Markerting Essay Example | Topics and Well Written Essays - 5250 words

Markerting - Essay Example Design/methodology/approach – The scope of the study qualitatively considered university student consumer behavior. The methodology implemented a case study and 100 questionnaires derived from the internet database (survey monkey), as well as 50 questionnaires from campus. The study implements a non-probability, self-selecting sampling technique. The qualitative methodology consisted of interviewing five university students. A semi-structured technique was used. Finding – A number of significant findings were determined. A significant number of university students owned netbook computers and they found the following things significant: battery life, price, product quality, storage memory, Bluetooth, design and elegance. The participants generally had a high knowledge of computer knowledge. They are also brand conscious. There were a few problems detected but most were anticipated in the literature review. The research investigates the university students’ perspectives on netbooks, in order to get the information about the students’ consumer trends regarding netbook products. And to determine the wants and needs university students have for netbook products. Netbook is an extremely lightweight personal computer. Intel introduced the netbook in February 2008 to describe small, low cost, light weight, and lean functioning subnotebooks designed for optimal internet use. PCWORLD (2008) towards the end 2008, netbooks had ultimately become a larger commodity than laptops. Figures estimate that nearly 30 times more netbooks were sold in (11.4 million, 70% of which were in Europe than in 2007 (400,000). For 2009 sales are expected to increase to 35 million, and an estimated 139 million in 2013. This trend is reinforced by the rise of web-based applications as well as mobile networking and, according to Wired Magazine, netbooks have changed into "super-portable laptops for professionals". PCWORLD (2008),

States and Territories of India and Chandigarh Essay Example for Free

States and Territories of India and Chandigarh Essay Chandigarh is a city and union territory in India that serves as the capital of two states, Haryana and Punjab. The name Chandigarh translates as The Fort of Chandi. The name is derived from an ancient temple called Chandi Mandir, devoted to the Hindu goddess Chandi, near the city.[3] The city of Chandigarh was the first planned city in India post independence in 1947 (pre-independence planned cities include Dispur in Assam, New Delhi in Delhi, Jaipur in Rajasthan, etc.) and is known internationally for its architecture and urban design. [4] The city has projects designed by architects such as Le Corbusier, Pierre Jeanneret, Jane Drew, and Maxwell Fry. The city tops the list of Indian States and Union Territories with the highest per capita income in the country at Rs.99,262 at current prices and Rs.70,361 at constant prices (2006–2007).[5] The city was reported in 2010 to be the cleanest in India, based on a national government study,[6] and the territory also headed the list of Indian states and territories according to research conducted using 2005 data by Human Development Index.[7] The Union Territory, Chandigarh lies in the foot of the Shivalik Ranges and shares its capital, Chandigarh city with that of Haryana and Punjab. Chandigarh finds Haryana in the East and Punjab in the North, West and South as its neighbours. After independence and partition of India in 1947, the capital Lahore of Punjab state had fallen in Pakistan and hence a need to construct a new capital was felt. Eventually, French Architect Le Corbusier was selected to design the new city, as a result of which, India saw its first planned city, Chandigarh, that acquired its name from the temple of Chandi (goddess of Shakti) and the adjoining garh (fort) to the temple. This cosmo-politan city, Chandigarh represents the flavour of modern India in its architecture, culture and life style. Well planned wide roads, spacious residential colonies and square markets take you to visit a completely different side of India which is affluent, systematic and serene unlike the metros and big cities of the country. The splendid view of Shivalik Hills guarding the city furtively is what makes Chandigarh even more charming.

