of private equity and VC deals from other sectors were also chosen. The size of investment of the second round is used as the dependent variable. The story of exits in Russia is not long, so we took the approach that the company which attracted the second round of PE&VC investment can be considered successfully developing. To analyse the influence of the previous round such determinant as the volume of the previous (first) round investments is used. There is also used a dummy variable in regression to examine that the participation of an investor with high reputation and experience in the previous round can influence the size of the next investment round. Finally, the predicted marked growth of the company examined at the moment of investment was taken into account. As the implication, the regression analysis of short-term interrelations between studied variables reveals prevailing influence of the volume of the first round investments on the venture investments volume of the second round. The most important determinant of the value of the second-round investment is the value of first-round investment, so it
means that the most competitive on the Russian market are the start-up teams which can attract more money on the start, and neither the target market growth nor the reputation of the investor is not the factor of the first importance.
Keywords: Venture industry, venture investment, determinants of the venture sector development, IT-sector.
Abbreviations
IPO — Initial Public Offering IT-sector — Information technology sector LBO — Leveraged Buy-Out MBO — Managerial BuyOut M&A — Mergers and Acquisit
Introduction
Among different types of start-up financing possibilities, venture capital stands alone because it possesses the unique characteristics of financing terms at the beginning of their life cycle, which allow mitigating risks.
Venture capital companies and individual venture investors play an important role in the economic activities in innovative sector. They are intended to finance new, growing companies that possess high levels of risk, but have substantial growth potential.
The topic of venture investments as a non-traditional way of financing different start-up projects has been booming in the last decades. The focus of venture investors is mostly directed to the companies that demonstrate dramatic growth rates or significant market potential. Considering plenty of success stories of venture financing and the scale of venture market (35-40 billion dollars per year in the world), the strong attention of researchers is drawn to tendencies and determinants of venture investment development at the level of particular company.
Considering the dynamics of venture investment development in Russia in the last years and growing need for data-mining, trend estimation, the research on drivers and constraints to development of start-ups, financed by venture capital, is an actual task. Nowadays, there is still lack of published fundamental econometric researches on the venture investments in the innovative projects of Russian companies including modern methods of empirical analysis. The vast majority of works on Russian VC market is descriptive and based on the high-level analysis of aggregated statistical reports.
The situation with data gathering in this field caused some major difficulties for researchers because of its inconsistency. Many details of announced deals were under nondisclosure agreement and the figures on many important deals are unpublished. In 2013, the rocket growth in the flow of new deal data was registered, new databases and sources
appeared, and appropriate information was disclosed for many deals. All these facts opened the way for conducting the deeper econometric research on Russian venture capital data, which was earlier impossible because of the inappropriate size of the possible data sample. The results obtained in this study are based on the recent information aggregated by RusBase portal [1], PwC [2], FastLane [3] data and Thomson Reuter's informational terminal.
Despite the absence of empirical research on this topic, the international experience is full of significant studies of similar kind.
So, the subject of this research were Russian companies operating in the information technology sector (IT) and attracting venture capital investments.
This work is the first Russian detailed analysis of determinants of the IT-sector venture deals that have taken place from 2010 to the first two quarters of 2014. Sixty-nine companies that attracted venture capital investments are investigated. The result of the work is the dependence that was explored between the responding variable, which is the second round investment values, and the explanatory variables; the first round investments values, participation of investors with first-class reputation, and the growth rate of market sector, in which the invested company operated.
The impact of the paper can be formulated in the following terms:
- to continue the research on venture determinates on the sample of Russian venture deals including the logit model;
- to find key determinants of the Russian venture development (by studying foreign academic researches) on the enlarged list of deals;
- to detect the drivers of the venture capital attraction (by means of empirical analysis) which can be the deal size on the previous round, reputation of the investor, the predicted market growth of the subsector, where does the invested company operate.
This work consists of introduction, three sections and conclusion. The list of references is also provided. Section 1 is the description of venture industry and the review of the academic literature that is devoted to the venture capital analysis. Furthermore, in section 2 the methodology of the research is discussed and hypotheses are set out. After that, in section 3, the model is outlined, with the high-level regression variables.
