Alternative investments as a financing tool for small and ... · of different sizes. They recommend specific financing tools such as leasing and factoring, as they can be useful in

Risk Market Journals Bulletin of Applied Economics, 2018, 5(2), 13-44| December 1, 2018

Alternative investments as a financing tool for small and medium

enterprises

Thomas Poufinas1 and Maria Polychronou

2

Abstract

Alternative investments more than ever have come to the spotlight as they have attracted over the last

few years the interest of asset owners and asset managers. The former are nothing but individual or

institutional investors, such as pension schemes. The latter are the individuals or organizations that

direct or allocate the available assets to the appropriate securities. Over the last decade there has been a

shift from traditional, listed equity and fixed income to venture capital - private equity, private debt,

exchange traded funds, and other investment means, also known as alternative investments. In this

paper we investigate the parameters that affect small and medium enterprise financing through

exchange traded funds and venture capital. We employ econometric models to find the link between the

exchange traded funds and venture capital that invest in small and medium enterprises in a country and

the economy of the relevant country. We find that the overall condition of the economy of a country as

represented by the macroeconomic figures and certain indices is important for the choice of the country

for the domiciliation, size or availability of exchange traded funds and venture capital.

JEL classification numbers: G20, G30, G32, O40, O50

Keywords: alternative investments, exchange traded funds, venture capital, small and medium

enterprises, financing.

1. Introduction

Small and medium enterprise (SME) financing has always been in the spotlight either from a

company characteristic perspective or from a country of origin angle. Especially during, but also in the

aftermath of the most recent financial crisis, the financing of SMEs has attracted the interest of the

relevant market as well as of the relevant research. Alternative sources have been examined by both the

market and the researchers. Exchange traded funds (ETFs) and venture capital (VC) emerged among the

candidate sources.

Exchange Traded Funds is a relatively new form of investment funds that exhibited

considerable growth over the last few years. The demand for ETFs has grown significantly the last 2

decades because of their appealing features, both for retail and institutional investors. As such, they

could be successful financing tools for enterprises seeking funding, as these enterprises could be

included among the ETF holdings.

Several factors have contributed to the popularity of ETFs from the investor perspective (ICI

Research Perspective, 2014). These include the intraday tradability, the tax efficiency, the rising

popularity of passive investments, the externalization of distribution fees, the standardization and

1 Department of Economics, Democritus University of Thrace, Greece

2 Graduate Program in Business Mathematics, University of Athens & Athens University of Economics and

Business

Article Info: Received: May 28, 2018. Revised: June 20, 2018

Published online: July 30, 2018

14 Thomas Poufinas and Maria Polychronou

transparency and the greater use of asset allocation models. Consequently it makes sense to examine

how they could become a successful financing vehicle also for SMEs.

Intraday tradability means that investors can trade existing ETF shares at marked to market

prices during trading hours on stock exchanges. This gives the required liquidity and access to a wide

range of asset classes. Tax efficiency is achieved by the small percentage of the distributed capital gains

and the reduced unrealized gains as a result of the in-kind redemptions. Passive investments have

increased popularity as they are materialized via index-oriented funds, allowing for lower fees. ETFs

are an efficient and cost-effective way to use an asset allocation model. Distribution fees are paid

directly to advisors; hence their performance is net of fees. As ETFs are traded in the stock exchange

they are standardized and characterized by transparency.

Another form of investment that could serve in this direction is Venture Capital. Venture

Capital has also seen fast growth since its inception a few decades ago and has evolved to be a

specialized form of financing for small privately owned companies (Kenney, 2000). Venture capital can

provide seed capital, capital for start-ups, as well as later stage capital (Invest Europe, 2015). It also

comprises a financing vehicle of great interest both for the investors and SMEs.

In this direction it is critical to examine what environments foster the growth of ETFs and

Venture Capital. To reveal that we investigate the link between on one hand ETFs and Venture Capital

and on the other hand certain metrics representative of an economy, such as GDP, market capitalization,

tax, unemployment, regulatory quality and economic freedom. In addition, it is important to find the

link between ETFs and Venture Capital themselves.

The trigger for our research, which also defines the problem we attempt to solve, is the need of

stock exchanges to identify what conditions and parameters are necessary for ETFs to be a successful

means of the financing of SMEs. This stems from the interest of such stock exchanges to play a role in

this direction. The need is more and more pertinent in peripheral economies of the Eurozone, where the

traditional financing means cannot properly operate. At the same time SMEs are looking for alternative

sources of funding, either for starting their operations or for growing and expanding. Consequently,

ETFs could be the vehicle that will cover this demand, indicating that there is an intersection of

interests with the stock exchanges.

Besides stock exchanges and SMEs, investors, both retail and institutional, are crucial

stakeholders, being the interested party that will provide the required funds. ETFs have a series of

advantages for investors, such as listing, liquidity, transparency, standardization, professional selection

of the companies to be included in the fund, diversification opportunities (in terms of market cap, sector

and geography) and index-tracking. In addition, they offer access to possibly higher performance, as

well as the ability to take both long and short positions and can therefore be used also for hedging.

In parallel, we study VC as a means that can be used for the funding of SMEs. The stakeholders

are similar. Among the advantages of VC are the knowhow, the economies of scale and the access to a

network of investors they offer.

In this paper we investigate the variables/ parameters that affect the volume of the activity of

ETFs and VC as sources of funding for SMEs in a country. In an earlier manuscript of one of the

authors (Poufinas and Kouskouna, 2017) the contribution of pension funds to the growth of a country

through the use of small and mid cap ETFs and VC has been investigated. However, in that paper a

smaller set of countries and a different set of independent variables (except for GDP and GDP per

capita) has been used. Beck et al (2008) have tried to link some financing sources (not limited to SMEs)

with the country characteristics, however not for ETFs and VC or to the extent that we do. Boscoianu et

al. (2015) follow a more strategic approach to construct a hybrid of funds, but they also do not evaluate

ETFs and VC as per our approach.

