The Macroeconomic Forces and Stock Prices Finance Essay

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The study examines the influence of a selective set of macroeconomic forces on stock market prices in Bangladesh. The Dhaka Stock Exchange All-Share Index (DSI) is used to represent the prices in the stock market while deposit interest rates, exchange rates, consumer price index (CPI), crude oil prices and broad money supply (M2) are selected to represent the macroeconomic variables affecting the stock prices. Using monthly data from 1992m1-2011m6, several time-series techniques were used which include Cointegration, Vector Error Correction Model (VECM), Impulse Response Functions (IRF) and Variance Decompositions (VDC). Cointegration analysis, along with the VECM, suggests that interest rates, crude oil prices and money supply are negatively related to stock prices, exchange rates are positively related to stock prices, and CPI is insignificant in influencing the stock prices, in the long-run. Both the IRF and VDC suggest that shocks to macroeconomic variables explain a small proportion of the forecast variance error of the DSI, but these effects persist for a long period. Stock markets, where shares and bonds are traded and issued through exchanges and over-the-counter markets, form an integral part of the financial markets and are important for the development of an economy. Stock markets contribute to the economy by providing businesses with access to capital and investors with opportunities for capital gains. Research has shown that stock market development contributes significantly to the economic growth of a country (Levine and Zervos, 1996) [1] . Since stock market prices are subject to fluctuations, it is essential to determine the forces influencing the stock prices for efficient functioning and development of the stock market and country. There are many reasons for there to be an interest to determine the forces influencing the stock prices. Firstly, this may interest investors, so they can forecast stock prices accurately to make apt decisions regarding their stock portfolio for maximum gains. Secondly, businesses may find this useful; as stock price is an indication of the financial health of the companies, businesses may be interested to determine the future stock prices as it will allow them to assess their ability to issue bonds or obtain financing in the future. Thirdly, policymakers and economists may find this useful, so they can predict stock prices as prices of stocks reflect changes in economic activity in the long run (Cheung and Lai, 1999). Stock prices are expected discounted dividends, i.e. discounted value of future cash flows derived from a stock. Theoretically, stock prices are modelled as: where P refers to the stock price, CF refers to the cash flows derived from a stock and R refers to the discount rate. Hence, any forces influencing the discount rates, R, or expected future cash flows derived from a stock, CF, will affect the stock prices. However, theoretical models do not provide an ‘identity’ of these exogenous economic forces (Bodhurta et al., 1989), i.e. do not identify the economic forces influencing the stock prices. Macroeconomic variables are potential candidates for these forces because macroeconomic changes simultaneously affect many firms’ cash flows and may influence the risk-adjusted discount rate (Shiller, 1981; Leroy and Porter, 1981; Flannery and Protopapadakis, 2002), or simply, discount rate. The purpose of the dissertation will be to try to find a long-term relationship between macroeconomic forces and stock prices for an emerging stock market in a less developed country. The dissertation will focus on an emerging market because the behaviour of stock prices in these countries is not tied to economic fundamentals (Gunasekarage et al., 2004) and, therefore, makes it difficult to predict the forces affecting the stock prices. Moreover, studies on emerging markets have shown that returns and risks in these markets are higher relative to those in stock markets in developed countries (Harvey, 1995a). It will be interesting to determine what factors cause these higher risks and returns and study the relationship between macroeconomic forces and stock prices in emerging stock markets. The dissertation will study the relationship between a selective set of macroeconomic variables and stock prices in the Bangladesh stock market. The Bangladesh stock market is an established capital market and deemed as the next Asian success story by JPMorgan Chase, Citigroup, and Merrill Lynch (Bloomberg, 1997); its stocks have performed well in recent years and prices gained nearly 50% over one year in 2010 (2nd highest in the world after Sri Lanka) [2] . However, the stock market is still developing and the analysis made in this study can, therefore, be used to shed light on other emerging stock markets. For the purpose of the study, the stocks from the Dhaka Stock Exchange (DSE), the primary stock market of Bangladesh, will be considered as it covers majority of the stocks in the country and will allow a more comprehensive analysis. The DSE uses three share price indices – Dhaka All-Share Price Index (DSI), Dhaka General Price (DGEN) Index, and DSE-20 [3] . The DSI will be used as a proxy for stock prices for the study as it covers all the stocks including Z-category shares. The study will cover the last two decades since the DSE became very active during this period due to developments in the capital markets of Bangladesh, such as exemption of tax dividends on stock market investments to increase stock trades, off-loading of shares of government-owned companies, allowing investment of black money [4] into the capital market, etc. The next section reviews the existing literature on the topic and discusses the methodologies used in the papers. Section 3 describes the macroeconomic variables used in the study along with their hypothesized relationship with the stock prices. Section 4 discusses the sources from which the data were collected and provides descriptive statistics of the data. Section 5 explains the econometric model and methodologies used in the study. Section 6 provides the empirical results with interpretations. Section 7 concludes the paper and offers further remarks.

