Technical Production in Film and Television - 1039 Words

Technical Production in Film Television Technical production in film and television is a broad category that is best discussed by breaking it down into the major elements: Composition mise en scÃÆ' ¨ne; editing; sound effects music, camera work, lighting color, and props locations. Most of these categorical elements have been more extensively developed in film than in television, primarily because television has tended to be filmed on sets and because films tend to have larger budgets and more flexible production. As a result of the natural constrictions imposed by television production, popular techniques have been slower to make their way to television. Sound effects and music have played an important part in television production because they enhance the realism of filming on a set and are relatively inexpensive to employ. The association between a particular television show and certain melodies is very strong most people who watch television regularly can reproduce the theme song of their favorite television shows. Props and locations (albeit, typically simulated on a set) have also been well-used supports to television production. The mise en scÃÆ' ¨ne of a television show is generally of a smaller scale than a film, such that composition of the scenes and the use of props to help tell the story achieve a relatively high degree of importance. Early and classic films, such as the vehicles in Harold and Maude, employed props to convey significance to viewers.Show MoreRelatedUniversal Studios Distribution of Despicable Me 2 Essay examples752 Words   |  4 PagesDespicable Me 2, an animated film distributed by Universal Studios and Illumination Entertainment in July of 2013 grossed $970,065,385 world-wide (BoxOffice, 2014). 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Improving The Risk Return Performance Of Portfolios Finance Essay - Free Essay Example

Sample details Pages: 22 Words: 6548 Downloads: 2 Date added: 2017/06/26 Category Finance Essay Type Essay any type Did you like this example? With the development of the Chinese capital market, more and more investors start to look for a more rational way to invest. To increase the investment return and decrease the risk, investors must learn to allocate their funds in order to diversify risk. However, due to the limited assets that can be invested in, the convenience and effectiveness of portfolio diversification must be studied. Don’t waste time! Our writers will create an original "Improving The Risk Return Performance Of Portfolios Finance Essay" essay for you Create order This paper mainly explores the function of futures in the ordinary stock portfolio through the study of risk-return performance. By comparing the efficient frontiers of different portfolios, the risk-return performance of the futures portfolio and mixed stock-futures portfolio is better than the stock only portfolio. Futures play an important role in upgrading the integrated portfolio of stock and futures. The results of this study provide investors with a feasible way to diversify their funds in multi-type investment portfolios, which is of great theoretical and practical significance. I. An introduction to Chinese capital market Ever since December 19, 1990, when Shanghai stock exchange opened, people become more and more interested in investing in the security market to make money. After twenty years, investing in stocks is a quite popular and important way for ordinary Chinese people to manage their money. However, stock market itself can not meet investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ needs of diversifying risk and increase capital return, and investment diversification becomes a natural solution and guiding concept. Although twenty years have passed since Shanghai stock exchange came in existence, development of Chinese capital market is quite slow, with limited kinds of investment products. Lack of varieties of trading tools and incomplete structure of capital market products make it difficult to diversify in Chinese capital market. In developed capital markets such as Hong Kong, over 80% of financial derivative instruments in international financial market have been introduced. In stock market, the trading o f index futures, options and warrants is quite active with a trending of exceeding the trading of spot market. Hong Kong bond market is even more diversified. Based on three basic kinds which are bond, note and certificate of deposits of fund-raising tools, many more complicated derivatives such as floating rate bonds, variable rate bonds, convertible bonds, credit card receivables, and the current debt instruments traded on the Hong Kong Stock Exchange listing has been increased to 129.(2009) On the contrary, despite of stocks, there are few more than five years investment instruments in mainland China capital markets. The trading of 1-5 year instruments is also confined so that the available trading instruments are quite limited. As an emerging market the risk of stock market is higher than normal, both systematic risk and market risk. The systematic flaws in Chinese stock market such as no trades of state owned and corporation owned stocks and lack of index futures  [1]  o r other kinds of hedging instruments make the whole stock exchange system more uncertain. The strong influence of state policy changing is also a reason for high uncertainty. As for the market risk, stock market is in sharp adjustment since the end of 2007. On the one hand, the overall risk has lowered a little; it is still too high compared with the mature capital markets. On the other hand, the low self-control ability of the participants involved in stock market makes the unsystematic risk higher than average. Investing only in stock market can not successfully diversify risk. Considering the incompleteness of Chinese warrant market, futures have been chosen to diversify risk. Chinese future market also started in 1990. After six years of cleaning up and reconstruction (1995-2000), future market is in good development. In 2002, stock market turned down, which made part of the stock market capital switch to future market and made it a hot deal. This situation is quite similar t o what happened in 2007-2008. Chinese future market developed from first pilot reform to rectification and now has entered a new stage of stable development. The legal operation and market discipline have been significantly improved. These features make futures possible as a component of portfolio. At present, research of the role of futures in the portfolio is focused on index futures and its hedging properties, while the research of commodity futures is focused on its function of price discovering. Adding futures into ordinary stock portfolio has not been well discussed so that this article will research on the performance of portfolio with commodity futures to see whether futures can effectively diversify risk and raise the return. How to optimize investment portfolio becomes the first and most important question that investors need to consider. Thus, modern portfolio theory becomes quite widely applied in practice. Portfolio means investors allocate certain amount of money to different kinds of assets in order to gain as much as possible return or to get the lowest possible risk. II. Past literature review in portfolio selection theories In 1959, Markowitz published his paper named Portfolio Selection: Eficient Diversification of Investments, which conducted a pioneering study of optimizing portfolio in the security market. Ever since then, modern finance and investment decision making comes into a quantitative stage. Portfolio theory is a set of theories and methods to help investors choose certain types and allocate their money from varieties of instruments to form efficient portfolio. In Markowitz theory, mean-variance model can be applied to any class of financial assets, as long as its expected return and the correlation of each asset can be accurately estimated (Markowitz, 1959). In his model, mean represents