1. Financial Analysis, Planning and Forecasting Theory and Application By Alice C. Lee San Francisco State University John C. Lee J.P. Morgan Chase Cheng F. Lee Rutgers University Chapter 2 Accounting Information, Regression Analysis, and Financial Management 1

2. Outline  2.1Introduction  2.2Financial statements: A brief review  2.3Critique of accounting information  2.4Static ratio analysis and its extension  2.5Cost-volume-profit analysis and its applications  2.6Accounting income vs. economic income  2.7Summary  Appendix 2A. Simple regression and multiple regression  Appendix 2B. Instrumental variables and two-stage least squares 2

3. 2.2Financial statements: A brief review  Balance sheet  Income statement  Cash flow statement  Equity statement  Annual vs. quarterly financial data 3

4. 2.1 Introduction Table 2.1 Consolidated Balance Sheets of Johnson & Johnson Corporation and Consolidated Subsidiaries (dollars in millions) 4

5. Income Statement Table 2.2: Consolidated Income Statements of Johnson & Johnson Corporation and Subsidiaries (dollars in millions) 5

6. Statement of Equity Table 2.3: Consolidated Statements of Equity of Johnson & Johnson Corporation and Subsidiaries (dollars in millions) 6

7. Statement of Equity Table 2.3: Consolidated Statements of Equity of Johnson & Johnson Corporation and Subsidiaries (dollars in millions) (Cont’d) 7

8. Statement of Cash Flows Table 2.4: Consolidated Statement of Cash Flow of Johnson & Johnson Corporation and Consolidated Subsidiaries, December 31, 2000, December 31, 2001, December 31, 2002, December 31, 2003, December 31, 2004, December 31, 2005, December 31, 2006. Annual vs. Quarterly Financial Data 8

9. 2.3Critique of accounting information  Criticism  Methods for improvement a) Use of Alternative Information b) Statistical Adjustments c) Application of Finance and Economic Theories 9

10. 2.4Static ratio analysis and its extension  Static determination of financial ratios  Dynamic analysis of financial ratios  Statistical distribution of financial ratios 10

11. Static determination of financial ratios Table 2.5: Company ratios period 2003-2004 Ratio ClassificationFormulaJ&JIndustry 2003200420032004 Liquidity Ratio Current Ratio1.711.961.591.7 Quick Ratio1.211.471.0481.174 Leverage Ratio Debt-to-Asset0.440.790.39 Debt-to-Equity0.39 0.150.09 Equity Multiplier1.801.682.292.24 Times Interest Paid12.614.623.827.3 11

12. Static determination of financial ratios Table 2.5: Company ratios period 2003-2004 (Continued) Ratio ClassificationFormulaJ&JIndustry 2003200420032004 Activity Ratios Average collection period57.3252.6658.356.6 Accounts receivable Turnover6.376.936.266.45 Inventory Turnover3.393.583.283.42 Fixed Asset Turnover2.92.84.54.7 Total Asset Turnover0.950.920.790.78 Profitability Ratios Profit margin13.2%15.3%17.19%17.97% Return on assets16.21%16.75%8.66%9.18% Return on equity29.04%29%18.51%18.89% Market value Price/earnings30.1524.221.3522.1 Price-to-book-value5.524.685.715.92 12

13. Dynamic Analysis of Financial Ratios (2.1) where 0  j  1, and  j = A partial adjustment coefficient; Y j,t = Firm’s jth financial ratio period t; Y j,t-1 = Firm’s jth financial ratio period t-1; and Y* j,t = Firm’s jth financial ratio target in period t, 13

14. Dynamic Analysis of Financial Ratios Y* j,t = CX j,t-1 +  j,t, (2.2) where Z j,t = Y j,t - Y j,t-1 ; W j,t-1 = X j,t-1 - Y j,t-1 ; A j and B j = Regression parameters, and  j,t = The error term. 14

15. Dynamic Analysis of Financial Ratios Z′ j,t = A′ j + B′ j W′ j,t-1 +  ′ j,t, (2.5) where Z′ j,t = log (Y j,t ) - log (Y j,t-1 ); W′ j,t-1 = log (X j,t-1 ) - log (Y j,t-1 ); and  ′ j,t = The Error term. 15

16. Dynamic Analysis of Financial Ratios 16

17. Dynamic Analysis of Financial Ratios Table 2.6: Dynamic adjustment ratio regression results * Partial adjustment coefficient significant at 95% level VariableCurrent RatioLeverage Ratio Mean Z 0.0075-0.03083 Mean W -0.145830.361666667 Var(Z) 0.0130390.006099 Cov(Z,W) 0.0740.009 Bj`Bj` 0.810*0.259 t-Statistics [3.53][1.06] Aj`Aj` 0.032-0.042 17

18. Dynamic Analysis of Financial Ratios Table 2.7: Ratio correlation coefficient matrix CRATGPMLR CR 1.0 AT -0.4438411.0 GPM 0.3632730.3813931.0 LR -0.511750.21961-0.050281.0 18

19. Dynamic Analysis of Financial Ratios Z 1,t = A 0 +A 1 Z 2,t + A 2 W 1 +  1,t, (2.9a) Z 2,t = B 0 + B 1 Z 1,t + B 2 W 2 +  2,t. (2.9b) where A i, B i (i = 0, 1, 2) are coefficients,  1 and  2 are error terms, and Z 1,t = Individual firm’s current ratio in period t - individual firm’s current ratio in period t-1; Z 2,t = Individual firm’s leverage ratio in period t - individual firm’s leverage ratio period t-1; W 1,t = Industry average current ratio in period t-1 - individual firm’s current ratio period t-1; W 2,t = Industry average leverage ratio in period t-1 - individual firm’s leverage ratio in period t-1. 19

