# Integrating Financial Statement Modeling and Sales Forecasting

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Integrating Financial Statement Modeling and Sales Forecasting John T. Cuddington, Colorado School of Mines Irina Khindanova, University of Denver ABSTRACT This paper shows how to integrate financial statement modeling and sales forecasting using the EViews software package. A model is developed to carry out stochastic simulations of the effects of estimated future sales forecasts (with the associated uncertainty) on various income and balance sheet items, along with their respective confidence intervals. We illustrate how the analyst can easily study the likelihood that cash balances will be exhausted a clear indicator of financial distress -- under various sales scenarios. INTRODUCTION Most business school programs include classes in financial accounting, including pro forma statement analysis. Often more extensive treatments of financial modeling are discussed. See, e.g., Benninga (2008). In addition, students are typically exposed to introductory statistical and econometric methods, including forecasting (esp. sales forecasting). See, e.g., Diebold (2007) for a representative treatment. While practitioners are exposed to both tools financial modeling and forecasting -- there is seldom an attempt to integrate them within a unified framework. The purpose of this paper is to show how this can be done using the EViews econometrics package, which is often used in introductory econometrics and forecasting courses. The paper begins with a simple sales-driven model that Benninga (2008, Ch.3) develops using the Excel spreadsheet program. We show how to set up the same model in EViews and confirm that our results match those in Benninga s Excel model. The EViews model has the advantage of being more transparent and easier to audit and debug because the equations in the model are written out explicitly in the MODEL object in EViews. The Excel formulation, on the other hand, will be more familiar to students who are comfortable with Excel. Next a very simple univariate sales forecasting equation along the lines of the example in Diebold (2007, pp.87-94) is estimated using EViews. It is a quadratic trend model for LOG(SALES) with an AR(2) error process. This forecasting equation is then linked to the pro forma statement model. We demonstrate how the sales equation can be used to generate a forecast of the time path of future sales, which is the pivotal driving variable in the Benninga model (and many similar sales driven financial models). The estimated error process in the sales equation and the standard error of regression are used to obtain conditional forecasts for sales and the associated standard errors. Using this information from the sales equation, the EViews MODEL can be used to calculate forecasts and associated confidence intervals for all items in the pro forma financial statements. This provides a wealth of useful additional 1

2 information to the financial analyst. She obtains not only the expected future values for the various pro forma statement items, but also the associated confidence intervals used to assess the degree of uncertainty of the financial forecasts. Our illustration focuses on sales forecast implications for the net cash position of the firm over time. The deterioration of the cash position towards zero would be a clear indicator of financial distress in most situations. The remainder of the paper is structured as follows. Section II discusses how to implement pro forma statement models in EViews. Section III develops an illustrative sales forecasting equation. The linking of the sales forecast to the pro forma model is covered in Section IV, and Section V summarizes our findings. PRO FORMA STATEMENT MODELING IN EVIEWS Benninga (2008, Ch.3) introduces a series of increasingly complicated models to demonstrate various aspects of corporate financial strategy. Typically, the models begin with a set of hypothesized initial figures for income statement and balance sheet items, such as those presented in Table 1 below. In real-world applications, these figures would be taken from actual financial statement information. For ease of reference, we have added to the left-most column of Table 1 our chosen EViews variable or series names. 1 When implementing a pro forma model in EViews, one must first set up an annual (or quarterly or monthly) frequency workfile and then declare series that have appropriate initial values, such as those in Table 1. If one has historical time series data on each of these items, rather than just the 2005 values, so much the better. For the sales forecasting exercise in Section III below, we ll need historical data for sales, but only the 2005 values for the other financial statement items. Appendix B (available on request) provides detailed instructions on how to get your Excel dataset into EViews and how to run both the deterministic and stochastic versions of the model, where the stochastic version is driven by the error process and coefficient estimation uncertainty implied by the sales forecasting equation. Table 1: Initial Year Data and EViews Series Names* EViews Series Initial Condition Name INCOME STATEMENT 2005 Needed SALES Sales Yes COGS Cost of Goods Sold No INT_DEBT Interest Expense on Debt 32.0 No INT_CASH Interest Earnings on Cash & Securities 6.0 No DEPN Depreciation Expense No PBT Profit Before Taxes No TAXES Taxes No PROFIT After-Tax Profit No DIVIDENDS Dividends 89.8 No RET_EARNINGS Retained Earnings No BALANCE SHEET 1 Note that EViews series names cannot contain spaces or unusual characters. 2

3 CASH Cash and Marketable Securities 80.0 Yes OCA Other Current Assets No FA_COST Fixed Assets at Cost Yes ACC_DEPN Accumulated Depreciation Yes NFA Net Fixed Assets No ASSETS TOTAL ASSETS No CL Current Liabilities 80.0 No DEBT Debt Outstanding Yes LIAB Total Liabilities No CAPITAL Paid-in Capital Yes ACC_EARNINGS Accumulated Retained Earnings Yes EQUITY Total Equity No Total Liabilities + Equity No * Initial conditions are given for Model 1 in Benninga, 2008, p Benninga lays out a simple set of equations to describe the interrelationships among his income and balance sheet variables at each future date. These formulas are shown in Table 2. Because many of these equations involve variables lagged one period, denoted by (-1) after the series name, the model is, in effect, a system of first-order difference equations. Given the initial conditions (e.g., the 2005 values), the system of equations is solved recursively going forward to obtain future-period values. Table 2: Formulas For Pro-Forma Financial Statements* EViews Variable Name Formula for Subsequent Periods Assumptions Constant Sales SALES SALES = SALES(-1)*1.10 growth rate COGS COGS = 0.50*SALES Sales Driven INT_DEBT INT_DEBT =.10*(DEBT+DEBT(-1))/2 Ratio assumption INT_CASH INT_CASH =.08*(CASH+CASH(-1))/2 Ratio assumption DEPN DEPN =.10*(FA_COST+FA_COST(-1))/2 Ratio assumption PBT PBT = SALES - COGS - INT_DEBT + INT_CASH - DEPN Identity TAXES TAXES =.40*PBT Ratio assumption PROFIT PROFIT = PBT - TAXES Identity DIVIDENDS DIVIDENDS =.40*PROFIT Ratio assumption RET_EARNINGS RET_EARNINGS = PROFIT - DIVIDENDS Identity CASH CASH = LIAB + EQUITY - NFA - OCA The "Plug" OCA OCA =.15*SALES Sales Driven FA_COST FA_COST = NFA + ACC_DEPN Identity ACC_DEPN ACC_DEPN = ACC_DEPN(-1) + DEPN Identity NFA NFA =.77*SALES Sales Driven 3

