Eating Out Vs. Eating In

Eating Out Vs. Eating In Eating out Vs. Eating In Cooking has always been a good hobby for people and part of relaxing and fun entertainment for some category of people. There is always something new to be learnt about cooking and its all about creativity and making a better taste of bland materials. It has been one of the humans interests since the beginning of their creation and has always been improved. Some people argue that cooking will be a thing of the past like many other humans past hobbies which have changed by the change of technology. Cooking at home will never be a thing of the past because many cultures continue their cooking traditions and it is more economical and healthier. Some people prefer eating outside because its easier and more convenient but cooking at home is becoming a hobby for most people. During history nations have created dishes that are special and delicious. Their decedents have learnt these dishes and got so attached to them. This is one big reason why cooking at home wont be a thing of the past. Its exactly like how do people always stick to their lands and they always go back to them no matter how long have they been away, they will always stick to their traditional food and their traditions of cooking at home no matter how much do they eat outside they will always go back to their roots and to the original way of cooking which is at home. For an example, its well known in the Arabic world that the Arabic men will never accept the idea of eating out side the house the whole time and not getting home made food at all. The reason behind this is because all Arabic mothers try to pamper their kids by cooking for them the food at home considering it as the healthiest way and the cleanest way of all. So they are used to a specific standard of food and they also feel like it is a way of care giving. Another example is the Japanese home cooking. In general, the Japanese nation is exceptionally healthy and lives much longer than any other civilization in the humankind. Why is this? Loads of people point it to the way they eat. They cook at home a lot. They use healthy delicious and very useful ingredients from the nature. They like to take advantage of everything god blessed mankind with. They will never give up on their on home cooking because they consider it a source of joy and a way of remaining healthy and a way of living longer. Some cultures look at cooking as an art and at cooks as artists. Once people declare that cooking is a fine art, they are just saying the truth. They dont come across cooking as an odd job, and they seem to see it as a mode of expressing peoples ingenuity. They appear to look at it as a method to state peoples originality. They look at the food they make, as their personal preparation and not only are they taking self-importance in their kitchens, they as well fall in love with the challenge it put forwards.( williamm,22 March 22, 2010) Each culture enjoys its way of cooking, its meals and food in general. For lots of people it is a joy and something they will never leave or stop. People will keep on cooking no matter what because human in their nature they get bored and thats a thing in all cultures. So we cant just eat outside the whole time or eat packed food or prepared food or frozen food. People will always want to eat something they know or to eat something they have cooked even if they are not great cooks but they long for that a lot. The quality of the food greatly affects peoples health. Many people prefer cooking at home because it makes them feel great and have lots of energy. There are few more reasons, why cooking at home has been so important for the health. It helps to have a strong immune system ,so people can stay in   healthy weight and look slim and trim and physically fit. To avoid certain diseases known to be a related to diet and nutrition, particularly cancer, heart disease, stroke, high blood pressure, high cholesterol, diabetes and obesity, we need safe and clean food. Eating a balance of healthy foods that contain carbohydrate, protein, and fat every day will help blood glucose stay in balance. It is also very important to have full knowledge over the quality of food and utensils. All ingredients, while people cooking, should be fresh and utensils should be the best quality. The World Health Organization predicts that depression will be the second highest cause of the global disease problem by 2020. It is clear, that eating fast food thereby increasing stress and tension and reducing joy and liveliness.   It is a fact that eating vegetables, fruits and well cooked fish helps to prevent depression. What humans eat controls the levels of brain chemicals, which regulate behavior (Gutierrez, 2009). Cooking salads at home is very healthy.   For instance, it is proven that eating just a few ounces of broccoli each day can reduce a humans risk of ulcers and stomach cancer, as well as eating cabbage, kale, mustard seed and rocket, amongst others. Research has also shows, that broccoli and other vegetables may not only prove to play important role in many cancers, it could be effective treatment as well (Adams, 2008). There are many healthy techniques to cook like stir -frying, boiling and steaming.   Stir-frying,   which is very common   in Asia, means cutting food into small pieces, putting them in a pan with very little oil   and cooking them very quickly at a very high heat. Because it is one of the fastest ways to cook, rich of vitamins, such as broccoli and carrots, contain more nutrients, as well as their texture and colour. Also stir-fry recipe requires only two to three tablespoons of oil. This small amount of oil can cook big chicken and any veggies. One of the most popular techniques is boiling. Boiling food involves no fat, so in a sense it is healthy. Therefore, the water that the food is boiled in is actually healthier and it can be used for other purposes, such as using it as stock or for making soup. This water is extremely nutrient rich. Boiling food is a safe way of ensuring that all disease-causing bacteria in the water or the food are destroyed.   Boiling water for a few minutes kills most bacteria, amoeba and other microbial pathogens. It can help prevent illnesses such as cholera, dysentery and other diseases caused by micro-organisms. As for food, boiling makes it is easy to classify. Boiling food on a high flame changes the shape of the food being cooked, while boiling on a low flame helps retain the shape. Some people believe steaming is the best way to go. Steaming preserves the vitamins and minerals in foods. It is also healthy,   because   it is does not   require   any oils or fats. Steam can actually help melt some of the excess fats in foods, which end up in the water that was heated to make the steam. When people using this technique,   it is also less danger of consuming carcinogens that are present in blackened foods. Eating outside is more expensive than eating inside. The cost of the items is different for the same quantity. The statement eating out saves much time and money cannot be convinced (Jeff D Gorman, n.d).   And that makes logical sense; we pay more in fast food restaurants than cooking at home for the same things. People are looking at the quantity. Were spending the same amount of time in both at home and at restaurants. At home, we shop for the ingredients and prepare our food and afterwards we clean the dishes. At restaurants, we either wait for our table to get ready, wait for the food to arrive and afterwards we wait for the restaurant to bring out the check (MetaFilter Network Inc, 1999). In a short term perspective, it may seem that dining out can be cost-effective and convenient. But in a long term perspective, it doesnt matter whether its convenient or not, but itll be very cost-effective (Christian Science Monitor, 2010). Most of the people who cant afford pricey things prefer to eat at home because they know it is cheaper. They wont complain about being mentally tired as long as the saved the money in their wallets. For a restaurant to make profit they need to put all the costs for their expenditure in the meals. They include the rent, employees salaries, and the maintenance bills in the meals. And add a little more to get the profit out of it. Now if someone knew this, how can they explain that its cheaper to eat out when youre paying for things you can do yourself and for things that probably does not concern you, and also for the same amount of time? Buying five pieces of chicken breasts for ten dirhams at a grocery store like Spinneys is much cheaper than ord ering a combo meal of chicken and rice from a fast food outlet for fifteen dirhams. The combo meal will last only for that day while the five pieces for chicken breast will last for three meals if the person is willing to do so. Even if we eat the cheapest food available, such as Chinese takeout, we can feed up to five people with the same amount of money we would spend for the meal to feed ourselves. Even if shopping takes a lot of time comparing when eating at a restaurant, we arent earning or saving any money when we are saving time from shopping. The majority of articles and books and talk about managing your budget when it comes to eating out against eating in would claim that eating out would make your money stretch. Also eating at home would depend all on how you buy your ingredients. It depends on if you buy in a bulk, with coupons, or buying premium meat or standard. It makes logical sense that companies are still selling food ingredients in supermarkets and people are still cooking at home. Dining out is for those going out on a special occasion. One reason to eat outside instead of cooking in the house is because its more convenient and easier to get. specially for that category of people who are lazier and more busy and prefer to not spend their time for cooking and preparing its material as they believe its more time consuming .This category of people believes it is always more convenient to go and get whatever they want and save a lot of time by not cooking. In first sight it may seem that eating outside is more convenient but it is actually more complicated and stressful than it look likes. By considering time people spend standing in the food cues, waiting for their meal and finding an appropriate place to sit and serve their meal will all count part of the time they spend to have a meal outside. In addition, stress and aggressiveness that people may get out of communicating with other people are all part of hassle of eating outside. In conclusion, eating will never be a thing of past because its one of humans life necessities and its more beneficial for human to keep their habit alive as it will be more economical, healthy and cultural.

