macroeconomic variables and stock markets: an
TRANSCRIPT
Applied Econometrics and International Development Vol. 19-1 (2019)
MACROECONOMIC VARIABLES AND STOCK MARKETS: AN
INTERNATIONAL STUDY
Francisco JAREÑO*
Ana ESCRIBANO
Alberto CUENCA
Abstract. This paper studies the potential correlation between the stock market of six relevant
countries (Germany, Italy, Spain, France, UK and US) and some important macroeconomic
factors, such as the gross domestic product (GDP), the consumer price index (CPI), the
industrial production index (IPI) and the unemployment (UNEMP). GDP and UNEMP show
statistically significant correlation with these international stock markets, mainly in the crisis
sub-period, finding, in addition, the expected signs.
Keywords: International Stock Market; Macroeconomic Factors; Correlation Analysis; US;
European Countries
JEL Classification: E32, F44, G15, O40, O51
1. Introduction and literature review.
A large part of the financial literature agrees that the globalization process begins to develop
at the beginning of the 21st century, fundamentally the globalization of financial systems,
which is the focus of this work. Financial globalization occurs mainly due to the liberalization
of national financial systems, which causes a greater connection between international
financial systems. Thus, this would be one of the main reasons for the rapid and general spread
of the global financial crisis of 2008 that affected the world economy.
The equity markets experienced a generalized growth during the beginning of the
century, showing the economic moment of growth that extends, approximately, until the year
2007. At the end of this year, a recession begins in the United States that mainly affects to the
stock markets. Financial globalization, therefore, is what makes the US recession begin to
move to markets throughout Europe and the rest of the world at the beginning of 2008. This
year there has been a generalized fall in yields in the international equity markets, which has
continued for several consecutive quarters, reaching even 2009, as shown by the data on the
evolution of stock prices in that period.
According to Chen et al. (1986), Humpe and Macmillan (2009), and Jareño and
Negrut (2016), among others, the aim is to analyze the possible relationship between
international stock market returns and a pool of relevant macro-economic variables, largely
gathered from the previous studies. Because of the recent sample period, this research may
observe whether changes in the economic cycle –before, during and after the recent global
financial crisis- affect in some way the relationship studied between the macro variables and
the returns of different international stock markets.
Many researches investigate the relationship between stock markets and
macroeconomic factors, although they do not find agreement in their conclusions. However,
according to Chen et al. (1986), Wasserfallen (1989), Schwert (1990), Peiró (1996 and 2016),
Humpe and Macmillan (2009) and Jareño and Negrut (2016), the expected signs of the most
relevant macroeconomic variables could be those collected in Table 1.
* Francisco Jareño, E-mail: [email protected]; Ana Escribano, E-mail:
[email protected]; Alberto Cuenca, E-mail: [email protected]. Department of
Economics and Finance. University of Castilla-La Mancha, Faculty of Economic and Business
Sciences, Plaza de la Universidad, 1, 02071, Albacete (Spain)
44
Table 1. Relationship between stock markets and macroeconomic factors: expected signs
GDP CPI IPI Unemployment
Stock Market Positive Uncertain Positive Negative
Source: Own preparation based on Chen et al. (1986), Wasserfallen (1989), Schwert (1990), Peiró
(1996 and 2016), Humpe and Macmillan (2009) and Jareño and Negrut (2016)
Thus, the study includes the most used macroeconomic factors in the previous
literature: the Consumer Price Index (CPI), the Industrial Price Index (IPI), the Gross
Domestic Product (GDP) and unemployment (UNEMP) during a sample period between 2000
and 2014. The impact of these variables on some international stock market indices is
analyzed, in concrete, for Germany, Spain, France, Italy, UK and US. In addition, the analysis
of the relationship between the selected macro-economic variables and different stock market
returns is carried out in a period that includes the recent global financial crisis, because this
paper aims to study if this relationship changes according to the phase of the economic cycle,
focusing attention on the global financial crisis phase.
The rest of the paper is structured as follows. Section 2 shows the data sample
analysed in this paper. Section 3 analyses the time evolution between the stock market and
the different macroeconomic variables. Section 4 shows correlation matrices between the
stock market price and the various macroeconomic factors. Finally, Section 5 shows the main
conclusions of this study.
2. Data
This paper examines the impact of some relevant macroeconomic variables (CPI,
GDP, IPI and UNEMP) on international stock market returns (Germany, Spain, France, Italy,
UK and USA) from 2000 q1 to 2014 q4.1 As previously said, this study breaks the whole
sample period into three different sub- periods: pre-crisis (2000-2006), crisis (2007-2010) and
post-crisis (2011-2014).
