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 25 Econometric Approach to Financial Analysis, Planning, and Forecasting

Outline  25.1 Introduction  25.2 Simultaneous nature of financial analysis, planning, and forecasting  25.3 The simultaneity and dynamics of corporate-budgeting decisions  25.4 Applications of SUR estimation method in financial analysis and planning  25.5 Applications of structural econometric models in financial analysis and planning  25.6 Programming vs. simultaneous vs. econometric financial models  25.7 Financial analysis and business policy decisions  25.8 Summary  Appendix 25A. Johnson & Johnson as a case study

25.1 Introduction

25.2 Simultaneous nature of financial analysis, planning, and forecasting  Basic concepts of simultaneous econometric models  Interrelationship of accounting information  Interrelationship of financial policies

25.3 The simultaneity and dynamics of corporate-budgeting decisions  Definitions of endogenous and exogenous variables  Model specification and applications

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.1 Endogenous and exogenous variables 1. The endogenous variables are: a) X 1,t = DIV t = Cash dividends paid in period t; b) X 2,t = IST t = Net investment in short-term assets during period t; c) X 3,t = ILT t = Gross investment in long-term assets during period t; d) X 4,t = -DF t = Minus the net proceeds from the new debt issues during period t; e) X 5,t = -EQF t = Minus the net proceeds from new equity issues during period t. 2. The exogenous variables are: 5 a) Y t = Σ X i,t = Σ X * i,t, where Y = net profits + i=1 i=1 depreciation allowance; a reformulation of the sources = uses identity.

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.1 Endogenous and exogenous variables (Cont.) b) RCB = Corporate Bond Rate (which corresponds to the weighted-average cost of long-term debt in the FR Model [Eqs. (20), (23), and (24) in Table 23.10], and the parameter for average interest rate in the WS Model [Eq. (7) in Table 23.1]. c) RDP t = Average Dividend-Price Ratio (or dividend yield, related to the P/E ratio used by WS as well as the Gordon cost-of-capital model, discussed in Chapter 8). The dividend-price ratio represents the yield expected by investors in a no-growth, no-dividend firm. d) DEL t = Debt Equity Ratio (parameter used by WS in Eq. (18) of Table 23.1). e) R t = The rates-of-return the corporation could expect to earn on its future long-term investment (or the internal rate-of-return discussed in Chapter 12). f) CU t = Rates of Capacity Utilization (used by FR to lag capital requirements behind changes in percent sales; used here to define the R t expected).

25.3 The simultaneity and dynamics of corporate-budgeting decisions 5 Σ X i,t = Σ X * i,1 = Y t, (25.1) i=1 i=1 where X 1,t, X 2,t, X 3,t, X 4,t, X 5,t, X * 1,t and Y t are identical to those defined in Table Expanding Eq. (25.1) we obtain X 1,t + X 2,t + X 3,t + X 4,t + X 5,t = X * 1,t + X * 2,t + X * 3,t + X * 4,t + X * 5,t = Y t. (25.1')

25.3 The simultaneity and dynamics of corporate-budgeting decisions X * t = AZ t, (25.2) where X * ' = (DIV * IST * ILT * - DF * - EQF * ), Z' = (1 Q1 Q2 Q3 Y RCB RDP DEL R CU), ┌ a 10 a a 19 ┐ │.. │ A = │.. │. │.. │ │ a 50 a a 59 │ └ ┘

25.3 The simultaneity and dynamics of corporate-budgeting decisions DIV * t = a 10 + a 11 Q 1 + a 12 Q 2 + a 13 Q 3 + a 14 Y t + a 5 RCB t + a 16 RDP t + a 17 DEL t + a 18 R t + a 19 CU t, IST * t = a 20 + a 21 Q 1 + a 22 Q 2 + a 23 Q 3 + a 24 Y t + a 25 RCB t + a 26 RDP t + a 27 DEL t + a 28 R t + a 29 CU t,

25.3 The simultaneity and dynamics of corporate-budgeting decisions ILT * t = a 30 + a 31 Q 1 + a 32 Q 2 + a 33 Q 3 + a 34 Y t + a 35 RCB t + a 36 RDP t + a 37 DEL t + a 38 R t + a 39 CU t, -DF * t = a 40 + a 41 Q 1 + a 42 Q 2 + a 43 Q 3 + a 44 Y t + a 45 RCB t + a 46 RDP t + a 47 DEL t + a 48 R t + a 49 CU t, -EQF * t = a 50 + a 51 Q 1 + a 52 Q 2 + a 53 Q 3 + a 54 Y t + a 55 RCB t + a 56 RDP t + a 57 DEL t + a 58 R t + a 59 CU t.