Literature Review
Chen, Roll and Ross (1986) (CRR) was one of the pioneer papers that tried to identify the macroeconomic variables that influenced stock returns and determined this relationship for the New York Stock Exchange (NYSE). They used a regression framework to test whether macroeconomic innovations such as monthly growth in industrial production, expected inflation and unexpected inflation [5] , and an interest rate spread variable have systematic influences on stock market returns. To this end, they estimated a Vector Autoregression (VAR) model of lagged stock market returns and used the residuals of the macroeconomic variables as the unanticipated innovations in the macroeconomic factors. They found that industrial production, changes in the risk premium, term structure, unexpected inflation and changes in anticipated inflation were significant in explaining expected stock returns in the NYSE. They also used value-weighted NYSE index as a macroeconomic variable, and found that it has an insignificant influence on expected returns. Poon and Taylor (1991) used the dataset for the London stock market and the same macroeconomic variables as CRR and found that no significant relationship exists between stock returns and the macroeconomic variables. Diacogiannis (1986) and Cheng (1995), similarly, determined that there is no conclusive result regarding the relationship of relevant macroeconomic variables with the capital market of the U.K. Günsel and Çukur (2007), using the same variables as CRR, looked into different industries in the U.K. and found that macroeconomic factors have a significant effect in the U.K. stock exchange market; however, each factor affect different industry in different manner. Further work on the topic has extended the analysis by incorporating different framework/setting and conducted the study using different econometric methods. Bodhurta et al. (1989) undertook an analysis for seven major industrial countries – United States, Japan, United Kingdom, Germany, France, Canada and Australia. They chose the same explanatory variables as CRR, and to incorporate an international setting, introduced deviations from Purchasing Power Parity, typified as real exchange rate changes, and interest rate parity. They were able to demonstrate that several of the international analogs of the CRR domestic variables – stock index returns, industrial production, bond returns, unanticipated inflation and oil prices are significant in explaining the average stock returns in the cross-section sample. Mukherjee and Naka (1995) used a different methodology to determine the relationship between macroeconomic forces and stock returns. They employed Johansen’s (1991) Vector Error Correction Model (VECM) to examine whether relevant macroeconomic variables and the Tokyo Stock Exchange (TSE) index were cointegrated for the period 1971-1990. They found that a cointegrating relationship exists and that stock prices contributed to the relation. The relationships between stock index and exchange rates, inflation, money supply, and industrial production were as hypothesized and the same as existing literature. However, the results for the interest rates were mixed. The relation between the TSE and long-term government bond rate (LGB) was negative, but positive between the TSE and call money rate; they state that this may be because the LGB is a better proxy for risk-free rate in the basic stock pricing model. Nasseh and Strauss (2000) used Johansen’s cointegration procedure and variance decompositions method for 1962-1995 for six European countries: France, Germany, Italy, The Netherlands, Switzerland and the U.K, and found support for the existence of a long-run cointegrating relationship between stock prices and domestic interest rates, consumer prices, real industrial production, business surveys of manufacturing orders, and International (German) movements in stock prices. There have only been a few studies focused on the emerging markets in less developed countries. Wongbangpo and Sharma (2004) studied the stock markets of the five ASEAN countries, namely Indonesia, Malaysia, Singapore, Philippines and Thailand, and their relationship with select macroeconomic variables. They found that in the long-run, the stock prices were positively related to growth in output, and negatively to the aggregate price level. A negative long-run relationship between stock prices and interest rates was observed in Philippines, Singapore and Thailand. High inflation in Indonesia and Philippines was found to influence the long-run negative relation between stock prices and the money supply, while the money growth in Malaysia, Singapore and Thailand was found to be responsible for the positive effect on their stock markets. Lastly, the exchange rate variable was positively related to stock prices in Indonesia, Malaysia, and Philippines, which can be explained by the high competition in the world exporting market. Mookerjee and Yu (1997) and Maysami and Koh (2000) studied the Singapore stock market and found that changes in Singapore’s stock market levels form a cointegrating relationship with changes in price levels, money supply, short- and long-term interest rates. Gunasekarage et al. (2004) examined the long-run relationship between macroeconomic factors and all-share price index from 1985-2001 for the Colombo Stock Exchange and found that the consumer price index and treasury bill rate coefficients are significant and negative, money supply coefficient is significant and positive, but exchange rate had no influence on share price indices. Frimpong (2009) conducted a study on Ghana for the period 1990-2006 and found that exchange rates are positively related to the Ghana Stock Exchange All-Share index (GSE), and inflation and money supply are negatively related to the GSE. Rahman and Moazzem (2011) attempted to identify the causal relationship between changes in the DSE stock prices and the regulatory decisions taken by the Securities and Exchange Commission. The results suggested that market price fluctuations were positively and significantly influenced by these decisions, a result that was opposite of what was expected. However, the methodology suffers from endogeneity as the number of decisions taken may be due to high price indices in the given month.