The contribution of our findings is that they can indicate what parameters a country and/or its

stock exchange should consider so as to successfully attract/ create ETFs for the financing of its SMEs.

The novelty in the route we follow is that we link the success of ETFs and VC (in terms of the volume

of their activity) with the global economic and financial environment of a country.

Alternative investments as a financing tool for small and medium enterprises 15

2. Literature Review

The SME financing literature does not exploit at all the use of ETFs and Venture Capital in the

sense we approach it in our paper. It primarily addresses the policies that can be applied so as to support

the SMEs. More precisely, Sarker (2017) evaluates the challenges SMEs face in financing new or

existing businesses and envisages different paths that SMEs explore for financial supports. He primarily

focuses on the financial needs in family controlled, women-led, and ethnic minority administered firms

and recommends that government policy of initiating various intervention funds for entrepreneurial

development should be encouraged. In our paper we also offer some directions that the interested

authorities can follow to increase the volume activity of ETFs and VC as sources of financing, but from

a global perspective.

Beck and Demirguc-Kunt (2006) realize that SMEs face larger growth constraints and have less

access to formal sources of external finance, potentially explaining the lack of SMEs’ contribution to

growth. They deem that financial and institutional development helps alleviate SMEs’ growth

constraints and increase their access to external finance and thus levels the playing field between firms

of different sizes. They recommend specific financing tools such as leasing and factoring, as they can

be useful in facilitating greater access to finance even in the absence of well-developed institutions, as

can systems of credit information sharing and a more competitive banking structure. We offer a

different route in our paper, this of alternative investments.

Beck et al. (2008) investigate how financial and institutional development affects financing of

large and small firms, using a database that includes large, small and medium-size firms and a broad

spectrum of financing sources, including leasing, supplier, development, and informal finance. They

perform regression analysis, in order to relate firms’ financing patterns with other firm and country

characteristics. They find that small firms and firms in countries with poor institutions use less external

finance, especially bank finance. They also realize that protection of property rights increases external

financing of small firms significantly more than of large firms, mainly due to its effect on bank finance.

They see that small firms do not use disproportionately more leasing or trade finance compared with

larger firms, so these financing sources do not compensate for lower access to bank financing of small

firms. They also find that larger firms more easily expand external financing when they are constrained

than small firms. In our research we use country characteristics as well but to link them with ETFs and

VC that invest in small and medium enterprises only.

Beck et al. (2013) explore the relationship between financial structure and firms’ access to

financial services. They consider the importance of three different types of financial institutions: low-

end financial institutions, specialized lenders, and banks to find that (a) dominance of the financial

system by banks is associated with lower use of financial services by firms of all sizes, while low-end

financial institutions and specialized lenders seem particularly suited to ease access to finance in low-

income countries and (b) there is no evidence that smaller institutions are better in providing access to

finance. In our paper we do not address financial institutions or their structure at all. We are therefore

exploiting a different direction.

Lee et al. (2015) find that innovative firms are more likely to be turned down for finance than

other firms, and this worsened significantly in the crisis. They show that the worsening in general credit

conditions has been more pronounced for non-innovative firms with the exception of absolute credit

rationing which still remains more severe for innovative firms. They therefore infer that there are two

issues in the financial system, namely a structural problem which restricts access to finance for

innovative firms and a cyclical problem that has been caused by the financial crisis and has impacted

relatively more severely on non-innovative firms. In our paper we do not address the ease of access to

finance.

Kersten et al (2017) conduct a systematic review and meta-analysis of the empirical literature

on SME finance effectiveness. They realize that in contrast to the microfinance literature, few SME

finance evaluations use experimental methods. They find a positive significant effect of SME finance

on investments, firm performance, and employment. They see that the summary effect on profitability

and wages within the supported firm is insignificant. They find that spillovers and poverty reduction

effects are scarcely addressed in these evaluations. We do not tackle the effect of SME finance to the

SME effectiveness.


Fowowe (2017) tries to link the access to financing with the growth of firms in African

countries. He uses two measures, on subjective and one objective, to realize that difficulties in

accessing financing can have a significant negative effect on the growth of a firm. He also finds that

firms that do not face credit constraints show faster growth compared with firms that do. This indicates

that financing is of key importance to firm growth. This is in a different direction compared to our

research, as we do not examine the importance of financing to the firm growth.

Quartey et al. (2017) attempt to provide some understanding about SMEs’ access to finance

within the West African sub-region with particular interest in establishing whether there are similarities

and/or differences in the determinants of SMEs access to finance across countries in Sub-Saharan

Africa. They find that, generally, at the sub-regional level, access to finance is strongly determined by

factors such as firm size, ownership, strength of legal rights, and depth of credit information, firm’s

export orientation and the experience of the top manager and that there are important differences in the

correlates of firms’ access to finance at the country level. In our study we do not look at the firm

specific characteristics.

Rupeika-Apoga (2014) highlights the importance of alternative resources such as external

financing for small developing countries as the Baltic ones. She confirms that SMEs’ access to

alternative financing in the Baltic States is improving and hopefully this market segment will be the

way for the Baltic States to become innovative driven economies. We look at two alternative

investments (ETFs and VC) but not limited to the Baltic countries. We have a much bigger dataset.

Boscoianu et al. (2015) propose new tools based on innovative mix of private management and

governmental support of a new type of financial public - private partnership and a way that creates a

strong support of the markets and changing public perception about investments in capital markets.

They examine the possibility of creating a tool, such as a closed end fund for SME manufacturing, with

an initial participation of the government (recommended 50%), which could attract foreign Venture

Capital Funds or Private Equity Funds that already exist and are interested in portfolio diversification.

This fund can turn into semi-open and periodically admit new entries and may provide loans or venture

capital or private equity funding. We do not examine the structure of specific financing schemes, but

rather the volume of trading of ETFs and VC as financing means.