Analysis of the Capital Assets Pricing Model

Analysis of the Capital Assets Pricing Model Chapter I The QUARREL ON THE CAPM: A LITERATURE SURVEY Abstract The current chapter has attempted to do three things. First it presents an overview on the capital asset pricing model and the results from its application throughout a narrative literature review. Second the chapter has argued that to claim whether the CAPM is dead or alive, some improvements on the model must be considered. Rather than take the view that one theory is right and the other is wrong, it is probably more accurate to say that each applies in somewhat different circumstances (assumptions). Finally the chapter has argued that even the examination of the CAPMs variants is unable to solve the debate into the model. Rather than asserting the death or the survival of the CAPM, we conclude that there is no consensus in the literature as to what suitable measure of risk is, and consequently as to what extent the model is valid or not since the evidence is very mixed. So the debate on the validity of the CAPM remains a questionable issue. 1. INTRODUCTION The traditional capital assets pricing model (CAPM), always the most widespread model of the financial theory, was prone to harsh criticisms not only by the academicians but also by the experts in finance. Indeed, in the last few decades an enormous body of empirical researches has gathered evidences against the model. These evidences tackle directly the models assumptions and suggest the dead of the beta (Fama and French, 1992); the systematic risk of the CAPM. If the world does not obey to the models predictions, it is maybe because the model needs some improvements. It is maybe because also the world is wrong, or that some shares are not correctly priced. Perhaps and most notably the parameters that determine the prices are not observed such as information or even the returns distribution. Of course the theory, the evidence and even the unexplained movements have all been subject to much debate. But the cumulative effect has been to put a new look on asset pricing. Financial Researchers have provided both theory and evidence which suggest from where the deviations of securities prices from fundamentals are likely to come, and why could not be explained by the traditional CAPM. Understanding security valuation is a parsimonious as well as a lucrative end in its self. Nevertheless, research on valuation has many additional benefits. Among them the crucial and relatively neglected issues have to do with the real consequences of the models failure. How are securities priced? What are the pricing factors and when? Once it is recognized that the models failure has real consequences, important issues arise. For instance the conception of an adequate pricing model that accounts for all the missing aspects. The objective of this chapter is to look at different approaches to the CAPM, how these have arisen, and the importance of recognizing that theres no single ‘right model which is adequate for all shares and for all circumstances, i.e. assumptions. We will, so move on to explore the research task, discuss the goodness and the weakness of the CAPM, and look at how different versions are introduced and developed in the literature. We will, finally, go on to explore whether these recent developments on the CAPM could solve the quarrel behind its failure. For this end, the recent chapter is organized as follows: the second section presents the theoretical bases of the model. The third one discusses the problematic issues on the model. The fourth section presents a literature survey on the classic version of the model. The five section sheds light on the recent developments of the CAPM together with a literature review on these versions. The next one raises the quarrel on the model and its modified versions. Section seven concludes the paper. 2. THEORETICAL BASES OF THE CAPITAL ASSET PRICING MODEL In the field of finance, the CAPM is used to determine, theoretically, the required return of an asset; if this asset is associated to a well diversified market portfolio while taking into account the non diversified risk of the asset its self. This model, introduced by Jack Treynor, William Sharpe and Jan Mossin (1964, 1965) took its roots of the Harry Markowitzs work (1952) which is interested in diversification and the modern theory of the portfolio. The modern theory of portfolio was introduced by Harry Markowitz in his article entitled â€Å"Portfolio Selection, appeared in 1952 in the Journal of Finance. Well before the work of Markowitz, the investors, for the construction of their portfolios, are interested in the risk and the return. Thus, the standard advice of the investment decision was to choose the stocks that offer the best return with the minimum of risk, and by there, they build their portfolios. On the basis of this point, Markowitz formulated this intuition by resorting to the diversifications mathematics. Indeed, he claims that the investors must in general choose the portfolios while getting based on the risk criterion rather than to choose those made up only of stocks which offer each one the best risk-reward criterion. In other words, the investors must choose portfolios rather than individual stocks. Thus, the modern theory of portfolio explains how rational investors use diversification to optimize their portfolio and what should be the price of an asset while knowing its systematic risk. Such investors are so-called to meet only one source of risk inherent to the total performance of the market; more clearly, they support only the market risk. Thus, the return on a risky asset is determined by its systematic risk. Consequently, an investor who chooses a less diversified portfolio, generally, supports the market risk together with the uncertaintys risk which is not related to the market and which would remain even if the market return is known. Sharpe (1964) and Linter (1965), while basing on the work of Harry Markowitz (1952), suggest, in their model, that the value of an asset depends on the investors anticipations. They claim, in their model that if the investors have homogeneous anticipations (their optimal behavior is summarized in the fact of having an efficient portfolio based on the mean-variance criterion), the market portfolio will have to be the efficient one while referring to the mean-variance criterion (Hawawini 1984, Campbell, Lo and MacKinlay 1997). The CAPM offer an estimate of a financial asset on the market. Indeed, it tries to explain this value while taking into account the risk aversion, more particularly; this model supposes that the investors seek, either to maximize their profit for a given level of risk, or to minimize the risk taking into account a given level of profit. The simplest mean-variance model (CAPM) concludes that in equilibrium, the investors choose a combination of the market portfolio and to lend or to borrow with proportions determined by their capacity to support the risk with an aim of obtaining a higher return. 2.1. Tested Hypothesis The CAPM is based on a certain number of simplifying assumptions making it applicable. These assumptions are presented as follows: The markets are perfect and there are neither taxes nor expenses or commissions of any kind; All the investors are risk averse and maximize the mean-variance criterion; The investors have homogeneous anticipations concerning the distributions of the returns probabilities (Gaussian distribution); and The investors can lend and borrow unlimited sums with the same interest rate (the risk free rate). The aphorism behind this model is as follows: the return of an asset is equal to the risk free rate raised with a risk premium which is the risk premium average multiplied by the systematic risk coefficient of the considered asset. Thus the expression is a function of: The systematic risk coefficient which is noted as; The market return noted; The risk free rate (Treasury bills), noted This model is the following: Where: ; represents the risk premium, in other words it represents the return required by the investors when they rather place their money on the market than in a risk free asset, and; ; corresponds to the systematic risk coefficient of the asset considered. From a mathematical point