Diabetes and the african-american population Essay

Diabetes and the african-american populace - Essay Example Additionally, diabetes is likewise connected with the advancement of nephropathy with possible renal disappointment, autonomic brokenness, and foot ulcers. Thirst, polyuria, obscuring of vision, and weight reduction are the trademark clinical introduction of diabetes. Diabetes can prompt ketoacidosis and hyperosmolar non-ketotic unconsciousness. People with diabetes are frequently asymptomatic and a few patients experience gentle side effects. For a long time, there has been an attention to various kinds of diabetes with shifting seriousness. Toward the start of the twentieth century, the likelihood that there are two unmistakable sorts of diabetes rose. The two sorts of diabetes are Type 1 diabetes (beta-cell annihilation), which is an idiopathic and immune system ailment, and Type 2 diabetes, which is described by insulin obstruction and insulin hyposecretion (Holt, 2010). Type 1 diabetes happens because of beta-cell demolition and mellow insulin obstruction. Insulin is required for endurance after the patient endures the underlying phases of the infection. Type 2 diabetes is described by insulin obstruction with relative insulin insufficiency. Type 2 diabetes is the predominant type of the illness around the globe (T. Metcalf and G. Metcalf, 2008). Diabetes is especially very predominant in the United States of America. As per the 2000 US Census, there are 37.4 million African American people in America which comprise around 12.3% of the complete populace. In African American kids, the paces of Type 1 diabetes are lower contrasted with American youngsters. The African American populace has a frequency pace of 5 to 8 for each 100,000 for every annum. Then again, the occurrence pace of diabetes in white populace is 14 to 17 for every 100,000 every year. (Joslin and Kahn, 2006).The various extents of racial admixture, especially with the white populaces, may be the explanation behind the particular frequency rates among the dark populace. A huge job is played by hereditary

Who Cares About Writing Anyway Essay Example | Topics and Well Written Essays - 1000 words

Who Cares About Writing Anyway - Essay Example is about oneself, including the individual and the aggregate inside the â€Å"I.† I might want to believe that individuals, notwithstanding, should contemplate what, why, and how they compose, on the grounds that composing renders huge force that ought not be weakened by continually expounding on close to home and shallow difficulties alone. The right to speak freely of discourse is a duty that is too essential to be in any way squandered on void notices that don't, at any rate some of the time, mean something more to individuals as people and as networks. This paper contends that individuals should think about composition, since whoever composes well can re-tell the past, acknowledge and influence the present, and re-characterize what's to come. History generates power; whoever composes and re-tells it, claims it and what's to come. Benjamin Franklin has composed his collection of memoirs that some commendation, some censure, or some simply feel nothing for. Be that as it may, he knows the intensity of the composed word. The composed word can be the reason for the said word as well. The composed word can bring importance and exercises that can affect the manner in which individuals think and act. Lawrence subverts Franklins virtues in â€Å"On Ben Franklins Virtues,† however he does as such through composing as well. He needs to record his own perspectives, since he realizes that composing will re-tell the past the manner in which he needs it to be told. He needs the past to not be deciphered and spread by a white man alone. He needs his own perspective as a â€Å"dark forest† (367). I need to pressure that composing comprises of communicating how individuals need others to consider themselves to be people and as a feature of their kin/s as well. In reality, each race has its people. Lawrence is an individual and Franklin is as well; they have a place with their â€Å"groups,† whether these gatherings converge or not. They can conflict in their contentions of excellencies and expound on it, since that is a piece of forming what their identity is. Simultaneously, they need to impact others, regardless of whether they are in the equivalent racial network or not, with the goal that they can understand their past as

The Ultimate Cheat Sheet for Writing Explainer Video Scripts

The Ultimate Cheat Sheet for Writing Explainer Video Scripts Internet marketing has been revolutionized by video.It is now official that video is the ruler of internet marketing. Although text certainly has its place, video is the king of content.Check out the below statistics.According to both Alexa and SimilarWeb, YouTube is the second most popular website globally after Google.59% of senior executives said that they preferred video to text.Almost 50% of internet users look for videos related to a product or service before visiting a store.Video traffic is estimated by Cisco to account for 82% of all internet traffic by 2022.With such records, you would be at a loss if you ignored the power of videos.There are many types of videos used to pass information. In this article, we will look at one of the more common ones: the explainer video.Explainer videos are used by brands to help customers or prospects understand what those businesses do. They can also be used to explain an aspect of the business, or a product which is somehow difficult to u nderstand.Simply put, they are a great way of educating people about your business.From education institutions and not-for profit organizations to large corporations, explainer videos are being widely used.But what’s really the big deal about them anyway?WHY EXPLAINER VIDEOS WORKFirst of all, all video content works. And just for clarity purposes, “work” here means generating leads and helping to make conversions.If all videos work, then why all the hype about explainer videos in particular?Below are some reasons to help you understand the situation.They are fun to create.When you talk to artists, graphic designers and video producers, they will tell you that one of the most important things in their jobs is creativity. And being creative is fun.Rarely will you get tired or bored while being creative.When you have a product to describe and are free to use simple language to pass the message, that is fun. You get to speak the natural language with no need for technical informat ion.Much of the fun is on the creation of characters and how to illustrate the story being told.The copy to be used can either come from the client or be written by the producers. This happens after they have understood the goals of the video. This is the way one of the top video producers does it. They are fun to watch.Going by the number of explainer videos out there, it would be safe to assume that you have watched at least a few. When you did, what went through your mind especially when you watched the first one?Explainer videos have a way of reminding you of the good old days of simple yet funny cartoons.Today, cartoons look and feel very different due to all the technological advancement.With more computing power and sophisticated graphic design software, more life-like cartoons are being created.This is however not a bad thing at all since there are 3D explainer videos which serve their purpose well. They actually rank very highly with big companies since they are very engagi ng.They also give a realistic, almost life-like feel to the created characters.When you are looking for information and you get an explainer video, you can be sure of enjoying it.One thing that explainer videos do very well is entertain, even as they help you understand what a product does.Generate more conversions.While videos generally attract more traffic to websites, explainer videos are proving to do it in a seemingly special way. Or bigger way. The sheer interest in them plays a big role in this.It all comes down to the quality and effectiveness of these videos.Since these videos are produced as high quality visuals, they do the job they are supposed to very well.Terminus is a good example of how video can be effective in bringing the kind of results needed. After incorporating video in their marketing, they experienced a 216% increase in response rate.One sure way to experience such benefits is to include video in your landing page. The page that receives the traffic from you r ads can really be a good place to start.Whether you are asking your site visitors to subscribe, order, register etc, you will experience better results with video.The simplicity of the message and entertaining aspect of it combine to form a powerful tool ready to help your business grow.More memorable.If these videos are fun to watch and easily get clicked on, then they already have a place reserved for them in the minds of many.Psychologically speaking, the expectation to enjoy a video creates an excitement and the mind is assured that it’s going to have a good experience. This is similar to what happens when people sign up to watch comedians.They buy tickets knowing for sure that they are going to enjoy the show. As such, it just happens that there is excitement building up even before the comedian shows up on stage. This makes the job of the comedian easier.It is a similar case with these videos. Since they have already created a good name for themselves, it becomes easier to attract new fans. More and more people will be drawn towards them.But how do they become more memorable?Assuming you have created an unquestionably helpful video, the mind first registers excitement in anticipation to watching it.Once the video is on, entertainment and learning starts happening.After the video is over, the mind is happy and informed.Why?Because learning in a fun environment ensures retention. This has been proven by studies. And if you want personal evidence, just go back to the days of practical learning in school.Theoretic learning in class was quite boring. But anything learned by experimenting in the lab easily stuck in memory.The environment always impacts learning and retention. Therefore, an expectation to enjoy and be informed makes it easy to remember the information passed.Easy to share.It is the hope of the marketing team that whatever content they release will be shared. Especially on social media where it can go viral.But just what determines the level of sharing?Among many other reasons, there are two things which make content shareable.The first is that it has to be practically helpful. The second is that it has to be funny. Or at least entertaining in some way.If you were to keenly check the kind of advertising videos being released of late, you will notice a trend. These videos are not necessarily showcasing the brand all over the video. They are just telling a story.These stories are what make the videos acceptable and shareable. The videos themselves are, in all honesty, not very helpful in terms of providing information about the product or brand.This is because marketing is an emotional affair.Well, explainer videos also touch on emotions but then they are a bit different. And the difference is so clear that it’s shown in their nameâ€"explainer video.These videos