Through the years, researchers investigated whether stock returns and macroeconomic vari-
ables are correlated. Chen, Roll and Ross (1986) provide evidence that macroeconomic vari-
ables influence stock prices. The goal of their research is to model stock returns as a function
of macroeconomic variables. Since their theory suggests that stock prices are responding to
exogenous shocks. Meaning that stock prices are only driven by macroeconomic variables.
Because by the diversification argument, risk of individual stocks can be avoided by diversi-
fying the portfolio. They test this for stocks listed in the New York Stock Exchange (NYSE)
and test whether the stocks are systematically affected by the inflation rate measured as the
consumer price index, the risk premium of low graded bonds against long-term government
bonds, the term structure of long-term government bonds and the treasury bill of one month,
and industrial production. Their results show that inflation, industrial production, the term
structure and the risk premium systematically affect stock returns for stocks listed in the
NYSE. And conclude that this set of macroeconomic variables are significantly priced.
In more recent studies they examine different macroeconomic variables than Chen et al.(1986)
did. For instance, Flannery and Protopapadakis (2002) identify macroeconomic risk factors
by using a GARCH model for the US stock exchange. They examine the impact of macroe-
conomic announcements on the daily stock returns and conditional volatility of returns. A
macroeconomic announcement is considered a risk factor if either the stock return or the
conditional volatility of the returns change. They considered seventeen macroeconomic an-
nouncement to have an impact on either stock returns or conditional volatility or even both.
They find significant results for six macroeconomic variables. The consumer and producer
price index affect only the stock returns. The balance of trade, employment report and hous-
ing starts affect only the conditional volatility of the returns. And monetary aggregate (M1)
affects both the returns and the conditional volatility. Flannery and Protopapadakis conclude
that macroeconomic variables do have an impact on either stock returns or the volatility in
Bhargava (2014) is investigating whether quarterly stock prices can be explained by firm
characteristics and macroeconomic variables. Bhargava is using a simple dynamic random
effects model and a comprehensive dynamic model to model quarterly stock prices for over
3000 US firms. Bhargava is using a autoregressive random effect model to test the null hy-
pothesis that stock prices follow a random walk. The comprehensive dynamic model consists
firm characteristics and macroeconomic variables to explain the stock prices. The explana-
tory variables in the comprehensive dynamic model are total assets, long-term debt, earnings
and dividend per share, unemployment rate, consumer price index and interest rate on trea-
sury bills. The main findings are that there exists persistence in quarterly stock prices, and
therefore the null hypothesis that stock prices follow a random walk can be rejected. The com-
prehensive dynamic model shows a negative effect of macroeconomic variables on quarterly
stock prices. The interest rate on treasury bills and the unemployment rate have significant
negative influence. The model also displays that total assets, long-term debt and earnings
and dividends per share are significant variables to predict stock prices.
Other researchers have conducted more detailed research in the sense whether macroeco-
nomic variables not just influence stock returns, but whether macroeconomic variables give
direction to stock returns, i.e. are they cointegrated. Such analysis has been done by Ratana-
pakorn and Sharma (2007) and Humpe and Macmillan (2007). Ratanapakorn and Sharma
(2007) are investigating the relationship between stock returns and macroeconomic variables
by means of a Vector Error Correction Model (VECM) coupled with Granger causality test.
They are examining whether stock prices and macroeconomic variables have a long-run equi-
librium and test whether the variables have a long- and short-term causal relationship. The
macroeconomic variables they consider are the money supply, industrial production, inflation,
the exchange rate, and the long- and short-term interest rate. They test this for stocks listed
in the S&P 500. Their results indicate a negative relation for long-term interest rates, and
a positive relation for money supply, industrial production, inflation, the exchange rate and
the short-term interest rate. Additionally, the six variables are Granger caused by the stock
prices in the long-run, but not in the short-run.
Humpe and Macmillan (2007) also find a cointegrated relation between US stocks and macroe-
conomic variables. They are modelling stock prices with a discounted value model (DVM)
to test for cointegration effects for a number of macroeconomic variables. The stock price
is determined by discounting the cash flows. The advantage of discounting the cash flows is
that it can be used on the long-run relationship between stock market and macroeconomic
variables. Because many long-term investors base their investment decision on the assump-
tion that the cash flow should grow in line with the economy. They examine whether there
exists a cointegrated relation between stock prices and industrial production, inflation, money
supply, and the long-term interest rate. Their results indicate that US stocks are positively
influenced by industrial production and negatively by inflation and the long-term interest
rate. Unfortunately, they were not able to find significant results for the money supply.
