Impact Of Money Supply On Stock Returns
The stock prices movements are an important indicator of the economy. In the well-organized stock market, the savings are mobilized and the investment projects are activated, which lead to economic activities in a country. Stock markets play very essential role in the economy. To act as mediator between savers and borrowers is the stock market’s key function.
It mobilizes savings from a large pool of small savers and channelizes these funds into fruitful investments. Stock market operations harmonize the preferences of the lenders and borrowers. The Stock market also allows transference of funds among corporations and sectors. It also provides liquidity for domestic expansion and credit growth. In this way stock markets help in improving the economic efficiency through the following means:
Growth of savings;
Efficient allocation of investment resources;
Alluring foreign portfolio investment.
Stock return consists of dividend and increases in price (capital gain). It is important to investors and business organization to know the company’s stock value and investment returns. The decision whether to choose a particular stock is one of the most important implications for the stock price. A lot of models and techniques have been developed and used by investors to help them obtain better returns on their stock investment.
Capital Asset Pricing Model (CAPM) is the most influential and widely used one factor pricing model. The model estimates the expected return of a stock, given the return for a theoretical risk free asset, market return and the stock’s sensitivity to the market risk. In other words, non diversifiable market risk is the only risk factor that is used in the model and it is sufficient to explain the risk-return trade-off with an efficient market portfolio. The model’s success depends on whether or not any persistent excess return can be made without taking additional market risk through β’s.
Capital asset pricing model is most practitioners’ favourite when estimating expected return for an individual stock. CAPM developed by Sharpe (1964) and Linter (1965) was the first theoretical model that explains the non diversifiable market risk’s impact on return. The model estimates the expected return of a stock. Non diversifiable risk is the only risk factor that is used in the model, which is represented by beta in the CAPM model.
However, Ross (1976) has developed the APT model arbitrage pricing theory, which argues that there are other factors that affect stock return rather than stock’s beta that is accounted for in CAPM model.
Ross (1976) argues that these factors which are macroeconomic factors are not incorporated in the CAPM and therefore should be incorporated when pricing or determining the required rate of return for any stock.
In Ross (1976)’s APT, the macroeconomic factors that affect stock price and return were not defined, the model only tells how strong is the effect for non-defined factors.
Yet, Chen, Roll & Ross (1986) hypothesised and tested a set of macroeconomic data series to explain US stock returns. Based on the Arbitrage Pricing Theory (APT) developed by Ross (1976), they investigate the sensitivity of macroeconomic variables to stock returns having employed seven macro series: term structure, industrial production, risk premium, inflation, market return, and consumption and oil prices. It is assumed that the underlying variables are serially uncorrelated and all innovations are unexpected. In their research, Chen, Roll & Ross (1986) found a strong relationship between the macroeconomic variables and the expected stock returns. Having first illustrated that economic forces affect discount rates, the ability of firms to generate cash flows, and future dividend payouts, Chen, Roll & Ross (1986) provided the basis for the belief that a long-term equilibrium existed between stock prices and macroeconomic variables. It was noted that industrial production, changes in risk premium, twist in the yield curve, and measure unanticipated inflation and changes in expected inflation during period when these macroeconomic variables are highly volatile, are significant in explaining expected returns. Evidence by Chen, Roll & Ross (1986) suggests that consumption, oil prices and market index are not priced by the financial market. According to their conclusion, stock returns are exposed to systematic news that is priced by the market.
In light of the above, this paper aims to examine the influence of macroeconomic forces on stock returns in Jordanian stock market, Amman stock exchange (ASE) index, using APT.
According to Fisher’s Hypothesis, the market interest rate incorporates the expected real rate of interest and expected inflation (Fisher, 1930). Real rate of interest was not affected by a permanent change in inflation rate in the long-run because nominal rate of interest and rate of inflation moved one-to-one.
To this extent, it was concluded that stock returns and rate of inflation moved in the same direction. Hence, real assets as well as shares perhaps provide hedge against inflation. Relationship between stock returns and inflationary trends in India was investigated by Chatrath et al. (1997). The study provided an evidence of a negative relationship between market returns and inflationary trends in India. In their study, a positive relationship between stock prices and inflation was reported by Ratanapakorn and Sharma (2007); while, a study by Humpe and Macmillan (2009) illustrated negative impact of inflation on stock prices.
