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Test ID: 32038241
Question #1 of 60
Question ID: 692270Questions 16 relate to Goldensand Jewelry, Ltd.
Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
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A) B) C)
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5 The per ounce price of gold that corresponds to the $2,000 cost of finished jewelry is closest to:
$687. $712. $3,240.
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Question #2 of 60
Question ID: 692269Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
n × R with k degrees of freedom
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
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A) B) C)
Question #3 of 60
Question ID: 692272Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5
Are Singh (Statement 1) and Hara (Statement 2) correct or incorrect regarding the usefulness of regression results described in Exhibit 1 and the value of the slope coefficient?
Both are correct.
One is correct, the other is incorrect. Both are incorrect.
Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
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Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
n × R with k degrees of freedom
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
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A) B) C)
Question #4 of 60
Question ID: 692274Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5 Is Biscayne correct with regard to the specification of the BreuschPagan test?
No, because it is an Ftest. No, because the wrong R is used.
No, because the degrees of freedom are equal to k and n k 1.
Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
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df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
n × R with k degrees of freedom
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
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A) B) C)
Question #5 of 60
Question ID: 692271Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5
n d d d d d d
39 1.33 1.66 1.27 1.72 1.22 1.79
40 1.34 1.66 1.29 1.72 1.23 1.79
45 1.38 1.67 1.34 1.72 1.29 1.78
...
Regarding the comments on the potential consequences of misspecification in the simple linear regression, is Singh correct or incorrect regarding his comment on his concern over stationarity, and is Biscayne correct or incorrect about the effect of omitting an important variable?
Only Singh is incorrect. Only Biscayne is incorrect. Both are incorrect.
Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
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Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
n × R with k degrees of freedom
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table 2
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A) B) C)
Question #6 of 60
Question ID: 692273Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5
n d d d d d d
39 1.33 1.66 1.27 1.72 1.22 1.79
40 1.34 1.66 1.29 1.72 1.23 1.79
45 1.38 1.67 1.34 1.72 1.29 1.78
... Is Hara's Statement 3 about multicollinearity accurate?
Yes.
No, because removal of independent variables is a remedy for residual autocorrelation. No, because the coefficient of determination would not increase.
Introduction
Rajesh Singh is the CFO of Goldensand Jewelry, Ltd, a Londonbased retailer of fine jewelry and watches. Singh has noticed that the price of gold has begun to increase. If economic activity continues to pick up, the price of gold is likely to accelerate its rate of increase as both the level of demand and inflation rates increase. Implications of Rising Gold Price
Singh has become concerned about the cost implications for Goldensand if gold prices continue to rise. He has requested a meeting with Anita Biscayne, Goldensand's COO. In preparation for the meeting, Singh asked one of his staff, Yasunobu Hara, to prepare a regression analysis comparing the price of gold to the average cost of Goldensand's purchases of finished gold jewelry. Hara provides the regression results as shown in Exhibit 1.
Exhibit 1: 19792009 Annual Data (31 Observations)
Variable Coefficient Standard Error of the Coefficient
Intercept 11.06 7.29
Cost of gold 2.897 0.615 standard error of the forecast = 117.8
Exhibit 2: Partial Student's tdistribution Table Level of Significance for OneTailed Test
df 0.100 0.050 0.025 0.010 0.005 0.0005
Level of Significance for TwoTailed Test
df 0.200 0.100 0.050 0.020 0.010 0.001
29 1.311 1.699 2.045 2.462 2.756 3.659 30 1.310 1.697 2.042 2.457 2.750 3.646 31 1.309 1.696 2.040 2.453 2.744 3.636
Reviewing the regression results, Biscayne becomes concerned about the implications for the cost of finished jewelry to Goldensand if the price of gold continues to rise. To remain profitable, the cost of finished jewelry should not exceed $2,000.
Regression Concerns
Overall Concerns
Singh's principal concern about the regression is whether the time period chosen is a good predictor of the current situation. He makes the following statement:
Statement 1: We may have a problem with parameter instability if the relationship between gold prices and jewelry costs has changed over the past 30 years.
