The Health and Wellness Survey Essay - 814 Words

By living in such a technically advanced society, I feel there are several things that account towards the decisions we make every day in regards to our health. To further describe the claim that we live in a technically advanced society, we are surrounded by images that are often directed towards health and image. Covers of magazines, famous people, and athletes are all images that most people strive to become or look like. Personally, growing up I always aspired to become a professional hockey player. Admiring my favorite hockey players in the NHL, I always perceived them to being the healthiest people on the planet. In many respects, I was not that far off. Professional athletes, pampered by the best medical care and nutritional advice,†¦show more content†¦Coaching is important, along with structured practices and skill instruction. It is times like pond hockey where there isn’t a coach there to yell at you, when you really learn to appreciate the sport and exerci se in general. Though reality struck, and my professional hockey dreams were tainted, maintaining good health is still something I strive to achieve for my well being, and performance at the level of hockey that I do play still today. On the other hand, in terms of the Hollywood hot-pink image that the media delivers to us, people generally feel pressured to look or live like famous people. Though it does not apply to me, or most people I know, there is always a minimal amount of influence or pressure that can affect my decisions with health. I believe that the people that surround us play some sort of role on the decisions we make. If a friend of mine is getting in really good shape and taking a protein supplement, my questioning of what type of protein he’s taking is proof that I strive to perform, or train like him. Another example is how advertisements can use beautiful people to claim that they used their product in order to gain customers whom want to appear like that b eautiful person. These few examples are things that have impacted my mental model of health and decisions that I make about health. Reflecting on the wellness and health survey, many of my answers to questions were answered based on experience. For example, IShow MoreRelatedWellness Plan1575 Words   |  7 PagesDeveloping an Organizational Employee Wellness Plan Sample Outline This outline is intended to be a starting point to guide state agencies in developing their own agency-specific wellness plan. The provided outline addresses specific points to consider in developing the agency plan, but each agency will need to add additional content to define adequately what and how each activity will be implemented in the agency. 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The Macroeconomic Forces And Stock Prices Finance Essay - Free Essay Example

Sample details Pages: 28 Words: 8501 Downloads: 3 Date added: 2017/06/26 Category Economics Essay Type Analytical essay Did you like this example? 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). Don’t waste time! Our writers will create an original "The Macroeconomic Forces And Stock Prices Finance Essay" essay for you Create order 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 f rom 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 dissertat ion 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 5 0% 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 governmen t-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 h as 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. Gà ¼nsel 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 State s, 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 Johansens (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 Johansens 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 f ocused 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 m arket. Mookerjee and Yu (1997) and Maysami and Koh (2000) studied the Singapore stock market and found that changes in Singapores 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 pri ces 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 (1 987), 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 Sc hwert, 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 act ivities 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. Sell in (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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt is the error term in the model. ÃÆ'Ã… ½Ãƒ ¢Ã¢â€š ¬Ã¢â€ž ¢0 represents the constant term in the model and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²1, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²2, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²3, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²4 and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²5 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 dÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¥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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ ´ refers to the existence of a trend and ÃÆ' Ãƒâ€šÃ‚ , to presence of unit root. The ADF test is carried out for two models with constant and trend, and constant with no trend (ÃÆ'Ã… ½Ãƒâ€šÃ‚ ´=0). The lag of dependent variable is included to account for autocorrelation. For the Phillips-Perron unit root tests, however ÃÆ'Ã… ½Ãƒâ€šÃ‚ ³j=0, and the PP test incorporates an automatic correction to the test on ÃÆ' Ãƒâ€šÃ‚ =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 combination  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, all owing 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 ÃÆ'Ã… ½Ãƒâ€¹Ã…“i is k x k. This can be manipulated and written as: where ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  Yt = Yt Yt-1, and ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒâ€šÃ‚ k = ÃÆ'Ã… ½Ãƒâ€¹Ã…“1 + ÃÆ'Ã… ½Ãƒâ€¹Ã…“2 Ik. If the rank of ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒâ€šÃ‚ k is zero, then there are no cointegrating vectors. In the presence of cointegration, ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒâ€šÃ‚ k has rank r ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤ k 1, and then, ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒâ€šÃ‚ k = ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±ÃƒÆ'Ã… ½Ãƒâ€šÃ‚ ², where ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± is k x r and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² is r x k. Then, this can be written as: where ÃÆ'Ã… ½Ãƒâ€šÃ‚ ² is the cointegrating matrix, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ²Yt represents the r linear combination and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ± represents the speed of adjustment towards the long-run equilibrium relationship. In o rder to perform Johansen tests, we need to compute the k eigenvalues of , which is the estimate of ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒâ€šÃ‚ 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 + ÃÆ'Ã… ½Ãƒâ€šÃ‚ »j) = 0 for j=r+1,ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦,k. Test for H0 : r ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤ 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 ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤ 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, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±1, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±2, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±3, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±4, ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±5 and ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±6 represent short-run elasticities, ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt-1 is the error correction term, with its coefficient ÃÆ' Ãƒ ¢Ã¢â€š ¬Ã‚  , 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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±1, ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦. ÃÆ'Ã… ½Ãƒâ€šÃ‚ ±6) in the dependent variable (Frimpong, 2009). The optimal num ber 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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ ¼ is the vector of intercepts and ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt ~ IN (0,). The IRF of a shock to variable, for instance, IR on variable DSI after k periods as: where ÃÆ'Ã… ½Ãƒâ€šÃ‚ µIR,t is the vector ÃÆ'Ã… ½Ãƒâ€šÃ‚ µt 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,ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦t). The IRF shows impulse responses of the select variable in the VECM system in regards to the time paths of the variables 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 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  DSI -12.83 (0)*** -12.81 (0)*** -12.85 (3)*** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  IR -5.78 (2)*** -6.03 (2)*** -14.43 (8)*** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  ER -4.07 (7)*** -4.06 (7)*** -13.00 (5)*** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  CPI -4.39 (10)*** -4.69 (10)*** -10.30 (16)*** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  OP -11.84 (0)*** -11.84 (0)*** -11.82 (1)*** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  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 th e 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 repor ted. 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] ÃÆ'Ã… ½Ãƒâ€šÃ‚ »max Statistic 95% Critical Value [Max.] 99% Critical Value [Max.] ÃÆ'Ã… ½Ãƒâ€šÃ‚ »trace Statistic 95% Critical Value [Trace] 99% Critical Value [Trace] r=0 41.58 39.37 45.10 104.51 94.15 103.18 rÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤1 32.59 33.46 38.77 62.93* 68.52 76.07 rÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤2 14.57 27.07 32.24 30.35 47.21 54.46 rÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤3 10.35 20.97 25.52 15.78 29.68 35.65 rÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤4 5.36 14.07 18.63 5.42 15.41 20.04 rÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ »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 ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤ 1, the ÃÆ'Ã… ½Ãƒâ€šÃ‚ »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 th at is used to determine the number of cointegrating vectors is ÃÆ'Ã… ½Ãƒâ€šÃ‚ »max. The ÃÆ'Ã… ½Ãƒâ€šÃ‚ »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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ »trace statistic, for r ÃÆ' ¢Ãƒ ¢Ã¢â€š ¬Ã‚ °Ãƒâ€šÃ‚ ¤ 1 and other values of r, the ÃÆ'Ã… ½Ãƒâ€šÃ‚ »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 ÃÆ'Ã… ½Ãƒâ€šÃ‚ »trace and ÃÆ'Ã… ½Ãƒâ€šÃ‚ »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 Poors (SP) 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 Gunesekarag e (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 models 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** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  DSIt-1 0.172 0.071 2.44** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  DSIt-2 -0.012 0.071 -0.17 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  DSIt-3 0.110 0.071 1.55 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  DSIt-4 -0.002 0.072 -0.02 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  IRt-1 -0.025 0.024 -1.03 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  IRt-2 0.028 0.024 1.15 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  IRt-3 -0.023 0.025 -0.92 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  IRt-4 0.017 0.025 0.65 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  ERt-1 -0.968 0.924 -1.05 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  ERt-2 0.093 0.935 0.10 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  ERt-3 -0.576 0.937 -0.61 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  ERt-4 -0.708 0.958 -0.74 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  CPIt-1 -0.172 0.883 -0.20 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  CPIt-2 0.247 0.895 0.28 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  CPIt-3 1.803 0.903 2.00** ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  CPIt-4 -0.020 0.889 -0.02 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  OPt-1 0.090 0.087 1.04 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  OPt-2 0.074 0.08 8 0.84 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  OPt-3 -0.056 0.088 -0.62 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  OPt-4 -0.023 0.087 -0.26 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  M2t-1 -0.186 0.282 -0.66 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  M2t-2 -0.026 0.319 -0.08 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  M2t-3 0.026 0.308 0.08 ÃÆ' ¢Ãƒâ€¹Ã¢â‚¬  Ãƒ ¢Ã¢â€š ¬Ã‚  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 incre ase 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 varia nce 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 4.9 76 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 Johansens 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 rate s 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. Ex change 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. References Bespoke Investment Group, 2010, 2010 Country Stock Market Performance, https://www.bespokeinvest.com/thinkbig/2010/9/3/2010-country-stock-market-performance.html, last accessed June 20, 2012. Bloomberg, 1997, Bangladesh Stock Market Lures Investors Hungry for Next Vietnam, retrieved from https://www.bloomberg.com/apps/news?pid=newsarchivesid=ajYCq0jTs6Forefer=india, last accessed 10th June, 2012 Bodhurta, N. J., Cho, C. D. and Senbet, W. L., 1989, Economic Forces and the Stock Market: An International Perspective, The Global Finance Journal 1(1) pp. 21-46 Bodie, Z., 1976, Common stocks as a hedge against inflation, Journal of Finance 31, pp. 459-470 Brown S., J. and Otsuki, T., 1990, Macroeconomic factors and the Japanese equity markets: The CAPMD Project, Japanese Capital Markets, Harper and Row: New York Bulmash, S. B. and Trivoli, W. G., 1991, Time-lagged interactions between stock prices and selected economic variables, Journal of Portfolio Management pp. 61-67 Cheng, A. C. S., 1995, The UK Stock Market and Economic Factors: A New Approach, Journal of Business Finance and Accounting 22(1), pp. 129-142 Chen, N. F., Roll, T. and Ross, A. S., 1986, Economic forces and the stock market, Journal of Business 59, 383-403. Cheung, Y-W, and Lai, S.K., 1999, Macroeconomic determinants of long-term stock market comovements among major EMS countries, Applied Financial Economics, 9(1), pp. 73-85 DeFina, R. H., 1991, Does inflation depress the stock market?, Business Review, Federal Reserve Bank of Philadelphia, 3-12 Diacogiannis, G. P., 1986, Arbitrage Pricing Model: A Critical Examination of its Empirical Applicability for the London Stock Exchange, Journal of Business Finance and Accounting 13 (4), pp. 489-504 Engle, R. F. and Granger, C. W. J., 1987, Co-integration and Error Correction: Representation, Estimation, and Testing,  Econometrica, 55(2), pp. 251-76 Fama, E. F., 1990, Stock returns, expected returns and real acti vity, Journal of Finance 45, pp. 1089-1108 Fama, F. E. and Gibbons, M., 1984, A comparison of inflation forecasts, Journal of Monetary Economics 13, pp. 327-248 Fama, F. E. and Schwert, W. G., 1977, Asset Returns and Inflation, Journal of Financial Economics 5, pp. 115-146 Flannery, J. M. and Protopapadakis, A. A., 2002, Macroeconomic Factor do influence aggregate stock returns, The Review of Financial Studies 15(3), pp. 751-782 Frimpong, M. J., 2009, Economic Forces and the Stock Market in a Developing Economy: Cointegration Evidence from Ghana, European Journal of Economics, Finance and Administrative Sciences 16, pp. 123-135 Goldsmith, R. W., 1969, Financial Structure and Development, New Haven: Yale University Press Gunasekarage, A., Pisedtasalasai, A. and Power, M. D., 2004, Macro-economic influences on the stock market: Evidence from an Emerging Market in South Asia, Journal of Emerging Market Finance 3(3) 285-304. Gà ¼nsel, N. and Çukur, S., 2007, Th e Effects of Macroeconomic Factors on the London Stock Returns: A Sectoral Approach, International Research Journal of Finance and Economics 10, pp. 140-152 Gurley, J. G. and Shaw, E. S., 1955, Financial Aspects of Economic Development, American Economic Review, 45, pp. 515-538 Gurley, J. G. and Shaw, E. S., 1960, Money in a Theory of Finance, Brookings Institution, Washington D.C. Gurley, J. G. and Shaw, E. S., 1967, Financial Development and Economic Development, Economic Development and Cultural Change 15(3), pp. 257-268 Harvey, C. R., 1995a, Predictable risk and returns in emerging markets, Review of Financial Studies 8, 773816 Hassan, M. G. and Hisham, A., 2010, Can Macroeconomic Factors Explain Equity Returns in the Long Run? The Case of Jordan, MPRA Paper 22713, University Library of Munich, Germany Hume, D., 1752, Political Discourses, Edinburgh: A. Kincaid A. Donaldson Johansen, S., 1991, Estimation and hypothesis testing of cointegrating vectors in Ga ussian vector autoregressive models, Econometrica 59, 1551-80 LeRoy, S. and Porter, R. D., 1981, The Present-Value Relation: Tests based on Implied Variance Bounds, Econometrica 49, pp. 555-574 Levine, R. and Zervos, S., 1996, Stock Market Development and Long-Run Growth, The World Bank Economic Review, 10(2), pp. 323-339 Levine, R., 2004, Finance and Growth: Theory and Evidence, National Bureau of Economic Research, Working Paper 10766 Lintner, J., 1973, Inflation and common stock prices in a cyclical context, National Bureau of Economic Research Annual Report Ma, C. K. and Kao, G.W., 1990, On Exchange Rate Changes and Stock Price Reactions, Journal of Business Finance and Accounting 17, pp. 441-449 Maysami, R. C. and Koh, T. S., 2000, A Vector Error Correction Model of the Singapore stock market. International Review of Economics and Finance 9, pp. 79-96 McKinnon, R.I., 1973, Money and Capital in Economic Development, Brookings Institution, Washington D.C. Mobarek, A. and Mollah, S. A., 2005, The General Determinants of Share Returns: An Empirical Investigation on the Dhaka Stock Exchange, Review of Pacific Basin Financial Markets and Policies 8(4), pp. 593-612 Mookerjee, R. and Yu, Q., 1997, Macroeconomic variables and stock prices in a small open economy: The case of Singapore, Pacific-Basin Finance Journal 5, pp. 377-388 Mukherjee, K. T. and Naka, A., 1995, Dynamic Relations between Macroeconomic Variables and the Japanese Stock Market: An Application of a Vector Error Correction Model, The Journal of Financial Research 18 (2) 223-237. Nasseh, A. and Strauss, J., 2000, Stock prices and domestic and international macroeconomic activity: A cointegration approach, The Quarterly Review of Economic