the expected return of an asset and its risk is represented by the variance. In order to use the Markowitz mean-variance method, we need to find the expected rate of return and risk. However, considering the ineffectiveness of Chinese stock market, the simple mean-variance is not applicable. Thus, more a ppropriate method of evaluating return and risk needs to be found. Among these different evaluating methods, people tend to agree using expected return as a representative of future earnings. The return of a financial asset is consisted of two parts: intertemporal cash flows and capital premium (asset price changes during the holding period). The return that this article is going to use is the daily logarithmic rate of return, so the intertemporal cash flows can be ignored. The yield can be expressed as: Because logarithmic rate of return can be simply added which facilitate the data processing by software and its value can be any real numbers, this article will use logarithmic rate of return as the evaluation of asset yields. The simplest way to get the expected rate of return is calculating its average. Its flaws are also quite obvious: the result is far from accurate. In order to find more accurate estimation, we need to fit time series data to appropriate model and find the unconditional expectation of asset return. In 1980s and 1990s, lots of literatures have discussed the predictability of stock market and suitable model of predicting asset returns. M.Hashem Pesaran and Allan Timmermann (1995) found that the predictable components of stock returns are highly correlated with business cycle and the magnitude of shocks influences the model more than expected. But because what they studied is a long term relationship in the stock market, the results can only be a consultation. As for the daily stock return, many researches suggest that it shows significant dependence on former returns. Vedat Akgiray found in his paper about the conditional heteroscedasticity in stock returns that the probability distribution of return lag of s days is dependent on return today for several values of s (1989). He used daily returns on the CRSP (Center for Research in Security Prices) value-weighted and equal-weighted index from January 1963 to December 1986 to find th at GARCH (1,1) shows the best fit and forecast ability among the econometric models. Noticing that the return he used is also logarithmic rate, the features of logarithmic rate in this article can be expected to be just like that in his study. Similar results can be obtained from other literatures. There is a positive relation between the expected risk premium and the predictable level of volatility and a negative relation between unpredictable component of stock market risk and excess holding period return (K. R. French et al, 1987). Although they can not determine a certain model to describe the exact relation (difficult to choose between ARIMA and GARCH-M), the relation between return and risk is quite significant. Studies about Chinese stock market also show evidence of fitting stock return data in ARMA or GARCH models. The daily returns of Shanghai and Shenzhen index indicates significant ARCH effect and the data fit in GARCH-M model well (Hua Tian and Jiahe Cao, 2003). I t is reasonable to choose ARMA or GARCH model to simulate the actual stock movement. But as for the measurement of risk there are comparably various methods. Markowitz explained the mean-variance theory in his 1959 portfolio selection paper which introduced the statistical concept of expectation and variance into the study of investment portfolio. Under a certain probability distribution of returns, he used the average deviation from the average return of all the random returns. Thus, risk can be quantified with the expectation of return as return expected and standard deviation as the measurement of risk. Although variance has some easy to use features such as simple calculating and easy understanding, it is only an approximate measurement of risk. Using variance needs the distribution to be systematic and does not take the investors different feeling about capital gain and loss into consideration. Given the same amount of gain and loss, the pain of loss is usually larger tha n the happiness of the capital earnings. Variance ignores this asymmetry while LPM (lower partial moments) would be a better measurement. Harlow proposed this new indicator as a more accurate way to describe risk (1991). LPM is an abbreviation of lower partial moment, which P (partial) stands for its measuring only one side of the returns compared with the fundamental rate and L (lower) stands for less than fundamental rate (downside risk). LPM is a risk measurement which meets the requirements of Von Neumann à ¢Ã¢â€š ¬Ã¢â‚¬Å" Morgenstern utility function and can cover almost all peopleà ¢Ã¢â€š ¬Ã¢â€ž ¢s risk preference. It shows a new way to describe risk apart from the traditional utility measurement which is the function of variance or the standard deviation. The expression of LPM is: , where n is called the order of LPM indicators, representing the risk aversion of investors, and z is called fundamental rate of return which is the minimum return that investors would accept. Different values of n would change LPM into different measurements of risk and therefore meet different investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ risk preference, from risk preference to risk neutral, then risk aversion. One advantage of LPM is that it can show only the pain or loss possibility when the return is lower than the expected. The other is it can show what investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ different risk preference can affect the feelings to the same asset by simply changing the order n. LPM is less popular in evaluating volatility than variance as the calculation of LPM is more complicated. Another reason is that LPM must be calculated separately for each variable while variance can be added or processed under certain assumptions. This means people need to program it in order to use LPM with comput er data processing programs. On the contrary, all the data processing programs have a default function of calculating variance. The way to evaluating the performance of asset portfolios is its efficient frontier. Every combination of risky assets can be plotted in a risk-return space, and those combinations with the highest return under the same risk or with the lowest risk under same return are called efficient portfolios. Usually, the upper part of the curve which describes risk-return features of efficient portfolios is called efficient frontier. Ordinary efficient frontier of investment portfolio is calculated by Markowitzà ¢Ã¢â€š ¬Ã¢â€ž ¢s mean-variance method. This article will use LPM to substitute variance to calculate efficient frontier which makes it more like investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ thoughts of risk. Merriken suggested that variance and LPM are suitable for the study of short-term investment (1994), which is quite popular in Chinese capital market. Based on the review of the related literatures, this article will use econometric models to get expectation daily return of stock and futures and both variance and LPM to calculate efficient frontiers to see whether adding futures into stocks would improve the performance of portfolios. III. Theoretical study and empirical data results i. Theories of econometric models and multi-type asset portfolio The econometric models used to estimating the expected return and risk are ARMA and GARCH models depending on the features of different stock and futures time series. ARMA is an abbreviation of autoregressive and moving average model, which is typically used in estimating autocorrelated time series. As what is mentioned in the literature, auto-correlation in daily logarithmic return is shown by theoretical study, and the empirical study of the realistic data also suggests this result. Typical ARMA model is consisted of two parts: AR (auto-regressive) part and MA (moving average) part. It