Overview of Jordanian Economy
Jordan is a small country with limited natural resources, but has improved much since its inception as a country. Its current GDP per capita soared by 351% in the Seventies. But this growth proved unsustainable and consequently shrank by 30% in the Eighties. But it rebounded with growth of 36% in the Nineties.
Jordan’s open economy can only rely on limited natural resources. Only 6 percent of the country is arable land and water resources are among the scarcest in the world. However, there are sizeable mining resources, primarily potash and phosphates, of which it is the third largest world exporter.
Jordan was heavily impacted by the war in Iraq, disruption of trade with Iraq (its main export market), having not only important economic consequences for the economy but also having an adverse impact on prospects for development. Moreover, tensions in the region contributed to a significant drop in foreign investor interest in Jordan, in addition to marked deterioration of income from tourism.
The leading sector in Jordan is services, which account for 70 percent of GDP and the role it plays in supporting production is underlined in the King’s new economic guidelines. While the agricultural and construction sectors account for only a small portion of GDP, they employ a significant percentage of the workforce. The main manufacturing industries are textiles, mining (potash and phosphates), fertilisers, pharmaceuticals, oil refining, and cement.
1.2 Problem Statement
It has been argued that there are other variable rather than systematic risk (non-diversifiable risk) is not the only risk factor that investors should be compensated for. However, researchers had been studying this gap for years, finding that using beta or systematic risk (CAPM) as the only factor that influences stock return does not lead to match the actual rate of return on the stocks in the market. Thus, researchers have started to think about macroeconomic variables. However, a new model has been developed for this purpose, which is arbitrage pricing theory (APT); this theory assumes that there are several macroeconomic variables that affect stock price and return. Unfortunately, this model does not identify what exactly are those macroeconomic variables, until (Roll and Ross 1984), who have identified some macroeconomic variables that might affect stock return, such variables as interest rates, inflation, industrial production, and the spread between high and low bonds grades, those variables were significantly priced in the stock market. On the other hand, they found that oil price risk was not separately rewarded in the stock market.
Al-Sharkas (2004) empirically investigated the relationship between macroeconomic variables in Jordan. The empirical evidence shows that there exists a cointegrating relation among the variables. However, most of the researchers have suggested further examination of the macroeconomic variables using different methodologies, time periods, samples. Therefore, as mentioned earlier, Jordanian economy has experienced heavy negative impact after the war in Iraq in the year 2003. Thus, the current study considers this event as a possible impact on the relationship between macroeconomic forces and stock returns in Jordan.
This study initially aims to examine the effect of some macroeconomic variables that exist in the Jordanian stock market on stock returns in Amman stock exchange (ASE). Therefore, research objectives can be written as follows:
To examine the relationship between inflation rate and stock returns;
To investigate the relationship between stock returns and interest rates;
To examine the impact of money supply on stock returns;
To examine the relationship between oil price risk and stock return.
In light of the research objectives, this paper questions the following:
Is there any relationship between inflation and stock return?
Is there any relationship between interest rates and stock return?
Is there any relationship between money supply and stock return?
Is there any relationship between oil price risk and stock return?
1.5 Significance of the Study
In the past many studies have examined the determinants of stock returns and prices, starting from using CAPM model as beta is the only risk factor that influences the stock, then using fundamentals such as price to earnings and market to book ratios to price the stocks. Yet, researchers have found that macroeconomic forces also contributing in the stock return. However, as an extension for this issue, the current study aims to examine these macroeconomic forces and their effect on stock returns in Amman Stock Exchange (ASE) during the years from 2000 to 2009.
1.6 Scope of the Study
This study will be one of the few studies carried out in Jordan, as an emerging market. Similar studies have been undertaken mostly in developed countries. A detail summary is presented and the findings compared to those in the developed countries. This study initially adopts a similar approach by grouping observed stock return, interest rate, inflation rate, money supply, and oil prices from the Jordan market which is an emerging market. The period of study spans from 2000 to 2009.
Numerous studies have been done on this issue in order to determine what the macroeconomic variables that significantly affect stock prices and returns are. Hereby, reviews of some relevant studies which can be helpful to better understand this issue.