How to Deal With Someone Threatening You With a Weapon

If you come face-to-face with someone threatening you with a gun, a knife, or any  other weapon, there are steps that you can take to make the situation less dangerous. Some are general, such as remaining calm, while others are more specific, including making eye contact with the assailant. Stay Calm The most important thing to do is probably one of the most difficult: remain calm. Remember that you will need all your wits to increase your chances of gaining control of the situation. If you are hysterical, it is unlikely that you will be able to maintain mental clarity. Its likely that the person holding the weapon wont be calm, and if you exhibit high anxiety, theres a good chance it will increase the assailants anxiety. Screaming can be particularly dangerous in this situation because it can make the assailant panicky or angry. Remaining calm can have the opposite effect. Make Eye Contact Many criminals who pull weapons on people can mentally dehumanize their victims. Making eye contact helps them view you more as a human being than a disposable object. Assess Assailants Goal Certain scenarios can result in having a weapon pulled on you. If the purpose is to kill you, then you would likely already be dead. Mass killers enter schools, places of employment, malls, etc., and start shooting, either randomly or with predetermined goals such as selecting a specific group of people. Most criminals who point a gun dont want to shoot it. Their goal might be to rob you,  steal a car for a joyride, hold you hostage to get out of a crime gone bad, or kidnap you for ransom. Usually in these situations the weapon is being used to control you, not to kill you. Follow Instructions Calmly Follow the instructions of the person with the weapon but be sure to communicate what youre about to do. For example, if they ask for your wallet, before reaching into your purse or pocket tell them what you are about to do. Then do it slowly and calmly. Dont make it appear that you have another intention than to do what you told them you were going to do. Dont Challenge If you have always wanted to be a hero, now is not the time. Not only could it cost you your life, but it also could harm others. Being physically or verbally aggressive to the person with the weapon probably will ignite the situation. Trying to grab the weapon will likely get you killed or seriously injured. Resisting their instructions will not only anger them, but it also will force them to show who is in charge. What you want to communicate is that you plan to cooperate. Talk Carefully If you have an opportunity to engage the assailant in light conversation, try to direct the chat so they talk about themselves and subtly feed their egos by making them feel that what they say is intelligent and has merit. Not only are you trying to connect with their human side, but you also want them to believe that you dont feel superior to them. If you get into a conversation, keep your voice low and your sentences short. Ask questions and avoid talking too much about yourself. They want you as their audience, not the other way around. If there is an opportunity to inject something short and personal, do it. For example, if they name the high school they attended, ask them if they knew your friend who went to the same school, even if that friend doesnt exist. If a controversial subject arises, such as politics or religion, this is not the time to get into a debate. Appear interested in their opinions. If asked, tell them that you see they know a lot about it and you appreciate their point of view. Note Assailants Appearance Notice what the person holding the weapon looks like, but dont stare. Rather than try to figure out their weight or height, pay attention to distinguishable things, such as tattoos, gang symbols, birthmarks, moles, and scars. Evaluate Hostage Situations Hostage situations have different dynamics from armed robberies. If, for example, you work at a bank where a robbery attempt has gone bad and you are being held hostage, do as you are told and remain quiet. Your goal should be to be invisible to the person holding the gun. If you see a chance to escape, do it, but only if the probability of success is high. If the assailant is negotiating with the authorities and you are chosen as a hostage to be released, go. It might be hard to abandon your co-workers or friends, but staying behind wont improve their situation. It will only anger and frustrate the person telling you to go. Remember that in hostage situations the police are probably making plans for your rescue, and your best chance of survival is not to be the main focus of the assailant. Try to position yourself as far from the assailant as possible. If the criminal is talking with a hostage negotiator and talks breaks down, the next step might be for sharpshooters to take aim. Avoid being grabbed as a human shield or inadvertently shot by a flying bullet. Keeping distant from the guy holding the gun is the best way to go. When Not to Cooperate There is no certainty that any of these suggestions will keep you alive. Relying on your common sense and instincts will ultimately be your best chance of survival. However, doing everything a criminal tells you might not be the best approach, though there is no instruction book to follow. Carjackings can be particularly perilous if the carjacker insists that you remain in the vehicle or instructs you to drive. Any diversion that you can create to avoid this situation could increase your chances of survival. Carjacking victims have pretended to faint outside the car. Others who have been forced to drive have driven into poles or parked cars in well-populated areas, But every situation is different, and you must rely on yourself to assess the situation and find the best way out. After the Ordeal If the ordeal ends before the police arrive, call 9-1-1 as soon as possible. Notifying law enforcement quickly will increase the chance of apprehending the suspect and preventing future victims. When they ask questions, provide as many details as you can and be available for follow-up interviews.

