In this chapter, theoretical and empirical review will be examined for literature. From the theoretical part, theories like the Asset Pricing Theory or Fisher hypothesis on interest rates will be observed; followed by empirical evidence from past studies.
2.1 THEORETICAL REVIEW
From basis financial theory, an efficient capital market is where new information are rapidly adjusted in the share price; that is the current share prices should reflect all available information. Basically, no investors should be able to employ readily available information so as to forecast future stock price movements to make a profit through the trading of shares.
Empirical researches on capital market efficiency had long been analysed, but Fama (1970) was the first to theorise capital market efficient, specially the semi-strong form efficient. As such, semi-strong form efficient implies that the stock prices should incorporate all relevant information including publicly available information; thus, this has fundamental implication for both investors and policy-markers.
For policy makers that need to implement certain macroeconomic policies, they should ensure that these policies will not conflict with stock trading activities. As for investors, the efficient market hypothesis (EMH) states that due to competition among investors whose main aim is to maximise profit, changes in macroeconomic variables will be fully incorporated in the current share prices. Consequently, no investors should be able to gain abnormal profit unless they have access to insider information- which practice is generally prohibited by laws. As such, based on the EMH, there should be no stock broking industry.
However, the concept of EMH had been overthrown based from empirical evidence accumulated throughout the past 40 years where key macroeconomics variables helped in predicting the time series of stock returns. In his articles, Economist Shiller(1981) [1] challenged the EMH by raising the question of whether the movements in stock prices can be justified by subsequent change in dividend and conclude that that stock prices are too volatile to be explained by dividends and earnings.
2.1.1 Stock Market Volatility
Investment in stock market can be a very lucrative yet volatile place where one can invest his money. The daily, monthly or even annual fluctuation in stock prices can be quiet striking, but it is in fact these fluctuations that produce the returns that investors seek.
Fluctuation in individual stock prices had long been observed by market observer, but it was only in the early 1970s that volatility indicators were constructs to test for the entire market. Volatility in fact measures the dispersion around the mean or average return of an asset. There tend to exist a negative influence between stock market and volatility; reductions in volatility induce a rise in stock market and wider fluctuation in volatility would result from a fall in stock market. Insinuations rising volatility generate higher risk and decreasing returns.
Factors affecting volatility:
Economic News: these relates to information concern announcement from monthly information release by statistical office such as employment rate. Therefore most investors would react to such news by buying and selling of shares. Arise in unemployment rate will indicates a reduction in disposable income circulating in the economy, thereby causing a fall in the stock market.
Financial News: Include information about both local and international interest rates, Exchange rates and treasury bills.
Unpredictable events: events such as cyclone, earthquake and terrorist attacks cause market to react negatively since government and individual will diver their fund to solve these problems, thereby causing investors to worry about the future thus pushing market price lower.
2.1.2 Arbitrage Pricing Theory
One way of linking macroeconomic variables and stock prices is through the Arbitrage Pricing Theory (APT) developed by Ross (1976); APT is a method used to estimates the price of an asset. The theory stipulates that returns of an asset can be predicted through the relationship that exists between the same asset and other common risk factor; that is, asset's return is dependent on various macroeconomic, market and security-specific factors. The APT is often viewed as an alternative to the Capital Asset Pricing Model (CAPM) (William Sharpe, 1944; John Lintner, 1965), since it is argued that the assumptions governing the APT is more flexible than that under the CAPM. Both asset pricing models operate under an efficient market, as explained by Lofthouse (2001, pp 91):
"Asset-pricing models need the EMT. However, the notion of an efficient market is not affected by whether any particular asset-pricing theory is true. If investors preferred stocks with a high unsystematic risk, that would be fine: as long as all information was immediately reflected in prices, the EMT theory would be true."
Some assumptions governing the APT:
The pricing theory assumes that asset/portfolio returns can be described by a multi-factor model and proceeds to derive the expected returns relation that follows from that assumption.