are truly helpful. They offer explanations as to why a product is better or can do what you expect it to do. It is not just about showing you how you can have a good experience.Consider, for example, the whiteboard animation explainer video.You basically have characters being drawn in front of your eyes. The characters, as narrated by the voice, are doing something you can identify with.You see the character struggling with the same challenge you do then he suddenly gets a solution to help him out of the situation.This approach will lead you to fully understand the solution being presented.And seeing the helpful nature of the video, you will naturally want to share with your friends and connections. HOW TO WRITE EXPLAINER VIDEO SCRIPTSFor any marketer, these are not benefits which can be ignored.You certainly understand that these will automatically lead to the one thing you are afterâ€"increased conversions.But then, how do you go about creating them? How do you develop the concept and come up with a winning masterpiece?The goal of this article is to help you be able to utilize this trend for your brand.In case you are someone who desire s to get into the business of making explainer videos for clients, then you will also benefit from this article.We have broken the whole process into a simple step by step procedure.Know Your AudienceThis is really the first step in everything. As long as you are looking to communicate something, you definitely have to know who you are talking to. This helps you craft your message accordingly.In seeking to know your audience, you have to understand their lifestyles.Know what they like, dislike, prefer, what kind of options they generally look for etc. You have to know what makes them choose what they choose and why they reject other options.Unless you are in the same category as the people you are trying to reach, you will have to do some research at this stage.Having this knowledge will go a long way in making your script powerful.Remember that it is the script that determines the illustrations and ultimately, the whole story.It is then this story that determines the success of the video.Know Your ProductThis is another critical part of the script writing project. If you are the marketer writing your own scripts, then this might be easier.You will simply use the knowledge available from your colleagues who developed the product.If you are a script writer intending to write for these videos, then you should be a researcher. Whether naturally gifted or self-trained, you need to know how to get information.When discussing with the client about the product you are to write a script for, ensure you have as much information as possible. Always be attentive and ask questions when communicating.In some cases, your clients will be very general with what they tell you. You will however need to go deeper than the general. Keep in mind that you are supposed to differentiate this product from the competitor’s.Also consider the fact that the people you will be communicating with are possible users of the competitor’s product.It is therefore necessary to know the points which you will emphasize.Define the ProblemYou are offering a solution. Meaning that there is a problem.In many cases, the viewers know that there is a problem but they haven’t actively identified it.What usually happens is that you are a user of a certain product. That product may not be perfect but you have chosen it over others for a particular reason.In your mind, you are convinced that you got the best deal. All the same, this best deal still falls short. But because it is the best deal at the moment, you tend to overlook the shortcomings.Therefore, as much as the product isn’t satisfying all your needs, and is probably inconveniencing you, you hardly actively think about it.This is the situation you need to understand as the scriptwriter.You have to know why people are using the solutions they are using. With that knowledge, then you are able to show how that apparent solution is actually not a solution at all.You should show how the current solution is actually creating another problem which no-one is really recognizing.The trick here is to paint a picture of how serious the situation is.In defining the problem, it is important that you touch on emotions. Emotions are, and will always remain to be, the triggers behind decisions.So get to those emotions.Show how much pain is caused by using what viewers are using.Include StatisticsDefining the problem will not do it by itself. You have to capture the wider picture. People will normally want to know that they are indeed in a bad situation.Statistics will help you achieve this. All the stats you use must paint a negative picture.Let’s say you are writing about a safer car. You will be better of giving stats about accidents.For example, you can tell the viewers that 250 people have died in road accidents in the past 6 months alone. This was caused by brake failures happening at least once per month in 8 out of 10 vehicles.Notice the mention of people being victims and the cause of it.For you to be su ccessful with the use of stats, you have to show how they affect human life.Remember that people generally want to live better lives and anything which interferes with that is frowned upon.This stage is meant to strengthen your case to make the problem you defined be seen as a real possibility which could happen to anyone.Use Positive LanguageThis is where your story starts turning towards being a copy.Always keep in mind that you are presenting a solution. As such, you have to show the viewers that despite the problem being experienced, there is hope. This hope is what you are bringing to them.Having built up their emotions towards the avoidance of pain and dissatisfaction, you should now start soothing that pain. Speak positively and show them that you have done some homework to bring them the best solution.Do not fail to mention some of the efforts made to come up with the solution. At this point, you are subtly endearing them to your product without directly pitching.Use Illustr atable WordsAs you write, choose your words wisely. Every word you write will be used to come up with an image.Your script is what helps the video creators show the story as it is being read. It is therefore essential that they be able to come up with the appropriate graphics.Although any skilled and creative designer can illustrate almost anything, making it difficult will only increase the cost of the project. Moreover, the message may become complicated.Even worse, the words being read may not coincide very well with the graphics. These will only cost you more and make success difficult to achieve.When writing your own script, it is advisable to stay open-minded during its discussion. If you are the scriptwriter as well as video creator, engage your client and advise on what would be best.Mention the CompetitionAs you build towards the climax of the video, do not be ignorant of the competition.Doing so will only make you sound like the traditional salesperson who seems oblivious to the fact that his product isn’t the only one in the market.Your viewers are well aware of the options available.Apart from mentioning the competition, try to give some information you are sure not many people know.Take note not to badmouth your competitors.Do not make accusations or seek to tarnish their name. You are not on a smear campaign. You are just showing the lack of ingenuity on their side while showing how customer-focused you are.This is to make the viewer doubt his current choice.Show the Uniqueness of Your SolutionAfter that, bring in your hero product and show how it works better than all others. Mention that it is a product in the same category (e.g. a car) but different with unique features.From your knowledge of the product, pick at least three features lacking in the products currently in the market.Touching on emotions, show how those features will make the life of users more comfortable. Watch the below video to learn how to touch on emotions and successfull y sell anything. And by the way, the video is a type of explainer video. If the product is a productivity tool, show how it will help create more time for family and friends. Show how it will lead to less stress and enough rest time.Using our initial example of a car, mention features which make the car more safe. Paint a picture of a family traveling in the car safely to a picnic site and back home.You can also mention the strength of the engine and show the car helping pull a friend’s car out of the mud.Whichever way you communicate the features, keep it within the context of human benefits.Imply Freedom to ChooseComing towards the close of your copy, make it clear that you are providing a choice. Don’t let the script sound as though you are pushing people to choose you.If the whole project is a good job, they will definitely buy.Come up with a way of communicating freedom while at the same time subtly directing them to choose your product.This is a balance you have to create. As you give them the opportunity to make a choice, you are also directing them towards the best choice. In the minds of those you are pitching, they have been on a journey with you as the guide.As such, they have already trusted you to direct them in the right path. So coming to this fork in the road, as much as they are the ones to decide, they will likely consider choosing you.Make It ShortExplainer videos are often short. In most cases, they will rarely be more than 6 minutes. However, the length of your video depends on many factors.Although there is data indicating that short (2 minutes) is better, context matters a lot. It also matters who is watching and the purpose of the video.For instance, can you imagine a 2-minute video about an engineering concept?Unless you are an engineer with an understanding of the basics, that won’t work.Taking all things into perspective, ensure you provide relevant information. But just to ensure you maintain people’s engagement, you can stay within 5 minutes.In this duration, you should have achieved maximum engagement and convinced everyone watching that your product is the best.Since you are writing a script, it would only be practical for us to advice on the number of words.On average, people speak 135 words per minute. Therefore, a 2-minute script would be around 270 words. Keep in mind that it also matters who will be reading it.Different people read at different paces.It also depends on the kind of story being told as the tone to be used will differ.Since the story is narrated, you also need to factor in pauses between the sentences and paragraphs.The story must sound natural just as though someone was talking to the viewers.After writing, it will be wise to practically track the length by reading it with a timer running to establish the length.To check whether your speed is okay, read it to a colleague or a friend. Ensure you explain to them what you are testing for so they can be helpful with their response.Onc e the script is delivered to the video creators, editing may still be needed. That means your availability is still crucial.CONCLUSIONYou are now well equipped to write explainer video scripts and nothing should hold you back.You can also write some sample scripts for practice so as to build your confidence before the main job.

