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Austin Clark, CFA, has been asked to analyze White Goods Corporation, a $9 billion company that owns a nationwide chain of stores selling appliances and other electronic goods. As part of his analysis of the White Goods Corporation, Clark's supervisor, David Horvath, asks Clark to forecast White Goods' 2009 sales using multiple regression analysis. The following model was developed:
sales = 20.1 + 0.001 GDP+ 1,000.6 TR + 0.1 CC -3.2 PC -40.3 UR
t-values: (1.1) (2.3) (1.75) (3.2) (-0.48) (-0.9)
Number of observations: 76
Standard error estimate: 15.67
Unadjusted R2: 0.96
Regression sum of squares: 412,522
Error sum of squares: 17,188
Independent Variable Descriptions
GDP = gross domestic product
TR = average coupon rate on 5-year U .S . Treasury securities
CC = most recent quarter end consumer confidence index value
PC = previous year's sales of personal computers
UR = most recent quarter end unemployment rate
Variable Estimates for 2009
GDP =8,000
TR = 0.05
CC =97
PC = 60,000
UR = 0.055
Critical Values For Student's t-Distribution
Clark's supervisor asks him to prepare a report explaining the implications of the regression analysis results. Clark writes the following conclusions concerning regression analysis in his report:
Interpreting the results of regression analysis can be problematic if certain assumptions of the ordinary least squares framework are violated. The regression output for White Goods Corporation is unreliable for the following reasons:
Finding 1: The correlation between regression errors across time is very close to 1.
Finding 2: There is a strong relationship between the regression error variance and the regression independent variables.
What is the F-value that tests the hypothesis that all of the coefficients are equal to zero?
The F-value is calculated as (mean regression sum of squares) / (mean squared error) = (412,522/5) / (17,188/70) = 336. (Study Session 3, LOS 12.e)
Carl Warner, CFA, has been asked to review the financial information of Global Drug World (GDW) in preparation for a possible takeover bid by rival competitor Consolidated Drugstores International (Consolidated). GDW has produced impressive results since going public via an initial public offering in 1998. Through a program of aggressive growth by acquisition, GDW is currently seen as a major player and a threat to Consolidated^ own plans for growth and profitability. In preparation for his analysis, Warner has gathered the following financial data from GDW's year-end statements:
As part of his analysis, Warner needs to forecast the free cash flow to the firm (FCFF) for 2009. The best information he has points to an increase in sales of 6%. The earnings before interest and tax (EBIT) margin is not expected to change from the rate of 6.4% achieved in 2008. Additional fixed capital spending is expected to be $36,470. Investment in net working capital is expected to be $24,313. Moreover, Warner notes that the only noncash charge is depreciation, which he estimates will be $60,000.
Warner has been asked to analyze the effect each of the following corporate events, if taken during 2009, would have on GDW's free cash flow to equity (FCFE):
* 20% increase in dividends per share.
* Repurchase of 25% of the firm's outstanding shares using cash.
* New common share offering that would increase shares outstanding by 30%.
* New issue of convertible bonds that are not callable for fi\e years and would increase the level of debt by 10%.
The 2008 free cash flow to the firm (FCFF) for Global Drug World (GDW) in dollars is closest to:
Free cash flow to the firm can be calculated in various ways. One approach to calculate FCFF is to start with net income:
De Jong continues her analysis of O'Connor. She is concerned that along with a dividend discount model approach she would also like to get a measure of the contribution that the key managers, Melanie and Arthur O'Connor, have made to the company's apparent ongoing success.
She considers using NOPAT and EVA to assess management performance. She believes that increasing invested capital to take advantage of projects with positive net present values increases both NOPAT and EVA .
However, De Jong decides to use residual income analysis instead. She provides the following justification for using the residual income model:
* The calculation of residual income depends primarily on readily available accounting data.
* The residual income model can be used even when cash flow is difficult to forecast.
* The residual income model does not depend on dividend payments or on positive free cash flows in the near future.
* The residual income model depends on the validity of the clean surplus relation.
She also considers the following assumptions about continuing residual income:
Assumption 1: Residual income is positive and continues at the same level year after year.
Assumption 2: As return on equity approaches the cost of equity, residual income tends to zero.
Assumption 3: Residual income growth declines overtime and eventually reaches zero.
De Jong gathers recent financial information data on O'Connor, as shown in Exhibit I.
De Jong has also determined that at the beginning of 2008, O'Connor had total capital of $324,000,000, of which $251,000,000 was debt and $73,000,000 was equity. The company's cost of debt before taxes is 7%, and the cost of equity capital is 8%. The company has a tax rate of approximately 34%. Weighted average cost of capital is 5.4%. Net operating profit after tax (before any adjustments) is $28,517,640.
De Jong is interested in obtaining the market's assessment of the implied growth rate in residual income and notes that the book value per share for O'Connor at the beginning of 2009 was $4.29, and the current market price is $70. She forecasts the return on equity (ROE) for 2009 to be 11.84%.