We can therefore realize that our approach is definitely adding value to the existing literature of

SME financing, as it studies the determinants of the volume of trading of ETFs and VC that invest in

SMEs. The volume of trading is seen as a success measure of the use of these two alternative

investment vehicles in providing SME funding.

The main ETF literature compares ETFs with similar investment funds, such as mutual funds

and closed-end funds. Harper, Madura and Schnusenberg (2006) compare the risk and return

performance of exchange-traded funds (ETFs) available for foreign markets and closed-end country

funds. They show (a) that ETFs exhibit higher mean returns than foreign closed-end funds, which is

attributed to lower expense ratios and (b) that ETFs have higher Sharpe ratios, on average, than

corresponding closed-end funds. This indicates that a passive investment strategy utilizing ETFs may

yield superior performance to an active investment strategy using closed-end country funds.

Paliwal (2014) examines and compares the investment performance of index mutual funds and

exchange traded funds (ETFs) tracking the same underlying index. He compares pre-tax returns of these

two products and measures their tracking errors relative to the underlying index. His results suggest that

for large-cap and broad-market indices, index funds perform relatively better than the corresponding

ETFs in terms of tracking errors. In contrast, for indices tracking narrower indices, mid-cap indices,

small-cap indices and a segment of large-cap indices, ETFs exhibit lower tracking errors compared to

the corresponding index funds.

On another direction Shin and Soydemir (2010), study ETFs to find tracking errors to be

statistically significant and negative. Further, statistically significant alpha values from testing the

relative performance of ETFs support the existence of tracking errors. With regard to the factors

affecting tracking errors, the change in the exchange rate is found to be an important factor impacting

tracking errors. The finding of negative Jensen’s alphas implies that investing in ETFs does not provide

a significant benefit compared to their benchmark returns. Their findings indicate that there appears to

be a greater divergence between market price and NAV of ETFs for the Asian markets relative to the

U.S. Therefore, the liquidity risk appears to be relatively higher for Asian ETFs. Our study is definitely


in a totally different direction compared to the existing literature, as we do not examine the investment

features of ETFs but rather the determinants of their volume of trading.

The Venture Capital literature focuses primarily in linking the Venture Capital investments

with (a) the sources of funding, such as funds, banks, insurance companies, pension funds, corporate

investors, individual investors, government, etc. (b) the stage at which the investment takes place (early,

middle, late), (c) the industry receiving the investment (life sciences, IT, electronics, manufacturing,

etc.), (d) the region (region within country, country, continent, world). Mayer, Schoors and Yafeh

(2004) investigate the above for Germany, Israel, Japan and the UK, to realize that VC investments

differ across countries with regards to the sources of investment, the stage, the sector and the

geographical focus. They conclude that neither financial systems, nor sources of finance are the main

explanations for the differences in VC activities.

The determinants of Venture Capital funding for 21 countries are examined by Jeng and Wells

(2000). They consider the importance of IPOs, GDP, market capitalization growth, labor market

rigidities, accounting standards, private pension funds and government programs on the different stages

and sources of VC financing, to realize that IPOs are the strongest driver of venture capital investing,

whereas GDP and market cap growth are not significant determinants. In addition, they find that early

stage venture capital investing is negatively impacted by labor market rigidities, while later stage is not.

Geronikolaou and Papachristou (2012) investigate the direction of causality between VC and

innovation to realize that in Europe causality runs from patents to VC, meaning that innovation seems

to create a demand for VC. When connected to our problem, as innovation is sought by most of SMEs

and as according to this paper there is evidence that innovation creates demand for VC, we can infer

that VC can be a good alternative source of funding for SMEs.

In our paper we investigate the determinants of VC financing with regards to its volume of

trading and not to its stages and sources or the link of VC to innovation. From that perspective the

existing VC literature and our contribution are complementary to each other.

By contrasting our research with the existing literature we confirm that it definitely has an

added value as (a) we are investigating the determinants of the volume of trading of ETFs and VC as

SME financing vehicles, which has not been examined in the past, (b) we are looking at ETFs as a

means of SME funding and (c) we are treating VC also as an SME financing tool. The assessment is

done based on a series of variables that reflect the volume of ETFs and VC, as analyzed in the

following section.

3. Data and methodology

Data

Our dataset consists of all small and medium cap equity ETFs, a total of 297 worldwide, as

found in Bloomberg (data extracted in June 2016). We use averages of the ETF figures for the period

1/1/2006 – 1/6/2016, as we want to capture the global trend. The average risk-free rate comes from the

same source (daily average for the period 1/1/2015-1/6/2016 for the 10-yr Government Bond). For the

country figures (GDP, GDP per capita, corporate tax rate) as well as for VC our source is the OECD

(2016a, 2016b, Koske et al. (2015), 2016c, 2011 – 2016). Consequently, this determines the countries

of interest to Australia, Austria, Belgium, Canada, China, Czech Republic, Denmark, Finland, France,

Germany, Greece, Hong Kong (SAR), China, Hungary, India, Israel, Ireland, Italy, Japan, Korea

(Republic of), Luxembourg, Mexico, Netherlands, New Zealand, Norway, Portugal, Russian

Federation, Slovak Republic, South Africa, Spain, Sweden, Switzerland, Taiwan, Turkey, United

Kingdom, United States, Estonia and Slovenia. The market capitalization and the foreign direct

investment data come from the World Bank (2016, Kaufmann and Kraay (2016)). The risk-free rate is

taken from Trading Economics (2016).