Macroeconomic Forces and the DSI: Hypothesized Relations
This section covers the macroeconomic variables chosen for the study and their hypothesized relationships with the DSI. The variables chosen were based on financial theory and established literature – deposit interest rates (IR), exchange rate (EXR), consumer price index (CPI), crude oil prices (OP) and broad money supply (M2) [6] . The intuition behind the relationship between deposit interest rates and stock prices forms the basis for the hypothesized negative relationship between the variables. Interest rates represent the opportunity cost for investors in the equity markets (Asprem, 1989). An increase in the interest rates results in high opportunity cost of holding cash and leads investors to substitute between stocks and other interest-bearing securities. Moreover, the interest rates, through their effect on the risk-free rate, will lead to an increase in the discount rate (Mukherjee and Naka, 1995). Thus, stock prices are expected to fall and vice versa. Solnik (1987), Soenen and Hennigar (1988) and Ma and Kao (1990), among others, indicate that exchange rates play a significant role in affecting the performance of a stock market. For this study, a positive relation is hypothesized between exchange rate (against the U.S. dollar) [7] and stock prices based on the classic theory of Hume (1752). As goods in the Bangladesh economy become relatively more expensive in the international market due to an appreciation of the Bangladeshi Taka (falling exchange rate) against the U.S. dollar, demand for exports reduce and, at the same time, demand for imports increase, thus leading to lower Taka-denominated cash flows into the Bangladeshi companies and hence, lowering stock prices. This is evident from the theoretical model of stock valuation. The opposite should hold when the Bangladeshi Taka depreciates against the U.S. Dollar. The relation between consumer price index and stock returns has been generally theorized to be negative (Fama and Schwert, 1977). A decrease in consumer price index decreases the nominal risk-free rate and lowers the discount rate in the stock valuation model, leading to higher stock prices. Mukherjee and Naka (1995) suggest that the effect of a lower discount rate would be neutralized if cash flows decrease with the CPI. DeFina (1991), however, documents that cash flows do not decrease at the same rate as inflation or CPI, and, hence, it is expected that the fall in discount rate will lead to higher stock prices. It must be noted though, that prices, in general, may be subject to greater fluctuations in the developing countries, which may render the relationship insignificant. Thus, a negative or insignificant relationship is expected between CPI and stock prices. Crude oil prices are used in this study following Hassan and Hashim (2010). Oil prices serve as an input for production in sectors, such as agriculture and manufacturing. Changes to these prices are likely to affect the economic activities taking place in the country. A negative relationship is hypothesized between oil prices and stock prices since higher oil prices may lead to a decrease in production and thereby lower cash inflows and stock prices. The opposite relationship is expected to hold as well. It should be noted that crude oil price is an external factor – the objective is to see whether international factors play a role in the stock markets of Bangladesh. The relationship between money supply and stock prices is not straightforward. It has been widely tested because changes in money supply have important direct effects on stock prices via portfolio changes, and indirect effects via its effects on real activity variables (Mookerjee and Yu, 1987). As money growth rate is likely to be positively related to inflation, it will increase the discount rate and, hence, lower stock prices. However, prices are constant in this study; hence, money supply may affect stock prices through other mechanisms. Sellin (2001) argues that a positive money supply shock will alter expectations about future monetary policy and lead people to anticipate tightening monetary policy in the future. The subsequent increase in bidding for bonds will drive up the current rate of interest. As the interest rate goes up, the discount rates go up as well, and the present value of future earnings decline, leading to decline in stock prices. The increase in money supply may also lead to a boost in companies’ cash flows resulting from the increased money supply (Mukherjee and Naka, 1995; Chaudhuri and Smiles, 2004), known as corporate earnings effect, which is likely to increase stock prices. For this study, a negative relationship is expected between money supply and stock prices since prices and interest rates are subject to greater fluctuations in developing countries.
Data
4.1 Data Sources
For the purpose of this paper, monthly data has been collected for the period January 1992 to June 2011. The period was chosen purposefully since the Bangladesh economy has undergone major changes during this period, such as trade liberalisation in the 1990s, stock market crash in 1996, and again in 2010-2011, capital market developments in the 2000s, etc., and it will be interesting to analyse the relationship between the macroeconomic variables and stock index during this period. Firstly, the monthly Dhaka Stock Exchange All-Share index data is obtained from the Dhaka Stock Exchange and its website [8] . Next, five macroeconomic variables have been chosen to determine their relationships with the stock prices. These include the interest rate (deposit rate), the exchange rate (domestic currency for US dollar), consumer price index (with a base year of 2005) as a measure of inflation, per barrel price of crude oil in U.S. Dollar, and broad money supply. Oil price is taken to serve as a proxy for international risk factors and external supply side shocks [9] . Data on consumer price index, exchange rate, deposit interest rates and crude oil prices were collected from the International Financial Statistics of the International Monetary Fund. Data on broad money was collected from the Monthly Trends publications of the Bangladesh Bank. Other variables were also considered for the study initially, such as call money rate and industrial production index, but due to unreliability and unavailability of data, they had to be excluded from the study.
Data Statistics
This sub-section provides descriptive statistics and time-plots (attached in Figure A-1a:f in the Appendix) for the data under study. The purpose is to observe the trends that the variables have displayed over the period and analyse the changes that have taken place. For the sake of the study, all the variables (except interest rates [10] ) have been converted into natural logarithms [11] . The following table gives a summary of the descriptive statistics of the variables: Table 4.: Descriptive Statistics of Variables – January 1992 to June 2011 Variable Summary Statistics – Logged Variables (except IR) DSI IR ER CPI OP Mean 6.99 8.22 3.98 4.42 3.44 St. Dev. 0.76 1.21 0.22 0.32 0.65 Maximum 8.87 11.39 4.30 5.05 4.89 Minimum 5.66 5.77 3.66 3.93 2.34 Note: DSI is Dhaka Stock Exchange All-Share Index, IR is deposit interest rate, ER is exchange rate, CPI is consumer price index, OP is oil prices and M2 is broad money. All the variables (except interest rates) are in natural logarithms Source: Dhaka Stock Exchange, Bangladesh Bank and International Financial Statistics The table above and the time plot in Figure A-1a show that in the span of 1992M1-2011M6, the DSI has registered high fluctuations in levels. Figure A-1a shows that the DSI has registered an upward trend over the period under consideration. The DSI series shows spikes in 1996 and 2010, both were due to bubbles [12] in the stocks. The deposit interest rates were fairly stable in the period under consideration, with low standard deviation in levels, as seen in Table 4.1 and Figure A-1b. The deposit interest rates were lower during the periods of the stock price crashes, as banks were forced then to lower the interest rates that they pay out on deposits to consumers. The exchange rate of Bangladeshi Taka against the U.S. Dollar has been on an upward trend for the entire period, as seen in Figure A-1c. The Bangladesh economy is highly reliant on imports for luxury products and raw materials. Since these transactions are conducted in U.S. Dollars, the exchange rate of the Bangladeshi Taka against the U.S. dollar has been rising. However, the appreciation of the U.S. dollar against the Bangladeshi Taka has ceased since the global financial crisis in 2006-2007, as transactions in U.S. Dollar have reduced. The consumer price index, which is taken to account for inflation [13] , has risen steadily over the entire period, with higher increase in recent periods, as shown in Figure A-1d. This is due to high food prices in recent years [14] . The crude oil prices data in Figure A-1e show that the prices have remained mostly stable until 2006. Since then, crude oil prices have seen major fluctuations with record-high prices during the recent global financial crisis. The prices were lower in 2008, affected by easing of tensions between the U.S. and Iran. A stronger US dollar in the international market and a likely decline in European demand are also among the causes of the decline. The broad money supply data in Figure A-1f show that M2 has remained stable except for two shocks in 1996 and 2006. However, the overall trend in the broad money data has been upward.