Influence of Terror on Pakistan Stock Market Returns

Influence of Terror on Pakistan Stock Market Returns Abstract This paper examines the influence of political instability and terror on Pakistan stock market returns between 1997 and 2010. The study constructs three variables that quantify political instability and terror and examine the effect on country stock return. This study seeks to apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to assess the impact of these variables on stock market returns and volatility using daily time series data for KSE. Results for KSE showed strong support for the hypothesis that bad news exerts more adverse effect on stock market volatility than good news of the same magnitude. Furthermore, terror and regime have significant negative impact while war has positive but insignificant effect on stock market volatility. JEL Classification: O40, C32. Keywords: Terror, Regime, political instability, growth, ARCH/GARCH. Introduction Many people agree that stock prices sometimes behave in bizarre ways. Markets are pretty tough and quite difficult. In the world of todays no one can negate the importance of stock markets. Stock market acts as a barometer for any countrys economy. In todays information-oriented world, news travels very fast and contagion can spread quickly and capital markets become more flexible and are absorb shocks brought on different news such as terrorism, political instability etc. Stock market of Pakistan is going through quite rough patch from many years. The change of political government and later on the terrorists attacks have badly affected the stock market and make the Pakistan Stock Market unreliable place for investment. As by seeing the overall scenario of Pakistans stock market during that time period it was not difficult for prices to follow certain patterns that support the rejection of Random Walk Hypothesis. This paper examines the impact of change in government, war and terror on economic growth in the Pakistan. Pakistan is one of those episodic-democratic countries who are facing continuous upheavals and socio-political disruptions since their inception. Military interventions could be witnessed in the political history of Pakistan. More over intervallic wars with India, strikes, antigovernment demonstrations and most importantly the ongoing war on terror have popped Pakistan to prominence on the socio-political platform. Such sociopolitical flux, terrorist attacks and other disruptions can have serious implications for stock price movement because stock prices reflect investors expectations about the future and these stock price movements on aggregate can generate a surged wave of activity. There has been an extensive work on study of stock market returns and volatility with respect to the fundamental variables and the macroeconomic variables but a diminutive work has been done so far to study the impact of socio-political factors on the stock market volatility in Pakistan. The existing literature on impact of socio-political factors on stock returns volatility is quite inadequate especially if we talk in context of Pakistani market. Masood Sergi (2008) analyzed Pakistans political risks and events that have affected the Pakistani stock market since its independence but their study chiefly covers the political events. Terrorism and strikes which have recently become the matters of intense interest and the source of unrest in the economy are the missing part there. The Karachi stock market is rapidly converting into a volatile market. If we see figure below it showed that there are high volatility during 1997 to 2010. This cannot be viewed as a positive sign for this emerging markets like Stock market of Pakistan. Though heavy fluctuations in stock prices are not an unusual phenomena and it has been observed at almost all big and small exchanges of the world. But focusing on the reasons for such fluctuations is instructive and likely to have important policy implications. The efficient market hypothesis argued that changes in stock prices are mainly dependent on the arrival of information regarding the expected returns from the stock and risk associated with that stock. (See Figure 1.1) So the purpose of our study is to examine empirically the impact of socio-political instability on Pakistani stock market. This study examines the three factors and their impact on the Pakistani stock market; the political instability due to military interventions, 1999 Kargil war, and terrorism. Literature Review A number of theoretical and empirical articles argue that these factors hinder economic growth of a country. Cutler, Poterba and Summers (1989) claimed that the sock prices move in response to the information other than about the fundamental values. They estimate the fraction of stock returns that can be accredited to various kinds of economic and non-economic events including assassinations of important political or national figures, war, invasions, raids and major policy change but their findings suggests a very small effect of non-economic news on the share price. Most of the studies have found a significant impact of political news or events on the stock market behavior. Chan Wei (1996) studied the impact of political news on the stock market volatility in Hong Kong and using GARCH-M model they found the strong evidence of the impact of political news on stock market volatility inferring that unfavorable political news is correlated to negative returns for the Hang Seng Index and vice versa. Mei Guo (1999) examined the impact of political insecurity on the financial crises in emerging markets and they observe that market volatility increased during political election and transition periods and political uncertainty could be a major contributory factor to financial crisis. Similarly Kim Mei (2001) infered through empirical analysis using GARCH(1,1) filter that the political risk affect the stock market volatility but this impact of political events or news is asymmetric, with bad news having a greater influence on volatility relative to good news. However Voth (2001) have argued that the impact of political factors in studies on German market has been over stated. He argued that the majority of events escalating political uncertainty had a minute or no effect on the value