is normally notified as ARMA (p, q) where p is the order of autoregressive part and q is the order of moving average part. AR part is written as: , where are the parameters and is the error term (usually white noise). The value of p suggests how many lags of are regressed on and therefore is a measurement of autocorrelation. For the need of stays stationary, usually we need the absolute value of is less than unit. MA part is written as: , where are the parameters, is the expectation of , and is still the error term (usually white noise). The value of q suggests how many error terms are included in the smoothing process of average and MA process is always a stationary time series. Thus, ARMA model is written as: , which is a combination of autoregressive part and moving average part. The value of parameters is generally determined by the least square method which minimized the residual error term. The value of p and q is chosen to better fit the model without too much lags or smoothing terms. The method used in this article is through the value of ACF (autocorrelation function, which is used to determine the order of moving average) and PACF (partial autocorrelation function, which is used to determine the order of autoregressive part). In spite of autocorrelation, there are other special features of financial time series data such as fat tails, extreme values and volatility clustering. Simple ARMA models assume that the error term is independently and identically distributed which does not meet the fact. Thus, Engle (1982) posed ARCH (Autoregressive Conditional Heteroscedasticity) model to analyze this volatility feature of financial data. Four years later, T.Bollerslev improved this mod el and made it GARCH which is a generalized ARCH model. GARCH model is developed specially for financial data and is widely used in the study of volatility. In addition to the normal econometric model, people use GARCH to better analyze and forecast volatility. GARCH model can be written as: where the first equation is a simple ARMA model, but this time is not an independently and identically distributed normal error term. is an independently and identically distributed error term and is called conditional variance which is estimated by the third equation (also an ARMA model). and are independent of each other and the distribution of is not restricted as normal but can be changed to satisfy actual situation. This makes GARCH a more accurate model in estimating the expected rate of return and risk. Hiroshi Konno and Katsunari Kobayashi (1997) made an attempt to add bonds into ordinary stock portfolio to find a new way of allocating investment. Their purpose is to extend the mean-variance model normally used in optimizing stock portfolios to integrated bond-stock portfolios. At that time, big scale mean-variance models were restricted in stock portfolios although the computer technology and mathematical methods in financial engineering developed fast. Although bonds seem always to be considered separately when people intend to invest in financial market, Hiroshi and Katsunari still want to add bonds into portfolios. The reason is that before 1980s, the return of bond was far less risky than that of stock due to the stable interest rate. However, after 1980s, interest rate became much more volatile and investors bore heavily loss and huge risks. Actually, the volatility of bonds at that time was even higher than that of stocks. Considering this, combining bonds and stocks into the same portfolio is of great realistic meanings. The method they used is mean-variance and mean-absolute deviation models where variance and absolute deviation are as the diff erent measurement of risk. The results are also quite satisfied as adding bonds into stock portfolios can increase the expected return under the same risk level. Never the less, Raimond Maurer and Frank Reiner in 2001 also used this idea of multi-type asset portfolio to discuss the possible outcomes of adding real estate securities into international asset portfolios under a shortfall risk frame. They noticed the fact that financial time series data had its own features and the tradition way of evaluating risk using variance can not reflect what investors think in the reality. Therefore, LPM was introduced as the way of measuring risk to reflect the asymmetry in the rate of return of asset. They compared the situation in Germany and in US by calculating the efficient frontiers of common portfolios, then calculating the efficient frontiers of adding real estate securities into portfolios. Because they studied between different countries, Raimond Maurer and Frank Reiner also cal culated the effects of hedging. The results are also quite satisfied as the efficient frontiers move to the left, especially for those high risk-averse investors in Germany. Also, hedging could improve the performance of portfolios, especially for the US investors. With hedging they can build investment portfolios with higher rate of return under a relatively low risk level. But as mentioned above in the introduction part, there are few commercial bonds besides the government bonds; the only possible type of asset besides stocks that can be added into investment portfolios is futures. This article will also calculate the efficient frontiers of stocks, futures and combined portfolios separately, using both variance and LPM as the measurement of risk. As to the number of assets that should be held in one portfolio, investors have different opinions. Most mutual funds in the US market hold more than 100 stocks. Although these over-sized investment portfolios may well diversified risks, the expected return can be just acceptable as higher operational fee are needed to maintain such a huge portfolio and these stocks usually contains some low return ones. Xianyi Lu (2006) discussed this question that how many stocks are suitable for Chinese investors to hold in a single portfolio. He constructed portfolios with different number of stocks to compare their risk-return performance. The measurement of risk he used is variance. He came to the conclusion that 20 stocks would be enough to diversify most of the risk. The close-up price of stock is quite easily obtained while to find suitable closing price of futures is somewhat tricky. Futures are contracts which specify certain quantity and quality of fundamental assets between two parties to trade at a specified date in the future with a price agreed today. Thus there can be various contracts with the same kind of fundamental asset in different delivery date. Considering the trading characteristics of Chinese fut ure market, Chengjie Ge and Yong Liang from a Chinese fund called Guotai Junan tried to construct a continuous future contract to get the daily closing price in 2008. When a contract first comes into market, the transactions are quite few. One contract is traded most actively just three or four months before delivery date, as the coming of specified date the trading volume begins to fall quickly. Those investors, especially the speculators would only trade those contracts that so-called à ¢Ã¢â€š ¬Ã…“dominant contractà ¢Ã¢â€š ¬?. Thus, each future contract is in good liquidity only for a short time period. A continuous future contract is selecting the most actively traded contract of same fundamental asset at the same time to form a new, artificial contract to get the continuous price time series of one asset. ii. Data collection and analysis This article uses daily closing price of stock and futures from the time period 04/01/2007 to 31/12/2008. The data is obtained from RESSET database  [4] Futures chosen are copper, aluminum, rubber and fuel oil from Shanghai Future Exchange, corn and soybean meal from Dalian Future Exchange and cotton and wheat gluten from Zhengzhou Future Exchange. In order to get daily return we need to construct continuous future contracts by selecting the most active contracts. As to the 8 futures used in this article, the most active contracts of wheat gluten, soybean meal, cotton, fuel oil and corn are those contracts