1. Revue and Background
1.1. Basic Information
During the last 20 years, Russia has been trying to build an effective hi-tech market system. The development of innovations and technologies is commonly considered as the best way to success. Existing advantages of previous early-stage financing alternatives,
for example public-based, often are not able to solve appearing problems of lack of capital available for seed and start-up projects.
Venture capital takes a special place in the innovation development and is considered a good solution. The venture money is the capital of investors that is involved in the financing of new growing firms with a rather specific deal pattern. The markets where venture capital operates differ from other types in some characteristics: the high level of risk, innovative nature of the subject, possibility of changing the market structure where the business operates, and an investor has enormous profit opportunities in the projects in case of success. Such famous companies as Intel, Microsoft, Google, Yandex etc. appeared due to the participation of venture capital and succeed because of venture funds.
Russia has become the highest growth venture capital market of Europe in 20112012, having climbed up the 4th place by the available venture capital volume in the high-tech sector by the 2012 totals [4], but due to the short story of Russian VC, and small number of exits we still cannot say that this trend is stable.
800 -
700 -
600 - - -
!, ■
-^^B-
I
2010 2011 2012 2013
■ Seed ■Round A "Round B "Round C,D,E Unattributed
Figure 1. Total number and total amount of the venture IT deals, 2010-2013 [6] (Source: FastLane venture team Deal book of Russian internet [5])
The presented charts illustrate that the volume of the Russian venture capital market was doubled in 2011. In 2012, the market growth ratio amounted 18% versus 4% in 2013. At the same time, there were 205 deals in amount of 643 mln. USD in 2012 with 245 deals in amount of 667 mln USD in 2013.
Thus, in spite of the fact that growth of the market has slowed down in 2013, the number and total amount of investments still grow. According to Thomson Reuters' customer, support the number of venture transactions in the first half of 2014 reached only
300
200
150
50
2010 Seed □ Round C,D,E
2011
2012
Round A □ Unattributed
2013 Round B ■ Debt
250
100
0
approximately 70 units but the average sum of investments totaled 2.71 million dollars that is much higher than for previous period.
The performed successful investors' exits became the main growth driver of the venture capital market in Russia. It is the most important indicator of the market development. The positive tendency of exists will stimulate further capital inflows to Russian venture industry and promote its development.
The growth also is driven by investments in IT-sector, whose main driver was the enormous growth of online retail. This market segment in Russia offers a number of easy innovation opportunities with a relatively short return on investments.
The volume of venture investment in the Russian IT sector in 2012 increased by over 50% to exceed USD 450 million. In 2013, it accounted for almost 87% of deals — 213 deals, and 93,5% of total investments — 623 mln USD.
The venture company as the subject of investments has the following stages of development:
- the seed stage (the stage when the idea of the business appears and only the first investments are made).
- the start-up stage (the beginning of the business).
- the early stage of the project (the project is gradually reaching the break-even point).
- the growth stage (the model of realization proved its successfulness, expansion of the business).
- pre-IPO stage.
- selling stage (investor sales the share by Initial Public Offering (IPO), Managerial Buyout (MBO), Trade Sale, Leveraged Buy-Out (LBO) or other ways).
There also can be other stages like pre-seed stage or late growth mezzanine stage.
It is necessary to notice that these 6 stages describe the «classic» successful venture project. And most projects do not have the final stages because of bankruptcy. In that work would use terms «first» and «second» round without the reference to the stage.
1.2. The Literature Review
In order to underpin the methodology, which is proposed in the next sections, the extensive literature review is given. The review is chronological, and will also provide the sample details and the methodology summary.
One of the first articles on the topic was the work by Tyzoon et. al [6]. The aim of this framework was to numerically assess the importance of the factors, which are interpreted by venture investment specialists in order to provide a definitive decision on the investment in the start-up. As at the time the financial statistics were not available to the necessary extent, the researchers had to use expert opinion as a key method. After carry-
ing out a survey on 41 venture funds, 5 main stages of a venture investment deal were identified.