4 ASSETS ASSETS = CASH + OCA + NFA Identity CL CL =.08*SALES Sales Driven Debt remains DEBT DEBT = DEBT(-1) constant LIAB LIAB = CL + DEBT Identity Paid-in Capital CAPITAL CAPITAL = CAPITAL(-1) remains constant ACC_EARNINGS = ACC_EARNINGS(-1) ACC_EARNINGS + RET_EARNINGS Identity EQUITY EQUITY = CAPITAL + ACC_EARNINGS Identity * Ratio assumptions match those for Model 1 in Benninga, 2008, p Note that sales growth rate is assumed to be a constant 10% over time, so current sales equal last year s sales multiplied by Several of the key variables are proportional to sales: cost of goods sold (COGS), other current assets (OCA), net fixed assets (NFA), and current liabilities (CL). This is the sense in which the model is sales driven. To complete the model, Benninga assumes that: Interest expense is 10% on the average debt outstanding (where average means the beginning and end of the period values divided by two). Interest income is 8% of the average cash and marketable securities. Depreciation expense is 10% of average fixed assets at cost. Dividend payout rates and corporate tax rates are constant at 40 and 40 percent, respectively. The model contains a number of identities, identified as such in the Table 2, and importantly, it is assumed that the cash and marketable securities item (CASH) adjusts to insure that the balance sheet identity holds. That is, CASH is the so-called plug that closes the model. The Benninga model is straightforward to implement and solve using EViews. After creating the annual frequency workfile and declaring series with the initial values assumed in Table 1, we create a blank MODEL object. Then, in text mode, the model equations are entered one-by-one using the syntax given in Table 2. Comment lines can be inserted in the MODEL object, as needed, to help readers interpret the various equations; these lines begin with a single quotation mark. Figure 1 shows the equations as entered into the EViews MODEL. The MODEL object is capable of solving first or higher-order (potentially) simultaneous equation systems using initial and/or terminal conditions, as appropriate. In the current application with first-order difference equations and initial conditions for the year 2005, the MODEL is solved from 2006 forward (for as many periods as the analyst is interested in). Figure 1: Benninga s Model 1 in the EViews MODEL Object ' ================================================= ' Model 1 Cash and marketable securities is the plug. Debt is constant. ' ' This model replicates the first Excel pro forma statement model in Benninga (2008, Ch. 3, p. 111). SALES = SALES(-1) *

5 ' Balance Sheet OCA = 0.15 * SALES CL = 0.08 * SALES NFA = 0.77 * SALES DEPN = 0.10 * (FA_COST(-1) + FA_COST) / 2 ACC_DEPN = ACC_DEPN(-1) + DEPN FA_COST = NFA + ACC_DEPN LIAB = DEBT + CL EQUITY = CAPITAL + ACC_EARNINGS CASH = LIAB + EQUITY - OCA - NFA ASSETS = CASH + OCA + NFA ' Income Statement COGS = 0.50 * SALES INT_DEBT = (DEBT(-1) + DEBT) / 2 * 0.10 INT_CASH = (CASH(-1) + CASH) / 2 * 0.08 PBT = SALES - COGS - INT_DEBT + INT_CASH - DEPN TAXES = 0.40 * PBT PROFIT = PBT - TAXES DIVIDENDS = 0.40 * PROFIT RET_EARNINGS = PROFIT - DIVIDENDS ACC_EARNINGS = ACC_EARNINGS (-1) + RET_EARNINGS ' ================================================= Figure 2 illustrates the EViews generated pro forma balance sheet and income statements. One can confirm that values of financial statements items coincide with those in Benninga s Excel pro-forma statements (Benninga, 2008, p. 111). Note that the company s cash balance position may rise or fall over time depending, in a complicated way, on both the sales growth rate and various ratio assumptions in the model. As Benninga points out, this model has the shortcoming that it is possible for CASH to become negative and to grow larger and larger in absolute value over time. This is surely unrealistic for any going concern! Therefore, he then goes on to generalize the model to eliminate this (and other) shortcomings. For example, one might assume that once the cash position hits zero (indeed even before that dire moment), the firm takes on additional debt. If the debt grows in a way that moves the firm from its target debt/equity ratio, then additional equity financing may ultimately be undertaken. 5

6 Figure 2: EViews-generated Financial Statements While these complications are interesting and straightforward to implement in EViews, we omit them here to simplify the exposition of how to integrate sales forecasting with a simple financial model. THE SALES FORECASTING EQUATION This section summarizes the estimation of a univariate sales forecasting equation, which will then be used in Section IV to forecast sales for future years. This variable is the exogenous driver in Benninga pro forma statement model. For purposes of illustration, aggregate U.S. retail sales published by the Bureau of Economic Analysis (rather than the sales for a particular corporation) will be used. 2 The seasonally adjusted monthly series is aggregated to annual frequency and rebased to create an index that equals 1000 in the year 2005 so that the resulting SALES series has an initial condition matching that used by Benninga. The estimated equation is a quadratic time trend model for the natural logarithm of SALES: 2 LOG( SALES ) = α + βt + βt + ε (3.1) t 1 2 t 2 This series was obtained from the Haver Analytics USECON database 6