Singh also focuses on the value of the slope coefficient. He expected it to be 4.0 based on his experience in the industry. Hara computes the appropriate test statistic and reports the following:
Statement 2: We fail to reject the null hypothesis that the slope coefficient is
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equal to 4.0 at the 5% level of significance.
Testing for Heteroskedasticity
Biscayne remarks that the dramatic increase in the price level over the past 30 years leads her to suspect heteroskedasticity in the regression results. She suggests to Singh that they should conduct a BreuschPagan chisquare test for heteroskedasticity by calculating the following test statistic:
n × R with k degrees of freedom
where:
n = number of observations
R = R of the regression of jewelry prices on gold prices k = number of independent variables
Model Misspecification
Biscayne and Singh have various views on the potential for model misspecification and the effect of any such misspecification.
Biscayne worries that the regression model is misspecified because it does not include a variable to measure the cost of the highly specialized labor used by manufacturing jewelers. She points out that the effect of omitting an important variable in a regression analysis is that the regression coefficients will be unbiased and inconsistent.
Singh adds that another common consequence of misspecifying a regression analysis is creating undesired stationarity. Multiple Regression
Hara conducts a series of regression analyses using all possible combinations of the suggested independent variables based on their average quarterly values. He returns with the following regression results as shown in Exhibit 3 for the equation which uses all suggested independent variables.
Exhibit 3: 19992009 Quarterly Data (44 Observations) Independent Variables Coefficient tStatistic
Intercept −3.9 3.7
Gold price 4.7 14.5
Silver price 1.2 7.8
Platinum price 3.5 3.1
Labor costs 0.82 2.4
GDP (EU) 0.000274 5.7
GDP (Middle East) 0.000049 3.6 Personal income (EU) 0.000314 2.1 Personal income (Middle East) 0.009876 2.2 R : 0.55
DurbinWatson: 3.89
Hara is concerned about the equation described in Exhibit 3. He makes the following statement:
Statement 3: The model appears to suffer from multicollinearity. Dropping one or more independent variables will increase the coefficient of determination.
Biscayne responds with the following statement:
Statement 4: An autocorrelation problem can be addressed by using the Hansen method to adjust the R.
Exhibit 4: Partial DurbinWatson Table
Critical Values for the DurbinWatson Statistic (∝ = 0.05)
K = 3 K = 4 K = 5 Is Biscayne correct regarding his statement concerning how to correct for autocorrelation?
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A) B) C)
Question #7 of 60
Question ID: 692275No, because the White method is used to adjust the R .
No, because the Hansen method adjusts the coefficient standard errors.
No, because the Hansen method is used to address the problem of multicollinearity.
Questions 712 relate to Kay Longton, CFA.
Kay Longton, CFA, works as an equity analyst for BKJE Services, a small advisory firm Longton founded with three colleagues she previously worked with. BKJE offers a range of services to both institutional and retail investors and prides itself on its ability to service both clients with relatively shallow knowledge of the markets as well as experienced veterans.
Currently Longton is engaged with Coreblue, a buyside client that has recently seen a significant downturn in the performance of several of its actively managed funds. As recently as 2014, Coreblue was featured in lists highlighting the best performing funds, but recent poor performance has resulted in a 24% drop in assets under management. A thorough inhouse review revealed that several of the historically bestperforming investments in one of Coreblue's biggest funds had not been subject to the mandatory screening process. Three of these investments subsequently saw decreases in market capitalization of more than 40% and were responsible for more than 70% of the drop in the fund's active return.
Longton is currently reviewing the investments in question in order to report to Coreblue whether any warning signs were evident from the financial statements. Longton hopes this report will lead to a much bigger project for BKJE involving redesign of Coreblue's screening and analysis process.
The first company Longton is reviewing, Reddyfast, Inc., rose to prominence in 2012 when it promised to deliver custom built kitchen/dining room extensions in customer backyards, which could be built onsite in a day. Longton intends to include in her report the following extracts from Reddyfast's financial statements shown in Exhibit 1. Exhibit 1 Reddyfast Financial Statements and Notes (Extracts, $ '000s)
2012 2013 2014
Revenue 14,000 13,720 15,915
Cost of Goods Sold (11,340) (10,976) (12,891)
Gross Profit 2,660 2,744 3,024
Accounts Receivable (note 1) 1,789 1,907 2,610
Note 1 Accounts Receivable Securitization
In 2014, the company received $400,000 from the sale of accounts receivable. These balances are not shown in the accounts receivable figure in the balance sheet. An associated finance charge has been disclosed in operating profit for the year.