Themes of Conformity in Society - 561 Words

As readers, we see how the analysis of conflictive themes has been taking over literature scenarios. When reading between the lines of certain books we can perceive themes such as conformity and rebellion. Mostly these themes can be seen in writings from times of social upheaval. According to The Oxford Dictionary, the word conformity means â€Å"behavior in accordance with socially accepted conventions or standards†, while rebellion means â€Å"the action or process of resisting authority, control, or convention.† These terms create a constant battle among societies. A certain amount of conformity needs to exist in life in order to avoid disorder. This is the reason society has laws, rules and expectations. Literature represents life, and these themes can be found at the root of many literary works. Conformity can also be considered as silence. This silence is based on the restrictions of real feelings and thoughts in some literary pieces. During the sixteenth centur y, the emphasis on conformity was at its height. Works of literature from this time period have often reflected a fascination and frustration with this particular characteristic. People do not want to be restricted in terms of expressing opinions and developing certain activities. When facing oppression, denial of their freedom, they rebel, and that contributes to the part of the process of change in society. Not all kinds of rebellions are rationally fundamental, but when they are, it can transform society for theShow MoreRelatedThe Movie Cool Hand Luke 1164 Words   |  5 Pagesprisoners. Unfortunately at the end, Luke ends up getting shot and killed. After viewing the movie â€Å"Cool Hand Luke† there were three main psychological themes that were very prevalent on the basis of the film. 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The Effects of Social Norms on Society Free Essays

Dr. Kenneth Hoffman Sociology 200-302 2 January 2013 The Effects of Social Norms on Society In a world where routine is dominant and change is difficult to adhere by, social norms have become major assets for a smoothly functioning day. With the experiment of breaking normative behavior I wanted to do something subtle yet startling. We will write a custom essay sample on The Effects of Social Norms on Society or any similar topic only for you Order Now Sometimes the smallest changes in behavior receive the toughest adjustments. First impressions have a strong influence on a person’s relationship with someone else. With that being said, I decided to change the way I went about greeting people. The opening greeting of shaking hands and smiling has become so common it has lost value and become routine. The subtle change I made to my greeting was that I decided to only say my name in a monotone voice and shake hands with the wrong hand, leading to responses of surprise, unease, and sometimes anger. With most people being right handed, I have made the assumption that this could be why the common greeting is to shake with one’s right hand. The â€Å"normal† greeting is to smile, say hello, and shake with your right hand. People have taken what use to be excitement to seeing each other to going through common motions repeated multiple times. This greeting is a sign that one may have come from a proper upbringing and family, possibly middle to upper class. It is also taken as a common sign of acceptance to strangers meeting for the first time. My sample size of people ranged from people of all ages and relationships to me. I decided I would greet with a plain face and state my name and reach out with my left hand with all introductions. People who did not know me well believed I was unpleasant and felt awkward. My new greeting was taken with surprise, as people who know me well believed I was in a bad mood or having a bad day. If I don’t do the norm I’m considered upset and negative. From the experiment I learned that people commonly receive me as joyful and energetic. In a way my own personal norm has evolved into me being upbeat whether my day has been good or bad. All people react to different stimuli in their own way. With that being said, I was surprised to find that out of the 20+ people I greeted against the norm, only one gave my greeting a â€Å"normal† response without interruption. The lone person was a 12 year old boy I see regularly and he just continued what he was doing without a hint of awkwardness. The older people would teach and try to correct what they took as my â€Å"ignorance† toward a common greeting. A few of these older experimentees actually refused to shake my hand unless it was a right handed shake. People around the same age as me were awkward with a sarcastic manner, laughed, or just went ahead and shook my left hand after a moment of hesitation. With all the immediate responses having large variance, the most correlated response was that people thought there was something wrong with my well-being and tried to cheer me up. The overall response to the experiment ended up being quite humorous. It also opened my eyes to how much the people in my life care about me. While I expected the awkward moments, I expected more people to just go along with it and not be startled. Even the slightest changes in one’s actions can affect the response and mood of how people interact with one another. In this case, it was subtle yet changed the entire complexion of the exchanges between the people I intercepted. With responses ranging from laughter to confusion, the smallest changes in normative behavior can have most astonishing results. How to cite The Effects of Social Norms on Society, Papers

Proposal For Product Innovation