Econometric Methodology
The purpose of the research is to determine if a long-run relationship exists between the DSI and macroeconomic factors for Bangladesh. The econometric model to be used for the paper is as follows: where the variables are as they have already been defined, and AŽAµt is the error term in the model. AŽ’0 represents the constant term in the model and AŽA²1, AŽA²2, AŽA²3, AŽA²4 and AŽA²5 represent long-run parameters. Time-series econometrics requires an analysis of the time-series properties and paths of the economic variables in a regression equation before estimation in order to assess if a long-run relationship can be estimated for the model. A long-run relationship exists if the variables are non-stationary in levels and stationary in first differences. More specifically, it should be ensured that the variables in the study are integrated of order d, where dA¢”°A¥1, i.e., they should be stationary in differenced forms, denoted as I(d). To test for stationarity, i.e. no unit root, in the variables, two tests are used – the Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) tests. ADF estimates the following equation: where AŽA´ refers to the existence of a trend and AA, to presence of unit root. The ADF test is carried out for two models – with constant and trend, and constant with no trend (AŽA´=0). The lag of dependent variable is included to account for autocorrelation. For the Phillips-Perron unit root tests, however AŽA³j=0, and the PP test incorporates an automatic correction to the test on AA=0 to allow for autocorrelated residuals. The PP test estimates: where , and the second term is the Newey-West estimator of the error variance which adjusts the statistics for the possibility of autocorrelated error. The optimum lag length to be used in ADF tests are decided using the Schwert maximum lag length, sequential t-test procedure, Akaike Information Criterion (AIC) and Schwarz Bayesian Information Criterion (SBIC). The lower the value of the criterion, the better the fit for the unit root tests. When the sample is large, say T > 250, it is better to rely on SBIC. However, if the sample contains observations below T < 250, AIC is a better fit and used to account for the optimal lag length. After testing the variables for unit root, the next step entails determining if cointegration exists among the variables. Engle and Granger (1987) suggest that a long-term equilibrium relation between stock prices and macroeconomic factors can be determined using cointegration analysis. If two or more series individually have unit root series, but some linear combinationA of them has a stationary process, then the series is said to be cointegrated. The Johansen (1991) method is an extension of Engle and Granger procedure, allowing for more than one cointegrating equation and it this procedure which will be undertaken. Suppose for a multivariate case, where Yt is a vector of k variables, and i – 1,2 AŽËœi is k x k. This can be manipulated and written as: where A¢Ë†” Yt = Yt – Yt-1, and A¢Ë†Ak = AŽËœ1 + AŽËœ2 – Ik. If the rank of A¢Ë†Ak is zero, then there are no cointegrating vectors. In the presence of cointegration, A¢Ë†Ak has rank r A¢”°A¤ k – 1, and then, A¢Ë†Ak = AŽA±AŽA²’, where AŽA± is k x r and AŽA²’ is r x k. Then, this can be written as: where AŽA²’ is the cointegrating matrix, AŽA²’Yt represents the r linear combination and AŽA± represents the speed of adjustment towards the long-run equilibrium relationship. In order to perform Johansen tests, we need to compute the k eigenvalues of , which is the estimate of A¢Ë†Ak. It is assumed that is the squared canonical relationship ordered from the largest to the smallest. If there are r cointegrating relationships, then log(1 + AŽA»j) = 0 for j=r+1,A¢â‚¬A¦,k. Test for H0 : r A¢”°A¤ r0 versus HA : r > r0, i.e. under the null, the number of cointegrating vector is at most r0, under the alternative, it is larger than r0. This is called a Trace test. The maximum eigenvalue test is also conducted to test for the number of cointegrating relationships. Under the maximum eigenvalue test, H0 : r A¢”°A¤ r0 versus HA : r = r0 + 1, i.e. under the null, the number of cointegrating vector is at most r0, under the alternative, it is equal to r0 + 1. If at least one cointegrating relationship exists among the variables, a causal relationship among them can be determined by estimating the Vector Error Correction Model (VECM). In this study, the short-run VECM equation with a lag length p is modeled as: where the variables are I(1) and as previously defined, AŽA±1, AŽA±2, AŽA±3, AŽA±4, AŽA±5 and AŽA±6 represent short-run elasticities, AŽAµt-1 is the error correction term, with its coefficient A” , which conveys the long-run information contained in the data and denotes the speed of adjustment to long-run equilibrium after a shock to the system. The VECM builds on cointegration by incorporating error correction terms that account for short-run dynamics, and, if a long-run equilibrium condition is valid and cointegration exists, it explains short-run fluctuations (as represented by the AŽA±1, A¢â‚¬A¦. AŽA±6) in the dependent variable (Frimpong, 2009). The optimal number of lags is determined by lag length in VAR using AIC. Impulse Response Functions (IRF) and Variance Decompositions (VDC) will be constructed after estimating the VECM. IRF is a useful tool for characterizing the dynamic responses implied by estimated VECMs. Consider a first-order VAR for the n-vector yt: where AŽA¼ is the vector of intercepts and AŽAµt ~ IN (0,). The IRF of a shock to variable, for instance, IR on variable DSI after k periods as: where AŽAµIR,t is the vector AŽAµt excluding the IR element. The IRF measures the effect of a shock of 1 unit occurring at period t-k, on variable DSI, k periods later, assuming there are no other shocks at period t-k, or in the other intervening periods (t-k+1,A¢â‚¬A¦t). The IRF shows impulse responses of the select variable in the VECM system in regards to the time paths of the variable’s own error shock against the error shocks to other variables in the system. Since the innovations of error terms are likely to be correlated, a mechanism of Impulse Response via the Generalized Impulse Response method is implemented, to ensure orthogonalization of the innovations. Unlike other mechanisms of orthogonalization of innovations where the interpretation of specific impulses rests on the ordering of variables within the VAR system, the Generalized Impulse Response has no such concern on the VAR ordering. The VDC is implemented to show the percentage of the movement of the t-step ahead forecast error variance of the select variable in the VECM system that is attributed to its own error shock in contrast to error shocks to other variables in the system (Gunasekarage, 2004).