Longton has isolated these figures as she believes that analysis of the relationship between receivables and revenue should have revealed cause for concern over the period 20122014. She intends to restate the financial statements for accounts receivables sold, and then compute the trend in days of sales outstanding using endof year receivables.
Longton plans to make the following statements concerning inventory management trends that Coreblue should be on the lookout for: Statement 1
A substantial and unexpected increase in sales in the final quarter may be a sign that a company is using billandhold transactions to give a oneoff boost to revenue and cash flows toward the end of a period.
Statement 2
Earnings are made up of a cash earnings component and an accrual earning component. The cash component of earnings is more persistent. A firm with a higher proportion of cash earnings will have a higher β in the following expression of earnings persistence:
Earnings = α + β(Earnings ) + ε
The second company in question is Ervington Boddan, Inc. (EB), a provider of heating solutions for recreational vehicles across the United States. Three board members of EB have also served as board members for Reddyfast since its inception in 2009.
Longton is concerned that EB's growth is fueled largely by income from associates, and, as a result, Longton intends to prepare a report showing the core ROE without including the results on such investments. In order to illustrate the driving forces behind ROE, she intends to perform a classic DuPont analysis that excludes the impact of associates from the margin and turnover ratios. One of Longton's interns has prepared the extracts shown in Exhibit 2 to assist with the analysis.
Exhibit 2 EB Financial Statements (Extracts, $ millions)
2014 2015 2016
Revenue 11,719 12,071 12,795
2
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Cost of Goods Sold 9,243 9,502 10,357
Research and Development 80 78 76
Depreciation and Amortization 831 839 864
Other Operating Expenses 590 625 675
Total Expenses 10,744 11,045 11,973
Operating Profit 975 1,026 822
Finance Costs (178) (183) (194)
Finance Income 23 23 23
Income From Associates 56 63 94
Profit Before Tax 876 929 745
Tax (157) (159) (160)
Profit After Tax 719 770 585
NonControlling Interest (16) (16) (16)
Net Income Attributable to
Shareholders of Parent 703 754 568
Balance Sheet
NonCurrent Assets
PPE 8,120 8,193 8,203
Intangibles 982 980 992
Investment in Associates 1,733 1,890 2,014 Other NonCurrent Assets 1,013 1,102 1,712
Total NonCurrent Assets 11,848 12,165 12,921
Total Current Assets 3,245 3,345 3,354
Total Assets 15,093 15,510 16,275
Total Liabilities 10,678 10,899 11,010
Shareholders' Equity 4,415 4,611 5,265
The third company under review is Yopatta Solutions, Inc. The company provides marketing and advertising services to a variety of clients, promising to deliver a "one stop shop" solution for all client customer communication needs.
Due to the nature of its business, Yopatta (like its peers) has relatively few tangible assets on its balance sheet. However, on reviewing the notes to the balance sheet, Longton identifies that Yopatta has a significant operating lease commitment. Using endofyear reported balance sheet data, Longton calculated Yopatta's debtto equity ratio to be 48%. She now intends to restate the ratio after capitalizing operating lease commitments using the information in Exhibit 3.
Exhibit 3 Yopatta Leverage
Balance Sheet 31 Dec 2015 As Reported
NonCurrent Assets 7,892
Current Assets 6,422
Total Assets 14,314
Debt 2,367
Other Liabilities 7,011 Total Liabilities 9,378 Shareholders' Equity 4,936
Notes:
Operating Lease Commitments
Yopatta is committed to making the following payments under noncancellable operating leases:
$ millions
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A)
B)
C)
Question #8 of 60
Question ID: 692276Year ended 31 December 2020 98 Years ending 31 December 202125 490
Longton assumes that all lease payments occur at the end of each year, and that the payments from 202125 are all equal. Yopatta recently went to the market and issued senior unsecured debt at a yield of 4%; Longton intends to apply this rate to capitalize the operating lease.
