Risk Assessment of Strident Marks - Essay Example

Risk Assessment of Strident Marks July 25, 2006 The risk assessment of Strident Marks is completed using the Capital Asset Pricing Model. This model is based on a regression analysis function performed in Microsoft Excel. Primarily, the regr4ession between Stock Total Return and Stock Excess Return is conducted. Market Total Return and Market Excess Return follow this. Every stock in the market has a certain return. This return is assumed to have a normal distribution. This means that the return can be described by two terms.

The first term is the mean (expected return) and the second is the variance of returns. This also computes a covariance of returns between any the stocks and the market value where they have positive covariance, and those that move in opposite directions will have negative covariance. The expected return and variance of several stocks, a portfolio of these stocks that has a desired variance (risk) with a certain expected return. The expected return is the measurement of investment risk, what variances can be expected by the amount of investment.

CAPM formula. The CAPM formula is:Expected Security Return = Riskless Return + Beta x (Expected Market Risk Premium)or:r = Rf + Beta x (RM - Rf)where:- r is the expected return rate on a security;Rf is the rate of a "risk-free" investment, i.e. cash;RM is the return rate of the appropriate asset class.(Bennings 2006) Beta is the overall risk in investing in a large market, like the New York Stock Exchange Beta is the R-squared statistic found in the regression analysis. The Beta of a Strident Marks is risk compared to the Beta (Risk) of the overall market.

Beta indicates the volatility of the security, relative to the asset class (Frontline Systems, Inc. 2006).The regression analysis of Stock Total Return and Stock Excess Return yields the following:Regression StatisticsMultiple R0.066948141R Square0.004482054Adjusted R Square-0.012682049Standard Error1.928341146Observations60By the above analysis, the residual r shows that there is a risk of expected loss based on the predictor Y (percentage of return).Secondly, the Market regression analysis of Market Total Return and Market Excess Return yields the following:Regression StatisticsMultiple R1R Square1Adjusted R Square1Standard Error8.

27025E-16Observations60This can further be shown by the prediction of Y (return) based on the Residual (Risk) for both Market and Stock as shown:ObservationPredicted Y MarketResiduals MarketPredicted Y StockResiduals Stock10.15-4.718E-160.27633392-0.106333920.1658.6042E-160.176204340.0037956630.171.3323E-150.126139550.0588604540.1658.6042E-160.22626913-0.051269150.164.1633E-160.27633392-0.106333960.1681.138E-150.32639871-0.161398770.164.1633E-160.27633392-0.106333980.153-2.22E-160.3764635-0.216463590.145-9.437E-160.

42652829-0.2715283100.153-2.22E-160.3764635-0.2164635110.164.1633E-160.27633392-0.1063339120.1614.996E-160.26632096-0.095321130.1691.2212E-150.166191380.01480862140.1761.8319E-150.116126590.06987341150.1691.2212E-150.21625617-0.0402562160.1614.996E-160.26632096-0.095321170.1691.2212E-150.31638575-0.1503858180.1614.996E-160.26632096-0.095321190.154-1.388E-160.36645054-0.2054505200.146-8.604E-160.41651533-0.2605153210.154-1.388E-160.36645054-0.2054505220.1614.996E-160.26632096-0.095321230.15-4.718E-160.

47659308-0.3265931240.1582.2204E-160.3764635-0.2164635250.1658.6042E-160.32639871-0.1613987260.1582.2204E-160.42652829-0.2715283270.15-4.718E-160.47659308-0.3265931280.1582.2204E-160.52665787-0.3816579290.15-4.718E-160.47659308-0.3265931300.143-1.11E-150.57672267-0.4367227310.135-1.804E-150.62678746-0.4917875320.143-1.11E-150.57672267-0.4367227330.15-4.718E-160.47659308-0.3265931340.15-4.718E-160.47659308-0.3265931350.1582.2204E-160.3764635-0.2164635360.1658.6042E-160.32639871-0.1613987370.1582.2204E-160.42652829-0.

2715283380.15-4.718E-160.47659308-0.3265931390.1582.2204E-160.52665787-0.3826579400.15-4.718E-160.47659308-0.3265931410.143-1.11E-150.57672267-0.4367227420.135-1.804E-150.62678746-0.4917875430.143-1.11E-150.57672267-0.4367227440.15-4.718E-160.47659308-0.3265931450.15-4.718E-160.47659308-0.3265931460.1582.2204E-160.3764635-0.2164635470.1658.6042E-160.32639871-0.1613987480.1582.2204E-160.42652829-0.2715283490.15-4.718E-160.47659308-0.3265931500.1582.2204E-160.52665787-0.3816579510.15-4.718E-160.

4765930814.5234069520.143-1.11E-150.57672267-0.4367227530.135-1.804E-150.62678746-0.4917875540.143-1.11E-150.57672267-0.4367227550.15-4.718E-160.47659308-0.3265931560.15-4.718E-160.47659308-0.3265931570.1582.2204E-160.3764635-0.2164635580.1658.6042E-160.32639871-0.1613987590.1582.2204E-160.42652829-0.2715283600.15-4.718E-160.47659308-0.3265931In conclusion, the Beta statistic defined by R-Square is positive 1 in the Market analysis, and 0.004 in the Stock analysis, it can be assumed, provided that the stock and market follow a normal distribution, that the stock holds a 40% greater risk than that marketReferencesBennings, Simon (2006) Principles of Finance with Excel: Includes CD (Hardcover)Oxford University Press, USAFrontline Systems, Inc. (2006) Optimization Solutions with the Microsoft Excel Solver Retrieved July 25, 2006 from www.solver.com