Question: Describe about for Proposal For Product Innovation? Answer: Introduction: According to Stania, (2013) implementation of innovation in existing products not only improves its quality but also provides organization with new business opportunities. However, innovation can be implemented at two stages. Generally the organizations bring innovation in their existing products continuously in order to enhance efficiency of these products. However such innovations are cost effective to the organizations as bringing gradual changes in a product has less negative impacts on the number of sales (Ford and Paladino, 2013). On other hand, Lee et al. (2013) opines that bringing disruptive can lead to significant financial losses for an organization. According to Jackson (2013) chances of product failure is high in case of disruptive innovations. However, often the companies launch new products to attain competitive advantages in market. The current proposal deals with designing Driverless cars. Although Google already designed a prototype of automated cars, driverless veh icles are not commercially available in market. Apart from this, driverless vehicles are completely new product in automobile industry and these cars can be considered as example of disruptive innovation. Description: Driverless cars will not require any human input for navigating. The car will contain different applications of network technology such as radar, GPS and computer vision. In the proposed cars sensors will be used for identifying the obstacles. However the sensors will also be used to identify the track for reaching destination. The car will use GPS technology to create a map for its surrounding areas (Anderson et al., 2015). However, the map will be updated regularly. Driverless vehicles can benefit its users in various ways. It can reduce the chances of accidents and traffic congestion. Use of driverless cars can reduce the chances of car theft also. Justification of Innovation: As this car will sense obstacles on its way, the chances of accidents will reduce significantly (Miller, 2014). On other hand, if any obstacle comes suddenly in front of the car, reaction time will be lower than that required by human driver. Thus the chances of accidents will reduce. Sensors in autonomous cars will direct to maintain minimum safety distance from other cars. It indicates that increase in the number of fully autonomous cars will reduce traffic congestion. As the maximum speed will be limited in driverless cars, number of accidents due to reckless driving will reduce. Apart from all these benefits, parking efficiency of cars will increase. It will provide better riding experiences to the passengers. However, functioning of autonomous cars depend on the quality of software used in design. As it uses Wi-Fi and Bluetooth networks, incidents related with privacy loss can increase. Technology requirement: Driverless cars will use sensors for getting information regarding the environment surrounding it. The cars will contain GPS technology to get information about its destination. However, the prototype car of Google used laser technology for mapping the environment (Miller, 2014). In the proposed car, laser technology can also be used for creating map. As the car will not use any human input, different applications of communication technology will be used in the design so that it can interact with other cars on road. In the proposed car, short range communication technologies such as Bluetooth or Wi-Fi will be used. Target market: It is expected that the driverless cars will be used extensively by transportation service providers (Lee et al.2013)). However these cars can also be used for transportation of products. Use of automated cars can be effective for testing the condition of a road. However use of driverless cars is expected to be high among visually impaired and aged persons as it does not require any input from passengers. Competitive advantages: As the need of fully autonomous cars is increasing, organizations in automobile industry are focusing more on the research and development of driverless cars. Most of these companies are expected to launch driverless cars in market by within a few years (Miller, 2014).as the demand of this car is high; launching such products earlier than other organizations can be advantageous for an organization. However, the organizations can also focus on keeping the prices low for attaining competitive advantage. Viability of products: Design of the driverless cars is based on the use if different technologies such as GPS, laser, Bluetooth, WI-Fi etc. However, the applications of these technologies are easily available in market. It indicates that manufacturing of driverless cars will be cost effective. On other hand, it is expected that autonomous vehicles will account for almost 75% in 2040 (Ieee.org, 2015). It indicates that the number of sale and revenue will be high. Conclusion: Although the analysis indicates that manufacturing of automated cars will be profitable, more research is required on its design to enhance the efficiency. It is also important that the proposed car will be user friendly. The design requires to be modified to keep the manufacturing cost lower. References: Ford, D. and Paladino, A. (2013). Enabling Innovation through Strategic Synergies. Journal of Product Innovation Management, 30(6), pp.1058-1072. Ieee.org, (2015). IEEE News Releases. [online] Available at: https://www.ieee.org/about/news/2012/5september_2_2012.html [Accessed 9 Mar. 2015]. Jackson, P. (2013). Integrated synoptic surveys using an autonomous underwater vehicle and manned boats. [Reston, Va.]: U.S. Department of the Interior, U.S. Geological Survey. Lee, S., Yoon, K. and Lee, J. (2013). Frontiers of intelligent autonomous systems. Berlin: Springer. Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C. and Oluwatola, O. (2015). Autonomous Vehicle Technology. [online] Available at: https://www.rand.org/content/dam/rand/pubs/research_reports/RR400/RR443-1/RAND_RR443-1.pdf [Accessed 9 Mar. 2015]. Miller, J. (2014). Google's driverless cars designed to exceed speed limit. [online] Available at: https://www.bbc.com/news/technology-28851996 [Accessed 9 Mar. 2015]. Stania, M. (2013). Mechatronics Systems of Autonomous Transport Vehicle. SSP, 198, pp.96-101.