with delivery date four months before the current month (not accounting current month); the most transacted contracts of rubber, aluminum and copper are those with delivery date two months before the current month (still not accounting current month). For example, current time is 19970201, so the contract which should be selected for cotton is the 199705 contract whose de livery month is May 1997. When it comes to 19970301, the contract selected for cotton should be 199703, and so on, so forth. After constructing eight continuous future contracts, we can get the time series of close-up price. The calculation of logarithmic rate of return, variance and LPM is just like the stock data. Table 1 shows the descriptive statistics of futures like the mean, the standard deviation, and some others. As the bond market is not mature in China, the risk free rate that used in this article is the three-month central bank bill rate which is also from the RESSET database, same database as the closing price of stocks and futures. From the statistics in the table we can find that the logarithmic daily return of futures shows asymmetry and fat tails, far from the assumption of mean-variance model that the distribution of returns should be normal distribution, or at least a symmetric bell-shaped distribution. Thus, using variance or standard deviation or any other ki nd of symmetric statistics would be less accurate. Fitting data into econometric models should provide a better estimation of expected rate of return and risk. Table 2.1-2.4 and Table 3.1-3.3 show the estimation of coefficients using ARMA and GARCH models. The models of stock returns are mostly ARMA models, but of futures are half GARCH models and half ARMA models. Table 2 is the results of future data and table 3 is the results of stock data. From the table we can see that there are four futures which are better fit in GARCH models and for the other four, ARMA is enough as the residual series after ARMA does not show significant heteroscedasticity in error terms. As for stocks, none of the 19 stock time series show significant heteroscedasticity which means ARMA could describe the features of stock price series. One interesting finding is that only 11 stock price time series show the correlation effect while the other 8 stock price series seem to be random walk. Table 2.1 and Table 2.2 are the GARCH results of future returns. Cotton, soybean meal, aluminum and copper show significant auto correlated heteroscedasticity. The basic model that used to estimate the return is ARMA model, and the first two lags show the most correlation with current logarithmic rate of return. The null hypothesis for all the coefficient in the model is the coefficient equals zero. The constant terms in the models are not significant despite that of soybean meal whose p-value is 0.0202, which means we can reject the null hypothesis under a 5% confidence level. The reason for not able to reject the null hypothesis of constant terms equaling zero may be the absolute value of daily logarithmic rate of return is too small, usually under 0.01. In such a low level the normal test to calculating p-value may become not suitable. So the value of constant terms is still used in the ultimate model to calculate the estimation of expected return although we can not reject the possibility th at it actually equals zero. Table 2.3 and Table 2.4 show the ARMA results of future returns. Wheat gluten, corn, fuel oil and rubber daily logarithmic rate of return are estimated by ARMA model. The null hypothesis is also that any coefficient equals zero with p-value stands for the probability of making mistakes when rejecting the null hypothesis. The problem is the same with that of GARCH models as the p-values are too large to reject. But still we accept this result and make forecast using the present model. In spite of the not-so-satisfying results in the constant term, the coefficients of AR term and MA term are quite significantly different from zero which can be tell from the p-values. This is also true in futures GARCH model and stocks ARMA models. The significance of correlations in logarithmic rate of return series matches the features of financial time series and is what we would like to expect when estimating these coefficients. There are 19 stock return series to be modeled, but only 11 of them shows autocorrelation with their lags. None of these shows significant heteroscedasticity in the error terms so the model chosen is ARMA model. The constant terms of each stock return model is smaller than that of future return model, and the p-value is bigger than 0.05 as expected. The current return of four stocks out of this eleven shows significant correlation with the six and seven lags, showing the existence of cycle effects in the stock market. For these four stocks, what happened in the week before affects the price of this week more compared with other time. Other seven stocks show the ordinary one or two lags correlation. The coefficients of AR and MA part are also of great significance and the null hypothesis can be rejected. For those 8 stocks which do not show the existence of autocorrelation, the processing method is to calculate the basic descriptive statistics such as mean and variance. This method may ignore the asymmetry and fat t ails of the data, but as there is no good econometric model to estimate random walk series, this simple way has its own advantage and also of quite high accuracy in estimating the expected rate of return and risk. This article use the forecast value of each model as the expected rate of return, and the variance of the sample as the expected risk for the mean-variance model of investment portfolios. For those 4 GARCH future models, the expected risk is the forecast value of the error part model. As for those eight stocks whose logarithmic daily return series are random walk, simply use the mean as the expected rate of return and the variance as the expected rate of risk. LPM1 is using the three-month central bank bill rate as fundamental rate of return because of its risk-free characteristic. The mean-LPM model also uses the results of expected rate of return from the forecast of GARCH and ARMA models as the only change in this new model is the risk measurement from variance to LP M. Someone may argue that different econometric models could cause different estimation of expected rate of return, thus the results of efficient frontiers become not so convincing. The purpose of this article is to compare the efficient frontiers of different asset portfolios, trying to find the possible improvement of adding futures into the ordinary stock portfolios. The econometric estimation is used to construct Markowitzà ¢Ã¢â€š ¬Ã¢â€ž ¢s mean-variance model. What can be seen from Table 2 and Table 3 is that most of the assets can be fitted into ARMA model. As a matter of fact, because the absolute value of daily logarithmic rate of return is too small, the difference of constant terms between GARCH and ARMA model for the same asset is very small that can be ignored. The calculation of efficient frontiers is using MATLAB financial tool box, and the original data is what has been done above. After calculating the correlation coefficient matrix of 19 stocks and 8 futures, there is not much correlation of each asset. In fact, most of the correlations coefficients are between 0.1 to 0.3, with some of them even to be negative correlated. It suggests that the risk diversify of investment portfolios should successful using these 27 assets according to the statement of Markowitz. Table 1: Descriptive statistics of futures Soybean meal Aluminum Copper Cotton Wheat gluten Fuel oil Rubber Corn mean 0.0275 0.1074 -0.164 0.0527 0.00117 -0.0207 0.1434 0.00632 Standard deviation 1.82 1.29 2.073 1.007 0.0105 2.0109 2.11 0.928 LPM1 0.667 0.476 0.875 0.353 0.365 0.751 0.821 0.315 Skewness -0.496 -0.577 -0.326 0.237 1.19 -0.742 -0.672 0.385 kurtosis 4.45 7.445 3.31 9.12 12.57 4.63 5.60 8.515 J/B 62.15 405 10.08 746 1936 93.6 164 624 (the mean, standard deviation and LPM1 are all in