Adrienne Clarkson Biography

A well-known CBC broadcaster, Adrienne Clarkson brought a new style to the role of Governor-General of Canada. Originally from Hong Kong, Adrienne Clarkson was the first immigrant and the first Chinese-Canadian to be Governor General. Adrienne Clarkson and her husband philosopher and author John Ralston-Saul kept a high profile, worked hard and traveled extensively to Canadian communities, both large and small, during her six years as Governor General. Reviews were mixed for Adrienne Clarksons tenure as Governor General. Many in the Canadian Forces, of which she was Commander-in-Chief, regarded Adrienne Clarkson fondly for going the extra mile for the troops. At the same time, some Canadians considered her elitist, and there was public criticism of her lavish spending, including taking a delegation on a $5-million circumpolar tour to Finland, Iceland, and Russia in 2003. Governor General of Canada 1999-2005 Birth Born February 10, 1939, in Hong Kong. Adrienne Clarkson came to Canada in 1942 as a refugee during the war and grew up in Ottawa, Ontario. Education BA, English Literature - University of TorontoMA, English Literature - University of TorontoPost-graduate work - La Sorbonne, Paris, France Profession Broadcaster Adrienne Clarkson and the Arts Adrienne Clarkson was a host, writer and producer at CBC Television from 1965 to 1982. Her CBC programs included Take ThirtyAdrienne at LargeFifth EstateAdrienne Clarksons Summer FestivalAdrienne Clarkson PresentsSomething Special Adrienne Clarkson also served as Agent General for Ontario in Paris from 1982 to 1987 and was Chair of the Board of Trustees of the Canadian Museum of Civilization from 1995 to 1999. Adrienne Clarkson as Governor General of Canada Adrienne Clarkson traveled extensively across Canada to meet Canadians where they live. In her first year as Governor General of Canada, she visited 81 communities and traveled 115,000 km (about 71,500 miles). She kept a similar pace for the next five years.One of the themes of Adrienne Clarksons time as Governor General was the North. In 2003, Adrienne Clarkson led a delegation on a three-week tour of Russia, Finland, and Iceland to raise Canadas profile and focus attention on northern foreign policy issues. She also spent time as Governor General in the Canadian north, including visits to the troubled communities of Davis Inlet and Sheshatshiu. Adrienne Clarkson established the Governor Generals Northern Medal to be awarded for achievements contributing to the evolution and reaffirmation of the Canadian North as part of the Canadian national identity.Adrienne Clarkson made a point of visiting Canadian troops in the field, going to Kosovo and Bosnia, spending Christmas on frigates i n the Gulf, and New Year 2005 in Kabul.Adrienne Clarkson was asked by Prime Minister Paul Martin to stay on an extra year to provide stability and experience when Parliament was faced with a minority government.When Adrienne Clarkson left office, it was announced that an Institute for Canadian Citizenship would be created in her honor, with up to $10 million in government backing.

What I Learned From An Audit Essay - 1277 Words

Introduction The overall goal of Professor Schafer for ACCT-8400 Seminar in Auditing course is to prepare for each student to enter the auditing professions and/or to work with auditors. The student should have ability to demonstrate proficiency in developing audit evidence that meet the standards of the profession, apply auditing standards in the real world, create an audit plan, and proposed to the senior managers. I recognize that having an effective audit system is important for a company because it protects the company from management and financial issues that might destroy the business. Three main things I learned from this class is how to plan an audit program, perform tests of control and substantive tests of transaction, and ethical responsibilities of accountants and auditors. Audit plan Firstly, in an audit plan, an auditor should know how to monitor, analyze and access risks of the organization. Essentially, auditors gather information on how an organization or company is operating, understand the company’s business, industry, ownership, and management, as well as observe how these things are being implemented. Then, the auditor can use information obtained from communications between previous auditors, analytical procedures to consider the inherent risk. Analytical procedures are evaluations of financial information through analysis of relationships among financial and non-financial data. In this procedure, we compute financial ratios to compare current toShow MoreRelatedEssay Ethics Case937 Words   |  4 Pagessupposed to go over a total of 6 hours. Pressured by the senior on the audit, he is caught between lying about his hours or telling the truth and going over budget on the audit, potentially causing the other accountants on the team to look bad prof essionally. 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Stability of Beta over Market Phases Free Essays

string(27) " of beta and its findings\." International Research Journal of Finance and Economics ISSN 1450-2887 Issue 50 (2010)  © EuroJournals Publishing, Inc. 2010 http://www. eurojournals. We will write a custom essay sample on Stability of Beta over Market Phases or any similar topic only for you Order Now com/finance. htm Stability of Beta over Market Phases: An Empirical Study on Indian Stock Market Koustubh Kanti Ray Assistant Professor, Financial Management at Indian Institute of Forest Management (IIFM), Bhopal, India. E-mail: raykk@iifm. ac. in Abstract The significant role played by beta in diverse aspects of financial decision making has forced people from small investors to investment bankers to rethink on beta in the era of globalization. In the present changing market condition, it is imperative to understand the stability of beta which augments an efficient investment decisions with additional information on beta. This study examined the stability of beta for India market for a ten year period from 1999 to 2009. The monthly return data of 30 selected stocks are considered for examining the stability of beta in different market phases. This stability of beta is tested using three econometric models i. e. using time as a variable, using dummy variables and the Chow test. The results obtained from the three models are mixed and inconclusive. However there are 9 stocks where all the three models reported similar signal of beta instability over the market phases. Keywords: Stability of Beta, Phase wise beta, Indian Market Beta, Dummy Variable, Chow Test 1. Introduction The Capital Asset Pricing Model (CAPM) developed by Sharpe (1964), Lintner (1965) and Mossin (1966) has been the dominating capital market equilibrium model since its initiation. It continues to be extensively used in practical portfolio management and in academic research. Its essential implication is that the contribution of an asset to the variance of the market portfolio – he asset’s systematic risk, or beta risk – is the proper measure of the asset’s risk and the only systematic determinant of the asset’s return. Risk is the assessable uncertainty (Knight, 1921) in predicting the future events that are affected by external and internal factors. Sharpe (1963) had classified risks as systematic risk and unsystematic risk . The elements of systematic risk are external to the firm. The external factors are changes in economic environment, interest rate changes, inflation, etc. On the other hand, internal factors are the sources of unsystematic risk. Unsystematic risks are categorized as business risk or financial risk specific to the firm. The systematic risk related with the general market movement cannot be totally eradicated through diversification. The unsystematic risk, which is confine to a firm, can be eliminated or reduced to a considerable extent by choosing an appropriate portfolio of securities. Some of the sources of unsystematic risk are consumer preferences, worker strikes and management competitiveness. These factors are independent of the factors effecting stock market. Hence, systematic risk will influence all the securities in the market, whereas unsystematic risk is security specific. International Research Journal of Finance and Economics – Issue 50 (2010) 175 Theoretically defined, beta is the systematic relationship between the return on the portfolio and the return on the market (Rosenberg and Marathe, 1979). It refers to the slope in a linear relationship fitted to data on the rate of return on an investment and the rate of return of the market (or market index). Beta is a technique of telling how volatile a stock is compared with the rest of the market. When the return on the portfolio is more than the return on the market, beta is greater than one and those portfolios are referred to as aggressive portfolios. That means, in a booming market condition, aggressive portfolio will achieve much better than the market performance. While in a bearish market environment the fall of aggressive portfolios will also be much prominent. On the other hand, when the return on portfolio is less than the market return, beta measure is less than one and those portfolios are treated as defensive. In case of defensive portfolios, when the market is rising, the performances associated with it will be less than the market portfolio. However, when the market moves down, the fall in the defensive portfolios would also be less than the market portfolio. In those situations where, the