2.1 Capital asset pricing model (CAPM)
William Sharpe (1964) published the capital asset pricing model (CAPM). Besides, one more parallel work was carried out by Treynor (1961) and Lintner (1965). CAPM extended Harry Markowitz's portfolio theory (1952) to introduce the notions of systematic and specific risk. For his work on CAPM, Sharpe shared the 1990 Nobel Prize in Economics with Harry Markowitz and Merton Miller.
Capital Asset Pricing Model is
r = Rf + Beta x (Rm - Rf)
r is the expected return rate on a security.
Rf is the rate of a "risk-free" investment.
Rm is the return rate of the appropriate asset class.
The above given formula is known as capital asset pricing model (CAPM). The model implies that the expected return on a security is linearly related to its beta. Since the return on the market has been higher than the average risk free rate over long periods of time, (Rm - Rf) is presumably positive. Thus the formula implies that the expected return on security is positively related to its beta.
CAPM considers a simplified world where there are no taxes or transaction costs. All investors have identical investment horizons. All investors have identical opinions about expected returns, volatilities and correlation of available investments.
In such a simple world, Tobin (1958) states that the super-efficient portfolio must be the market portfolio. He argued that all investors would hold the market portfolio, leveraging or de-leveraging it with positions in the risk-free asset with the purpose of achieving a desired level of risk.
According to CAPM a portfolio's risk is decomposed into systematic and specific risk. Systematic risk refers to the risk of holding the market portfolio. As the market moves, from these movements each individual asset is more or less affected. To the extent that any asset participates in such general market moves, that asset entails systematic risk. Specific risk refers to the risk which is unique to an individual asset. Specific risk represents the component of the return of an asset which is uncorrelated with general market moves.
According to the CAPM, investors are compensated by the marketplace for taking systematic risk but not for taking specific risk. This is because specific risk can be diversified away. When an investor holds his market portfolio, each individual asset in the portfolio involves specific risk, but through diversification, the investor's net exposure is just the systematic risk of the market portfolio.
There is a number of empirical research and developmental research on CAPM. This was one of the important topics to be investigated until the 1990s. As found by Fama and French (1992) the relationship between the Beta (β) and the average return was weak over the era from 1941 to 1990 and virtually nonexistent from 1963 to 1990.
2.2 Macroeconomic Forces and Stock Returns Using APT
In (1976) Ross has suggested that CAPM is not enough to determine stock returns, because CAPM is a one factor model, meaning that it has only one risk factor which is the company’s beta. However, Ross (1976) argues that there many variable that should be included when determining the price or required return on stocks, these variables are some unknown macroeconomics, therefore, Ross (1976) has developed the APT.
Yet, Chen, Roll & Ross (1986) hypothesised and tested a set of macroeconomic data series to explain US stock returns. Based on the Arbitrage Pricing Theory (APT) developed by Ross (1976), they investigate the sensitivity of macroeconomic variables to stock returns having employed seven macro series: term structure, industrial production, risk premium, inflation, market return, and consumption and oil prices. It is assumed that the underlying variables are serially uncorrelated and all innovations are unexpected. In their research, Chen, Roll & Ross (1986) found a strong relationship between the macroeconomic variables and the expected stock returns.
Having first illustrated that economic forces affect discount rates, the ability of firms to generate cash flows, and future dividend payouts, Chen, Roll & Ross (1986) provided the basis for the belief that a long-term equilibrium existed between stock prices and macroeconomic variables. It was noted that industrial production, changes in risk premium, twist in the yield curve, and measure unanticipated inflation and changes in expected inflation during period when these macroeconomic variables are highly volatile, are significant in explaining expected returns. Evidence by Chen, Roll & Ross (1986) suggests that consumption, oil prices and market index are not priced by the financial market. According to their conclusion, stock returns are exposed to systematic news that is priced by the market.
Hondroyiannis and Papapetrou (2001) in their study, investigate whether movements in the indicators of economic activity affect the performance of the stock market for Greece. In the study, they implied the variables such as industrial production as a measure of output, real oil prices, interest rate, exchange rate, the performance of the foreign stock market (difference between the continuously compounded return on S & P 500 index and the USA inflation rate) and domestic real stock returns (difference between the continuously compounded return on the Athens general stock market index and Greek inflation rate). They found that the domestic market economic activity affects the performance of domestic stock market while impulse response analysis carried out in the study showed that all the macroeconomic variables are important in explaining stock price movements. It was found that growth in industrial production responded negatively to a real stock return shock, implying that an increase in real stock returns did not necessarily lead to a higher level of industrial production. The empirical results of the study suggested that the Greek stock market returns did not rationally signal changes in the overall macroeconomic activity. Interest rate shocks were also responded negatively by real stock returns. However, it was found that a depreciation of the currency lead to higher real stock market returns.