percentage. LPM1 is order 1 lower partial moment with the fundamental rate is the risk free interest rate.) Table 2.1: the estimated coefficients of each model Cotton Soybean meal ARMA equation: r=c+ar(1)*r(-1)+ma(1)*e(-1) ARMA equation: r=c+ar(2)*r(-2)+ma(2)* e(-2) coefficient p-value coefficient p-value c -0.000269 0.4884 c 0.000752 0.0202 AR(1) 0.813401 0.0007 AR(2) 0.965705 0 MA(1) -0.851776 0.0001 MA(2) -0.96641 0 Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) Variance equation: e = C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) C(4) 4.83E-06 0.002 C(4) 1.18E-06 0 C(5) 0.063917 0.0002 C(5) -0.01277 0 C(6) 0.887646 0 C(6) 1.013578 0 Table 2.2: the estimated coefficients of each model (continued) Aluminum Copper ARMA equation: r=c+ar(1)*r(-1)+ar(4)*r(-4)+ma(1)*e(-1)+ma(4)*e(-4) ARMA equation: r=c+ar(1)*r(-1)+ar(2)*r( -2)+ma(1)*e(-1)+ma(2)*e(-2) coefficient p-value coefficient p-value c 0.000757 0.3249 c -0.00091 0.2975 ar(1) -0.12668 0.4223 ar(1) 0.675058 0 ar(4) 0.822443 0 ar(2) -0.48379 0.0007 ma(1) 0.117813 0.3959 ma(1) -0.7834 0 ma(4) -0.85309 0 ma(2) 0.64634 0 Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) Variance equation: e= C(4) + C(5)*RESID(-1)^2 + C(6)*e(-1) C(4) 5.20E-06 0.0208 C(4) 6.77E-06 0.0336 C(5) 0.046297 0.0048 C(5) 0.128242 0.0009 C(6) 0.942352 0 C(6) 0.860077 0 Table 2.3: the estimated coefficients of each model (continued) Wheat gluten Fuel oil ARMA equation: r=c+ar(3)*r(-3)+ma(3)*e(-3) ARMA equation: r=c+ar(2)*r(-2)+ar(3)*r(-3)+ma(2)*e(-2)+ma(3)*e(-3) coefficient p-value coefficient p-value c -2.68E-05 0.9622 c -0.00057 0.787 AR(3) 0.772531 0 AR (2) 0.421234 0.0041 AR(3) 0.514736 0.0005 MA(3) -0.747211 0 MA(2) -0.3081 0.0378 MA(3) -0.55602 0.0002 Table 2.4: the estimated coefficients of each model (continued) Rubber Corn ARMA equation: r=c+ar(2)*r(-2)+ma(2)* e(-2) ARMA equation: r=c+ar(1)*r(-1)+ma(1)*e(-1) coefficient p-value coefficient p-value c -0.00161 0.2191 c -7.88E-05 0.8534 AR(2) 0.665529 0 AR(1) -0.821529 0 MA(2) -0.556 0.0002 MA(1) 0.768263 0 Table 3.1: the estimated coefficients of each model stock 01 stock 07 ARMA model: r=c+ar(6)*r(-6)+ma(6)* e(-6) ARMA model: r=c+ar(6)*r(-6)+ma(6)* e(-6) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.00125 0.0022 C -0.004621 0.0697 AR(6) 0.892999 0 AR(6) 0.972813 0 MA(6) -0.975019 0 MA(6) -0.980419 0 à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ à £Ã¢â€š ¬Ã¢â€š ¬ stock 02 stock 09 ARMA model: r=c+ar(6)*r(-6)+ar(7)*r(-7)+ma(6)*e(-6)+ma(7)*e(-7) ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) coefficient p-value coefficient C -0.00057 0.5591 C -0.000835 0.3755 AR(7) -0.580883 0 AR(2) -0.832358 0 AR(6) 0.306957 0.0093 MA(2) 0.768651 0 MA(7) 0.594833 0 MA(6) -0.388734 0.0007 Table 3.2: the estimated coefficients of each model (continued) stock 03 stock 11 ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) ARMA model: r=c+ar(2)*r(-2)+ma(2)* e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -3.32E-05 0.9565 C 0.000128 0.8024 AR(2) 4.55E-01 0.0777 AR(2) 0.699258 0 MA(2) -0.567395 0.0176 MA(2) -0.763239 0 stock 04 stock 13 ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C 4.12E-05 0.9448 C -0.000181 0.8822 AR(1) -1.010713 0 AR(2) 0.51781 0.0152 AR(2) -0.813403 0 AR(1) 0.214893 0.5356 MA(1) 1.080601 0 MA(2) -0.538276 0.0054 MA(2) 0.880392 0 MA(1) -0.099508 0.772 stock06 stock 15 ARMA model: r=c+ar(3)*r(-3)+ar(4)*r(-4)+ma(3)*e(-3)+ma(4)*e(-4) ARMA model: r=c+ar(1)*r(-1)+ar(2)*r(-2)+ma(1)*e(-1)+ma(2)*e(-2) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.000439 0.6981 C -0.001621 0.5432 AR(4) -0.525641 0.0006 AR(1) 0.895432 0 AR(3) 0.273493 0.0475 AR(2) 0.097598 0.5815 MA(4) 0.611753 0 MA(1) -0.787578 0 MA(3) -0.145839 0.2854 MA(2) -0.226713 0.2128 Table 3.3: the estimated coefficients of each model (continued) stock 18 ARMA model: r=c+ar(6)*r(-6)+ar(7)*r(-7)+ma(6)*e(-6)+ma(7)*e(-7) à £Ã¢â€š ¬Ã¢â€š ¬ coefficient p-value C -0.00026 0.7174 AR(6) -0.35917 0.002 AR(7) -0.45655 0.0006 MA(6) 0.261067 0.0289 MA(7) 0.505517 0.0003 Figure 1: the efficient frontiers of stock, future and mixed portfolios using mean-variance model 19 stocks portfolio 8 futures portfolio Stock and future portfolio (The green line is the efficient frontiers of 19 stocks portfolio, the purple line (in the middle) is of 8 futures portfolio and the blue line is of the mixed stock and future portfolio.) Compared these three efficient frontiers, we can find that adding futures into the ordinary stock portfolio can greatly improve the performance of portfolios, which is even greater under lower risk level. Single future portfolio also performs well compared with single stock portfolio as it can offer higher rate of return under the same risk level. From Figure 1 we can find with the same expected return of 0.4ÃÆ'—10-3, the mixed stock and future portfolio can reduce the risk from 0.012 of single stock portfolio to less than 0.006. This more than fifty percent of risk reduction shows great practical meaning of multi-type asset investment portfolios. Figure 2: the efficient frontiers of stock, future and mixed portfolios using mean-LPM model Figure 2 shows the same results as the Fig ure 1. The mixed stock and future investment portfolio can improve the risk-return performance of portfolios. Similarly, future portfolio performs much better than stock portfolio, and it can greatly raise the expected return under higher risk level. The mixed portfolioà ¢Ã¢â€š ¬Ã¢â€ž ¢s improvement is mainly under low risk level, as the risk becomes bigger, the performing difference between future portfolio and mixed portfolio are not so significant, for the efficient frontiers overlap each other. The efficient frontiers are straight lines in Figure 2 while they are curves in Figure 1. The different risk measurement may result in this. Because LPM only calculates the downside risk, the risks of the portfolios which provide same return are not the same. Every single LPM must be calculated separately. So the shape of the new efficient frontiers may look different from the traditional hyperbola-shaped curves in mean-variance models. Both the mean-variance model and the mean-LPM model show that only investing in stock market can not get as much return as investing only in future market under the same risk level because the efficient frontier of stock portfolio is to the right of that of future portfolio and the distance between the two efficient frontiers is quite large. It reveals a fact that investing only in stock market can not guarantee ideal revenue. Although twenty years has passed since the establishment of Chinese stock market, there still exist some system flaws which raise the systematic risk of stocks. Thus, 19 biggest market value stocks from the market can not efficiently diversify the risk. Chinese future market resumes development since 2001, but the return of future portfolio is quite high. The efficient frontier is to the left which suggests low risk under the same rate of return. Moreover, margin trade system is implemented in future exchange, and the average leverage ratio can reach as much as fifteen. Both of them can insure a very impressive return when investing in the future market. An argument for this result is that whether it is consistent with other stocks and futures. These two figures are based on the most representative stocks and futures of Chinese capital market. Other stocks and futures may not provide such a high return but as long as they are not correlated much with each other, this improvement can also be expected as the results come from the traditional risk diversification theory of Markowitz. Although Chinese capital market is not so mature, the results of multi-type asset investing portfolios shows the similar results as that in the US and German market. Adding different types of assets into the original single-type asset investing portfolio can extremely improve the risk-return performance, with a much obvious improvement under relatively low risk level. The difference in the methods of measuring risk does not affect this conclusion. In order to get higher return, investors should