Since the intention is to maximize returns, the investor holds a number of securities so that unsystematic risk becomes negligible.
In time, arbitrageurs will exhaust all potential opportunities for riskless profits and the market will be in equilibrium.
Under the APT, investors can make profit by taking advantage of asset mispricing. While in CAPM formula requires only market's expected return, APT uses the risk premium of a number of macro-economic factors and risky asset's expected return.
APT gives the expected return on asset i as:
E(ri) = rf + bijRP1 + b2jRP2 + b3jRP4 + ... + bnjRPn
Where:
E(rj) = the asset's expected rate of return
rf = the risk-free rate (i.e. interest on Treasury Bonds)
bj = the sensitivity of the asset's return to the particular factor
RP = the risk premium associated with the particular factor
The APT assumes that there are n factors that cause asset returns to deviate from its expect returns. No specific number of n is giving in theory and nor does it identified these factors. To avert arbitrage, Ross demonstrates that, an asset's expected return must be a linear function of its sensitivity to the n factors.
2.2.2 Fisher's Theory
The theory of interest rates was first introduced by Irving Fisher in 1907 (!!!)
Classical theory posits that fluctuation in interest rate equates to the amount of savings and investments. According to empirical evidence, policy makers and economist also found that investments are also influenced by prices and other government policies.
From assumption, fluctuation in inflation should not cause any effect on stocks valuations. According to Fisher (1930), he affirmed a relationship between inflation and both real and nominal interest rates. Fisher hypothesis (or Fisher effects) states that real interest rates is the sum of nominal interest rates less expected inflation. Letting r denote the real interest rates, i denote the nominal interest rate, and let \pidenote the inflation rates, the Fisher equation is:
Linear approximation: i \approx r + \pi
Exact methodology: 1 + i = (1 + r)(1 + \pi).
From Fisher Hypothesis, expected real rate of the economy is determined by the real factors such as productivity of capital and time preference of savers and is independent of the expected inflation rate; if Fisher effect holds, there is no change in the nominal stock prices and in inflation since stock returns are allowed to hedge for inflation (Olweny and Omondi, 2011).
2.2 EMPIRICAL REVIEW
In his paper, Tangjitprom (2012) classified variables used from different studies under four main groups: variables reflecting general economic conditions, other variables related to interest rates and monetary policy, price level, and some variables related to international activities.
2.2.1 Economics Conditions
Variables that are classified in this group deal with general economic conditions that can be used as proxy for cyclical factors.
2.2.1.1 Gross Domestic Products
Prominent variables that measures economic conditions are gross domestic products (GDP) or national output. In the research undertaken by Huss (2003) which applied cointergartion to investigate the linkage between the Swiss stock market and macroeconomic variables used GDP as one of its main independent variables. Wong (2010) applied advance econometrics model - AR-EGARCH and LA-VAR model- for four macroeconomic variables effects on the Chinese's stock market. Results indicate that no causal relationship exist between stock market and real GDP, that is no real GDP is not significant in explaining stock market volatility.
2.2.12 Industrial Production Index
But many researches instead used the Industrial Production Index (IPI) as proxy for economic growth. Based on theory, industrial production index will increase during economic growth and contract during recession therefore, changes in industrial production act as a signal for the economy.
Maysami et al. (2004), for Singapore employ the VECM for a 7 year period from February 1995 to December 2001indicates that industrial production is positively and significantly related to the stock returns. Humpe and Macmillian (2007) used Industrial production index along with three other macroeconomic variables on a monthly basis for a period of 40 years (1965-2005) using VEC model. Their analysis was to investigate the long term relationship that exists for macroeconomic variables and stock prices as a comparison between US and Japan. From the cointergartion vector, stock prices were found to be positively related to industrial index for both US and Japan.
Ozbay (2009) examined the Turkish Stock market by employing Granger-causality test and conclude that industrial production is statistically insignificant meaning that the variable is neither a result variables nor the cause variable of the stock price movement.