return of the portfolio accurately matches the return of the market, beta is equal to one that rarely happens in real life situations. Beta estimation is central to many financial decisions such as those relating to stock selection, capital budgeting, and performance evaluation. It is significant for both practitioners and academics. Practitioners use beta in financial decision making to estimate cost of capital. Beta is also a key variable in the academic research; for example it is used for testing asset pricing models and market efficiency. Given the importance of this variable a pertinent question for both practitioners and academics is how to obtain an efficient estimation. This study is aimed at testing the beta stability for India. Further the stability of beta is of great concern as it is a vital tool for almost all investment decisions and plays a significant role in the modern portfolio theory. The estimation of beta for individual securities using a simple market model has been widely evaluated as well as criticized in the finance literature. One important aspect of this simple market model is the assumption of symmetry that propounds the estimated beta is valid for all the market conditions. Many studies questioned this assumption and examined the relationship between beta and market return in different market conditions, but the results are mixed and inconclusive. In this paper, an attempt is made to investigate the stability of beta in the Indian stock market during the last 10 years i. . from August 1999 to August, 2009. With this objective, the paper is divided into five sections including the present section. Section 2 reviews the existing literature and discusses the findings of major empirical researches conducted in India and other countries. Section 3 describes the data sources and methodology. Section 4 outlines the results of tests for investigating the stabili ty of beta and its findings. You read "Stability of Beta over Market Phases" in category "Papers" Section 5 is dedicated to summary, conclusion and scope for further research in the area. 2. Literature review Several studies are carried out to study the nature and the behavior of beta. Baesel (1974) studied the impact of the length of the estimation interval on beta stability. Using monthly data, betas were estimated using estimation intervals of one year, two years, four years, six years and nine years. He concluded that the stability of beta increases significantly as the length of the estimation interval increases. Levy (1971) and Levitz (1974) have shown that portfolio betas are very stable whereas individual security betas are highly unstable. Likewise Blume (1971) used monthly prices data and successive seven-year periods and shown that the portfolio betas are very stable where as individual security betas are highly unstable in nature. He shows that, the stability of individual beta increases with increase in the time of estimation period. Similar results were also obtained by Altman et al (1974). In both the cases, initial and succeeding estimation periods are of the same length. Allen et al. (1994) have considered the subject of comparative stability of beta coefficients for individual securities and portfolios. The usual perception is that the portfolio betas are more stable than those for individual securities. They argue that if the portfolio betas are more stable than those for individual securities, the 176 International Research Journal of Finance and Economics – Issue 50 (2010) larger confidence can be placed in portfolio beta estimates over longer periods of time. But, their study concludes that larger confidence in portfolio betas is not justified. Alexander and Chervany (1980) show empirically that extreme betas are less stable compared to interior beta. They proved it by using mean absolute deviation as a measure of stability. According to them, best estimation interval is generally four to six years. They also showed that irrespective of the manner portfolios are formed, magnitudes of inter-temporal changes in beta decreases as the number of securities in the portfolios rise contradicting the work of Porter and Ezzell (1975). Chawla (2001) investigated the stability of beta using monthly data on returns for the period April 1996 to March 2000. The tability of beta was tested using two alternative econometric methods, including time variable in the regression and dummy variables for the slope coefficient. Both the methods reject the stability of beta in majority of cases. Many studies focused on the time varying beta using conditional CAPM (Jagannathan and Wang (1996) Lewellen and Nagel (2003)). These studies concluded that the fluctuations and events that influence the market might change the leverage of the firm and the variance o f the stock return which ultimately will change the beta. Haddad (2007) examine the degree of return volatility persistence and time-varying nature of systematic risk of two Egyptian stock portfolios. He used the Schwert and Sequin (1990) market model to study the relationship between market capitalization and time varying beta for a sample of investable Egyptian portfolios during the period January, 2001 to June, 2004. According to Haddad, the small stocks portfolio exhibits difference in volatility persistence and time variability. The study also suggests that the volatility persistence of each portfolio and its systematic risk are significantly positively related. Because of that, the systematic risks of different portfolios tend to move in a different direction during the periods of increasing market volatility. The stability of beta is also examined with reference to security market conditions. For example, Fabozzi and Francis (1977) in their seminal paper considered the differential effect of bull and bear market conditions for 700 individual securities listed in NYSE. Using a Dual Beta Market Model (DBM), they established that estimated betas of most of the securities are stable in both the market conditions. They experienced it with three different set of bull and bear market definitions and concluded with the same results for all these definitions. Fama and French (1992, 1996), Jegadeesh (1992) and others revealed that betas are not statistically related to returns. McNulty et al (2002) highlight the problems with historical beta when computing the cost of capital, and suggest as an alternative- the forward-looking market-derived capital pricing model (MCPM), which uses option data to evaluate equity risk. In the similar line, French et al. (1983) merge forward-looking volatility with istorical correlation to improve the measurement of betas. Siegel (1995) notes the improvement of a beta based on forward-looking option data, and proceeds to propose the creation of a new derivative, called an exchange option, which would allow for the calculation of what he refers to as â€Å"implicit† betas. Unfortunately the exchange options discussed by Siegel (1995) are not yet traded, and the refore his method cannot be applied in practice to compute forward-looking betas. A few studies are carried out to explore the reason for instability of beta. For example, Scott Brown (1980) show that when returns of the market are subjected to measurement errors, the concurrent autocorrelated residuals and inter-temporal correlation between market returns and residual results in biased and unstable estimates of betas. This is so even when true values of betas are stable over time. They also derived an expression for the instability in the estimated beta between two periods. Chen (1981) investigates the connection between variability of beta coefficient and portfolio residual risk. If beta coefficient changes over time, OLS method is not suitable to estimate portfolio residual risk. It will lead to inaccurate conclusion that larger portfolio residual risk is associated with higher variability in beta. A Bayesian approach is proposed to estimate the time varying beta so as to provide a precise estimate of portfolio residual risk. Bildersee and Roberts (1981) show that during the periods interest rates fluctuate, betas would fluctuate systematically. The change would be in tune with their value relative to the market and the pattern of changes in interest rate. International Research Journal of Finance and Economics – Issue 50 (2010) 177 Few research studies are available in the Indian context to examine the factors influencing systematic risk. For example, Vipul (1999) examines the effect of company size, industry group and liquidity of the scrip on beta. He considered equity shares of 114 companies listed at Bombay Stock Exchange from July 1986 to June 1993 for his study. He found that size of the company affects the value of betas and the beta of medium sized companies is the lowest which increases with increase or decrease in the size of the company. The study also concluded that industry group and liquidity of the scrip do not affect beta. In another study, Gupta Sehgal (1999) examine the relationship between systematic risk and accounting variables for the period April 1984 to March 1993. There is a confirmation of relationship in the expected direction between systematic risk and variables such as debt-equity ratio, current ratio and net sales. The association between systematic risk and variables like profitability, payout ratio, earning growth and earnings volatility measures is not in accordance with expected sign. The relationship was investigated using correlation analysis in the study. 