Statistical Arbitrage in Emerging Markets: A Global Test of Efficiency

Abstract

In this paper, we use a statistical arbitrage method in different developed and emerging countries to show that the profitability of the strategy is based on the degree of market efficiency. We will show that our strategy is more profitable in emerging ones and in periods with greater uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of 39 countries, and the performance of the strategy is analyzed during the period from 1 January 2000 to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and different portfolios are also considered.

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mathematics
Article
Statistical Arbitrage in Emerging Markets: A Global Test
of Efficiency
Karen Balladares 1, José Pedro Ramos-Requena 2, Juan Evangelista Trinidad-Segovia 2,*
and Miguel Angel Sánchez-Granero 3


Citation: Balladares, K.;
Ramos-Requena, J.P.;
Trinidad-Segovia, J.E.;
Sánchez-Granero, M.A. Statistical
Arbitrage in Emerging Markets: A
Global Test of Efficiency. Mathematics
2021,9, 179. https://doi.org/10.33
90/math9020179
Received: 7 December 2020
Accepted: 15 January 2021
Published: 18 January 2021
Publisher’s Note: MDPI stays neu-
tral with regard to jurisdictional clai-
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Copyright: c
2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Facultad de Ciencias Administrativas, Universidad de Guayaquil, 090514 Guayaquil, Ecuador;
karen.balladaresp@ug.edu.ec
2Departamento de Economía y Empresa, Universidad de Almería, Carretera Sacramento s/n,
La Cañada de San Urbano, 04120 Almería, Spain; jpramosre@ual.es
3Departamento de Matemáticas, Universidad de Almería, Carretera Sacramento s/n,
La Cañada de San Urbano, 04120 Almería, Spain; misanche@ual.es
*Correspondence: jetrini@ual.es
Abstract:
In this paper, we use a statistical arbitrage method in different developed and emerging
countries to show that the profitability of the strategy is based on the degree of market efficiency.
We will show that our strategy is more profitable in emerging ones and in periods with greater
uncertainty. Our method consists of a Pairs Trading strategy based on the concept of mean reversion
by selecting pair series that have the lower Hurst exponent. We also show that the pair selection with
the lowest Hurst exponent has sense, and the lower the Hurst exponent of the pair series, the better
the profitability that is obtained. The sample is composed by the 50 largest capitalized companies of
39 countries, and the performance of the strategy is analyzed during the period from
1 January 2000
to 10 April 2020. For a deeper analysis, this period is divided into three different subperiods and
different portfolios are also considered.
Keywords:
emerging markets; pairs trading; Hurst exponent; financial markets; long memory;
co-movement; efficiency
1. Introduction
The Efficient Market Hypothesis (EMH) was introduced by Cootner [
1
] and
Samuelson [2]. Recently, Fama [3] developed three states of efficiency:
•
Strong form efficiency states all information in a market, whether public or private, is
contained in the stock price.
•
Semi-strong efficiency means that all public information is calculated into a stock’s
current share price.
•
Weak form efficiency implies that all past prices of a stock are reflected in today’s
stock price.
When markets are fully efficient, neither technical analysis, fundamental analysis nor
insider information enable an investor to obtain returns greater than those that could be
obtained by holding random portfolios or individual stocks with the same risk.
Today, many financial economists and investors accept that the presence of the first
two scenarios are almost impossible even in high capitalized markets (
Campbell et al. [4]
and Grossman and Stiglitz [
5
]); this is why the weak-form version of market efficiency is
the most tested criterion in the financial literature. To test the level of market efficiency has
been a quite popular topic in financial literature because, if a market is not efficient, this
means that future stock prices are somewhat predictable based on past stock price which
enable investors to earn excess risk adjusted rates of return.
One of the most interesting works is the one by Markiel and Fama [
6
], where authors
considered that the weak and the semi strong forms of efficiency were strongly supported
Mathematics 2021,9, 179. https://doi.org/10.3390/math9020179 https://www.mdpi.com/journal/mathematics

Mathematics 2021,9, 179 2 of 20
by their results. After this work, some researchers have tested whether technical analysis is
able to provide abnormal returns to the investors (see, for example, Fama and Blume [
7
],
Fama and French [
8
], Olson [
9
], Rosillo et al. [
10
], Shynkevich [
11
], Metghalchi et al. [
12
] or
Bobo and Dinica [13]).
Other researchers decided to analyze price adjustments after market events (see, for
example, Pettit [
14
], Asquith and Mullins [
15
] and Michaely, Thaler and Womack [
16
],
Aharony and Swary [17] and Kalay and Loewenstein [18], among others).
Another part of the financial literature has tested the EMH based on the statistical
implication of this hypothesis: that stock returns follow a random walk. Interesting con-
tributions are Lo and MacKinlay [
19
], Lima and Tabak [
20
],
Fifield and Jetty [21]
, Charles
and Darné [22], Al-Ajmi and Kim [23] or Mlambo and Biekpe [24] between others.
Recent contributions have come from some mathematicians and physicians that have
focused their attention on Mandelbrot’s critics to the EMH [
25
]. Mandelbrot proposed
that stock prices follow a fractional Brownian motion and not a random walk. One of
the implications of this assumption is that stock prices exhibit long memory, which is
against the EMH. On this basis, to explore the presence of market memory is an alternative
method to test the market efficiency. Relevant contributions are Beben and Orłowski [
26
],
DiMatteo et al. [
27
], Zunino et al. [
28
], Cajueiro [
29
],
Kristoufek [30]
, Ferreira et al. [
31
],
Kristoufek [32] and Dimitrova et al. [33].
This paper goes along a similar line, and it is based on the recent novel approach intro-
duced by Sanchez et al. [
34
], where the authors analyzed the relationship between market
efficiency and a statistical arbitrage technique based on Hurst exponent [
35
]. We propose
to extend the analysis to the 50 largest capitalized companies in 39 so-called advanced and
emerging countries to see if a Pairs Trading strategy based on Hurst Exponent, which is a
market memory indicator, can obtain a significant profit during different periods.
Our results will show that emerging markets are not efficient while the developed
ones are, for most of the period, under study. We will prove that the method used is
robust by studying the return of the trades with each pair, considering the estimated
Hurst exponent of the pair selection. We will show that, as expected, the lower the Hurst
exponent of the pair series, the higher the return of the pair in the Pairs Trading strategy.
The paper is structured as follows: Section 2introduces the Pairs Trading technique
based on Hurst exponent. In this section, we present the fundamentals of the Pairs
Trading technique as well as the main contributions done by the financial literature. This
section also introduces some relevant questions about Hurst Exponent and the Pairs
Trading strategy.
Section 3
contains the results of the different strategies developed. Finally,
Section 4presents the conclusions.
2. A Pairs Trading Technique Based on Hurst Exponent
2.1. Fundamentals of Pairs Trading
The strategy of statistical arbitrage arose in the 1980s. Since its birth, there have been
different studies in this area.
The pioneer in investigating this strategy was Gatev et al. [
36
] who found statistically
significant results using US market values during the period 1962–1997. Gatev et al. [
37
]
carried out the study again extending the period until 2002 and obtaining average annual-
ized higher returns of up to 11%. The authors concluded that these higher returns from
this strategy are due to a reward for the application of the Law of One Price.
Elliott et al. [
38
] used a Gaussian Markov chain model to measure dispersion, while
Do et al. [
39
] employed theoretical pricing methods. The cointegration approach was used
by Vidyamurthy [40], Burgess [41], and Haque and Haque [42].
Perlin [
43
] used a Pairs Trading strategy in the Brazilian market, concluding that it
works significantly, pointing out that positive superior returns are significant.
Do and Faff ([
44
,
45
]) used the distance method introduced by Gatev et al. during
the period 2000–2009 and concluded that the Pairs Trading strategy was still profitable,
but the profitability decreases over the time. This decline was attributed to a worsening