Specifically, we use quarterly data for the 2000-2014 sample period. Furthermore,
data on the selected macroeconomic variables were obtained from the Eurostat website
(http://ec.europa.eu/eurostat) and the National Bureau of Economic Research
(http://www.nber.org/). Data from differente international stock markets were obtained from
the Econstats (http://www.econstats.com/).
For comparison reasons, this research studies six different international stock
markets, such as Germany, Spain, France, Italy, UK and USA. Thus, we analise the following
stock market indices: DAX30 (Germany), IBEX35 (Spain), CAC40 (France), MIB30 (Italy),
FTSE100 (UK) and S&P500 (US). International market indices have been incorporated into
the analysis through the yields of the quarterly closing quotations.
Finally, the explanatory variables have been incorporated into the analysis as growth
rates, which guarantees that the variables included in the analysis are stationary variables.
Thus, the four macroeconomic variables used in this research are defined as follows: (1) the
Gross Domestic Product (GDP) represents the value of all goods and services produced in the
United States; in concrete, this study uses two different measures of GDP: GDP in real terms,
and the growth rate in percentage; in addition, these measures are seasonally adjusted; (2) the
1 The sample period ends in 2009 in the case of Italy, due to a lower availability of data from this
country for the most recent dates.
45
Consumer Price Index (CPI) is the original data used to obtain the US inflation rate; in
particular, we have considered this factor as an index and as an inflation rate; (3) the Industrial
Production Index (IPI) according to the National Statistics Institute is a cyclical indicator that
measures the productive activity of the industrial sector (excluding construction); this factor
has been considered in the analysis as an index (in levels), and the growth rate (seasonally
adjusted); finally, (4) the unemployment (UNEMP) represents the total number of individuals
who are not working but are actively seeking employment.
Table 2. Market indices and macroeconomic variables
International Stock Market Indices Macro Variables
DAX30 (Germany) Consumer Price Index (CPI)
IBEX35 (Spain) Industrial Production Index (IPI)
CAC40 (France) Gross Domestic Product (GDP)
MIB30 (Italy) Unemployment
S&P500 (US)
FTSE100 (UK)
According to Table 1, based on Jareño and Negrut (2016), among others, a positive
relationship between the stock market and both GDP and IPI may be expected. Thus, higher
prices in the stock market are associated with higher values for both variables (GDP and IPI),
and their behavior proceeds according to the stock market cycle: good news in the financial
economy also means good news in the real economy and vice versa. By contrast, the
unemployment and interest rates are negatively related to the stock market; that is, higher
prices on the DJ index are associated with lower values for these macroeconomic factors,
showing anti-cyclical behavior. Again, good news in the financial economy produces good
news in the real economy (because these factors, in principle, are better when the values are
lower). Moreover, the relationship between the inflation rate and the stock market is uncertain
because it can fluctuate according to the needs of the economy.
3. Analysis of the time evolution between the stock market and the different
macroeconomic variables This section collects the graphs that show the evolution of the explanatory variables
and the respective stock market index of each country. Figure 1 shows CPI, IPI, and GDP,
since previous literature hypothesizes a positive relationship with market returns. Figure 2
exhibits the relationship between unemployment and international stock market indices,
assuming an inverse relationship.2 The explanatory variables collected in Figure 1 and 2 are
shown in levels, although next section includes growth rates. In addition, these graphs show
a shaded area that refers to the global financial crisis period in 2008. In particular, shaded
areas in Figure 1 indicate recession periods based on the NBER dating.3 Thus, the beginning
of the aforementioned economic recession was in 2008q1, with the end of it being dated in
2009q2. This concrete period is the one that has been highlighted in the following graphs with
a shaded area.
2 They are included separately to show more clearly their different evolution, since while the first three
have a positive relationship, the fourth is negative. 3 This page offers information on the start and end dates of the different phases of the economic
cycle.
46
Figure 1. Graphs of the combined evolution of the stock market and macroeconomic factors (CPI,
IPI, GDP): Germany, Spain and France, Italy, USA, UK
Germany
Spain
France
Italy
UK
USA
Note: Shaded areas in this figure indicate recession periods based on the NBER dating.
