Organizational information systems and their functionalities

Organizational information systems and their functionalities The concept of an information system is used in organizations in reference to a computer system which provides the management and other personnel with updated information on the organizational performance such as current inventory and sales. The organizational information systems are designed/developed administered and maintained in order to capture, analyze, quantify, compile, manipulate and share the information. The management information systems are those systems that serve the general, predictable management functions with technological advancements, the enterprise resource planning software and the executive information systems which have improved functionality, integrated and high flexibility. (Hoganson, 2001). The internet further accelerates the information system processes and information exchange through avenues such as e-mail, intranets and the extranets. It allows for wider accessibility and a faster rate of information exchange. The internet technologies such as web-casting and videoconferencing allows for quick and real-time exchange of information. Other modes of mobile computing in line with the internet communication have also increased the accessibility these include; the multi-functional mobile phones, and the IPad. Organizations information systems and their functionalities Organizations implement the information systems for the purposes of improving on efficiency and the effectiveness, to achieve the purpose of implementation, the organization should determine the capabilities of the information system, the work systems and the development/implementation methodologies. The traditional information was composed of executive information systems, the decision support system, management information systems and lastly the transaction processing. The advancement of technology has led to the emergence of new categories of information systems that include; Databases Enterprise systems The enterprise resource planning Office automations Global information systems Expert systems Human Resource Management Systems Information system functionalities The information system functions are varied, for the Riordan Manufacturing Virtual Organization the functions of the information systems will include; The document and record management This is the most crucial aspect of information system especially for the manufacturing organizations such as the Riordan Manufacturing Virtual Organization, the information in the documents is analyzed, quantified compiled and manipulated to enable the organization to determine the means to create the new products and services, the information also provides the market information such as demand and supply, procurement, shipment and customer billing. (Fairhtorne, 1961) The documents include accounting, marketing, financial, manufacturing and human resource. The information system serves as the organizational library since the information is collected and indexed according to the requirements and type of the organization. The information systems improves the accessibility of the information Information, this is because the location and retrieval of the archived information involves a direct and logical process. The designing of the information systems employs a very careful plan which outlines how the information is organized and indexed. Information system as a collaborative tool These serve the purpose of data and information sharing through the use of the information systems software and hardware. The information systems allows the exchange and of data between different departments of the organization and resource sharing in the context of real-time communication, therefore the utilization of the information systems as the collaborative tool in a manufacturing organization such as the Riordan Manufacturing Virtual Organization greatly improves its efficiency. Data mining The information systems enable the analysis of empirical data and the extrapolating of information. Manufacturing and even processing organizations use the extrapolated results in the forecasting and definition of the future trends in the market. Information systems as Query tools The information systems functions as the query tools by allowing the sorting and filtering of information in accordance to the management specification and the system administration. These enable the user to find specific information that is needed in performing a given function. The organizations have a workforce with a varied and wider knowledge base, the information systems ensure a successful navigation of various information levels. The information system administrator helps in ascertaining that the varied collection, retrieval and the analyzation of the systems operate on a common platform. The querying of the information also employs the use of the intelligent agents which customizes the information in order to fit to the individual needs of the organization. The Human Resource Management Systems These systems employ the use of the client-server, the Application Service Provider, and the Software. Human Resource Management Systems have increased the administrative control. Human Resource Management Systems encompass; the Payroll, Work Time, HR management Information system, Recruiting and Training (Learning Management System) Global interconnectivity The payroll module enables the automation of the pay process through gathering data about the employees attendance, calculation of various deductions and the taxes, and the generation periodic pay cheque and reports The work time module collects the standardized time and work related efforts, it provides a broad flexibility for data collection, the distribution of labor and the data analysis features. The HR management module covers many aspects of HR ranging from the application to the retirement. These include; the system records, the basic demographic, the address data, the training and development plans, and the compensation plan records. The advanced systems provide the ability to read the applications and data entry in relevance to applications of the database fields, through the global interconnections the employers are notified and provided with a good management position and overall control. The Human resource management function involves; recruitment, placement, evaluating, compensation and the development of the organizations employees. (Caldelli Parmigiani, 2004) The businesses use the computer based information systems to; produce the pay checks and payroll reports; maintain the personnel records; and enhance Talent Management. The global interconnectivity enables online recruitment and analyzing of personnel usage in organizations; identification of the potential applicants; and the recruitment through online recruiting sites or publications that market to both recruiters and applicants. The globalization also provides the training module creates the system for organizations to easily administer and track the employees training and career development efforts. HR managers can also track the education qualifications and skills of their employees, the process of outlining training courses or the web based learning materials are also enabled through the interconnection of the HRMS. A failure in the operation of a major system of operation of the human resource management system could lead to fatal losses that can cause the fall of an organization. Therefore the main reason for the protection of this system is because it the backbone of all the operations of the organizations which depend on human resource for the utilization of other resources.