Mathematics 2021,9, 179 3 of 20
of arbitrage risks and an increase in market efficiency. This is the first contribution where
transaction costs are considered, showing that, from 2002 onwards, it generated losses.
Bowen et al. [
46
] developed their study taking intraday values and concluded that
this strategy may be affected by transaction costs and speed of execution. They showed
that the highest profitability is achieved at the first and last minute. Similar results are
obtained by
Liu et al. [47]
, where authors introduced an intraday trading strategy based
on a conditional modeling to model spreads between pairs of stocks. The authors found
remarkable returns including transaction costs during specific periods.
Huck [
48
] introduced a forecasting methodology using a combination of Neural Net-
works techniques and multi-criteria decision-making methods; Xie and Wu [
49
] proposed
an alternative approach using the copula technique that is able to capture the structure of
dependence of co-movement between two assets. Göncü and Akyildirim [
50
] supported
the strategy of statistical arbitrage by assuming that the dispersion of two assets follows an
Ornstein–Uhlenbeck process around a long-term equilibrium level.
Avellaneda et al. [
51
] studied the strategy of statistical arbitrage employing US actions.
To do this, they used Principal Component Analysis and sectorial ETFs. In both cases,
they detected opposite trading signals, considering the waste the returns of the shares and
modeling the investment process.
Krauss [
52
] examined the literature on pairs trading strategy. It did so by dividing it
into five groups; firstly, it studied the distance method; secondly, it used the co-integration
method, it used the stochastic approach to identify the optimal portfolio trends, and, finally,
it selected other approaches with some limitations in the literature.
Rad et al. [
53
] studied the performance of Pairs Trading based on the distance, cointe-
gration, and copula methods on the entire US equity market from 1962 to 2014 including
trading costs. The authors found that all strategies show positive results and mainly during
periods of significant volatility.
Ramos-Requena et al. [
35
] introduced the Hurst exponent as a selection method in
Pairs Trading. The authors found that this new methodology gives better results than the
classical methodologies such as the distance or the correlation method. In a recent contri-
bution [
54
], these authors proposed an alternative method to correlation and cointegration
called the HP method.
Finally, all of these contributions are focused on the methodology for pairs selection.
In a different line, Ramos-Requena et al. [
55
] introduced different models to calculate the
amount of money that must be allocated to each stock. The authors showed these new
alternatives perform better than the usual Equal Weight method.
2.2. Notes on Hurst Exponent
The hypothesis that price variations are well-described by means of a fractional
Brownian motion was introduced by Mandelbrot [
25
]. This process is a long memory
generalization of the Brownian motion process with a self-similarity exponent (called
Hurst exponent) different to 0.5. Thus, when the process is a Brownian motion, the Hurst
exponent (H) is equal to 0.5; when it is persistent, H will be greater than 0.5, and, finally,
when it is anti-persistent, then H will be less than 0.5.
The Hurst exponent was introduced by Hurst in 1951 [
56
] to deal with the problem of
reservoir control near the Nile River Dam, but, in recent decades, its application has been
widely extended in economics in general and in finance in particular (see López-García
and Ramos-Requena [
57
] for a literature and methodological review). There is also some
novel applications, like Trinidad-Segovia et al. [
58
] and Nikolova et al. [
59
], where it was
used to study volatility clusters.
Ramos-Requena et al. [
35
] used the Hurst exponent as a management tool for statistical
arbitrage strategies based on the concept of reversion to the mean.
Since the first method introduced by Hurst [
56
], the R/S analysis, different method-
ologies have been proposed. In this paper, we use the Generalized Hurst exponent (
GH E
)
introduced by Barabasi and Vicsek [60].