7 The residual correlogram and Ljung-Box Q statistics from this OLS estimation were examined to check for the presence of serially correlated errors. The correlogram suggested that the residuals followed an AR(2) process: ε = ρε + ρε + u (3.2) t 1 t 1 2 t 2 t Thus, our chosen specification is equation (3.1) with the AR(2) error process in (3.2). The regression output for the selected model is shown in Table3. The Q(3) statistic is reported as confirmation that we are unable to reject the null hypothesis that the innovations in the AR(2) process are white noise. In our stochastic version of the Benninga model in Section IV, the SALES equation is used to forecast future sales and confidence intervals for future sales are obtained by Monte Carlo simulation. The Monte Carlo simulation assumes that the error process in the sales equation has a normal distribution with mean zero and standard Table 3: Estimated SALES Equation Dependent Variable: LOG(SALES) Variable CoefficientStd. Error t-statistic Prob. C AR(1) AR(2) R-squared Adjusted R-squared Q(3) Stat S.E. of regression p-value deviation equal to the estimated standard error of regression (s.e.=0.011). 3 Coefficient uncertainty (as the OLS estimates are only estimates of the true but unknown parameters) is also taken into account in the simulations below. Parenthetically, the procedures outlined in this paper can be implemented with any sales forecasting equation one prefers; it may be a single equation or one equation in a multi-equation model. It is straightforward to introduce several sources of uncertainty into the analysis by including several estimated equations in the model. In this case, the forecasts correctly capture interactions among the forecasted variables. Confidence intervals reflect the error variances in all equations as well as their covariances. 3 EViews also has the option of using bootstrapping rather than assuming that error terms are multivariate normal, if one prefers. 7

8 INTEGRATING THE SALES FORECAST AND THE PRO FORMA STATEMENT MODEL This section presents a stochastic generalization of Benninga s model for financial statements described in Section II. First, SALES are forecasted into the future. Then, using the sales forecasts and associated standard errors, forecasts and confidence bands for all (or specified) items in pro-forma statements are derived. Lastly, uncertainty of the financial forecasts for particular variables here, PROFIT and CASH balances are analyzed. We use the sales equation estimated in Section III (SALES_EQ) to forecast future sales. The sales equation is used to replace the constant sales growth equation in Benninga s model, as in EViews in Section II by linking the estimated equation in the workfile to the MODEL object. This is done by typing :SALES_EQ in the MODEL object. (The colon creates the link to the estimated equation in the workfile.) In solving the MODEL, we elect to account coefficients uncertainty. By using the stochastic solution feature in EViews, one can easily produce forecasted future SALES and a 95% (or other user selected) confidence band as shown in Figure 3. Figure 3: SALES Forecasts 1,800 1,700 1,600 1,500 1,400 1,300 1,200 1,100 1, SALES (Baseline Upper) SALES (Baseline Lower) SALES (Baseline Mean) The forecasted time path of SALES, of course, differs from Benninga s illustrative constant sales growth rate assumption. Figure 4 shows the implied SALES growth rate over time with the 95% confidence bounds. The SALES forecast equation implies that the SALES growth rate varies between 0% and 5%, while trending down slowly over time. The 95% confidence band for SALES widens over time, reflecting growing SALES uncertainty as one goes further into the future. The width of the SALES confidence band in 2020 reaches 683.1, which equals 49.4% of expected SALES in that year. 8

9 Figure 4. SALES Growth Rate SALES Growth Rate (Baseline Upper) SALES Growth Rate (Baseline Lower) SALES Growth Rate (Baseline Mean) We examine impacts of sales forecasts on other financial variables in the MODEL under two scenarios. In the first scenario, we assume that the ratio of cost of goods sold to sales (COGS/SALES) equals 0.50 as in Benninga s first model. Next we consider an alternative scenario where COGS/SALES = 0.75 a value that produces quite different outcomes for the company s future cash position. Figures 5 and 6 show the time paths of CASH and PROFIT, respectively, in the COGS/SALES =.50 case. CASH balances increase over time, with a relatively narrow 95% Figure 5: CASH Balances for the COGS/SALES =.50 Scenario 2,000 1,600 1, CASH (Baseline Upper) CASH (Baseline Lower) CASH (Baseline Mean) 9

10 Figure 6: PROFIT for the COGS/SALES =.50 Scenario PROFIT (Baseline Upper) PROFIT (Baseline Lower) PROFIT (Baseline Mean) confidence band that slowly widens over time. The width of the CASH confidence band in 2020 is (or 10.8% of expected CASH balances). PROFIT increases in the initial four years with a growth rate of up to 4 % and then declines. The 95% confidence band for PROFIT expands to in 2020, which equals 90.8% of expected PROFIT. Comparing the SALES, PROFIT, and CASH forecasts in the COGS/SALES = 0.50 case, one finds that the PROFIT forecasts have highest degree of uncertainty (90.8%), measured by the ratio of the width of the 95% confidence band to the mean or expected forecast level; the SALES projections have lower degree of uncertainty (49.4%); and the CASH forecasts exhibit the lowest degree of uncertainty (10.8%). An analysis of volatilities (standard deviations) of the SALES, PROFIT, and CASH projections shows that SALES have highest volatility, followed by CASH, and PROFIT. See Figure 7. Figure 7: Volatilities of SALES, CASH, PROFIT for the COGS/SALES =.50 Scenario SALES (Baseline S.D.) PROFIT (Baseline S.D. CASH (Baseline S.D.) 10

