should you tilt your equity portfolio to smaller countries...
TRANSCRIPT
1
Should You Tilt Your Equity Portfolio to Smaller Countries?
Abstract
This paper examines the relation between country size, measured as the aggregate market
capitalization of the listed stocks in a country, and individual stock returns. We find that stocks
from small countries tend to have higher average returns than stocks from large countries. The
country size effect is largely independent of the firm size effect and other country quantitative
factors such as book-to-market and momentum. We conjecture that the country-size effect is due
to home bias and provide mixed evidence in support of this conjecture.
Contact Info:
Gregg S. Fisher
Gerstein Fisher Head of Quantitative Research and Portfolio Strategy 565 Fifth Avenue, 27th Floor New York, NY 10017 [email protected]
Ronnie Shah
University of Texas at Austin - Department of Finance McCombs School of Business Austin, TX 78712
Sheridan Titman1
University of Texas at Austin - Department of Finance McCombs School of Business Walter W. McAllister Centennial Chair in Financial Services Austin, TX 78712 [email protected]
Keywords: Size Premium, International Investing, Portfolio Construction
1Contact Author. Sheridan Titman is an advisor to Gerstein Fisher, which employs quantitative equity investment
strategies that tilt towards smaller countries. The views expressed here are those of the authors and not necessarily those of any affiliated institution. We thank Chris Meeske, Ashvin Viswanathan, Tianyu Wang and other members of the Gerstein Fisher Investment Strategy & Research Group for their research assistance. This research has benefited from discussions with Rawi E. Abdelal of Harvard Business School.
2
Introduction
US investors continue to shift investments into foreign markets. Indeed, over the past 10
years, US equity mutual funds have experienced $834 billion in net outflows, compared to $643
billion of net inflows into international funds.2 These flows have increased the share of equity
mutual funds assets that invest in international markets from 23% to 27%, and the average asset
allocation fund currently invests 30% of all equity assets internationally.3 The proliferation of long-
only equity strategies that invest in multiple countries necessitates a country allocation policy that
takes into account the trade-offs of investing in different financial markets.
Traditional portfolio theories, like the CAPM, suggest that the value-weighted combination
of the country portfolios provides an efficient allocation. However, a variety of frictions associated
with international investing can lead to substantial deviations from value-weighting. Indeed, a
number of authors starting with Keppler and Traub (1993, 2011) have argued that returns in
smaller countries are higher than returns in larger countries, so tilting towards smaller country
stocks may increase returns as well as provide diversification benefits. Other authors have
observed a momentum effect in country indices, suggesting that one might not want to hold
country weights constant.
In this paper we will reexamine what we will be referring to as the small country effect, or
the tendency for stocks in smaller financial markets to out-perform stocks in larger financial
markets. To what extent is this driven by the inclusion of Japan, the largest foreign market, which
also had terrible returns in the 1990s? To what extent is it driven by the fact that the average
market capitalization of stocks in smaller countries tends to be smaller? Perhaps the small country
effect is simply a manifestation of the small firm effect. Finally, we consider the relation between
the country momentum effect and the small country effect.
We also consider a potential explanation for the small country effect. Intuitively, if
investors are risk averse and subject to home bias, the expected rates of return of stocks should
be higher in smaller countries because investors in these countries, who have less diversification
2Source: 2015 Investment Company Institute Factbook. 3Source: Morningstar. Calculated using Total Net Assets from US and International Equity Mutual funds from December 2005 to December 2016. Asset Allocation data based on weighted average of US and non-US equity share of assets for mutual funds categorized by Morningstar as Asset Allocation/Target Date.
3
opportunities, require higher rates of return. Small country stocks may also attract fewer foreign
investors, because the small countries receive less attention from sell-side analysts and they tend
to be less regulated and provide lower investor protection. The smaller markets are also potentially
more vulnerable to the risks associated with the fickle nature of global portfolio flows. Each of
these effects is likely to be less important in developed markets that are more open to foreign
investors, and the larger stocks in the smaller markets that have access to international investors
are likely to be less affected. Hence, the small country effect should be strongest for the smaller
stocks in the emerging markets, and increased globalization of financial markets should weaken
the country size premium over time.
To address these issues we analyze individual stock returns and measure the extent to
which the size of the market in which the firm is domiciled influences returns after controlling for
firm size. We find that there is a small country effect, and that it is not a manifestation of the small
firm effect. These results are influenced by the presence of Japan, a large country with poor
returns, but the results are still significant in a sample that does not include Japan. We also find
that our results are not driven by other country-level factors such as momentum or the average
book-to-market ratio in the country.
Our evidence provides mixed support for our conjecture that the small country effect is
generated because of home bias and market frictions that reduce the access of small country
stocks to international investors. We find that the small country effect is less pronounced in the
more recent period, which is consistent with the idea that impediments to international investing
has been reduced over time. We also find that the small country effect is stronger in emerging
markets, which is consistent with both international investors having limited access to these
markets, as well as the fact that investors in these markets tend to have less ability to invest
internationally. Our most puzzling result, however, is that the small country effect is as strong for
large capitalization stocks as small capitalization and mid-capitalization stocks. However, at least
part of this observation can be attributed to differences in analyst following, which may be a proxy
for the interest in these stocks by international investors. Among large stocks, we find that stocks
from smaller markets tend to have significantly lower analyst coverage when compared to stocks
from larger markets. We do not find this relationship for small capitalization and mid-capitalization
stocks, but the coverage of these stocks is fairly minimal.
4
In addition to the earlier cited papers that directly focus on the small country effect, the
issues raised in this paper relate to the more general international investment literature. Keppler
and Encinosa (1993, 2011) analyze 18 equity markets that are components of the MSCI World
Index and show that a capitalization-weighted portfolio that invests in the six smallest markets has
a 12.79% annualized compound return, outperforming the capitalization-weighted portfolio that
invests in all 18 markets by 5.02% over the period January 1970 to December 2009. Asness, Liew
and Stevens (1997) show that country-level size, momentum and value (aggregate price-to-book)
explain differences in country returns. Desrosiers, L’Her and Plante (2004) analyze the
performance of global investment strategies based on country indices from the 18 largest stock
markets and find that country momentum explains differences in country returns, but aggregate
book-to-market does not. Li and Pritamani (2015) find country size and momentum effects for
various emerging and frontier markets. Angelidis and Tessaromatis (2016) explore how to best
construct a multi-country global portfolio using value, momentum, low volatility and size country-
level variables.
Our paper contributes to this literature in a number of ways, in addition to providing an
explanation for the country size effect. First, larger countries tend to have larger firms - our
regression methodology allows us to test whether the firm size effect drives the country size effect,
while the majority of the previous literature in this area reports portfolio return differences for
combinations of country indices. Second, we show that our results are not driven by Japan, which
is the largest country but happens to have among the poorest returns in our sample. Third, we
find evidence of a significant country size effect in both developed and emerging markets and
show that this relationship has weakened over time. Last, we control for other country-level
quantitative factors such as momentum and the book-to-market ratio, and our results generally
suggest the country size premium is independent of these other sources of expected return.