Mathematics 2021,9, 179 4 of 20
This algorithm is calculated as follows:
Kq(τ) = <|X(t+τ)−X(t)|q>
<|X(t)|q>(1)
where
X
is the series (in this paper,
X
will be the pair series as defined in Section 2.3 ),
τ
can vary between 1, and
τmax
,
τmax
is usually chosen as a quarter of the length of the series,
and <·>denotes the sample average over the time window.
Therefore, the GHE is defined on the basis of the behavior on the scale of the statistic
given in (1), given by the power law:
Kq(τ)∝τqH(q)·(2)
where H(q)is the Hurst exponent, which characterizes the power law scaling.
The GHE is calculated by linear regression after taking logarithms in Equation (2),
for different values of τ[27,61].
2.3. Methodology
Our trading methodology is developed as follows:
Firstly, we normalize the stock prices. If we consider shares Aand B, and their share
prices are PAand PB, respectively, the pair series is defined as:
log(PA)−b∗log(PB)
where bis a constant, and it is used to normalize the stock prices.
To calculate the value of
b
, we will use the method described by Ramos-Requena et al. [
55
],
called minimizing distance.
The function
f(b) = ∑txA(t)−bxB(t)
will be minimized, looking for the value of
b
,
such that
xA
and
bxB
have the minimal distance, where
xA(t) = log PA(t)−log PA(
0
)
and
xB(t) = log PB(t)−log PB(0).
Now, the pairs will be selected, using the Hurst exponent approach as developed in
Section 2.2. Here, we look for pairs such that the Hurst exponent of their pair series is as
low as possible.
Finally, the trading strategy will be developed. If
s
is the pair series,
m
the average of
the series s, and σthe standard deviation of m−s[62], then:
•
If
m+
2
σ>s>m+σ
, the pair will be sold at
s0
. The position will be closed when
s<mor s>s0+σ.
•
If
m−
2
σ<s<m−σ
, the pair will be bought at
s0
. The position will be closed when
s>mor s<s0−σ.
3. Experimental Results
This section shows the main results obtained by applying the Pairs Trading strategy.
This will be done by taking the 50 largest capitalized companies in 39 countries. Three
sub-periods (From 1 January 2000 to 31 December 2007, the second period is from 1 January
2007 to 31 December 2014 and the last period is from 1 January 2014 to 10 April 2020) and
different portfolios (of 30, 40, and 50 pairs) are also considered. In this study, we considered
transaction costs of 0.01%. Table A1 includes the classification of the countries studied
between emerging and advanced.
Tables A2–A4 show the results obtained for the period 2000–2007 for the portfolios
composed of 30, 40, and 50 pairs. It can be seen that the highest profits after transaction
costs are obtained for emerging countries, especially in South Africa (71.21%, 65.84%, and
65.68%), Japan (32.61%, 28.99%, and 28.87%) and Israel (28.99%, 23.54%, 25.76%). Japan is
not an emerging country, but the long-lasting negative bias in its stock market has caused
many investors to forget about it. Regarding Israel, the country cannot be considered an
emerging country, but its stock market posseses a very low capitalization, even below

Mathematics 2021,9, 179 5 of 20
emerging countries such as South Africa or Indonesia. For the same period, developed
markets, especially the United States, show significantly negative returns after transaction
costs (
−
12.87%,
−
11.61%, and
−
10.05%) as well as other European countries such as
Norway, Russia, and Portugal.
If we look at the Sharpe ratio values, we can see that a value above 1 is obtained for
countries such as South Africa (1.98, 1.6 and 1.45), Lebanon (1.11), Namibia (1.1), Israel,
and Japan. Against these values, we find Norway with a negative Sharpe ratio (
−
0.74),
and the United States (−0.35, −0.31, and −0.26).
Tables A5–A7 show results obtained for the period 2007–2014. During this period,
we are faced with the subprime crisis, which led to a large drop in the values of the main
world stock market indexes as a consequence of the volatility increase. Despite this, it can
be seen that the greatest benefits are obtained for emerging countries such as Israel (54.25%,
51.59% and 47.32%) and South Africa (33.80%, 32.73% and 29.88%). In this ranking, we
find some European countries with significant benefits, such as Portugal (36.82%, 24.75%
and 10.91%), Netherlands (26.26%, 31.27% and 27.94%), and Greece (19.85%, 19.88% and
19.89%). It is not like in the previous period, when it is clear that the developed countries
are at a disadvantage in this strategy; for example, the United States is making positive
gains in this period (1.30%, 4.13%, and 4.66%). The positive results could be attributed to
the high volatility and correlation during financial crisis. These results are congruent with
previous finding of Ramos Requena et al. [35] and Lopez García et al. [63].
The Sharpe ratio will indicate that the best investment options will be in countries
such as Lebanon (2.53), Israel (1.44, 1.35, and 1.23), and South Africa (1.31, 1.28, and 1.08).
Finally, Tables A8–A10 present the main results obtained for the period 2014–2020 for
the portfolios composed of 30, 40, and 50 pairs. In this period, the highest profitability after
transaction costs is for Greece (59.71%, 47.70%, 42.39%). Two other emerging countries
(Colombia and South Africa) are among the most profitable to apply the Pairs Trading
strategy during this period. France, Spain, or Dubai are the least profitable to invest in,
with negative returns during this period.
If we look at the risk, the values of the Sharpe ratio indicate that Lebanon (3.85) and
Colombia (2.11, 1.9 and 1.84) are the most appropriate countries. However, according to
this ratio, it is not advisable to invest in countries such as Mexico or Dubai with a negative
Sharpe ratio value.
One of the main features of Pairs Trading’s strategy is its market neutrality. As pre-
sented by Ramos-Requena et al. [
35
], to comply with this property, investors must consider
pairs with a value of the Hurst exponent (H) below 0.5.
Figures 1–3show the relationship between the
H
value and the average return ob-
tained for each of the periods studied (2000–07, 2007–14, 2014–20), in which all the countries
considered in this paper are included. It can be seen that, as the value of
H
decreases,
the average profitability increases for the three periods studied, it being significant that
pairs with a value of 0.5 give negative average profitability. Therefore, those countries that
select their pairs with an Hclose to 0 get a higher average return.
It is also important to note that the selection of the pairs based on the Hurst exponent
is refreshed each six months with data from the previous year; therefore, the selected pairs
are used for the next six months, without refreshing the calculation of the Hurst exponent.
Consequently, these results are a kind of robustness check of the pair selection method,
since we can see that the pairs with the lowest Hurst exponent in the past are the one for
which the mean reversion strategy best work in the future.
Figure 4gives the relationship between the value of
H
and the average return for the
period 2000–2007, for Brazil, Colombia, Israel, and Saudi Arabia. We can see that, in all
cases, if only pairs with small values of
H
are selected, they would obtain their highest
returns. The case of Brazil (a) is significant, as pairs with values between 0.1 and 0.2
would obtain an average return of around 1%. In the case of Brazil (a), Colombia (b), and
Saudi Arabia (d), when the value of
H
of a pair is between 0.4 and 0.5, the strategy get
negative returns.