of German assets and the volatility of their returns. Instead, it was inflation that is mainly responsible for most of the variability in stock returns. He suggests that there is no direct linkage between the political factors and the stock market, however through channel it impacts. But Voth (2002) in a panel study of a set of 10 countries using panel regression confess that during great depression political risks changed dramatically over the period, and are adequate to account for a large part of the boost in stock price volatility. Beaulieu, Cosset Essaddam (2002) examined the impact of political risk in Canada on the volatility of stock returns, covering important political events in the country. Their study suggested that political news performs a significant role in the volatility of stock returns. Moreover the volatility of stock returns also depends on the degree of how much a firm is exposed to political risk i.e. the structure of its assets and the level to which there is foreign involvement. Kutan Perez (2002)Â  also found a significant impact of social and political factors on stock return volatility in their study conducted on Colombian stock market. Bautista (2003) applied Regime-switching-ARCH regression on Philippine stock returns to estimate its conditional variance and the estimated volatility was then related to major political and economic events. Their study revealed that the Philippine stock market is sensitive to radical changes in the political situation. Moreover the series of military takeover attempts during late 1980s in Philippines lead to hefty fluctuations in stock market index. Masood Sergi (2008) analyzed political risks and events that have affected the Pakistans stock markets since its foundation. They have found that Pakistans political risk carries a significant risk premium of between 7.5% and 12%. They made forecasts using Bayesian hierarchical modeling and Markov Chain Monte Carlo (MCMC) techniques and found that there is relatively high probability of occurrence of events with an average arrival rate of approximately 1.5 events per year. Many others also wrote that political instability warped the future path of investment decisions (Calvo and Drazen (1997), lessened public investment leading to a shift of government budgets from capital spending to government consumption (Darby, Li and Muscatelli (1998), and makes governments less inclined to make improvements to the legal system (Svensson (1993) Wars and unrest at the borders creates instability and panic among the investors that could affect the stock market movement at large. The affect of war has been analyzed in many studies including Cutler, Poterba and Summers (1989), Aggarwal, Incaln Leal (1999) and in Pakistan Masood Sergi (2008). Aggarwal, Incaln Leal (1999) examined the sort of events that cause large swings in volatility of emerging stock markets. For this purpose they examine various social, political and economic events both at global and domestic level to find out their explanatory power in context of the returns volatility in the emerging markets including the impact of gulf war. Though at small scale but the impact of gulf war was felt in those emerging markets. Similarly Masood Sergi (2008) found that among other factors that they studied, wars with India, 1948, 1965, 1971 and 1999 kargil war negatively influenced the Pakistani stock market. Evia et al. (2008) examined the affect of socio-political conflict in Bolivia on economic performance. Factors studied widespread during the conflicts as strikes, demonstrations, road blockades, and conventional rent-seeking. Their results showed that economic growth due to external factors is positively related to conflict while growth due to productive investment is negatively related to conflict. Terrorism is another as put that has been studied in relation to economic activity. Many studied in this distance; produced conflicting results as Becker and Murphy (2001) argue that economic performance are not much affected, because terrorist attacks usually devastate only a small portion of the overall stock of capital in a country. By contrast, Abadie and Gardeazabal (2005) repeated that terrorism shape overall economic risk in a country and lead to the economic shakiness in the country. They also conclude their study that higher level of terrorism risks results into the lower levels of foreign direct investment (FDI). Almost all studies on terrorism and its influence on stock prices limited to only on a single or few events, such as the 11 September 2001 attacks, as considered by Hon et al. (2004) Chen and Siems (2003) study. Chen Siems (2003), used event study methodology to capture the aftermath of terrorism on global capital markets. They studied on the reaction of U.S. capital markets in response to terrorist attacks. Their results showed that capital markets of US are more resilient flexible than in the past and recover quicker from terrorist attacks than other global capital markets. Their study suggests this increased market resilience to be partially explained by a stable financial sector in US that provides adequate liquidity to support market stability and reduce the spread panic. Methodology and Data Description Stock index data is taken from Karachi Stock Exchange, Yahoo Finance. This is a well known and reliable source of business information in Pakistan. The daily closing value of KSE-100 index is used for calculating the daily returns. The continuously compounded annual rate of return is used to measure the returns for the specific period as; Rt = ln (Pt / Pt-1) The closing prices of KSE-100 index for Karachi Stock Exchange are taken for the period July 2, 1997 to Oct 13, 2010. Our proxies are TERROR, a dummy variable of terrorist incidents during this period; REGIME, a dummy variable for government changes from fully democratic government to Marshal Law or democratic under such condition; a dummy variable for the period of the Kargal War in 1999. We applied regression model and Arch/Garch technique to capture the results. ARCH/GARCH Study Models This section presents the methodology of the paper. Daily data for Karachi stock markets were obtained from Yahoo finance and data for terror, kargal war and regime were obtained from South East Asia Terrorism Portal, and Different News