Variables

In our analysis we use variables relevant to the ETFs, to VC and to the economy of the

countries of interest. The variables relevant to the ETFs are the number of ETFs available per country,

the number of ETFs domiciled in a country, the number of ETFs that invest at a country, the total ETF

assets, the ETF amount invested per country and the number of holdings per ETF. The variable relevant

to VC is the total VC amount averaged for the years of interest. These variables are the measures of the


volume of activity of ETFs and VC and are the dependent variables of our regressions. The variables

relevant to the metrics of an economy are the GDP, the GDP per capita, the market capitalization, the

corporate income tax (as a percent of GDP), the regulatory quality, the product market regulation, the

registration and licensing requirements, the barriers to entrepreneurship, the state control, the barriers to

trade and investment, the index of economic freedom, the freedom from corruption, the fiscal freedom,

the labor freedom the trade freedom, the investment freedom, the unemployment, the competitiveness

index, the risk free rate, the 10-year government bond yield rate, the government debt (as a percent of

GDP) and the foreign direct investments (FDI). These are the parameters that are tested for their impact

on the volume of activity measures and are the independent variables of our regressions.

Methodology

We employ linear regression to assess the relation among the volume of activity measures of

ETFs and VC and the parameters that affect it. We run the linear regressions with the Stata econometric

software using Ordinary Least Squares (OLS). We rely on White’s test to detect potential

heteroskedasticity and we use Robust Standard Errors to tackle it when present. The regressions we run

use one dependent and one independent variable and have the following general equation:

upS 10

where S is any one of the aforementioned volume of activity measures and p is any one of the

parameters that determine it as defined in the variable session. The only variation is the number of ETF

holdings, which was regressed solely with the expense ratio.

4. Results

The number of ETFs available in a country is positively correlated at all levels with the market

capitalization, the competitiveness index, the GDP and the FDI. It is positively correlated with the labor

freedom at the 10% significance level. Moreover, it is positively correlated at all levels with the VC

amount. The remaining of the variables shows no statistical significance. However, it is worth

mentioning that it is negatively correlated with the product market regulation, the barriers to

entrepreneurship, the state control, the barriers to trade and investment, the unemployment, the risk-free

rate and the 10-year government bond yield, whereas it is positively correlated with the regulatory

quality, the index of economic freedom, the freedom from corruption, the trade freedom, the investment

freedom, the GDP per capita and the government debt.

The number of ETFs domiciled in a country is positively correlated at all levels with the market

capitalization, the GDP and the FDI. It is positively correlated at the 10% significance level with the

labor freedom. In addition, it is positively correlated at all levels with the VC amount. Although the

remaining of the variables exhibit no statistical significance we observe that it is negatively correlated

with the product market regulation, the barriers to entrepreneurship, the state control, the barriers to

trade and investment, the unemployment, the risk-free rate and the 10-year government bond yield,

whereas it is positively correlated with the regulatory quality, the index of economic freedom, the

freedom from corruption, the trade freedom, the GDP per capita and the government debt.

The ETF amount invested per country shows positive correlation at all significance levels with

the market capitalization, the GDP and the FDI and at the 5% level with the labor freedom. It is also

positively correlated at all levels with the VC amount. The other variables post no statistical

significance. However, as before, there is negative correlation with the product market regulation, the

barriers to entrepreneurship, the state control, the barriers to trade and investment, the unemployment,

the risk-free rate and the 10-year government bond yield, whereas it is positively correlated with the

regulatory quality, the index of economic freedom, the freedom from corruption, the trade freedom, the

GDP per capita and the government debt.

The number of ETFs that invest at a country is positively correlated at all levels with the market

capitalization, the GDP, the GDP per capita, the competitiveness index and the FDI at all levels. It is

positively correlated at the 5% confidence level with the freedom from corruption and the labor

freedom and at the 10% level with the index of economic freedom and the regulatory quality. It is

negatively correlated at the 10% level with the state control. As before, it is positively correlated at all

levels with the VC amount. There seems to be no other variable that has some statistical significance.

However, there is negative correlation with the product market regulation, the barriers to


entrepreneurship, the state control, the unemployment, the risk-free rate and the 10-year government

bond yield; there is positive correlation with the trade freedom and the government debt.

The total assets of the ETFs domiciled in a country exhibit positive correlation at all levels with

the market capitalization, the GDP and the FDI. It is also positively correlated at all levels with the VC

amount. The remaining of the variables exhibits no statistical significance. There seems to be though

negative correlation with the product market regulation, the barriers to entrepreneurship, the state

control, the unemployment, the risk-free rate and the 10-year government bond yield; there is positive

correlation with the government debt.

The average total assets per ETF domiciled in a country are positively correlated at all levels

with the government debt. It is positively correlated with the labor freedom at the 5% significance level.

It is negatively correlated with the trade freedom at the 10% significance level. The other variables

show no statistical significance. There appears to exist though negative correlation with the barriers to

entrepreneurship, the state control, the unemployment, the risk-free rate and the 10-year government

bond yield; there is positive correlation with the fiscal freedom, the GDP, the competitiveness index and

the market capitalization.

The VC amount per country is positively correlated at all levels with the market capitalization,

the GDP, the labor freedom and the FDI. The other variables seem to have no statistical significance. It

is worth mentioning their correlation though. Similarly to the other dependent variables, the VC amount

per country is negatively correlated with the product market regulation, the barriers to entrepreneurship,

the state control, the unemployment, the risk-free rate, whereas it is positively correlated with the

regulatory quality, the index of economic freedom, the freedom from corruption, the trade freedom, the

competitiveness index and the GDP per capita.

The number of ETF holdings is negatively correlated with the expense ratio at all significance

levels. This means that ETFs that hold a higher number of holdings tend to have lower expense ratios.

White’s test

As mentioned earlier, we used White’s test to detect heteroskedasticity and then we corrected it

accordingly with the use of robust standard errors. There were a few cases that it could not be corrected

and these are (i) the number of ETFs that invest at a country with the state control, (ii) the ETF amount

invested per country with the labor freedom, (iii) the ETF amount invested per country with the GDP,

(iv) the number of ETFs available in a country with the labor freedom, (v) the number of ETFs

available in a country with the GDP, (vi) the number of ETFs domiciled in a country with the labor

freedom and (vii) number of ETFs domiciled in a country with the GDP.

In addition, (viii) number of ETFs that invest at a country with the labor freedom and (ix)

number of ETFs that invest at a country with the GDP were corrected at the 10% level (lower

significance compared to the initial one).