Longton believes that this recalculation is essential for all companies with operating leases as she believes that U.S. GAAP will very soon be updated to require the capitalization of all operating leases longer than one year. As a result, Longton will add the following comment on the impact on the income statement in her report:
Potential Accounting Policy Change
The requirement to capitalize operating leases will impact not only leverage ratios, but also coverage ratios based on the income statement. This lease capitalization will result in a decrease in operating profit, a decrease in interest expense, and a decrease in interest coverage ratios.
... Using the information in Exhibit 1 and Longton's stated method of calculation, Longton is most likely to conclude that Reddyfast's:
days of sales outstanding increased by approximately 28% between 2012 and 2014, possibly due to aggressive revenue recognition or quicker receivables collection.
days of sales outstanding increased by approximately 47% between 2012 and 2104, possibly due to aggressive revenue recognition or poor receivables management.
receivables turnover increased by approximately 21% between 2012 and 2014, possibly due to increased collection periods and aggressive revenue recognition policies.
Kay Longton, CFA, works as an equity analyst for BKJE Services, a small advisory firm Longton founded with three colleagues she previously worked with. BKJE offers a range of services to both institutional and retail investors and prides itself on its ability to service both clients with relatively shallow knowledge of the markets as well as experienced veterans.
Currently Longton is engaged with Coreblue, a buyside client that has recently seen a significant downturn in the performance of several of its actively managed funds. As recently as 2014, Coreblue was featured in lists highlighting the best performing funds, but recent poor performance has resulted in a 24% drop in assets under management. A thorough inhouse review revealed that several of the historically bestperforming investments in one of Coreblue's biggest funds had not been subject to the mandatory screening process. Three of these investments subsequently saw decreases in market capitalization of more than 40% and were responsible for more than 70% of the drop in the fund's active return.
Longton is currently reviewing the investments in question in order to report to Coreblue whether any warning signs were evident from the financial statements. Longton hopes this report will lead to a much bigger project for BKJE involving redesign of Coreblue's screening and analysis process.
The first company Longton is reviewing, Reddyfast, Inc., rose to prominence in 2012 when it promised to deliver custom built kitchen/dining room extensions in customer backyards, which could be built onsite in a day. Longton intends to include in her report the following extracts from Reddyfast's financial statements shown in Exhibit 1. Exhibit 1 Reddyfast Financial Statements and Notes (Extracts, $ '000s)
2012 2013 2014
Revenue 14,000 13,720 15,915
Cost of Goods Sold (11,340) (10,976) (12,891)
Gross Profit 2,660 2,744 3,024
Accounts Receivable (note 1) 1,789 1,907 2,610
Note 1 Accounts Receivable Securitization
In 2014, the company received $400,000 from the sale of accounts receivable. These balances are not shown in the accounts receivable figure in the balance sheet. An associated finance charge has been disclosed in operating profit for the year.
Longton has isolated these figures as she believes that analysis of the relationship between receivables and revenue should have revealed cause for concern over the period 20122014. She intends to restate the financial statements for accounts receivables sold, and then compute the trend in days of sales outstanding using endof year receivables.
Longton plans to make the following statements concerning inventory management trends that Coreblue should be on the lookout for: Statement 1
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Statement 2
Earnings are made up of a cash earnings component and an accrual earning component. The cash component of earnings is more persistent. A firm with a higher proportion of cash earnings will have a higher β in the following expression of earnings persistence:
Earnings = α + β(Earnings ) + ε
The second company in question is Ervington Boddan, Inc. (EB), a provider of heating solutions for recreational vehicles across the United States. Three board members of EB have also served as board members for Reddyfast since its inception in 2009.
Longton is concerned that EB's growth is fueled largely by income from associates, and, as a result, Longton intends to prepare a report showing the core ROE without including the results on such investments. In order to illustrate the driving forces behind ROE, she intends to perform a classic DuPont analysis that excludes the impact of associates from the margin and turnover ratios. One of Longton's interns has prepared the extracts shown in Exhibit 2 to assist with the analysis.