Empirical Results
6.1 Results of Unit Root Tests
It is essential to confirm the order of integration of all the variables before the model is estimated and tested for cointegration. The Augmented Dickey Fuller and Phillips-Perron tests are employed to test for unit roots and the results are reported in Table 6.1. The tests were conducted for all the variables with both a constant and a time trend, with lags for the ADF tests selected as per the Akaike Information Criterion, unless otherwise stated. The lags were also tested for significance prior to their results being reported. For the PP test, Bandwidth or the lag truncation parameter was chosen using the estimation method of Bartlett kernel. The null hypothesis for the ADF and PP tests is that the selected variable has a unit root. When the test statistic for a variable was greater than the critical value for the test, the null of unit root was rejected and vice versa. Table 6.: Results of Unit Root Tests Variable Augmented Dickey Fuller Phillips Perron Constant Trend and Constant Constant Trend and Constant Level DSI -0.95 (1) -2.08 (1) -0.92 (5) IR -2.26 (1) -2.73 (1) -2.76 (8) ER -0.58 (10) -2.53 (10) -0.31 (2) CPI 2.01 (4) -1.73 (4) 1.68 (9) OP -0.67 (1) -2.94 (1) -0.61 (4) M2 -3.65 (5) 0.21 (5) 1.18 (1) First Differences A¢Ë†” DSI -12.83 (0)*** -12.81 (0)*** -12.85 (3)*** A¢Ë†” IR -5.78 (2)*** -6.03 (2)*** -14.43 (8)*** A¢Ë†” ER -4.07 (7)*** -4.06 (7)*** -13.00 (5)*** A¢Ë†” CPI -4.39 (10)*** -4.69 (10)*** -10.30 (16)*** A¢Ë†” OP -11.84 (0)*** -11.84 (0)*** -11.82 (1)*** A¢Ë†” M2 -7.79 (5)*** -11.66 (4)*** -26.04 (1)*** The ADF and PP critical values for t-statistics at 1% and 5% significance levels for the model with the constant are -3.46 and -2.88 respectively; the model including both the trend and constant are -4.00 and -3.43 respectively ***, ** and * denote the rejection of unit root/non-stationarity for the ADF and PP tests at 1%, 5% and 10% significance levels respectively The numbers in parentheses for the ADF tests correspond to the optimum lag length, as per Akaike Information Criterion, unless otherwise stated The numbers in parentheses for the PP tests correspond to the Bandwidth, based on Newey-West using Bartlett Kernel for PP MacKinnon (1996) critical values are used for ADF and PP tests. The variables show non-stationarity in levels and stationarity in first differences, for both the ADF and PP tests. The lags for interest rates in levels under the ADF test estimated using the AIC were insignificant with a constant and trend at 5% significance level, hence, the lags were changed until they were significant and then the appropriate test statistic reported. The lags for the first differences of the exchange rates’ data under the ADF test estimated using the AIC were insignificant with a constant and trend at 10% significance level. Similar to the tests for interest rates, the lags were then modified until they showed significance. As can be observed from Table 6.1 above, all the variables are non-stationary in levels, and stationary in first differences, a necessary pre-condition for cointegration analysis.