Our paper is also related to research on the benefits of international diversification. Asness,
Israelov and Liew (2011) suggest that investors benefit from international diversification in the
long-run as economic growth drives variation in country returns. Braymen and Johnson (2015)
show that a trade-adjusted weighting scheme can be used to improve risk-adjusted performance
relative to a GDP-weighted portfolio. Goetzmann, Li and Rouwenhurst (2005), Eun and Lee (2010),
and Christoffersen, Errunza, Jacobs and Langlois (2011) suggest that emerging markets may be
less integrated than developed markets and thus provide greater diversification benefits when
compared to portfolios that consist of only developed market equities. Our paper, in contrast,
5
suggests that forming more diverse portfolios by under-weighting large countries and over-
weighting small countries not only improves diversification but can also increase expected returns.
The rest of the paper is organized as follows: The first section explains the data sources
used in this study and presents descriptive statistics of value-weighted portfolios of country indices.
The second section provides country-level analysis on the country size premium. The third section
discusses whether home bias explains the country size effect. The fourth section reviews evidence
on analyst coverage and country size. The final section concludes.
I. Data Sources and Summary Statistics
Our research examines stocks from markets that MSCI either classifies as developed or
emerging.4 While the definitions are dynamic, we use the initial country classification at the
beginning of the sample period, January 1990, to ensure no forward-looking bias. Our analysis
also excludes various emerging market countries. We exclude Argentinian stocks due to the
transition from a floating rate currency (prior to 1992) to a fixed rate currency (until 2001) and
back to a floating rate currency. We exclude Chinese stocks, since until recently foreigners could
not purchase these stocks due to government restrictions, and Canadian stocks, as a high
proportion of those stocks are also traded on US exchanges. We also exclude stocks from Russia,
the Czech Republic, Egypt, Qatar, United Arab Emirates and Colombia, which lack sufficient data,
and we exclude ADRs, GDRs, and stocks that are headquartered in a country that is different from
country of the stock exchange that the stock is listed on.
Our final sample consists of 37 different international markets. For developed markets, our
sample starts in January 1990. For emerging markets, our sample starts in January 1996, due to
lack of stock return and foreign exchange data in certain countries. Both samples end in December
2015. Stock returns, stock exchange country codes, country incorporation codes and currency
codes for international stocks are taken from the Compustat Global Security Daily file. Information
on foreign exchange rates comes from Bloomberg. For developed market countries, we obtain
4MSCI classifies an equity market by its stage of financial development into three groups: developed, emerging, and frontier. During our sample period, three countries were reclassified by MSCI. Greece was upgraded from emerging to developed in May 2001, and then downgraded back to emerging in November 2013. Israel was upgraded from emerging to developed in May 2010. Portugal was upgraded to developed markets in November 1997.
6
aggregate book-to-market from Ken French’s website.5 We obtain the number of sell-side analysts
reporting next-year EPS estimates from I/B/E/S.
For many of our tests that follow, we sort stocks into group by firm size. Specifically, we
use an aggregate market capitalization breakpoint methodology which is also applied by index
providers such as MSCI and other asset managers. Specifically, at the beginning of each year we
assign a capitalization score (Fk) based on the sum of those stocks’ market capitalization with the
same or lower market capitalization divided by the total market capitalization of the eligible
universe (either Developed or Emerging Market stocks) multiplied by 100. A stock’s score captures
the percentage of aggregate market capitalization of stocks that have lower or equal market
capitalization values. For example, a stock with a market capitalization of $2 billion would have a
score of 75 if 75% of the total capitalization of the stock market consistents of stocks with market
capitalizations of less than $2 billion.
𝐹𝑘 =∑ 𝐶𝑎𝑝𝑗
𝑘𝑗=1
∑ 𝐶𝑎𝑝𝑖𝑁𝑗=𝑖
× 100 ∀ 𝑛, 𝑗 𝑤ℎ𝑒𝑟𝑒 𝐹𝑘 ≥ 𝐹𝑗
Take for example three stocks, A, B, and C, which have capitalizations of $200 MM, $300
MM, and $500 MM, respectively. The capitalization score for stock A is equal to 200/(200 + 300 +
500) × 100 = 20, and the score for security B is equal to (200 + 300)/(200 + 300 + 500) × 100
= 50. The score of 50 for stock B indicates that 50% of the aggregate market capitalization
(including stock B) has the same or a lower market capitalization, while 50% of the market has a
higher market capitalization.
Using each stock’s capitalization score, which is calculated annually at the end of
December, we put stocks into three groups: Large (Fk≥30), Mid (30>Fk≥15) and Small (15>Fk≥1).
We exclude the bottom 1% of stocks, which consist of Microcap stocks that are likely to be illiquid
and hard to trade. While this is similar to the methology that MSCI uses, we depart in one key way
– instead of dividing stocks into groups by country or region breakpoints, we define the breakpoints
across the entire universe of developed or emerging market stocks. Thus, even though Great
5 For more information, see Ken French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
7
Britain has larger firms on average, its mid-cap stocks are similar in size to mid-cap stocks from
other countries. In this way, our breakpoints are homogenous across the respective developed
and emerging universes.
Exhibit 1 displays index weights for multi-country portfolios that are weighted by end-of-
year market capitalization. Each chart reports (i) the largest country’s weight, (ii) the sum of the
next four largest countries’ weights, and (iii) the sum of the remaining countries’ weights. The top
chart shows weight distributions for developed markets; the bottom chart illustrates results for
emerging markets. The following 19 countries were classified as developed: Australia, Austria,
Belgium, Denmark, Finland, France, Germany, Great Britain, Hong Kong, Ireland, Italy, Japan,
Netherlands, New Zealand, Norway, Singapore, Spain, Sweden, Switzerland; while 18 markets
were classified as emerging: Brazil, Chile, Greece, Hungary, India, Indonesia, Israel, Malaysia,
Mexico, Peru, Philippines, Poland, Portugal, South Africa, South Korea, Taiwan, Thailand and
Turkey.
[Insert Exhibit 1 Here]
Investing in foreign markets provides exposures to different economic (GDP growth,
inflation, unemployment, and industrial production) trends, which can provide diversification
benefits to a portfolio that currently only invests in domestic assets. Exhibit 1 illustrates one of the
major challenges associated with using a capitalization-weighted passive index to gain
international exposure: in most cases, only a few countries make up most of the index. For
developed countries displayed in the top graph, Japan is the largest country, representing 67.2%
of the developed sample at the end of 1989. While Japan is still the largest country at the end of
the period, its weight had dropped to 18.4% of developed markets at the end of 2015. The trend
is for the larger countries to represent less of the portfolio over time. Despite this decline, the five
largest countries (black plus shaded line regions) still represent 63% of the developed markets at
the end of the sample period.
The bottom graph displays results for emerging markets and also shows a decline, with
68% of the index initially being represented by the following five markets (ranked descending by
8
aggregate market capitalization): Thailand, Malaysia, South Africa, Taiwan, South Korea at the end
of 1995, while the top five countries, India, South Korea, Taiwan, Thailand and South Africa still
represent 65% of the Index at the end of 2015. Exhibit 1 suggests more generally that while
capitalization-weighted passive international indices invest in multiple countries, the actual
economic exposure is concentrated in only a few large countries due to those countries being
much larger in terms of market size when compared to the other, smaller countries.