3. Data Type and Research Methodology The data related to the study is taken for 30 stocks from BSE-100 index. The top 30 stocks are chosen on the basis of their market capitalization in BSE-100 index. These 30 stocks are selected from BSE100 stocks in such a way that the continuous price data is available for the study period. The adjusted closing prices of these 30 stocks were collected for the last 10 years period i. e. from August 1999 to August 2009. The stock and market (BSE-100) data has been collected from prowess (CMIE) for the above period. BSE-100 index is a broad-based index and follows globally accepted free-float methodology. Scrip selection in the index is generally taken into account a balanced sectoral representation of the listed companies in the universe of Bombay Stock Exchange (BSE). As per the stock market guideline, the stocks inducted in the index are on the basis of their final ranking. Where the final rank is arrived at by assigning 75 percent weightage to the rank on the basis of three-month average full market capitalization and 25 percent weightage to the liquidity rank based on three-month average daily turnover three-month average impact cost. The average closing price for each month of 30 socks is computed for the period August 1999 to August 2009. Therefore we have 120 average monthly prices for each of the 30 stocks included in the research. The following method has been used to compute the monthly return on each of the stock. P i,t – P i,t-1 ri,t = –––––––––– P i, t-1 Where: P i,t = Average price of stock â€Å"i† in the month t Pi,t-1 = Average price of stock â€Å"i† in the month t-1 r i,t= Return of ith stock in the month t. The monthly market return is computed in the following way: Bt – Bt-1 mt = –––––––––– B t-1 Where: Bt = BSE-100 Index at time period t Bt-1 = BSE-100 Index at time period t-1 mt = Market return at time period t. After the monthly stock and market returns are calculated as per the above formula, we identified the different market phases to compute beta separately. The market phases are identified, by creating a cumulative wealth index from the market returns. The cumulative wealth index data is presented in annexure-1. As per the cumulative wealth index, we identified five different market 178 International Research Journal of Finance and Economics – Issue 50 (2010) hases in BSE-100 index. We recognized that there are three bullish phases (Jan-1999 to Feb-2000, Oct-2001 to Dec-2007 and Dec-2008 to August 2009) and two bearish phases (Mar-2000 to Sept2001, Jan-2008 to Nov-2008). The summary of different market phases is depicted in Table -1 figure-1 below. Table-1: Different Market Phases Market Phases Phase I Phase II Phase III Phase IV Phase V Market Phase Timing Sta rt End Jan-1999 Feb-2000 Mar-2000 Sep-2001 Oct-2001 Dec-07 Jan-2008 Nov-08 Dec-2008 Aug-09 Market Type Bullish Bearish Bullish Bearish Bullish Figure-1: Different Market Phases After these five market phases are identified, the beta value has been computed for each stock for each market phases following the below mentioned regression equation. ri,t = ? + ? mt + e (1) ri,t = Return on scrip i at time period t mt = Market rate of return at time period t e = Random error ? = Parameters to be estimated The above regression equation is applied to calculate beta coefficient of each stocks for each market phases separately and taking the entire ten years period. As the objective of the paper is to test the stability of beta in different market phases, the hypothesis has been set accordingly. The null hypothesis (H0) being the beta is stable over the market phases, whereas the alternative hypothesis (H1) is that the beta values are not stable and varies according to phases in the market. The hypothesis has been tested with the help of three econometric models- using time as a variable, using dummy variables to measure the change of slope over the period and through Chow test. International Research Journal of Finance and Economics – Issue 50 (2010) 179 3. 1. Testing the Stability of Beta using time as a variable In case of measuring stability of beta using time as a variable, in the above regression model (1) another variable i. e. † t mt† is used as a separate explanatory variable. Where the time variable t takes a value of t=1 for the first market phase, t=2 for the second market phase and so on for all other market phases identified. In this method the objective is to see whether the beta values are stable over time or not. After including the tmt variable, the above regression model (1) can be written as: ri,t = ? + ? 1mt + ? 2( t*mt) + e (2) The above regression equation can be re-framed as below: ri,t = ? + (? + ? 2*t )*mt + e (2) To test the stability of beta, we basically have to see whether the expression ? 2 is significant or not. If it is significant, we need to reject the null hypothesis and accept alternative hypothesis. It is implied that the sensitivity of stock return to market return i. e. (? 1 + ? 2*t)* mt changes with time, and hence, beta is not stable. If ? 2 is not significant, (? 1 + ? 2*t)* mt will get reduced to ? 1*mt , implying that ? 1, or the beta of stock, does not vary with time and is thus stable over time. The statistical significance of ? 2 is tested using the respective p-values. . 2. Testing the Stability of Beta using dummy variable In case of the second method of testing the beta stability, dummy variables are used in above mentioned regression equation (1) for the slope coefficients. As five market phases discovered, there are 4 dummy variables used in the new equation (Levine et al. 2006). The new regression equation is reframed as follows: ri,t = ? 0 + ? 1* mt + ? 2*D1* mt + ? 3*D2* mt + ? 4*D3* mt + ? 5*D4*mt + e (3) Where: D1 = 1 for phase 1 (Jan 1999 to Feb 2000) data = 0 otherwise. D2 = 1 for phase II (May 2000 to Sept 2001) data = 0 otherwise D3 1 for phase III (Oct 2001 to Dec 2007) data = 0 otherwise D4 = 1 for phase IV (Jan 2008 to Nov 2008) data = 0 otherwise = return on stock I in period t. r i,t mt = retur n on market in period t. e = error term and ? 0, ? 1, ? 2, ? 3, ? 4 ? 5 = coefficients to be estimated. As there are 5 market phases, we use 4 dummy variables in the above equation (3). The use of 5 dummy variable would lead to a dummy variable trap. We treat the 5th phase viz. Dec-08 to Aug-09 as the base period. The significance of ? 2, ? 3, ? 4 and ? 5 will tell us whether the beta is stable over the time periods or not. For the beta to be truly stable over the entire period, all coefficients like, ? 2, ? 3, ? 4 and ? 5 should be statistically insignificant and where we need to accept the null hypothesis. The logic is that if ? 2, ? 3, ? 4 and ? 5 are insignificant, the equation reduces to the following, thus implying that beta is stable over time. ri,t = ? 0 + ? 1*mt + e (4) th 3. 3. Testing for Structural or Parameter Stability of Regression Model: The Chow Test In the third method, for structural or parameter stability of regression models, the Chow test has been conducted (Gujarati, 2004). When we use a regression model involving time series data, it may happen 180 International Research Journal of Finance and Economics – Issue 50 (2010) that there is a structural change in the relationship between the regress and the regressors. By structural change, we mean that the values of the parameters of the model do not remain the same through the entire time period. We divide our sample data into five time periods according to the different market phases identified earlier. We have six possible regressions for each stock (five regressions for each market phases and one for the whole ten year period). The regression equations are mentioned below. ri,t = ? 1 + ? 2mt + ut (5) (6) r i, t = ? 1 + ? 2mt + ut Equation (5) is for each market phases and equation (6) is for the whole period. There are 128 observations (n=128) for the whole period and n1=14, n2=19, n3=75, n4=11 and n5=9 are the number of observations for phase-I to phase-V respectively. The u’s in the above regression equations represent the error terms. Regression (6) assumes that there is no difference over the five time periods and therefore estimates the relationship between stock prices and market for the entire time period consisting of 128 observations. In other words, this regression assumes that the intercept as well as the slope coefficient remains the same over the entire period; that is, there is no structural change. Now the possible differences, that is, structural changes, may be caused by differences in the intercept or the slope coefficient or both. This is examined with a formal test called Chow test (Chow, 1960). The mechanics of the Chow test are as follows: First the regression (6) is estimated, which is appropriate if there is no parameter instability, and obtained the restricted residual sum of squares (RSSR) with df = [(n1+n2+n3+n4+n5) ? k], where k is the number of parameters estimated, 2 in the present case. This is called restricted residual sum of squares because it is obtained by imposing the restrictions that the sub-period regressions are not different. Secondly estimated the phase wise other regression equations and obtain its residual sum of squares, RSS1 to RSS8 with degrees of freedom, df = (no of observations in each phase ? ). Since the five sets of samples are deemed independent, in the third step we can add RSS1 to RSS8 to obtain what may be called the unrestricted residual sum of squares (RSSUR) with df = [(n1+n2+n3+n4+n5)? 