Mathematics 2021,9, 179 6 of 20
Figure 1.
Comparison between the value of H and the mean return of the portfolios for the pe-
riod 2000–2007.
Figure 2.
Comparison between the value of H and the mean return of the portfolios for the pe-
riod 2007–2014.

Mathematics 2021,9, 179 7 of 20
Figure 3.
Comparison between the value of H and the mean return of the portfolios for the pe-
riod 2014–2020.
(a) Brazil (b) Colombia
(c) Israel (d) Saudi Arabia
Figure 4. Comparison between the value of H and the mean return between countries for the period 2000–2007.

Mathematics 2021,9, 179 8 of 20
Figure 5shows the comparison between average returns and the value of
H
, for the
countries Brazil (a), Israel (b), Mexico (c), and South Africa (d) for the period 2007–2014.
As in the previous period, as the value of
H
decreases, the average return increases. It is
significant in the case of Brazil and South Africa that, for all the values of
H
, it obtains a
positive profitability.
(a) Brazil (b) Israel
(c) Mexico (d) South Africa
Figure 5. Comparison between the value of H and the mean return between countries for the period 2007–2014.
Finally, Figure 6shows for Colombia (a), Pakistan (b), Thailand (c), and Hong Kong (d)
the average profitability vs. the
H
value of the pair series for the period 2014–2020. As we
have been seeing, as the value of the Hurst exponent (H) decreases, the average return
increases. If we observe what happens in the case of Thailand, we would only obtain a
positive average return if the value if His between 0.2 and 0.3.
Therefore, we can also conclude that it is interesting to form the pairs of shares that
make up the portfolios with the lowest possible value of the Hurst exponent of the pair
series, as this would mean an increase in the profitability of the strategy.

Mathematics 2021,9, 179 9 of 20
(a) Colombia (b) Pakistan
(c) Thailand (d) Hong Kong
Figure 6. Comparison between the value of H and the mean return between countries for the period 2014–2020.
4. Conclusions
According with the EMH, arbitrage strategies cannot over perform random portfolios
with the same class of risk. In this paper, we look at market efficiency by comparing
the performance of an arbitrage technique based on the Hurst exponent in emerging and
developed markets.
We found that our statistical arbitrage strategy is consistent in emerging markets and
it can obtain a significant profit during the period considered. This is the case of South
Africa, Colombia, or Lebanon where the strategy obtains important results. However,
in the case of the developed markets, only during high volatility periods, such as after the
financial crisis, does the strategy performance properly. After the financial crisis, there
are several markets where the Pairs Trading give significant results. The cases of Portugal
and Greece are interesting, which are countries seriously affected by the financial crisis in
Europe. These results are consistent with the previous findings of Ramos-Requena [35].
These results are also consistent with previous works of DiMatteo et al. [
27
],
Zunino et al. [28]
, and Kristoufek [
30
], and they are a clear proof of the degree of inef-
ficiency of emerging markets. Again, we consider that the performance of arbitrage
methods in developed markets during specific periods could be considered a proof of the
Adaptative Markets hypothesis [64].
On the other hand, we have studied the degree of incidence that the value of the
Hurst exponent of the pair series has on the strategy performance, as proposed by
Ramos-Requena et al. [35]
. We have proved that the main characteristics of the Pairs
Trading strategy, the mean reversion, are achieved with a low
H
. Another interesting result
is that, when the value of
H
is around 0.1 or 0.2, the performance of the strategy is greater.

Mathematics 2021,9, 179 10 of 20
To conclude, we would like to remark that the selection methodology shows that the
strategy is robust because the pairs with the lowest Hurst exponent in the past are the one
for which the mean reversion strategy best works in the future.
Next, we highlight some possible limitations of this study (we thank the anonymous
referees for pointing these out). The main issue is that the inefficiency of some markets may
be due to various market frictions. For example, short selling banning on some countries is
not taken into consideration for the difficulties to short sell some stocks in some countries.
We have considered transaction fees, but we have not considered any cost or revenue
incurring by the short selling positions, as well as any revenue for interest on cash not used.
We have used daily closing prices to open or close positions. Though we have considered
the most capitalized stocks in each country, it is still possible that the scale of the strategy
may impact those prices. Therefore, the real implementation of this strategy may suffer
some difficulties and the profitability of the strategy may be lower due to market frictions.
However, we are mainly interested in the inefficiency of the markets, and it is beyond the
scope of this paper (though very interesting) to determine the origin of this inefficiency.
Author Contributions:
Conceptualization, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Methodology,
K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Software, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Validation,
K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Formal Analysis, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.;
Investigation, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Resources, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.;
Data Curation, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Writing—Original Draft Preparation, K.B.,
J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Writing—Review and Editing, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.;
Visualization, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Supervision, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-
G.; Project Administration, K.B., J.P.R.-R., J.E.T.-S., and M.A.S.-G.; Funding Acquisition, J.E.T.-S.
and M.A.S.-G. All authors have read and agreed to the published version of the manuscript.
Funding:
Juan Evangelista Trinidad-Segovia is supported by grant PGC2018-101555-B-I00 (Min-
isterio Español de Ciencia, Innovación y Universidades and FEDER) and UAL18-FQM-B038-A
(UAL/CECEU/FEDER). Miguel Ángel Sánchez-Granero acknowledges the support of grants
PGC2018-101555-B-I00 (Ministerio Español de Ciencia, Innovación y Universidades and FEDER) and
UAL18-FQM-B038-A (UAL/CECEU/FEDER) and CDTIME.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can
be found here: Investing.com.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Classification of Emerging and Advanced Countries
Table A1. Classification of emerging and advanced countries (following MSCI).
Country Classification
Argentina Emerging
Bahrain Emerging
Belgium Advanced
Brazil Emerging
Colombia Emerging
Czech Republic Emerging
Denmark Advanced
Dubai Emerging
Finland Advanced
France Advanced
Greece Emerging
Hong Kong Advanced