Paper of Pakistan. Study apply ARCH/GARCH tools to see the long term relationship of these variable taking stock return as dependent variable and terror, regime and kargal war as independent variables. As aggregate uncertainty may be a function of political instability, we proceed to model uncertainty directly. It is natural to look at the conditional variance of output. Thus, we examine GARCH processes, in a more general framework than in the previous section. The model estimated here is a GARCH (1,1) process. Engle (1982) argue that in high frequency data large and small disturbance errors appear in group therefore error term variances can be shown as a function of their lagged values. He calls it Autoregressive conditional Heteroskedasticity (ARCH). As an investor or policy maker, we might be interested in investigating the returns and variance financial assets over observable period of time (conditional) rather than long run estimate of variance (unconditional). Engle (1982) shows that it is possible to describe the conditional mean and conditional variance of a financial asset using information set of previous period; Where is the return of financial asset in time t conditional on the information set at time t-1. E represents the expected value in statistics. Consider the simple model Where Where the rate of is return and are the regression parameters. A typical ARCH model can be written as follows: Conditional Mean Equation; Error Decomposition OR where ‘v is the part of variance which is homoskedastic and is the conditional variance which is Heteroskedasticity. This conditional variance can be shown as ARCH Conditional variance Equation, i.e. where and are non negative. Engle (1982) has also derived a Lagrange Multiplier (LM) based principle to test the hypothesis of. Another useful variant of ARCH methodology, proposed by Bollerslev (1986) is the generalized ARCH or GARCH model. Bollerslev (1986) argues that conditional variance in financial series is not only the function of its lagged error term but also the function of its lagged conditional variances. Therefore, GARCH (1, 1) process would be So GARCH model helps to explain the conditional variance with the help of past squared error term and conditional variance lag value. Which also means that conditional variance at time‘t would be function of long run variances and also variances conditional on past information set (short run) or observed shocks i.e. . Testing for ARCH/GARCH effects: Before estimating Arch/Garch techniques, it is first important to check for possible presence of Arch effect in order to know which model is requires the ARCH estimation instead of OLS (Ordinary Least Squire). The presence of ARCH effects in a regression model does not invalidate OLS estimation. However it implies that there is more efficient nonlinear estimator than OLS. (See Table 1.1) Obs*R-Squared is 147.26 and has a probability limit of 0.000. This clearly suggested that ARCH effect is present and presence of Heteroskedasticity suggested that ARCH/GARCH is appropriate model for this type of time series data. So we can apply ARCH/GARCH model on this data instead of ordinary least squire regression. Result of GARCH effects: The results of GARCH are presented in Table 1.3. The first column presents the regression results when we include as independent variables dummy values of the regime, terror, and war. In most of the cases, the variables enter with the anticipated signs, but not all of them are consistently significant at the 0.05 level. We can see an evidence of significant negative impact of terror, regime that show due to bomb blast in Pakistan and change in government negatively impact the country stock return in long run while insignificant positive impact of war on the country stock return. The results can further explained that stock return volatility every day is explained by approximately 71% of the previous months return volatility for Karachi stock exchange. This is significant for KSE returns. The coefficient of return innovation are statistically significant for market implying that new information arrival into the markets has significant impact on predicting next days stock market volatility. Because, the constant term in the variance equation for KSE is significant. The results of GARCH (1,1) are presented in Table 1.3 (Table 1.2) The model can be written as; Mean Equation: = 0.001188+ 0.064048* R_KSE(-1) Variance Equation: GARCH = 4.01E-05 + 0.20721*ARCH+ 0.713458 GARCH(-1) 1.21E-05*Terror + 1.93E-05*War -1.48E-05*Regime The persistence parameter for KSE Durbin-Watson stat = 1.943, which is > 1. This show a very explosive volatility in KSE returns. It also demonstrates the capability of past volatility to explain current volatility (Engle and Bollerslev, 1986) and because it is very high, the rate at which it diminishes is rather very slowly. For ACRH/GARCH, conditional standard deviation and conditional varience graph were as shown in figure 1.2 and 1.3; The GARCH coefficient is both statistically significant and conforms to expectation. This implies that past variances exert significantly positive effect on stock return volatility in KSE. On the basis of these results, it is evident that there is significant time varying volatility in Pakistan stock market returns during the sample periods. Conclusions and Recommendations In this paper, we have estimated a nonlinear GARCH model for daily stock returns volatility and terror, Kargal war and regime in Pakistan. Data for the estimation of GARCH (1,1) models was obtained from Yahoo finance and South Asia Terror Portal and news paper of Pakistan. The asymmetric effect of terror, war and regime on stock returns and volatility was investigated. Preliminary investigation into the nature of the data reveals that study had to employ ARCH/GARCH techniques for data analysis. Firstly, results show evidence of time varying volatility in stock market returns across the market and from the asymmetric model, results indicate that bad news has larger impact on stock volatility than good news in the KSE. The result for KSE showed that terror and regime has negativity impact on returns of KSE while war has positively effect, it may be due to short term period of the war. All three variable are significantly have their impact on the returns.