2.2.1.2 Employment
Employment level can also be considered in examining the effects of economic conditions on stock return. The use of employment level is mostly done in event studies concerning macroeconomic news announcement. Announcement of employment level can have a more prominent impact on stock returns than the use of IPI or GDP. In the study of Flannery and Protopapadeltis (2002) employed IPI, GDP and employment announcement as determinant of stock returns and found that the IPI and GDP to be insignificant whereas employment announcement proffer a significant impact on the market. Boyd et al. (2005) also found that rising unemployment announcement can significantly affect the stock market.
2.2.2 Interest rate and money supply
In this group we depict variables relating to interest rates and monetary policy.
2.2.2.1 Interest rate
In general, interest rates that are employed in these studies use government securities (Three months Treasury bill and 10-years Treasury Bonds) as an alternative to interest rate.
Alam and Uddin (2009) employed time series and Panel regressions to explore the relationship between interest rates and stock prices using a mixture of fifteen developed and developing countries on a monthly basis. Results from their analysis indicate that for all countries interest rate has a significant negative relationship with stock prices. Olugbenga (2011) investigates the macroeconomic indicators on stock price in Nigeria from individual firm's level for the period 1985 to 2009 on a quarterly basis. By employing interest rate as one of its six determinants, he found that interest rates exert negative impact on stock prices for most of the selected firms.
For Maysami et al. (2004) test for both long term and short term interest rates, results indicates that the long term interest rate exert a negative impact on the Singapore's stock market and as for the short term interest rate wield a significant positive force on the stock market. Adam and Tweneboah (2008) for Ghana indicate the relationship between stock prices and interest rates is negative and statistically significant. From Chen et al. (1986) indicates that interest rate had a positive impact on stock returns.
Long term interest rates (10-years Treasury Bonds) can also be employed to test for long term relationship between stock prices and macroeconomic variables, Humpe and Macmillan (2007) found that stock prices are negatively influenced by interest rates for US.
As for Gan et al. (2006) using monthly data to explore the long term and short term dynamics relationship that exist between the New Zealand Stock Market index and seven macro economic variables among which is interest rates (lending rates for long run and deposit rates for short run). By using Johansen Maximum Likelihood and Granger-causality test found that interest rate does in fact have a positive effect on the Stock Index.
The use of default spread can also be use as proxy to interest rate; default spread is the difference between the yields on risk free assets (that is, government bond) and risky assets (corporate bonds). In the paper of Chen et al. (1986), they measured the default spread by using different yield on government bonds and low-grade bonds. The results demonstrate a positive relationship between the default spread and the stock returns.
2.2.2.2 Money Supply
From previous studies, money supplies are used as monetary policies. Generally an economy is influenced by monetary policy through the transmission mechanism. Both a restrictive and an expansionary monetary policy might have bilateral effects. In case of expansionary monetary policy, the government creates excess liquidity by engaging in open market operation, which results in an increase in bond price and lower interest rates. The lower interest rate would lead to the lower required rate of return and thus, the higher stock price.
Additionally, an increase in monetary growth indicates excess liquidity available for buying stocks, eventually resulting in higher stock prices due to an increase of demand of stocks. However, monetary growth might result in higher inflation and hence, higher nominal interest rate according to Fisher equation. The higher interest rate leads to the higher required rate of return, which will result in the lower stock price.
In case of a restrictive monetary policy, to reduce the growth rate of money supply would result in a decrease in the supply of funds for working capital and expansion for all business. Additionally, a restrictive monetary policy would raise market interest rate and hence firm's cost of capital. Furthermore, an increase in interest rate would make it more expensive for individuals to finance mortgage payments and the purchase of other durable goods. However, a decrease in money supply might result in the lower inflation, hence the lower required rate of return via the lower nominal interest rate. Thus, this would lead to the higher stock prices.