Appendix Exhibit 1 provides country codes for developed and emerging market countries
ranked on aggregate market capitalization used in this study. The country rankings in our paper
are different from rankings based simply on total country market capitalization due to exclusion of
stocks that are incorporated but trade on a different exchange (such as an ADR or GDR). These
restrictions particularly reduce the aggregate market capitalization of those countries like Brazil
that have historically had large corporations such as Petrobras and Vale that trade on larger
international stock exchanges. Despite these exclusions, our value-weight country index returns
generally have very high correlations (>95%) with reported MSCI index returns.
Do large countries that make up more of a passive index have lower average returns when
compared to smaller countries? Exhibit 2 illustrates the growth of wealth by investing in a value-
weighted portfolio consisting of stocks for the same groupings of countries (Largest Country, Next
Four Largest Countries, and Rest of the Market) as Exhibit 1. The first graph reports results for
Developed Markets and shows that a $1,000 investment in the largest country made on January
1, 1990 grows to only $1,015 by December 31, 2015. In contrast, investing in the next four largest
countries (which often includes Great Britain, France and Germany) yields $5,108, and investing
in the remaining fourteen countries generates $9,227 at the end of the sample period. Our results
are consistent with previous studies.
[Insert Exhibit 2 Here]
The second graph in Exhibit 2 presents results for Emerging Markets and finds that $1,000
invested in January 1, 1996 declines to $92 for an investment in the largest country, $3,037 for
the next four largest countries and $4,961 for the remaining thirteen countries. The extremely
poor returns associated with an investment in the largest country for the emerging market sample
9
requires some explanation. First, the starting point for the sample is just before the Asian Financial
Crisis (1997), which adversely affected many of the larger countries in Emerging Markets, including
Indonesia, Malaysia, Russia, South Korea, Taiwan, and Thailand6. For example, Thailand which
was the largest market in December 1995, declined 36% in 1996. As a result of that decline,
Malaysia became the largest Emerging Markets country in December 1996, and subsequently
experienced a drop of 68% in 1997. In contrast, an investment in the thirteen smallest markets
increased 12% in 1996 and 2% in 1997.
II. Do Small Country Indices Have Higher Average Returns Than Large
Country Indices?
Exhibit 3 reports performance summary statistics for different countries in Developed
Markets (Panel A) and Emerging Markets (Panel B). The left-hand side of the exhibit presents
summary statistics on the country-level, while the right-hand side shows results on the firm-level.
The countries are ranked by aggregate market capitalization as of the start of the sample period.
The five largest developed market countries have average annual returns of 5.2% per year,
compared to 7.8% for the other fourteen markets. The fourteen smaller markets have average
annual volatility of 21.7%, which is slightly higher than the 18.6% for the largest five markets.
The higher volatility for smaller countries is likely due to having fewer firms listed on their markets,
which reduces diversification. Despite the slightly higher volatility, the smallest markets have high
risk-adjusted returns (Average Sharpe Ratio of 0.34), which is 50% higher than the performance
for the largest five markets (Average Sharpe Ratio of 0.23). Smaller markets tend to have slightly
higher betas of 0.97 (measured against the MSCI World Index), compared to an average of 0.92
for the five largest markets.7
The five largest developed markets have slightly lower average stock volatility (31.3%)
when compared to the smaller fourteen markets (32.1%). We also find slightly higher stock-level
betas for stocks from smaller markets (0.80 compared to 0.75 for the five largest markets). Larger
6 For more information on the 1997 Asian financial crisis please see https://en.wikipedia.org/wiki/1997_Asian_financial_crisis. 7 For country-level summary statistics, we report compound annual returns, volatilities and betas with respect to the MSCI World Index using value-weighted country index monthly returns. The Sharpe Ratio is the average monthly return less the monthly risk-free rate (US one-month treasury rate) divided by the standard deviation of the difference between the monthly country return less the risk-free rate multiplied by the square root of 12.
10
markets tend to have larger firms (13.6 BN geometric-weighted average market capitalization)
when compared to the fourteen smaller markets (9.4 BN) and a much greater average number of
firms (732 average number of firms each year for the five largest markets compared to 104 firms
for the remaining, smaller markets).8 There are, however, some exceptions, such as Finland, which
is the fourth smallest developed country but has an average market capitalization of 12.5 BN due
to Nokia, an information technology firm with a nearly $40 BN market capitalization as of the end
of December 2015.
[Insert Exhibit 3 Here]
Panel B reports results for the emerging market sample and shows a similar picture as
Panel A. The five largest markets have average annual returns of 2.4% per year, with volatility of
27.2% and a Sharpe Ratio of 0.146. The smaller thirteen markets have average annual returns of
6.2% per year, with 29.0% in volatility and an average Sharpe Ratio of 0.29. We also find that
larger emerging markets have slightly lower index betas, higher firm volatilities, higher stock-level
betas, higher average firm size and many more firms when compared to smaller emerging markets.
The last two rows of each Panel present summary statistics for combinations of developed
and emerging markets country indices. The second-to-last row reports value-weighted results that
weight countries by market capitalization as with a passive index. The last row reports equal-
weight results that weight countries equally (the country-indices themselves, however, are value-
weighed). As we show, the return associated with an equal-weight portfolio of country indices
(8.1%) is nearly twice as big as that of the value-weighted portfolio (4.3%), which has a much
greater weight for larger countries. Interestingly, the volatility of the equal-weighted developed
market portfolio (16.2%) is actually slightly lower than for the value-weighted portfolio (16.6%),
as the benefits of diversifying across countries outweighs the slighty higher volatility and beta
associated with smaller countries. The result of reducing weight of large countries and reallocating
weight to smaller countries results in a 140% increase in risk-adjusted returns as measured by the
Sharpe Ratio. For Emerging Markets in Panel B, the equal-weight portfolio has average returns of
8 For stock-level summary statistics, we report time-series averages of volatilities and betas with respect to the MSCI World Index based on daily returns over the prior year.
11
7.5% and volatility of 20.2% per year, resulting in a Sharpe Ratio of 0.35. In contrast, the value-
weighted portfolio yields returns of 3.9%, volatility of 20.5%, and a Sharpe Ratio of only 0.18.
We start our analysis by examining Fama-MacBeth regressions of country index returns on
different country-level factors. Exhibit 4 forms country indices by capitalization-weighting stocks
from a particular country. By examining country indices rather than individual stocks, the
methodology in this section is more in line with past literature that originally identified the country
effect. We perform this exercise including all stocks (All Cap) and also excluding the largest stocks
(Mid/Small). Exhibit 4 is organized as follows. We first run univariate regressions of four different
factors – natural log of country size, natural log of average firm size within a country, natural log
of aggregate country book-to-market, and past 12-month country momentum. We then regress
country index returns on all four factors and repeat this exercise for country indices formed from
only mid- and small-capitalization firms.9
[Insert Exhibit 4 Here]
The univariate regressions show that country size Ln (Ctry) and average firm size LN (Avg.
