Passing Reflection Essay Topics

Passing Reflection Essay TopicsA Larkspur passing reflection essay topic can get you back on track with your life if you do not go astray for the time being. There are a lot of good guidelines that will help you get back on track.You need to realize that your answers will be measured by those people who have actually taken the course by Noam Chomsky, Fred Siegel and Joseph Stiglitz. They will be rating the answers that you give and taking notes about the answers that you give. They will be giving them the best possible grade and so will other people who will be grading the course materials.One of the important things that you should never forget is that your concentration questions have to be based on concrete examples. They have to be based on practical applications. They have to be based on real life events that you have personally experienced.The time that you spend studying these essay topics will not be wasted if you don't make the most of them. All the attention you get will he lp you in the long run.Don't just study these essay topics because you want to prove to yourself that you are intelligent. They are an excellent way to gain self-esteem as well as to motivate yourself. Your knowledge of these topics will also help you when you are in real life situations.You should also avoid commenting to your peers and acquaintances. They are not the only ones who would care about what you are doing with your life. You will be giving them a great boost, too.So, don't think that you have to proveyourself to Noam Chomsky or Fred Siegel or Joseph Stiglitz. They are not going to give you a perfect score.

Power Utility Consumption Capm in Uk Stock Markets Free Essays

string(114) " for values of risk aversion \(\? \) between 0 and 10 and values of the beta coefficient \(\? \) between 0 and 1\." Pricing of Securities in Financial Markets 40141 – How well does the power utility consumption CAPM perform in UK Stock Returns? ******** 1 Hansen and Jagannathan (1991) LOP Volatility Bounds Volatility bounds were first derived by Shiller (1982) to help diagnose and test a particular set of asset pricing models. He found that to price a set of assets, the consumption model must have a high value for the risk aversion coefficient or have a high level of volatility. Hansen and Jagannathan (1991) expanded on Shiller’s paper to show the duality between mean-variance frontiers of asset portfolios and mean-variance frontier of stochastic discount factors. We will write a custom essay sample on Power Utility Consumption Capm in Uk Stock Markets or any similar topic only for you Order Now Law of one price volatility bounds are derived by calculating the minimum variance of a stochastic discount factor for a given value of E(m), subject to the law of one price restriction. The law of one price restriction states that E(mR) = 1, which means that the assets with identical payoffs must have the same price. For this constraint to hold, the pricing equation must be true. Hansen and Jagannathan use an orthogonal decomposition to calculate the set of minimum variance discount factors that will price a set of assets. The equation m = x* + we* + n can be used to calculate discount factors that will price the assets subject to the LOP condition. Once x* and e* are calculated, the minimum variance discount factors that will price the assets can be found by changing the weights, w. Hansen and Jagannathan viewed the volatility bounds as a constraint imposed upon a set of discount factors that will price a set of assets. Therefore, when deriving the volatility bounds, we calculate the minimum variance stochastic discount factors that will price the set of assets. Discount factors that have a lower variance than these values will not price the assets correctly. Furthermore, Hansen and Jagannathan showed that to price a set of assets, we require discount factors with a high volatility and a mean close to 1. After deriving these bounds, we can use this constraint to test candidate asset pricing models. Models that produce a discount factor with a lower volatility than any discount factor on the LOP volatility can be rejected as they do not produce sufficient volatility. Hansen and Jagannathan find evidence that using LOP volatility bounds, we can reject a number of models such as the consumption model with a power function analysed in papers such as Dunn and Singleton (1986). 2 Methodology To test whether the power utility CCAPM prices the UK Treasury Bill (Rf) and value weighted market index returns, we first calculate the LOP volatility bounds. The volatility bound is derived by calculating the minimum variance discount factors that correctly price the two assets for given values of E (m). The standard deviations of the stochastic discount factors are then plotted on a graph to give the LOP volatility bound shown in figure one. Figure 1 here The CCAPM stochastic discount factors are then calculated for different levels of risk aversion. The mean and standard deviation of these discount factors are then plotted on the graph and compared to the LOP discount factor standard deviations. Pricing errors can then be calculated and analysed to see whether the assets are priced correctly by the candidate model. To accept the CCAPM model in pricing the assets, we expect the stochastic discount factors variance to be greater than the variance of the LOP volatility bounds. It is also expected that pricing errors and average pricing errors (RMSE) will be close to zero. These results will be analysed more closely in the later questions. 