Anorexia and Bulimia Essay -- Causes of Bulimia Nervosa, Anorexia

  Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  Bulimia Nervosa [also known as Bulimia] is a very serious and dangerous eating disorder. The disorder can be describe as bingeing and then followed by purging or a person who eats a large amount of food in short periods of time and then vomits after eating to prevent on gaining the weight cause by the food. There is different ways of going about ways to prevent the weight gain, making oneself throw up, taking pills, or laxatives which will increase how fast the food will move through your body, exercising excessively, eating a little amount or not at all, or taking other pills to pass urine This disorder is mostly between the ages of 15 and 35, even if they have no specific food disorders. Studies made in Europe and USA have underlined the fact that roughly 10% of women in this age bracket say they and provoke vomiting occasionally 2% vomit once or more per week. This disorder is most commonly found in girls and in their late teen years, but that’s not to s ay it does not happen to boys it does but it is very rare. With a person who has bulimia it is very hard to tell by their appearance because when you have Bulimia you look average weight or just below it.   Ã‚  Ã‚  Ã‚  Ã‚  This disorder is split up into 3 different illnesses they all have a lot in common but also have differences. The first type is Simple, simple bulimia nervosa usually affects girls at age 18 years old. The cause for this illness is under confident about oneself but come from good backgrounds. This illness is usually triggered by a short time of depression, such as a break up with a boyfriend. The girls who have simple bulimia nervosa have had a normal life as made friends normally and then they will have the short depression period and they will learn to hate themselves and their appearance. Usually diets start to boost up ones self esteem and then they cheat during the diet and vomiting will occur after eating. This form of bulimia is the least out of all 3 forms severe. But that is not to say that the girls who do this don’t progress into worst conditions. Another form of the disorder is Anorexic Bulimia Nervosa this is almost a mixture between Anorexic a nd Bulimia. It starts off as just a brief period of anorexic and then when that is over the sufferer recovers the illness by a short period of stabilized weight... ... and mental health professionals come in to help the patient recover. In different cases of the disorder family therapy is the best way to help overcome the disorder and helps eat healthy again. The family has a big effect on the sufferer and has to help the person realize that there body shape if perfectly fine and doesn’t need to be thin to make anyone happy. There is always Clinics that may help treat the disorder, the treatment is structured and controlled so that they have to eat in a better and healthier way. If there is no desire and hospitalizing is not necessary for the sufferer then there is the choice of outpatients by having weekly visits to a therapist and sometimes needs psychotherapy for the medicine, which can be more effective in worst cases. These disorders should not be left alone or untreated they can damage the body and soul and if left untreated the disorder can kill and at the best leave you looking terrible and feeling worse. These disorders may hel p you lose weight or not let you gain any, but in the long run it can seriously harm one and not only do these disorders affect the sufferer it also affects the people around them.   Ã‚  Ã‚  Ã‚  Ã‚  

Hot Seat Chapter 16 Essay

1 . The fundamental ideas associated with the mercantile theory were: that everything was to benefit the mother country, each nation was trying to achieve self-sufficiency, colonies and agriculture would improve economy and raw materials, and the country must benefit at the expense of others. For the most part, these ideas along with a few other minor pieces helped European nations to conquer much of the New and old world. Great Britain was the most successful with a vast overseas empire through North America, the West Indies, Africa and into India. They governed their colonies effectively and efficiently maximizing power and economic strength over the world. The least successful would probably be Portugal. They initially started out strong, finding trade routes along Africa into Asia and conquering Brazil, however by the 18th century their empire had diminished to slight control of Brazil and almost no other colonies. 2. The main points of conflict between Britain and France in North America were in the St. Lawrence River valley and the Ohio River valley. These areas were huge enters of trade and influence of the Native Americans that both the French and English desired. In the West Indies the conflict was mainly over crops and through naval battles. These skirmishes affected overseas trade and the flow of resources. In India, the conflict was mainly restricted to port cities and factories set up by the early English and French settlers along the Indian subcontinent. 3. Triangle trade was an extremely useful trading method to transport goods, raw materials, and resources between Europe, the Americas, and Africa. European sailors ould travel down to Africa and exchange weapons (mostly) for African slaves from West African kings. These slaves were typically prisoners of war that the rival African cities wanted to get rid of. Europeans would transport these slaves to the West Indies and North American colonies were they would be traded for bullion and raw materials found and grown in the New World. Finally those ships would take the materials back to Europe were they could be sold and traded to the rest of the world. Triangle trade was an effective way for mercantilist empires to become more self- ufficient and grow economically. 