25.3 The simultaneity and dynamics of corporate-budgeting decisions X i,t = X i,t-1 + δ i (X * i,t - X i,t-1 ) (25.3) or (a) X 1,t = X 1,t-1 + δ 1 (X * 1,t - X 1,t-1 ), (b) X 2,t = X 2,t-1 + δ 2 (X * 2,t - X 2,t-1 ), (c) X 3,t = X 3,t-1 + δ 3 (X * 3,t - X 3,t-1 ), (d) X 4,t = X 4,t-1 + δ(X * 4,t - X 4,t-1 ), (e) X 5,t = X 5,t-1 + δ 5 (X * 5,t - X 5,t-1 ).

25.3 The simultaneity and dynamics of corporate-budgeting decisions 5 Σ X * i,t = Σ X i,t = Y t. i=1 i=1 X 2,t = X 2,t-1 + (1 - δ 1 )(X * 1,t - X 1,t-1 ). 5 X i,t = X i,t-1 + Σ δ ij (X * j,t - X j,t-1 ) (i = 1, 2, 3, 4, 5), (25.4) j=1 5 Σ δ ij = 1. i=1

25.3 The simultaneity and dynamics of corporate-budgeting decisions X t = X t-1 + D(X * t - X t-1 ) = X t-1 + D(AZ t - X t-1 ) (25.5) = X t-1 + DAZ t - DX t-1 = (I - D)X t-1 + DAZ t, ┌ δ 11 δ δ 15 ┐ │.. │ │.. │. D = │.. │ │ δ 51 δ δ 55 │ └ ┘

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.2 An expanded version of Eq. (25.5) X = BX t-1 + CZ t + U t, (25.6)

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.3 An expanded form of Eq. (25.6)

25.3 The simultaneity and dynamics of corporate-budgeting decisions D = i - B, (25.7) A = D -1 C. (25.8) 5 Σ X it = Y t for every period t. i=1 5 5 Σ b ij = Σ ĉ ik = 0 for all j and all k≠4, i=1 i=1 and that 5 Σĉ = 1. i=1

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.4 Adjustment coefficients of

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.4 Adjustment coefficients of (Cont.)

25.3 The simultaneity and dynamics of corporate-budgeting decisions TABLE 25.5 Summary of results

25.3 The simultaneity and dynamics of corporate-budgeting decisions Fig (From Spies, R. R., “The dynamics of corporate capital budging,” Journal of Finance 29 (September 1974): Fig. 1. Reprinted by permission.)

25.4 Applications of SUR estimation method in financial analysis and planning  The role of firm-related variables in capital-asset pricing  The role of capital structure in corporate-financing decisions

25.4 Applications of SUR estimation method in financial analysis and planning R 1t = α 1 + ß 1 R mt + γ 11 X 11 + γ 12 X 12 + γ 13 X 13 + E 1t, R 2t = α 2 + ß 2 R mt + γ 21 X 21 + γ 22 X 22 + γ 23 X 23 + E 2t,. R nt = α n + ß n R mt + γ n1 X n1 + γ n2 X n2 + γ n3 X n3 + E nt, (25.9)

25.4 Applications of SUR estimation method in financial analysis and planning where R jt = Return on the jth security over time interval t (j = 1, 2,..., n), R mt = Return on a market index over time interval t, X j1t = Profitability index of jth firm over time interval t (j = 1, 2,..., n), X j2t = Leverage index of jth firm over time period t (j = 1, 2,..., n), X j3t = Dividend policy index of jth firm over time period t (j = 1, 2,..., n), γ jk = Coefficient of the kth firm-related variable in the jth equation (k = 1, 2, 3), ß j = Coefficient of market rate-of-return in the jth equation E jt = Disturbance term for the jth equation, and a j 's are intercepts ( j = 1, 2,..., n). R jt = α′ + ß ′ + E jt. (25.10)