11 The model forecasts are quite different if we assume that the COGS/SALES ratio equals 0.75, rather than CASH balances increase in the initial four years, then level off in the following four years. After 2012, the CASH position rapidly declines, becoming negative from 2018 forward (see Figure 8) 4. While SALES increase over the forecasted period and the CASH balances increase in the initial periods, the PROFIT levels decline after 2007, becoming negative after 2014 (Figure 9). In 2020, the width of the 95% confidence band for CASH reaches 463.8, Figure 8: CASH Balances for the COGS/SALES =.75 Scenario CASH (Baseline Upper) CASH (Baseline Lower) CASH (Baseline Mean) Figure 9: PROFIT Levels for the COGS/SALES =.75 Scenario PROFIT (Baseline Upper) PROFIT (Baseline Lower) PROFIT (Baseline Mean) 4 Benninga s Model 2 (2008, p. 120) prevents CASH from becoming negative by increasing debt levels as needed. We have also implemented this slightly more complicated financial model in EViews. See Appendix A for a brief description. 11

12 which is 196.8% of (the absolute value of) of expected CASH balances. In the same year (2020), the width of the 95% confidence band for PROFIT is equal to 20.3% of expected PROFIT. The confidence band around PROFIT is narrower than the confidence bands around SALES and CASH, what implies less uncertainty in PROFIT levels. As in the COGS/SALES = 0.50 scenario, volatility of SALES is greater than volatility of CASH, which in turn is greater than volatility of PROFIT, as shown in Figure 10. Figure 10: Volatilities of SALES, CASH, PROFIT for the COGS/SALES =.75 Scenario SALES (Baseline S.D.) PROFIT (Baseline S.D.) CASH (Baseline S.D.) A comparison of the two scenarios for the COGS/SALES ratio shows that the PROFIT forecasts have greater uncertainty in the first scenario (COGS/SALES = 0.50), while the CASH forecasts demonstrate higher uncertainty in the second scenario (COGS/SALES = 0.75). These comparisons illustrate that a sales-driven pro-forma statement model can result in different degrees of uncertainty in financial forecasts for various variables depending on the underlying ratio assumptions. 5 This emphasizes the nontrivial nature of the interdependences among financial variables that must be taken into account when assessing the degree of uncertainty for various financial statement variables. These interdependences are easily captured when using stochastic simulations to study how sales forecast uncertainty impacts other variables in the pro forma financial statement model. Another benefit of integrating the sales forecasting and financial model simulation using EViews is that it is straightforward to allow for the coefficient uncertainty that is inherent in the econometric estimation. In the current application, a comparison of forecasts with and without coefficients uncertainty suggests that: (i) confidence bands are narrower under the no coefficient uncertainty assumption, implying these confidence bands overestimate the precision of financial forecasts; (ii) volatility of variables is lower under the no coefficients uncertainty assumption; (iii) confidence bands widen faster in the coefficients uncertainty case. 5 These ratio assumptions could, in principle, be replaced by estimated relationships. 12

13 CONCLUSIONS This paper demonstrates how to integrate pro forma financial statements with sales forecasting. The integration was performed in five steps: (i) modeling of financial statements using the MODEL object in the EViews econometrics software package; (ii) estimation of a sales forecasting equation; (iii) linkage of the sales forecasting equation and the EViews model of financial statements; (iv) forecasting sales and other items in the pro forma statements and constructing confidence intervals for those forecasts; and (v) analysis of uncertainty of financial forecasts. The illustration developed in this paper shows that EViews modeling of financial statements has the benefit of being more transparent and easier to audit and debug than the same financial model implemented in the Excel spreadsheet program. Advantages of the use of sales forecasting equation include projection of the sales series with a realistic (and typically variable) growth rate, consideration of coefficient uncertainty, estimation of the expected values for various pro forma statement items, obtaining the confidence bands for financial forecasts. The confidence intervals can be used to assess the degree of uncertainty associated with the financial forecasts. The procedures outlined in the paper can be implemented with any sales forecasting equation. It may be a single equation or one equation in a multiple equation model. It is possible to introduce several sources of uncertainty into the analysis, with accounting for correlations among the error terms. We intend to develop financial modeling with multiple sources of uncertainty in our future work. REFERENCES Diebold, Francis X Elements of Forecasting. Fourth Edition. South-Western College Publishing Co. Simon Benninga Financial Modeling. Third Edition. Cambridge, MA: MIT Press. Ch. 3: Financial Statement Modeling. 13

14 APPENDIX A: BENNINGA S MODEL 2 IN THE EVIEWS MODEL OBJECT In an extension of his first model (discussed in this paper), Benninga (2008, Ch. 3, p. 120) develops a model where CASH is the plug as long as cash balances are non-negative. The possibility that CASH could become negative is precluded by additional borrowing (DEBT increases) as needed. To implement this model in EViews, we need to construct a dummy variable (DUM) to determine the sign of CASH balances in the absence of the non-negativity constraint. When CASH>0, it is the plug. When CASH would become negative, it is constrained to equal zero and DEBT becomes the plug. Below, we reproduce the equations to be entered into the MODEL object in order to implement this model in EViews. ============================= ' Model 2 Cash and marketable securities is the plug as long as it is non-negative. Debt is increased as needed to prevent negative cash balances from materializing. ' This model replicates the second Excel pro forma statement model in Benninga (2008, Ch. 3, page 120) SALES = SALES(-1) * 1.20 ' Balance Sheet OCA = 0.20 * SALES CL = 0.08 * SALES NFA = 0.80 * SALES DEPN = 0.10 * (FA_COST(-1) + FA_COST) / 2 ACC_DEPN = ACC_DEPN(-1) + DEPN FA_COST = NFA + ACC_DEPN EQUITY = CAPITAL + ACC_EARNINGS ' ' create a dummy variable DUM that is equal to one when (OCA + NFA) exceed (CL - DEBT(-1) - EQUITY) and zero otherwise DUM = OCA + NFA - CL - DEBT(-1) - EQUITY > 0 ' ' DEBT equals its value from the previous period unless there is a need to undertake additional borrowing to prevent CASH from becoming negative: DEBT = (OCA + NFA - CL - EQUITY) * DUM + DEBT(-1) * (1 - DUM) LIAB = DEBT + CL ' CASH equals the minimum of its value in model 1 or zero; it can t be negative CASH = (LIAB - OCA - NFA + EQUITY) * (1 - DUM) ASSETS = CASH + OCA + NFA 14

