Exchange rate is defined as the price of one country's currency expressed in another country's currency. In other words, it is the rate at which one currency can be exchanged for another. Exchange rate may be fixed or flexible. It is called a fixed exchange rate when two countries agree to maintain a fixed rate through the use of monetary policy. On the other hand, an exchange rate is flexible, or floating, when two countries agree to let international market forces determine the rate through demand and supply.
Industrial Production
Industrial production is used to measure changes in output for the industrial sector of the economy. The industrial sector includes manufacturing, utilities etc. It is an important tool for forecasting future GDP and economic performance. Industrial production are also used by central banks to measure inflation, as high levels of industrial production can lead to uncontrolled levels of consumption and rapid inflation.
2.1.2 Stock Market indices
Stock market indices are essential means of measuring a section of the stock market. As the stocks in a group change value, the index also changes value. If an index goes up by 5% , it means that the total value of the securities which make up the index have gone up by 5% in value.
Stock market indices are important in the sense that if one invests in mutual funds or individual stocks, then it is important to measure the performance of the investment against a relevant market index. If the investments consistently lag behind the index, new investment strategies might be devised.
One of the most common indices is the Dow Jones Industrial Average which is an index of 30 "blue chip" U.S. stocks of industrial companies (excluding transportation and utility companies).
The S&P 500 Composite Stock Price Index is an index of 500 stocks from major industries in the U.S. economy. There are indices for almost every conceivable sector of the economy and stock market.
The FTSE 100 index is a share index of the 100 most highly capitalized UK companies listed on the London Stock Exchange.
The Nikkei 225 is a stock market index for the Tokyo Stock Exchange. It is one of the oldest and most well known indices in the world. Unlike the Dow Jones, the Nikkei is designed to reflect the overall market; there is no specific weighting of the industries.
2.1.3 Relationship between macroeconomic variables and stock market
A significant literature now exists which investigates the relationship between stock market and a range of macroeconomic and financial variables, across a number of different stock markets and over a range of different time horizons. Existing financial economic theory provides a number of models that provide a framework for the study of this relationship.
The Arbitrage Pricing Theory
One way of linking macroeconomic variables and stock market returns is through the Arbitrage Pricing Theory (APT) (Ross, 1976)- a theoretical alternative to the Capital Asset Pricing Model (CAPM).
The APT states that the expected return of a financial asset can be modeled as a linear function of various macroeconomic factors or theoretical market indices, where sensitivity to changes in each factor is represented by a factor specific beta coefficient.
Some of the macroeconomic variables significant in explaining security returns, as identified by Chen, Roll and Ross (1986) are
Surprises in inflation
Surprises in GNP as indicated by an industrial production index
Surprises in investor confidence due to changes in default premium in corporate bonds;
Surprise shifts in the yield curve
The Dividend Discount Model
A common theoretical framework connecting stock prices to macroeconomic fundamentals is the dividend discount model.
Assuming constant growth in dividends,
P=D1/ (k-g) (1)
Where P= stock price, D1=dividends after ï¬rst period, g= constant growth rate of the dividends and k= required rate of return on the stock.
This model postulates that the current share price equivalents the present value of future cash flows, which depends on the growth of a company. As a company's growth depends on domestic macroeconomic condition as well as its major trading partners, the co-movement of macroeconomic variables across countries may influence the comovement of stock prices in those countries. Accordingly, macroeconomic variables will affect stock prices if it impacts on either expectations about future dividends, discount rates, or both.
The Present Value Model
An alternative, but not inconsistent, approach to explain the relationship between macroeconomic variables and stock market is the discounted cash flow or present value model (PVM). This model relates the stock price to future expected cash flows and the future discount rate of these cash flows. Again, all macroeconomic factors that influence future expected cash flows or the discount rate by which these cash flows are discounted should have an influence on the stock price. The advantage of the PV model is that it can be used to focus on the long run relationship between the stock and the macroeconomic variables.
2.2 Empirical Review
There are a number of existing studies that attempt to determine the relationship between stock market and macroeconomic variables. Numerous studies have focused on developed countries, in more recent times, however, there has been an encouraging number emphasising on developing economies.