2k]. Now the idea behind the Chow test is that if in fact there is no structural change (i. e. , all phases regressions are essentially the same), then the RSSR and RSSUR should not be statisticall y different. Therefore in the fourth step the following ratio is formed to get the F-value. F = [(RSSR ? RSSUR)/k] / [(RSSUR)/ ((n1 + n2+n3+n4+n5) ? 2k)] ~ F [k, ((n1+n2+n3+n4+n5) ? 2k)] (7) We cannot reject the null hypothesis of parameter stability (i. e. , no structural change) if the computed F value is not statistically significant (F value does not exceed the critical F value obtained from the F table at the chosen level of significance or the p value). Contrarily, if the computed F value is statistically significant (F value exceeds the critical F value), we reject the null hypothesis of parameter stability and conclude that the phase wise regressions are different. 4. Test Results and Findings Initially the beta coefficient is calculated using the Ordinary Least Square (OLS) technique as defined in equation (1). The estimation was carried out by using monthly return data for the 5 market phases for each of the 30 stocks. To compare the phase wise beta estimation with the entire 10 year period, the same estimation also carried out taking the whole 10 years for each stock separately. Stock wise beta values over 5 market phases and the entire period is reported in appendix-2. From annexure-2, it is revealed that there are 14 stocks beta value is greater than 1 in phase I. This figure (beta value greater than 1) has reduced to 6, 11, 12 and 10 for phase-2 to phase-5 respectively. It is also illustrated that, there are 8 stocks whose beta value is greater than 1 in respect to overall between Jan-99 to Aug-09 and highest being for Wipro of 1. 47. The stocks having beta value International Research Journal of Finance and Economics – Issue 50 (2010) 181 more than 1 are considered to be volatile securities. It is noticed that, as we increase the period of estimation to full ten years period, there are less number of stocks proved to be more volatile. Out of the total 30 stocks considered in the study, only one company i. e. LT has beta more than 1 in all phases including the overall period. But none of the company’s overall beta value is more than the phase wise betas. There are seven companies (RIL, NALCO, ITC, GAIL, Hindustan Lever, Hero Honda and Cipla) whose beta values are less than 1 all through the phases including overall period. These stocks are considered to be less volatile than the market. There are 3 companies (Cipla, ITC and Hindustan Lever) recent beta value (Dec 2008 to August 2009) is negative, where Cipla’s phase I beta value is also negative along with other two stocks like SAIL and NALCO. It is observed from annexure-2 that there are only two companies’ from the software sector (Infosys and Wipro) whose beta values are consistently declining over time. However there are 7 stocks viz. Cipla, Sunpharma, Wipro, Grasim, Hindustan Lever, Infosys and ITC whose beta values are showing a decreasing trend from phase 3 onwards, while Tata steel is the only stock whose beta values are showing an increasing trend during the same period. It is observed from the annexure-2 that, on an overall basis 29 out of 30 stocks have their beta values statistically significant at 5% level. This number has varied from 8 to 30 over the various phases, indicating that the beta values of the stocks have fluctuated significantly. This implies that the volatility of the stocks depend on the market phases i. e. bearish or bullish. Thus the result rejects the null hypothesis that the beta is stable over various market phases. The null hypothesis is rejected in 29 out of 30 cases in case of overall period, while 30 out of 30 cases in respect to phase-3. Since the period of estimation of beta is more in case of overall period and in phase-3, the obtained results are similar in both the cases. But the remaining phase wise results do not follow any pattern. In respect of period of estimating the value of beat the results are comparable to the finding of Baesel (1974) and Altman et al (1974). It is mentioned earlier that to examine the stability of beta over different market phases, three separate models have been used in paper. The results obtained from these models are interpreted in the following paragraphs. The estimated results for regression model-2 that includes t*mt as a separate variable are depicted in annexure-3. It is observed that the value of R2, a measure of goodness of fit varies from 0. 11 to 0. 61. It is only in 5 out of 30 regression results, the value is greater than 0. 50. The coefficient of mt (? 1) is found to be highly statistically significant at 5% level in 19 out of 30 cases. It is in 11 regressions, the coefficient is statistically insignificant. As discussed earlier, the significance of the coefficient of variable t*mt implies the rejection of the null hypothesis of stable beta over time. It is observed that the coefficient (? ) is significant in 14 cases out of 30. The regression results indicate that in 50% cases the null hypothesis of stability of beta over the market phases is rejected. This means 50% stocks reported stability of beta over different phases. So model (2) cannot infer that beta is not stable over market phases. The estimated results for coefficients for regression model-3 that incorporates dummy variables are depicted in annexure-4. It is noticed from the results that the R2 value fluctuates from 0. 15 to 0. 62 and in case of 8 stocks this value is greater than 0. 0. It is mentioned earlier that the null hypothesis of stability of beta will be rejected if any of the coefficients (? 2, ? 3, ? 4 ? 5) corresponding to D1*mt, D2*mt, D3*mt or D4*mt were found to be statistically significant. It is observed from the results presented in appendix-4, that there are 17 out of 30 stocks represented statistically significant at 5% level at least one of the coefficient. There are only 2 cases where 3 coefficients are significant and none of the stocks reported significant for all the 4 coefficients. Further in 6 cases where 2 out of 4 coefficients are reported significant, where as in 9 cases depicted significant only for one coefficient. The outcome of this model in brief can be stated that, in case of 17 stocks out of 30 stocks, the stability of beta hypothesis is rejected meaning, in rest 13 cases there is a stability of beta over the market phases. 182 International Research Journal of Finance and Economics – Issue 50 (2010) The estimated results of Chow test are depicted in annexure-5. The results show that, 12 out of 30 cases the F-value is statistically significant and rest 18 stocks are reported insignificant at 5% level. Based on the F- statistics and its corresponding p-values, the null hypothesis of beta stability over the market phases is rejected in 12 cases and accepted in 18 cases. The F-values are also supported by log likelihood ratio and it p-values, which also reported statistical significance in 12 cases. The outcome of Chow test confirms that the beta values are not stable or there is a structural change in 12 out of 30 stocks in different market phases. But the rest 18 stocks reported stability or no structural change in beta values over the market phases. From the above deliberations, it is observed that all the three models described above exhibit a mixed and inconclusive result. There are 14, 17 and 12 stocks are statistically significant as per model2, model-3 and model-7 respectively. This means as per model-2 the beta values of 14 stocks out of 30 stocks are instable over the period. But this number is 17 and 12 in case of model3 and 7 respectively. However, on the basis of results obtained from different models, it is not possible to conclude that the beta values of the stocks are stable or instable over the market phases. But if we closely glance at the results obtained from three models, it is very apparent that in case of 9 stocks where all the three models represented similar results and rejected the null hypothesis. These stocks include Sun pharmaceutical, Wipro, Tata motors, Tata Steel, Hindalco, Hindustan Unilever, HDFC, Infosys and Zee Entertainment. This indicates that beta values are not stable over the market phases in these 9 stocks. Similarly there are 6 stocks where two models recommended instability of beta and 4 stocks where only one model reported a change in beta values over the period. There are 11 cases where none of the models rejected the null hypothesis, which proved that the beta values are stable over the time in these stocks. 