Dream School Essay Example For Students

Dream School Essay I am going to write my paper on my vision of the perfect school. In describingmy dream school I will explain how it will operate, involve special educationstudents and prepare students for life after high school. A strong site basedadministration is not only my preference, but is also the system that is mosteffective. I will first identify the individual positions of my dream school andthen define their roles. I will call my school Dream High School. Dream Highwill have a traditional administrative chain, consisting of a principal and avice-principal. There would be a dean of discipline for each grade, and a FAC(Faculty Advisory Committee) made up of two teacher representatives from eachgrade; one person from the classified staff and two from the parent teacherassociation. There will also be one student from each grade on the FAC. With theassistance of the two deans of discipline, the student body would establish aPeer Court. This would consist of seven members elected from the stude nt body atlarge. Dream Supreme Court would be made up of the principal, vice principle,both deans and two teacher representatives. The principal would be responsible,primarily, for the business management of the school. Budgets, building,schedules, transportation and personnel issues fall into the area covered by theprincipal. The vice-principal would assist the principal in all areas. Inaddition, the VP must deal with staff development concerns. He or she willfacilitate the mentoring program, manage the training schedules and ensure thatall staff is allowed to attend seminars each year to further their expertise. All discipline issues not covered by the PEER COURT system-suspension,expulsion, safety- will be dealt with by the deans of discipline. The PEER COURTwould be responsible for holding weekly sessions to address routine concerns. Students accused of violating a rule will come before the PEER COURT for ahearing. In my school the accused will have an opportunity to present theirside, witnesses may be called and may testify. The PEER COURT will thendetermine guilt and impose consequences. The accused may appeal to the deans ofdiscipline. The VP and deans must approve of the imposed consequences. The DreamSupreme Court must automatically intervene when issues of safety, security, andor state law are involved. Otherwise, the decisions of the peer court are final. The Parent Teacher Association is an integral part of the running of my school. Formally, they are represented on the FAC. It is the representativesresponsibility to bring to light concerns, issues and desires of the community. It is an absolute mandate that the staff be responsive to community concerns. The school, primarily through the PTA, must actively seek not only their in-putregarding concerns, but also their needs and wants. The expertise of everyone inthe site community must be sought to enhance the overall effectiveness. Eachprofessional at Dream High will be assigned to his or her best area ofexpertise. The staff would have the FAC to deal with their issues while thestudents would have representatives on the FAC as well as their own PEER COURT. I believe that when those involved have direct control over their lives, theresults will benefit all involved. Giving control to the local community,students and staff members will increase the feeling of belonging and investmentof all who come in contact with the school. DREAM SCHOOL ISSUE: SPECIAL EDDISCIPLINE Dream High will include its Special Ed students with the mainstreampopulation with regards to discipline. Since federal law interjects formaldiscipline guidelines, someone must be placed in charge to over see thisprocess. The case manager (Special Ed teacher) for each student will act as thelegal advocate for the Special ED student on his or her caseload. Thisutilization of the teacher will accomplish several things: First, federal andstate laws will be followed. It is simply not reasonable to expect all staff,FAC members and student PEER COURT officers to be familiar with all legalstatues. Therefore, the case manager will guide all involved through thediscipline process. Nex t is the possible communication concern. Many Special Edstudents are dramatically delayed in the communication and social skill areas. Having the case manager present will alleviate some of this concern. Thestudent(s) will certainly feel much more comfortable with a known spokesperson. The student will be able to voice his or her case through the advocate. IDEA mandates that schools discipline Special ED students as mainstream studentsare treated. Therefore, it will be required that ALL students be disciplined inaccordance with the Dream High School governance system. CURRICULUM DreamHighs curriculum will be based upon the fact that it is a two year (11-12)school. The Base curriculum will be made up of the following core classes: MathEnglish Social Studies Science Physical Education Humanities In addition to theabove-mentioned classes Dream High will have an interactive agreement with thelocal university to provide college level math, English and science courses tothe students. These courses will be offered during the evenings and weekends soas to allow students the opportunity to enjoy a variety of classes both duringthe school day and outside of the school day if the student elects. Thealternative classes offered will include: Welding Auto-Shop Cooking Wood WorkingCarpentry Computer Technology Cosmetology Drama For all of the above m entionedclasses industry leaders will be called upon to help develop the curriculum andcourse content. It only makes sense that the people that are working on a day today basis in the fields that apply to these courses would be better suited toestablish the levels of importance of course content. An engineer working at IBMfor example would be working not only on the Computer Technology class but onthe math curriculum as well. Construction firms and local architects would beable to build a class that would target what skills are most important for astudent that is interested in working in their field. Skilled Chefs would beworking with our curriculum design team to identify the basics that anapprentice Chef would need to know when entering the workplace. In short, it isimportant that the people with the most expertise in the selected areas shouldhave input on what course content should be. Because technology and techniqueschange on a rapid basis the curriculum would be reviewed on a bi-annual basisand innovations would be implemented at that time. Specialty Area-Music Themusic program in most high schools is probably one of the most under appreciatedprograms in general. Administrators tend to believe that these programs do notneed the funding or support that athletics or core classes need but often thisis not the case. It has been proven through numerous studies that students witha musical background perform better in school, have better study habits andgrasp abstract thinking better that those students that are not