Beginning with Chen, Roll and Ross (1986), the authors used some macroeconomic variables to explain stock returns in the US stock market. They used simple arguments to choose a set of economic state variables that, a priori, were candidates as sources of systematic asset risk. Several of these economic variables were found to be significant in explaining expected stock returns, most remarkably industrial production, changes in risk premium, and twists in the yield curve. Both anticipated and unanticipated inflation rates were concluded to be negatively related to the expected stock returns.
Similar to Chen, Roll and Ross (1986), Hamao (1988) determined whether the observed relationship between macroeconomic variables and share returns were applicable when the analysis is conducted in the Japanese marketplace. The latter, besides the other macroeconomic variables included by Chen, Roll and Ross (1986), incorporated international trade variables. Except from industrial production which appeared insignificant in asset pricing Hamao's findings were consistent with Chen, Roll and Ross (1986) research.
In their study on whether current economic activities in Korea could explain stock market returns, Kwon and Shin (1999), concluded that Korean stock market reflects macroeconomic variables on stock market indices. The cointegration and vector error correction model illustrate that stock price indices are cointegrated with a set of macroeconomic variables-that is the production index, exchange rate, trade balance and money supply-which provides a direct long run equilibrium relation with each stock price index. However, they concluded that the stock price indices are not a leading indicator for economic variables.
Nasseh and Strauss (2000) find support for the existence of significant long run relationships between stock market prices and domestic and international economic activity in six countries which included France, Italy, Switzerland, Germany, Netherlands and the U.K. Johansen's cointegration tests confirmed that stock price levels were significantly related to industrial production, business of manufacturing orders, short- and long-term interest rates as well as foreign stock prices, short-term interest rates, and production. Nasseh and Strauss (2000) also used variance decomposition methods that supported the strong explanatory power of macroeconomic variables in contributing to the forecast variance of stock market prices. They recognized the usefulness of Johansen's framework for analyzing stock market and macroeconomic activity-it incorporates dynamic co-movements or simultaneous interactions, allowing the researchers to study the channels through which macroeconomic variables affected asset pricing, as well as their relative importance. Their variance decomposition methods, based on a vector auto regression with orthogonal residuals, showed that macroeconomic factors explained a substantial part of the variation in stock prices in the medium and short runs. Nasseh and Strauss (2000) found that although stock prices were explained by economic fundamentals in the medium and short-run, the underlying volatility inherent in stock prices was related to macroeconomic movements in the long run.
McMillan (2001), using US data, undertook to investigate whether a cointegrating vector existed between variables such as industrial production, inflation, money supply, interest rate and stock market indices. The findings provided positive support of cointegration between both the US market index Dow Jones Industrial Average index (DJIA) and the S&P 500 and macroeconomic activity variables. The established relationship is positive and significant for industrial production and inflation, negative and significant for long term interest rates, and negative and insignificant for money supply and short term interests rates. The results are consistent with the belief that changes in output which affect expected future cash flows have a positive effect on stock prices, that stocks act as an inflation hedge and that changes in the discount rate have an inverse effect on prices. In addition, variance decompositions show that long-term rates explain a substantial amount of variability in stock prices, whilst short-term rates, industrial production and inflation also have some explanatory power.
Maysami and Sims (2002, 2001a, 2001b) employed the Error Correction Modeling technique to study the relationship between macroeconomic variables and stock returns in Hong Kong and Singapore (Maysami and Sims 2001b), Malaysia and Thailand (Maysami and Sims 2001a), and Japan and Korea (Maysami and Sims 2001b). Through the employment of Hendry's approach which allows making inferences to the short run relationship between macroeconomic variables as well as the long run adjustment to equilibrium, they analysed the influence of interest rate, money supply, real activity, inflation and exchange rate, along with a dummy variable to capture the impact of the 1997 Asian financial crisis. The results confirmed the influence of macroeconomic variables on the stock market indices in each of the six countries under study, though the type and magnitude of the associations differed depending on the country's financial structure.