3 Power Utility CCAPM vs LOP Volatility Bounds In order for the power utility CCAPM to satisfy the Law of One Price volatility bound test at any level of risk aversion, the standard deviation f the CCAPM stochastic discount factor at that level of risk aversion must be above the Law of One Price standard deviation bound for the mean value of the CCAPM stochastic discount factor at the same level of risk aversion. This is the null hypothesis and if it is accepted then the model satisfies the test. The alternative hypothesis is that it the stand ard deviation of the stochastic discount factor is below the Law of One Price standard deviation bound for the mean value of the stochastic discount factor. If the null hypothesis is rejected and the alternative hypothesis is accepted then the model does not satisfy the test. Table 1 here Figure 2 here Figure 2 shows LOP volatility bounds and the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20. It is obvious the standard deviations (Sigma(m)) of the CCAPM stochastic discounts factors are much lower than the LOP volatility bounds corresponding to the means (E(m)) of the CCAPM stochastic discount factors. This is true for any level of risk aversion, because the entire CCAPM (green) line lies below the LOP volatility bounds (dark blue) line. Table 1 shows the standard deviations of the stochastic discount factors and the precise LOP volatility bound values, corresponding to the stochastic discount factor means so that the CCAPM can be formally tested. All of the standard deviations are lower than their respective volatility bound values. Therefore the null hypothesis is to be rejected and the alternative hypothesis is to be accepted for all levels of risk aversion between 1 and 20. Furthermore it would take a risk aversion of at least 54 to accept the null hypothesis. Therefore the power utility CCAPM stochastic discount factor does not satisfy the Law of One Price volatility bound test. These results are consistent with the equity premium puzzle study by Mehra and Prescott (1985). The study examines whether a consumption growth based model with a risk aversion value restricted to no more than 10 accurately prices equities. They have found that according to the model equity premiums should not exceed 0. 5% for values of risk aversion (? ) between 0 and 10 and values of the beta coefficient (? ) between 0 and 1. You read "Power Utility Consumption Capm in Uk Stock Markets" in category "Papers" However the average observed equity premium based on the average real return on nearly riskless short-term securities and the SP 500 for the period 1989-1978 was 6. 18%. This is clearly inconsistent with the predictions of the model. In particular if risk aversion is close to 0 and individuals are almost risk neutral, the model fails to explain why the sample’s average equity returns are so high. If risk aversion is significantly positive the model does not justify the low average risk-free rate of the sample. The results of Mehra and Prescott’s (2008) empirical study are consistent with our results, because the power utility CAPM did not satisfy our empirical tests. 4 Kan and Robotti (2007) Confidence Intervals The Law of One Price volatility bounds calculated in part 2 are subject to sampling variation. We have calculated point estimates of the volatility bounds, but we did not take into account that our results are based on a finite sample of Treasury Bill and market returns. To more accurately test whether the power utility CCAPM passes the LOP volatility bounds test, we need to identify the area in which the population volatility bound may lie. The area used is that between the upper and lower 95% confidence intervals for Hansen-Jagannathan volatility bounds obtained by Kan and Robotti (2007), shown in table 2. If the standard deviations of the CCAPM stochastic discount factors lie below that area for values of risk aversion between 1 and 20, then the power utility CCAPM model is to be rejected according to this test. Table 2 here Figure 3 here Figure 3 contains point estimates of the LOP volatility bounds, the standard deviations and means of the CCAPM stochastic discount factors for levels of risk aversion between 1 and 20 and the 95% confidence intervals for the volatility bounds. All of the standard deviations are below the area in between the upper and lower confidence intervals for the volatility bounds. This indicates that at a 95% certainty the CCAPM does not satisfy the LOP volatility bound test even when sampling errors are taken into account. Performance of Power Utility CCAPM In recent academic literature on the subject of asset pricing models a common formal method of evaluating model performance is to calculate the pricing errors on a set of test assets. In this report the test assets are the Treasury Bill and Market Index quarterly