Wongbangpo and Sharma (2002) studied the relationship between stock prices and some macroeconomic factors in five ASEAN countries (Indonesia, Malaysia, Philippines, Singapore and Thailand). Their results in the study suggest that stock prices are positively related to growth in output in the long-run. However, stock prices are found to be functions of past and current values of macroeconomic variables in the short-run.
The study by Habibullah et al (2000) determines the lead and lag relationships between Malaysian stock market and five key macroeconomic variables such as money supply aggregate, nominal income (GNP), price level (CPI), interest rate (3-month T-bill rate) and the exchange rate (Real Effective Exchange Rate). The results of the study show that stock prices lead nominal income, the price level and the exchange rate, but money supply and interest rate lead the stock price.
Maysami et al (2004) argued that relationship between macroeconomic variables and stock market return is well documented in the finance literature, and they further examine the long run relationship between some selected macroeconomic variables and the Singapore stock market index, they found that Singapore market index changes along with, short and long-term interest rates, industrial production, price levels, exchange rates, and money supply.
Serkan Yilmaz Kandir (2008) paper investigates the role of macroeconomic factors in explaining Turkish stock returns. A macroeconomic factor model is employed for the period that spans from July 1997 to June 2005. Macroeconomic variables used in this study are, growth rate of industrial production index, change in consumer price index, growth rate of narrowly defined money supply, change in exchange rate, interest rate, growth rate of international crude oil price and return on the MSCI World Equity Index. This study uses data for all non-financial firms listed on the ISE. A multiple regression model is designed to test the relationship between the stock portfolio returns and seven macroeconomic factors. Empirical findings reveal that exchange rate, interest rate and world market return seem to affect all of the portfolio returns, while inflation rate is significant for only three of the twelve portfolios. On the other hand, industrial production money supply and oil prices do not appear to have any significant affect on stock returns.
Nil Günsel et al (2007) investigated the performance of the Arbitrage Pricing Theory (APT) in London Stock Exchange for the period of 1980-1993 as monthly. The study developed seven pre-specified macroeconomic variables. The term structure of interest rate, the risk premium, the exchange rate, the money supply and unanticipated inflation are similar to those derived in Chen, Roll and Ross (1986). This study extends the approach of Chen, Roll and Ross, by adding industry specific variables, such as sectored dividend yield and sectored unexpected production.
Melina Dritsaki et al (2001) have empirically tested the existence of a long-run relationship between the Greek Stock Market Index (GEN) and its fundamentals, namely industrial production, inflation and interest rates. However, by using a macroeconomic multi-regression they found that there is a significant relationship between Greek stock market returns and its fundamentals.
Humpe and Macmillan (2000) have examined the relationship of some macroeconomic variables with stock prices in the US and Japan. the long term relationship between industrial production, the consumer price index, money supply, long term interest rates and stock prices in the US and Japan have been examined in this study. For the US they found the stock prices are positively related to industrial production and negatively related to both the consumer price index and a long term interest rate. However, for the Japanese data they found that stock prices are influenced positively by industrial production and negatively by the money supply. Moreover, they found industrial production to be negatively influenced by the consumer price index and a long term interest rate. These contrasting results may be due to the slump in the Japanese economy during the 1990s and consequent liquidity trap.
Frimpong (2009) applies a cointegration analysis to examine the long term relationship between exchange rates, the consumer price index, money supply, interest rates and stock prices on the Ghana Stock Exchange. The study reveals that all of the macroeconomic factors employed have significant negative relationship with stock prices in Ghana.
In this chapter, the methodology used is highlighted. This chapter provides an explanation for research design and reliability of measurement for the methods of the study, using data collection method, a brief description of the method used to conduct the study, instruments and methods used to analyze the data.