Wongbangpo and Sharma (2002) explored the relationship between the stock returns for the ASEAN-5 countries of Indonesia, Malaysia, the Philippines, Singapore, and Thailand and five macroeconomic variables. By observing both short and long run relationships between respective stock indexes and the macroeconomic variables of gross national product (GNP), the consumer price index (CPI), the money supply, the interest rate, and exchange rate they found that in the long-run all five stock price indexes were positively related to growth in output and negatively to the aggregate price level. However, a negative long-run relationship between stock prices and interest rates was noted for the Philippines, Singapore, and Thailand, and was found to be positive for Indonesia and Malaysia. In the end, causality tests detected an overall relationship between macroeconomic variables and stock prices for all five ASEAN equity markets
Chakravarty (2005) made an attempt to study the causal relationship between macroeconomic variables and stock price for India for the period of April 1991 to December 2005 using Granger causality tests. The results obtained consistent causality results for some consecutive lag structures along with the optimal choice of the lag using Akaike information criteria (AIC) and Schwartz criterion (SC), so the results are robust. The nine empirical results suggested that exchange rate does not Granger cause stock price nor stock price Granger cause exchange rate. Moreover, the empirical results further suggested that the index of industrial production and the rate of inflation Granger cause the behaviour of stock price but does not Granger cause index of industrial production and inflation, so the causation is unidirectional. In addition, the causal relation between the behaviour of stock price and M3 is unidirectional, the behaviour of stock price causing M3. Gold price which is included in the model does not show any relationship with stock price.
Gay (2008) investigates the time series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, Indian and China using the Box Jenkins ARIMA model. No significant relationship was found between respective exchange rate and oil price on the stock market index prices of either country., this may be due to the influence other domestic and international macroeconomic factors on stock market returns as explained by the author. In addition, no significant relationship was found between present and past stock market returns, suggesting the markets of Brazil, Russia, India and China exhibit the weak form of market efficiency.
Mahmood and Dinniah (2009) examine the dynamics relationship between stock prices and economic variables in six Asian Pacific selected countries of Malaysia, Korea, Thailand, Hong Kong, Japan and Australia. The monthly data on stock price indices, foreign exchange rates, consumer price index and industrial production index that spans from January 1993 to December 2002 are used. The focus of the analysis is on the long run equilibrium and short run multivariate causality between these variables. The results indicated the existence of a long run equilibrium relationship between and among variables in only four countries that is Japan, Korea, Hong Kong and Australia. As concerned short run relationships, all countries except for Hong Kong and Thailand showed some interactions. Hong Kong shows relationship only between exchange rate and stock price while Thailand reported significant interaction only between output and stock prices.
The table below summarises the empirical research conducted by various authors in different countries under different time horizons. It also summarises the findings.
Name of Author(s)
Country under study/Period
Methodology
Findings
Mukherjee and Naka(1995)
Tokyo-January 1971 to December 1990
Johansen's VECM
A cointegrating relationship exists and that stock prices contribute to this relation.
Cheung and Ng (1998)
Canada, Germany, Italy, Japan, US
Johansen Cointegration techniques
Evidence of long run co-movements between five national stock market indexes and measures of aggregate real activity
Ibrahim(1999)
Kuala Lampur
Observing that macroeconomic variables led the Malaysian stock indices, he concluded that the market place was informationally inefficient.
Islam (2003)
Kuala Lampur
Hendry's approach
Statistically significant short run and long run relationships between index and variables under study.
Omran (2003)
Egypt
Cointegration analysis through Error Correction Mechanism
Long run and short run relationships between the variables.
Vuyyuri (2005)
India/1992-2002(monthly observations)
Cointegration and Causality tests
Cointegration test supported the long run equilibrium relationship between financial and the real sector, and the granger causality between the financial sector and the real sector of the economy
Maghyereh (2002)
Jordan
Cointegration analysis
Macroeconomic variables were reflected in stock prices.
Islam and Watanapalachaikul (2003)
Thailand
Cointegration analysis
Strong, significant long run relationship between stock prices and macroeconomic factors
Christopher G, Minsoo L, Hua H, Jun. Z (2006)
New Zealand
Cointegration analysis
No evidence that the New Zealand Stock Index is a leading indicator for changes in macroeconomic variables.