Firm Size) are negatively related to average stock returns, while book-to-market Ln (Ctry B/M) and
momentum Ctry MOM are positively related to average stock returns. Our variable of interest,
country size, has a t-statistic that is close to or greater than 2 in each of the univariate regressions
(reported as the first regression in each set of five regressions). While average firm size is only
significant for Emerging Markets, country momentum is only significant for developed markets and
country aggregate book-to-market ratio is not significant for the Developed Market sample.
The last two regressions consider all four factors together in a multi-variate setting. As we
show, for All Countries (ALL) and Developed Markets (DM), we find coefficients for country size of
-0.14 to -0.16 with t-statistics that range from 1.91 to 2.58, respectively. There is not much
difference between using all stocks when forming country indices or using only mid- and small-
capitalization stocks. In contrast, the average firm size and book-to-market ratio’s coefficients are
insignificant for the ALL and DM samples. For the ALL sample, the coefficient on momentum is
9 We repeated our univariate regressions using only mid- and small-capitalization stocks to form value-weighted country indices and found similar (unreported) results to using all stocks to form country indices.
12
significant, but only for the ALL country sample, which uses only Mid/Small Capitalization stocks
when forming country indices. In Emerging Markets, we find that adding average firm size and
country momentum causes country size to be an insignificant predictor of average returns. We
find some evidence of average firm size predicting negative future returns after controlling for
country momentum and country size, but only when large stocks are included – this variable also
becomes much weaker when we only use Mid/Small stocks when forming country indices. Multi-
collinearity is a bigger issue in Emerging Markets, as small countries also tend to have had positive
momentum and tend to have smaller average firm size.
III. Do Stocks in Small Countries Have Higher Returns than Stocks in Large Countries?
In this section we change focus slightly and examine the returns of individual stocks rather
than those of indices. The advantage of examining individual stocks is that we can explicitly
separate the effect of country size and firm size. In addition, we can examine the interaction
between the small country effect and firm size. Recall that we conjectured that because home bias
affects small stocks more than large stocks,the small country effect will be stronger for smaller
cap stocks.
Exhibit 5 reports value-weighted monthly returns for stocks sorted on firm size (Large, Mid
and Small) and country size (Largest Country, Next four Largest Countries and Remaining
Countries). As we show in Panel A, the largest country has very poor performance, ranging from
0.09% - 0.25% for Developed Markets. In contrast, average performance for the Next Four Largest
Countries and Remaining Countries is 0.70% and 0.90%, respectively. Panel B presents results for
Emerging Markets and finds average returns for the Next Four Largest Countries is 0.76%, while
for Remaining Countries the average return is 0.92%. The returns for investing in the largest
country in Emerging Markets is abysmal, ranging from -0.76% for small capitalization stocks to -
0.62% for large capitalization stocks, compared to an average return of 0.57% for the Next Four
Largest Countries and 0.69% for the Remaining Countries. Our results presented in Exhibit 5
suggest that the country size premium is largely independent of firm size.
[Insert Exhibit 5 Here]
13
A major difference between the analysis in this section relative to the analyses presented
in Exhibit 4 is that by using index returns rather than individual stock returns, we are effectively
weighting each country equally. Our main analysis is presented in Exhibit 6, which reports results
from regressions of stock returns on the natural log of firm size, country size and a dummy for
emerging markets. Our methodology involves using individual stock returns, which enables us to
control for firm size when examining whether country size explains stock returns. The first set of
four regressions shows that within all size groupings, the log of Country Size is a negative and
significant predictor of future stock returns. We see a slightly stronger country size effect among
Large and Mid-Capitalization stocks, and contrary to the early US evidence we do not find evidence
of a small firm effect. Emerging markets also have under-performed Developed Markets during
our sample period, leading to a negative relation between the EM Dummy and average stock
returns.
[Insert Exhibit 6 Here]
The second set of regressions examines all stock markets but excludes Japan, which is the
biggest market in our sample. Dropping Japan reduces the sample by roughly 25% across all size
groups. While the Ln (Ctry) coefficient drops in each regression, we still find that country size is a
significant negative predictor of future stock returns. For the non-large size groupings, we also
find a significant positive coefficient on firm size. The third and fourth sets of regressions divide
stocks by Developed and Emerging Markets. As we show, the natural log of country size is
negatively related to average stock returns in both tests, with slightly stronger results in Emerging
Markets. The last two sets of regressions split the sample by time period. Note that in the earlier
time period prior to 2003, there are far fewer small stocks when compared to the sample after
2002. For the earlier sample period, regression coefficients are roughly two to three times as large
as in the later sample period. Also, the t-statistics for the coefficient on Ln (Ctry) are not
significantly different from zero for the regressions using the later time period between 2003 and
2015.
These results address a number of issues that were raised in the introduction. First, we
show that the country size effect is largely independent of the firm size effect and is a significant
negative predictor of future returns among groups of firms with different sizes. Second, we find
14
that our country size results persist even if we exclude the largest market, Japan. Third, this is not
an emerging markets effect; we observe a country size effect in both emerging and developed
countries.
Recall that our main explanation involved an interaction between home bias and market
size. Our results thusfar are mixed on whether this interpretation explains the country size effect.
For example, the lack of capital market access for firms in small markets would suggest these
stocks are riskier – consistent with the hypothesis that there is weak evidence that smaller
countries have higher volatilities and betas to the world index. Second, small stocks (relative to
large stocks) should be more sensitive to the country size effect. We find the country size effect
exists among different sized stock groups, and in certain circumstances is stronger among large
stocks, which is inconsistent with this explanation. Last, we should expect that over time,
globalization forces and foreign flows into smaller markets should improve capital market access
for firms in small markets. As we show, our results supporting this premise are in fact stronger in
the earlier part of the sample period.
IV. Do Small Countries Attract Less Analyst Coverage?
In this section we directly examine whether smaller countries are more subject to home
bias by looking at analyst coverage. Our conjecture is that there is a fixed cost associated with
covering the stocks in a particular country, and because of those costs investors and analysts may
choose to ignore the smaller countries. If this is the case, then when we control for firm size and
industry we should observe less coverage of stocks in smaller countries.
Exhibit 7 reports annual panel regressions of the natural log of one plus the number of
analysts covering a stock on firm and country size. We calculate our measure once a year on
January 1st, using the number of analysts that report EPS estimates in December of the prior year.
We control for year and industry fixed effects and the natural log of firm size, LN(Firm). We also
cluster errors by firm and time period.
[Insert Exhibit 7 Here]
The first four panel regressions in Exhibit 7 report results for developed markets across
different sized firms (All Cap, Large, Mid and Small Cap); the second set of regressions reports
results for emerging markets. The All Cap and Small Cap results are similar, as close to 80% of
15
firms in the All Cap developed market sample are Small Cap firms. With the exception of large
firms in developed markets, our regression results show that bigger firms are significantly more
likely to have more analysts following those stocks and that much of the explanatory power in
these regressions comes from firm size. Our main variable of interest is the natural log of country
size, Ln (Ctry).
