# Hello, I am looking for someone to write an article on Risk Assessment of Strident Marks. It needs to be at least 500 words.

Hello, I am looking for someone to write an article on Risk Assessment of Strident Marks. It needs to be at least 500 words. 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 Statistics

Multiple R

0.066948141

R Square

0.004482054

Adjusted R Square

-0.012682049

Standard Error

1.928341146

Observations

60

By 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 Statistics

Multiple R

1

R Square

1

Adjusted R Square

1

Standard Error

8.27025E-16

Observations

60

This can further be shown by the prediction of Y (return) based on the Residual (Risk) for both Market and Stock as shown:

Observation

Predicted Y Market

Residuals Market

Predicted Y Stock

Residuals Stock

1

0.15

-4.718E-16

0.27633392

-0.1063339

2

0.165

8.6042E-16

0.17620434

0.00379566

3

0.17

1.3323E-15

0.12613955

0.05886045

4

0.165

8.6042E-16

0.22626913

-0.0512691

5

0.16

4.1633E-16

0.27633392

-0.1063339

6

0.168

1.138E-15

0.32639871

-0.1613987

7

0.16

4.1633E-16

0.27633392

-0.1063339

8

0.153

-2.22E-16

0.3764635

-0.2164635

9

0.145

-9.437E-16

0.42652829

-0.2715283

10

0.153

-2.22E-16

0.3764635

-0.2164635

11

0.16

4.1633E-16

0.27633392

-0.1063339

12

0.161

4.996E-16

0.26632096

-0.095321

13

0.169

1.2212E-15

0.16619138

0.01480862

14

0.176

1.8319E-15

0.11612659

0.06987341

15

0.169

1.2212E-15

0.21625617

-0.0402562

16

0.161

4.996E-16

0.26632096

-0.095321

17

0.169

1.2212E-15

0.31638575

-0.1503858

18

0.161

4.996E-16

0.26632096

-0.095321

19

0.154

-1.388E-16

0.36645054

-0.2054505

20

0.146

-8.604E-16

0.41651533

-0.2605153

21

0.154

-1.388E-16

0.36645054

-0.2054505

22

0.161

4.996E-16

0.26632096

-0.095321

23

0.15

-4.718E-16

0.47659308

-0.3265931

24

0.158

2.2204E-16

0.3764635

-0.2164635

25

0.165

8.6042E-16

0.32639871

-0.1613987

26

0.158

2.2204E-16

0.42652829

-0.2715283

27

0.15

-4.718E-16

0.47659308

-0.3265931

28

0.158

2.2204E-16

0.52665787

-0.3816579

29

0.15

-4.718E-16

0.47659308

-0.3265931

30

0.143

-1.11E-15

0.57672267

-0.4367227

31

0.135

-1.804E-15

0.62678746

-0.4917875

32

0.143

-1.11E-15

0.57672267

-0.4367227

33

0.15

-4.718E-16

0.47659308

-0.3265931

34

0.15

-4.718E-16

0.47659308

-0.3265931

35

0.158

2.2204E-16

0.3764635

-0.2164635

36

0.165

8.6042E-16

0.32639871

-0.1613987

37

0.158

2.2204E-16

0.42652829

-0.2715283

38

0.15

-4.718E-16

0.47659308

-0.3265931

39

0.158

2.2204E-16

0.52665787

-0.3826579

40

0.15

-4.718E-16

0.47659308

-0.3265931

41

0.143

-1.11E-15

0.57672267

-0.4367227

42

0.135

-1.804E-15

0.62678746

-0.4917875

43

0.143

-1.11E-15

0.57672267

-0.4367227

44

0.15

-4.718E-16

0.47659308

-0.3265931

45

0.15

-4.718E-16

0.47659308

-0.3265931

46

0.158

2.2204E-16

0.3764635

-0.2164635

47

0.165

8.6042E-16

0.32639871

-0.1613987

48

0.158

2.2204E-16

0.42652829

-0.2715283

49

0.15

-4.718E-16

0.47659308

-0.3265931

50

0.158

2.2204E-16

0.52665787

-0.3816579

51

0.15

-4.718E-16

0.47659308

14.5234069

52

0.143

-1.11E-15

0.57672267

-0.4367227

53

0.135

-1.804E-15

0.62678746

-0.4917875

54

0.143

-1.11E-15

0.57672267

-0.4367227

55

0.15

-4.718E-16

0.47659308

-0.3265931

56

0.15

-4.718E-16

0.47659308

-0.3265931

57

0.158

2.2204E-16

0.3764635

-0.2164635

58

0.165

8.6042E-16

0.32639871

-0.1613987

59

0.158

2.2204E-16

0.42652829

-0.2715283

60

0.15

-4.718E-16

0.47659308

-0.3265931

In 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 market

References

Bennings, Simon (2006) Principles of Finance with Excel: Includes CD (Hardcover)

Oxford University Press, USA

Frontline Systems, Inc. (2006) Optimization Solutions with the Microsoft Excel Solver Retrieved July 25, 2006 from www.solver.

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