returns from Q1 1963 to Q4 2009. The pricing error is calculated as [pic] Where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pri cing errors. Table 3 here For a model to correctly price an asset it would require that the pricing errors are as close to zero as possible since the pricing error is a measure of the distance between the model pricing kernel and the true pricing kernel. From Table 3 we can see that the pricing errors for the different values of risk aversion are not close to zero and the size of the errors actually increases with the level of risk aversion. We can also see that the Route Mean Square Pricing Error (RSME) which measures the average distance from zero of the pricing errors is not as close to zero as we would hope and also increases with the level of risk aversion. If we note the case for a risk aversion level of 20 then the RSME is 6. 76%, since this is quarterly data this works out to an annual RSME of approximately 27%. With such large pricing errors we would not expect this model to perform strongly. Hansen and Jagannathan (1997) found that for different levels of risk aversion the pricing errors do not vary greatly. As noted above, this is not the case in our sample in which the error increases with the level of risk aversion, thus creating an ever wider dispersion of pricing errors. This is counterintuitive to what we would usually assume as with increased levels of risk aversion the consumer is only willing to accept a certain level of return for lower and lower levels of risk, therefore we would expect at some point that the mean variance level would pass the volatility bounds and therefore correctly price the assets. Conforming with this report Cochrane and Hansen (1992) found that in order to satisfy the levels of variance necessary to surpass the volatility bounds a risk aversion level of at least 40 was necessary. It should be noted that in reality this is quite unreasonable and also that for this level of variance to be attained the expected return might also have to drop below the level necessary to surpass the volatility bounds. Table 4 here From Hansen and Jagannathan (1991) we know that in order to price a set of assets correctly the stochastic discount factor (SDF) should be close to one and have high levels of volatility. Table 4 shows that SDF’s at low levels of risk aversion are relatively close to one but have very low levels of volatility. When the level of risk aversion increases the SDF’s get further and further away from one yet the volatility also increases. Therefore it seems reasonable to conclude that we would not expect any of these SDF’s to price the assets correctly. The results illustrated above are consistent with the earlier analysis and point to the conclusion that the power utility CCAPM does not do a good job in pricing the two test assets and thus does not perform well in UK stock returns. Cochrane and Hansen (1992) agree with this conclusion but Kan and Robotti (2007) find the opposite. The reason for this could be the use of sampling error in the Kan and Robotti paper and the different data used the in the analysis. This report illustrates that there exists not only an equity premium puzzle but also a risk free rate puzzle. This risk free rate puzzle as noted by Weil (1989) states that if consumers are extremely risk averse, a result of the equity premium puzzle, then why is the risk free rate so low. Weil cites market imperfections and heterogeneity as the probable causes of this puzzle; however, this is not the explanation that Bansal and Yaron (2004) find. Using a model that accounts for investor reaction to news about growth rates and economic uncertainty they are able to go some way to resolving not only the risk free rate puzzle but also the equity risk premium puzzle. One method that could be used to improve the performance of the power utility CCAPM would be to construct the model using conditioning information; this would enlarge the possible payoff space available to investors. Kan and Robotti (2006) find that including conditioning information in models reduces the pricing errors by allowing the prices of volatility to move in line with the market. Although as Roussanov (2010) finds, conditioning information does not necessarily improve model performance and may actually exacerbate the problem. 6 Sampling Error in the Volatility Bounds When using the volatility bounds as specified by Hansen and Jagannathan (1991) to test asset pricing models we must be wary of sampling error in the bounds. As noted previously if a model does not lie within the Hansen and Jagannathan volatility bounds then we can conclude that it does not price the test assets correctly. However, Gregory and Smith (1992) and Burnside (1994) first noted that this test does not take into account significant sampling variation and could therefore reject models that price assets correctly. Burnside (1994) uses Monte-Carlo simulation to illustrate that over repeated samples if sampling error is ignored the volatility bounds test performs poorly. Gregory and Smith (1992) state that the sampling error could be due to large variability in the estimated bounds or the use of sample data in the analysis. Kan and