De Jong discusses her analyses with a colleague, who makes the following general statements:
Statement 1: It is usually the case that value is recognized later in the residual income model than in the dividend discount model.
Statement 2: When the present value of expected future residual income is
negative, the justified P/B based on fundamentals is less than one.
The implied residual income growth rate for 2009, based on the residual income model, is closest to:
We need to solve (or g in the relationship:
Solving for g, wc get g = 7.75%. (Study Session 12, LOS 43.g)
Carl Warner, CFA, has been asked to review the financial information of Global Drug World (GDW) in preparation for a possible takeover bid by rival competitor Consolidated Drugstores International (Consolidated). GDW has produced impressive results since going public via an initial public offering in 1998. Through a program of aggressive growth by acquisition, GDW is currently seen as a major player and a threat to Consolidated^ own plans for growth and profitability. In preparation for his analysis, Warner has gathered the following financial data from GDW's year-end statements:
As part of his analysis, Warner needs to forecast the free cash flow to the firm (FCFF) for 2009. The best information he has points to an increase in sales of 6%. The earnings before interest and tax (EBIT) margin is not expected to change from the rate of 6.4% achieved in 2008. Additional fixed capital spending is expected to be $36,470. Investment in net working capital is expected to be $24,313. Moreover, Warner notes that the only noncash charge is depreciation, which he estimates will be $60,000.
Warner has been asked to analyze the effect each of the following corporate events, if taken during 2009, would have on GDW's free cash flow to equity (FCFE):
* 20% increase in dividends per share.
* Repurchase of 25% of the firm's outstanding shares using cash.
* New common share offering that would increase shares outstanding by 30%.
* New issue of convertible bonds that are not callable for fi\e years and would increase the level of debt by 10%.
The 2009 estimate of FCFF is closest to:
When depreciation is the only noncash charge, FCFF can be estimated from:
Ernie Smith and Jama! Sims are analysts with the firm of Madison Consultants. Madison provides statistical modeling and advice to portfolio managers throughout the United States and Canada.
In an effort to estimate future cash flows and value the Canadian stock market. Smith has been examining* the country's aggregate retail sales. He runs two autoregressive regression models in an attempt to determine whether there are any patterns in the data, utilizing nine years of unadjusted monthly retail sales data. One model uses a lag one variable and the other adds a lag twelve variable. The results of both regressions are shown in Exhibits 1 and 2.
Sims has been assigned the task of valuing the U .S . stock market and uses data similar to the data that Smith uses for Canada. He decides, however, that the data should be transformed. He takes the natural log of the data and uses it in the following model:
Smith and Sims are concerned that the data for Canadian retail sales may be more appropriately modeled with an ARCH process. Smith states, that in order to find out, he would take the residuals from the original autoregressive model for Canadian retail sales and then square them.
Sims states that these residuals would then be regressed against the Canadian retail sales data using the
where e represents the residual terms from the original regression and X represents the Canadian retail sales data. If is statistically different from zero, then the regression model contains an ARCH process.
Smith also examines the quarterly inflation data for an emerging market over the past nine years. He models the data using an autoregressive model with a lag one independent variable which he finds is statistically different from zero. He wonders whether he should also include lag two and lag four terms, given the magnitude of the autocorrelations of the residuals shown in Exhibit 4, assuming a 5% significance level. The critical t-values, assuming a 5% significance level and 35 degrees of freedom, are 2.03 for a two-tail test and 1.69 for a one-tail test.
where: FF is the Federal Funds rate in the United States (US), and BY is the bond yield in the European Union (E) and Great Britain (B).
Before he runs this regression, he investigates the characteristics of the dependent and independent variables. He finds that the Federal Funds rate in the United States and the bond yield in Great Britain have a unit root but that the bond yield in the European Union does not. Furthermore, the Federal Funds rate in the United States and the bond yield in Great Britain are cointegrated but the Federal Funds rate in the United States and the bond yield in the European Union are not.
Will Sims' regressions of European and British bond yields on the U .S . Federal Funds rate produce valid results?
In the first regression, the Federal Funds rate in the United States has a unit root but the bond yield in the European Union does not. So the former data series is not covariance stationary but the latter is. In this case, the regression results will not be valid.
In the second regression, both the Federal Funds rate in the United States and the bond yield in Great Britain have a unit root. So both data series are not covariance stationary. However, because they are cointcgrated, the regression results will be valid.
To sum up the possibilities you may face on exam day:
* If neither data series has a unit root, the regression results are valid.
* If only one data series has a unit root, the regression results are invalid.
* If both data series have a unit root and they are cointegrated, the regression results are valid.
* If both data series have a unit root and they are not cointegrated, the regression results are not valid.
(Study Session 3, LOS 13.j,m)