According to Figure 1, which shows the historical evolution of the different market
indices and the macro-economic variables (in levels), in Germany -which serves as an
example of the evolution of data in Europe-, it is observed how until the year 2002 certain
variables suffer decreases and changes of tendency to, from that moment, begin a joint
evolution that leads to the beginning of the crisis. The shaded period, which refers to the
global financial crisis, reflects the decreasing trend and changes in the evolution of the
variables. The rest of Figure 1 verifies the increasing tendency of the magnitudes in almost
all its route, with the exception of certain periods of decrease.
The rest of the countries show a similar evolution until the global financial crisis. As
of 2010, according to the economic policies developed and their different impact, for instance,
in France and Spain it is found that some indicators do not recover the growth trend after the
2000
3000
4000
5000
6000
7000
8000
9000
10000
80
85
90
95
100
105
110
RECESSION CPI IPI GDP DAX30
5000
7000
9000
11000
13000
15000
70.00
75.00
80.00
85.00
90.00
95.00
100.00
105.00
110.00
115.00
RECESSION CPI IPI GDP IBEX35
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
80.00
85.00
90.00
95.00
100.00
105.00
110.00
RECESSION CPI IPI GDP CAC40
15000
20000
25000
30000
35000
40000
45000
50000
55000
75.00
80.00
85.00
90.00
95.00
100.00
105.00
110.00
20
00.1
20
00.2
20
00.3
20
00.4
20
01.1
20
01.2
20
01.3
20
01.4
20
02.1
20
02.2
20
02.3
20
02.4
20
03.1
20
03.2
20
03.3
20
03.4
20
04.1
20
04.2
20
04.3
20
04.4
20
05.1
20
05.2
20
05.3
20
05.4
20
06.1
20
06.2
20
06.3
20
06.4
20
07.1
20
07.2
20
07.3
20
07.4
20
08.1
20
08.2
20
08.3
20
08.4
20
09.1
20
09.2
RECESSION CPI IPI GDP MIB30
3500
4000
4500
5000
5500
6000
6500
7000
65.00
75.00
85.00
95.00
105.00
115.00
RECESSION CPI IPI GDP FTSE100
750
950
1150
1350
1550
1750
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
RECESSION CPI IPI GDP SP500
47
crisis, keeping their variables horizontal (stability) or even decreasing. Thus, a similar
evolution in the GDP is observed, since it follows the evolution of the market index, or even
in the IPI, which does not reach its previous growth rate.
Figure 2. Graphs of the combined evolution of the stock market and macroeconomic factors
(UNEMP)
Germany
Spain
France
Italy
UK
USA
Note: Shaded areas in this figure indicate recession periods based on the NBER dating.
In Spain, at least until 2010, a correspondence is observed in the evolution of all the
variables, since the Consumer Price Index continues to increase along with the IPI until the
final stretch, where the latter remains constant. In its case, GDP continues to maintain an
evolution similar to the market index (although softened), which decreases in the final tranche.
The CPI maintains a rhythm of growth that only stops during the recession, reaching a decline
in this period. The IPI does maintain a behavior similar to the market index for much of the
2000
3000
4000
5000
6000
7000
8000
9000
5
6
7
8
9
10
11
RECESSION UNEMP DAX30
5000
7000
9000
11000
13000
15000
7.5
9.5
11.5
13.5
15.5
17.5
19.5
21.5
23.5
25.5
27.5
RECESSION UNEMP IBEX35
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
RECESSION UNEMP CAC40
15000
20000
25000
30000
35000
40000
45000
50000
55000
5.0
6.0
7.0
8.0
9.0
10.0
11.0
20
00.1
20
00.2
20
00.3
20
00.4
20
01.1
20
01.2
20
01.3
20
01.4
20
02.1
20
02.2
20
02.3
20
02.4
20
03.1
20
03.2
20
03.3
20
03.4
20
04.1
20
04.2
20
04.3
20
04.4
20
05.1
20
05.2
20
05.3
20
05.4
20
06.1
20
06.2
20
06.3
20
06.4
20
07.1
20
07.2
20
07.3
20
07.4
20
08.1
20
08.2
20
08.3
20
08.4
20
09.1
20
09.2
RECESSION UNEMP MIB30
3500
4000
4500
5000
5500
6000
6500
7000
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
RECESSION UNEMP FTSE100
750
950
1150
1350
1550
1750
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
RECESSION UNEMP SP500
48
total period, because until the crisis maintain a common growth. After this, it continues with
its previous growth rate, which is only slowed down in the last years analyzed, where it
remains constant, moving into a growth phase, as well as the stock market returns (between
2012 and 2014).