r easonably allocate their fund into stock and future market to construct a multi-type asset investing portfolios instead of investing only in stock or future market. IV. Conclusion The aim of this dissertation is to find whether adding futures into a stock portfolio can improve its risk-return performance. This article uses Chinese capital market data to construct three investment portfolios: only stock portfolio, only future portfolio and the mixed stock and futures portfolio. The way to evaluate their performance is to calculate their own efficient frontiers. The main problems that exist in Chinese capital market include as follows: the behavior of market players is not standard and their corporate structure is imperfect; market structure is irrational and dysfunctional; investorsà ¢Ã¢â€š ¬Ã¢â€ž ¢ expectation violates greatly because of some history issues such as policy-controlled market; the regulatory functions and approaches can not meet the needs of fast developing market. These problems reduce the efficiency of capital market and make the actual distribution of returns far from the assumption of using ordinary mean-variance model. Considering the immaturity of the Chinese capital market, the simple statistical mean and variance can not reflect the true value. Under this circumstances, this article use econometric model to fit the data of security and future returns, trying to find the best estimation of expected rate of return and risk. Thus ARMA and GARCH models are introduced to better estimate the expectation. Also, the traditional way of using variance as the measurement of risk has been challenged in recent years, so this article uses the LPM (lower partial moment) to measure risk, which is more reasonable in meeting the utility function of investors. The results of sample asset portfolio analysis using mean-variance model and mean-LPM model suggest that diversifying investment into different types of assets can efficiently reduce risks. Through comparing three efficient frontiers we can find that adding futures into stock portfolio can significantly increase the expected return under same risk level. Although the ri sk measurement is not the same in mean-variance model and mean-LPM model, the conclusion of multi-type asset investment portfolio reducing risk remains the same. With this conclusion, we can find that investing in any single market can not get the expected rate of return under acceptable risk level. Especially after the huge volatility of Chinese stock market during 2007-2008, investors who only built stock portfolios suffered huge losses. One possible solution is to allocate fund into different markets to construct multi-type asset investment portfolios, which ensures a higher rate of return under a relatively low risk. Considering the reality of Chinese capital market, the best choice is to invest both in stock and future market. Investors need to find suitable stocks and futures to build up portfolios according to their own preference of risk and return. Generally speaking, higher return is companied by higher risk. As to the choosing of specific stocks and futures, investors should pay attention to the less correlated ones in order to better diversify the non-systematic risk. Because of the time and knowledge restriction, this article only discusses the general performance of stock and future portfolios. The best proportion of fund allocating to each stock and future under the sample data is not calculated, and the sample studied are only stock and future market data. As the development in Chinese capital market, more and more investment tools will appear, and the investment policy would also change with the maturing of market. The new stock index future is a very useful tool in hedging portfolios. The accurate proportion of fund allocation and adding more types of asset into portfolios are the next topics to be studied.

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The Scarlet Letter, By Nathaniel Hawthorne - 1951 Words

One Cannot Escape the Truth The Buddha once said, â€Å"Three things cannot be long hidden: the sun, the moon, and the truth.† The romantic novel The Scarlet Letter by Nathaniel Hawthorne explores the importance of the truth and the effects on an individual when they attempt to escape from it. This novel mostly follows the main character Hester, who is doomed to display the scarlet letter, signifying adultery, for the rest of the duration of her life. Pearl, Hester’s daughter and the result of her adultery, is thought of by the Puritan community to be unworldly, wild, and an offspring of Satan. However, despite the Puritans’ ill view on her, Hawthorne purposely uses her character to portray truth and nature in this gloomy and dark setting. The actions of Pearl make it apparent that one cannot hide or escape from the truth. Pearl represents nature and the truth through her existence and actions. 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Society Does Emphasis on Phsycal Appearance Free Essays

Beauty and Vibrancy It is important to take care of yourself and try to look your best. Letting your looks go or becoming unhealthy and sloppy is unappealing and disrespectful to yourself and those you love. On the other hand, when people focus exclusively on how they look in a vain effort to seek validation, they lose a certain vibrancy that comes from depth and self-confidence. We will write a custom essay sample on Society Does Emphasis on Phsycal Appearance or any similar topic only for you Order Now In today’s society of never-ending nips and tucks, we forget that the essence of beauty stems from a person’s inner vitality and depth. Vitality often results from leading a multi-dimensional life that involves pursuing one’s passions, being creative, having purpose, and carrying on meaningful relationships. Demeanor True beauty is reflected more by demeanor than by a person’s perfect features. In his autobiography, Alan Alda describes being backstage watching an actress play the part of a hideous woman. Alda thought the actress was perfect for the part—she was ugly, had thick fingers, no neck, and a pudgy nose. He was shocked when in the last act, she transformed into a beautiful woman without any change in makeup. Her fingers and neck actually became slender and long, and her face suddenly looked regal. He realized that the most dramatic transformation comes from the way people hold themselves. Self-Assurance Attractiveness is also a reflection of how we view ourselves. I have a cousin who has a long scar down one side of his face from an accident. When he was a teenager, his father told him that he looked disfigured and pressured him to get plastic surgery. Embarrassed by his scar, he decided to get the surgery. A few days beforehand, his sister in law—a British catwalk model whom he adored—caught sight of him looking in the mirror while covering the â€Å"damaged† side of his face with shaving cream to see how it would look if it were gone. When he told her about the impending surgery, she leaned toward him and whispered emphatically, â€Å"Don’t do it. It’s dreadfully sexy. † Her comment transformed the way he viewed his physical appearance, and needless to say, he kept the scar as an asset. Why People Seek Beauty Being admired for being attractive feels good. When something feels good, it’s easy to want more of it, whether it’s beauty, wealth, popularity, fame, food, or wine. A dentist once told me â€Å"You can never be too beautiful or too rich. † Yet, there is a point where too much focus on beauty (or anything else) tragically takes away from other important facets of your life. Desperate efforts to look young or sexy sends the message that you have nothing to offer but your youth and beauty. A person with perfect features and flawless skin who feels insecure and resentful cannot, and does not, radiate beauty, only anxiety. When people start â€Å"running for their lives†Ã¢â‚¬â€i. . , running to plastic surgeons biannually, the message they send is one of fear and insecurity. There will always be younger and more beautiful people, so