6.2 Results of Optimum Lag Length Tests
In choosing the specification of the cointegration model, it is necessary to specify the number of lags in the autoregressive specification (Chaudhuri and Smiles, 2004). For this purpose, the Likelihood Ratio, Final Prediction Error, Akaike, Schwarz and Hannan-Quinn Information Criterion were used to determine the appropriate lag length. The AIC, SIC and HQIC are chosen based on lowest values over the lags considered (allowed for a maximum ten lags in this case). The Akaike criterion suggests that a lag of five is optimal, whereas the Schwarz criterion indicates a lag of one. Since the number of observations considered in the study is below 250, the AIC is a better fit for the model. In addition, overestimating the order of the VAR is a bigger mistake than underestimating it and, hence, it is better to rely on AIC. Table 6.: VECM Lag Order Selection Criteria Lag LR FPE AIC SIC HQIC 0 4.6-9 -2.16 -2.07 -2.12 1 4374.6 2.1-17 -21.37 -20.73* -21.11 2 123.86 1.7-17 -21.60 -20.41 -21.12* 3 66.87 1.7-17 -21.58 -19.84 -20.88 4 74.43 1.7-17 -21.59 -19.30 -20.67 5 86.98 1.6-17* -21.66* -18.82 -20.51 6 57.24 1.7-17 -21.59 -18.21 -20.22 7 75.44 1.7-17 -21.60 -17.68 -20.02 8 61.34* 1.8-17 -21.56 -17.08 -19.75 9 38.71 2.2-17 -21.41 -16.38 -19.38 10 44.92 2.5-17 -21.29 -15.71 -19.04 *indicates lag order selected by the criterion
6.3 Results of Johansen Cointegration Tests
Table 6.3 shows the results for the Johansen Cointegration test performed to investigate the long-run relationships of the variables in the model. However, the number of cointegrating vectors generated by the Johansen test may be sensitive to the lag length. Hence, the optimum lag length estimated in the previous section via AIC (five) will be used to determine the number of cointegrating relations. Table 6.: Results for Johansen Cointegration Test No. of CE(s) [H0] AŽA»max Statistic 95% Critical Value [Max.] 99% Critical Value [Max.] AŽA»trace Statistic 95% Critical Value [Trace] 99% Critical Value [Trace] r=0 41.58 39.37 45.10 104.51 94.15 103.18 rA¢”°A¤1 32.59 33.46 38.77 62.93* 68.52 76.07 rA¢”°A¤2 14.57 27.07 32.24 30.35 47.21 54.46 rA¢”°A¤3 10.35 20.97 25.52 15.78 29.68 35.65 rA¢”°A¤4 5.36 14.07 18.63 5.42 15.41 20.04 rA¢”°A¤5 0.06 3.76 6.65 0.06 3.76 6.65 *denotes rejection of the hypothesis at the 5% and 1% significance level r denotes the number of cointegrating relationships CE refers to cointegrating equations The first column in Table 6.3 shows the null hypothesis assumed for the Maximum Eigenvalue and Trace Tests. The value of AŽA»trace under the null of r = 0 (no cointegration) is 104.51, which is greater than 94.15, the 5% critical value reported from Osterwald-Lenum (1992), so the null of no cointegration can be rejected in favour of one cointegrating equation. For r A¢”°A¤ 1, the AŽA»trace measure is less than the critical value at 1% and 5% significance levels, which forms the basis for accepting the null hypothesis of at least one cointegrating vector. An alternative measure that is used to determine the number of cointegrating vectors is AŽA»max. The AŽA»max shows that at the 5% significance level, the null hypothesis of no cointegrating vector is rejected since the value of 41.58 is greater than 39.37. However, similar to the AŽA»trace statistic, for r A¢”°A¤ 1 and other values of r, the AŽA»max measure is less than the critical value at 5% significance level. Therefore, it can be assumed that there is at least one cointegrating vector. According to both AŽA»trace and AŽA»max statistics, it can be confirmed that there is at least one long-run equilibrium relationship between the Dhaka Stock Exchange All-Share Index and macroeconomic variables. Lags of six and seven were considered to check for robustness; they also indicated one cointegrating relationship.
6.4 Results of Long-Run Cointegration Model
Table 6.4 shows the long-run cointegrating model. The long-run relationships were as hypothesized in the study and these are reported below: Table 6.: Long Run Cointegrating Model Regressor Coefficient Std. Error t-statistics Constant -1.39


Interest Rate -0.12 0.07 -1.79* Exchange Rate 5.26 1.33 3.94*** Consumer Price Index 3.01 2.26 1.33 Crude Oil Prices -0.69 0.21 -3.28*** Broad Money Supply -2.66 1.19 -2.23** ***, ** and * denote significance of variables at the 1%, 5% and 10% significance levels. Note: Dependent Variable – DSI According to the table, the actual long-run relationship can be represented by: DSI = -0.12IR + 5.26ER + 3.01CPI – 0.69OP -2.66M2 In the long-run, deposit interest rates and the DSI are significantly and negatively related. This was expected as high deposit interest rates mean that rational investors would be less interested to invest in risky assets in the Dhaka Stock Exchange. Consequently, this will lower the stock prices and hence, the DSI. The relationship was found to be significant at 10% level in the long-run. The finding is consistent with the literature, though early studies dealt primarily with Treasury-bill (short-term) rate and government bond rate (long-term) [15] . Mukherjee and Naka (1995), Maysami and Koh (2000), and Bulmash and Trivoli (1991) found a positive relation between the short-term interest rates and stock market prices, and a negative relationship between long-term interest rates and stock prices. The relationship between deposit interest rates and stock prices in the study are, therefore, consistent with the results of the long-term interest rates. The exchange rate and the DSI are significantly and positively related in the long-run. This was also hypothesized since increasing exchange rates (Taka depreciation against the U.S. Dollar) result in money inflows, and, consequently higher investment in the stocks. A higher investment in the Dhaka Stock Exchange would lead to higher prices for the stocks and higher DSI. Mukherjee and Naka (1995) and Brown and Otsuki (1990) also report the same relation for the Japanese stock market. This is in contrast to Maysami and Koh (2000) and Gunasekarage (2004). Maysami and Koh (2000) found that the Singapore Dollar exchange rate (against the U.S. Dollar) and the Singapore stock market are negatively related. They state that an appreciation of the Singaporean Dollar lowers imported inputs and allows the exporters in the country to be more competitive internationally. This is received as favourable news for the Singapore stock markets and, hence, positive stock returns are generated as a result. Gunasekarage (2004) found that exchange rates have no significant