As we show, All and Small-Cap developed market stocks have a significant negative relation
between country size and analyst following. This result is inconsistent with our conjecture that
analyst coverage should be less in smaller countries; however, it should be noted that this result
is driven by small cap stocks that have very little analyst coverage. We do find a significant positive
relation between country size and analyst following among developed market large capitalization
stocks, which is consistent with our conjecture that smaller country stocks receive less attention
for international investors. As we expect, the positive relation between large capitalization
coverage and country size is especially strong in the emerging markets.
V. Conclusion
This paper takes a closer look at the country size premium; i.e., the tendency of stocks
from smaller markets to have higher returns than stocks in the largest markets. Our measure of
country size is the sum of all stocks’ market capitalization within a particular country. We find that
the country size effect is largerly independent of the firm size effect, exists when excluding the
largest country (Japan), and potentially subsumes other country-level quantitative factors such as
value and momentum.
Our working hypothesis was that the small country effect was due to home bias that
depressed small country stock prices more than large country stock prices because of the small
countries’ more limited investor base. We presented evidence that is consistent with this conjecture
– in particular, the small country effect seems to be declining as access to international markets
improves; and that it is stronger in emerging markets than in developed markets. We were
somewhat surprised, however, that the small country effect is as strong for large capitalization
stocks as it is for small capitalization stocks. Our analysis of analyst coverage suggests a potential
explanation for this phenomenon. We find that for large stocks, analyst coverage is in fact
somewhat less in smaller countries, suggesting that at least historically, home bias affected even
larger stocks in small countries.
16
References Angelidis, Timotheos and Nikolaos Tessaromatis. "Global Style Portfolios Based on Country Indices." MPRA Working Paper (2016). Asness, Clifford S., John M. Liew and Ross L. Stevens. "Parallels between the Cross-Sectional Predictability of Stock and Country Returns." The Journal of Portfolio Management, Vol. 23, No.3 (1997), pp. 79-87. Asness, Clifford S., Roni Israelov, and John M. Liew. "International Diversification Works (Eventually)." Financial Analysts Journal, Vol. 67, No.3 (2011), pp. 24-38. Baele, Lieven, and Koen Inghelbrecht. "Time-Varying Integration and International Diversification Strategies." Journal of Empirical Finance, Vol. 16, No.3 (2009), pp. 368-387. Banz, Rolf W. "The Relationship between Return and Market Value of Common Stocks." Journal of Financial Economics, Vol.9, No.1 (1981), PP. 3-18. Braymen, Charles, and Robert R. Johnson. "International Diversification: The Weighting is the Hardest Part." The Journal of Portfolio Management, Vol.42, No.1 (2015), pp. 53-62. Chan, Kalok, Allaudeen Hameed, and Wilson Tong. "Profitability of momentum stragegies in the international equity market." Journal of financial and quantitative analysis 35.02 (2000): 153-172. Christoffersen, P., V. Errunza, K. Jacobs and H. Langlois, “Is the Potential for International Diversification Disappearing?” working paper, Rotman School of Management, University of Toronto (2011). Eun, Cheol S., and Jinsoo Lee. "Mean–Variance Convergence around the World." Journal of Banking & Finance, Vol. 34, No.4 (2010), pp. 856-870. Goetzmann, William N., Lingfeng Li and K. Geert. Rouwenhorst. "Long-Term Global Market Correlations." Journal of Business, Vol.78, No.1 (2005), pp. 1-38. Keppler, Michael and Heydon D. Traub. "The Small-Country Effect: Small Markets Beat Large Markets." The Journal of Investing, Vol.2, No.3 (1993), pp. 17-24. Keppler, Michael and Peter Encinosa. "The Small-Country Effect Revisited." The Journal of Investing, Vol.20, No.4 (2011), pp. 99-103. La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny. "Legal Determinants of External Finance." Journal of Finance (1997), pp. 1131-1150. Li, Tianchuan and Mahesh Pritamani. "Country Size and Country Momentum Effects in Emerging and Frontier Markets." The Journal of Investing, Vol.24, No.1 (2015), pp. 102-108. Santis, Giorgio and Bruno Gerard. "International Asset Pricing and Portfolio Diversification with Time‐Varying Risk." The Journal of Finance, Vol.52, No.5 (1997), pp. 1881-1912.
17
Sharpe, William F. "The Sharpe Ratio." The Journal of Portfolio Management, Vol.21, No.1 (1994), pp. 49-58. Statman, Meir and Jonathan Scheidb. "Global Diversification.” Journal of Investment Management, Vol.3, No.1 (2005), pp. 1-11.
18
Exhibit 1. Country Weights for Developed and Emerging Market Multi-Country Indices. For each
chart, we report the largest country, next four largest countries and remaining countries’ aggregate portfolio weight based on a capitalization-weighted index using end-of-year country aggregate market
capitalizations. The developed markets sample begins in 1990; the emerging markets sample begins in 1996. Both samples end in 2015.
Developed Markets
Emerging Markets
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Largest Country Next Four Largest Countries Rest of the Market
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Largest Country Next Four Largest Countries Rest of the Market
19
Exhibit 2. Growth in Wealth of $1,000 Invested in Different-sized Financial Markets. For each
chart, we report the growth in wealth associated with investing in a value-weighted portfolio consisting of the largest country, next four largest countries, and remaining countries’ using end-of-year country
aggregate market capitalizations, rebalanced on January 1 of each year. The developed markets sample begins in January 1990; the emerging markets sample begins in January 1996. Both samples end in
December 2015.
Developed Markets
Emerging Markets
0
2000
4000
6000
8000
10000
12000
1990
1990
1991
1992
1993
1993
1994
1995
1996
1996
1997
1998
1999
1999
2000
2001
2002
2002
2003
2004
2005
2005
2006
2007
2008
2008
2009
2010
2011
2011
2012
2013
2014
2014
2015
Rest of the Market Next Four Largest Countries Largest Country
0
1000
2000
3000
4000
5000
6000
7000
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Rest of the Market Next Four Largest Countries Largest Country
20
Exhibit 3. Summary Statistics by Country. For each country, we report summary statistics based on
country indices (left-hand side) and individual stocks (right-hand side). The country characteristics include
compound annual return (Return), annualized volatility (Volatility), Beta (Beta to MSCI World) and
annualized Sharpe Ratio. The firm characteristics include annualized volatility (Avg. Firm Volatility), beta
(Avg. Firm Beta), median average firm market capitalization in billions (Median Cap ($BN)), geometric
average firm market capitalization in billions (Avg. Cap ($BN)) and the average number of firms (N). Stock-
level annualized volatility and beta are based on value-weighted averages of daily returns over the previous
year. Countries are sorted in descending order, with the largest country by aggregate market capitalization
as of the beginning of the sample period listed at the top. Panel A displays results for Developed Market
countries from January 1990 to December 2015. Panel B displays results for Emerging Market countries
from January 1996 to December 2015. The two rows of each panel display summary statistics for value-
weighted (VW) and equal-weighted (EW) portfolios that consist of country indices from all countries in
either Developed or Emerging Markets.