4. Initially, the Spanish conquered and controlled the largest empire in the Americas. They had colonies in much of the rich West Indies, all throughout Central America, most of South America, and the South West of North America. They would split much of their territories into Judicial councils called audiencias. Each audiencia had a local official loyal to the Spanish crown called a Corregidor. Before the Bourbon reforms, Queen Isabella had assigned much of the colonial control to the Council of the Indies, hich nominated viceroys for the New World. Trade was mostly ruled by a flota system of yearly shipping with Spain. With the Bourbon reforms, Charles Ill attemoted to reassert Spanish control over the colonies. He allowed more than one Spanish city (Cadiz) to trade with the New World and opened more Caribbean ports. colonists) as the heads of society. They were the elites while the creoles were subordinate. 5. Slavery was introduced to the Americas through the triangle trading networks where large numbers of blacks were brought over form Africa. This wasn’t the first nstitution of slavery, however it is one of the worst recordings of slavery throughout history. Slavery became a fundamental part of the plantation system and completely necessary to the New World colonies’ economies. Without slaves, much of the intense economic growth experienced by the New World and Europe would not have occurred. The plantation system helped drive inhumane treatment of slaves because they were then seen as property, farm tools, that could be replaced. When they misbehaved or didn’t function properly, the plantation owners would either fix them through torture) or kill them. Despite the harsh treatment of slaves, our country and many others would arguably never have gotten to where it is today without them. 6. By the end of the Seven Years’ War, France and Austria came out defeated. In Europe, almost no borders or politics changed. Germany was still disputed and Prussia remained a strong power with England at its back. In North America, France lost all of their colonies and possessions. England and her North American colonies defeated the French and with the Treaty of Paris, cast them out. The only real foothold France now held in the New World were through its West Indies possessions. Great Britain now came out as a world power and probably the strongest nation up until the USA’s uprising. 7. Many European, especially British, events and ideas helped influence the American Revolution and drive the colonists to rebel. The John Wilkes affair which challenged the Kings power and the influence of Parliament. John Wilkes criticized the peace negotiations with France after the Seven Years’ War and gained much support from mall property owners and the nobles who wished to drain the kings power. America saw these demonstrations of proof as to the tyrannical nature of a monarchy. The Glorious Revolution also showed to the colonists how sometimes a new government must be instated to protect the people. Thinkers like John Locke and Thomas Paine also widely influenced the minds of many Americans. The American Revolution also caused a domino effect over much of the New World colonies like Haiti and other South American areas. It displayed the Enlightenment characteristics and helped inspire the French revolution. Great British political radicals saw that taxation of their North American colonies as far and Just. England had protected and defended them throughout the Seven Years’ War and they must share some of the burden. Also, American colonists paid significantly less taxes than the English citizens in Great Britain so they had no reason to complain. Americans were outraged because they were only represented through virtual representation. They felt that if the Parliamen t wished to tax the colonies, they must give them fair representation in Parliament, â€Å"no taxation without

SPSS analysis on modern portfolio theory-optimal portfolio strategies in today’s capital market

Abstract This paper provides information on specific ideas embedded in single index model/construction of optimal portfolios compared to the classic Markowitz model. Important arguments are presented regarding the validity of these two models. The researcher utilises SPSS analysis to demonstrate important research findings. This type of analysis is conducted to explore the presence of any significant statistical difference between the variance of the single index model and the Markowitz model. The paper also includes implications for investors. Introduction In the contemporary environment involving business investments, selecting appropriate investments is a relevant task of most organisations. Rational investors try to minimise risks as well as maximise returns on their investments (Better, 2006). The ultimate goal is to reach a level identified as optimal portfolios. The focus in this process is on initiating the portfolio selection models, which are essential for optimising the work of investors. Research shows that the Markowitz model is the most suitable model for conducting stock selection, as this is facilitated through the use of a full covariance matrix (Bergh and Rensburg, 2008). The importance of this study reflects in the application of different models so as to develop adequate portfolios in organisations. It is essential to compare certain models because investors may be provided with sufficient knowledge about how they can best construct their portfolios. In this context, the precise