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Financial Analysis, Planning and Forecasting Theory and Application

This chapter explores risk estimation and diversification in financial analysis and planning. Topics covered include risk classification, portfolio analysis, market rate of return, commercial lending rates, dominance principle, and performance evaluation.

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Financial Analysis, Planning and Forecasting Theory and Application

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  1. Financial Analysis, Planning and ForecastingTheory and Application Chapter 8 Risk Estimation and Diversification By Cheng F. Lee Rutgers University, USA John Lee Center for PBBEF Research, USA

  2. Outline • 8.1 Introduction • 8.2 Risk classification • 8.3 Portfolio analysis and applications • 8.4 The market rate of return and market risk premium • 8.5 Determination of commercial lending rates • 8.6 The dominance principle and performance evaluation • 8.7 Summary • Appendix 8A. Estimation of market risk premium • Appendix 8B. The normal distribution • Appendix 8C. Derivation of Minimum-Variance Portfolio • Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight

  3. 8.1 Risk classification • Total risk=Business risk + Financial risk

  4. 8.1 Risk classification 8.1A : Method (8.1) (8.2) (8.3) ROI: return on investment

  5. 8.1 Risk classification 8.1B : Example Table 8-1

  6. 8.2 Portfolio analysis and applications • Expected rate of return on a portfolio • Variance and standard deviations of a portfolio • The efficient portfolios • Corporate application of diversification

  7. 8.3 Portfolio Analysis and Application (8.4) (8.5)

  8. 8.3 Portfolio Analysis and Applications (8.6) (8.7)

  9. 8.3 Portfolio analysis and applications Figure 8-2 Two Portfolios with same mean and different variance

  10. 8.3 Portfolio analysis and applications (8.8) (8.9) (8.10) (8.11)

  11. 8.3 Portfolio analysis and applications Figure 8-3 The Correlation Coefficients

  12. 8.3 Portfolio analysis and applications Efficient Portfolios Under the mean-variance framework, a security or portfolio is efficient if E(A) > E(B) and var(A) = var(B) Or E(A) = E(B) and var(A) < var (B).

  13. 8.3 Portfolio analysis and applications Figure 8-4 Efficient Frontier in Portfolio Analysis

  14. 8.3 Portfolio analysis and applications Table 8-2 Variance-Covariance Matrix

  15. 8.3 Portfolio analysis and applications

  16. 8.4The market rate of return and market risk premium Table 8-3 Market Returns and T-bill Rates by Quarters

  17. 8.4The market rate of return and market risk premium Table 8-3 Market Returns and T-bill Rates by Quarters (Cont’d)

  18. 8.5 Determination of commercial lending rates Table 8.4

  19. 8.5 Determination of commercial lending rates Table 8-5

  20. 8.5 Determination of commercial lending rates

  21. 8.6The dominance principle and performance evaluation Figure 8-5 Distribution of Leading Rate(R)

  22. 8.6The dominance principle and performance evaluation Figure 8-6 The Dominance Principle in Portfolio Analysis

  23. 8.6The dominance principle and performance evaluation The example of Sharpe Performance Measure Table 8-6

  24. 8.7 Summary In Chapter 8, we defined the basic concepts of risk and risk measurement. Based on the relationship of risk and return, we demonstrated the efficient portfolio concept and its implementation, as well as the dominance principle and performance measures. Interest rates and market rates of return were used as measurements to show how the commercial lending rate and the market risk premium are calculated.

  25. Appendix 8A. Estimation of market risk premium Table 8A-1 Summary Statistics of Annual Returns (1926-2006)

  26. Appendix 8A. Estimation of market risk premium Exhibit 8A-1: Derived Series: Summary Statistics of Annual Component Returns (1926-2006)

  27. Appendix 8B. The Normal Distribution Figure 8B-1 Probability Density Function for a Normal Distribution, Showing the Probability That a Normal Random Variable Lies between a and b (Shaded Area)

  28. Appendix 8B. The Normal Distribution Figure 8B-2 Probability Density Function of Normal Random Variables with Equal Variances: Mean 2 is Greater Than 1. Figure 8B-3 Probability Density Functions of Normal Distributions with Equal Means and Different Variances

  29. Appendix 8B. The Normal Distribution Table 8B-1 Probability, P, That a Normal Random Variable with Mean and Standard Deviation σ lies between K – σ and K – σ. Mean =12. If the investor may believe there is a 50% chance that the actual return will be between 10.5% and 13.5%. K=(13.5-10.5)/2=1.5 and K/ σ=0.674 Then σ=1.5/0.674=2.2255, =4.95

  30. Appendix 8B. The Normal Distribution Figure 8B-5 For a Normal Random Variable with Mean 12, Standard Deviation 4.95, the Probability is .5 of a Value between 10.5 and 13.5

  31. Appendix 8C. Derivation of Minimum-Variance Portfolio (8.C.2) By taking partial derivative of with respect to w1, we obtain

  32. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight where = expected rates of return for portfolio P. = risk free rates of return = Sharpe performance measure as defined in equation (8.C.1) of Appendix C

  33. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.1) (8.D.2) (8.D.3) (8.D.4)

  34. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.5) (8.D.6) (8.D.7)

  35. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.8) (8.D.9)

  36. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.10) (8.D.11)

  37. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.12)

  38. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight Left hand side of equation (8.D12):

  39. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight Right hand side of equation (8.D.12)

  40. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight (8.D.13) (8.D.14)

  41. Appendix 8D. Sharpe Performance Approach to Derive Optimal Weight

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