Robotti (2007) derive the finite sample distribution of the Hansen and Jagannathan bounds in order to take account of this sampling error. They argue that confidence intervals that take into account the variation can be constructed and used to test asset pricing models. The importance of this new method of testing cannot be underestimated as it could affect the decision to reject an asset pricing model or not, this is best illustrated with reference to examples. Kan and Robotti test the equity premium puzzle using data from Shiller (1989) to show the implications of taking into account sampling error. Through constructing the 95% confidence intervals for the Hansen and Jagannathan volatility bounds they are able to show that the time-separable power utility model being tested may not be rejected at low levels of risk aversion. This is in stark contrast to the findings when sampling error is not taken into account where the model is strongly rejected except for unfeasible levels of risk aversion. From Figure 3, as noted earlier, even when sampling error is taken into account for the model tested in this report it does not fall within the volatility bounds. However, it does decreases the distance between the model and the volatility bounds which is the major consequence of the Kan and Robotti paper. This new method goes some way to solving the problem noted by Cecchetti, Lam, and Mark (1994) who found using classical hypothesis tests that the Hansen and Jagannathan bounds without sampling error rejected true models too often. Again, an extension here could be to use conditioning information to improve the volatility bounds by using the methods of Ferson and Siegel (2003) and as a result hopefully reduce the sampling error in the bounds. References Bansal, R. and A. Yaron, 2004, Risks for the long run: A potential resolution of asset pricing puzzles, Journal of Finance, American Finance Association, vol. 59(4), pages 1481-1509, 08. Burnside, C. , 1994, Hansen-Jagannathan Bounds as Classical Tests of Asset-Pricing Models,† Journal of Business Economic Statistics, American Statistical Association, vol. 12(1), pages 57-79 Cecchetti, S. G. , P. Lam, and N. C. Mark, 1994, Testing Volatility Restrictions on Intertemporal Marginal Rates of Substitution Implied by Euler Equations and Asset Returns, Journal of Finance, 49, 123–152. Cochrane, J. H. and L. P. Hansen, 1992, Asset Pricing Explorations for Macroeconomics, NBER Chapters, in: NBER Macroeconomics Annual 1992, Volume 7, pages 115-182 National Bureau of Economic Research, Inc. Dunn, K. , and K. Singleton, 1986, Modelling the term structure of interest rates under Non-separable utility and durability of goods, Journal of Financial Economics, 17, 1986, 27-55. Ferson, W. E. , and A. F. Siegel, 2003, Stochastic Discount Factor Bounds with Conditioning Information, Review of Financial studies, 16, 567–595. Gregory, A. W. and G. W Smith, 1992. Sampling variability in Hansen-Jagannathan bounds, Economics Letters, Elsevier, vol. 38(3), pages 263-267. Hansen, L. P. and R. Jagannathan, 1991, Implications of Security Market Data for Models of Dynamic Economies, Journal of Political Economy, Vol. 99, No. 2 (Apr. , 1991), pp. 225-262   Hansen, L. P. and R. Jagannathan, 1997. Assessing specification errors in stochastic discount factor models. Journal of Finance 52, 591-607. Kan, R. , and C. Robotti, 2007, The Exact Distribution of the Hansen-Jagannathan Bound. Working Paper, University of Toronto and Federal Reserve Bank of Atlanta. Mehra, R. , and E. C. Prescott, (1985), The equity premium: A puzzle, Journal of Monetary Economics 15, 145-161. Roussanov, N. , 2010, Composition of Wealth, Conditioning Information, and the Cross-Section of Stock Returns, NBER Working Papers 16073, National Bureau of Economic Research, Inc. Shiller, R. , 1982, Consumption, Asset Markets and Macroeconomic fluctuations, Carnegie–Rochester Conference Series on Public Policy, Vol. 17. North-Holland Publishing Co. , 1982, pp. 203–238. Shiller, R. J. , 1989, Market Volatility, MIT Press, Massachusetts. Journal of Economic Behavior Organization, Elsevier, vol. 16(3), pages 361-364. Weil, P. , 1989, The equity premium puzzle and the risk free rate puzzle, Journal of Monetary Economics 24. 401-422. Appendix [pic] Figure 1 LOP Volatility Bounds. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. [pic] Figure 2 LOP Volatility Bounds with CCAPM. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line) for values of risk aversion between 1 and 20. [pic] Figure 3 LOP Volatility Bounds with CCAPM and Confidence Intervals. The figure shows the LOP volatility bounds (dark blue line) which were found by using Treasury Bill and market returns as test assets. It also shows the means and corresponding standard deviations of the CCAPM stochastic discount factors (green line) for values of risk aversion between 1 and 20. The figure contains the confidence intervals, with a 95% level of confidence, estimated by Kan and Robotti (2007) for E(m) between 0. 97 and 1. 