25.4 Applications of SUR estimation method in financial analysis and planning TABLE 25.6 OLS and SUR estimates of oil industry

25.4 Applications of SUR estimation method in financial analysis and planning TABLE 25.6 OLS and SUR estimates of oil industry (Cont.) *t-values appear in parentheses beneath the corresponding coefficients. †Denotes significant at 0.10 level of significant or better for two-tailed test. ‡Denotes significant at 0.05 level of significant or better for two-tailed test. From Lee, C. F., and J. D. Vinso, “Single vs. simultaneous-equation models in capital-asset pricing: The role of firm-related variables,” Journal of Business Research (1980): Table 3. Copyright 1980 by Elsevier Science Publishing Co., Inc. Reprinted by permission of the publisher.

25.4 Applications of SUR estimation method in financial analysis and planning TABLE 25.7 OLS parameter estimates of oil industry-Sharpe Model* * t-values appear in parenthesis beneath the corresponding coefficients From Lee, C.F., and J.D. Vinso, “Single vs. simultaneous-equation models in capital-asset pricing: The role of firm-related variables.” Journal of Business Research (1980): Table 2. Copyright 1980 by Elsevire Science Publishing Co., Inc. Reprinted by permission of the publisher.

25.4 Applications of SUR estimation method in financial analysis and planning TABLE 25.8 Residual correlation coefficient matrix after OLS estimate

25.4 Applications of SUR estimation method in financial analysis and planning ΔLDBT = α 1 (LDBT * - LDBT t-1 ) + α 2 (PCB * - PCB t-1 - RE) + α 3 STOCKT + α 4 RT + ε 1, (25.11) ΔGSTK = ß 1 (LDBT * - LDBT t-1 ) + ß 2 (PCB * - PCB t-1 - RE) + ß 3 STOCKT + ß 4 RT + ε 2, (25.12) STRET = η 1 (LDBT * - LDBT t-1 ) + η 2 (PCB * - PCB t-1 - RE) + η 4 RT + ε 3, (25.13) ΔLIQ = LIQ * + γ 2 (TC * - TC t-1 ) + γ 3 (ΔA - RE) + γ 4 RT + ε 4, (25.14)

25.4 Applications of SUR estimation method in financial analysis and planning ΔSDBT = ΔLIQ * + λ 2 (TC * - TC t-1 ) + λ 3 (ΔA - RE) + λ 4 RT + ε 5, (25.15) where LDBT * = bSTOCK (i/i) = A target for the book value of long-term debt, STOCK = Market value of equity, b = LDM/STOCK = Desired debt-equity ratio, LDM = Market value of debt = (LDBT)(i/i), i/i = Ratio between the average contractual interest rate on long-term debt outstanding and the current new-issue rate on long-term debt, LDBT t-1 = Book value of long-term debt in previous period, PCB * = Permanent capital (book value) = net capital stock (NK) + the permanent portion of working assets (NWA), PCB t-1 = Permanent capital in the previous period, RE = Stock retirements, STOCKT = Stock-market timing variable = average short-term market value of equity divided by average long-term market value of equity, RT = Interest timing variable, weighted average (with weight 0.67 and 0.33) of two most recent quarters' changes in the commercial paper rate, TC * = Target short-term capital = short-term asset-liquid assets, TC t-1 = Short-term debt in the previous period, ΔA = Changes in total assets, ΔLIQ * = Change of target liquidity assets.

25.5Applications of structural econometric models in financial analysis and planning  A brief review  AT&T’s econometric planning model

25.2 Applications of structural econometric models in financial analysis and planning Fig Flow chart of FORECYT. (From Davis, B. E., G. C. Caccappolo, and M. A. Chaudry, “An econometric planning model for American Telephone and Telegraph Company,” The Bell Journal of Economics and Management Science 4 (Spring 1973): Fig. 2. Copyright © 1973, The American Telephone and Telegraph Company. Reprinted with permission.

25.3Applications of structural econometric models in financial analysis and planning Fig Tripartite structure of FORECYT.