why not appreciate and cultivate greater depth and breadth within ourselves? How others view you does not lead to fulfillment. Too much emphasis on our looks steals from us th e enjoyment of many other pursuits—intellectual, athletic, and spiritual, for example. How we look has little to do with the fulfillment that comes from meaningful relationships, humor, and creativity, as well as from work, wisdom, solitude, and philanthropy. Skin-deep beauty, particularly if manufactured, will only attract others who are not interested in much else. Lacking depth and substance, even the most gorgeous woman or handsome man will receive only superficial and short-term interest from others, usually from people looking for a hot evening, a trophy wife, or a cabana boy. Self-Presentation Imagine being extremely beautiful or handsome, and receiving endless adulation. Although the attention may feel good, it can also create increased dependency on other people’s opinion of how you appear. This dependency develops into a tendency toward self-presentation, that is, presenting only the parts of yourself that will get a desired reaction. You become afraid of developing wrinkles—even smile wrinkles–or showing up without makeup. Your fear of losing admiration has the paradoxical impact of increasing fear of rejection about aspects of yourself that remain undisclosed—other interests and ideas. The interesting thing about beauty is that there isn’t one measure for it, even in one short lifetime. Styles of clothes change, as do the concepts of beauty. The emaciated look may be in now, but not historically so. In â€Å"Fiddler on the Roof,† one of the lines is â€Å"If I were a rich man, my wife would have a double chin. † Historical excesses in forced or artificial beauty point to the transitory nature of our own current preferences: African or South American wooden plugs in ear lobes to stretch out the lobes; the old Chinese custom of binding little girls’ feet to keep them small; the Poof, made popular by Marie Antoinette, whose hairdresser piled pads and pomades to raise the hair three feet high—all come and gone. Beauty care customs that are often viewed as â€Å"must-have† in their time can seem almost ludicrous by other cultures in a later era. But at the time, beauty products are endowed with the promise of helping us conform with current trends. Attitude In reality, true beauty is without artifice. Your character eventually shines through any amount of make up or plastic surgery. The way you treat others is remembered always, no matter how flawless your complexion. I’m all for continuing to do things that will preserve or enhance what nature has given us—that may include having work done for some. But the key lies in choosing a positive attitude about life rather than allowing desperation to take over. Knowing and accepting our aging process liberates us to pursue our life through our own lens, not someone else’s. Acceptance and confidence in yourself can sustain passion in a relationship better than liposuction and restilin. With each year, the inner self expresses itself more strongly in each line and wrinkle. It becomes impossible to hide your true self. Each person has his or her own individual passions and life experiences that are often best reflected in those very wrinkles we abhor. If we choose to have the expression of our life erased, what does that say about ourselves? Modified or not, the face becomes the true mirror of the soul. We tend to replicate objects we consider beautiful because it can make us feel better about ourselves. Surrounding ourselves with beauty and/or making ourselves look beautiful can help one boost up there self esteem and confidence. By replicating beauty it shows that one is not strong enough to show public who he/she really is. Replicating beauty is a shield to hide ones real self and personality, and modeling themselves to fit within the standards placed on them by society. Certain things such as makeup and accessories usually used by women, to replicate beauty are usually used to hide ones inner self. These items are to hide ones true beauty and to show a beauty that is more popular or fashionable to fit in with society. Society may have their own view of beauty, for example nice facial complexion, a certain body size and a certain way to act. See beauty can be judged on many bases but reality has made man perceive it as only physical. Society has gone so far in replicating objects we consider beautiful that they have gone to all extremes. Items such as a nice expensive car or a huge house are used for one to feel that they are surrounded by beauty. These items are usually used as a footstone to make someone feel better about them and also expect a certain reward or treatment from others because of the items of huge financial status. People want society to judge them on the objects they have as if these objects provide them with the beauty they have worked so hard to replicate. They are often scared that people may not like them for who they really are and instead hide behind these items. How to cite Society Does Emphasis on Phsycal Appearance, Papers

Patricide Essay Example For Students

Patricide Essay In the time of the Romans, the punishment for patricide was to be sewn up in a sack that had a monkey, snake, rooster, and dog inside, and then to be thrown in a river. Each of the animals in the bag had some specific meaning to them, and being sewn up in a sack and tossed into the river also had a specific function to the murderer. Thus this punishment became the proper way to punish the guilty. In the Roman era, patricide had become a major problem, so it was decided that for whomever held a title in Rome, there would be a meeting to discuss how to get rid of the problem and punish appropriately. The title holders decided that the best way to punish the young men, and to stop them from thinking of committing the sin, was to make them die, as well as make them feel everything their father had, and to regret their crime. This decision then became the chosen consequence for the crime of patricide. The significance of the animals was to torture the perpetrator in a particular way for his crime. The importance of the snake was that the snake was evil, dating back to the Garden of Eden, where it posed as the Devil and deceived Eve. While the victim was alive, the snake would be there to remind him of the ultimate sin-the deception of ones own father. The rooster is primarily known for his crowing, and thus his crows would remind the sinner of his guilt, so that he couldnt escape from what he did. The dogs function in the sack would be to howl, not only to be deafening and frightening, but also to evoke the wrath of the gods upon him. The monkey represents torture, because it is capable of mimicking human actions. It would mimic the sons behavior and re-enact the murder of the sons All four of these animals perform at least one role in torturing the boy, and so that he would be forced to think about what he had done to his father. The purpose of the sack was to increase tenfold the agony which his father suffered, and also to make him regret his decision to kill his father. With each passing moment, the torment would get progressively worse, so that the boy would get a taste of the Hell that was to be his afterlife, as punishment for committing patricide. The sack represented a way in which to make the boy suffer much more, and quickly before he drowned. The son was thrown into the river so that he could feel the way his fathers panic when he killed him. The water would serve to scare the son in the way his father felt when he realized that his own son had turned on him. The sewn sack would prevent the sons escape so he would realize there would be no turning back from his actions. These different elements of punishment combined to make the murderer truly suffer each aspect of the crime through the torture. The closed sack with animal reminders of different aspects of the murder would serve as a deterrent to living observers. This ritual is a fitting punishment for the crime.

