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

Outline  2.1Introduction  2.2Financial statement: 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

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

2.2Financial statement: A Brief Review  Balance Sheet  Income Statement  Retained Earnings Statement  Statement of changes in financial position  Annual vs. Quarterly Financial Data

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

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

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

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, Annual vs. Quarterly Financial Data

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

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

Static determination of financial ratios Table 2.5: Company ratios period Ratio ClassificationFormulaJ&JIndustry Liquidity Ratio Current Ratio Quick Ratio Leverage Ratio Debt-to-Asset Debt-to-Equity Equity Multiplier Times Interest Paid

Static determination of financial ratios Table 2.5: Company ratios period (Continued) Ratio ClassificationFormulaJ&JIndustry Activity Ratios Average collection period Accounts receivable Turnover Inventory Turnover Fixed Asset Turnover Total Asset Turnover Profitability Ratios Profit margin13.2%15.3%17.19%17.97% Return on assets14.91%15.96%7.34%7.06% Return on equity26.79%26.75%14%12.44% Market value Price/earnings Price-to-book-value

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,

Dynamic Analysis of Financial Ratios 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.

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.

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 Mean W Var(Z) Cov(Z,W) Bj`Bj` 0.810*0.259 t-Statistics [3.53][1.06] Aj`Aj`

Dynamic Analysis of Financial Ratios Table 2.7: Ratio correlation coefficient matrix CRATGPMLR CR 1.0 AT GPM LR

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.

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) [-1.80] [-5.52] [1.20] (2.9b) [-1.59] [-6.07] [0.91]

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

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.

Statistical Distribution of Financial Ratios

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

2.5.1 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.

2.5.1 Deterministic Analysis (cont’d) If operating profit is equal to zero, Eq. (2.12) implies that Q(P-V)-F=0 or that Q(P-V)=F, that is, Equation (2.13) represents the break-even quantity, or that quantity of sales at which fixed costs are just covered. The definition of the degree of operating leverage (DOL) is, Based upon the definition of linear break-even quantity defined in Eq. (2.13), the degree of operating leverage can be rewritten as

2.5.2 Stochastic Analysis In reality, net profit is a random variable because the quantity used in the analysis should be the quantity sold, which is unknown and random, rather than the quantity produced, which is internally determined. This is the simplest form of stochastic CVP analysis; for there is only one stochastic variable and one need not be concerned about independence among the variables. The distribution of sales is shown graphically in Fig. 2.5.

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.

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.

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

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

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

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

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

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

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

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

Variance of

(2.A.10) (2.A.11) (2.A.12)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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