Figure 2 contains the temporal evolution of international market indices and
unemployment (in levels). In concrete, in France, in most of the period the relationship is
inverse, especially in the shaded part, during the crisis, in which the decrease in market prices
is accompanied by an increase in unemployment. Previously, the increase in market returns
is accompanied by a large decline in unemployment. Therefore, unemployment is expected
to have an inverse relationship with the market index, as shown in Table 1. Graphically, Figure
2 seems to confirm this relationship during a large part of the period analyzed. This
relationship is more clearly reflected in the case of the US, although it is also observed in the
European countries, because in the pre-crisis period we find falling unemployment rates and
increasing market returns. Once the period of recession begins, the market index falls sharply
and unemployment begins to have a constant growth rate that places it several percentage
points above its previous data.
In sum, a visual inspection of the graphs that reflect the temporal evolution of the variables
analyzed by country allows us to anticipate the potential existence of a relationship between
the different macroeconomic variables and the stock market returns in the different countries.
Thus, there may be a direct relationship between three out of four explanatory variables (CPI,
IPI and GDP) and the international stock market indices, and inverse in one of the cases
(unemployment). However, we find certain quarters in which this relationship is diffuse or
does not come into existence. Therefore, we confirm that Unemployment seems to show an
inverse relationship with the stock market indices during a large part of the sample period.
There is also a direct relationship with GDP, since both variables show a similar evolution
during the whole sample period. The CPI, however, has an almost continuous trend of growth,
which makes it go away in times of recession in the market index, showing a direct
relationship at times of economic growth. As for the IPI, to a lesser extent than the GDP, it
also exhibits some direct relationship with the equity market, decreasing at times when the
market indices show a negative trend, especially in the crisis period (2008q1 - 2009q2).
4. Relationship between international stock market returns and some macroeconomic
factors
For robustness, to study the existence of a relationship between the explanatory
variables included and the stock market returns of different countries, we check our
preliminary results through a correlation analysis and scatter plots.
For this second analysis, the variables are expressed in growth rates (one quarter
compared to the previous quarter) to guarantee the stationarity of the explained and
explanatory variables. The analysis is carried out by countries with their respective variables
for the entire period (2000-2014) in a first matrix. Later we will proceed to show the matrices
for each sub-period mentioned previously: pre-crisis (2000-2006), crisis (2007-2010), and
post-crisis (2011-2014). The complete sample period is divided into sub-periods to check if
the relationship is greater at certain times and eliminate distortions that may have occurred in
certain periods of time, thus affecting the whole sample.
4.1. Scatter plots to show the relationship between international stock market returns and
some macroeconomic factors
49
Figure 3, in the Annex, exhibits the relationship between the international stock
market returns and the explanatory variables of each country, collected with scatter plots.
Again, a positive relation between stock market returns and the GDP (growth rate), IPI
(growth rate) and the inflation rate is observed, and, on the other hand, an inverse relationship
between the international market returns and the unemployment rate. This last relation is the
clearest, since the cloud of points perfectly shows the inverse relationship that exists with
unemployment, since at a lower rate of unemployment the returns are, in general, higher. In
addition, the slope of the represented regression line seems to show a greater slope than the
rest.
The GDP growth rate, on the other hand, also shows a revealing relationship in the
US scatter plot, although in this case with a positive trend line. This result would indicate that
at times of higher stock market returns, the GDP growth rate would also be expected to be
higher. Furthermore, in US this direct relationship show a higher slope in the case of GDP
and IPI. The US inflation rate show a weaker relationship in the whole sample period, because
the trend line is almost horizontal. Finally, the unemployment rate shows a clearly inverse
relationship with a negative trend line and with a steep slope.
Thus, in cases where the relationship is positive, the GDP growth rate is the one that
reflects a relationship with a steeper slope. Therefore, the higher growth rates of this variable
correspond to higher stock market returns, although it is true that some distortion of the results
is observed at specific moments in the sample period analyzed.
UK shows a reality very similar to what happens in the rest of Europe. However, in
this case the inflation rate (CPI growth rate) seems to show a slightly higher correlation with
stock market returns. In addition, the unemployment rate in the United Kingdom would again
show a markedly steep slope as it happens in the rest of the European countries analyzed,
because even though the point cloud is presented in a dispersed way, the trend line indicates
the negative relationship between the unemployment rate and stock market returns. As in the
rest of the countries, the unemployment rate exhibits a clearer relationship with stock market
returns. This overview will contrast with the correlation matrices presented later. As in the
previous case, the GDP growth rate shows a clear positive relationship with respect to stock
market returns, generally observed in the rest of the countries analyzed. The same happens
with the growth rate of the IPI, which although sometimes with a less pronounced trend line,
shows scatter plots for all countries with a clear positive trend.