Mathematics 2021,9, 179 11 of 20
Table A1. Cont.
Country Classification
India Emerging
Israel Advanced
Italy Advanced
Japan Advanced
Jordan Emerging
Kuwait Emerging
Lebanon Emerging
Mauritius Emerging
Mexico Emerging
Morocco Emerging
Namibia Emerging
Netherlands Advanced
Norway Advanced
Oman Emerging
Pakistan Emerging
Palestine Emerging
Poland Emerging
Portugal Advanced
Romania Emerging
Russia Emerging
Saudi Arabia Emerging
South Africa Emerging
Spain Advanced
Sweden Advanced
Switzerland Advanced
Thailand Emerging
United States Advanced
Appendix B. Results
Below is a comparison between the main results obtained.
Appendix B.1. Period 2000–2007
Table A2.
Results obtained for the period 2000–2007 (30 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 30 1666 0.80% 0.31 4.14%
Bahrain 30 6 −0.10% −0.53 −0.20%
Belgium 30 3617 1.50% 0.57 10.99%
Brazil 30 3127 1.10% 0.26 7.06%
Colombia 30 1612 −0.80% −0.25 −4.34%
Czech Republic 30 1224 −0.40% −0.26 −3.31%
Denmark 30 2710 −0.30% −0.11 −2.60%
Dubai 30 509 −0.50% −0.30 −2.67%
Finland 30 2591 2.00% 0.72 14.64%
France 30 3284 1.50% 0.54 11.71%
Greece 30 2644 0.20% 0.05 0.32%
Hong Kong 30 63 0.20% 0.78 1.28%
India 30 3116 0.80% 0.22 5.46%
Israel 30 3628 3.60% 0.96 28.99%
Italy 30 3249 1.80% 0.68 13.22%
Japan 30 2983 4.00% 1.04 32.61%
Jordan 30 1980 0.50% 0.16 2.14%

Mathematics 2021,9, 179 12 of 20
Table A2. Cont.
Country N Operations AAV Sharpe Ratio Profits
Kuwait 30 2452 0.20% 0.07 0.58%
Lebanon 30 171 0.60% 1.11 2.64%
Mauritius 30 1855 −0.50% −0.23 −3.92%
Mexico 30 2362 1.40% 0.48 7.91%
Morocco 30 882 0.60% 0.35 3.91%
Namibia 30 1590 3.00% 1.06 12.07%
Netherlands 30 3322 3.20% 0.80 27.59%
Norway 30 679 −1.10% −0.74 −8.63%
Oman 30 740 −0.10% −0.08 −0.85%
Pakistan 30 2116 1.90% 0.51 14.19%
Palestine 30 264 0.50% 0.42 2.51%
Poland 30 4 0.90% 1.37 0.20%
Portugal 30 2772 −0.50% −0.16 −4.72%
Romania 30 34 −0.30% −0.71 −1.21%
Russia 30 2887 −0.90% −0.27 −7.86%
Saudi Arabia 30 2530 0.30% 0.12 1.86%
South Africa 30 4682 6.90% 1.45 65.84%
Spain 30 304 0.00% −0.07 −0.40%
Sweden 30 4057 0.00% −0.01 −1.55%
Switzerland 30 3552 0.60% 0.19 3.32%
Thailand 30 2779 0.80% 0.21 4.77%
United States 30 2736 −1.50% −0.26 −11.61%
Table A3.
Results obtained for the period 2000–2007 (40 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 40 1776 0.50% 0.27 2.66%
Bahrain 40 6 −0.10% −0.53 −0.20%
Belgium 40 4714 1.40% 0.61 10.12%
Brazil 40 3923 0.40% 0.12 2.32%
Colombia 40 1851 −0.50% −0.18 −2.76%
Czech Republic 40 1224 −0.30% −0.26 −2.51%
Denmark 40 3477 −0.20% −0.10 −2.27%
Dubai 40 509 −0.40% −0.30 −2.03%
Finland 40 3393 2.10% 0.78 15.15%
France 40 4158 1.70% 0.67 12.96%
Greece 40 3499 −0.20% −0.04 −1.87%
Hong Kong 40 63 0.10% 0.78 0.98%
India 40 4084 0.90% 0.25 6.18%
Israel 40 4658 3.00% 0.91 23.54%
Italy 40 4124 1.80% 0.77 13.47%
Japan 40 3795 3.60% 1.02 29.45%
Jordan 40 2539 −0.10% −0.03 −1.13%
Kuwait 40 3204 0.20% 0.09 1.00%
Lebanon 40 171 0.40% 1.11 2.06%
Mauritius 40 2335 −0.50% −0.28 −4.08%
Mexico 40 3032 0.50% 0.19 2.24%
Morocco 40 1018 0.40% 0.32 3.15%
Namibia 40 1706 2.50% 1.10 10.17%
Netherlands 40 4486 3.40% 0.95 28.78%
Norway 40 679 −0.80% −0.74 −6.47%
Oman 40 809 0.10% 0.07 0.20%
Pakistan 40 2620 2.00% 0.65 15.54%