variance of the portfolio selection model is important, as it reflects portfolio risk (Bergh and Rensburg, 2008). Information on the parameters of different models is significant to make the most appropriate decisions regarding portfolio creation. Markowitz is a pioneer in the research on portfolio analysis, as his works have contributed to enhancing investors’ perspectives on the available options regarding specific models of constructing optimal portfolios (Fernandez and Gomez, 2007). Research Methodology The research question presented in this study referred to the exploration of ideas embedded in single index model/construction of optimal portfolios and comparing them with the classic Markowitz model. The focus was on the construction of optimal portfolios, as the researcher was concerned with the evaluation of constructed portfolios with specific market parameters (Better, 2006). Moreover, the researcher paid attention to the stock market price index, including stocks of organisations distributed in three major sectors: services, financial, and industrial (Fernandez and Gomez, 2007). The behaviour of this index was explored through the implementation of SPSS analysis. The data covered a period of seven years, starting on January 1, 2000 and ending on December 31, 2006. It was essential to evaluate the effectiveness parameters of the single index model/construction of optimal portfolios and the Markowitz model. The criteria for the selection of companies included that all organisati ons shared the same fiscal year (ending each year on December 31) as well as they have not demonstrated any change in position. Results and Data Analysis The research methodology utilised in the study is based on the model of single index/optimal portfolios and the Markowitz model. The exploration of the relationship between these two models required the selection of 35 equally weighted optimal portfolios, as two sizes of portfolio were outlined. An approximate number of 10 optimal portfolios represented the first size, which further generated 12 portfolios. In addition, the researcher considered the option of simulating of optimal portfolios represented at second sizes (Bergh and Rensburg, 2008). The criterion of queuing randomise portfolio selection has been used to generate approximate 23 portfolios from the second size category. The researcher selected five and 10 stocks to analyse the data. The portfolio size split allowed the researcher to explore how the portfolio size could be used to affect the relationship between the single index model/optimal portfolios and the Markowitz model (Fernandez and Gomez, 2007). Results of testin g the data are provided in the table below: Optimal portfolio numberVariance of Single Index ModelVariance of the Markowitz ModelOptimal portfolio numberVariance of the Single Index ModelVariance of the Markowitz Model 100.00370.003950.00210.0023 100.00140.001750.00280.0038 100.00210.002850.00420.0051 100.00200.002150.00250.0030 100.00310.003550.00260.0024 100.00190.001950.00330.0038 100.00880.008650.00670.0071 100.00280.003750.00370.0053 100.00250.002450.00380.0043 100.00220.002350.00210.0020 100.00190.002050.00630.0061 100.00230.002650.02120.0202 Table 1: Variance of Five and 10 Optimal Portfolios Based on the results provided in the table, it can be concluded that the variance between the single index model/construction of optimal portfolios and the Markowitz model is similar. For instance, values of 0.0020 and 0.0019 for the variance of the two models are similar. This means that the results do not show substantial statistical differences between the two models. The tables below contain a descriptive summary of the results presented in the previous table: MeasureSingle Index ModelMarkowitz Model Mean0.00440.0047 Minimal0.00210.0020 Maximum0.02120.0202 Standard Deviation0.00370.0035 Table 2: Descriptive Summary of 10 Optimal Portfolios The results in Table 2 were derived from testing the performance of 10 optimal portfolios. It has been indicated that the mean for the single index model of 10 portfolios is 0.0044, while the mean for the Markowitz model is 0.0047, implying an insignificant statistical difference. The minimal value of the single index model is reported at 0.0021, while the minimal value of the Markowitz model is 0.0020. The difference is insignificant. The maximum value of the single index model is 0.0212, while the same value of the Markowitz model is 0.0202. Based on these values, it can be argued that there is a slight difference existing between the two models. The standard deviation of the single index model is 0.0037, while the standard deviation of the Markowitz model is 0.0035, which also reflects an insignificant statistical difference. MeasureSingle Index ModelMarkowitz Model Mean0.00280.0031 Minimal0.00140.0017 Maximum0.00880.0086 Standard Deviation0.00200.0019 Table 3: Descriptive Summary of 5 Optimal Portfolios Table 3 provides the results for five optimal portfolios. These results are similar to the ones reported previously (10 optimal portfolios). The mean for the single index model of 5 optimal portfolios is 0.0028, while the mean for the Markowitz model is 0.0031, implying an insignificant statistical difference. There are insignificant differences between the two models regarding other values, such as minimal and maximum value as well as standard deviation. Furthermore, the researcher performed an ANOVA analysis of 10 optimal portfolios, which are presented in the table below. It has been indicated that the effective score for the single index model and the Markowitz model is almost the same. Yet, an insignificant difference was reported between the two means and standard deviations for both