of view, this one corresponds to the ratio of the covariance of the assets return and that of the market return and the variance of the market return. Where: ; represents the standard deviation of the market return (market risk), and ; is the standard deviation of the assets return. Subsequently, if an asset has the same characteristics as those of the market (representative asset), then, its equivalent will be equal to 1. Conversely, for a risk free asset, this coefficient will be equal to 0. The beta coefficient is the back bone of the CAPM. Indeed, the beta is an indicator of profitability since it is the relationship between the assets volatility and that of the market, and volatility is related to the returns variations which are an essential element of profitability. Moreover, it is an indicator of risk, since if this asset has a beta coefficient which is higher than 1, this means that if the market is in recession, the return on the asset drops more than that of the market and less than it if this coefficient is lower than 1. The portfolio risk includes the systematic risk or also the non diversified risk as well as the non systematic risk which is known also under the name of diversified risk. The systematic risk is a risk which is common for all stocks, in other words it is the market risk. However the non systematic risk is the risk related to each asset. This risk can be reduced by integrating a significant number of stocks in the market portfolio, i.e. by diversifying well in advantage (Markowitz, 1985). Thus, a rational investor should not take a diversified risk since it is only the non diversified risk (risk of the market) which is rewarded in this model. This is equivalent to say that the market beta is the factor which rewards the investors exposure to the risk. In fact, the CAPM supposes that the market risk can be optimized i.e. can be minimized the maximum. Thus, an optimal portfolio implies the weakest risk for a given level of return. Moreover, since the inclusion of stocks diversifies in advantage the portfolio, the optimal one must contain the whole stocks on the market, with the equivalent proportions so as to achieve this goal of optimization. All these optimal portfolios, each one for a given level of return, build the efficient frontier. Here is the graph of the efficient frontier: The (Markowitz) efficient frontier The efficient frontier Lastly, since the non systematic risk is diversifiable, the total risk of the portfolio can be regarded as being the beta (the market risk). 3. Problematic issues on the CAPM Since its conception as a model to value assets by Sharpe (1964), the CAPM has been prone to several discussions by both academicians and experts. Among them the most known issues concerning the mean variance market portfolio, the efficient frontier, and the risk premium puzzle. 3.1 The mean-variance market portfolio The modern portfolio theory was introduced for the first time by Harry Markowitz (1952). The contribution of Markowitz constitutes an epistemological shatter with the traditional finance. Indeed, it constitutes a passageway from an intuitive finance which is limited to advices related to financial balance or to tax and legal nature advices, to a positive science which is based on coherent and fundamental theories. One allots to Markowitz the first rigorous treatment of the investor dilemma, namely how obtaining larger profits while minimizing the risks. 3.2 The efficient frontier 3.3 The equity premium puzzle 4. Background on the CAPM â€Å"The attraction of the CAPM is that it offers powerful and intuitively pleasing predictions about how to measure risk and the relation between expected return and risk. Unfortunately, the empirical record of the model is poor poor enough to invalidate the way it is used in applications. The CAPMs empirical problems may reflect theoretical failings, the result of many simplifying assumptions.† Fama and French, 2003, â€Å"The Capital Asset Pricing Model: Theory and Evidence†, Tuck Business School, Working Paper No. 03-26 Being a theory, the CAPM found the welcome thanks to its circumspect elegance and its concept of good sense which supposes that a risk averse investor would require a higher return to compensate for supported the back-up risk. It seems that a more pragmatic approach carries out to conclude that there are enough limits resulting from the empirical tests of the CAPM. Tests of the CAPM were based, mainly, on three various implications of the relation between the expected return and the market beta. Firstly, the expected return on any asset is linearly associated to its beta, and no other variable will be able to contribute to the increase of the explanatory power. Secondly, the beta premium is positive which means that the market expected return exceeds that of individual stocks, whose return is not correlated with that of the market. Lastly, according to the Sharpe and Lintner model (1964, 1965), stocks whose return is not correlated with that of the market, have an expected return equal to the risk free rate and a risk premium equal to the difference between the market return and the risk free rate return. In what follows, we are going to examine whether the CAPMs assumptions are respected or not through the empirical literature. Starting with Jensen (1968), this author wants to test for the relationship between the securities expected return and the market beta. For this reason, he uses the time series regression to estimate for the CAPM ´ s coefficients. The results reject the CAPM as for the moment when the relationship between the expected return on assets is positive but that this relation is too flat. In fact, Jensen (1968) finds that the intercept in the time series regression is higher than the risk free rate. Furthermore, the results indicate that the beta coefficient is lower than the average excess return on the market portfolio. In order to test for the CAPM, Black et al. (1972) work on a sample made of all securities listed on the New York Stock Exchange for the period of 1926-1966. The authors classify the securities into ten portfolios on the basis of their betas.They claim that grouping the securities with reference to their betas may offer biased estimates of the portfolio beta which may lead to a selection bias into the tests. Hence, so as to get rid of this bias, they use an instrumental variable which consists of taking the previous periods estimated beta to select a securitys portfolio grouping for the next year. For the estimate of the equation, the authors use the time series regression. The results indicate, firstly, that the securities associated to high beta had significantly negative intercepts, whereas those with low beta had significantly positive intercepts. It was proved, also, that this effect persists overtime. Hence, these evidences reject the traditional CAPM. Secondly, it is found that the relation between the mean excess return and beta is linear which is consistent with the CAPM. Nevertheless, the results point out that the slopes and intercepts in the regression are not reliable. In fact, during the prewar period, the slope was sharper than that predicted by the CAPM for the first sub period, and it was flatter during the second sub period. However, after that, the slope was flatter. Basing on these results, Black, Fischer, Michael C. Jensen and Myron Scholes (1972) conclude that the traditional CAPM is inconsistent with the data. Fama and MacBeth (1973) propose another regression method so as to overcome the problem related to the residues correlation in a simple linear regression. Indeed, instead of estimating only one regression for the monthly average returns on the betas, they propose to estimate regressions of these returns month by month on the betas. They include all common stocks traded in NYSE from 1926 to 1968 in their analysis. The monthly averages of the slopes and intercepts, with the standard errors of the averages, thus, are used to check, initially, if the beta premium is positive, then to test if the averages return of assets which are not correlated with the market return is from now on equal to the average of the risk free rate. In this way, the errors observed on the slopes and intercepts are directly given by the variation for each month of the