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
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Financial Analysis, Planning and Forecasting
Theory and Application
ByAlice C. Lee
San Francisco State UniversityJohn C. Lee
J.P. Morgan ChaseCheng 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 decisionsTABLE 25.1 Endogenous and exogenous variables
1. The endogenous variables are:
a) X1,t = DIVt = Cash dividends paid in period t;
b) X2,t = ISTt = Net investment in short-term assets during period t;
c) X3,t = ILTt = Gross investment in long-term assets during period t;
d) X4,t = -DFt = Minus the net proceeds from the new debt issues during period t;
e) X5,t = -EQFt = Minus the net proceeds from new equity issues during period t.
2. The exogenous variables are: 5 5
a) Yt = Σ Xi,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) RDPt = 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) DELt = Debt Equity Ratio (parameter used by WS in Eq. (18)
of Table 23.1).e) Rt = 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) CUt = Rates of Capacity Utilization (used by FR to lag capital requirements behind changes in percent sales; used here to define the Rt expected).
25.3 The simultaneity and dynamics of corporate-budgeting decisions
5 5
Σ Xi,t = Σ X*i,1 = Yt, (25.1)
i=1 i=1
where X1,t, X2,t, X3,t, X4,t, X5,t, X*1,t and Yt are identical to those defined in Table 25.
1.
Expanding Eq. (25.1) we obtain
X1,t + X2,t + X3,t + X4,t + X5,t = X*1,t + X*
2,t + X*3,t + X*
4,t + X*5,t = Yt. (25.1')
25.3 The simultaneity and dynamics of corporate-budgeting decisions
X*
t = AZt, (25.2)where X*' = (DIV*IST*ILT* - DF* - EQF*),
25.4 Applications of SUR estimation method in financial analysis and planningwhere Rjt = Return on the jth security over time interval t (j = 1, 2, ..., n), Rmt = Return on a market index over time interval t, Xj1t = Profitability index of jth firm over time interval t (j = 1, 2, ..., n), Xj2t = Leverage index of jth firm over time period t (j = 1, 2, ..., n), Xj3t = Dividend policy index of jth firm over time
period t (j = 1, 2, ..., n), γjk = Coefficient of the kth firm-related variable in
the jth equation (k = 1, 2, 3), ßj = Coefficient of market rate-of-return in the jth equation Ejt = Disturbance term for the jth equation, and aj's are intercepts ( j = 1, 2, ..., n).
Rjt = α′ + ß ′ + Ejt. (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 planningTABLE 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,” Jou
rnal 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 planningTABLE 25.7 OLS parameter estimates of oil industry-Sharpe Model*
* t-values appear in parenthesis beneath the corresponding coefficientsFrom 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
25.3 Applications of structural econometric models in financial analysis and planning
Fig. 25.3 Tripartite structure of FORECY
T.
25.6Programming 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 24.
3. 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.
NOTES5. The constraint on the values of δij is a result of the "uses-equals-sources“ identity. Sum
ming Eq. (18.4) over i gives 5 5 5 5
Σ Xi,t = Σ Xi,t-1 + Σ Σ δij(X*
j,t-1), i=1 i=1 i=1 i=1
This can be rewritten as
Σ (Xi,t - Xi,t-1) = Σ (X*j,t - Xj,t-1) Σ δij.
i j i
The identity ensures that ΣjXj,t = ΣjX*
j,t, and therefore,
Σ (Xi,t - Xi,t-1) = Σ (Xj,t - Xj,t-1) Σ δij
i j i
Changing the notation slightly, this becomes
Σ (Xj,t - Xj,t-1) = Σ (Xj,t - Xj,t-1) Σ δij
j j i
or 1 = Σ δij. i
NOTES
6. This constraint ensures that the "uses-equals-sources" identity will hold for the estimated equations. First of all, we know that Σi ij = 1, since
1 -b ij for i = j, ij = -b ij for i . Therefore,
Σ ij = 1 - Σb ij = 1 - 0 = 1. i i
In addition, it can be shown that X*i,t = Yt. To show this, it is necessary
only to show that
0 for all k 4, Σ ajk = j 1 for all k = 4.
Note that ik = Σjijajk. Since we have constrained
0 for all k 4, Σc ik = i 1 for all k = 4.
NOTES6 (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 Xi,t = Σ X*i,t = Yt.
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