Power Utility Consumption Capm in Uk Stock Markets

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. 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 th is 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 t he 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 standard 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 bou nds (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 excee d 0. 5% for values of risk aversion (? ) between 0 and 10 and values of the beta coefficient (? ) between 0 and 1. However the average observed equity premium based on the average real return on nearly riskless short-term securities and the S&P 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 volatilit y 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 ar ea 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 pricing errors. Table 3 hereFor 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 differe nt 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 s o 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 bo unds 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 tha t 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.Howe ver, 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, Elsev ier, 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. 094953 | |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 av ersion 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. 1821 | |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

Discuss How Different Approaches to Learning Can Affect Student Success in Higher Education Essay

It utilized a questionnaire based on an academic text, gathering some students, asking them to read the text then answer the questionnaire. Two distinctive groups were formed: students with high levels of understanding and perfect answers, named deep approach learners, and another with lower level, referred to as surface approach learners (Ramsden, 2003). Later, another approach was discovered and named as the strategic approach to learning (Chin, 2000). This essay recommends the deep approach to learning to be followed as a key of success in higher education, arguing particularly about the advantages and disadvantages of both deep and surface approaches to learning. Advantages of surface approach: The expression of the word surface means â€Å"the top layer of something† (Cambridge, 2009). Students who are surface learners are characterized by mechanical memorization (Chin, 2000), which stands for memorizing facts without understanding their objectives. These students learn only to pass exams or to meet a demand. Surface approach has only a lone advantage which can only benefit some students and not all. It is applicable particularly for the students who work while they are studying or who suffer from work loads such as preparing for academic assignments and doing extensive homework. This can fulfill their need of acquiring a time saving approach that enables them to succeed in their studies. Disadvantages of surface approach: In contrast, surface approach has many disadvantages. Some of these disadvantages can be summarized in five main ways. First, the students who follow this route of learning can not demonstrate the new ideas learnt thoroughly, neither can they relate them with other fields (Ramsden, 2003). Second, it directs the student to be a dependent learner. For instance, if a chemistry instructor asked his students to prove an experiment practically, then the surface learners will depend on their peers’ idea to verify the experiment. If they do not, then they will easily give up and this can be considered as a third disadvantage. The forth disadvantage is that it makes them easily ignore the points that they do not understand. As in the first example, those students neglect and forget about the ideas that were not helpful in doing their experiment. Finally, it brings the learner to forget the knowledge learnt easily and fast (Johansson, n. d). Advantages of deep approach: The expression of the word deep means â€Å"being a long way down from the top or surface to the bottom† (Cambridge, 2009). So, deep learners are the students who search for the full of meaning of the subjects they learn by following strategic ways to achieve that. Deep learners, unlike surface learner, use memorization when necessary but not always. There are many advantages related to deep approach. First of all, deep approach encourages the students to become more interested in their subjects and to have the curiosity to learn further. The second is that it assists the students to predict new information by analyzing recent ideas and connecting them with their prior experience and with other fields, as a result forming a complete image of the task required (Chin, 2000). Thirdly, it enables the students to have high quality outcomes in higher education (Johansson, n. d. ). The last is that it encourages the students to be independent learners (Entwistle, 1990). Disadvantages of deep approach: However, there is only one disadvantage of deep approach, which can be described as the obsession and passion that the student may follow in order to learn everything about the subject being learnt (Johansson, n. d). This can waste time and cause irregularity for other subject timetables. For instance, many deep learners like to know the whole idea about everything they learn, however they are not supposed to know everything, but this obsession leads them to waste time unconsciously. This situation can occur sometimes within the period of final exams revision, which can drive the student to have lower marks than expected for a deep learner. Conclusion: After the classification of the students into deep and surface learners, many universities recommended their students to follow the deep rather than the surface approach to learning owing to its benefits that their students are going to obtain. Perhaps the surface approach is applicable for some students but not all. Nevertheless, the advantages of deep approach to learning are more than surface approach; in addition the disadvantages of the deep approach are much less than the surface approaches. Therefore, by following the deep approaches to learning, students’ success in higher education will be advantageous. 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