regression coefficients, which detect the effects of the residues correlation over the variation of the regression. Their study led to three main results. At first, the relationship between assets return and their betas in an efficient portfolio is linear. At second, the beta coefficient is an appropriate measure of the securitys risk and no other measure of risk can be a better estimator. Finally, the higher the risk is, the higher the return should be. Blume and Friend (1973) in their paper try to examine theoretically and empirically the reasons beyond the failure of the market line to explain excess return on financial assets. The authors estimate the beta coefficients for each common stock listed in the New York Stock Exchange over the period of January 1950 to December 1954. Then, they form 12 portfolios on the basis of their estimated beta. They afterwards, calculate the monthly return for each portfolio. Third, they calculate the monthly average return for portfolios from 1955 to 1959. These averaged returns were regressed to obtain the value of the beta portfolios. Finally, these arithmetic average returns were regressed on the beta coefficient and the square of beta as well. Through, this study, the authors point out that the failure of the capital assets pricing model in explaining returns maybe due to the simplifying assumption according to which the functioning of the short-selling mechanism is perfect. They defend their point of view while resorting to the fact that, generally, in short sales the seller cannot use the profits for purchasing other securities. Moreover, they state that the seller should make a margin of roughly 65% of the sales market value unless the securities he owns had a value three times higher than the cash margin. This makes a severe constraint on his short sales. In addition to that, the authors reveal that it is more appropriate and theoretically more possible to remove the restriction on the short sales than that of the risk free rate assumption (i.e., to borrow and to lend on a unique risk free rate). The results show that the relationship between the average realized returns of the NYSE listed common stocks and their correspondent betas is almost linear which is consistent with the CAPM assumptions. Nevertheless, they advance that the capital assets pricing model is more adequate for the estimates of the NYSE stocks rather than other financial assets. They mention that this latter conclusion is may be owed to the fact that the market of common stocks is well segmented from markets of other assets such as bonds. Finally, the authors come out with the two following conclusions: Firstly, the tests of the CAPM suggest the segmentation of the markets between stocks and bonds. Secondly, in absence of this segmentation, the best way to estimate the risk return tradeoff is to do it over the class of assets and the period of interest. The study of Stambaugh (1982) is interested in testing the CAPM while taking into account, in addition to the US common stocks, other assets such as, corporate and government bonds, preferred stocks, real estate, and other consumer durables. The results indicate that testing the CAPM is independent on whether we expand or not the market portfolio to these additional assets. Kothari Shanken and Sloan (1995), show that the annual betas are statistically significant for a variety of portfolios. These results were astonishing since not very early, Fama and French (1992), found that the monthly and the annual betas are nearly the same and are not statistically significant. The authors work on a sample which covers all AMEX firms for the period 1927-1990. Portfolios are formed in five different ways. Firstly, they from 20 portfolios while basing only on beta. Secondly, 20 portfolios by grouping on size alone. Thirdly, they take the intersection of 10 independent beta or size to obtain 100 portfolios. Then, they classify stocks into 10 portfolios on beta, and after that into 10 portfolios on size within each beta group. They, finally, classify stocks into 10 portfolios on size and then into 10 portfolios on beta within each size group. They use the GRSP equal weighted portfolio as a proxy for the whole market return market. The cross-sectional regression of monthly return on beta and size has led to the following conclusions: On the one hand, when taking into account only the beta, it is found that the parameter coefficient is positive and statistically significant for both the sub periods studied. On the other hand, it is demonstrated that the ability of beta and size to explain cross sectional variation of the returns on the 100 portfolios ranked on beta given the size, is statistically significant. However, the incremental economic benefit of size given beta is relatively small. Fama and French published in 1992 a famous study putting into question the CAPM, called since then the Beta is dead paper (the article announcing the death of Beta). The authors use a sample which covers all the stocks of the non-financial firms of the NYSE, AMEX and NASDAQ for the period of the end of December 1962 until June 1990. For the estimate of the betas; they use the same test as that of Fama and Macbeth (1973) and the cross-sectional regression. The results indicate that when paying attention only to the betas variations which are not related to the size, it is found that the relation between the betas and the expected return is too flat, and this even if the beta is the only explanatory variable. Moreover, they show that this relationship tend to disappear overtime. In order to verify the validity of the CAPM in the Hungarian stock market, Andor et al. (1999) work on daily and monthly data on 17 Hungarian stocks between the end of July 1991 and the beginning of June 1999. To proxy for the market portfolio the authors use three different indexes which are the BUX index, the NYSE index, and the MSCI world index. The regression of the stocks return against the different indexes return indicates that the CAPM holds. Indeed, in all cases it is found that the return is positively associated to the betas and that the R-squared value is not bad at all. They conclude, hence, that the CAPM is appropriate for the description of the Hungarian stock market. For the aim of testing the validity of the CAPM, Kothari and Jay Shanken (1999), study the one factor model with reference to the size anomaly and the book to market anomaly. The sample used in their study contains annual return on portfolios from the GRSP universe of stocks. The portfolios are formed every July from 1927 to 1992. The formation procedure is the following; every year stocks are sorted on the basis of their market capitalization and then on their betas while regressing the past returns on the GRSP equal weighted index return. They obtain, hence, ten portfolios on the basis of the size. Then, the stocks in each size portfolio are grouped into ten portfolios based on their betas. They repeat the same procedure to obtain the book-to-market portfolios. Using the Fama and MacBeth cross-sectional regression, the authors find those annual betas perform well since they are significantly associated to the average stock returns especially for the period 1941-1990 and 1927-1990. Moreover, the ability of the beta to predict return with reference to the size and the book to market is higher. In a conclusion, this study is a support for the traditional CAPM. Khoon et al. (1999), while comparing two assets pricing models in the Malaysian stock exchange, examine the validity of the CAPM. The data contains monthly returns of 231 stocks listed in the Kuala Lumpur stock exchange over the period of September 1988 to June 1997. Using the cross section regression (two pass regression) and the market index as the market portfolio, the authors find that the beta coefficient is sometimes positive and some others negative, but they do not provide any further tests. In order to extract the factors that may affect the returns of stocks listed in the Istanbul stock exchange, Akdeniz et al. (2000)make use of monthly return of all non financial firms listed in the up mentioned stock market for the period that spans from January 1992 to December 1998. They estimate the beta coefficient in two stages using the ISE composite index as the