Panel A. Developed Markets (January 1990 - December 2015)
Country-Level Characteristics Firm-Level Characteristics
Country Return Volatility
Beta to MSCI
World
Sharpe
Ratio
Avg. Firm
Volatility
Avg. Firm
Beta
Median
Cap ($BN)
Avg. Cap
($BN) N
Japan 0.1 20.2 0.87 -0.04 36.0 0.62 0.43 7.5 1988
Great Britain 7.7 15.1 0.85 0.37 29.8 0.72 0.51 21.5 676
Germany 3.9 21.4 1.02 0.16 31.6 0.69 0.57 15.0 307
France 5.8 18.7 1.00 0.24 31.0 0.76 0.57 16.9 331
Australia 8.8 17.7 0.84 0.40 28.3 0.96 0.39 7.0 315
Switzerland 10.3 16.8 0.86 0.50 25.8 0.74 0.56 22.1 159
Netherlands 9.1 18.7 0.97 0.41 29.0 0.89 1.09 18.5 112
Spain 6.8 22.0 1.07 0.28 29.7 0.84 1.09 13.7 110
Italy 2.0 23.0 1.03 0.08 32.3 0.90 0.58 9.5 169
Sweden 9.7 23.5 1.20 0.39 34.3 0.85 0.56 6.2 149
Belgium 11.3 18.5 0.82 0.52 28.9 0.66 0.52 10.6 86
Hong Kong 11.7 25.3 1.01 0.45 33.4 0.98 0.68 14.8 114
Singapore 7.0 23.0 1.02 0.28 32.0 0.95 0.35 3.5 169
Denmark 11.3 18.1 0.84 0.53 30.7 0.64 0.47 4.9 78
Norway 7.7 22.4 0.99 0.32 34.6 0.83 0.37 5.1 84
Finland 4.0 28.8 1.19 0.18 41.0 0.80 0.54 12.5 76
Austria 2.9 22.8 0.91 0.12 33.1 0.65 0.55 2.4 51
Ireland 7.0 22.2 1.04 0.29 37.7 0.82 0.74 6.5 51
New Zealand 8.8 18.3 0.69 0.39 27.1 0.65 0.32 1.2 44
Dev. Mkts(VW) 4.3 16.6 1.00 0.16 31.9 0.76 0.48 11.3 5067
Dev. Mkts(EW) 8.1 16.2 0.96 0.38 31.9 0.76 0.48 11.3 5067
21
Panel B. Emerging Markets (January 1996 - December 2015)
Country-Level Characteristics Firm-Level Characteristics
Country Return Volatility
Beta to MSCI
World
Sharpe
Ratio
Avg. Firm
Volatility
Avg. Firm
Beta
Median
Cap ($BN)
Avg.
Cap ($BN) N
Thailand 0.1 30.2 1.03 0.08 44.5 0.68 0.14 1.6 182
Malaysia 2.0 26.0 0.76 0.12 33.1 0.79 0.12 2.0 252
South Africa 6.3 23.9 1.01 0.28 35.3 0.78 0.33 4.4 153
Taiwan 1.7 25.9 0.94 0.10 36.5 0.84 0.17 3.0 460
Korea 2.4 30.1 1.20 0.15 46.9 0.92 0.09 4.4 367
India 8.5 29.0 0.97 0.35 42.8 0.81 0.15 4.0 339
Indonesia 5.8 38.0 1.29 0.28 50.2 0.92 0.17 2.6 108
Mexico 12.9 23.4 1.09 0.54 33.6 0.82 0.68 7.4 76
Phillipines 2.3 26.7 0.90 0.13 38.9 0.81 0.18 1.7 66
Brazil 5.1 32.3 1.19 0.25 58.0 1.01 0.57 2.2 26
Chile 6.3 21.2 0.84 0.29 26.6 0.64 0.36 2.9 81
Israel 8.9 22.1 0.87 0.39 33.4 0.72 0.15 2.3 111
Turkey 5.7 39.6 1.64 0.32 52.8 1.27 0.16 2.3 108
Portugal 3.1 21.9 0.89 0.15 31.8 0.70 0.41 4.1 31
Greece -3.0 34.2 1.27 0.01 47.2 1.01 0.22 2.0 73
Peru 9.2 22.7 0.65 0.40 34.8 0.43 0.20 1.3 30
Poland 4.6 32.0 1.35 0.23 38.1 1.19 0.11 2.0 80
Hungary 10.8 33.4 1.44 0.41 40.1 0.90 0.21 3.3 21
Emer.Mkts(VW) 3.9 20.5 1.05 0.18 40.3 0.85 0.15 3.3 2564
Dev. Mkt(EW) 7.5 20.2 1.07 0.35 40.3 0.85 0.15 3.3 2564
22
Exhibit 4. Fama-MacBeth Regressions of Monthly Country Index Returns on Country Size,
Aggregate Country Book-to-Market and Past One-Year Momentum for Developed and
Emerging Market Stocks. This table reports the results of a set of Fama-MacBeth regressions of monthly
country returns on country size, country average firm size, aggregate country book-to-market and country-
level momentum. EM/DM refers to whether the regressions only use emerging market stocks (EM) or
developed market stocks (DM). Time period reflects the starting and ending year for each regression. N is
the average number of firms or countries in the sample each year. Ln (Ctry) is the natural log of the
aggregate market capitalization of a country measured as of the end of December of the previous year. Ln
(Firm) is the natural log of the geometric average firm size for stocks from a specific country measured as
of the end of December of the previous year. LN (B/MCtry) is the natural log of the ratio of aggregate book
equity divided by aggregate market capitalization for stocks from a specific country measured as of the end
of December of the previous year. Ctry MOM is the past one-year country value-weighted return. For the
sake of brevity, the intercept is not reported. T-statistics are reported in parentheses to the right of each
estimate and are based on Newey-West corrected standard errors with a lag of 12 months.
EM/DM Size Ln (Ctry) t-stat Ln (Firm) t-stat Ln (B/MCtry) t-stat
Ctry
MOM t-stat
EM
Dummy t-stat N Time Period
ALL All Cap -0.17 (2.66) -0.22 (0.99) 32 1991-2015
ALL All Cap -0.12 (1.56) -0.18 (0.86) 32 1991-2015
ALL All Cap 0.92 (1.81) 0.08 (0.45) 32 1991-2015
ALL All Cap -0.14 (1.91) 0.06 (0.53) 0.78 (1.64) -0.15 (0.83) 32 1991-2015
ALL Mid/Small -0.14 (1.92) 0.05 (0.45) 1.29 (2.52) -0.09 (0.51) 32 1991-2015
DM All Cap -0.12 (2.23) 19 1991-2015
DM All Cap -0.03 (0.41) 19 1991-2015
DM All Cap 0.45 (1.70) 19 1991-2015
DM All Cap 1.25 (2.33) 19 1991-2015
DM All Cap -0.16 (2.58) 0.13 (1.22) 0.32 (1.46) 0.84 (1.33) 19 1991-2015
DM Mid/Small -0.14 (2.05) 0.08 (0.87) 0.21 (0.75) 1.03 (1.60) 19 1991-2015
EM All Cap -0.28 (1.97) 18 1997-2015
EM All Cap -0.36 (2.40) 18 1997-2015
EM All Cap 0.47 (0.69) 18 1997-2015
EM All Cap -0.05 (0.40) -0.33 (1.99) -0.05 (0.70) 18 1997-2015
EM Mid/Small -0.10 (0.80) -0.22 (1.06) 1.15 (1.60) 18 1997-2015
23
Exhibit 5. Value-weighted Monthly Returns for Different-sized Financial Markets by Firm Size Group.