Finally, the relationship found between international stock market returns and the
growth rate of the CPI (inflation rate) and the IPI (growth rate of Industrial Production Index)
is positive but slightly lower than the rest. In both cases, there is a positive trend line but little
pronounced. This would show some positive relationship in this case but with a very scattered
cloud of points that makes the relationship observed in the graphics somewhat less clear.
4.2. Correlation matrices between international stock market returns and some
macroeconomic factors
To confirm the relationships observed in the previous scatter plots, and in order to
improve the perception of the possible relationship between the stock market returns and the
growth rates of the different macro-economic variables, we show some correlation matrices
by countries that express numerically these relationships.
This analysis shows four correlation matrices since, as previously said, the analysis
has splitted the whole sample period into three different sub-periods. First, the total sample
period (2000-2014) is analyzed, since it is the period used in the previous section of the
50
dispersion diagrams. In this way, you can confirm the previous results with those obtained
through the correlation matrices.
Thus, tables 3-6 present the correlation matrix to indicate whether the relationships
between the six international stock market indices and the analyzed macroeconomic variables
are statistically significant. To that end, Student’s t distribution and the associated
probabilities (p-value) are used to confirm the statistically significance.
4.2.1. Correlation matrix: the whole sample period (2000-2014)
Table 3 shows the Pearson correlation coefficients between macro variables and
international stock market returns (from Germany, Spain, France, Italy, UK and US) during
the whole sample period. When the coefficient shows a positive sign, it means that the
relationship is direct, as seen in the GDP growth rate, the inflation rate, and the growth rate
of the IPI. In the case of the unemployment growth rate, the coefficient and, therefore, the
relationship is negative. Thus, at higher values in the market returns, lower values are found
in terms of the unemployment rate.
Table 3. Correlation matrix between stock market returns and some macroeconomic
factors: the whole sample period (2000-2014)
GDP CPI IPI UNEMP
Germany 0.3210 * 0.2947 ** 0.1081 -0.6793 **
Spain 0.5150 ** 0.0832 0.3069 * -0.3298
France 0.4216 * 0.1468 0.3878 -0.6794 **
Italy 0.5752 ** 0.2395 0.5129 ** -0.4329 *
US 0.2726 * 0.1568 0.1869 -0.4443 **
UK 0.1654 0.2268 * 0.2590 * -0.3252 *
Note: As usual, *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
In this way, it is observed that the unemployment rate is the macro-economic variable
with the highest correlation (in absolute values) and the greatest statistical significance. This
variable is statistically significant at 5% level in half of the countries, and at 10% level in two
countries. In a paradoxical manner, only in Spain UNEMP is not statistically significant. In
addition, the negative coefficients are around 0.5, which indicates a linear and inverse
relationship between both variables, with an average correlation. This value confirms the
results obtained in the scatter plots, with the unemployment rate being the variable that
exhibits the greatest relationship with respect to stock market returns. Finally, if we compare
the results by countries, France and Germany show higher coefficients, with a correlation
close to 68% in the case of the unemployment rate.
On the other hand, the GDP growth rate shows a positive and statistically significant
relationship with the international stock market returns in most countries, corroborating some
previous results observed in the scatter plots. This relationship is statistically significant in
most countries, except in the UK. In addition, in the rest of the countries there are coefficients
that, as in the case of the unemployment growth rate, hover around 0.5 with a positive sign
(although the coefficients, in absolute value, are slightly lower). Furthermore, the countries
that show a higher correlation coefficient are, in this order, Italy (0.58) and Spain (0.52).
The growth rate of the IPI shows an insignificant relationshipt in the case of certain
countries, situation that was illustrated in the previous scatter plots. The same case is observed
in the inflation rate (growth rate of the CPI). Both variables show a relationship that is only
statistically significant in the case of certain countries. This may be due to different behaviors
51
depending on the phase of the economic cycle, which are compensated in the entire period,
showing an inconclusive result.
4.2.2. Correlation matrix: pre-crisis, crisis and post-crisis sub-periods
A second analysis of correlation matrices linked to the different sub-periods of pre-
crisis, global financial crisis and post-crisis could provide complementary information,
mainly in the case of growth rates of the IPI and the inflation rate, with inconclusive results
in the full sample period.