market portfolio. First, they employ the OLS regression and estimate for the betas each month for each stock. Then, once the betas are estimated for the previous 24 months (time series regression), they rank the stocks into five equal groups on the basis of the pre-ranking betas and the average portfolio beta is attributed to each stock in the portfolio. They, afterwards, divide the whole sample into two equal sub-periods and the estimation procedure is done for each sub-period and the whole period as well. The results from the cross sectional regression, indicate that the return has no significant relationship with the market beta. This variable does not appear to influence cross section variation in all the periods studied (1992-1998, 1992-1995, and 1995-1998). In a relatively larger study, Estrada (2002) investigates the CAPM with reference to the downside CAPM. The author works on a monthly sample covering the period that spans from 1988 to 2001 (varied periods are considered) on stocks of 27 emerging markets. Using simple regression, the authors find that the downside beta outperforms the traditional CAPM beta. Nevertheless, the results do not support the rejection of the CAPM from two aspects. Firstly, it was found that the intercept from the regression is not statistically different from zero. Secondly, the beta coefficient is positive and statistically significant and the explanatory power of the model is about 40%. This result stems for the conclusion according to which the CAPM is still alive within the set of countries studied. In order to check the validity of the CAPM, and the absence of anomalies that must be incorporated to the model, Andrew and Joseph (2003) try to investigate the ability of the model to predict book-to market portfolios. If it is the case, then the CAPM captures the Book-to-market anomaly and theres no need to further incorporate it in the model. For this intention, the authors work on a sample that covers the period of 1927-2001 and contains monthly data on stocks listed in the NYSE, AMEX, and NASDAQ. So as to form the book-to-market portfolios, they use, alike Fama and French (1992), the size and the book-to-market ratio criterion. To estimate for the market return, they use the return on the value weighted portfolios on stocks listed in the pre-cited stock exchanges and to proxy for the risk free rate; they employ the one-month Treasury bill rate from Ibbotson Associates. They, afterwards, divide the whole period into two laps of time; the first one goes from July 1927 to June 1963, and the other one span from July 1963 to the end of 2001. Using asymptotic distribution the results indicate that the CAPM do a great job over the whole period, since the intercept is found to be closed to zero, but there is no evidence for a value premium. Hence, they conclude that the CAPM cannot be rejected. However, for the pre-1963 period the book to market premium is not significant at all, whereas for the post-1963 period this premium is relatively high and statistically significant. Nevertheless, when accounting for the sample size effect, the authors find that there is an overall risk premium for the post-1963 period. The authors conclude then that, taken as a whole, the study fails to reject the null that the CAPM holds. This study points to the necessity to take into account the small sample bias. Fama and French (2004), estimate the betas of stocks provided by the CRSP (Center for Research in Security Prices of the University of Chicago) of the NYSE (1928-2003), the AMEX (1963-2003) and the NASDAQ (1972-2003). They form, thereafter, 10 portfolios on the basis of the estimated betas and calculate their return for the eleven months which follow. They repeat this process for each year of 1928 up to 2003. They claim that, the Sharpe and Lintner model, suppose that the portfolios move according to a linear line with an intercept equal to risk free rate and a slope which is equal to the difference between the expected return on the market portfolio and that of the risk free rate. However, their study, and in agreement with the previous ones, confirms that the relation between the expected return on assets and their betas is much flatter than the prediction of the CAPM. Indeed, the results indicate that the expected return of portfolios having relatively lower beta are too high whereas expected return of those with higher beta is too low. Moreover, these authors indicate that even if the risk premium is lower than what the CAPM predicts, the relation between the expected return and beta is almost linear. This latter result, confirms the CAPM of Black which assumes that only the beta premium is positive. This means, analogically, that only the market risk is rewarded by a higher return. In order to test for the consistency of the CAPM with the economic reality, Thierry and Pim (2004) use monthly return of stocks from the NYSE, NASDAQ, and AMEX for the period that spans from 1926-2002. The one -month US Treasury bill is used as a proxy for the risk free rate, The CRPS total return index which is a value-weighted average of all US stocks included in this study is used as a proxy for the market portfolio. They sort stocks into ten deciles portfolios on the basis of historical 60 months. They afterwards, calculate for the following 12 months their value weighted returns. They obtain, subsequently, 100 beta-size portfolios. The results from the time series regression indicate, firstly, that the intercepts are statistically indifferent from zero. Secondly, it is found that the betas coefficients are all positive. Furthermore, in order to check the robustness of the model, the authors split the whole sample into sub-samples of equal length (432 months). The results indicate, also, that for all the periods studied the intercepts are statistically not different from zero except for the last period. In his empirical study, Blake T (2005) works on monthly stocks return on 20 stocks within the SP 500 index during January 1995-December 2004. The SP 500 index is used as the market portfolio and the 3-month Treasury bill in the Secondary Market as the risk free rate. His methodology can be summarized as follows; the excess return on each stock is regressed against the market excess return. The excess return is taken as the sample average of each stock and the market as well. After estimating of the betas, these values are used to verify the validity of the CAPM. The coefficient of beta is estimated by regressing estimated expected excess stock returns on the estimates of beta and the regression include intercept and the residual squared so as to measure the non systematic risk. The results confirm the validity of the CAPM through its three major assumptions. In fact, the null hy

Banner in the Sky by James Ramsey Ullman Essay -- essays research pape

The book that I have read chose to review is Banner in the Sky by James Ramsey Ullman. James ramsey Ullman was born in New York City in 1907. His highest-honored book was Banner in the Sky, but four of his books, including this one, were made into major motion pictures.   Ã‚  Ã‚  Ã‚  Ã‚  The main characters of this story are Rudi Matt, Franz Lerner, Frau Matt, John Winter, and Emil Saxo. Rudi is the son of the legendary mountain guide of the Alps, Josef Matt. He has mountain climbing in his blood and is destined to become a guide. He is the main character of the story. Franz Lerner is Rudi’s uncle. He was with Josef Matt hours before he died while trying to climb the Citadel, and now he is looking out for Rudi. Frau Matt is Rudi’s mother. She does not want Rudi to become a guide like his father because she fears that he will die the same way. John winter is a famous guide in Switzerland. Rudi saved his life and now Winter wants him to climb the Citadel with him. Emil Saxo is a famous Swiss guide form the village of Broli. Winter asks him to be the guide for the journey up the Citadel.   Ã‚  Ã‚  Ã‚  Ã‚  Rudi Matt has been given a chance to avenge his father’s death when Capt. John Winter asks him to climb the Citadel, the last unconquered peak in the Alps, along with him. Rudi is eager to do it but he cannot get consent from his mother, so he lies and tells Winter that he can go. They start up the Citadel with the famous guide Emil Saxo, who is also ve...