At the beginning of each year, stocks are sorted into three groups according to market capitalization: Large
(Top 70% of aggregate capitalization), Mid (70% to 85%), and Small (85% to 99%). Within these three
groups based on size, stocks are further divided into three groups: (i) largest country, (ii) next four largest
countries, and (iii) remaining countries using end-of-year country aggregate market capitalizations,
rebalanced on January 1 of each year. We form value-weighted portfolios comprised of stocks in these
various groups. The rankings of countries are dynamic and change each year. Panel A displays developed
market results starting in January 1990; Panel B reports emerging market results starting in January 1996.
Both sample periods end in December 2015.
Panel A. Developed Markets (January 1990 – December 2015)
Panel B. Emerging Markets (January 1996 – December 2015)
0.09%
0.62%0.81%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Large
0.22%
0.72%
1.01%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Mid
0.25%
0.77%0.88%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Small
-0.62%
0.61%0.80%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Large
-0.43%
0.93% 1.01%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Mid
-0.76%
0.75%0.95%
LargestCountry
Next 4Largest
Countries
RemainingCountries
Small
24
Exhibit 6. Fama-MacBeth Regressions of Monthly Stock Returns on Firm Size and Country Size
for All, Developed, Developed ex-Japan and Emerging Markets. This table reports the results of a
set of Fama-MacBeth regressions of monthly stock returns on firm and country size. EM/DM refers to
whether the regressions only use emerging market stocks (EM), developed market stocks (DM), both (ALL)
or all stocks excluding Japan (All [ex-JP]). Time period reflects the starting and ending year for each
regression. N is the average number of firms in the sample each year. Ln (Firm) is the natural log of the
market capitalization measured as of the end of December of the previous year. Ln (Ctry) is the natural log
of the aggregate market capitalization of a country measured as of the end of December of the previous
year. The intercept is not reported for brevity. T-statistics are reported in parentheses to the right of each
estimate and are based on Newey West corrected standard errors with a lag of 12 months.
EM/DM Size Ln (Firm) t-stat Ln (Ctry) t-stat EM Dummy t-stat N Time Period
All All Cap 0.050 (1.27) -0.232 (2.43) -0.381 (1.34) 7906 1990-2015
All Large -0.033 (0.46) -0.298 (3.17) -0.637 (2.01) 758 1990-2015
All Mid 0.204 (1.63) -0.289 (3.57) -0.144 (0.55) 978 1990-2015
All Small 0.091 (1.44) -0.208 (2.10) -0.230 (0.81) 6169 1990-2015
ALL (ex-JP) All Cap 0.090 (2.69) -0.179 (2.57) -0.346 (1.23) 6075 1990-2015
ALL (ex-JP) Large 0.003 (0.03) -0.200 (2.41) -0.598 (2.02) 618 1990-2015
ALL (ex-JP) Mid 0.381 (3.04) -0.197 (2.43) -0.144 (0.57) 767 1990-2015
ALL (ex-JP) Small 0.191 (3.30) -0.162 (2.33) -0.163 (0.60) 4691 1990-2015
DM All Cap 0.063 (0.93) -0.180 (1.73) 4667 1990-2015
DM Large -0.054 (0.46) -0.240 (2.77) 436 1990-2015
DM Mid 0.155 (1.84) -0.243 (2.80) 566 1990-2015
DM Small 0.121 (1.05) -0.162 (1.50) 3665 1990-2015
EM All Cap 0.058 (0.93) -0.452 (2.34) 4210 1996-2015
EM Large -0.049 (0.46) -0.457 (2.37) 419 1996-2015
EM Mid 0.376 (1.84) -0.426 (2.30) 536 1996-2015
EM Small 0.124 (1.05) -0.437 (2.23) 3255 1996-2015
All All Cap 0.041 (0.58) -0.324 (2.34) -0.684 (1.48) 5420 1990-2002
All Large 0.071 (0.56) -0.481 (3.30) -1.087 (2.04) 662 1990-2002
All Mid 0.216 (0.94) -0.418 (3.58) -0.427 (1.21) 797 1990-2002
All Small -0.012 (0.11) -0.281 (1.98) -0.537 (1.22) 3962 1990-2002
All All Cap 0.059 (1.68) -0.139 (1.10) -0.078 (0.26) 10391 2003-2015
All Large -0.138 (2.72) -0.115 (1.21) -0.186 (0.66) 855 2003-2015
All Mid 0.191 (1.84) -0.159 (1.59) 0.138 (0.37) 1160 2003-2015
All Small 0.194 (3.90) -0.134 (1.00) 0.078 (0.24) 8376 2003-2015
25
Exhibit 7. Panel Regression Explaining Number of Analysts Covering a Stock. This table reports results
from panel regressions of the natural log of the number of analysts plus one covering a stock as of
December 31st of the previous year. Ln (Firm) is the natural log of the market capitalization measured as
of the end of December of the previous year. Ln (Ctry) is the natural log of the aggregate market
capitalization of a country measured as of the end of December of the previous year. For brevity, the
intercept is not reported. Each regression includes industry (Based on GICs sector definitions) and year
fixed effects, which are also not reported. T-statistics are reported in parentheses based on robust
standard errors that are clustered by firm and year. N is the average number of firms per year.
EM/DM Size Ln (Firm) t-stat Ln (Ctry) t-stat R2 N Time Period
DM All Cap 0.34 (22.1) -0.12 (9.0) 0.25 4667 1990-2015
DM Large 0.00 (0.1) 0.08 (2.3) 0.13 436 1990-2015
DM Mid 0.23 (4.4) -0.04 (1.7) 0.17 566 1990-2015
DM Small 0.43 (21.9) -0.14 (0.2) 0.23 3665 1990-2015
EM All Cap 0.38 (20.2) 0.01 (0.2) 0.36 4210 1996-2015
EM Large 0.18 (4.6) 0.22 (3.9) 0.20 419 1996-2015
EM Mid 0.56 (9.1) 0.09 (2.2) 0.12 536 1996-2015
EM Small 0.30 (21.5) -0.03 (1.4) 0.18 3255 1996-2015
26
Apendix 1. Country FIC codes sorted on Market Size by Year. At the beginning of each year, we
report the country FIC code for the largest (Big) to smallest (Small) markets based on aggregate market
capitalization as of December of the previous year. Panel A presents results for developed markets from
1990 -2015. Panel B presents results for emerging markets from 1996 – 2015.