Mathematics 2021,9, 179 13 of 20
Table A3. Cont.
Country N Operations AAV Sharpe Ratio Profits
Palestine 40 264 0.40% 0.42 1.83%
Poland 40 4 0.70% 1.37 0.10%
Portugal 40 3710 −0.30% −0.11 −3.43%
Romania 40 34 −0.20% −0.71 −0.91%
Russia 40 3503 −1.00% −0.33 −8.58%
Saudi Arabia 40 3320 0.00% 0.00 −0.73%
South Africa 40 6073 6.90% 1.60 65.68%
Spain 40 304 0.00% −0.07 −0.28%
Sweden 40 5049 −0.40% −0.16 −4.26%
Switzerland 40 4734 0.30% 0.12 1.32%
Thailand 40 3589 0.00% 0.00 −0.90%
United States 40 3461 −1.70% −0.35 −12.87%
Table A4.
Results obtained for the period 2000–2007 (50 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 50 1776 0.40% 0.27 2.14%
Bahrain 50 6 −0.10% −0.53 −0.10%
Belgium 50 5735 1.40% 0.66 10.25%
Brazil 50 4747 0.90% 0.24 5.65%
Colombia 50 2080 −0.50% −0.20 −2.72%
Czech Republic 50 1224 −0.20% −0.26 −2.04%
Denmark 50 4307 −0.20% −0.09 −2.06%
Dubai 50 509 −0.30% −0.30 −1.60%
Finland 50 4213 1.90% 0.76 13.96%
France 50 4987 1.80% 0.77 13.80%
Greece 50 4315 −0.40% −0.14 −3.66%
Hong Kong 50 63 0.10% 0.78 0.79%
India 50 4870 0.90% 0.28 6.23%
Israel 50 5710 3.30% 1.09 25.76%
Italy 50 4973 1.60% 0.75 12.21%
Japan 50 4637 3.60% 1.08 28.87%
Jordan 50 3071 0.20% 0.08 0.59%
Kuwait 50 3904 0.20% 0.09 0.82%
Lebanon 50 171 0.30% 1.11 1.57%
Mauritius 50 2782 −0.50% −0.34 −4.16%
Mexico 50 3573 0.50% 0.23 2.59%
Morocco 50 1144 0.40% 0.28 2.47%
Namibia 50 1706 2.00% 1.10 8.16%
Netherlands 50 5586 3.10% 0.96 26.08%
Norway 50 679 −0.70% −0.74 −5.24%
Oman 50 828 0.00% 0.03 −0.07%
Pakistan 50 2989 1.90% 0.69 14.50%
Palestine 50 264 0.30% 0.42 1.55%
Poland 50 4 0.50% 1.37 0.10%
Portugal 50 4643 −0.60% −0.23 −5.43%
Romania 50 34 −0.20% −0.71 −0.71%
Russia 50 4114 −0.60% −0.21 −5.52%
Saudi Arabia 50 4075 0.00% −0.01 −1.12%
South Africa 50 7458 7.40% 1.98 71.21%
Spain 50 304 0.00% −0.07 −0.26%
Sweden 50 5989 −0.50% −0.21 −4.90%
Switzerland 50 5847 0.50% 0.19 2.63%
Thailand 50 4179 0.10% 0.05 0.16%
United States 50 4180 −1.30% −0.31 −10.04%

Mathematics 2021,9, 179 14 of 20
Appendix B.2. Period 2007–2014
Table A5.
Results obtained for the period 2007–2014 (30 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 30 2877 0.10% 0.03 −0.06%
Bahrain 30 186 0.90% 1.04 4.94%
Belgium 30 4076 2.90% 0.92 23.64%
Brazil 30 3756 2.00% 0.53 14.55%
Colombia 30 3844 1.00% 0.41 6.62%
Czech Republic 30 506 1.30% 0.52 9.93%
Denmark 30 3024 1.70% 0.40 12.69%
Dubai 30 2836 −2.00% −0.56 −15.25%
Finland 30 4103 2.00% 0.48 15.03%
France 30 3470 1.50% 0.48 10.94%
Greece 30 3739 2.50% 0.48 19.85%
Hong Kong 30 1425 1.70% 0.55 5.72%
India 30 4051 3.20% 0.51 25.75%
Israel 30 3735 6.10% 1.23 54.25%
Italy 30 3817 1.80% 0.52 13.83%
Japan 30 3010 3.70% 0.84 30.30%
Jordan 30 2359 0.60% 0.15 3.41%
Kuwait 30 2862 −0.10% −0.03 −1.85%
Lebanon 30 318 0.70% 2.53 5.39%
Mauritius 30 1153 0.40% 0.28 2.72%
Mexico 30 2535 1.00% 0.34 7.35%
Morocco 30 2635 0.60% 0.23 3.82%
Namibia 30 2537 1.60% 0.50 11.95%
Netherlands 30 3415 3.10% 0.71 26.26%
Norway 30 2091 −0.90% −0.41 −7.50%
Oman 30 1816 −1.30% −0.57 −10.11%
Pakistan 30 2805 0.80% 0.16 5.06%
Palestine 30 492 −0.60% −0.69 −4.56%
Poland 30 1886 1.80% 0.88 13.37%
Portugal 30 3836 4.20% 0.73 36.82%
Romania 30 723 2.50% 0.83 19.76%
Russia 30 3081 0.70% 0.15 4.27%
Saudi Arabia 30 3508 0.50% 0.14 2.33%
South Africa 30 4557 3.60% 1.08 29.88%
Spain 30 2087 0.20% 0.07 0.80%
Sweden 30 4072 2.50% 0.55 19.14%
Switzerland 30 4033 2.90% 0.79 22.86%
Thailand 30 3217 −1.10% −0.27 −8.67%
United States 30 2996 0.30% 0.11 1.30%
Table A6.
Results obtained for the period 2007–2014 (40 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 40 3660 0.20% 0.05 0.48%
Bahrain 40 186 0.70% 1.04 3.65%
Belgium 40 5243 2.50% 0.87 20.29%
Brazil 40 4796 2.20% 0.67 16.60%
Colombia 40 4846 0.70% 0.32 4.19%
Czech Republic 40 506 1.00% 0.52 7.37%
Denmark 40 4008 0.90% 0.23 6.20%
Dubai 40 3524 −1.40% −0.43 −11.28%
Finland 40 5308 1.80% 0.46 13.47%