Changes In The Earths Environment Essay -- essays research papers

Changes in the Earth's Environment The 20th century, especially in the second half, has been one of rapid change in the Earth's environment. The impact of humans on the physical form and functioning of the Earth have reached levels that are global in character, and have done so at an increasingly mounting speed. 20 years ago the environment was seen as posing a threat to the future of humanity as death rates from natural hazards had increased dramatically since the turn of the century. The Earth though has always been plagued by natural disasters. Now, with the world population growing at a rapid rate more people are living in hazard prone areas. Events which may have gone unnoticed previously, only become hazards when there is intervention with humans and their lifestyle. With the discovery of the ozone hole in the 1980's attention was now more focused on the threat humans were posing to the environment. With scientific evidence to back up pessimistic predictions of our future, most people, through media coverage, political pressures and general concern now see the environment as being truly threatened by human progress and in desperate need of help. Natural hazards have been defined as â€Å"...extreme geophysical events greatly exceeding normal human expectations in terms of their magnitude or frequency and causing significant damage to man and his works with possible loss of life.† (Heathcote,1979,p.3.). A natural hazard occurs when there is an interaction between a system of human resource management and extreme or rare natural phenomena (Chapman,1994). As McCall, Laming and Scott (1991) argue, strictly speaking there is no hazard unless humans are affected in some way. Yet the line between natural and human-made hazards is a finely drawn one and usually overlapping. Doornkamp ( cited in McCall et al, 1992) argues that many hazards are human induced or at least made worse by the intervention of humans. In the 1970's, natural hazards were an important subject of topical study, as the nature of their impact on human populations and what they valued was increasing in frequency at quite a rapid rate (Burton, Kates, White, 1978). During the 75 years after 1900 the population of the earth increased by a staggering 2.25 billion people. People who needed land on which to live and work. As the population rose people were dispersed in more places and in larger numb... ...cote and B.G. Thom (eds): Natural Hazards in Australia. 3-12, Australian Academy of Science, Canberra. Kevies, D.J. (1992). Some Like it Hot. New York Review of Books. 39:31-39. McCall, G.J.H. (1992). Natural and Man Made Hazards: Their Increasing Importance in the End 20th Century World in G.J.H.McCall, D.J.C.Laming and S.C.Scott (eds): Geohazards: Natural and Man Made. 1-4, Chapman and Hall, London. McKibben,B. (1990). The End of Nature. Penguin, Middlesex. Meyer, W.R. and Turner, B.L. (1995). The Earth Transformed: Trends, Trajectories and Patterns in R.J. Johnson, P.J. Taylor and M.J.Watts (eds): Geographies of Global Change. 302-317, Blackwell, Oxford. Pearce, D. (1995). Blueprint 4: Capturing Environmental Value. Oxford Uni. Press, New York. Perry,A.H. (1981). Environmental Hazards in the British Isles. Allen and Unwin. London. Schnieder, S.H. (1989). Global Warming: Are We Entering The Greenhouse Century ?. Sierra Club Books, New York. Stow, D.A.V. (1992). Preface in G.J.H.McCall, D.J.C.Laming and S.C.Scott (eds): Geohazards: Natural and Man Made. i-ii, Chapman and Hall, London. Suzuki,D. and Gordon, A. (1990). It's a Matter of Survival. Harvard Uni Press, Harvard.

Girl, Interrupted Essay -- essays research papers

Girl, Interrupted Part I: Critical Analysis Author: Susanna Kaysen. Girl, Interrupted: New York Division of Random House. Inc 1993. 1.  Ã‚  Ã‚  Ã‚  Ã‚  What is the author’s topic? The author’s topic is about a teenager name Susanna Kaysen. At 18 she voluntarily turned herself into McLean Hospital. 2.   Ã‚  Ã‚  Ã‚  Ã‚  Identify the author’s main idea(s). In other words, what is the main point the author is attempting to make about the book?   Ã‚  Ã‚  Ã‚  Ã‚  The author’s has 2 main points; one point is about her two-year stay McLean hospital. The second main point talks about how she handles and gets treated for being depressive and suicidal. 3.  Ã‚  Ã‚  Ã‚  Ã‚  Identify the author’s overall pattern(s) of organization. Give examples and/or details to explain your answer.   Ã‚  Ã‚  Ã‚  Ã‚  The pattern the author uses is time order; she starts off the book with her case record, which was submitted in 1967. The last example given is her recovery record; she left and had a recovery of her depression and being suicidal in 1969. 4.  Ã‚  Ã‚  Ã‚  Ã‚  What biases, if any, can you detect from the author’s writing? Give examples and details to support your answer.   Ã‚  Ã‚  Ã‚  Ã‚  The author isn’t biased about anything, she wasn’t for anything and she wasn’t against anything. Susanna Kaysen was simply trying to find her place in the world through the world of medicine. 5.  Ã‚  Ã‚  Ã‚  Ã‚  Is this book mostly concerned with factual information, or does the author use conjecture and opinion to make his/her point? What detail and examples support your answer?   Ã‚  Ã‚  Ã‚  Ã‚  Girl, Interrupted is all factual information. Susanna Kaysen the author has put her reports from the doctors that were helping her in the book. On August 9,1967 the author Susanna Kaysen 6.  Ã‚  Ã‚  Ã‚  Ã‚  Identify the author’s purpose(s) for writing this book. In your opinion, does the author accomplish his/ her purpose? Support your answer with concrete evidence.   Ã‚  Ã‚  Ã‚  Ã‚  The author’s purpose to the story is to make people notice how people are being treated in a mental institution. Susanna Kaysen writes about the girls in the ward as if nothing is wrong with them. She writes about being in a pschychiatri... ...ok. The girls in the book make up swear words about the weirdest things or how they act if they don’t get something they want. In a way I can relate to that because my mom is just a little bit like some of the girls in the story. If she doesn’t get what she wants she gets mad at the world and it is unnecessary, and that’s for everything. I think that’s just too spoiled. The girl I can most relate to in the story is Polly, because I can see myself listening to everyone and not complaining about anything really in life. But she was also a burn victim that didn’t complain about her face. I’m almost like her, I never once think I’m cute or beautiful, and I don’t complain about anything about my body. I know I have to live with what I have, so what’s the point in complaining about it. One more way I can relate to the story is my cousin is like my cousin she tried to the whole I swallowed this many pill thing. I never got why she did it. She never told me why, on the other hand she had some problems in the past with some things. So maybe that might have been the problem. But she never passed out like Susanna did in the beginning of the book. I’m just glad my cousin didn’t die.