Table 4. Correlation matrix between stock market returns and some macroeconomic
factors: the pre-crisis sub-period (2000-2006) GDP CPI IPI UNEMP
Germany 0.5201 ** 0.1538 0.2821 -0.7829 ***
Spain 0.5147 ** 0.0104 0.2935 -0.3196
France 0.3739 * -0.0964 0.3739 * -0.6546 **
Italy 0.5060 ** -0.0092 0.3021 -0.4220 **
US 0.2312 0.0289 -0.0475 -0.2309
UK -0.2978 0.2013 0.0825 0.0257
Note: As usual, *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
Table 4 that shows the results in the pre-crisis sub-period and shows a situation similar
to that collected in the analysis of the whole sample period. In this case, however, the
existence of statistically significant correlations between the macro-economic variables and
the stock market returns is lower. Only statistically significant correlations are observed for
the growth rate of GDP and the unemployment rate in a part of the countries analyzed. The
unemployment rate in the United Kingdom shows a Pearson correlation coefficient that is
virtually zero. The other two variables (IPI growth rate and inflation rate) show little
correlation for this period, except in the case of France for the IPI. The rest of correlations
show values very close to zero, which implies the non-existence of a linear relationship.
Negative coefficients are also found for these variables, since the time series shows that,
during the pre-crisis period (2000-2002), the market indices are falling while the explanatory
variables (IPI and CPI) exhibit a positive trend. In the sub-period of the global financial crisis,
a greater correlation between macroeconomic variables and the respective international stock
market returns is observed. Table 5 shows higher Pearson correlation coefficients than in the
previous samples, since all the variables find a statistically significant relationship depending
on the country. Specifically, the growth rate of the GDP is the variable with the greatest
relationship with respect to the stock market returns –with the exception of the UK-.
Table 5. Correlation matrix between stock market returns and some macroeconomic
factors: the crisis sub-period (2007-2010) GDP CPI IPI UNEMP
Germany 0.6049 * 0.7306 ** 0.5278 * -0.3604
Spain 0.7370 ** 0.3204 0.4446 -0.5532 *
France 0.5347 * 0.4634 * 0.5651 -0.7875 **
Italy 0.8214 ** 0.3187 0.6463 * -0.3335
US 0.6348 * 0.5240 ** 0.5763 ** -0.5785 *
UK 0.3233 0.1081 0.4552 -0.7074 ** Note: As usual, *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
The unemployment rate, on the other hand, also shows a relationship with stock
market returns in four out of six countries analyzed. This inverse relationship implies that in
this stage of crisis, while the stock market returns experience a downward trend, the
52
unemployment rate begins to increase –and vice versa-. The coefficients are close to the value
0.5, which would imply an average level of correlation, although in France and the UK they
have a correlation level of 70%.
The inflation rate and the growth rate of the IPI in this case show a stronger Pearson
correlation coefficient than in the correlation matrices presented previously. In the crisis sub-
period they show a higher level of probability and coefficients with higher values. However,
we find some countries in which these variables are not statistically significant. Again, these
macro-economic variables exhibit a lower correlation with the stock market returns. In
short, the financial crisis sub-period seems to show a greater correlation between the
explanatory variables and the international market returns, because this period corresponds to
a recession that affects the economy as a whole. This translates into falls and loss of the
positive trend of almost all of the variables for the different countries analyzed. Finally, the
unemployment rate changes its tendency to decrease in 2007 due to a continuous increase in
unemployment. When dealing with an economic crisis that affects most variables, they begin
a cycle of depression that translates into linear movements of the macro-magnitudes in a
framework of crisis.
Finally, Table 6 shows the Pearson correlation coefficients of the post-crisis period,
in which Europe and the United States may show quite different situations. Potentially diverse
economic policies could have carried out depending on the geographical zones, which may
affect the respective countries differently. This could explain why the variables act differently
depending on the country analyzed. Thus, the correlation between macro variables and stock
market returns would be affected by this context, oscillating each one differently and even
erratically.
Table 6. Correlation matrix between stock market returns and some macroeconomic
factors: the post-crisis sub-period (2011-2014) GDP CPI IPI UNEMP
Germany 0.0502 -0.1971 -0.3888 0.1880
Spain 0.4339 0.0337 0.4887 -0.4292
France 0.3488 0.0210 0.1067 -0.5877 *
Italy 0.2416 -0.3205 -0.2715 -0.3131
US 0.3412 -0.1342 0.0858 -0.5306 * Note: As usual, *, **, *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
Thus, the only macro-economic variable that shows a statistically significant Pearson
correlation coefficient in the post-crisis sub-period is the unemployment rate, and only in
France and the UK, but with relatively low coefficients with respect to samples previously
analyzed. One can get to appreciate a positive correlation between UNEMP and German stock
market returns, situation that is not expected by the inverse behavior that assumes of both. In
the rest of the countries, no statistically significant correlation is observed.