Chen (2008) is examining whether macroeconomic variables can predict economic recessions,
i.e. bear markets. Chen uses the Markov-switching model and the Bry-Boschan dating
method to distinguish cyclical variations in stock prices from recessions. After identifying
recession periods Chen is investigating whether these recession periods can be predicted by
macroeconomic variables. The various macroeconomic variables Chen considers are the inter-
est rate spread, inflation rates, money stocks, aggregate output, unemployment rates, federal
funds rate, federal government debt, and nominal effective exchange rates. Chen is using
the S&P 500 index for his research. The results suggests that only the spread in interest
rates and inflation rates were significant, consistent and useful in predicting bear markets.
However, they did not find evidence that one was better over the other and conclude that the
term spread and the inflation rate have equal forecasting accuracy. Also, Chen found that
macroeconomic variables are better able to predict bear markets than market returns.
Researchers in Asia also started investigating cointegrated relationships between stock prices
and macroeconomic variables, especially the countries in the growth engine of Asia (e.g.
Japan, Singapore, Malaysia, and Korea). Mukherjee and Naka (1995), for example, enlarge
the findings of Chen et al. (1986) for the Japanese stock market. They try to find a cointe-
grated relation between six macroeconomic variables and the Tokyo Stock Exchange (TSE).
The variables they use were industrial production, the exchange rate, long-term government
bond rate, money supply, inflation, and call money rate. By applying a VECM model they
try to determine the relationship between the six macroeconomic variables and the returns of
the TSE. Mukherjee and Naka find a positive relation for industrial production, call money
rates, and money supply, and a negative relation for inflation and long-term government bond
rates. A possible reason why the long and short term interest rate have mixed results is that
the long-term government bond rates are a better proxy for the nominal risk free rate than
the short-term rate (call money rate) as discount rate for the discounted value model (DVM).
Likewise, Kwon and Shin (1999) investigate whether macroeconomic fluctuations can explain
stock returns for the Korean Stock Exchange (KSE). They also use Granger causality test
to find a cointegrated relation between the KSE and four macroeconomic variables. Kwon
and Shin also use a VECM model to determine whether a cointegrated relation exists. The
variables they consider are foreign exchange rates, trade balance, production level, and money
supply. Their results show that there does not exists a cointegrated relation with the KSE and
a single macroeconomic variable. However, there exists a cointegrated relation between the
KSE and a combination of the four macroeconomic variables. They conclude that there ex-
ists a long-run equilibrium, though, they argue that KSE is a lagging indicator, contradicting
the findings that the stock market rationally reflects changes in the economy. They suggest
that the movements in the KSE are rather due to international trading activities than to, for
instance, inflation or interest rate. According to Kwon and Shin, a possible explanation could
be that the KSE is more sensitive to speculative activities, manipulations and government
interventions than a more developed market, e.g. US market.
Maysami, Howe and Hamzah (2004) are investigating the cointregrated relationship between
the Singapore stock index, the Stock Exchange of Singapore (SES) All-S Equities Finance
Index, the SES All-S Equities Property Index, and the SES All-S Equities Hotel Index and
various macroeconomic variables. They employ a VECM model to examine the long-term
equilibrium relationship between the stocks and macroeconomic variables. The variables
they consider are the long- and short-term interest rate, industrial production, price lev-
els, exchange rate and money supply. The results of the VECM model indicates that the
Singapore stock exchange and the SES All-S Equities Property index both have significant
cointegrated relationships with all the variables. While the SES All-S Equities Finance Index
is only affected by inflation rates, exchange rates, and long- and short-term interest rates.
And the SES All-S Equities Hotel Index only the exchange and inflation rate were signifi-
cantly priced. They conclude that there exists inefficiencies in the Singapore stock exchange
and stock picking could lead to superior returns.
Vejzagic and Zarafat (2013) test for cointegrated relation between the FTSE Bursa Malaysia
Hijrah Shariah Index (FBMHS) and four macroeconomic variables. The FBMHS index is
a response of increasing interest in Shariah compliant investments. The constituents of the
index are complying the principles of the Koran. The variables that Vejzagic and Zarafat
consider are the interest rate, money supply, consumer price index, and exchange rate. They
use a VECM model to determine the cointegrated relation between the index and the macroe-
conomic variables. Their results show that the FBMHS is influencing and leading macroe-
conomic variables. The FBMHS is significantly related to the money supply, consumer price
index, and exchange rate. If the FBMHS is deviating from its equilibrium, it is positively
affecting the money supply, and negatively the interest- and exchange rate. Unfortunately,
they did not find any significant results for the consumer price index variable.