15 ' Income Statement COGS = 0.50 * SALES INT_DEBT = (DEBT(-1) + DEBT) / 2 * 0.10 INT_CASH = (CASH(-1) + CASH) / 2 * 0.08 PBT = SALES - COGS - INT_DEBT + INT_CASH - DEPN TAXES = 0.40 * PBT PROFIT = PBT - TAXES DIVIDENDS = 0.50 * PROFIT RET_EARNINGS = PROFIT - DIVIDENDS ACC_EARNINGS = ACC_EARNINGS (-1) + RET_EARNINGS ' ====================================================== 15

### Integrating Financial Statement Modeling. and Sales Forecasting Using EViews

Forthcoming: Journal of Applied Business and Economics (211) Integrating Financial Statement Modeling and Sales Forecasting Using EViews John T. Cuddington Colorado School of Mines Irina Khindanova University

### Incorporating Macroeconomic and Firm-Level Uncertainties in Stochastic Pro-Forma Financial Modeling

Incorporating Macroeconomic and Firm-Level Uncertainties in Stochastic Pro-Forma Financial Modeling John T. Cuddington W.J. Coulter Professor of Mineral Economics Colorado School of Mines Irina Khindanova

### Incorporating Macroeconomic and Firm-Level Uncertainties in Stochastic Pro-Forma Financial Modeling

Incorporating Macroeconomic and Firm-Level Uncertainties in Stochastic Pro-Forma Financial Modeling John T. Cuddington Colorado School of Mines* Irina Khindanova University of Denver** This paper demonstrates

### In this presentation I will introduce some basic elements of financial forecasting and how they connect with the financial plan.

In this presentation I will introduce some basic elements of financial forecasting and how they connect with the financial plan. 1 The pro forma financial statements in the financial plan will include

### Financial Planning and Growth. Background

Financial Planning and Growth (Text reference: Chapter 26) background detailed examples factors affecting growth AFM 271 - Financial Planning and Growth Slide 1 Background financial planning may be thought

### Integrated Resource Plan

Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 650-962-9670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1

### Computing Liquidity Ratios Current Ratio = CA / CL 708 / 540 = 1.31 times Quick Ratio = (CA Inventory) / CL (708 422) / 540 =.53 times Cash Ratio =

1 Computing Liquidity Ratios Current Ratio = CA / CL 708 / 540 = 1.31 times Quick Ratio = (CA Inventory) / CL (708 422) / 540 =.53 times Cash Ratio = Cash / CL 98 / 540 =.18 times 2 Computing Leverage

### CHAPTER 3 LONG-TERM FINANCIAL PLANNING AND GROWTH

CHAPTER 3 LONG-TERM FINANCIAL PLANNING AND GROWTH Answers to Concepts Review and Critical Thinking Questions 5. The sustainable growth rate is greater than 20 percent, because at a 20 percent growth rate

### Introduction to Financial Models for Management and Planning

CHAPMAN &HALL/CRC FINANCE SERIES Introduction to Financial Models for Management and Planning James R. Morris University of Colorado, Denver U. S. A. John P. Daley University of Colorado, Denver U. S.

### CHAPTER 3 LONG-TERM FINANCIAL PLANNING AND GROWTH

CHAPTER 3 LONG-TERM FINANCIAL PLANNING AND GROWTH Answers to Concepts Review and Critical Thinking Questions 1. Time trend analysis gives a picture of changes in the company s financial situation over

### CHAPTER 2 FINANCIAL STATEMENTS AND CASH FLOW

CHAPTER 2 FINANCIAL STATEMENTS AND CASH FLOW Solutions to Questions and Problems NOTE: All end-of-chapter problems were solved using a spreadsheet. Many problems require multiple steps. Due to space and

### Chapter 4. Long-Term Financial Planning and Growth. Overview of the Lecture. September 2004. What Is Financial Planning. Financial Planning Models

Chapter 4 Long-Term Financial Planning and Growth September 2004 Overview of the Lecture What Is Financial Planning Financial Planning Models The Percentage of Sales Approach xternal Financing and Growth

### Chapter 14. Web Extension: Financing Feedbacks and Alternative Forecasting Techniques

Chapter 14 Web Extension: Financing Feedbacks and Alternative Forecasting Techniques I n Chapter 14 we forecasted financial statements under the assumption that the firm s interest expense can be estimated

### Online Appendices to the Corporate Propensity to Save

Online Appendices to the Corporate Propensity to Save Appendix A: Monte Carlo Experiments In order to allay skepticism of empirical results that have been produced by unusual estimators on fairly small

### NWC = current assets - current liabilities = 2,100

Questions and Problems Chapters 2,3 pp45-47 1. Building a balance sheet. Penguin Pucks, Inc., has current assets of \$3,000, net fixed assets \$6,000, current liabilities of \$900, and long-term debt of \$5,000.