Mathematics 2021,9, 179 15 of 20
Table A6. Cont.
Country N Operations AAV Sharpe Ratio Profits
France 40 4551 1.20% 0.42 8.56%
Greece 40 4873 2.50% 0.53 19.88%
Hong Kong 40 1831 1.00% 0.35 3.34%
India 40 5287 3.60% 0.63 29.78%
Israel 40 4858 5.90% 1.35 51.59%
Italy 40 4841 2.30% 0.77 18.49%
Japan 40 3919 3.00% 0.72 23.12%
Jordan 40 3023 0.70% 0.18 4.14%
Kuwait 40 3839 −0.10% −0.02 −1.36%
Lebanon 40 318 0.50% 2.53 4.02%
Mauritius 40 1317 0.30% 0.20 1.67%
Mexico 40 3283 0.70% 0.25 4.48%
Morocco 40 3307 0.60% 0.27 3.97%
Namibia 40 3004 1.50% 0.57 10.95%
Netherlands 40 4517 3.60% 0.91 31.27%
Norway 40 2536 −0.80% −0.40 −6.43%
Oman 40 2395 −1.10% −0.52 −8.50%
Pakistan 40 3607 1.40% 0.31 9.70%
Palestine 40 492 −0.50% −0.69 −3.42%
Poland 40 2435 1.60% 0.82 11.59%
Portugal 40 4981 3.00% 0.62 24.75%
Romania 40 889 2.10% 0.90 16.88%
Russia 40 3996 1.20% 0.30 8.70%
Saudi Arabia 40 4594 0.80% 0.27 4.85%
South Africa 40 6004 4.00% 1.28 33.80%
Spain 40 2678 0.50% 0.20 3.33%
Sweden 40 5126 2.00% 0.49 15.12%
Switzerland 40 5175 1.70% 0.55 12.71%
Thailand 40 4267 −0.90% −0.27 −7.87%
United States 40 3882 0.70% 0.28 4.13%
Table A7.
Results obtained for the period 2007–2014 (50 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 50 4279 −0.20% −0.06 −2.16%
Bahrain 50 186 0.50% 1.04 2.96%
Belgium 50 6496 3.10% 1.14 25.60%
Brazil 50 5854 2.40% 0.79 18.43%
Colombia 50 5762 0.70% 0.34 4.05%
Czech Republic 50 506 0.80% 0.52 5.90%
Denmark 50 4884 0.90% 0.25 6.32%
Dubai 50 4144 −1.20% −0.40 −10.03%
Finland 50 6471 1.80% 0.51 13.51%
France 50 5527 1.30% 0.49 9.89%
Greece 50 6049 2.50% 0.57 19.89%
Hong Kong 50 2218 1.10% 0.41 3.76%
India 50 6529 3.20% 0.60 25.79%
Israel 50 5917 5.50% 1.44 47.32%
Italy 50 5987 2.80% 0.99 23.10%
Japan 50 4813 2.50% 0.64 18.94%
Jordan 50 3703 0.60% 0.22 4.06%
Kuwait 50 4698 0.60% 0.23 4.16%
Lebanon 50 318 0.40% 2.53 3.14%
Mauritius 50 1493 0.20% 0.14 1.10%
Mexico 50 3921 0.60% 0.23 3.72%
Morocco 50 3965 0.40% 0.20 2.31%

Mathematics 2021,9, 179 16 of 20
Table A7. Cont.
Country N Operations AAV Sharpe Ratio Profits
Namibia 50 3395 1.30% 0.59 9.62%
Netherlands 50 5798 3.30% 0.92 27.94%
Norway 50 2941 −0.80% −0.48 −6.59%
Oman 50 2805 −1.00% −0.49 −7.46%
Pakistan 50 4395 1.40% 0.37 9.92%
Palestine 50 492 −0.40% −0.69 −2.80%
Poland 50 2935 1.50% 0.84 10.91%
Portugal 50 6108 3.00% 0.70 24.88%
Romania 50 1045 1.80% 0.88 14.09%
Russia 50 4998 1.20% 0.33 8.40%
Saudi Arabia 50 5611 0.90% 0.34 6.08%
South Africa 50 7346 3.90% 1.31 32.73%
Spain 50 3100 0.30% 0.15 1.98%
Sweden 50 6263 2.30% 0.62 17.45%
Switzerland 50 6294 2.00% 0.73 15.54%
Thailand 50 5249 −1.40% −0.45 −11.05%
United States 50 4723 0.70% 0.32 4.66%
Appendix B.3. Period 2014–2020
Table A8.
Results obtained for the period 2014–2020 (30 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 30 2514 0.00% 0.00 −0.94%
Bahrain 30 5 0.00% 0.16 0.00%
Belgium 30 3048 3.90% 1.82 24.98%
Brazil 30 2375 2.40% 0.70 13.61%
Colombia 30 3543 5.00% 2.11 31.02%
Czech Republic 30 1009 −0.70% −0.48 −4.44%
Denmark 30 2089 1.30% 0.56 7.10%
Dubai 30 2307 −1.30% −0.35 −7.87%
Finland 30 3001 0.90% 0.30 4.30%
France 30 2411 −0.60% −0.31 −4.20%
Greece 30 2978 8.60% 1.35 59.71%
Hong Kong 30 1997 2.20% 0.57 11.43%
India 30 2760 2.50% 0.47 14.38%
Israel 30 2590 1.40% 0.71 7.64%
Italy 30 2687 1.40% 0.55 7.60%
Japan 30 2108 1.90% 0.60 10.60%
Jordan 30 1682 −0.30% −0.17 −2.46%
Kuwait 30 2834 2.70% 0.85 15.36%
Lebanon 30 182 1.30% 3.85 7.04%
Mauritius 30 233 0.00% 0.08 0.12%
Mexico 30 1808 −1.40% −0.67 −8.90%
Morocco 30 1606 0.20% 0.12 0.76%
Namibia 30 2260 2.00% 0.71 11.55%
Netherlands 30 2467 1.70% 0.59 9.58%
Norway 30 2381 0.80% 0.29 4.41%
Oman 30 1005 −0.20% −0.12 −1.44%
Pakistan 30 2149 1.00% 0.25 5.28%
Palestine 30 358 0.00% 0.04 −0.02%
Poland 30 2804 2.00% 0.65 11.47%
Portugal 30 2243 0.00% 0.01 −0.55%
Romania 30 2036 0.50% 0.15 2.22%
Russia 30 2095 0.00% −0.01 −0.90%
Saudi Arabia 30 2780 2.60% 0.82 14.77%