The implication of our results is that - if we exclude the size of an economy as measured by its

GDP and GDP per capita - it is the market capitalization, as well as the economic, investment and

regulatory environment, along with the global competitiveness of the country and its capacity to attract

foreign direct investments that makes it appealing to small and mid cap ETFs – as measured by

availability, domiciliation and investment at a country. The same applies to VC. It is therefore

important that countries that wish to exploit these alternative forms of investment pay attention to the

entire equity market, as well as the perceived quality of their economic, investment and regulatory

environment. They also need to focus to their overall competitiveness as well as their FDIs.

Interestingly enough the public debt is not a showstopper. Moreover, excluding the significance level, it

seems to exhibit positive correlation with the key volume-of-activity metrics for ETFs and VC. This can

be an opportunity for countries that went through an adjustment program, such as Greece, whose

government debt as a percent of GDP is comparatively high.

At the same time, the stock exchange needs to give the opportunity to qualifying SMEs to be

listed to the appropriate part/ sector of the market so that fund managers feel secure in investing at

them. The capitalization thresholds need to be adapted to the size of the economy of the country. This

may require a significant number of SMEs to be listed, so that the creation of a fund or the allocation of

a portion of a fund to these SMES is justified. Consequently, their inclusion in global indices will be

facilitated. The potential historical competitive advantage of the countries that initially attracted ETFs

and VC is not reflected in our study. It could be for example that the pioneers (such as Ireland) were the


first to offer lower tax rates, suitable legal and regulatory environment and other incentives that led

investment firms and fund managers to choosing them as domiciliation countries.

5. Further Research

All papers have certain limitations. We have identified some of ours that we leave for further

research. They have to do with the use of alternative investments for the funding of SMEs on one hand

and the methodology employed on the other hand.

When it comes to the former, we have left for future research the deeper study of the

domiciliation of the ETF holdings in connection to the tax regime at the time of their initiation. Such a

study will allow us to better view the (corporate) tax perspective and quantify the potential tax

advantages of the ETFs. There is a series of additional sources of funding such as Crowd Funding,

Leasing, Factoring, Business Angels, Incubators, etc. whose study is also left for further research, as the

relevant data need to be retrieved. Finally, the link between bank lending and ETFs is to be studied in

the future when we gain access to bank lending data.

As far as the latter is concerned, we leave for the immediate future the use of panel data, along

with the inclusion of more than one dependent variable in our model. In this manuscript we used

averages of the available data (for the period 1/1/2006 – 1/6/2016) as we wanted to capture the global

trend. This was also due to the fact that we faced different frequencies of the available data for the

variables we used.

6. Conclusions

In this paper we managed to identify the parameters of volume of activity of ETFs and VC in a

country. More specifically, ETFs seem to grow simultaneously with VC. ETFs seem to grow in

countries with higher GDP, GDP per capita, market capitalization, competitiveness, foreign direct

investments, regulatory quality, economic freedom, trade freedom and lower barriers to

entrepreneurship, corruption, state control, product market regulation, risk-free rate, government bond

yield and unemployment. VC investments tend to be higher in countries with higher GDP amounts,

market cap, foreign direct investment, labor freedom, regulatory quality, economic freedom, freedom

from corruption, trade freedom, competitiveness index, GDP per capita and lower product market

regulation, barriers to entrepreneurship, state control, unemployment and risk-free rate. Consequently,

countries that would like to exploit ETFs and VC as forms of funding for SMEs, besides looking at their

GDP capacity need to focus on their overall perceived competitiveness and shape an environment

comfortable for the investors in terms of regulatory quality and perceived freedom (economic, state,

from corruption, etc.). They have to globally attract investments via the stock market or FDI and

maintain low interest rates. As far as VCs are concerned, they seem to move in parallel with ETFs, with

the GDP and market cap primarily affecting them.

Acknowledgements We thank Professor Angelos Antzoulatos, from the Department of Banking and Financial

Management of the University of Piraeus, with whom we started discussing part of this project together,

for his valuable insights.


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Kersten, R., Harms., J., Liket, K. and Maas, K. (2017), Small Firms, large Impact? A systematic review

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Appendix: Regression tables

Variables/

Regressions

Dependent

variables

ETF amount

invested X X X X X X X X

Independent

variables

Regulatory

quality

1905.322

(0.24)

Market

capitalization

7.19e-09***

(14.18)

Product market

regulation

-11757.33

(-1.01)

Registration and

licensing

requirements

2496.421

(0.72)

Barriers to

entrepreneurship

-10899.55

(-1.04)

State control

-12733.18

(-1.39)

Barriers to trade

and investment

-2132.255

(-0.18)

Index of

economic

freedom

450.9584

(0.72)

Constant 3989.033

(0.37)

-6402.881**

(-2.53)

25063.2

(1.31)

-2302.841

(-0.17)

25835.6

(1.33)

35740.69

(1.64)

7934.498

(0.81)

-25310.14

(-0.57)

Observations 37 30 35 35 35 35 35 37

Adjusted R-

squared -0.0269 0.8735 0.0007 -0.0143 0.0023 0.0266 -0.0293 -0.0137


Variables/

Regressions

Dependent

variables

ETF amount

invested X X X X X X X X

Independent

variables

Freedom from

corruption

165.3736

(0.55)

VC amount

6.537605***

(90.77)

Fiscal freedom

-27.49461

(-0.06)

Labor freedom

872.7654**

(2.35)

Trade freedom

375.9142

(0.36)

Investment

freedom

-91.71356

(-0.28)

GDP per capita

.366032

(1.03)

Unemployment

-707.0825

(-0.64)

Constant -4635.507

(-0.23)

-1413.09***

(-3.57)

8007.576

(0.27)

-48086.57**

(-2.04)

-25858.86

(-0.29)

13100.99

(0.53)

-7768.093

(-0.51)

12860.17

(1.15)