0082 for the Law of One Price volatility bounds for their first set of test assets. The light blue line shows the upper bounds of the confidence intervals and the red line shows the lower bounds of the confidence intervals. Table 1 CCAPM stochastic discount factors’ means and standard deviations and corresponding LOP volatility bounds CCAPM |LOP volatility bounds |CCAPM | | |means | |st. dev. | | |0. 985121 |0. 82806186 |0. 011749 | |0. 980404 |1. 2067111 |0. 023503 | |0. 975849 |1. 57451579 |0. 035275 | |0. 971456 |1. 93015539 |0. 04708 | |0. 967223 |2. 27320637 |0. 58934 | |0. 963151 |2. 60350158 |0. 070853 | |0. 959239 |2. 92096535 |0. 082854 | |0. 955486 |3. 22555764 |0. 0 94953 | |0. 951893 |3. 5172513 |0. 107169 | |0. 94846 |3. 7960217 |0. 11952 | |0. 945187 |4. 06184126 |0. 132027 | |0. 942074 |4. 31467648 |0. 14471 | |0. 939121 |4. 5448604 |0. 15759 | |0. 93633 |4. 7812196 |0. 17069 | |0. 933701 |4. 99481688 |0. 184033 | |0. 931234 |5. 19520693 |0. 197645 | |0. 928931 |5. 38230757 |0. 211552 | |0. 926792 |5. 55602479 |0. 225781 | |0. 92482 |5. 71625225 |0. 240361 | |0. 923016 |5. 8628708 |0. 255322 | This table shows the means of the CCAPM stochastic discount factors for levels of risk aversion between 0 and 20, the corresponding LOP volatility bounds and the standard deviations of the CCAPM stochastic discount factors. Table 2 95% confidence intervals for E(m) between 0. 97 and 1. 0082 E(m) Lower Upper 0. 9700 3. 1823 5. 2069 0. 9710 2. 9385 4. 8383 0. 9719 2. 7038 4. 4830 0. 9729 2. 4781 4. 1411 0. 9738 2. 2617 3. 8125 0. 9748 2. 0544 3. 4974 0. 9757 1. 8565 3. 1959 0. 9767 1. 6680 2. 9080 0. 9776 1. 4890 2. 6337 0. 9786 1. 3195 2. 3731 0. 9795 1. 1597 2. 1262 0. 805 1. 0097 1. 8931 0. 9815 0. 8696 1. 6739 0. 9824 0. 7394 1. 4685 0. 9834 0. 6194 1. 2770 0. 9843 0. 5096 1. 0993 0. 9853 0. 4101 0. 9356 0. 9863 0. 3212 0. 7857 0. 9873 0. 2429 0. 6497 0. 9882 0. 1755 0. 5275 0. 9892 0. 1190 0. 4192 0. 9902 0. 0736 0. 3248 0. 9912 0. 0393 0. 2445 0. 9922 0. 0160 0. 1784 0. 9931 0. 0030 0. 1275 0. 9941 0 0. 0938 0. 9951 0 NaN 0. 9961 0 0. 0938 0. 9971 0. 0029 0. 1279 0. 9981 0. 0159 0. 1798 0. 9991 0. 0395 0. 2474 1. 0001 0. 0745 0. 3302 1. 0011 0. 1212 0. 280 1. 0021 0. 1796 0. 5408 1. 0031 0. 2498 0. 6689 1. 0041 0. 3317 0. 8123 1. 0051 0. 4255 0. 9714 1. 0061 0. 5309 1. 1461 1. 0072 0. 6481 1. 3368 1. 0082 0. 7769 1. 5437 This table shows the upper and lower bounds of the 95% confidence intervals Kan and Robotti (2007) calculated for the volatility bounds for their first set of test assets. The confidence intervals presented are for values of E(m) between 0. 97 and 1. 0082. Table 3 Pricing errors for the Treasury Bill (Rf) and the value weighted UK market index (Rm), and the Root Mean Square Pricing Error (RSME) for each level of risk aversion Level of Risk Aversion |Error Rf |Error Rm |RSME | |1 |-0. 0104 |0. 0047 |0. 0080 | |2 |-0. 0152 |-0. 0001 |0. 0107 | |3 |-0. 0199 |-0. 0049 |0. 0144 | |4 |-0. 0244 |-0. 0094 |0. 0184 | |5 |-0. 287 |-0. 0138 |0. 0225 | |6 |-0. 0329 |-0. 0180 |0. 0265 | |7 |-0. 0369 |-0. 0221 |0. 0304 | |8 |-0. 0408 |-0. 0260 |0. 0342 | |9 |-0. 0445 |-0. 0297 |0. 0378 | |10 |-0. 0480 |-0. 0333 |0. 413 | |11 |-0. 0514 |-0. 0367 |0. 0446 | |12 |-0. 0546 |-0. 0399 |0. 0478 | |13 |-0. 0577 |-0. 0430 |0. 0508 | |14 |-0. 0606 |-0. 0459 |0. 0537 | |15 |-0. 0634 |-0. 0487 |0. 0564 | |16 |-0. 660 |-0. 0513 |0. 0590 | |17 |-0. 0684 |-0. 0537 |0. 0614 | |18 |-0. 0706 |-0. 0560 |0. 0636 | |19 |-0. 0727 |-0. 0580 |0. 0657 | |20 |-0. 0747 |-0. 0600 |0. 0676 | | | | | | The pricing errors above are calculated as [pic], where [pic], [pic] Treasury Bill and Market Index returns, and [pic] is the pricing errors. The RSME is simply the average pricing error of the stochastic discount factor for each level of risk aversion. Table 4 Summary Statistics for power utility CCAPM stochastic discount factor |Level of Risk Aversion |Average |St Dev |Min |Max | |1 |0. 9851 |0. 0117 |0. 9551 |1. 0436 | |2 |0. 804 |0. 0235 |0. 9214 |1. 1000 | |3 |0. 9758 |0. 0353 |0. 8889 |1. 1595 | |4 |0. 9715 |0. 0471 |0. 8575 |1. 2223 | |5 |0. 9672 |0. 0589 |0. 8273 |1. 2884 | |6 |0. 9632 |0. 0709 |0. 7981 |1. 3581 | |7 |0. 592 |0. 0829 |0. 7699 |1. 4316 | |8 |0. 9555 |0. 0950 |0. 7428 |1. 5090 | |9 |0. 9519 |0. 1072 |0. 7166 |1. 5906 | |10 |0. 9485 |0. 1195 |0. 6913 |1. 6767 | |11 |0. 9452 |0. 1320 |0. 6669 |1. 7674 | |12 |0. 421 |0. 1447 |0. 6434 |1. 8630 | |13 |0. 9391 |0. 1576 |0. 6207 |1. 9638 | |14 |0. 9363 |0. 1707 |0. 5988 |2. 0701 | |15 |0. 9337 |0. 1840 |0. 5777 |2. 18 21 | |16 |0. 9312 |0. 1976 |0. 5573 |2. 3001 | |17 |0. 9289 |0. 116 |0. 5377 |2. 4245 | |18 |0. 9268 |0. 2258 |0. 5187 |2. 5557 | |19 |0. 9248 |0. 2404 |0. 5004 |2. 6940 | |20 |0. 9230 |0. 2553 |0. 4827 |2. 8397 | This table shows the average value, standard deviation, minimum and maximum for the stochastic discount factor at each level of risk aversion. ———————– 24th November 2011 How to cite Power Utility Consumption Capm in Uk Stock Markets, Papers

Information Systems Management and Strategy