Mathematics 2021,9, 179 17 of 20
Table A8. Cont.
Country N Operations AAV Sharpe Ratio Profits
South Africa 30 3036 3.90% 1.27 23.79%
Spain 30 2321 −0.80% −0.28 −5.37%
Sweden 30 2653 2.60% 1.05 15.82%
Switzerland 30 2259 0.80% 0.32 4.35%
Thailand 30 1869 0.00% 0.01 −0.52%
United States 30 2090 0.80% 0.41 4.20%
Table A9.
Results obtained for the period 2014–2020 (40 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 40 3177 −0.40% −0.10 −3.19%
Bahrain 40 5 0.00% 0.16 0.00%
Belgium 40 3860 3.70% 1.88 23.03%
Brazil 40 3062 1.70% 0.56 9.63%
Colombia 40 4505 4.00% 1.84 24.47%
Czech Republic 40 1087 −0.60% −0.48 −3.47%
Denmark 40 2634 1.00% 0.49 5.64%
Dubai 40 3107 −1.60% −0.48 −9.68%
Finland 40 3774 0.30% 0.11 0.76%
France 40 3017 −0.10% −0.04 −1.15%
Greece 40 4008 7.10% 1.28 47.70%
Hong Kong 40 2593 1.70% 0.46 8.65%
India 40 3436 2.40% 0.51 13.84%
Israel 40 3276 1.30% 0.71 6.88%
Italy 40 3441 1.20% 0.51 6.44%
Japan 40 2677 2.30% 0.83 13.63%
Jordan 40 2276 −0.20% −0.14 −1.97%
Kuwait 40 3753 3.20% 1.14 18.86%
Lebanon 40 182 0.90% 3.85 5.15%
Mauritius 40 233 0.00% 0.08 0.04%
Mexico 40 2230 −1.10% −0.60 −7.16%
Morocco 40 2078 −0.10% −0.06 −1.12%
Namibia 40 2847 2.00% 0.74 11.39%
Netherlands 40 3245 1.20% 0.51 6.79%
Norway 40 3031 0.40% 0.16 1.74%
Oman 40 1088 −0.10% −0.05 −0.67%
Pakistan 40 2851 0.90% 0.27 4.89%
Palestine 40 358 0.00% 0.04 0.01%
Poland 40 3669 1.50% 0.51 7.78%
Portugal 40 2961 0.30% 0.09 1.06%
Romania 40 2664 0.10% 0.03 −0.27%
Russia 40 2747 0.30% 0.09 1.11%
Saudi Arabia 40 3571 2.40% 0.84 13.51%
South Africa 40 3818 3.40% 1.18 20.05%
Spain 40 2901 −0.70% −0.30 −5.03%
Sweden 40 3239 2.10% 0.98 12.69%
Switzerland 40 2878 1.10% 0.48 6.08%
Thailand 40 2525 −0.70% −0.32 −4.53%
United States 40 2643 0.90% 0.51 4.64%

Mathematics 2021,9, 179 18 of 20
Table A10.
Results obtained for the period 2014–2020 (50 Pairs), where N is the number of pairs; AAV
Average annualized return; and Profit is the profitability for the full period with transaction costs.
Country N Operations AAV Sharpe Ratio Profits
Argentina 50 3799 −1.40% −0.38 −8.56%
Bahrain 50 5 0.00% 0.16 0.00%
Belgium 50 4687 3.30% 1.83 20.36%
Brazil 50 3817 1.20% 0.43 6.64%
Colombia 50 5354 3.80% 1.90 22.83%
Czech Republic 50 1112 −0.40% −0.47 −2.82%
Denmark 50 3128 1.00% 0.48 5.27%
Dubai 50 3821 −1.70% −0.57 −10.36%
Finland 50 4485 0.40% 0.15 1.40%
France 50 3692 −0.20% −0.12 −1.84%
Greece 50 5026 6.50% 1.28 42.39%
Hong Kong 50 3221 1.90% 0.48 9.66%
India 50 4085 2.00% 0.48 11.38%
Israel 50 3962 1.50% 0.89 8.21%
Italy 50 4296 0.90% 0.40 4.64%
Japan 50 3224 2.30% 0.85 13.46%
Jordan 50 2757 0.20% 0.12 0.55%
Kuwait 50 4597 3.30% 1.29 19.38%
Lebanon 50 182 0.70% 3.85 4.16%
Mauritius 50 233 0.00% 0.08 0.05%
Mexico 50 2731 −0.90% −0.54 −6.05%
Morocco 50 2496 0.20% 0.15 0.90%
Namibia 50 3386 1.70% 0.72 9.72%
Netherlands 50 3871 1.40% 0.65 7.73%
Norway 50 3640 0.50% 0.23 2.47%
Oman 50 1172 0.00% 0.02 −0.13%
Pakistan 50 3583 1.80% 0.55 9.88%
Palestine 50 358 0.00% 0.04 0.03%
Poland 50 4355 1.30% 0.50 6.83%
Portugal 50 3636 0.30% 0.08 0.87%
Romania 50 3263 −0.10% −0.04 −1.15%
Russia 50 3452 −0.10% −0.04 −1.29%
Saudi Arabia 50 4358 1.90% 0.75 10.83%
South Africa 50 4587 2.50% 0.90 14.38%
Spain 50 3468 −0.50% −0.23 −3.59%
Sweden 50 3935 1.80% 0.85 10.51%
Switzerland 50 3511 1.00% 0.50 5.60%
Thailand 50 3215 −0.40% −0.22 −3.14%
United States 50 3102 0.60% 0.38 2.98%
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