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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.
16 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 17
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
18 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 19
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
20 Thomas Poufinas and Maria Polychronou
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.
Alternative investments as a financing tool for small and medium enterprises 21
References
Beck, T. and Demirguc-Kunt, A. (2006), Small and medium-size enterprises: Access to finance as a
growth constraint, Journal of Banking & Finance, 30 (11), 2931–2943.
Beck, T., Demirguc-Kunt, A. and Maksimovic, V. (2008), Financing patterns around the world: Are
small firms different?, Journal of Financial Economics, 89 (3), 467-487.
Beck, T., Demirguc-Kunt, A. and Singer, D. (2013), Is Small Beautiful? Financial Structure, Size and
Access to Finance, World Development, 52, 19-33.
Bloomberg (2016), ETF data, June 2016.
Boscoianu M., Prelipean, G., Calefariu E. and Lupan, M. (2015), Innovative instruments for SME
financing in Romania - a new proposal with interesting implications on markets and institutions,
Procedia Economics and Finance 32, 240 – 255.
Eurostat (2016), Tax revenue statistics, http://ec.europa.eu/eurostat/statistics-explained/ index.
php/Tax_revenue_statistics#Further_Eurostat_information (2016).
Fowowe, B. (2017), Access to finance and firm performance: Evidence from African countries, Review
of Development Finance 7, 6-17.
Geronikolaou, G. and Papachristou, G. (2012), Venture Capital and Innovation in Europe, Modern
Economy, 3, 454-459.
Harper, J.T., Madura, J. and Schnusenberg (2006), Performance comparison between exchange-traded
funds and closed-end country funds, Int. Fin. Markets, Inst. and Money, No. 16 (2006), 104–122.
ICI RESEARCH PERSPECTIVE (2014), Understanding Exchange-Traded Funds: How ETFs Work,
Vol. 20, No. 5, September 2014, www.ici.org
Invest Europe (2015), 2015 European Private Equity Activity, Statistics on Fundraising, Investments &
Divestments, May 2016, www.investeurope.eu
Jeng, L.A. and Wells, P.C. (2000), The determinants of venture capital funding: evidence across
countries, Journal of Corporate Finance, No. 6 (2000), 241–289.
Kaufmann D. and Kraay A (2016), The Worldwide Governance Indicators (WGI) project, The World
Bank, http://info.worldbank.org/governance/wgi, (2016).
Kenney, M. (2000), Note on “Venture Capital”, Berkeley Roundtable on the International Economy,
BRIE Working Paper 142.
Kersten, R., Harms., J., Liket, K. and Maas, K. (2017), Small Firms, large Impact? A systematic review
of the SME Finance Literature, World Development, 97, 330-348.
Koske, I., I.Wanner, R. Bitetti and O. Barbiero (2015), “The 2013 update of the OECD product market
regulation indicators: policy insights for OECD and non-OECD countries”, OECD Economics
Department Working Papers, No. 1200 (2016).
Lee, N., Sameen H. and Cowling, M. (2015), Access to finance for innovative SMEs since the financial
crisis, Research Policy, 44 (2), 370-380.
Mayer, C., Schoors, K. and Yafeh, Y. (2005), Sources of funds and investment activities of venture
capital funds: evidence from Germany, Israel, Japan and the United Kingdom, Journal of
Corporate Finance, No. 11 (2005), 586 – 608.
Miller, T. and Kim A. B. (2016), Index of Economic Freedom – Promoting Economic Opportunity and
Prosperity, Institute for Economic Freedom and Opportunity, The Heritage Foundation – In
partnership with The Wall Street Journal (2016).
OECD (2011, 2012, 2013, 2014, 2015, 2016), Entrepreneurship at a glance (2011, 2012, 2013, 2014,
2015, 2016).
OECD (2016a), Data, https://data.oecd.org/gdp/gross-domestic-product-gdp.htm (2016).
OECD (2016b), Data, https://data.oecd.org/unemp/unemployment-rate.htm (2016).
OECD (2016c), Overall statutory tax rates on dividend income, http://stats.oecd.org/ (2016).
Poufinas, T. and Kouskouna, E. (2017), On the split of social security contributions between funded and
pay-as-you-go pension schemes; contribution to growth, The Greek Debt Crisis - In Quest of
Growth in Times of Austerity, Palgrave – Macmillan, 129-152.
Quartey, P., Turkson, E., Abor, J. Y. and Iddrisu, A. M. (2017), Financing the growth of SMEs in
Africa: What are the contraints to SME financing within ECOWAS?, Review of Development
Finance 7, 18-28.
22 Thomas Poufinas and Maria Polychronou
Rupeika-Apoga, R. (2014), Alternative financing of SMEs in the Baltic States: myth or reality?,
Procedia - Social and Behavioral Sciences, 156, 513 – 517.
Sarker, S. R. (2017), Small and Medium Enterprise Financing, The Cost and Management, No 45 (2),
2-8.
Shin S. and Soydemir, G. (2010), Exchange-traded funds, persistence in tracking errors and information
dissemination, Journal of Multinational Financial Management, J. of Multi. Fin. Manag., No. 20
(2010), 214–234.
The World Bank (2016), Data Extract from World Development Indicators, http://data.worldbank
.org/indicator/CM.MKT.LCAP.CD (2016).
Trading Economics (2016), www.tradingeconomics.com/bonds, July 4, 2016.
Alternative investments as a financing tool for small and medium enterprises 23
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
24 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 25
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
26 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 27
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
28 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 29
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
30 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 31
Variables/
Regressions
Dependent
variables
Number of ETFs
available X X X X X X X
Independent
variables
Corporate tax -214.497
(-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
32 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 33
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
34 Thomas Poufinas and Maria Polychronou
Variables/
Regressions
Dependent
variables
Number of ETFs
domiciled X X X X X X X
Independent
variables
Corporate tax -608.8712
(-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
Alternative investments as a financing tool for small and medium enterprises 35
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
36 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 37
Variables/
Regressions
Dependent
variables
VC amount X X X X X X X
Independent
variables
Corporate tax -40235.8
(-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
38 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 39
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
40 Thomas Poufinas and Maria Polychronou
Variables/
Regressions
Dependent
variables
Total assets X X X X X X X
Independent
variables
Corporate tax 632946.6
(-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
Alternative investments as a financing tool for small and medium enterprises 41
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
42 Thomas Poufinas and Maria Polychronou
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
Alternative investments as a financing tool for small and medium enterprises 43
Variables/
Regressions
Dependent
variables
Average total
assets per ETF
domiciled
X X X X X X X
Independent
variables
Corporate tax 9569.464
(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
44 Thomas Poufinas and Maria Polychronou
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).