By analysing the Cypriot equity market, Tsoukalas (2003) employed the VAR model to determine the Granger causality effect for the selected macroeconomic variables on the equity market. His investigation confirms that money supply along with the other variables is strongly related to equity market. Furthermore, this result indicates that the Cypriot market is in weak form efficient, that is, where the equity price includes past information about macroeconomic policies.
Maysami et al. (2004) reveal that a positive correlation exists between changes in money supply (M2) and the Singapore's stock returns. For the Sri Lanka stock market, Menike (2006) examines the effect of macroeconomic variables in selected companies quoted on the Colombo stock exchange market. The use of broad money (M2) results was consistent with theories, that is, M2 exert a positive impact related to stock price. Buyaksalvara (2010) results follow the trend by investigating money supply (M2) for the Turkish stock market and again a positive relationship emerge from the analysis.
Hsing (2011) used money supply (M3) as a percentage of GDP for the South African stock market by using the exponential GARCH model and result indicates that the ratio of money supply to GDP positively determine volatility in the stock market. In contrast, Humpe and Macmillan (2007) employed M1 as a proxy for money supply and an insignificant (but positive) relationship was found between the US stock prices and Money supply.
2.2.3 Price Level
In this section, we regroup all variables regarding price level.
2.2.3.1 Inflation
According to literature, a negative relationship is argued to exist between stock prices and inflation since an increase in the rate of inflation is accompanied by both lower expected earnings growth and higher required real returns (Ozbay, 2009). Empirical findings from Fama and Schwert (1977) and Chen et al (1986), confirm that stock returns are negatively affected by inflation. According to Chen et al (1986), they measured the inflation level through two separate variables: unexpected inflation - the difference between actual [2] and expected inflation rates, and expected inflation - the forecasted inflation reflected from other economic factors. Their results attest a negative relationship between stock returns and both of the inflation variables.
Yogaswari et al. (2012) analyse three macroeconomic variables on the Jakarta Composite Index, Agriculture sector and basic industry sector. Results conclude that changes in inflation negatively affect all three dependent variables. In the report of Humpe and Macmillan (2007) where they investigate the relationship for US and Japan; conclude that consumer price index indeed negatively affect stock prices.
In contrast, Mittal (2011) for India, studied the long run relationship between the capital market and macroeconomic variables. Through the use of quarterly data and by testing for Error Correction Mechanism (ECM) indicates that inflation rates exert a significant impact on both the BSE Sensex and the S&P CNX. Furthermore, research such as, Adam and Tweneboah (2008) for Ghana, and Maysami et al. (2004) for Singapore conclude that there subsist a positive relationship between Stock prices and inflation (CPI). Moreover, Solnik and Solnik (1997) and Schotman and Schweitzer (2000) confirm fisher hypothesis that a positive relationship exist between the variables over as horizons widens.
2.2.3.2 Commodity prices
Other studies have focused on oil prices and gold prices, which can be essential assets for both consumption and production specially, the oil prices. These variables can be viewed as a proxy to cost-push inflation. Olugbenga (2011) test the effect of oil prices along with five other variables in Nigeria on individual firm's level. His analysis was made on a quarterly period from 1985 to 2009 using panel model; results perceive that for majority of the firm, movement in oil prices influence stock price movement.
In contrast, Buyuksalvarci (2010) found a negative effect running from oil prices to stock returns for Turkey. Another alternative investment for investors is to invest in gold and some studies include gold as macroeconomic factor such as Buyuksalvarci. But the outcome from his analysis reveals that the effect of gold price is insignificant as opposed to the other macroeconomic variables used.
2.2.4 International Activities
With the advent of globalisation, international activities had become important since other countries fluctuation affects the local market too.
2.2.4.1 Foreign exchange rate and Stock return
Exchange rates play a vital role in a country's level of trade, which is critical to every free market economy in the world. One very basic definition of exchange rates is the rates at which one unit of a country's currency can be exchanged into another one. As such, the observation of exchange rates is crucial and its one of the most watched, analysed and fluctuate economic variables. But exchange rates do matter on a smaller scale as well: they impact the real return of an investor's portfolio.