Panel A. Developed Markets10 (1990 – 2015)
Big 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Small
1990 JPN GBR DEU FRA AUS CHE NLD ESP ITA SWE BEL HKG SGP DNK NOR FIN AUT IRL NZL
1991 JPN GBR DEU FRA ITA CHE NLD ESP AUS SWE BEL HKG SGP DNK AUT NOR FIN IRL NZL
1992 JPN GBR DEU FRA CHE AUS NLD ITA ESP SWE HKG SGP BEL DNK AUT NOR IRL FIN NZL
1993 JPN GBR FRA DEU CHE NLD AUS HKG ITA ESP SGP BEL SWE DNK AUT NOR IRL NZL FIN
1994 JPN GBR FRA DEU NLD HKG CHE AUS SGP ITA ESP SWE BEL DNK AUT NOR FIN IRL NZL
1995 JPN GBR DEU FRA NLD CHE AUS SGP HKG ITA SWE ESP BEL DNK FIN IRL NOR AUT NZL
1996 JPN GBR DEU FRA NLD CHE AUS HKG SGP ITA SWE ESP BEL DNK FIN NOR IRL AUT NZL
1997 JPN GBR DEU FRA NLD CHE AUS HKG SWE ITA SGP ESP BEL DNK FIN IRL NOR AUT NZL
1998 JPN GBR DEU FRA NLD CHE ITA AUS SWE ESP HKG BEL SGP IRL DNK FIN NOR AUT NZL
1999 JPN GBR DEU CHE AUS SWE HKG NLD IRL SGP DNK NOR NZL FRA ESP AUT FIN ITA BEL
2000 JPN GBR DEU FRA NLD ITA CHE ESP FIN AUS SWE HKG BEL IRL SGP DNK NOR AUT NZL
2001 JPN GBR FRA DEU NLD FIN CHE ITA ESP HKG AUS SWE BEL IRL SGP DNK NOR AUT NZL
2002 JPN GBR FRA DEU NLD CHE ITA FIN ESP AUS HKG IRL SWE BEL SGP DNK NOR AUT NZL
2003 JPN GBR FRA DEU NLD CHE ITA ESP AUS FIN HKG BEL SWE IRL SGP DNK NOR AUT NZL
2004 JPN GBR FRA DEU NLD CHE ESP ITA AUS FIN HKG SWE BEL IRL SGP DNK NOR AUT NZL
2005 JPN GBR FRA DEU NLD ESP ITA CHE AUS HKG SWE BEL FIN IRL SGP DNK NOR AUT NZL
2006 JPN GBR FRA DEU NLD ESP CHE ITA AUS HKG SWE BEL FIN SGP IRL NOR DNK AUT NZL
2007 JPN GBR FRA DEU NLD ESP CHE AUS ITA HKG SWE BEL FIN SGP IRL NOR DNK AUT NZL
2008 JPN GBR FRA DEU ESP NLD AUS ITA CHE HKG SWE FIN BEL SGP NOR IRL DNK AUT NZL
2009 JPN FRA GBR DEU ESP ITA CHE AUS NLD HKG SWE FIN SGP BEL NOR DNK IRL AUT NZL
2010 JPN GBR FRA DEU ESP AUS ITA CHE NLD HKG SWE SGP BEL FIN NOR DNK IRL AUT NZL
2011 JPN GBR FRA DEU AUS ESP CHE HKG NLD ITA SWE SGP BEL FIN NOR DNK IRL AUT NZL
2012 JPN GBR FRA DEU AUS ESP CHE NLD HKG ITA SWE SGP BEL NOR IRL FIN DNK AUT NZL
2013 JPN GBR FRA DEU AUS CHE NLD HKG ESP ITA SWE SGP BEL NOR IRL FIN DNK AUT NZL
2014 JPN GBR FRA DEU ESP NLD AUS CHE HKG ITA SWE BEL NOR SGP IRL FIN DNK AUT NZL
2015 JPN GBR FRA DEU HKG CHE ESP NLD AUS ITA SWE BEL SGP NOR IRL FIN DNK AUT NZL
10FIC codes for developed markets are as follow: JPN (Japan), GBR (Great Britain), DEU (Germany), FRA (France), AUS (Australia),
CHE (Switzerland), NLD (Netherlands), ESP (Spain), ITA (Italy), SWE (Sweden), BEL (Belgium), HKG (Hong Kong), SGP (Singapore), DNK (Denmark), NOR (Norway), FIN (Finland), AUT (Austria), IRL (Ireland), NZL (New Zealand).
27
Panel B. Emerging Markets (1996 - 2015)11
Big 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Small
1996 THA MYS ZAF TWN KOR IND IDN MEX PHL BRA CHL ISR TUR PRT GRC PER POL HUN
1997 MYS TWN ZAF THA KOR IND IDN MEX PHL BRA CHL ISR PRT TUR GRC POL PER HUN
1998 TWN ZAF MEX IND BRA MYS CHL TUR PRT THA KOR PHL ISR GRC IDN HUN POL PER
1999 TWN ZAF KOR MYS IND MEX GRC THA BRA CHL TUR PHL ISR IDN POL HUN PER PRT
2000 TWN KOR IND GRC ZAF MYS MEX TUR BRA THA PRT CHL ISR IDN PHL POL HUN PER
2001 TWN KOR IND MYS ZAF MEX BRA TUR PRT CHL ISR THA POL PHL HUN IDN GRC PER
2002 TWN KOR MYS MEX IND ZAF GRC BRA THA PRT ISR CHL TUR POL HUN IDN PHL PER
2003 TWN KOR MYS IND ZAF MEX THA GRC PRT CHL ISR TUR BRA POL IDN HUN PHL PER
2004 TWN KOR IND MYS ZAF THA MEX GRC CHL PRT TUR ISR BRA IDN POL HUN PHL PER
2005 TWN KOR IND ZAF MYS MEX THA GRC CHL TUR PRT ISR IDN POL HUN BRA PHL PER
2006 KOR IND TWN ZAF MEX MYS THA TUR GRC CHL ISR PRT POL IDN HUN BRA PHL PER
2007 KOR IND TWN ZAF MEX MYS GRC THA TUR CHL POL IDN PRT ISR HUN PHL BRA PER
2008 IND KOR TWN ZAF MYS MEX THA TUR GRC POL IDN CHL ISR PRT PHL HUN PER BRA
2009 IND KOR TWN ZAF MYS MEX THA TUR CHL ISR POL IDN GRC PRT PHL HUN PER BRA
2010 IND KOR TWN ZAF THA MEX MYS TUR CHL IDN ISR POL PRT GRC PHL HUN PER BRA
2011 IND KOR TWN ZAF THA MEX MYS IDN CHL TUR ISR POL PHL PRT PER GRC HUN BRA
2012 IND KOR TWN THA ZAF MYS IDN MEX CHL TUR PHL ISR POL PER PRT HUN GRC BRA
2013 IND KOR TWN THA ZAF MYS MEX IDN TUR CHL PHL POL ISR PER PRT HUN GRC BRA
2014 KOR IND TWN THA MYS ZAF MEX IDN CHL TUR PHL POL ISR PRT GRC PER HUN BRA
2015 IND KOR TWN THA ZAF MYS MEX IDN TUR PHL CHL POL ISR PRT PER GRC HUN BRA
11
FIC codes for emerging markets are as follows: THA (Thailand), MYS (Malaysia), ZAF (South Africa), KOR (South Korea), IND
(India), IDN (Indonesia), MEX (Mexico), PHL (Philippines), BRA (Brazil), CHL (Chile), ISR (Israel), TUR (Turkey), PRT (Portugal),
GRC (Greece), PER (Peru), POL (Poland) and HUN (Hungary).