These main stages included:
1. Deal origination, meaning the information about a project reaching the interesting parties.
2. Deal screening, the primary high-level selection of deals which did not fit the criteria of the venture investor.
3. Deal evaluation, the deep analysis of the projects, e.g. the projected returns and inherent risks, the assessment of the start-up non-numerical data. As a result of this stage, a weighted score is obtained implementing all the available data. Consequently, a decision on the investment is made.
4. Deal structuring, this stage happens in case of an accepted project and includes the transaction negotiations on the price, share to be bought and minor juridical issues, associated with the transaction.
5. Post-investment activities, which implies a stage when an investor actively participates in the operational activities of the invested company.
Obtaining a sample of 90 deals, which reached the 3rd step from 41 funds (while being rejected by 100 for confidentiality matters), the researchers required additional data concerning the qualities of the project. In order to get it, they gathered information on 23 characteristics of each project (including required return and risks). Most of the companies in the sample were from the electronics industry, on average requiring 1 mln. USD of external financing. After the analysis of the results, the criteria were narrowed down to 5 basic ones. The list is provided below:
- market attractiveness (the market share, growth opportunities, level of monopolization);
- the level of product differentiation (the uniqueness of the product, patents, innovative technology);
- quality of management (professional level of managers in areas of finance, marketing);
- the stability in case of external shocks (the level of technological development of the market segment, business-cycle susceptibility, stability in case of recession);
- the exit strategy possibilities (potential opportunities for M&A (Mergers and Acquisitions), IPO, LBO, MBO and other possibilities of exit).
The regression analysis of the data showed that for the calculation of the required return of the project investors use such factors as the market attractiveness and level of product differentiation (R2 = 0.22), and for the risk assessment the quality of management criteria and stability are most relevant (R2 = 0.33). Moreover, the analysis proved that in
89,4% of the cases the result of an investment decision was dependent on the risk/return ratio.
It is important to note, that comparing the results with previous works, the researchers emphasized the same key factors on project evaluation stage. At the end of the research project, 7 representatives from venture funds were invited. The results of the analysis proved to be satisfactory, however, the experts thought that the management role was overvalued as a key factor, and some of them pointed out that it should be more relevant in the risk assessment, but not in the return assessment.
In another relevant article by Chen et. al [7], the venture investments were analysed from the point of view of the presence of successful venture projects in the same region. Working with the 1000-deal sample, the research was concentrated mostly on the San-Francisco, Boston and New-York areas, since these areas have most start-ups that were venture-financed. In conclusion, a positive relation between a number of venture funds and a number of successful venture projects in each city was found. However, the success of venture projects in the "home" region was more significant than of the projects financed by the same companies but in other regions, in terms of returns.
The research by Zarustkie [8] tried to estimate the impact of personal qualities of a venture fund manager on the funds' financials. Based on the sample of 1184 funds, the conclusion was that the experience of venture financing proved to be crucial, and the managers have a significantly large number of companies in their portfolios. The start-up management experience nearly doubled the strength of this relationship. It is also interesting to note that at the seed stage the impact was the strongest, other stages were much less influenced by the managerial experience.
The article by Munarii and Toschi [9] focused on another issue arising from venture investment decision. The authors analysed the bias of venture investors towards the startups created based on educational institution rather than a private project. The sample of 247 companies was also divided between venture funds and public funds. As a result, the hypothesis of such a bias was rejected. The amount of venture capital raised by venture was deemed most dependent on intellectual property rights, the business model and the prestige of educational institution.
Another branch of articles focuses on the determinants of the venture activities from the macro-economic standpoint. The first article under review from this sphere is by Schertler [10]. The research was focused on the drivers of venture capital investments in Western Europe. Analysing the issue from the narrow definition of venture (seed-stage) and wider (later financing stages), the conclusions were drawn that the key drivers are liquidity and the capitalization of the stock exchange in the region, human capital potential and the stability of labour market. The regressions, however, proved that at the later stages of financing the results are independent of these issues.
Another study, addressing the same issues from a different perspective, is the one by Felix and Pires [11]. The hypothesis tested empirically in the article was about the level of dependence of venture capital activities on the size of the M&A market. The sample included companies from 23 countries for the period 1998-2003. The null hypothesis was proven in that case, the relevance of M&A market was significant, moreover, the level of entrepreneurial activities and unemployment rate played a major role in the volume of venture capital market. The issues, which were highlighted for the future researches were mostly considering the asymmetric information and the exit environment.