With regard to the correlation between the growth rate of the GDP and the stock
market returns, wich shows statistically significance in previous analyses, it does exhibit an
insignificant correlation for all countries in the post-crisis sub-period. The other two macro
variables (IPI growth rate and inflation rate) show lower Pearson correlation coefficients than
other magnitudes, since there is no statistically significant correlation for the countries
studied. The coefficients extracted from the correlation matrix in Table 6 show Pearson
correlation coefficients close to zero in most cases.
53
Therefore, in the post-crisis sub-period there is no clear correlations between
macroeconomic variables and stock market returns. This situation may be due to the different
economic policies adopted in each country to face the situation after a period of economic
recession. The instruments used and the policies implemented may affect the variables and
markets in different ways depending on the situation of each country, which would cause a
multitude of trends in the different variables and countries, distorting or masking the
correlation that, a priori, is expected. In summary, as expected, positive Pearson correlation
coefficients are found between stock marke returns and the macro variables IPI growth rate,
inflation rate and GDP growth rate. On the contrary, a negative and statistically significant
correlation is observed between market returns and the unemployment rate. In addition, these
last two varaibles (GDP and unemployment) are the most intense, with more pronounced
trend lines. Furthermore, the analysis by sub-periods shows how these correlations are more
relevant in the crisis sub-period, but reflect minimum values (both in terms of correlation
coefficients and statistical significance) in the post-crisis stage.Moreover, the Pearson
correlation coefficients between stock market returns and the growth rate of GDP and the
unemployment rate show the highest values and significance levels. On the other hand, the
inflation rate and the growth rate of the IPI do not show a clear correlation with stock market
returns. Thus, they show scatter plots with a horizontal trend line in many cases. Second, the
correlation matrices show Pearson correlation coefficients very close to zero and statistically
insignificant.
5. Summary and concluding remarks
The aim of this research is to analyze if the expected relationships between a set of
relevant macro-economic variables (Consumer Price Index: CPI, Industrial Production Index:
IPI, Gross Domestic Product: GDP and Unemployment: UNEMP) and six international stock
markets are verified: DAX30 (Germany), IBEX35 (Spain), CAC40 (France), MIB30 (Italy),
FTSE100 (United Kingdom) and S & P500 (United States).
A priori, the relationship between stock market returns and the CPI variable would be
uncertain, positive for GDP and IPI, and negative for UNEMP. The results obtained in the
previous studies reviewed vary in many cases depending on the different periods analyzed,
the economic cycle, the sample size, or even the methodology used.
The time evolution of the macro-economic variables and the benchmark indices of
the stock markets analyzed show that the UNEMP and GDP variables are those that,
apparently, show a fairly clear relationship with the market performance, the first inversely
and the second directly. The other two variables, CPI and IPI, show a seemingly less intense
and clear relationship.
The correlation analysis between the macro variables and the international stock
market indices shows greater and statistically significant correlations during the crisis sub-
period. In addition, this analysis corroborates that the UNEMP variable shows an intense and
inverse correlation with the market performance, and the GDP variable a strong but positive
correlation, confirming that these variables seem to be the most correlated with the stock
market returns. On the other hand, the IPI and IPC variables show a lower correlation, which
is only remarkable at certain moments of time and with less statistical significance.
The Pearson correlation coefficients by country show statistical significance in the
crisis period for all the macroeconomic variables at least in some of the countries analyzed,
and in all countries there is at least one statistically significant correlation. In the pre-crisis
period, there is a smaller number of statistically significant correlations (only for GDP and
54
UNEMP) for countries such as Germany, France, Italy and Spain. After the crisis, the
variables obtain less correlation with the market, possibly due to the different effects of the
recession. Only statistically significant correlations are observed in the post-crisis period for
UNEMP in countries such as the UK and France.
In general, UNEMP and GDP are the magnitudes that show a clearer result, since in
most of the tests carried out there is a statistically significant relationship with the stock
market returns. In addition, this relationship appears with a positive sign for GDP, which
indicates movements in the same direction in stock market returns and GDP. In the case of
UNEMP, the sign is negative, that is, there would be movements in the opposite direction in
the stock market returns and UNEMP, as expected. Both correlations are stronger in times of
crisis and in countries where policies accompany these joint movements.