Observations 37 31 37 37 37 37 34 33

Adjusted R-

squared -0.0198 0.9964 -0.0285 0.1120 -0.0249 -0.0262 0.0019 -0.0187


Variables/

Regressions

Dependent

variables

ETF amount

invested X X X X X X X

Independent

variables

Corporate tax -252047.7

(-0.54)

GDP

6.030567***

(5.31)

Competitiveness

index

17406.21

(1.60)

10-year

government

bond yield rate

-919.6913

(-0.23)

Risk free rate

-673.1102

(-0.32)

Government

debt

145.8074

(0.86)

FDI

2.99e-07***

(4.70)

Constant 14933.31

(0.98)

-5166.739

(-1.07)

-80944.56

(-1.48)

14200.74

(0.83)

8175.237

(1.10)

-3455.239

(-0.23)

-7349.152

(-1.41)

Observations 30 34 37 20 34 29 36

Adjusted R-

squared -0.0249 0.4519 0.0415 -0.0524 -0.0279 -0.0096 0.3762


Variables/

Regressions

Dependent

variables

Number of ETFs

that invest X X X X X X X X

Independent

variables

Regulatory

quality

15.66858*

(1.86)

Market

capitalization

5.33e-12***

(5.54)

Product market

regulation

-17.59859

(-1.40)

Registration and

licensing

requirements

-1.383543

(-0.36)

Barriers to

entrepreneurship

-8.883747

(-0.76)

State control

-16.53834*

(-1.66)

Barriers to trade

and investment

-15.69371

(-1.24)

Index of

economic

freedom

1.277143*

(1.91)

Constant 29.8308***

(2.64)

39.50978***

(8.21)

76.49111***

(3.68)

53.59622***

(3.60)

64.46903***

(3.00)

86.68547***

(3.67)

59.22855***

(5.65)

-41.30515

(-0.88)

Observations 37 30 35 35 35 35 35 37

Adjusted R-

squared 0.0638 0.5056 0.0273 -0.0262 -0.0124 0.0492 0.0156 0.0684


Variables/

Regressions

Dependent

variables

Number of

ETFs that

invest

X X X X X X X X

Independent

variables

Freedom from

corruption

.7209614**

(2.31)

VC amount

.004709***

(4.80)

Fiscal freedom

-.5395175

(-1.12)

Labor freedom

.9539996**

(2.32)

Trade freedom

1.003927

(0.87)

Investment

freedom

.3173671

(0.90)

GDP per capita

.0009709***

(2.71)

Unemployment

-.7731434

(-0.64)

Constant .7176358

(0.03)

43.93212***

(8.15)

83.65005***

(2.57)

-11.43141

(-0.44)

-37.68612

(-0.38)

23.9607

(0.88)

10.39308

(0.68)

55.03368***

(4.49)

Observations 37 31 37 37 37 37 34 33

Adjusted R-

squared 0.1073 0.4235 0.0069 0.1083 -0.0070 -0.0054 0.1618 -0.0188


Variables/

Regressions

Dependent

variables

Number of ETFs

that invest X X X X X X X

Independent

variables

Corporate tax 58.90024

(0.12)

GDP

.0049488***

(3.36)

Competitiveness

index

41.87303***

(4.07)

10-year

government

bond yield rate

-3.17998

(-0.97)

Risk free rate

-2.828079

(-1.30)

Government

debt

.1936143

(1.06)

FDI

3.94e-10***

(6.67)

Constant 48.233***

(2.91)

39.00828***

(6.21)

-161.7109***

(-3.12)

68.0968***

(4.79)

55.55067***

(7.14)

35.97331**

(2.27)

30.66371***

(6.33)

Observations 30 34 37 20 34 29 36

Adjusted R-

squared -0.0352 0.2381 0.3017 -0.0030 0.0202 0.0046 0.5544


Variables/

Regressions

Dependent

variables

Number of ETFs

available X X X X X X X X

Independent

variables

Regulatory

quality

4.156706

(0.62)

Market

capitalization

5.32e-12***

(11.16)

Product market

regulation

-12.34726

(-1.30)

Registration and

licensing

requirements

.9208941

(0.33)

Barriers to

entrepreneurship

-10.50818

(-1.25)

State control

-12.0065

(-1.57)

Barriers to trade

and investment

-6.232089

(-0.62)

Index of

economic

freedom

.4341904

(0.82)

Constant 6.515986

(0.71)

1.717541

(0.70)

31.09506**

(2.04)

8.882126

(0.83)

30.53833

(1.98)

38.99368**

(2.20)

15.97254

(2.06)

-19.06727

(-0.51)

Observations 35 28 33 33 33 33 33 35

Adjusted R-

squared -0.0186 0.8206 0.0215 -0.0287 0.0172 0.0437 -0.0194 -0.0100


Variables/

Regressions

Dependent

variables

Number of

ETFs available X X X X X X X X

Independent

variables

Freedom from

corruption

.2382826

(0.95)

VC amount

.0048085***

(21.53)

Fiscal freedom

-.2429784

(-0.68)

Labor freedom

.5767292*

(1.94)

Trade freedom

.5768226

(0.63)

Investment

freedom

.0378992

(0.13)

GDP per capita

.0004504

(1.63)

Unemployment

-.6942587

(-0.80)

Constant -4.404334

(-0.25)

6.646853***

(5.25)

27.51873

(1.16)

-24.41321

(-1.29)

-37.84745

(-0.48)

8.583718

(0.37)

-5.723887

(-0.48)

18.53078

(2.06)

Observations 35 29 35 35 35 35 32 31

Adjusted R-

squared -0.0031 0.9429 -0.0159 0.0750 -0.0180 -0.0298 0.0509 -0.0122


Variables/

Regressions

Dependent

variables

Number of ETFs

available X X X X X X X

Independent

variables


(-0.60)

GDP

.0045803***

(5.10)

Competitiveness

index

18.26282**

(2.17)

10-year

government

bond yield rate

-1.598276

(-0.54)