20. Dynamic Analysis of Financial Ratios Table 2.8: Johnson & Johnson empirical results for the simultaneous equation system A 0 (B 0 )A 1 (B 1 )A 2 (B 2 ) (2.9a)-0.071 [-1.80] -0.378 [-5.52] 0.080 [1.20] (2.9b)-0.0577 [-1.59] -0.842 [-6.07] 0.074 [0.91] 20

21. Statistical Distribution of Financial Ratios where  and  2 are the population mean and variance, respectively, and e and  are given constants; that is,  = 3.14159 and e = 2.71828. 21

22. Statistical Distribution of Financial Ratios There is a direct relationship between the normal distribution and the log-normal distribution. If Y is log- normally distributed, then X = log Y is normally distributed. Following this definition, the mean and the variance of Y can be defined as: where exp represents an exponential with base e. 22

23. Statistical Distribution of Financial Ratios 23

24. 2.5 COST-VOLUME-PROFIT ANALYSIS AND ITS APPLICATIONS  Deterministic analysis  Stochastic analysis 24

25. Deterministic Analysis Operating Profit = EBIT = Q(P － V) － F, (2.12) where Q = Quantity of goods sold; P = Price per unit sold; V = Variable cost per unit sold; F = Total amount of fixed costs; and P - V = Contribution margin. 25

26. Deterministic Analysis Operating profit = EBIT = Q π (P π － V π ) － F. (2.16) 26

27. Deterministic Analysis 27

28. 2.6 ACCOUNTING INCOME VS. ECONOMIC INCOME E t = A t + P t, (2.17) where E t = Economic income, A t = Accounting earnings, and P t = Proxy errors. 28

29. 2.7 SUMMARY In this chapter, the usefulness of accounting information in financial analysis is conceptually and analytically evaluated. Both statistical methods and regression analysis techniques are used to show how accounting information can be used to perform active financial analysis for the pharmaceutical industry. In these analyses, static ratio analysis is generalized to dynamic ratio analysis. The necessity of using simultaneous-equation technique in conducting dynamic financial ratio analysis is also demonstrated in detail. In addition, both deterministic and stochastic CVP analyses are examined. The potential applications of CVP analysis in financial analysis and planning are discussed in some detail. Overall, this chapter gives readers a good understanding of basic accounting information and econometric methods, which are needed for financial analysis and planning. 29

30. Appendix 2A. Simple regression and multiple regression 2. A.1 INTRODUCTION 2. A.2 SIMPLE REGRESSION Variance of Multiple Regression 30

31. Appendix 2A. Simple regression and multiple regression (2.A.1a) (2.A.1b) (2.A.2a) (2.A.2b) 31

32. Appendix 2A. Simple regression and multiple regression (2.A.3) (2.A.4) (2.A.5a) (2.A.5b) 32

33. Appendix 2A. Simple regression and multiple regression (2.A.6a) (2.A.6b) 33

34. Appendix 2A. Simple regression and multiple regression (2.A.7) (2.A.7a) 34

35. Appendix 2A. Simple regression and multiple regression (2.A.8) (2.A.8a) 35

36. Variance of Equation (2.A.7a) implies that: (2.A.7b) Where 36

37. Variance of (2.A.7c) (2.A.9) 37

38. Variance of 38

39. Variance of (2.A.10) (2.A.11) (2.A.12) 39

40. Multiple Regression (2.A.13a) The error sum of squares can be defined as: Where 40

41. Multiple Regression (2.A.14a) (2.A.14b) (2.A.14c) 41

42. Multiple Regression 0 = na + b(0) + c(0), (2.A.15a) (2.A.15b) (2.A.15c) 42

43. Multiple Regression (2.A.16a) (2.A.16b) (2.A.17) 43

44. Multiple Regression (2.A.13b) (2.A.18) (2.A.19) 44

45. Multiple Regression (2.A.20) where TSS = Total sum of squares; ESS = Residual sum of squares; and RSS = Regression sum of squares. 45

46. Multiple Regression (2.A.21) (2.A.22) where and k = the number of independent variables. 46

47. Multiple Regression (2.A.23) where F(k-1, n-k) represents F-statistic with k － 1 and n － k degrees of freedom. 47

48. Appendix 2B. Instrumental variables and two- stage least squares 2. B.1 ERRORS-IN-VARIABLE PROBLEM 2. B.2 INSTRUMENTAL VARIABLES 2. B.3 TWO-STAGE, LEAST-SQUARE 48

49. 2. B.1 ERRORS-IN-VARIABLE PROBLEM (2.B.1) (2.B.2) (2.B.3) 49

50. 2. B.1 ERRORS-IN-VARIABLE PROBLEM (2.B.4) (2.B.5) 50

51. 2. B.2 INSTRUMENTAL VARIABLES (2.B.6) (2.B.7) (2.B.8a) (2.B.8b) 51

52. 2. B.2 INSTRUMENTAL VARIABLES (2.B.9a) (2.B.9b) (2.B.10a) (2.B.10b) 52

53. 2.B.3 TWO-STAGE, LEAST-SQUARE (2.B.11a) (2.B.11b) (2.B.10′a) (2.B.10′b) 53

54. 2.B.3 TWO-STAGE, LEAST-SQUARE (2.B.12a) (2.B.12b) 54