On the other hand, the other two macro-magnitudes, CPI and IPI, show, in general, a
lower correlation with stock market returns, which is only remarkable at certain moments in
time for any specific country. So the evolution of these two macro variables does not seem to
be linked to that of the stock markets, but rather acts more independently.
These results may allow us to make certain predictions of the future movements that
the international stock market returns may experience to changes in the macro-economic
variables GDP and unemployment. Statistically significant correlations mean that increases
in GDP and UNEMP decreases would allow improving stock market returns, while
movements in the opposite direction indicated in the macro-economic variables would lead
us to a deterioration of the international stock market returns in the countries analyzed (with
the particularities of each of the countries included in the analysis).
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Annex on line at the journal Website: http://www.usc.es/economet/eaat.htm
55
Figure 3. Scatter graphs with the trend of the stock market and macroeconomic factors
Germany (Dax) and Spain (Ibex)
-.08
-.06
-.04
-.02
.00
.02
.04
.06
-.4 -.2 .0 .2 .4
Dax30 rdto.
Des
emp.
Tas
a
-.008
-.004
.000
.004
.008
.012
-.4 -.2 .0 .2 .4
Dax30 rdto.
ipc
tasa
-.03
-.02
-.01
.00
.01
.02
.03
.04
-.4 -.2 .0 .2 .4
Dax30 rdto.
ipri
tasa
-.06
-.04
-.02
.00
.02
.04
-.4 -.2 .0 .2 .4
Dax30 rdto.
pib
tasa
-.10
-.05
.00
.05
.10
.15
.20
.25
-.3 -.2 -.1 .0 .1 .2 .3
Ibex rdto
Des
emp.
Tas
a
-.02
-.01
.00
.01
.02
.03
-.3 -.2 -.1 .0 .1 .2 .3
Ibex rdto
ipc
tasa
-.04
-.02
.00
.02
.04
-.3 -.2 -.1 .0 .1 .2 .3
Ibex rdto
ipri
tasa
-.020
-.015
-.010
-.005
.000
.005
.010
.015
-.3 -.2 -.1 .0 .1 .2 .3
Ibex rdto
pib
tasa
56
France (CAC) and Italy (MIB)
-.08
-.04
.00
.04
.08
.12
-.3 -.2 -.1 .0 .1 .2 .3
Cac40 rdto
Dese
mp.
Tas
a
-.010
-.005
.000
.005
.010
.015
-.3 -.2 -.1 .0 .1 .2 .3
Cac40 rdto
ipc ta
sa
-.04
-.03
-.02
-.01
.00
.01
.02
.03
-.3 -.2 -.1 .0 .1 .2 .3
Cac40 rdto
ipri
tasa
-.020
-.015
-.010
-.005
.000
.005
.010
.015
-.3 -.2 -.1 .0 .1 .2 .3
Cac40 rdto
pib
tasa
-.08
-.04
.00
.04
.08
-.3 -.2 -.1 .0 .1 .2 .3
Mib30 rdto.
Des
em
p. T
asa
-.008
-.004
.000
.004
.008
.012
-.3 -.2 -.1 .0 .1 .2 .3
Mib30 rdto.
ipc
tasa
-.04
-.03
-.02
-.01
.00
.01
.02
.03
-.3 -.2 -.1 .0 .1 .2 .3
Mib30 rdto.
ipri
tasa
-.03
-.02
-.01
.00
.01
.02
-.3 -.2 -.1 .0 .1 .2 .3
Mib30 rdto.
pib
tas
a
57
UK (FTSE) and USA (SP)
Note: UNEMP (Desemp in Spanish), CPI (ipc in Spanish), IPI (ipri in Spanish), GDP (pib in
Spanish). Source: Compiled by the authors from the Eurostat and Yahoo Finance websites
-.08
-.04
.00
.04
.08
.12
-.3 -.2 -.1 .0 .1 .2 .3
FTSE100 rdto
Des
emp.
Tas
a
-.010
-.005
.000
.005
.010
.015
.020
.025
-.3 -.2 -.1 .0 .1 .2 .3
FTSE100 rdto
ipc
tasa
-.04
-.02
.00
.02
.04
.06
.08
-.3 -.2 -.1 .0 .1 .2 .3
FTSE100 rdto
ipri
tasa
-.03
-.02
-.01
.00
.01
.02
-.3 -.2 -.1 .0 .1 .2 .3
FTSE100 rdto
pib
tasa