Risk free rate

-1.513487

(-0.87)

Government

debt

.0939728

(0.71)

FDI

2.40e-10***

(4.95)

Constant 19.54879*

(1.68)

3.36156

(0.86)

-80.0997*

(-1.89)

20.50244

(1.59)

15.02957***

(2.57)

6.251203

(0.55)

.5025893

(0.12)

Observations 29 32 35 20 32 28 34

Adjusted R-

squared -0.0234 0.4465 0.0982 -0.0388 -0.0081 -0.0184 0.4156


Variables/

Regressions

Dependent

variables

Number of ETFs

domiciled X X X X X X X X

Independent

variables

Regulatory

quality

1.590432

(0.13)

Market

capitalization

6.52e-12***

(9.67)

Product market

regulation

-17.5083

(-1.07)

Registration and

licensing

requirements

4.072097

(0.77)

Barriers to

entrepreneurship

-15.35986

(-1.04)

State control

-18.07555

(-1.35)

Barriers to trade

and investment

-8.512015

(-0.48)

Index of

economic

freedom

.4221237

(0.43)

Constant 12.11436

(0.70)

-3.343249

(-0.79)

44.72559

(1.54)

2.489633

(0.13)

44.38837

(1.51)

58.27905

(1.76)

21.86864

(1.31)

-16.19696

(-0.23)

Observations 21 19 19 19 19 19 19 21

Adjusted R-

squared -0.0517 0.8372 0.0084 -0.0228 0.0047 0.0442 -0.0445 -0.0426


Variables/

Regressions

Dependent

variables

Number of

ETFs

domiciled

X X X X X X X X

Independent

variables

Freedom from

corruption

.159624

(0.33)

VC amount

.005768***

(18.08)

Fiscal freedom

-.1312626

(-0.17)

Labor freedom

1.016143

(1.96)

Trade freedom

.8410656

(0.55)

Investment

freedom

-.0528824

(-0.10)

GDP per capita

.000323

(0.66)

Unemployment

-.6380666

(-0.29)

Constant 3.18

(0.09)

3.998306

(1.59)

22.98914

(0.43)

-50.09597

(-1.48)

-56.65039

(-0.44)

17.99846

(0.47)

1.876798

(0.08)

21.10969

(1.10)

Observations 21 15 21 21 21 21 18 17

Adjusted R-

squared -0.0465 0.9588 -0.0510 0.1238 -0.0362 -0.0520 -0.0343 -0.0605


Variables/

Regressions

Dependent

variables

Number of ETFs

domiciled X X X X X X X

Independent

variables


(-0.90)

GDP

.0055872***

(3.64)

Competitiveness

index

23.81694

(1.19)

10-year

government

bond yield rate

-.8947213

(-0.25)

Risk free rate

-1.491442

(-0.47)

Government

debt

.223163

(0.79)

FDI

3.00e-10***

(3.55)

Constant 39.14567

(1.50)

-.8270747

(-0.09)

-109.2538

(-1.05)

17.63627

(1.14)

18.57118

(1.55)

2.449692

(0.10)

-4.564659

(-0.52)

Observations 15 18 21 20 19 14 20

Adjusted R-

squared -0.0137 0.4191 0.0201 -0.0519 -0.0453 -0.0295 0.3796


Variables/

Regressions

Dependent

variables

VC amount X X X X X X X X

Independent

variables

Regulatory

quality

210.5497

(0.12)

Market

capitalization

1.20e-09***

(28.27)

Product market

regulation

-3478.037

(-1.10)

Registration and

licensing

requirements

474.1071

(0.79)

Barriers to

entrepreneurship

-3744.272

(-1.21)

State control

-3268.981

(-1.57)

Barriers to trade

and investment

482.8927

(0.18)

Index of

economic

freedom

100.0931

(0.76)

Constant 1063.582

(0.44)

-527.6886**

(-2.41)

6369.162

(1.36)

-283.8178

(-0.13)

7413.801

(1.45)

8428.122

(1.82)

1061.523

(0.60)

-5726.5

(-0.61)

Observations 31 25 31 31 31 31 31 31

Adjusted R-

squared -0.0340 0.9708 0.0068 -0.0125 0.0156 0.0465 -0.0333 -0.0142


Variables/

Regressions

Dependent

variables

VC amount X X X X X X X

Independent

variables

Freedom from

corruption

24.46225

(0.41)

Fiscal freedom

9.918367

(0.12)

Labor freedom

158.847**

(2.36)

Trade freedom

38.98187

(0.16)

Investment

freedom

-35.25892

(-0.51)

GDP per capita

.0579572

(0.92)

Unemployment

-124.8149

(-0.71)

Constant -352.6955

(-0.08)

694.0404

(0.13)

-8618.243**

(-2.00)

-2028.047

(-0.10)

4076.459

(0.74)

-1083.856

(-0.39)

2401.619

(1.34)

Observations 31 31 31 31 31 31 31

Adjusted R-

squared -0.0285 -0.0340 0.1320 -0.0335 -0.0253 -0.0052 -0.0167


Variables/

Regressions

Dependent

variables

VC amount X X X X X X X

Independent

variables


(-0.55)

GDP

1.661013***

(14.46)

Competitiveness

index

3167.14

(1.61)

10-year

government

bond yield rate

14.17967

(0.01)

Risk free rate

-85.11466

(-0.19)

Government

debt

23.9984

(0.86)

FDI

5.58e-08***

(5.32)

Constant 2611.185

(1.08)

-1202.611***

(-3.11)

-14626.58

(-1.47)

2647.503

(0.70)

1611.254

(1.22)

-421.7814

(-0.17)

-987.1667

(-1.19)

Observations 29 31 31 14 28 27 31

Adjusted R-

squared -0.0254 0.8740 0.0508 -0.0833 -0.0370 -0.0100 0.4763


Variables/

Regressions

Dependent

variables

Total assets X X X X X X X X

Independent

variables

Regulatory

quality

-895.8571

(-0.06)