Phy31 Lab

Lab 2 Physics 190 Acceleration â€Å"g† Due to Gravity – Method 2 Introduction Tonight we will measure the acceleration due to gravity again. This time however, we will collect more data and the analysis will be different. We will first fit the data using a second order polynomial. Recall for a mass falling from rest, that 1 (1. 1) y ? a yt 2 2 Suppose a mass falls through n successively greater displacements, each time starting from rest. The displacements can be expressed a 2 y? ? y t? ; ? 1 n ? . (1. 2) 2 Analyzing the Data Data for y? is not linear in time t?. We have two unique ways we can analyze the data.The first is to simply plot the data with vertical displacement on the y-axis and time on the x-axis and perform a 2nd order polynomial curve fit. We can then extract acceleration from the coefficient of the 2nd order term. The second method involves transforming the nonlinear data into a linear form by means of the logarithm from which we can extract accelerati on. We are going to use both methods because it demonstrates the power of mathematics as a data analysis tool. Fitting the Data to a 2nd Order Polynomial Free-fall data is shown in figure 1 and has the form y ? At2 ? Bt ? C (1. 3) Figure 1.Free-fall plot (dots) and 2nd order fit (solid line). If we fit ideal free-fall data to equation (1. 3) we should find that B = 0, C = 0, and A = ay/2. If you look at the polynomial fit equation embedded in figure 1 you will see BWhitecotton Page 1 of 7 Lab 2 Physics 190 that B = -10-13, C = -10-14, and A = -4. 905. So the data is not perfect but essentially both B and C are zero while A = -4. 0905. If you compare the polynomial equation to our kinematic equation†¦ y ? At 2 ? Bt ? C a y ? y t 2 ? vyit ? yi 2 †¦it becomes immediately evident that B corresponds to initial velocity, C the initial position, and A = ay/2.If dropped from rest, initial velocity and position are zero. This all boils down to the fact that fitting a second order p olynomial to free-fall data should provide the acceleration due to gravity directly. Simply plot displacement (yaxis) vs. time (x-axis) and use Excel, Vernier, calculator, or any tool that will perform a polynomial fit of order 2. Then ay = 2A which in the example above gives ay = 2(-4. 905) = -9. 81. Using the Logarithm to Linearize Data and Fit We begin with equation (1. 2), generalize and take absolute value ay m y? ? t? . 2 Vertical in figure Time Equation (1. 4) is plotted as data belowDisplacement vs2. 5 (1. 4) 20 |y(t)| (m) 15 10 5 0 0 0. 5 1 t (sec) 1. 5 2 2. 5 Figure 2. Absolute value of vertical displacement versus freefall time. Taking the log we obtain ? ay ? ?. log ? yn ? ? m log ? tn ? ? log ? ? 2 ? ? ? mXn Y n (1. 5) B Equation (1. 5) has the slope-intercept form of a line. Plotting the log of the data of figure 2, we obtain figure 3. The curve fits a straight line that has the form of Y = mX + B with m = 2. 0108 and B = 0. 6896. BWhitecotton Page 2 of 7 Lab 2 Physics 190 Linearized Data 1. 5 y = 2. 0108x + 0. 6896 R2 = 1 1 0. 5 Log( |y(t)| ) 0 -1. 2 -1 -0. 8 -0. 6 -0. 4 -0. 2 -0. 5 0 0. 2 0. 4 1 -1. 5 Log(t) Figure 2. Linearized data from figure 1 data above. Recalling that B = log(|ay|/2) = 0. 6896, we can solve for the acceleration ay. Inverting we get ay ? 100. 6896 2 ay ? 4. 893 . 2 a y ? 9. 787 Recall that our lab is at latitude ? = 32. 745 °. Therefore the acceleration due to gravity in our lab should have magnitude g? ? 9. 795 . Computing experimental error we find ?a y ? g? g? ? ? 100% ? ?9. 787 ? 9. 795? ?100% ? ?0. 0863% . 9. 795 This is quite respectable but also uncharacteristically low for experiments in our lab. This experiment, if carefully done, can yield 1% error. BWhitecottonPage 3 of 7 Lab 2 Procedure Physics 190 Set up the apparatus as we did last week. See figure 3 below for typical arrangement – this should look familiar. Spherical mass to= 0 s Digital Timer 0. 013s tf = t Figure 3. Setup for the free-fall experim ent. You must complete 3 trials for each of 10 height settings. Use Table 1 to record data. Common Steps ? Set up the apparatus. ? ? Set the ball clamp to the first height y1 = 0. 53 m. ? Place the ball in the mount and measure the exact vertical displacement from the bottom of the ball to the compressed target mat. Please be sure to measure the displacement each time! Record the magnitude of y1 in Table 1 as your first of 3 trials. ? Make sure the timer is set in the correct mode and reset to zero. ? Release the ball and record the time of freefall in Table 1 as well. ? Repeat this procedure until columns |y? | and t? of Table 1 are complete. Polynomial Fit Steps ? Compute the means and record y? and t? of Table 1. ? ? Using your analysis tool of choice, plot y? vs. t? and label the axes appropriately. Fit a 2nd order polynomial to the mean data and instruct the tool to display the fit equation and the R2 value. You may need to omit a few of the lowest values if they are excessive outliers due to ? measurement uncertainty. This is legitimate when we understand equipment limitations. BWhitecotton Page 4 of 7 Lab 2 Physics 190 ? Compute ay from the 2nd order term: ay = _____________ m/s2. Show work here Log Method Steps ? Next, take log (use base 10) of y? and t? and complete the last two columns ? ? of table 1. Plot log( y? ) vs. log( t? ) and once again label the axes appropriately. Fit a 1st order polynomial (linear regression) to the data and instruct the tool to display the fit equation and the R2 value. You may need to omit a few of the lowest values if they are excessive outliers due to ? measurement uncertainty. This is legitimate when we understand equipment limitations. Obtain the y-intercept term B = log(ay/2). Compute ay from the y-intercept: ay = _____________ m/s2. ? ? Show work here Error Analysis Compute percent error for ay with respect to g? in the cases of the Polynomial Fit Method and the Logarithm Linearization Fit Method. Lastly compute th e percent difference between the acceleration values determined from these methods. Questions 1. What are sources of error in this lab? 2. Why is it necessary to use the absolute value of the displacements when computing the log values? . Which of these methods gave the best results and why do you think that is? 4. What does the R2 value indicate when curve fitting to data? BWhitecotton Page 5 of 7 Lab 2 Formal Lab Report Physics 190 I want you to write a formal report on this lab. Follow the guidelines described in the formal report document available on my Cuyamaca homepage. Your focus should be on tabulation of data and the analysis (plotting of both raw and linearized data) including error analysis. Your final results should be emphasized and any error(s) discussed with thoughtful insight.I want original work from each student with name and group name on the first page. Due ____________________ Logarithm Refresher Recall that the logarithm of an argument returns the exponent tha t operated on a base producing the argument. I know it sounds confusing. Let’s take a look. Suppose I had the number 1000. Well, 1000 is the same as 10 3. Here, 10 is the base and 3 is the exponent. If I operate on the value 1000 with the base-10 logarithm (denoted log10) like so, log10(1000), I obtain the result 3 which is the exponent that would operate on base-10 to produce 1000.The operation can be expressed log10 ? 