An analysis of the cross-country data is also a valid source of knowledge about the venture financing. The study by Jeng and Wells [12] was focused on the macro-factors of venture capital activities. Among the variables in the model were the GDP, IPO number, data on the capital markets etc. The results showed the significance of IPO market for the success of venture investing, especially in the later stages, since the exit strategy is an important factor then. The government-owned venture capital funds showed little relationship with economic variables, and that have proven to be a good ground for future research.
Since the cross-country data proved to give some valuable insights into the venture capital markets, another source of relevant hypothesis can be found in studying venture financing for funds investing domestically and internationally diversified funds. This issue was researched in the Wang [13] paper. The sample was based on the Chinese companies and proved that the results of venture investing were strongly dependent on the amount of experience of the fund in the country's operations. The networking, relations and value created by it are the key to venture capital success in a particular investment. Therefore, investing locally was in most cases the better strategy.
Through the literature review it could be seen, that the wide and developed methodology for the study of VC market development factors is present now in the economical theory. Nevertheless, there is a literature gap for Russian market, as there almost no available research exist (the previous works of the authors should be mentioned). This work eliminates the gap and gives the extended research of Russian VC market by the tested methodology.
Importance of this research is defined by the following factors. Firstly, the aim is to continue the studying of rather new Russian VC market including the application of the logit model. Secondly, to study which determinant is the most important on the young VC market, among the quantity previously invested, growth of the companies' market, reputation of the previous round investor. Thirdly, to introduce a method of determination the probability of second-round investment if the reputable investor of the first round exists.
Although not complete (as many information of deals is partially closed and could not be included in the database), this models can useful tools for the investors to deter-
mine how much to invest in different companies, as the crucial factor of the success is the quantity money that was earlier attracted in the company.
2. Data and Methodology
2.1. Research Settings
The analysed data for Russia was collected based on the RusBase — a web-project that collects information about the venture market [1] and Thomson Reuters' one [4]. Moreover, FastLane Ventures base was used [3]. It is an investment company that creates and develops successful internet businesses using their own unique model and it publishes annual reports. Furthermore, the annual analytic reports of PricewaterhouseCoopers for Russian PE and VC market were used [2].
As a result, there are taken into consideration venture investments made by business-angels, investment companies, and private, corporate and state venture funds in the first round of financing, according to open data sources. If a company attracts investments during two or more rounds, then every round is seen as a separate deal.
According to some experts, the modern conditions for creating the venture industry in Russia are becoming more favourable due to active government policy and intensive growth of the IT-sector (Information technology sector). Therefore, only Russian IT companies are investigated in this paper.
2.2. Selection Methodology
By means of the process mentioned, the sample of 125 companies that attracted venture investments was formed and was used to make an assessment by logit-regression model. Then, the companies that attracted venture capital more than once were selected for the OLS-regression analysis. Finally, 69 companies with all relevant information are taken into account.
The information required is about the value of investments in the first and second rounds, the name of the business-angel, an investment company, a private, corporate and state venture fund and the date of the deal. The investor was considered reputable if he has successful exits or represents a significant brand, for example Intel Capital or Mail.ru Group.
All companies in the sample operate on such markets as IT services, mobile apps, protection from leaks of confidential data. This information is completed by the forecasted market growth on the moment of the deal. It was supposed that in the process of decision-making investors focus on market trends and use the estimations of market growth forecast provided by leading marketing agencies just before the moment of the deal. This
factor is the main landmark for investors' decisions. So the market growth predictions made by the leading Russian marketing agencies were also taken as a variable.
2.3. Hypothesis
That is more convenient for future analysis to divide all the hypothesizes into two groups: those, which are tested in according with the OLS-regression model, and those, which testing method is logit-regression construction.
OLS-regression model
The value of investment of the second round is influenced by such factors as the value of the first round deal, the participation of a reputed investor and also the forecasted data about the market growth.