### CHAPTER 5. Exercise Solutions

CHAPTER 5 Exercise Solutions 91 Chapter 5, Exercise Solutions, Principles of Econometrics, e 9 EXERCISE 5.1 (a) y = 1, x =, x = x * * i x i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 y * i (b) (c) yx = 1, x = 16, yx

### Long-term Financial Planning

Long-term Financial Planning What is Financial Planning? Formulates the way financial goals are to be achieved. Requires that decisions be made about an uncertain future. Recall that the goal of the firm

### Topics Covered. Objectives. Chapter 18 Financial Planning

Chapter 18 Financial Planning Topics Covered What is Financial Planning? Financial Planning Models Planners Beware External Financing and Growth Objectives Forecast Financial Statements with the Percentage

### Using simulation to calculate the NPV of a project

Using simulation to calculate the NPV of a project Marius Holtan Onward Inc. 5/31/2002 Monte Carlo simulation is fast becoming the technology of choice for evaluating and analyzing assets, be it pure financial

### Eviews Tutorial. File New Workfile. Start observation End observation Annual

APS 425 Professor G. William Schwert Advanced Managerial Data Analysis CS3-110L, 585-275-2470 Fax: 585-461-5475 email: schwert@schwert.ssb.rochester.edu Eviews Tutorial 1. Creating a Workfile: First you

### Module 2: Preparing for Capital Venture Financing Building Pro-Forma Financial Statements

Module 2: Preparing for Capital Venture Financing Building Pro-Forma Financial Statements Module 2: Preparing for Capital Venture Financing Building Pro-Forma Financial Statements TABLE OF CONTENTS 1.0

### Web Extension: Financing Feedbacks and Alternative Forecasting Techniques

19878_09W_p001-009.qxd 3/10/06 9:56 AM Page 1 C H A P T E R 9 Web Extension: Financing Feedbacks and Alternative Forecasting Techniques IMAGE: GETTY IMAGES, INC., PHOTODISC COLLECTION In Chapter 9 we forecasted

### CHAPTER 2 INTRODUCTION TO CORPORATE FINANCE

CHAPTER 2 INTRODUCTION TO CORPORATE FINANCE Solutions to Questions and Problems NOTE: All end of chapter problems were solved using a spreadsheet. Many problems require multiple steps. Due to space and

Module 2: Preparing for Capital Venture Financing Financial Forecasting Methods Module 2: Preparing for Capital Venture Financing Financial Forecasting Methods 1.0 FINANCIAL FORECASTING METHODS 1.01 Introduction

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### CHAPTER 8: FINANCIAL PLANNING MODELS *

CHAPTER 8: FINANCIAL PLANNING MODELS * this version: 24 July 2003 Chapter contents Overview... 1 8.1. Initial accounting statements for a financial planning model... 3 8.2. Building a financial planning

### Time Series Analysis

Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos

### 2. Linear regression with multiple regressors

2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions

### Introduction to Regression and Data Analysis

Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it

### Simulation and Risk Analysis

Simulation and Risk Analysis Using Analytic Solver Platform REVIEW BASED ON MANAGEMENT SCIENCE What We ll Cover Today Introduction Frontline Systems Session Ι Beta Training Program Goals Overview of Analytic

### PROFITCENTS ANALYTICAL PROCEDURES EXPECTED VALUE METHODOLOGY

PROFITCENTS ANALYTICAL PROCEDURES EXPECTED VALUE METHODOLOGY INTRODUCTION This document includes an analysis of the projection methodology used in ProfitCents Analytical Procedures in calculating expectations

### Sensex Realized Volatility Index

Sensex Realized Volatility Index Introduction: Volatility modelling has traditionally relied on complex econometric procedures in order to accommodate the inherent latent character of volatility. Realized

### Chapter 5: Basic Statistics and Hypothesis Testing

Chapter 5: Basic Statistics and Hypothesis Testing In this chapter: 1. Viewing the t-value from an OLS regression (UE 5.2.1) 2. Calculating critical t-values and applying the decision rule (UE 5.2.2) 3.

### Multiple Choice Questions (45%)

Multiple Choice Questions (45%) Choose the Correct Answer 1. The following information was taken from XYZ Company s accounting records for the year ended December 31, 2014: Increase in raw materials inventory

### Chapter 8: Interval Estimates and Hypothesis Testing

Chapter 8: Interval Estimates and Hypothesis Testing Chapter 8 Outline Clint s Assignment: Taking Stock Estimate Reliability: Interval Estimate Question o Normal Distribution versus the Student t-distribution:

### 14. Calculating Total Cash Flows.

14. Calculating Total Cash Flows. Greene Co. shows the following information on its 2008 income statement: Sales = \$138,000 Costs = \$71,500 Other expenses = \$4,100 Depreciation expense = \$10,100 Interest

### Section 3 Financial and stock market ratios

Section 3 Financial and stock market ratios Introduction 41 Ratio calculation 42 Financial status ratios 43 Stock market ratios 45 Debt: short-term or long-term? 47 Summary 48 Problems 49 INTRODUCTION

### Corporate Defaults and Large Macroeconomic Shocks

Corporate Defaults and Large Macroeconomic Shocks Mathias Drehmann Bank of England Andrew Patton London School of Economics and Bank of England Steffen Sorensen Bank of England The presentation expresses

### The Bootstrap. 1 Introduction. The Bootstrap 2. Short Guides to Microeconometrics Fall Kurt Schmidheiny Unversität Basel

Short Guides to Microeconometrics Fall 2016 The Bootstrap Kurt Schmidheiny Unversität Basel The Bootstrap 2 1a) The asymptotic sampling distribution is very difficult to derive. 1b) The asymptotic sampling

### Cashflow, Free Cashflow, and Proforma Forecasting

Cashflow, Free Cashflow, and Proforma Forecasting Proforma Forecasting Models Proforma forecasting models are designed to use the balance sheet and income statement from a corporation from a number of

### CHAPTER 2 ACCOUNTING STATEMENTS, TAXES, AND CASH FLOW

CHAPTER 2 ACCOUNTING STATEMENTS, TAXES, AND CASH FLOW Answers to Concepts Review and Critical Thinking Questions 1. True. Every asset can be converted to cash at some price. However, when we are referring

### THE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Homework Assignment #2

THE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Homework Assignment #2 Assignment: 1. Consumer Sentiment of the University of Michigan.

