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

returns.

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-

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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

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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

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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.