- H1. There is supposed to be a stable positive relationship between the second round investments and the first round ones. It is possibly based on the factors connected with the company's operating process ('enough money is required to pass the first stage'), and the fact that in Russia the money available for the company is the most crucial factor of competitiveness (not the operating skills).
On the other hand, the participation of experienced (reputed) investor in the previous round can influence the size of next investments.
- H2. The participation of an experienced (reputed) investor can influence the next rounds negatively because they are supposed to be thrifty and economize on the expansion efforts (according to [13]).
- H3. Finally, because of the anticipation of a dramatic increase in the volume of investments after the deal, experienced investors choose the optimal level of their investments that is lower than the level of others.
Logit-regression model
- H4. The presence of "experienced" investors in the first round increases the probability of investment in the second round. This hypothesis will be tested by plotting the logit model based on a sample of 69 observations in the statistical package STATA. In this case all the observations are considered, even those, in which there was no investment in the second round.
3. The Model
OLS-regression model
The sample consists exclusively of Russian companies, which operate in the IT sector and had some kind of venture transactions for the period under review. Number of observations, representing considered companies, that is discussed in this context, is equal to 69 units, and the reviewed period is from 2007 to the end of 2014.
The responding variable of the regression is a value of the second round investment. It is nominated in US dollars. The explanatory variables are the first round investments values, participation of investors with first-class reputation (the similar factor as in [13]), and the growth rate of the market sector (the similar factor as in [6]). The choice of the first round of investment was our own approach, as in Russia it was too little exits till now, and the possibility to attract the second round was considered as a successful dynamics.
The "reputed investor" is the investor that is well known in the venture market (took part in plenty of successful deals or makes big investments or this investor is an expert in the field he invests in) or the fund with corporate participation. This is supposed to be a dummy variable: 1 — the reputed investor takes part in the deal unless 0.
As for market growth, it is different for all parts of the highly diversified IT-sector. Therefore, it is relevant to check this influence on investment decisions.
The best way to estimate this influence is the Ordinary least squares method. Regression statistics are presented in Tab.1, the parameters of the regression are in Tab. 2.
Table 1. Regression Statistics
Multiple R 0.624261
R square 0.389702
Adjusted R Square 0.362376
Standart Error 25.12874
Observations 71
Coefficients St. error P-value
Intercept 2753991 3910360 0.4864
1 round 1.312883 0.225755 1.90 10-/
branded investor 7072816 4513371 0.12181
market growth -81170.6 102812 0.4326
Table 2. Regression Statistics
Multiple R 0.619697
R square 0.384025
Adjusted R Square 0.365908
Standart Error 186964
Observations 71
Coefficients St. error P-value
Intercept 999361 3208437 0.7564
1 round 1.330758 0.223994 1.08 10-7
branded investor 7051297 4500771 0.12183
So that, the getting model is the following:
Y =2753991.1 + 1.3128833Xj + 7072816.2X2 -81170.57X3, where Y — the value of investment of the second round; X1 — the volume or the first round investments are significant at a level of 1%; X2 — the participation of branded investor is significant at a level of 15%; X3 — the forecasted market growth now of deal is non-significant variable.
The positive dependence between Y and X1 that is demonstrated by the model is evident: investments in the previous stage attract the following investors. So the H1 is not rejected. The participation of branded investor in the first round influences the second round investments positively since investors trust them and try to follow their way. So the H2 is rejected.
The market growth is insignificant variable. Moreover, the dependence is negative. It is important to conclude, that this variable is not the factor all investors pay attention to — H3 is rejected.
So that there is a regression which has two significant regress variables from the last model:
Y =999316 + 1.330758X + 7051297X2, where Y — the value of investment of the second round; X1 — the volume or the first round investments; X2 — the participation of branded investor.
Both regressors (1) and (2) now are significant (significance level — 12%). As it can be seen from the descriptive statistics, the dependence between Y and X1 and X2 remains positive.
Logit-regression model
During the construction of logistic regression the probability of the investment in the second round, depending on the same three factors, was estimated according to the following function:
P ( rnd2 = 1) = F(rndln1, inv, grateln).