Market

capitalization

8.09e-09***

(8.02)

Product market

regulation

-17474.12

(-0.82)

Registration and

licensing

requirements

2794.333

(0.41)

Barriers to

entrepreneurship

-18747.14

(-0.99)

State control

-20698.17

(-1.20)

Barriers to trade

and investment

222.1179

(0.01)

Index of

economic

freedom

448.2495

(0.35)

Constant 16619.25

(0.75)

-6245.356

(-0.99)

46472.79

(1.23)

8302.497

(0.34)

52616.01

(1.39)

66325.02

(1.55)

16895.51

(0.79)

-16663.55

(-0.18)

Observations 21 19 19 19 19 19 19 21

Adjusted R-

squared -0.0524 0.7786 -0.0181 -0.0485 -0.0012 0.0234 -0.0588 -0.0457


Variables/

Regressions

Dependent

variables

Total assets X X X X X X X X

Independent

variables

Freedom from

corruption

24.23618

(0.04)

VC amount

7.140927***

(10.54)

Fiscal freedom

-14.12321

(-0.01)

Labor freedom

1085.317

(1.58)

Trade freedom

-167.5223

(-0.08)

Investment

freedom

-204.6919

(-0.32)

GDP per capita

.2558822

(0.40)

Unemployment

-1793.957

(-0.65)

Constant 13846.17

(0.32)

3180.424

(0.60)

16460.41

(0.24)

-53057.54

(-1.19)

29594.49

(0.18)

30611.68

(0.63)

6726.836

(0.22)

31838.09

(1.31)

Observations 21 15 21 21 21 21 18 17

Adjusted R-

squared -0.0525 0.8872 -0.0526 0.0698 -0.0522 -0.0471 -0.0517 -0.0371


Variables/

Regressions

Dependent

variables

Total assets X X X X X X X

Independent

variables


(-0.72)

GDP

6.908867***

(3.41)

Competitiveness

index

28499.32

(1.10)

10-year

government

bond yield rate

-518.2894

(-0.11)

Risk free rate

-1624.019

(-0.40)

Government

debt

277.3901

(0.79)

FDI

3.44e-07***

(2.98)

Constant 42789.4

(1.27)

-2893.337

(-0.25)

-132095.9

(-0.98)

18058.18

(0.91)

20806.95

(1.35)

-3301.36

(-0.11)

-5881.254

(-0.49)

Observations 15 18 21 20 19 14 20

Adjusted R-

squared -0.0353 0.3851 0.0107 -0.0548 -0.0491 -0.0293 0.2929


Variables/

Regressions

Dependent

variables

Average total

assets per ETF

domiciled

X X X X X X X X

Independent

variables

Regulatory

quality

-280.3328

(-0.38)

Market

capitalization

2.34e-11

(0.22)

Product market

regulation

271.3604

(0.26)

Registration and

licensing

requirements

-505.8794

(-1.66)

Barriers to

entrepreneurship

-43.03166

(-0.05)

State control

-69.64539

(-0.08)

Barriers to trade

and investment

1059.78

(1.00)

Index of

economic

freedom

-7.060855

(-0.12)

Constant 1397.741

(1.33)

1022.479

(1.54)

672.8369

(0.37)

2716.337

(2.47)

1211.07

(0.65)

1295.215

(0.61)

307.0649

(0.31)

1555.285

(0.35)

Observations 21 19 19 19 19 19 19 21

Adjusted R-

squared -0.0446 -0.0558 -0.0545 0.0887 -0.0587 -0.0584 0.0002 -0.0519


Variables/

Regressions

Dependent

variables

Average total

assets per ETF

domiciled

X X X X X X X X

Independent

variables

Freedom from

corruption

-16.84316

(-0.58)

VC amount

.0016408

(0.02)

Fiscal freedom

17.26721

(0.36)

Labor freedom

-11.95243

(-0.34)

Trade freedom

-167.2253

(-1.93)

Investment

freedom

-15.8923

(-0.52)

GDP per capita

-.0130039

(-0.43)

Unemployment

-149.9421

(-1.17)

Constant 2200.339

(1.07)

1301.863

(1.65)

-121.3786

(-0.04)

1803.64

(0.80)

15114.62

(2.07)

2221.591

(0.95)

1709.814

(1.17)

2274.8

(2.01)

Observations 21 15 21 21 21 21 18 17

Adjusted R-

squared -0.0345 -0.0769 -0.0454 -0.0462 0.1198 -0.0381 -0.0504 0.0231


Variables/

Regressions

Dependent

variables

Average total

assets per ETF

domiciled

X X X X X X X

Independent

variables


(0.22)

GDP

.0217942

(0.17)

Competitiveness

index

91.38842

(0.07)

10-year

government

bond yield rate

-28.68334

(-0.13)

Risk free rate

-118.9428

(-0.61)

Government

debt

18.38641***

(4.62)

FDI

-4.39e-09

(-0.66)

Constant 970.0108

(0.59)

1075.636

(1.47)

575.2827

(0.09)

1195.358

(1.26)

1421.656*

(1.95)

-716.8579*

(-2.07)

1358.636*

(1.94)

Observations 15 18 21 20 19 14 20

Adjusted R-

squared -0.0728 -0.0606 -0.0523 -0.0545 -0.0358 0.6105 -0.0310


Variables/

Regressions

Dependent

variables

Number of

ETF holdings X

Independent

variables

Expense ratio -327.0309***

(-2.67)

Constant 582.2241***

(8.93)

Observations 268

Adjusted R-

squared 0.0224

Note: t-values in parenthesis; ***statistically significant at the 1% level; **statistically significant at the 5% level; *statistically significant at the 10% level

Source: Regressions run by the authors with data coming from Bloomberg (2016), OECD (2016a, 2016b, Koske et al. (2015), 2016c, 2011 – 2016), the World Bank (2016,

Kaufmann and Kraay (2016)) and Trading Economics (2016).

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