5. Conclusion The objective of the present study is to examine the stability of beta in different Indian market phases. For the purpose of the study monthly return data of 30 stocks for the period from 1999 to 2009 is considered. Considering the bullish and bearish condition in the Indian market, we divided the whole 10 years into 5 different market phases. Initially the beta has been estimated for different market phases and also taking the whole 10 years period. The results show that the beta values are not showing any particular pattern but in the overall phase almost all the stocks are statistically significant. Further the beta stability is examined using three different models. In the first method the beta coefficient is calculated considering the market phases as time variable. The results show that in 50% of cases the null hypothesis is rejected as the beta is stable over different market phases. In the similar line the results obtained in respect to model two states that in 17 out of 30 cases the null hypothesis is rejected. This confirms that in 17 cases the stability of beta is not there over the market phases but in rest 13 cases it stable over the market phases. In the third method of investigating beta stability, the Chow test has been conducted. The F-statistics under Chow test reveals that, beta is instable in 12 out of 30 stocks considered in the study in different market phases. We can thus finally conclude that the results obtained from different models are mixed and inconclusive in nature, where it is less ground to conclude that the beta values are stable or instable over the market phases. But there are 9 stocks which gives a strong indication that their beta values are not stable over the market phases. In these 9 cases, all the three models reported similar signal of beta instability over the market phases. The instability of beta has its implications in taking sound corporate financial decisions. Financial decisions should not be based on the overall beta of the company. Rather, the company’s periodical beta should be relied upon for taking certain managerial decisions. Considering the inconclusive results obtained from present study, it is suggested that the future research on beta in Indian market may be investigated from (a) industry wise stability of beta in different market phases (b) stability of beta from portfolio point of view (c) optimal time limit for stability of beta (d) forward looking beta and its stability (e) impact of market and company specific factors and stability of beta and (f) market efficiency study using phase wise beta under the event study methodology. 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[26] [27] [28] [29] 30] [31] [32] [33] [34] 185 International Research Journal of Finance and Economics – Issue 50 (2010) Annexure-1: Month December 1998 January 1999 February 1999 March 1999 April 1999 May 1999 June 1999 July 1999 August 1999 September 1999 October 1999 November 1999 December 1999 January 2000 Febru ary 2000 March 2000 April 2000 May 2000 June 2000 July 2000 August 2000 September 2000 October 2000 November 2000 December 2000 January 2001 February 2001 March 2001 April 2001 May 2001 June 2001 July 2001 August 2001 September 2001 October 2001 November 2001 December 2001 January 2002 February 2002 March 2002 April 2002 May 2002 June 2002 July 2002 August 2002 September 2002 October 2002 November 2002 December 2002 January 2003 February 2003 March 2003 April 2003 May 2003 June 2003 July 2003 August 2003 September 2003 October 2003 November 2003 December 2003 January 2004 February 2004 Identification of Market Phases Closing Price Return (R) 1+R Cumulative Wealth Index Market Phases 1359. 03 1461. 52 1506. 95 1651. 37 1449. 64 1714. 02 1790. 51 1988. 06 2192. 94 2213. 33 2071. 50 2253. 29 2624. 49 2875. 37 3293. 29 2902. 20 2396. 22 2156. 99 2397. 06 2153. 26 2306. 07 2075. 67 1916. 99 2061. 18 2032. 20 2209. 31 2139. 72 1691. 71 1682. 1 1763. 35 1630. 02 1564. 46 1534. 73 1312. 50 1389. 17 1557. 01 1557. 22 1592. 27 1707. 72 1716. 28 1671. 63 1596. 71 1650. 34 1506. 23 1580. 55 1473. 88 1458. 78 1594. 03 1664. 67 1600. 87 1628. 72 1500. 72 1470. 31 1641. 44 1819. 36 1893. 45 2229. 25 2314. 62 2485. 43 2594. 34 3074. 87 2946. 14 2923. 99 0. 08 0. 03 0. 10 -0. 12 0. 18 0. 04 0. 11 0. 10 0. 01 -0. 06 0 . 09 0. 16 0. 10 0. 15 -0. 12 -0. 17 -0. 10 0. 11 -0. 10 0. 07 -0. 10 -0. 08 0. 08 -0. 01 0. 09 -0. 03 -0. 21 -0. 01 0. 05 -0. 08 -0. 04 -0. 02 -0. 14 0. 06 0. 12 0. 00 0. 02 0. 07 0. 01 -0. 03 -0. 04 0. 03 -0. 09 0. 05 -0. 07 -0. 01 0. 09 0. 04 -0. 04 0. 2 -0. 08 -0. 02 0. 12 0. 11 0. 04 0. 18 0. 04 0. 07 0. 04 0. 19 -0. 04 -0. 01 1. 08 1. 03 1. 10 0. 88 1. 18 1. 04 1. 11 1. 10 1. 01 0. 94 1. 09 1. 16 1. 10 1. 15 0. 88 0. 83 0. 90 1. 11 0. 90 1. 07 0. 90 0. 92 1. 08 0. 99 1. 09 0. 97 0. 79 0. 99 1. 05 0. 92 0. 96 0. 98 0. 86 1. 06 1. 12 1. 00 1. 02 1. 07 1. 01 0. 97 0. 96 1. 03 0. 91 1. 05 0. 93 0. 99 1. 09 1. 04 0. 96 1. 02 0. 92 0. 98 1. 12 1. 11 1. 04 1. 18 1. 04 1. 07 1. 04 1. 19 0. 96 0. 99 1. 08 1. 11 1. 22 1. 07 1. 26 1. 32 1. 46 1. 61 1. 63 1. 52 1. 66 1. 93 2. 12 2. 42 0. 88 0. 73 0. 65 0. 73 0. 65 0. 70 0. 63 0. 58 0. 63 0. 62 0. 67 0. 65 0. 51 0. 51 0. 54 0. 9 0. 48 0. 47 0. 40 1. 06 1. 19 1. 19 1. 21 1. 30 1. 31 1. 27 1. 22 1. 26 1. 15 1. 20 1. 12 1. 11 1. 21 1. 27 1. 2 2 1. 24 1. 14 1. 12 1. 25 1. 39 1. 44 1. 70 1. 76 1. 89 1. 98 2. 34 2. 24 2. 23 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 186 March 2004 April 2004 May 2004 June 2004 July 2004 August 2004 September 2004 October 2004 November 2004 December 2004 January 2005 February 2005 March 2005 April 2005 May 2005 June 2005 July 2005 August 2005 September 2005 October 2005 November 2005 ecember 2005 January 2006 February 2006 March 2006 April 2006 May 2006 June 2006 July 2006 August 2006 September 2006 October 2006 November 2006 ecember 2006 January 2007 February 2007 March 2007 April 2007 May 2007 June 2007 July 2007 August 2007 September 2007 October 2007 November 2007 December 2007 January 2008 February 2008 March 2008 April 2008 May 2008 June 2008 July 2008 August 2008 September 2008 October 2008 November 2008 December 2008 January 2009 February 2009 March 2009 April 2009 May 2009 June 2009 July 2009 August 2009 International Research Journal of Finance and Economics – Issue 50 (2010) 2966. 31 3025. 14 2525. 35 2561. 16 2755. 22 2789. 07 2997. 97 027. 96 3339. 75 3580. 34 3521. 71 3611. 90 3481. 86 3313. 45 3601. 73 3800. 24 4072. 15 4184. 83 4566. 63 4159. 59 4649. 87 4953. 28 5224. 97 5422. 67 5904. 17 6251. 39 5385. 21 5382. 11 5422. 39 5933. 77 6328. 33 6603. 60 6931. 05 6982. 56 7145. 91 6527. 12 6587. 21 7032. 93 7468. 70 7605. 37 8004. 05 7857. 61 8967. 41 10391. 19 10384. 40 11154. 28 9440. 94 940 4. 98 8232. 82 9199. 46 8683. 27 7029. 74 7488. 48 7621. 40 6691. 57 4953. 98 4600. 45 4988. 04 4790. 32 4516. 38 4942. 51 5803. 97 7620. 13 7571. 49 8176. 54 8225. 50 0. 01 0. 02 -0. 17 0. 01 0. 08 0. 01 0. 07 0. 01 0. 10 0. 07 -0. 02 0. 03 -0. 04 -0. 05 0. 9 0. 06 0. 07 0. 03 0. 09 -0. 09 0. 12 0. 07 0. 05 0. 04 0. 09 0. 06 -0. 14 0. 00 0. 01 0. 09 0. 07 0. 04 0. 05 0. 01 0. 02 -0. 09 0. 01 0. 07 0. 06 0. 02 0. 05 -0. 02 0. 14 0. 16 0. 00 0. 07 -0. 15 0. 00 -0. 12 0. 12 -0. 06 -0. 19 0. 07 0. 02 -0. 12 -0. 26 -0. 07 0. 08 -0. 04 -0. 06 0. 09 0. 17 0. 31 -0. 01 0. 08 0. 01 1. 01 1. 02 0. 83 1. 01 1. 08 1. 01 1. 07 1. 01 1. 10 1. 07 0. 98 1. 03 0. 96 0. 95 1. 09 1. 06 1. 07 1. 03 1. 09 0. 91 1. 12 1. 07 1. 05 1. 04 1. 09 1. 06 0. 86 1. 00 1. 01 1. 09 1. 07 1. 04 1. 05 1. 01 1. 02 0. 91 1. 01 1. 07 1. 06 1. 02 1. 05 0. 98 1. 14 1. 16 1. 00 1. 07 0. 85 1. 00 0. 88 1. 12 . 94 0. 81 1. 07 1. 02 0. 88 0. 74 0. 93 1. 08 0. 96 0. 94 1. 09 1. 17 1. 31 0. 99 1. 08 1. 01 2. 26 2. 30 1. 92 1. 95 2. 10 2. 13 2. 28 2. 31 2. 54 2. 73 2. 68 2. 75 2. 65 2. 52 2. 74 2. 90 3. 10 3. 19 3. 48 3. 17 3. 54 3. 77 3. 98 4. 13 4. 50 4. 76 4. 10 4. 10 4. 13 4. 52 4. 82 5. 03 5. 28 5. 32 5. 44 4. 97 5. 02 5. 36 5. 69 5. 79 6. 10 5. 99 6. 83 7. 92 7. 91 8. 50 0. 85 0. 84 0. 74 0. 82 0. 78 0. 63 0. 67 0. 68 0. 60 0. 44 0. 41 1. 08 1. 04 0. 98 1. 07 1. 26 1. 66 1. 65 1. 78 1. 79 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 International Research Journal of Finance and Economics – Issue 50 (2010) Annexure-2: Beta values of individual securities over all the five phases Overall Phase I Phase II Phase III Phase IV ? p-val ? p-val ? p-val ? p-val ? p-val Bharat Heavy Electricals Ltd. 0. 86 0. 00* 0. 67 0. 21 1. 18 0. 00* 1. 10 0. 00* 0. 80 0. 02* Bharat Petroleum Corpn. Ltd. 0. 80 0. 00* 1. 02 0. 15 0. 66 0. 06 1. 13 0. 00* 1. 30 0. 06 Cipla Ltd. 0. 51 0. 00* -0. 04 0. 95 0. 75 0. 02* 0. 80 0. 00* 0. 51 0. 07 Sun Pharmaceutical Inds. Ltd. 0. 69 0. 00* 1. 13 0. 15 0. 80 0. 08 0. 57 0. 00* 0. 74 0. 00* Ranbaxy Laboratories Ltd. 0. 94 0. 00* 1. 19 0. 3 0. 63 0. 03* 0. 78 0. 00* 1. 07 0. 10 Wipro Ltd. 1. 47 0. 00* 2. 79 0. 02* 2. 63 0. 00* 0. 88 0. 00* 0. 87 0. 00* Reliance Infrastructure Ltd. 1. 24 0. 00* 1. 38 0. 02* 0. 26 0. 39 1. 20 0. 00* 1. 50 0. 00* Larsen Toubro Ltd. 1. 30 0. 00* 1. 12 0. 08 1. 70 0. 00* 1. 21 0. 00* 1. 07 0. 00* State Bank Of India 1. 01 0. 00* 1. 22 0. 08 0. 86 0. 00* 1. 03 0 . 00* 1. 08 0. 01* Tata Motors Ltd. 1. 20 0. 00* 1. 07 0. 08 -0. 13 0. 65 1. 11 0. 00* 1. 20 0. 00* Oil Natural Gas Corpn. Ltd. 0. 79 0. 00* 0. 43 0. 47 0. 59 0. 03* 1. 06 0. 00* 1. 03 0. 01* Steel Authority Of India Ltd. 1. 23 0. 00* -0. 31 0. 68 0. 99 0. 00* 1. 54 0. 0* 1. 12 0. 01* Tata Steel Ltd. 1. 22 0. 00* 0. 79 0. 17 0. 64 0. 05* 1. 25 0. 00* 1. 39 0. 00* Grasim Industries Ltd. 0. 94 0. 00* 1. 24 0. 13 0. 91 0. 01* 0. 95 0. 00* 0. 86 0. 00* H D F C Bank Ltd. 0. 79 0. 00* 1. 38 0. 03* 0. 36 0. 10 0. 68 0. 00* 0. 98 0. 00* Hero Honda Motors Ltd. 0. 47 0. 00* 0. 24 0. 64 0. 04 0. 85 0. 79 0. 00* 0. 93 0. 00* Hindalco Industries Ltd. 1. 00 0. 00* 0. 03 0. 95 0. 39 0. 06 1. 22 0. 00* 1. 44 0. 00* Hindustan Unilever Ltd. 0. 49 0. 00* 0. 78 0. 01* 0. 42 0. 06 0. 77 0. 00* 0. 67 0. 00* HDFC Ltd. 0. 74 0. 00* 0. 77 0. 01* 0. 50 0. 06 0. 85 0. 00* 1. 01 0. 00* Infosys Technologies Ltd. . 91 0. 00* 1. 33 0. 05* 1. 30 0. 00* 0. 73 0. 00* 0. 67 0. 06 G A I L (India) Ltd. 0. 49 0. 00* 0. 00 1. 00 0. 46 0. 11 0. 79 0. 00* 0. 34 0. 18 I C I C I Bank Ltd. 0. 84 0. 00* 1. 85 0. 05* 0. 06 0. 88 0. 50 0. 00* 0. 57 0. 14 I T C Ltd. 0. 37 0. 00* 0. 54 0. 13 0. 57 0. 01* 0. 42 0. 00* 0. 27 0. 24 National Aluminium Co. Ltd. 0. 49 0. 00* -0. 31 0. 75 0. 24 0. 37 0. 73 0. 00* 0. 21 0. 69 Indian Oil Corpn. Ltd. 0. 87 0. 10 0. 32 0. 56 0. 65 0. 00* 1. 24 0. 00* 0. 75 0. 01* Reliance Industries Ltd. 0. 51 0. 00* 0. 34 0. 47 0. 08 0. 81 0. 41 0. 00* 0. 74 0. 06 Sterlite Industries (India) Ltd. 1. 11 0. 00* 0. 99 0. 14 1. 3 0. 09 0. 87 0. 00* 0. 01 0. 96 Tata Communications Ltd. 0. 78 0. 00* 1. 10 0. 05* 1. 