25.6 Programming vs. simultaneous vs. econometric financial models

25.7 Financial analysis and business policy decisions

25.8 Summary Based upon the information, theory, and methods discussed in previous chapters, we discussed how the econometrics approach can be used as alternative to both the programming approach and simultaneous-equation approach to financial planning and forecasting. Both the SUR method and the structural simultaneous-equation method were used to show how the interrelationships among different financial-policy variables can be more effectively taken into account. In addition, it is also shown that financial planning and forecasting models can also be incorporated with the environment model and the management model to perform business-policy decisions.

NOTES 1. The stacking technique, which was first suggested by de Leeuw (1965), can be replaced by either the SUR or the constrained SUR technique. (See the next section and Appendix A for detail.) It should be noted that these techniques themselves can be omitted from the lecture without affecting the substance of the econometric approach to financial analysis and planning. 2. This section is essentially drawn from Spies' (1974) paper. Reprinted with permission of the Journal of Finance and the author. Basic concepts of matrix algebra used in this section can be found in Chapter 3. The simultaneous equation used in this section can be found in Appendix 2B in Chapter 2 of this book. In addition, the autoregressive model used in this chapter can be found in section 24.6 in chapter Theoretical development of this optimal model can be found in Spies' (1971) dissertation. 4. Bower (1970) provides an interesting discussion of corporate decision- making and its ability to adapt to a changing environment.

NOTES 5. The constraint on the values of δ ij is a result of the "uses-equals-sources “ identity. Summing Eq. (18.4) over i gives Σ X i,t = Σ X i,t-1 + Σ Σ δ ij (X * j,t-1 ), i=1 i=1 i=1 i=1 This can be rewritten as Σ (X i,t - X i,t-1 ) = Σ (X * j,t - X j,t-1 ) Σ δ ij. i j i The identity ensures that Σ j X j,t = Σ j X * j,t, and therefore, Σ (X i,t - X i,t-1 ) = Σ (X j,t - X j,t-1 ) Σ δ ij i j i Changing the notation slightly, this becomes Σ (X j,t - X j,t-1 ) = Σ (X j,t - X j,t-1 ) Σ δ ij j j i or 1 = Σ δ ij. i

NOTES 6. This constraint ensures that the "uses-equals-sources" identity will hold for the estimated equations. First of all, we know that Σ i ij = 1, since  1 -b ij for i = j, ij =   -b ij for i . Therefore, Σ ij = 1 - Σb ij = = 1. i i In addition, it can be shown that X * i,t = Y t. To show this, it is necessary only to show that  0 for all k  4, Σ a jk =  j  1 for all k = 4. Note that ik = Σ jijajk. Since we have constrained  0 for all k  4, Σc ik =  i  1 for all k = 4.

NOTES 6 (Cont.) we can see that ΣC ik = Σ Σa ijajk i i j = Σ (Σa ij ) jk j i = Σ (1)a jk j = Σa jk. j Therefore,  0 for all k  4, Σa jk =  j  1 for all k = 4. From all this it is clear that ^ ^ Σ X i,t = Σ X * i,t = Y t. i i

NOTES 7. Major portion of this section was drawn from Lee and Vinso (1980). Reprinted with permission of Journal of Business Research. 8. The economic forecasts from other econometrics models (e.g., Chase Econometric and Wharton Econometrics can also be used as inputs for corporate-analysis planning and forecasting.

Appendix 25A. Johnson & Johnson as a case study  25.A.1 INTRODUCTION  25.A.2 STUDY OF THE COMPANY’S OPERATIONS Consumer Pharmaceuticals Medical Devices and Diagnostics  25.A.3 ANALYSIS OF THE COMPANY’S FINANCIAL PERFORMANCE

Appendix 25A. Johnson & Johnson as a case study DivisionSalesProfitsSalesProfitsSalesProfitsSalesProfits %% Consumer18%13%18%11%18%12%18%10% Pharmaceuticals47%56%47%58%44%48%44%48% Medical Devices and Diagnostics 36%31%36%31%38%40%38%43% Total100% Domestic60% 59% 56% International40% 41% 44% Total100% TABLE 25.A.1 Sales in Different Segment