relationship with the Colombo stock prices. This was due to limited participation by the foreign investors. It was hypothesized that the relationship between CPI and the DSI in the long-run will be either negative or insignificant due to large price changes. However, long-run cointegrating model shows that the relationship between CPI and stock prices is positive; a possible reason for the positive relationship is that high prices of essentials might have led to an increase in the stock prices. The relationship, though, was insignificant, which confirms that large price changes in Bangladesh affect the theorized relationship. Price fluctuations in developing countries are more prevalent due to lower regulations, competition, etc. and explain why the relationship was found insignificant. The result found here is not consistent with early evidence in the literature – Lintner (1973), Oudet (1973), Bodie (1976), Nelson (1976), Mukherjee and Naka (1995) and Gunasekerage (2004) found a negative relationship between CPI and stock prices. The relationship between crude oil prices and the DSI was found to be negative in the long-run. Chen et al. (1986) found an insignificant relationship between oil prices and the NYSE. Hassan and Hisham (2010) found a negative relationship between crude oil prices and the Jordan Stock Exchange. However, it has been recently seen that crude oil prices and stock prices are positively related, ignoring the theorized relationship. For e.g., the Standard & Poor’s (S&P) 500 Index and the oil prices from 1998-2008 have demonstrated a positive relationship with each other with a correlation of 0.55 and the correlation has increased to 0.86 since 2008 (Smirnov, 2012). However, for the DSI, the relationship was negative with oil prices and consistent with the hypothesis. The relationship between money supply and stock prices was found to be negative and significant at 5% level in the long-run. As money supply is increased, it leads people to expect a tightening monetary policy in the future and hence, a higher interest and discount rate, and lower stock prices. This is consistent with Frimpong (2009), who similarly found a negative relationship between the Ghana stock prices and money supply. However, most of the studies in the literature have found a positive relationship between stock prices and money supply, such as Bulmash and Trivoli (1991), Mukherjee and Naka (1995) and Gunesekarage (2004). To check for robustness, lags of six and seven were considered. All the variables reported the same relationship with the stock prices at six lags; however, oil prices and money supply were insignificant in explaining stock price changes. Lags of seven rendered the relationship between oil and stock prices significant, but the relationship between money supply and stock prices remained insignificant. This shows that money supply and oil prices are sensitive to lag lengths and the results for these variables are not robust.
6.5 Results of Short-Run Cointegration Model
Table 6.5 below reports the short-run results of the Vector Error Correction Model. The sign and magnitude of the error correction coefficient (speed adjustment term) indicates the direction and speed of adjustment towards the long-run equilibrium path. A negative error correction coefficient implies that the model’s deviation from the long-run relation, in the absence of variation in the independent variables, is corrected by changes in the dependent variable. This confirms the existence of a long-run relationship. The size of the coefficient of the error correction term in this study implies that about 5.3% of the disequilibrium in the long-run model is corrected every month. The error term coefficient was significant at the 5% level. The short-run results indicate that DSI and interest rates positively affect the DSI at the first lag; the results from the latter lags are insignificant. CPI positively affects the stock prices at the third lag, but it was insignificant for other lags. Exchange rates, oil prices and money supply mostly affect the DSI negatively, but these variables are also statistically insignificant at most lags. Other lags were also considered for robustness and better results. Lags of four and seven revealed a negative sign for the error correction term but it was not significant. The other variables, similarly, did not result in more significant or robust estimates. Due to statistical insignificance of the variables, Impulse Response Function is employed to explain the short-run results better. Table 6.: Vector Error Correction Model Error Correction: Coefficient Standard Error t-statistics Speed of Adjustment -0.053 0.022 -2.42** A¢Ë†” DSIt-1 0.172 0.071 2.44** A¢Ë†” DSIt-2 -0.012 0.071 -0.17 A¢Ë†” DSIt-3 0.110 0.071 1.55 A¢Ë†” DSIt-4 -0.002 0.072 -0.02 A¢Ë†” IRt-1 -0.025 0.024 -1.03 A¢Ë†” IRt-2 0.028 0.024 1.15 A¢Ë†” IRt-3 -0.023 0.025 -0.92 A¢Ë†” IRt-4 0.017 0.025 0.65 A¢Ë†” ERt-1 -0.968 0.924 -1.05 A¢Ë†” ERt-2 0.093 0.935 0.10 A¢Ë†” ERt-3 -0.576 0.937 -0.61 A¢Ë†” ERt-4 -0.708 0.958 -0.74 A¢Ë†” CPIt-1 -0.172 0.883 -0.20 A¢Ë†” CPIt-2 0.247 0.895 0.28 A¢Ë†” CPIt-3 1.803 0.903 2.00** A¢Ë†” CPIt-4 -0.020 0.889 -0.02 A¢Ë†” OPt-1 0.090 0.087 1.04 A¢Ë†” OPt-2 0.074 0.088 0.84 A¢Ë†” OPt-3 -0.056 0.088 -0.62 A¢Ë†” OPt-4 -0.023 0.087 -0.26 A¢Ë†” M2t-1 -0.186 0.282 -0.66 A¢Ë†” M2t-2 -0.026 0.319 -0.08 A¢Ë†” M2t-3 0.026 0.308 0.08 A¢Ë†” M2t-4 0.045 0.259 0.17 Constant 0.001 0.015 0.05
6.6 Results of Impulse Response Functions and Variance Decompositions
The results for the Impulse Response Functions are reported in Figure A-2. Impulse Response Analysis entails analysing the incremental impact of a shock to the whole system. Hence, we are primarily concerned about the behaviour of the short-run dynamics of the model in the presence of any external shock to the system. Figure A-2 shows the effect of macroeconomic shocks on the DSI. Four years of data are forecasted and their effects plotted via Impulse Response Functions. An interest rate shock causes the DSI to fluctuate initially, however, from the 15th month onwards, the effect from the shock recedes and in the 30th month, the effect dies out. An exchange rate shock results in a big dip in the DSI; this is in contrast to the long-run relationship found. The resulting fall from the shock increases over time, but from the 6th month onwards, the gap remains steady. There are no signs of convergence here, unlike the interest rates. A consumer price index shock leads to an increase in the DSI initially, but then the relationship