Impact of Effective Communication on Change Management

Question: Discuss about the Impact of Effective Communication on Change Management for Global Organizations. Answer: Introduction Today, organizations have started to look beyond the domestic boundaries. The marketplace has expanded and organizations have started to look for expansion in the global market. It is believed that that communication is vital when dealing with changes and strategic planning. Encouraging input and ideas from those on the front-lines in order to create a strong plan or means for success. Leaders really must have an interest for the development of their team and those dependent upon them, this will only create a stronger team (Petronio, 2013). The objective of this research work is to study the factors that have positive impact on change management. The research work would primarily focus to study the impact of effective communication on change management for global organizations. The initial research suggests that the lack of communication and a thoughtful process can create difficulty on having others jump-on board with the idea. It can be said that change is always constant in our ever-changing world. That is where the vision and the mission statement must remain constant. Literature review Customer service is the key to success and the strategies must be moulded around customer service planning. Communication, requiring accountability and responsibility for staff and faculty, mandatory training and also including everyone in the change process (all levels), respecting their input and encouraging it, also assisting with technology and providing information about new technological changes. Authors argued that change management cannot be successful in organizations without effective communication strategies in place. Stark (2015) argued that from a strategic planning perspective, communication is a major support function for effective change management. Communication has been the biggest contributor to the successful progress being made. With effective communication strategies in place, all employees receive a consistent message and all stakeholders participate in decision making. By instituting a broad strategy of becoming highly reliable and safe, organizations are identifying opportunities to improve processes that affect daily operation, customer service, quality, and employee satisfaction. This in turn improves our community image and financial stability. When there is clear understanding that change is happening in all levels of the organization people are a little more willing to contribute to the changes being implemented. The people who would usually have the most difficult time contributing to change are those that have been doing things the same way for many years (Carsrud Brannbeck, 2014). Those employees usually will take longer to transition into the changes that are being implemented. Once there is lack of interest from subordinates then it becomes challenging to get momentum back from followers (Petronio, 2013). Part of the strategic planning should include constant communication and motivation while changes are implemented. Lack of consensus can be a challenge to strategic planning. No matter how much efforts someone add to a planning, if there is no consensus then companies fail. For example, in Toyota organization (Frankland Mitchell, 2013). there was conflict between to employees and it lead to recall of vehicles. Research questions and hypotheses Research questions The three key research questions of this research can be highlighted as: What is the impact of effective communication strategies on change management within global and multinational organizations? How can organizations manage the change effectively without compromising on organizational productivity? Hypotheses The effective communication is the key for organizations to manage change within the organization and effective communication has a positive impact on organizational change management. Operational definitions and measurement The key variable involved in the research work can be discussed as: Independent variable: The effectiveness of the communication strategies used in the global organizations. Dependent variable: The effectiveness of change management within the organization Key definitions: Change Management: Refers to strategic changes within large and global organizations. For example, organization introducing new performance management system or organization introducing new ERP system (Loewenstein, 2015). Effective Communication: It included both verbal and non-verbal communication channels that are used within an organization (Simoes, 2104). Research methodologies: Data collection and analysis Sampling techniques For this research work, the researcher would use cluster sampling technique. The researcher would create three clusters and the random sampling method would be used for each of the sample (Lies, 2012). The three clusters decided for this research work are: Cluster 1: The employees who work for large and multinational organizations in Australia. For example, the organizations like Toyota Cluster 2: The people who are part of senior management Cluster 3: Industry experts The methodology of cluster sampling methods can be shown as: Method of analysis. The researcher would use a mix method of research. It means that the researcher would use primary research methods and secondary research methods. Primary research methods: The primary research methods mean that the research would be done specifically for this research work. In this research work, the researcher would use online survey method to complete the primary research. An online questionnaire of 20-30 questions would be used. The online questionnaire would have both open-ended questions and close ended questions (Sturgeon, 2013). It is always advisable to use the open-ended questions in the survey as the respondents would find it easy to answer the open-ended questions as compared to the close ended questions. A brief sample of open-ended question and close-ended question can be shown as: Example: Close ended questions: On a scale of 7 (1 being least and 7 being best), rate the importance of effective communication strategies in change management Example: Open ended questions: What should large and multinational organizations do to have effective change management in place? Secondary research methods: The secondary research would be used to support the primary research. In this research work, researcher would use the secondary research methods of literature review (Harrison, 2013). The researcher would refer to various online and offline sources to complete the secondary research. The secondary research method would provide qualitative data. Research process The key phases in the research work can be discussed as: Identification of research problem This phase is already done. The objective of this research work is to study the factors that have positive impact on change management. The research work would primarily focus to study the impact of effective communication on change management for global organizations. Background Study and Research Questions formulation The three key research questions of this research can be highlighted as: 1.What is the impact of effective communication strategies on change management within global and multinational organizations? 2.How can organizations manage the change effectively without compromising on organizational productivity? Literature Review The literature review would be a key part of this research. The researcher would use the resources from the library and other online sources to complete the research. Data collection and analysis The data collection methods would include both quantitative and qualitative methods. The researcher would also use the data analysis tools like Microsoft Excel and SPSS. Expected outcomes It is expected that researcher would be able to establish a positive relationship between change management and effective communication at work place. Conclusion The above paper discusses the research proposal to study the factors that have positive impact on change management. The researcher would use primary research methods and secondary research methods to complete the research. The cluster sampling methods would be used and the researcher would use three clusters to collect data and information from respondents. References Carsrud, A.L. and Brannbeck, M., 2014. Handbook of Research Methods and Applications in Entrepreneurship and Small Business. Frankland, R., Mitchell, C.M., Ferguson, J.D., Sziklai, A.T., Verma, A.K., Popowski, J.E. and Sturgeon, D.H., Applications In Internet Time, Llc, 2013. Integrated change management unit. U.S. Patent 8,484,111. Harrison, R.L., 2013. Using mixed methods designs in the Journal of Business Research, 19902010. Journal of Business Research, 66(11), pp.2153-2162. Lies, J., 2012. Internal communication as power management in change processes: Study on the possibilities and the reality of change communications. Public Relations Review, 38(2), pp.255-261. Matos Marques Simoes, P. and Esposito, M., 2014. Improving change management: How communication nature influences resistance to change. Journal of Management Development, 33(4), pp.324-341. Ocasio, W., Loewenstein, J. and Nigam, A., 2015. How streams of communication reproduce and change institutional logics: The role of categories. Academy of Management Review, 40(1), pp.28-48. Petronio, S., 2013. Brief status report on communication privacy management theory. Journal of Family Communication, 13(1), pp.6-14. Stark, J., 2015. Product lifecycle management. In Product Lifecycle Management (pp. 1-29). Springer International Publishing.