involved in amusic program. It is for this reason that Dream High will place great value andpride in both the music program and the athletic program. The following classesin music will be offered: Music Theory Music Appreciation Concert Band ConcertChoir Jazz Band Jazz Choir Students entering their first year at the school willbe required to take music theory and music appreciation regardless if they playan instrument. This is to provide the student with a basic knowledge of musicand its history so that the student may have a well rounded education. Bandand Choir students will be offered lessons free of charge from an agreement madewith the local music store. Experienced staff and instructors are compensatedfor their time by having students promote music throughout the community byvisiting local elementary schools, civic events, and entertaining the residentsof local nursing homes. Students will also experience seminars given throughoutthe year by experienced professional musicians so that they better understandthe demands and rewards of a career in music Individual Educational ProgramsStudents at Dream High will be participating in academic classes until the 10thgrade when they would be tested in academics, take an interest inventory test,and complete a comprehensive Individualized Education Program (IEP). Studentswith academic abilities who wished to continue on a college prep schedule,would do so. Those who are not as stron g academically would address possiblevocations that would appear in the Interest Inventory Test. At that point, eachstudent would develop a comprehensive IEP. Most students have a good idea ifthey want to go to college or not and what they are good at. Most have thoughtabout, and may know, what they want to do when they are grown-up. Most of them,however, do not know whats involved in achieving their goals. An IEP is veryhelpful in developing a plan of attack. It gives the student manageablesteps to take to be successful. Having goals helps keep students motivated. Community Service Experience EssayThere are fewer discipline issues and their transition into adulthood is mucheasier. The IEPs would focus on the present levels of achievement,social-emotional adaptation, future goals and objectives as well as address theseven intelligences. The seven intelligences are; interpersonalrelationships, introspective abilities, spatial (visualization), athletic,musical, and verbal or mathematical intelligences. The principle would beHow are you smart, not how smart are you? At my school I would endorsethese intelligences to where students are in a state of flow, thatlearning lane that is challenging, but not so much as to cause anxiety. Studentswould be learning within their strong suit and therefore, would be motivated andsuccessful, requiring very little direct instruction. Students in their junioryear, who did not plan to go to college, would be placed in a vocationaltraining program and in their senior year, they would work as apprentices withinthe work force of their chosen area. Instruction Business people would be actingas teachers in the work force, with teacher support when needed. Businesspeople are not trained educators, and therefore, tend to fall into incompleteteaching naturally. They present the task with brief directions and then,usually, walk away, leaving the student to rely and hone his problem solvingskills based on his or her short term memory and seven intelligences. Thistype of learning is what is lacking in the schools today, because students donot have a personal interest in the topic and very little to figure out, theyare simply required to use rote memorization. Special Program Preparation forLife: A transition program from school to community Abstract of Program:Preparation for Life would be a program that is designed to prepare individualswith special needs for the transition from school life to adult life. Throughclasses and experiences, individuals with special needs will gain insight intothe areas o f work, leisure activities, adult living realities, and skills neededto be independent in the community. The program has 3 levels, based on the gradeand need of the student. In the following composition, the 3 levels of theprogram will be described. Level One: The first level would be for 9th and 10thgraders. During this level, students receive much of their instruction in highschool classes that are geared towards the individual student needs and goals. Instruction during the 9th and 10th year is intended to prepare students for thenext level. Students at this level will be taken into the community to learnaspects of transition, including; grocery shopping, going to the doctor, goingto the post office, public/private transportation, etc. Students will have theopportunity to be paid for school-based work experiences, to build andstrengthen positive working skills. Academic training during this level would bebased on practical needs of individual students. If a student is not ready tomove on to the next level, then that student will stay in level one until he/sheis ready to move on. Level Two: Level two would be made up of 11th and 12thgraders. At this level, students spend more time in the community. Most studentsspend ? of their school day in classes and the other ? in the community,usually being paid to work at a business, in a group or individually. Classes atthe school would be based on job seeking skills (where to look for a job , how tocontact job sites, how to keep a job, etc.), how to fill out variousapplications, interview skills, how to be a good employee, etc. Students wouldalso become more independent in the community. When students have successfullycompleted this level of the program they will go through the graduation processto signify the completion of high school. Based on what the need is, somestudents will not go onto level three, they will stay in level two until their22nd birthday. Those students will not go through the graduation ceremony untilthat point. If a student moves on, he/she goes to level three. Level Three: Themain focus of this level is independence. Once students have reached this level,they no longer attend high school classes. Students are hooked up with adultsupport programs, such as Vocational Rehabilitation, JOIN, OARC, etc. The schoolwould still support individuals, but the support would come from as job/lifecoaching in the community. At this time the students would be giv en the optionto work full time for pay. Living arrangements may include; at home with familyor in assisted living situations. It would be expected that the student displaya certain amount of responsibility by showing up to work on time. Once a studentturns 22 he/she will have completed level three. At this point the individualshould be ready to be an active part of the community. A program such asPreparation for Life would be designed to prepare individuals with variousspecial needs for a life after school. The program would help to ease thetransition from school life to adult life, a successful transition based onindividual needs and goals.