becomes negative, which is consistent with the study. This effect is seen to increase slowly over time. Oil prices shock lead to a large increase in the DSI initially and the effect increases with time. Money supply shock leads to a slight fall in the DSI the first month, but then increases the DSI over time, which supports the cash flow effect of money supply. Table 6.6 reports the Variance Decomposition test results. Twenty months of the model are forecasted and the results indicate that most of the variations in the DSI are explained by the DSI itself. In the 5th period, 94.58% of the variation in the DSI was explained by shocks to itself, 2.09% by ER and 2.54% by OP. At the end of the 10th period, 4.98% and 5.65% of the variations in the DSI were explained by shocks to ER and OP respectively. The results obtained from VDCs combined with IRF indicate that the macroeconomic shocks explain a minority of the forecast error variance in the DSI, though, exchange rates and oil prices have a significant influence on the stock prices. It must be noted though that most of the shocks from the macroeconomic variables are permanent and persist for a long period. Table 6.: Variance Decomposition of DSI Period Std. Error DSI IR ER CPI OP M2 1 0.099 100.000 0.000 0.000 0.000 0.000 0.000 2 0.151 99.032 0.199 0.336 0.015 0.414 0.005 3 0.188 97.691 0.129 0.595 0.011 1.553 0.020 4 0.223 96.372 0.108 1.059 0.249 2.127 0.086 5 0.253 94.578 0.085 2.098 0.443 2.541 0.255 6 0.281 92.929 0.069 2.985 0.513 3.063 0.441 7 0.305 91.460 0.064 3.680 0.511 3.617 0.667 8 0.326 89.925 0.066 4.206 0.476 4.292 1.034 9 0.346 88.560 0.068 4.613 0.442 4.988 1.328 10 0.363 87.356 0.073 4.976 0.411 5.650 1.535 11 0.380 86.291 0.078 5.239 0.382 6.299 1.711 12 0.395 85.270 0.083 5.479 0.355 6.934 1.879 13 0.410 84.255 0.085 5.732 0.330 7.546 2.053 14 0.423 83.282 0.084 5.977 0.310 8.136 2.210 15 0.436 82.348 0.082 6.211 0.294 8.714 2.351 16 0.448 81.449 0.079 6.417 0.282 9.279 2.493 17 0.459 80.579 0.076 6.605 0.273 9.833 2.633 18 0.470 79.731 0.073 6.784 0.268 10.375 2.769 19 0.481 78.910 0.069 6.949 0.265 10.908 2.898 20 0.491 78.109 0.067 7.102 0.265 11.434 3.022 To test for robustness, a VAR model was constructed in first differences, and IRF and VDC drawn from its estimates. The IRF demonstrated short-run results very similar to the IRF from the VECM model. The results for the VDC from the VECM and VAR models are also similar. A very low percentage of the changes in stock prices is explained by the variables, though exchange rate explains a high proportion of the changes in stock prices.
Conclusion
The study investigates the long-term relations between macroeconomic variables and the Dhaka stock market prices using Johansen’s methodology of multivariate cointegration analysis and Vector Error Correction Model. Variables such as interest rates, exchange rates, consumer price index, crude oil prices and money supply were used to represent the macroeconomic forces while the Dhaka Stock Exchange All-Share Index was used to represent changes in the Dhaka stock market prices. The main findings revealed that there is a long-term relationship between the stock prices and the macroeconomic variables. According to the cointegration analysis and the VECM estimated in the study, the stock prices and macroeconomic variables are related significantly and in accordance with the hypothesized relationship. The interest rates were negatively related with the stock prices, implying that investors shift away from stocks when the deposit interest rates are high and vice versa. The exchange rates are positively related with the stock prices – meaning that Taka appreciation leads to lower money inflows and, hence, lower stock prices and vice versa. The consumer price index is found to be insignificant in explaining the stock prices. This was hypothesized as large price changes in Bangladesh may render an insignificant relationship between CPI and stock prices. The relationship between crude oil prices and stock prices was found to be negative and significant. The broad money supply is negatively related with the stock prices which confirms that expectations about tightening money supply in the future leads to higher interest rates and lower stock prices. This was significant at the 5% significance level. The short-term results of the VECM revealed that around 5.3% of the disequilibrium in the long-run model is corrected every month. The DSI and interest rates were negatively related with the stock prices and at the first lag; though, the latter lags were insignificant. Exchange rates, consumer price index, oil prices and money supply were also insignificant at most lags. Since the VECM results were inconclusive, the Impulse Response Functions and Variance Decompositions were undertaken. An interest rate shock causes the DSI to fluctuate initially, but a tendency to converge to equilibrium is seen. An exchange rate shock results in a large fall in the DSI initially, but the gap remains steady afterwards. A consumer price index shock leads to an increase in the DSI initially, but then the relationship becomes negative, which is consistent with the literature. Oil prices shock lead to a large increase in the DSI initially and the effect seems to increase slowly over time. Money supply shock increases the DSI over time, which supports the corporate earnings effect. The Variance Decomposition results indicate that that the macroeconomic shocks explain a small proportion of the forecast error variance in the DSI, though, exchange rates and oil prices have a significant influence on the stock prices. Most of the shocks from the macroeconomic variables are permanent and persist for a long period. In light of the analysis made in the study, policymakers and economists in Bangladesh need to be careful when they try to influence the economy through changes in key macroeconomic variables comprising the interest rates, exchange rates, consumer prices index and money supply. Since the Bangladesh economy is small, they should also be aware of international factors such as crude oil prices as these also have a significant impact on the economy. The Bangladesh stock market is an established capital market and its development is crucial for the growth of the country. Thus, the government and policymakers should aim to influence the key macroeconomic variables in a way that ensures that stock prices are stabilized and stock markets are performing in an efficient and effective manner.
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