18 0. 00* 0. 87 0. 00* 0. 85 0. 09 Unitech Ltd. 0. 79 0. 00* 0. 47 0. 14 0. 48 0. 02* 0. 87 0. 00* 0. 21 0. 47 Zee Entertainment Ent. Ltd. 1. 00 0. 00* 1. 39 0. 08 0. 72 0. 07 0. 78 0. 00* 1. 13 0. 03* * indicates significance of coefficient at 5% level of significant Name of the Company Annexure-3: 187 Phase V ? p-val 0. 74 0. 00* 0. 48 0. 03* -0. 13 0. 65 0. 16 0. 55 1. 96 0. 01* 0. 78 0. 10 2. 46 0. 00* 1. 77 0. 00* 1. 55 0. 00* 1. 33 0. 02* 0. 94 0. 01* 1. 66 0. 00* 2. 07 0. 00* 0. 41 0. 29 0. 96 0. 00* 0. 29 0. 21 1. 63 0. 01* -0. 1 0. 68 0. 95 0. 00* 0. 07 0. 83 0. 38 0. 03* 1. 35 0. 02* -0. 01 0. 95 0. 50 0. 19 0. 98 0. 02* 0. 57 0. 10 0. 85 0. 03* 0. 43 0. 15 1. 27 0. 11 0. 74 0. 07 Estimates of regression equation using Time as a Variable Name of the Company Bharat Heavy Electricals Ltd. Bharat Petroleum Corpn. Ltd. Cipla Ltd. Sun Pharmaceutical Inds. Ltd. Ranbaxy Laboratories Ltd. Wipro Ltd. Reliance Infrastructure Ltd. Larsen Toubro Ltd. State Bank Of India Tata Motors Ltd. Oil Natural Gas Corpn. Ltd. Steel Authority Of India Ltd. Tata Steel Ltd. Grasim Industries Ltd. H D F C Bank Ltd. Hero Honda Motors Ltd. Hindalco Industries Ltd. Hindustan Unilever Ltd. HDFC Ltd. Constant 0. 02 0. 01 0. 02 0. 03 0. 01 0. 01 0. 01 0. 01 0. 01 0. 00 0. 01 0. 02 0. 01 0. 01 0. 02 0. 02 0. 00 0. 00 0. 02 mt (? 1) 0. 56 (0. 03) 0. 79 (0. 02) 0. 94 (0. 00) 1. 69 (0. 00) 0. 63 (0. 05) 3. 35 (0. 00) 0. 25 (0. 44) 1. 10 (0. 00) 0. 71 (0. 00) 0. 61 (0. 02) 0. 25 (0. 38) 0. 26 (0. 51) 0. 01 (0. 99) 0. 97 (0. 00) 0. 92 (0. 00) 0. 19 (0. 42) -0. 12 (0. 60) 0. 91 (0. 00) 0. 37 (0. 04) t*mt (? 2) 0. 10 (0. 22) 0. 00 (0. 96) -0. 14 (0. 10) -0. 33 (0. 00)* 0. 10 (0. 29) -0. 62 (0. 00)* 0. 33 (0. 00)* 0. 07 (0. 37) 0. 10 (0. 17) 0. 20 (0. 02)* 0. 18 (0. 03)* 0. 32 (0. 01)* How to cite Stability of Beta over Market Phases, Papers

Coca Cola Branding Strategies free essay sample

Coca-Cola is one of the most recognizable brands around the globe. The history of Coca-Cola began over a century ago since 1886. Today Coca-Cola sells products in over 300 countries world-wide, and has over 3,000 different beverages. The brand is familiar to people all around the world, and is available in many different varieties. The company takes pride in the development of unique marketing strategies, which have allowed growth and access to various places throughout the world. Their extremely recognizable branding is one of Coca-Colas greatest strengths and the simplicity of its bottling is a part of a great marketing strategy (Spring,2002). Coca-Cola makes money primarily from selling their very famous cola, allowing many across the globe to share the experience. Coca-Cola’s desired outcome from their marketing communications program is to move from creative excellence to content excellence. The purpose of their content excellence is to create ideas so contagious it cannot be controlled in terms of business objectives, brands and consumer interest, all linked together. We will write a custom essay sample on Coca Cola Branding Strategies or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page They developed a conversation model that begin with brand stories that create the linked ideas which provoke conversations that need us to act and react to 365 days a year. Coca Cola now intend to double the size of their business by observing a distribution of creativity in their advertising. No one now has the smarts on ideas and in fact, consumer generated stories outnumber Coca cola Company stories on most of their brands and they constantly fuel both of those truths. Secondly is by the distribution of technology. They now have greater connectivity and consumer empowerment than ever before. This has driven an on demand culture where consumers can turn their demands on 24 hours a day. Technology can enable brilliant connectivity; we can generate ideas where we cannot separate the message from technology. With that they develop direct relationship with technology companies. Coca cola focuses a lot on storytelling with their brand image growth; they focus on the evolution of storytelling by moving from one way storytelling to dynamic storytelling. Dynamic storytelling is the development of incremental elements of a brand idea that get dispersed systematically across multiple channels of conversation for the purposes of creating a unified and coordinated brand experience. This includes serial storytelling where one discovers the taste of coke by just opening the bottle and tasting it  themselves, multi-faceted storytelling where one carries the drink wherever they go, spreadable storytelling where we speak of it through social media, immersion and discovery storytelling where one is already addicted and tells everyone about the goodness of it and engagement through storytelling through word of mouth (Spring,2002) Storytelling is at the heart of all families, communities and cultures a nd it’s something that the Coca Cola Company has excelled at for more than 125 years now. They also do sponsorship with different college and schools and sponsors their extra curriculum activities to get market share. Their brand stories, just like most other brands shows commitment to making the world a better place with the existence of it. The equity of Coca Cola is rather tricky as people don’t distinguish all brands owned by the company, perhaps not knowing what they’ve bought or eating is a Coca Cola product. The availability and expected quality of Coca Cola makes it the most purchased brand for its visibility. Coke has become a part of a modern world culture through its advertising, a corporation worth close to $70 billion ranking as number one under brand equity. Coca-Cola’s brand image has one of the most successful plan layouts compared to many other beverage companies that are competing. Their physical elements of their identity define who they are, who they intend to be and how they are perceived. Coca cola built their brand image to be known similarly all around the world, as happiness. The brand has the taste that consumers are looking for and connects with their lifestyle and daily lives. It is a globalized product that gives the pleasure of refreshment during a break. Applying the SWOT analysis in Coke, among strengths that they have is being the world’s leading brand. Besides having strong brand recognition, they also have a leading brand value and a strong brand portfolio. The company owns four of the top five soft drink brands in the world which are Coca-Cola, Diet Coke, Sprite and Fanta. Strong brands allow the company to introduce brand extensions such as vanilla coke, cherry coke and coke with lemon. Over the years, the company has made large investments in brand promotions. Next strength is that they have a large scale of operations. Coca-Cola is the largest manufacturer, distributor and marketer of non-alcoholic beverage concentrates and syrups in the world. The company currently sells its products in more than 200 countries. The company’s operations are supported by revenues from South East Asia, and strong infrastructure across the world. Among weaknesses they have is a negative publicity where The Coca-Cola Company has been involved in a number of controversies and lawsuits related to its relationship with human rights violations and other perceived unethical practices. The company produces products that are not considered â€Å"healthy†, the large portion of their beverages are high in sugar content, and as the world’s waistline keeps growing, people are going to eventually look towards healthier alternatives. (Dart and Frost, 1956, as cited in Todd Daft,2002) The opportunities they have is their extensive and effective distribution network allows the company to take any product and have it in nearly every store around the world in days. Besides that, there is an increasing demand for healthy food and beverages so it’s something else they could excel in. Besides that, they have the opportunity to have more brand recognition. Threats that Coca Cola faces is with the PepsiCo Company that has for decades been battling with Coca-Cola for the money of the consumer, and this threat will not disappear anytime in the near future. Also, recently some select states have imposed taxes on the sale of soft drinks, and have banned the sale of drinks larger than a certain size, trying to battle obesity. However, coke continues to outsell Pepsi in most areas of the world. Comparing their asset rate in the world, Pepsi’s asset turnover rate is 91.  25% whereas Coke’s asset turnover rate is 58. 2% and the profit margin of Coke is 60. 86% leaving Pepsi with 52. 49% (Dart and Frost, 1956, as cited in Todd Daft,2002). Among weaknesses that Pepsi has is the decline of taste with low consumer knowledge about their brand. Their recognition is wide as well but their attempt to grow their brand image isn’t as efficient as Coca Cola, ca using a financial downfall for them. It’s now observed that Coca Cola is perceived quite positively as it has been projected. People are aware how the brand and awareness of coke is quite high in the market and when a product is launched, coke drinkers choose this soda over any other competitor simply because it’s a Coca Cola product and they trust it. Although coke has been into controversies, people still prefer to stay loyal to the brand because it’s a coke brand and they trust it. When people buy Coca Cola they’re not just buying the beverage but also the image that goes with it, therefore even if they have a higher price it shows that the product is off better quality and that the consumer is not cheap.