This study is conducted on firms listed on the Amman Stock Exchange (ASE) with data collected from Data Stream in Universiti Utara Malaysia and website of the central bank of Jordan.
3.1 Theoretical Framework
This study explores the method of using a framework for independent variables inflation rates, interest rates, money supply, and oil prices and their influence on stock return:
Figure 1: Theoretical Framework
Inflation RatesINDEPENDENT VARIABLES DEPENDENT VARIABLE
3.1.1 Independent and Dependent Variables
In this study we are aiming the independent variable which is monthly stock return with other variables which are inflation rate, interest rate, money supply, and crude oil prices. Our aim is to examine the influence these variables on stock returns from ASE in Jordan.
184.108.40.206 Inflation Rates
The independent variable is explained as the yearly absolute rate of inflation obtained from central bank of Jordan. The sample is the listed companies from ASE. Interestingly, inflation rates in Jordan have responded positively to war in Iraq in 2003 and global financial crisis in 2008.
The impact of inflation rate on stock prices is well documented in the literature of this area, there are many evidences that such a relationship exist, and therefore, this paper suggests the following hypothesis:
H1: There is a significant relationship between inflation rate ratio and stock return.
220.127.116.11 Interest Rate
This independent variable will be taken as the interest rate on the treasury bills; this data is available and will be obtained from the central bank of Jordan similar to the inflation rate.
For this variable this paper suggests the following hypothesis:
H2: There is a significant relationship between interest rate and stock return.
18.104.22.168 Money Supply
Money supply in this study is defined as the yearly M3; the data required to obtain M3 is also available and will be obtained from central bank of Jordan. However, the study suggests the following hypothesis:
H3: There is a significant relationship between money supply and stock return.
22.214.171.124 Crude Oil Price
The crude oil prices in order to measure this independent variable are being obtained from the OPEC pricing for crude oil price. Jordan as 100% importing country for oil is reported to relate its economic condition to the oil prices, before 2003 war to Iraq Jordan used to get its needs of oil from Iraq in special prices; lately after 2003 the Jordanian government has started to follow global prices for oil.
However, this study suggests the following hypothesis regarding this independent variable:
H4: There is a significant relationship between crude oil price and stock return.
126.96.36.199 Stock Return
In this study the dependent variable is stock return which is the explanatory variable toward inflation rate, interest rates, money supply, and crude oil price as independent variables. This method of analysis is based on previous studies that have been conducted on the developed capital markets. In this study our aim is to investigate the influence of these macroeconomic forces towards stock return of ASE.
3.2 Data Collection Method
This research will be conducted by using secondary data collected from ASE sources such as other researcher’s work, case studies, electronic journals and some other related company’s financial statements.
Data for dependent variable will be obtained from the data stream Universiti Utara Malaysia Library, sample is chosen from ASE in the period that spans from 2000 to 2009. There are 174 companies listed in ASE, all companies listed will be included in the sample unless for the financial sector that will be excluded.
3.3 Data analysis
3.3.1 Descriptive Analysis
This decretive study reduced the mean, minimum, maximum, and standard deviation for each variable for the sample that is chosen in this study.
3.3.2 Correlation of Variables
This study shows how one variable is related to another. The results of this analysis represent the nature, direction and significance of the correlation of the variables used in this study and the correlation between the variables will be analyzed by using the Pearson correlation.
3.4 Model Specification and Multiple Regression
Multiple regression method is used to examine the relationship between the stock return in ASE companies and the macroeconomic forces mentioned above.
The result of regression analysis is an equation that represents the best prediction of a dependent variable from several independent variables. This method is used when the independent variables are correlated with one another and with the dependent variable.
The following regression equation is estimated as follow:
Si = α + β1INF + β2INT+ β3M3+ β4OIL, Where:
α = constant
Si = stock return
INF = the yearly inflation rate
INT = treasury bills interest rate
M3 = money supply
OIL = crude oil price
To examine the relation between the whole set of predictors and the dependent variable. In this model, all independent variables enter the regression equation at once. The aim of this analysis is to determine which of the independent variables are more highly significant to the stock return.
This chapter discusses the methodology that is used in this research. It also explained the hypothesis that is listed earlier in this chapter. This chapter provides an explanation for research framework and reliability of measurement for the methods of study. The procedures for collecting, measuring and analyzing data of this study are also discussed.
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