Financial Analysis, Planning and Forecasting Theory and ...

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.


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).


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')


X*

t = AZt, (25.2)where X*' = (DIV*IST*ILT* - DF* - EQF*),

Z' = (1 Q1 Q2 Q3 Y RCB RDP DEL R CU),

┌ a10 a11 ... a19 ┐ │ . . │ A = │ . . │ . │ . . │ │ a50 a51 ... a59 │ └ ┘


DIV*t = a10 + a11 Q1 + a12 Q2 + a13 Q3 + a14 Yt

+ a5 RCBt + a16 RDPt + a17 DELt

+ a18 Rt + a19 CUt,

IST*t = a20 + a21 Q1 + a22 Q2 + a23 Q3 + a24 Yt


+ a28 Rt + a29 CUt,


ILT*t = a30 + a31 Q1 + a32 Q2 + a33 Q3 + a34 Yt


+ a38 Rt + a39 CUt,

-DF*t = a40 + a41 Q1 + a42 Q2 + a43 Q3 + a44 Yt


+ a48 Rt + a49 CUt,

-EQF*t = a50 + a51 Q1 + a52 Q2 + a53 Q3 + a54 Yt


+ a58 Rt + a59 CUt.


Xi,t = Xi,t-1 + δi(X*i,t - Xi,t-1) (25.3)

or

(a) X1,t = X1,t-1 + δ1(X*1,t - X1,t-1),

(b) X2,t = X2,t-1 + δ2(X*2,t - X2,t-1),

(c) X3,t = X3,t-1 + δ3(X*3,t - X3,t-1),

(d) X4,t = X4,t-1 + δ(X*4,t - X4,t-1),

(e) X5,t = X5,t-1 + δ5(X*5,t - X5,t-1).

25.3 The simultaneity and dynamics of corporate-budgeting decisions 5 5

Σ X*i,t = Σ Xi,t = Yt.

i=1 i=1

X2,t = X2,t-1 + (1 - δ1)(X*1,t - X1,t-1).

5

Xi,t = Xi,t-1 + Σ δij(X*j,t - Xj,t-1) (i = 1, 2, 3, 4, 5), (25.4)

j=1

5

Σ δij = 1.

i=1


Xt = Xt-1 + D(X*t - Xt-1)

= Xt-1 + D(AZt - Xt-1) (25.5)

= Xt-1 + DAZt - DXt-1

= (I - D)Xt-1 + DAZt,

┌ δ11 δ12 ... δ15 ┐ │ . . │ │ . . │ . D = │ . . │ │ δ51 δ52 ... δ55 │ └ ┘

25.3 The simultaneity and dynamics of corporate-budgeting decisionsTABLE 25.2 An expanded version of Eq. (25.5)

X = BXt-1 + CZt + Ut, (25.6)

* * * * *1 1 11 1 12 1 13 1 14 1 15 1

* * * * *2 1 21 1 22 1 23 1 24 1 25

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) (

t t t t t t t t t t t t

t t t t t t t t t t t

X DIV DIV DIV DIV IST IST ILT ILT DF DF EQF EQF

X IST IDT DIV DIV IST IST ILT ILT DF DF EQF E

1

* * * * *3 1 31 1 312 1 33 1 34 1 35 1

* * * *4 1 41 1 52 1 43 1 44 1 45

)

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) (

t


t t t t t t t t t t

QF

X ILT ILT DIV DIV IST IST ILT ILT DF DF EQF EQF

X DF DF DIV DIV IST IST ILT ILT DF DF

*1

* * * * *5 1 51 1 52 1 53 1 54 1 515 1

)

( ) ( ) ( ) ( ) ( )

t t


EQF EQF

X EQF EQF DIV DIV IST IST ILT ILT DF DF EQF EQF


TABLE 25.3 An expanded form of Eq. (25.6)

CU

R

DEL

RDP

RCB

Y

Q

Q

Q

I

EQF

DF

LTI

STI

DIV

ccccccccccbbbbb

ccccccccccbbbbb

ccccccccccbbbbb

ccccccccccbbbbb

ccccccccccbbbbb

EQF

DF

ILT

IST

DIVt

t

t

t

t

t

t

t

t

t

3

2

1

1

1

1

1

1

595857565554535251505554535251

494847464544434241404544434241

393837363534333231303534333231

292827262524232221202524232221

191817161514131211101514131211

25.3 The simultaneity and dynamics of corporate-budgeting decisions D = i - B, (25.7)

A = D-1 C. (25.8)

5

Σ Xit = Yt for every period t. i=1

5 5

Σ bij = Σ ĉik = 0 for all j and all k≠4, i=1 i=1

and that 5

Σĉ = 1. i=1


TABLE 25.4 Adjustment coefficients of 1 tt DIVDIV

25.3 The simultaneity and dynamics of corporate-budgeting decisionsTABLE 25.4 Adjustment coefficients of (Cont.) 1 tt DIVDIV

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

25.3 The simultaneity and dynamics of corporate-budgeting decisionsFig. 25.1 (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


R1t = α1 + ß1Rmt + γ11X11 + γ12X12 + γ13X13 + E1t,

R2t = α2 + ß2Rmt + γ21X21 + γ22X22 + γ23X23 + E2t,

.

.

.

Rnt = αn + ßnRmt + γn1Xn1 + γn2Xn2 + γn3Xn3 + Ent,

(25.9)

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)


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.


TABLE 25.8 Residual correlation coefficient matrix after OLS estimate


ΔLDBT = α1(LDBT* - LDBTt-1) + α2(PCB* - PCBt-1 - RE)

+ α3 STOCKT + α4RT + ε1, (25.11) ΔGSTK = ß1(LDBT* - LDBTt-1) + ß2(PCB* - PCBt-1 - RE)

+ ß3 STOCKT + ß4RT + ε2, (25.12) STRET = η1(LDBT* - LDBTt-1) + η2(PCB* - PCBt-1 - RE) + η4RT + ε3, (25.13) ΔLIQ = LIQ* + γ2(TC* - TCt-1) + γ3(ΔA - RE) + γ4RT + ε4, (25.14)


ΔSDBT = ΔLIQ* + λ2(TC* - TCt-1) + λ3 (ΔA - RE) + λ4RT + ε5, (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,

LDBTt-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),

PCBt-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,

TCt-1 = Short-term debt in the previous period,

ΔA = Changes in total assets,

ΔLIQ* = Change of target liquidity assets.

25.5 Applications of structural econometric models in financial analysis and planning

A brief review

AT&T’s econometric planning model

25.2Applications of structural econometric models in financial analysis and planning

Fig. 25.2 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.

25.3 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

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

2003 2004 2005 2006

Division Sales Profits Sales Profits Sales Profits Sales Profits

% % % %

Consumer 18% 13% 18% 11% 18% 12% 18% 10%

Pharmaceuticals 47% 56% 47% 58% 44% 48% 44% 48%

Medical Devices and Diagnostics 36% 31% 36% 31% 38% 40% 38% 43%

Total 100% 100% 100% 100% 100% 100% 100% 100%

Domestic 60% 59% 56% 56%

International 40% 41% 44% 44%

Total 100% 100% 100% 100%

TABLE 25.A.1 Sales in Different Segment

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