1000 ? ? log10 103 ? 3 ? ? There are many rules for using the logarithm. A few important ones for us are shown in the following examples†¦ log ? k ? r ? ? log( k ) ? log(r ) ? d? log ? ? ? log(d ) ? log(b) . ?b? log c7 ? 7 log(c ) ? ? (See me or refer to the appendix in the back of the text if you need more help on logarithms) BWhitecotton Page 6 of 7 Lab 2 Table 1. Raw and processed data. Setup : Positions 1: Set y ? 0. 53 m trial 1 trial 2 trial 3 mean 2: Set y ? 0. 66 m trial 1 trial 2 trial 3 mean 3: Set y ? 0. 9 m trial 1 trial 2 trial 3 mean 4: Set y ? 0. 92 m trial 1 trial 2 trial 3 mean 5: Set y ? 1. 05 m trial 1 trial 2 trial 3 mean 6: Set y ? 1. 18 m trial 1 trial 2 trial 3 mean 7: Set y ? 1. 31 m trial 1 trial 2 trial 3 mean 8: Set y ? 1. 44 m trial 1 trial 2 trial 3 mean 9: Set y ? 1. 57 m trial 1 trial 2 trial 3 mean 10: Set y ? 1. 70 m trial 1 trial 2 trial 3 mean Physics 190 Raw Data Polynomial Logarithm log( y? t? y? t? y? ) log( t? ) ? ? ? ? ? ? ? ? ? ? Use this table for data collection but make your own table in your report! BWhitecotton Page 7 of 7

Internal and external data Essay

The Bowmer and Kirkland human reasources department will collect and use external labour market information for planning in the business. Unemployment statistics : The current labour market figures show that in the east midlands current unemployment is only 4.1%, this means Bowmer and Kirkland may have difficulty finding people to employ as so many already have a job. As the higher the unemployment rate the more people there will be for Bowmer and Kirkland to employ.The unemployment rate of the east midlands is 1% lower than that of the whole UK. In Derbyshire alone the unemployment rates are paticually low and numbers are still falling. Although rates are low, GDP per head still lags behind the UK and European averages, skill levels and productivity in manufacturing are also below average. 31.6% of people working in east midlands have no qualifications compared with 28.9% of England, so when Bowmer and Kirkland need to emply someone it will alot of the time also include the costs of training them up. So in conclusion the unemployment rates in the east midlands could cause a problem when it comes to Bowmer and Kirkland recruiting staff. For a start there are only 4.1% people avaliable to work, and they have got appreciate that not all that 4.1% will have the knowlage and skills recuired to work at Bowmer and Kirkland. This will result in costing Bowmer and Kirkland alot of time and money, mainly on training. One way of getting round the low unemployment rates would be to take staff they already have and train them up for hiarachy positions. Although this will leave another problem of having to find someone to fill their origional position, so they cant win really. Employment and Unemployment in the construction industry : In the last few years turnover has gone up and the ammount of people employed has remained static. The avaliability of contructors in the labour market is a problem to Bowmer and Kirkland as there are not alot of qualified workers out there so most of there employees have the standard ammount of GCSE’s and A-levels. However Bowmer and Kirkland can train people up. The business’ in the local area that Bowmer and Kirkland are compeating for for the same employees are, Thorntons, Denby pottery, Rolls royce, these are the major compaines in this area, but construction companies down to Birmingham also cause competition for workers. Internal information The Bowmer and Kirkland human reasources team will also collect and use Internal staffing information for planning in the business. Labour turnover : Bowmer and Kirkland measure Labour turnover because they believe it is important to know how many people they need in the business. This also allows them to be able to compare their figures to other industrys and see how they match up. From the figures they have 20% of people have left the company this year, the main few reasons people are leaving are to branch out and move on, some are only probation periods and dont have their contract renuied, and some could have been sacked or made redundant. Although saying that some of them sometimes come back to the company depending on why they left. If someone is wanting to leave they have to hand in a 4 week or week notice and have the exit interview. Bowmer and Kirkland do this so they can see if there is a trend in why people are leaving, then they may be able to do something about it. For example †¦ most of the leavers may be from one department, and it may narrow down to the fact they are all finding better paid jobs elsewhere, so then Bowmer and Kirkland can sort out that problem. Bowmer and Kirkland however see this as a good thing as this means they can bring in new people/staff and they will bring in new ideas to the company. Bowmer and Kirkland on average employ 20 – 25 new starters each month, the majority of them have minimum qualifications and 10% are usually apprentices, however Bowmer and Kirkland will train them up. Sickness and Accident rates : Bowmer and Kirkland dont compare their Sickness and Accident rates against the national average. However they compare it internally within the company. They have weekly reports to monitor staff absences and the health and safety department monitor all their staff accidents. Of course if someone is found to be having an unusual ammount of absences they will have to have words with them and see the reason behind this as it would not be good for the company. Age, Skills and Training : Bowmer and Kirkland look at their demographic trends. Within the company 92 % of their employees are male and only 8 % female, this is purly because of the concept of he industry, it is more appealing to the male population. Also new training scemes are being used more in Bowmer and Kirkland now so the older generation is being left behind slightly, for example†¦ Modern apprentaships.