### WHY NOT VALUE EQUITY CFs DIRECTLY?

WHY NOT VALUE EQUITY CFs DIRECTLY? WHAT HAVE WE DONE SO FAR? Discount the firm s projected Free Cash Flows at their Weighted Average Cost of Capital to get the aggregate value of the firm s securities

### Foundations of Financial Management - 10th Canadian Edition - Block et al.

Chapter 04 1. In using a systems approach to financial planning, it is not necessary to develop a: A. pro forma income statement. B. cash budget. C. pro forma balance sheet. D. contingent liability plan.

### Factors affecting online sales

Factors affecting online sales Table of contents Summary... 1 Research questions... 1 The dataset... 2 Descriptive statistics: The exploratory stage... 3 Confidence intervals... 4 Hypothesis tests... 4

### The Determinants and the Value of Cash Holdings: Evidence. from French firms

The Determinants and the Value of Cash Holdings: Evidence from French firms Khaoula SADDOUR Cahier de recherche n 2006-6 Abstract: This paper investigates the determinants of the cash holdings of French

### Stochastic Analysis of Long-Term Multiple-Decrement Contracts

Stochastic Analysis of Long-Term Multiple-Decrement Contracts Matthew Clark, FSA, MAAA, and Chad Runchey, FSA, MAAA Ernst & Young LLP Published in the July 2008 issue of the Actuarial Practice Forum Copyright

### 2. What are the theoretical and practical consequences of autocorrelation?

Lecture 10 Serial Correlation In this lecture, you will learn the following: 1. What is the nature of autocorrelation? 2. What are the theoretical and practical consequences of autocorrelation? 3. Since

### What Do Short-Term Liquidity Ratios Measure? What Is Working Capital? How Is the Current Ratio Calculated? How Is the Quick Ratio Calculated?

What Do Short-Term Liquidity Ratios Measure? What Is Working Capital? HOCK international - 2004 1 HOCK international - 2004 2 How Is the Current Ratio Calculated? How Is the Quick Ratio Calculated? HOCK

### Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm

Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has

### Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025

Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025 In December 2014, an electric rate case was finalized in MEC s Illinois service territory. As a result of the implementation of

### ENTREPRENEURIAL FINANCE: Strategy Valuation and Deal Structure

ENTREPRENEURIAL FINANCE: Strategy Valuation and Deal Structure Chapter 7. Methods of Financial Forecasting: Integrated Financial Modeling Questions and Problems 1. The cash cycle is the time between when

### AFM 472. Midterm Examination. Monday Oct. 24, 2011. A. Huang

AFM 472 Midterm Examination Monday Oct. 24, 2011 A. Huang Name: Answer Key Student Number: Section (circle one): 10:00am 1:00pm 2:30pm Instructions: 1. Answer all questions in the space provided. If space

### Statistics in Retail Finance. Chapter 6: Behavioural models

Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics:- Behavioural

### Regression step-by-step using Microsoft Excel

Step 1: Regression step-by-step using Microsoft Excel Notes prepared by Pamela Peterson Drake, James Madison University Type the data into the spreadsheet The example used throughout this How to is a regression

### Pro Forma Financial Statements

Pro Forma Financial Statements INTRODUCTION Pro forma financial statements are an essential part of the strategic planning and annual budgeting processes. The phrase pro forma literally means as a matter

### A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study

A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study But I will offer a review, with a focus on issues which arise in finance 1 TYPES OF FINANCIAL

### How To Use A Monte Carlo Study To Decide On Sample Size and Determine Power

How To Use A Monte Carlo Study To Decide On Sample Size and Determine Power Linda K. Muthén Muthén & Muthén 11965 Venice Blvd., Suite 407 Los Angeles, CA 90066 Telephone: (310) 391-9971 Fax: (310) 391-8971

### For a balance sheet item, an asset increase is a Debit a liability increase is a Credit

1 CASH FLOW ANALYSIS 1.1 Introduction For a further discussion on cash flow, please refer to the guide to financial statements in the Learning Centre. The key to cashflow analysis is recognition of cashflow,

### USING THE EQUITY RESIDUAL APPROACH TO VALUATION: AN EXAMPLE

Graduate School of Business Administration - University of Virginia USING THE EQUITY RESIDUAL APPROACH TO VALUATION: AN EXAMPLE Planned changes in capital structure over time increase the complexity of

### Chapter 3 Financial Statements Analysis and Long-Term Planning

University of Science and Technology Beijing Dongling School of Economics and management Chapter 3 Financial Statements Analysis and Long-Term Planning Sep. 2012 Dr. Xiao Ming USTB 1 Key Concepts and Skills

### NIKE Case Study Solutions

NIKE Case Study Solutions Professor Corwin This case study includes several problems related to the valuation of Nike. We will work through these problems throughout the course to demonstrate some of the

### Financial Planning for East Coast Yachts

Financial Planning for East Coast Yachts Prepared for East Coast Yachts Prepared by Dan Ervin, Mary-Ann Lawrence, Kevin Klepacki, Katie Wilson, Andrew Wright January 1, 2010 Table of Contents iii Table

### The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series.

Cointegration The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series. Economic theory, however, often implies equilibrium

### Stock market booms and real economic activity: Is this time different?

International Review of Economics and Finance 9 (2000) 387 415 Stock market booms and real economic activity: Is this time different? Mathias Binswanger* Institute for Economics and the Environment, University

### C: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)}

C: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)} 1. EES 800: Econometrics I Simple linear regression and correlation analysis. Specification and estimation of a regression model. Interpretation of regression

### E2-2: Identifying Financing, Investing and Operating Transactions?

E2-2: Identifying Financing, Investing and Operating Transactions? Listed below are eight transactions. In each case, identify whether the transaction is an example of financing, investing or operating

### Lecture 5 - Financial Planning and Forecasting

Lecture 5 - Financial Planning and Forecasting Strategy A company s strategy consists of the competitive moves, internal operating approaches, and action plans devised by management to produce successful

### G2G Guide to Financial Calculations and Valuation Principles G2G GUIDE TO FINANCIAL CALCULATIONS AND VALUATION PRINCIPLES

G2G GUIDE TO FINANCIAL CALCULATIONS AND VALUATION PRINCIPLES G2G Guide to Financial Calculations and Valuation Principles Introduction... 3 1 Basic Accounting Principles... 3 1.1 Profit & Loss statement...

### Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini

NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building

### Time Series Analysis

Time Series Analysis Forecasting with ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos (UC3M-UPM)

### Capital Investment Analysis and Project Assessment

PURDUE EXTENSION EC-731 Capital Investment Analysis and Project Assessment Michael Boehlje and Cole Ehmke Department of Agricultural Economics Capital investment decisions that involve the purchase of

### CHAPTER FOURTEEN. Simple Forecasting and Simple Valuation

CHAPTER FOURTEEN Simple Forecasting and Simple Valuation Concept Questions C14.1 Book values give a good forecast when they are reviewed at their fair value: applying the required return to book value

### Chapter 6: Multivariate Cointegration Analysis

Chapter 6: Multivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie VI. Multivariate Cointegration

### A Fuel Cost Comparison of Electric and Gas-Powered Vehicles

\$ / gl \$ / kwh A Fuel Cost Comparison of Electric and Gas-Powered Vehicles Lawrence V. Fulton, McCoy College of Business Administration, Texas State University, lf25@txstate.edu Nathaniel D. Bastian, University

### 1 Short Introduction to Time Series

ECONOMICS 7344, Spring 202 Bent E. Sørensen January 24, 202 Short Introduction to Time Series A time series is a collection of stochastic variables x,.., x t,.., x T indexed by an integer value t. The

Management Accounting 137 Comprehensive Business Budgeting Goals and Objectives Profit planning, commonly called master budgeting or comprehensive business budgeting, is one of the more important techniques

### Chapter 12: Time Series Models

Chapter 12: Time Series Models In this chapter: 1. Estimating ad hoc distributed lag & Koyck distributed lag models (UE 12.1.3) 2. Testing for serial correlation in Koyck distributed lag models (UE 12.2.2)

### How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

STRUCTURAL EQUATION MODELING, 9(4), 599 620 Copyright 2002, Lawrence Erlbaum Associates, Inc. TEACHER S CORNER How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power Linda K. Muthén

### Financial Time Series Analysis (FTSA) Lecture 1: Introduction

Financial Time Series Analysis (FTSA) Lecture 1: Introduction Brief History of Time Series Analysis Statistical analysis of time series data (Yule, 1927) v/s forecasting (even longer). Forecasting is often

### Measurement of Banks Exposure to Interest Rate Risk and Principles for the Management of Interest Rate Risk respectively.

INTEREST RATE RISK IN THE BANKING BOOK Over the past decade the Basel Committee on Banking Supervision (the Basel Committee) has released a number of consultative documents discussing the management and

### Penalized regression: Introduction

Penalized regression: Introduction Patrick Breheny August 30 Patrick Breheny BST 764: Applied Statistical Modeling 1/19 Maximum likelihood Much of 20th-century statistics dealt with maximum likelihood

### Financial Risk Assessment Part I.1

Financial Risk Assessment Part I.1 Dimitrios V. Lyridis Assoc. Prof. National Technical University of Athens DEFINITION of 'Risk The chance that an investment's actual return will be different than expected.

### Asymmetry and the Cost of Capital

Asymmetry and the Cost of Capital Javier García Sánchez, IAE Business School Lorenzo Preve, IAE Business School Virginia Sarria Allende, IAE Business School Abstract The expected cost of capital is a crucial

### Chapter 6 Statement of Cash Flows

Chapter 6 Statement of Cash Flows The Statement of Cash Flows describes the cash inflows and outflows for the firm based upon three categories of activities. Operating Activities: Generally include transactions

### Calculating Interval Forecasts

Calculating Chapter 7 (Chatfield) Monika Turyna & Thomas Hrdina Department of Economics, University of Vienna Summer Term 2009 Terminology An interval forecast consists of an upper and a lower limit between

### CONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE

1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,

### Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are

### 11. Analysis of Case-control Studies Logistic Regression

Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:

### OFFICIAL FILING BEFORE THE PUBLIC SERVICE COMMISSION OF WISCONSIN DIRECT TESTIMONY OF JANNELL E. MARKS

OFFICIAL FILING BEFORE THE PUBLIC SERVICE COMMISSION OF WISCONSIN Application of Northern States Power Company, a Wisconsin Corporation, for Authority to Adjust Electric and Natural Gas Rates Docket No.

### Market Risk: FROM VALUE AT RISK TO STRESS TESTING. Agenda. Agenda (Cont.) Traditional Measures of Market Risk

Market Risk: FROM VALUE AT RISK TO STRESS TESTING Agenda The Notional Amount Approach Price Sensitivity Measure for Derivatives Weakness of the Greek Measure Define Value at Risk 1 Day to VaR to 10 Day

### A. Retained Earnings the basic source of retained earnings is income from operations.

Chapter 16 Stockholders Equity: Retained Earnings LECTURE OUTLINE The material in this chapter is straight-forward and can be covered in two class periods. Students are often not familiar with dividend

### Financing Costs of Additional Funds Needed: A Modified Equation Approach

Financing Costs of Additional Funds Needed: A Modified Equation Approach By: Daniel T. Winkler Winkler, D. T. "Financing Costs of Additional Funds Needed: A Modified Equation Approach," Financial Education

### 03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS

0 The full syllabus 0 The full syllabus continued PAPER C0 FUNDAMENTALS OF BUSINESS MATHEMATICS Syllabus overview This paper primarily deals with the tools and techniques to understand the mathematics