Question: Discuss about the Information Systems Management and Strategy. Answer: Introduction Database management system is always an important part for any business whether is big or small it does not matter. In this document the database management system is been discussed based on the selected case study that is on boots plc. The online shopping site where the database management system is plays a critical role as it handles most of the data management tasks with relevant to the shopping site such as customers data, vendors data, product information and many more. In this document we have discussed about how the database management system is helping the online shopping company Boots Plc by enhancing their business intelligence and knowledge management so that the company can make right decisions for their business. Overview of the Company The author has chosen the name of the company as Boots Plc. which is situated in UK, Nottingham. It is one the most leading retailers in UK and along with that it is been the key marketer and manufacture of cosmetics, nonprescription drugs and toiletries. Boots Company is one of the largest owners of shopping centers in United Kingdom. The company is expanding their business globally by acquisition, more than 90 to 95 percent of entire sales are still been generated from home. Their objective is to maximize the company value for the shareholders benefits. They have outlets in most high streets, airport terminals and shopping centers. The company has been merged with the Alliance UniChem and it has become Alliance Boots. Boots Plc operates many numbers of stores across UK and Ireland. The main product of Boots Plc is products related with beauty and health. Moreover it also provides hearing care and eye care services within their stores. The company also runs a retailing websites and operates a loyalty card even branded as the Advantage card. The company also has charitable trust known as Boots Charitable Trust. Features and capabilities of DBMS DBMS have many capabilities regarding management of an information system. In the Boot Plc. Company the database management system is introduced to manage a large amount of customer data. The database management system is capable to manage the customer database; data regarding a customer can be fetched from the database by posting a query in the database system. The customer data is backed up and replicated in multiple servers to get them in case of any disaster. Rules can be enforced in the database such that any unnecessary records cannot be inputted in the database. The database management system is much secure, the database is managed by a specific group of individual who have the permission to access the database.Sorting, calculating the sum, calculating average can be done with the implementation of the DBMS in the company. Records of who have accessed the database can be kept to track the changes made in the database. Some of the database softwares can automatically optimize t he data inputted in the system, a monitoring tool can also be provided to analyze the record and provide a graph to show the user about the current scenario. Thus implementation of DBMS increases the capability of the Boot Plc. Company. The challenges to implement database management system Cost To implement a DBMS in Boot Plc. an IT budget should be done and the hardware and the software cost must be analyzed. People cost is also analyzed because skilled employees are required for the administration of the database. DBA Talent The database administrator must be quick and efficient for the management of the database. The downtime cost of a database is very high and an experienced DBA can find the fault in the database quickly. Keeping up-to-date the database must be kept up to date and latest technology should be used to manage the database. The software application for the database management is becoming complex with time and it is difficult to understand the programming. The DBA needs training to learn the database and act accordingly. Proactive vs Reactive The DBA might have to handle different database or need to spend time in managing the hardware of the database. The DBA might not have time to monitor the activity instead they might act directly to the problem. This might have an negative impact on the business process. Cover The data residing in the database should be available to the user all the time. Database Versions Some of the customer uses old and backdated database softwares. This can risk to the business as the vendor might stop the support for the older version of the software. Benefits of the database management system The database management system reduces data replication. The customer data kept in the database of Boot Plc. is not replicated. It also enhances the integrity of data to the database and thus improving the flexibility and availability of the data. When a customer record is updated the reflection is seen throughout the database. The data are stored in a centralized pattern such that the user with administrative rights can access the data from any remote location. The data is accessible by the administrators and any other unauthorized access is restricted for illegal use. Critique and Discussion of the Improvement in Knowledge Base System in the Company Necessary as the company operates the concept of business to consumer and consumer to business fundaments. The knowledge management based system involves the type of program that is generally used to solve many complex issues. The prevailing issues in Boots Plc. are lack of specialized programs in the field