Where rnd2— the probability that for a certain period of time (2007-2014) there will be more investments in the company under review; rndln1 — logarithm of the volume of investments in the first round; inv — the presence of an experienced investor (dummy); grateln — logarithm of the forecast growth rate.
H4 hypothesis is not rejected at the 1% level of significance. This means that the presence of an experienced investor in the first round effect on future investment in the second round. Most likely, it is because investors chase high returns and this behaviour helps neutralize the risks.
Table 3. Regression Statistics
Iteration 0: log likelihood = -58.373938
Iteration 1: log likelihood = -46.093129
Iteration 2: log likelihood = -45.454471
Iteration 3: log likelihood = -45.452815
Iteration 4: log likelihood = -45.452815
Logistic regression
Log likelihood = -45.452815
rnd2 coef p-value
rndln1 -0.3242273 0.016
inv 1.962967 0.001
grateln 0.8353225 0.033
_cons 2.415313 0.230
The negative coefficient before the variable rndln explains a negative correlation between the volume of investments in the first round and the likelihood of investment in the second one. Perhaps, such a situation arises due to the fact that investors, analysing the value of investments in the first round, realize that they can not afford such sums to spend on the project under review, i.e. they do not have enough votes in decision-making or do not want to work in collaboration with existing investors.
In this model, the variable rate of growth of the market is significant at the 5% level and the probability of investing in the project for the second time depends positively on the variable grateln. Therefore, it is important to note, that before making a decision investors make a careful review of all their actions, and the market growth rate is one of the determinants of this analysis.
4. The Results
In this paper the following results of testing the formulated hypotheses in according with the OLS-regression model were obtained:
- H1 hypothesis is not rejected — investments in the previous stage attract the following investors, and the amount of the first round of the investment positively influences the second one.
- H2 hypothesis is rejected — the participation of branded investor in the first round influences the second round investments positively since investors trust them and try to follow their way.
- H3 hypothesis is rejected — the market growth is insignificant variable, so the investor of the second round looks on the first round size of the investment, and the reputation of the first-round investors. The more and the earlier a company attracts the money on the first round, the quicker in can expand and develop, and a smart and reputable first-round VC investor can contribute to the right speed and direction.
The results, received from testing with the logit-regression construction, are the following:
- H4 hypothesis is not rejected (at the 1% level of significance) — the presence of an experienced investor in the first round effect on future investment in the second round.
So, according to the research we can formulate the recommendation for a VC investor operating in the young market, that he or she should look for companies which already have enough development speed and resources (due to the money previously invested) and a smart and reputable investor in the board of directors. Perspectives of the market are the factor of the second importance.
It is also worth noting, that in fact, one of the main conclusions of this research project is a confirmation of the hypotheses constructed on in correspondence with the described-above literature study, but more variables should be explored. Therefore, this study cannot be considered as a complete one.
5. Conclusion
The main conclusion of this empiric research is that investments in the previous stage attract the following investors stimulating the further capital inflows to Russian venture industry and promote its development. And it is also worth to take into consideration, that the development of the venture capital market and its usefulness for the IT and other consumer sectors depends on the experience of participating investors, as their presence in the first round increases the probability of investment in the second round. Expected market growth of target companies plays the secondary role in investment decisions. The VC continue invest in companies who have attracted investors and reputed investors among them, not in trends
The world experience shows the efficiency and importance of venture schemes for the financing of risky early-stage projects. Venture investments are a good instrument for the competitive battle for the most valuable and promising start-ups and projects.
However, the topic has not been adequately investigated from the point of view of determinants of venture capital activities in Russia at the macro and micro-economical level. In this research project, the focus was maintained on the determinants of the volume of successful venture capital investments based on the emerging market sample. The results proved that the venture capital in Russia can be explored econometrically and allow future analysis.