There exist no theoretical consensus on the existence or the direction of any relationship between stock prices and exchange rates. But instead, classical economic theory hypothesis discuss the linkage between stock prices and exchange rates trough two models: 'flow oriented' model (Dornbusch and Fischer, 1980 and Gavin, 1989) and the 'stock-oriented' model [3] (Branson, 1983 and Frankel, 1983).
The flow oriented model assumes that important determinants of exchange rates through a country's current account and balance of trade performance. Under this model, it is assumed that exchange rate affects the valuation of a firm through its competitiveness since it affects the cost of capital borrowed from overseas and also earnings made in foreign currencies. This in-turn influences real economic variables such as real income and output subsequently affects the current and future cash flows of companies and thus their stock prices. This goes in line with what had been discussed by Dornbuscher and Fischer which states that stock prices is defined as discounted present value of a firm's expected future cash flows; thereby any events affecting a firm's cash flow will be projected in the firm's stock prices. [4]
The relationship between stock prices and exchange rates can be examined in the case of an import-dominant country and export-dominant country. One study which provides a foundation for further studies was by Ma and Kao (1990), whereby they test the degree of stock prices reaction to exchange rates changes in different countries. The differences among countries were explained by the nature of their economies, specifically by the extent to which the economy depends on exports and imports. As per their research, an appreciating currency negatively affects the domestic stock market for a country with larger export sector and positively affects the domestic stock market for an import-dominant country.
Earlier researches were based on US markets (Aggarwal, 1981; Frank and Young, 1972; Solnik, 1987) and results from these studies are varies from one another. Aggarwal (1981) examined the relationship between the US stock market indexes and stock prices for the period 1974 to 1978 and finds a positive relationship between the two. In contrast, Soenen and Hernigar (1988) for the period 1980 to 1986 reported a strong negative relationship between US stock indexes and a fifteen currency weighted value of the dollar.
Olwneny and Omondi (2011) focus their research on the Nairobi stock exchange by using EGARCH and TGARCH models; found that the impact of foreign exchange rate on stock return is low but still significant. Through the use of Johensen cointegration and VECM, a significant connection between stock prices and exchange rates can be found in the paper of Sohail and Hussain (2011) for the Pakistan stock market. Ozbay (2009) shows that foreign transaction has a significantly positive influence on stock price.
Olugbenga (2011), using selected firms from the Nigerian Stock market for the period 1985 to 2009 found that exchange rates exert a negative relationship with stock prices of majority of the sampled firms. Menika (2006) for Sri Lanka and Yogaswari et al. (2012) for Indonesia found an inverse linkage between exchange rates and stock returns.
One study by Adjasi et al. (2011) investigates the relationship between stock prices and exchange rates for Mauritius along with six other African countries by employing the VAR Model. Their finding stipulates that stock returns in Mauritius reduce with a shock induces by the Exchange rates. One important conclusion drawn from their research is that either shocks by stock market returns or exchange rates changes seems to be more protracted in Mauritius (eight months) which indicates that misalignments in the movements of exchange rates and stock markets leave longer lasting dictions in the economy of Mauritius.
2.2.4.2 Other variables
Hsing (2011) uses the U.S. and U.K stock market index as proxy of world stock prices to examine its impact of on the South African stock price index. Using the EGARCH model, results envisage that a higher U.S. stock price or a lower U.S. government bond yield would help the South African stock market.
2.3 CONCLUSION
From the above empirical reviews, different macroeconomic variables are employed depending on the purpose of study. Furthermore, diverse methods of linking macroeconomic variables with stock prices can be found: Multiple regression techniques (Chen et al., 1986), long-term relationship through the use of VAR and Cointergartion techniques (Maysami et al., 2004; Humpe and Macmillian, 2007; Ozbay, 2009), and Volatility Clustering and GARCH - family model (Wong, 2010; Hsing, 2011; Olwendy and Omondi, 2011).