of programming applications. There is need of urgency to develop the sequence of well and qualified employees in order to make continuous improvement in knowledge base management system. The concept base of knowledge management schemes are defined different in different company management. On the other hand, knowledge management is a basic term that virtually promotes the research and development cell of the company management. It implies gathering of economical knowledge management preferences in terms of resources and documents. Concept of Intelligent Data Support System In Boots Plc, the Intelligent Data Support System implies the concept of artificial intelligence techniques used in the system of e business fundamentals. The company implements the concept of intelligent data support system with the motive of maintaining the segments of customer management service, manipulation of personnel database and making certain compulsion in campaigning and transactions management. Use of database in the company as a source of Business Intelligence The author has analyzed that the use of database management system in Boot Plc. as a source of business intelligence is exclusively important in terms of maintaining the database of customer segmentation, measuring the fluctuation of customer orders in terms of analyzing the basic segments and making constant analysis in making customer responses and reviews. Elements of Business Intelligence in the Company The components of business intelligence in Boots Plc is central repository, data integration services, master data services, reporting customer services, analytic client services and predictive company analytics services. The company has manipulated certain changes in strategic and operational business intelligence department so as to maintain a constant monopoly system in entire market segmentation. Technical, Ethical and Legal Issues in the Company In a company like Boots Plc. there are various types of issues that affect an entire organizational management which generally includes technical issues, ethical issues and legal usual. The author has mentioned some major issues below: Technical issues: The Boot Plc. Company is facing some technical issues like lack of visitors in the company websites, low rates of conversation, high cost overhead in adopting advanced technologies, maintain ace of faith in customer attributes. Ethical issues: The ethical issue is the major issue that all company management is facing; since there are no possible outcomes. The Boot Plc. Company is facing some major ethical issues which generally includes web spoofing, cyber-squatting, invasion of privacy, online piracy, spamming of company websites email and fake accounts authentication. Legal issues: The author has estimated and manifested various intellectual legal issues that the company is facing. Some of these include trademark and copyright, online terms and conditions, dilemma of legislations and electronic business legal issues. Analytics tool for improvement in knowledge management and decision making in the company Depend upon the selected case study of Boot Plc. the author has manipulated that there is a requirement of urgency in impoverishing the identification of decision making and the base of knowledge management scheme in electronic commerce company management system. Additionally, the author has reflected and highlighted some major implications of knowledge management system and in decision making system in the company Boot Plc. Structured environment: It is very essential to maintain and reinvent the virtualized environment in corporate industry; so that several complex decisions that affect organization superiors and subordinates may weight up various intellectual options. The conduction of shareholders analysis and followed up decision making model may assist the base of decision making of an organizational management. Thorough up investigation: The author has stated that it is very crucial to make a clear understandings relating to organizational situation. The shifts in single department in Boot Plc make the entire counterproductive in nature. Flexibility alternatives: In a company like Boot Plc the flexibility alternatives implies generating several options with the extension of company alternatives that helps them to find out from various angles, maintaining customer segmentation, manipulating large amount of data and segregating company optimum resources are the main work of making certain alternatives. Concept of Non-Relational Database management System in the company The concept non-relational database management system is not mandatory in the field of electronic based company like Boots Plc. The entire website design paradigm is totally relying upon the SQL based programming language application. It is very similar to the concept of relational database management system that basically promotes SQL programming languages. This type of data management system does not need advanced system applications; it main wok is to manipulate customer records and data. Opportunities of Cloud Database Management System in the company In a sector of electronic commerce company like Boot Plc. there are huge opportunities in