models. ANOVA AnalysisSum of squaresDfConditionMeanStandard DeviationStandard Error MeanFSig. Between Groups.00011.000.003125.0018704.0005399.089.768 Within Groups.000222.000.002892.0019589.0005655 Total.00023 Table 4: ANOVA Analysis for the Variance between the Single Index Model and the Markowitz Model of 10 Portfolios From the conducted analysis, it can be also concluded that the F-test presents an insignificant statistical value, implying that the researcher rejected the hypothesis of a significant difference existing between portfolio selections with regards to risk in both models used in the study (Fernandez and Gomez, 2007). Hence, the hypothesis of a significant difference between the variance of the single index model and the Markowitz model was rejected (Lediot and Wolf, 2003). In the table below, the researcher provided the results of an ANOVA analysis conducted on five optimal portfolios: ANOVA AnalysisSum of SquaresDfConditionMeanStandard DeviationStandard Error MeanFSig. Between Groups.00011.000.004852.0036535.0007618.096.758 Within Groups.001442.000.004509.0038595.0008048 Total.00145 Table 5: ANOVA Analysis for the Variance between the Single Index Model and the Markowitz Model of 5 Portfolios The results from Table 5 show that the variance between the single index model and the Markowitz model of five optimal portfolios is almost the same. Regardless of the stock number in the selected optimal portfolios, there is no significant statistical difference between the single index model and the Markowitz model. The main finding based on the reported data is that the single index model/construction of optimal portfolios is similar to the Markowitz model with regards to the formation of specific portfolios (Bergh and Rensburg, 2008). As indicated in this study, the precise number of stocks in the constructed optimal portfolios does not impact the final result of comparing the two analysed models. The fact that these models are not significantly different from each other can prompt investors to use the most practical approach in constructing optimal portfolios (Haugen, 2001). Placing an emphasis on efficient frontiers is an important part of investors’ work, as they are focused on generating the most efficient portfolios at the lowest risk. As a result, optimally selected portfolios would be able to generate positive returns for organisations. This applies to both the single index model and the Markowitz model (Fernandez and Gomez, 2007). Conclusion and Implications of Research Findings The results obtained in the present study are important for various parties. Such results may be of concern to policy makers, investors as well as financial market participants. In addition, the findings generated in the study are similar to findings reported by other researchers in the field (Bergh and Rensburg, 2008). It cannot be claimed that either of the approaches has certain advantages over the other one. Even if the number of stocks is altered, this does not reflect in any changes of the results provided by the researcher in this study. Yet, the major limitation of the study is associated with the use of monthly data. It can be argued that the use of daily data would be a more viable option to ensure accuracy, objectivity as well as adherence to strict professional standards in terms of investment (Better, 2006). In conclusion, the similarity of the single index model and the Markowitz model encourage researchers to use both models equally because of their potential to generate optimal portfolios. Moreover, the lack of significant statistical differences between the variance of the single index model and the Markowitz model can serve as an adequate basis for investors to demonstrate greater flexibility in the process of making portfolio selection decisions (Haugen, 2001). The results obtained in the study were used to reject the hypotheses that were initially presented. As previously mentioned, the conducted F-test additionally indicates that the single index model and the Markowitz model are almost similar in scope and impact (Fernandez and Gomez, 2007). Investors should consider that portfolio selection models play an important role in determining the exact amount of risk taking while constructing optimal portfolios. Hence, investors are expected to thoroughly explore those models while they select their portfolios (Garlappi et al., 2007). Both individual and institutional investors can find the results generated in this study useful to facilitate their professional practice. A possible application of the research findings should be considered in the process of embracing new investment policies in the flexible organisational context (Bergh and Rensburg, 2008). Future research may extensively focus on the development of new portfolio selection models that may further expand the capacity of organisations to improve their performance on investment risk taking indicators. References Bergh, G. and Rensburg, V. (2008). ‘Hedge Funds and Higher Moment Portfolio Performance Appraisals: A General Approach’. Omega, vol. 37, pp. 50-62. Better, M. (2006). ‘Selecting Project Portfolios by Optimizing Simulations’. The Engineering Economist, vol. 51, pp. 81-97. Fernandez, A. and Gomez, S. (2007). ‘Portfolio Selection Using Neutral Networks’. Computers & Operations Research, vol. 34, pp. 1177-1191. Garlappi, L., Uppal, R., and Wang, T. (2007). ‘Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach’. The Review of Financial Studies, vol. 20, pp. 41-81. Haugen, R. (2001). Modern Investment Theory. New Jersey: Prentice Hall. Lediot, O. and Wolf, M. (2003). ‘Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection’. Journal of Finance, vol. 10, pp. 603-621.