References
[1] RusBase portal. http://rusbase.vc
[2] PwC and RVK research «Overview of Russian IT venture capital deals in 2011», 2012
[3] FastLane venture team Deal book of Russian internet. Investments in Russian internet companies and start-ups in 2010-2011, 2012
[4] Thomson Reuters' Customer Support. (http://thomsonreuters.com/)
[5] PwC MoneyTreeTM research «Navigator of the venture capital market», 2013
[6] FastLane venture team Deal book of Russian internet. Investments in Russian internet companies and start-ups in 2012-2013, 2014
[7] Tyebjee T. T., Bruno A. V. (1984) A model of venture capitalist investment activity. School of Business, University of Santa Clara, Santa Clara, California.
[8] Chen H., Gompers P., Kovner A., Lerner J. (2009) Buy Local? The Geography of Successful and Unsuccessful Venture Capital Expansion. Harvard Business School.
[9] Zarutskie R. (2007) Do Venture Capitalists Affect Investment Performance? Evidence from First-time Funds. Fuqua School of Business, Duke University.
[10]Munarii F., Toschi L. (2009) Do venture capitalists have a bias against investment in academic spin-offs? Evidence from the micro- and nanotechnology sector in the UK. University of Bologna.
[11] Schertler A. (2003) Driving Forces of Venture Capital Investments in Europe: A Dynamic Panel Data Analysis. Kiel Institute for World Economics.
[12] Felix G. E. S., Pires P. C., Gulamhussen M. A. (2012) The Determinants of Venture Capital in Europe. Evidence Across Countries. Springer Science+Business Media.
[13] Leslie A. J., Wells P. C. (2000) The determinants of venture capital funding: evidence across countries. Journal of Corporate Finance, 6, 241-289.
[14] Wang L., Wang S. (2011) Cross-border venture capital performance: Evidence from China.
Pacific-Basin Finance Journal, 19, 71-97.
Влияние внешних факторов на выбор проекта в процессе венчурного инвестирования. Анализ Российских данных
А. С. Семенов1'2, О. А. Соколова1
1Национальный исследовательскоий университет «Высшая школа экономики», 101000, Москва, ул. Мясницкая, 20
2Московский технологический институт, 119334, Москва, Ленинский пр., 38A
e-mail: semenov.venture@mail.ru
Аннотация. В настоящее время венчурный капитал становится передовым и наиболее эффективным источником финансирования. И несмотря на то, что финансирование инновационных продуктов практически всегда сопряжено с высокой степенью риска, в развитых странах венчурный капитал играет ключевую роль в преобразовании новых проектов в успешный бизнес и способствует их дальнейшему процветанию на более поздних стадиях развития. Эта статья посвящена определению ключевых факторов становления венчурной индустрии России на базе эмпирической выборки, и определению ключевых факторов влияния. Основной переменной выступает объем инвестирования в компании, осуществленный на первом и втором раундах.
Ключевые слова: венчурная индустрии, венчурные инвестиции, детерминанты развития венчурного сектора, ИТ-сектор.
Литература
[1] RusBase portal. http://rusbase.vc
[2] PwC and RVK research «Overview of Russian IT venture capital deals in 2011», 2012
[3] FastLane venture team Deal book of Russian internet. Investments in Russian internet companies and start-ups in 2010-2011, 2012
[4] Thomson Reuters' Customer Support. http://thomsonreuters.com/)
[5] PwC MoneyTreeTM research «Navigator of the venture capital market», 2013
[6] FastLane venture team Deal book of Russian internet. Investments in Russian internet companies and start-ups in 2012-2013, 2014
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[10]Munarii F., Toschi L. Do venture capitalists have a bias against investment in academic spinoffs? Evidence from the micro- and nanotechnology sector in the UK. — University of Bologna, 2009
[11] Schertler A. Driving Forces of Venture Capital Investments in Europe: A Dynamic Panel Data Analysis. — Kiel Institute for World Economics, 2003
[12] Félix G. E. S., Pires P. C., Gulamhussen M. A. The Determinants of Venture Capital in Europe.—Evidence Across Countries. — Springer Science+Business Media, 2012
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Авторы:
Семенов Александр Сергеевич — кандидат физико-математических наук, старший преподаватель Департамента финансов Национального исследовательского университета «Высшая школа экономики», преподаватель Московской бизнес-школы Московского технологического института
Соколова Ольга Андреевна — магистрант департамента финансов Национального исследовательского университета «Высшая школа экономики»