adopting the concept of cloud based management system. The author has reflected and highlighted some minor implications that generally include: lower cost overhead implications, minimization of IT maintain ace and manipulation, fostering of government laws and policies and protection of customer data and information. Challenges of the company in context with Database Management System There are different challenges for Boot Plc. Company in relation with the concept of database management system. After viewing various strategies in the above case study of the company some major challenges include: Increase in population growth percentage: The main issue that is prevailing all around an organizational management is increase the rate of population growth. In a company like Boot Plc; increase in customer and client segmentation details may increase the task of database administrators; that may also lead to increase in labor salary overhead. Transfer of volume based compensation: The constant move from volume based and value based compensation is totally inevitable in nature. There is a huge risk of company increase in revenue and operating cost with that of customer behaviors and practices. Comparison with Traditional Database Management System in the Company In Boots Plc the company maintains three types of database system namely: compulsive database management system, relational database management system and traditional database system. The concept of traditional database management system implies the use of SQL query language in building up the company databases. There are some challenges in traditional database management system in context with company requirements which include consistency and data corruption. Conclusion The report concludes about the capabilities of database management system in the selected case study that is Boots Plc. A detailed explanation is been provided in this document for the database management system and relevant case study. The study explains the role of database administrator and its capabilities. Moreover various aspects such as findings, technical and ethical issues and other factors have been discussed in this report. The overall idea for presenting the report is to have a clear idea about how database management system is utilizing in the selected study and its impact on the business and various challenges and advantages. Recommendations Based on the relevant case study, the researcher has highlighted some recommendations which are presented below: As seen above that the company maintains the pirated database management system; which varies according to customers orders and perceptions. It is strictly advisable to adopt and implement the advance concept of relational database management system to avoid the frequency of population growth. The company must configure the base concept of cloud database management system. As mentioned above that the prevailing technical issues like lack of visitors in the company websites, low rates of conversation, high cost overhead in adopting advanced technologies, maintain ace of faith in customer attributes and etc may severe affect the entire company management. Bibliography Alam, M. and Shakil, K.A., 2013. Cloud database management system architecture. UACEE International Journal of Computer Science and its Applications, 3(1), pp.27-31. Boots.com (2016). Boots - Beauty | Health | Pharmacy and Prescriptions - Boots. [online] Available at: https://www.boots.com/ [Accessed 30 Jun. 2016]. Chung, P.T. and Chung, S.H., 2013, May. On data integration and data mining for developing business intelligence. In Systems, Applications and Technology Conference (LISAT), 2013 IEEE Long Island (pp. 1-6). IEEE. Coronel, C. and Morris, S., 2016. Database systems: design, implementation, management. Cengage Learning. Grefen, P., Pernici, B. and Snchez, G. eds., 2012. Database support for workflow management: the WIDE project (Vol. 491). Springer Science Business Media. Gupta, S., 2013. Online shopping cart application (Doctoral dissertation, North Dakota State University). Kerdvibulvech, C. and Yin, L., 2014. A new online database system based on virtually digital mapping. International Journal of Information Processing and Management, 5(2), p.14. Khodakarami, F. and Chan, Y.E., 2014. Exploring the role of customer relationship management (CRM) systems in customer knowledge creation. Information Management, 51(1), pp.27-42. Lavra , N., Keravnou-Papailiou, E. and Zupan, B. eds., 2012. Intelligent data analysis in medicine and pharmacology (Vol. 414). Springer Science Business Media. Liebowitz, J. and Frank, M. eds., 2016. Knowledge management and e-learning. CRC press. Nunan, D. and Di Domenico, M., 2013. Market research and the ethics of big data. International Journal of Market Research, 55(4), pp.2-13. Zicari, R.V., Rosselli, M., Ivanov, T., Korfiatis, N., Tolle, K., Niemann, R. and Reichenbach, C., 2016. Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization. In Big Data Optimization: Recent Developments and Challenges (pp. 17-47). Springer International Publishing.

Managing Waste, To Save Our World Have You Checked Your Garbage Lately

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