Exhibit 2 EB Financial Statements (Extracts, $ millions)
2014 2015 2016
Revenue 11,719 12,071 12,795
Cost of Goods Sold 9,243 9,502 10,357
Research and Development 80 78 76
Depreciation and Amortization 831 839 864
Other Operating Expenses 590 625 675
Total Expenses 10,744 11,045 11,973
Operating Profit 975 1,026 822
Finance Costs (178) (183) (194)
Finance Income 23 23 23
Income From Associates 56 63 94
Profit Before Tax 876 929 745
Tax (157) (159) (160)
Profit After Tax 719 770 585
NonControlling Interest (16) (16) (16)
Net Income Attributable to
Shareholders of Parent 703 754 568
Balance Sheet
NonCurrent Assets
PPE 8,120 8,193 8,203
Intangibles 982 980 992
Investment in Associates 1,733 1,890 2,014 Other NonCurrent Assets 1,013 1,102 1,712
Total NonCurrent Assets 11,848 12,165 12,921
Total Current Assets 3,245 3,345 3,354
Total Assets 15,093 15,510 16,275
Total Liabilities 10,678 10,899 11,010
Shareholders' Equity 4,415 4,611 5,265
The third company under review is Yopatta Solutions, Inc. The company provides marketing and advertising services to a variety of clients, promising to deliver a "one stop shop" solution for all client customer communication needs.
Due to the nature of its business, Yopatta (like its peers) has relatively few tangible assets on its balance sheet. However, on reviewing the notes to the balance sheet, Longton identifies that Yopatta has a significant operating lease commitment. Using endofyear reported balance sheet data, Longton calculated Yopatta's debtto equity ratio to be 48%. She now intends to restate the ratio after capitalizing operating lease commitments using the information in Exhibit 3.
Exhibit 3 Yopatta Leverage
Balance Sheet 31 Dec 2015 As Reported
NonCurrent Assets 7,892
Current Assets 6,422
Total Assets 14,314
Debt 2,367
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A) B) C)
Question #9 of 60
Question ID: 692277Debt 2,367
Other Liabilities 7,011 Total Liabilities 9,378 Shareholders' Equity 4,936
Notes:
Operating Lease Commitments
Yopatta is committed to making the following payments under noncancellable operating leases:
$ millions
Year ended 31 December 2016 148 Year ended 31 December 2017 148 Year ended 31 December 2018 148 Year ended 31 December 2019 148 Year ended 31 December 2020 98 Years ending 31 December 202125 490
Longton assumes that all lease payments occur at the end of each year, and that the payments from 202125 are all equal. Yopatta recently went to the market and issued senior unsecured debt at a yield of 4%; Longton intends to apply this rate to capitalize the operating lease.
Longton believes that this recalculation is essential for all companies with operating leases as she believes that U.S. GAAP will very soon be updated to require the capitalization of all operating leases longer than one year. As a result, Longton will add the following comment on the impact on the income statement in her report:
Potential Accounting Policy Change
The requirement to capitalize operating leases will impact not only leverage ratios, but also coverage ratios based on the income statement. This lease capitalization will result in a decrease in operating profit, a decrease in interest expense, and a decrease in interest coverage ratios.
... Statement 1 by Longton is most likely to be:
correct.
incorrect with respect to revenue. incorrect with respect to cash flows.
Kay Longton, CFA, works as an equity analyst for BKJE Services, a small advisory firm Longton founded with three colleagues she previously worked with. BKJE offers a range of services to both institutional and retail investors and prides itself on its ability to service both clients with relatively shallow knowledge of the markets as well as experienced veterans.
Currently Longton is engaged with Coreblue, a buyside client that has recently seen a significant downturn in the performance of several of its actively managed funds. As recently as 2014, Coreblue was featured in lists highlighting the best performing funds, but recent poor performance has resulted in a 24% drop in assets under management. A thorough inhouse review revealed that several of the historically bestperforming investments in one of Coreblue's biggest funds had not been subject to the mandatory screening process. Three of these investments subsequently saw decreases in market capitalization of more than 40% and were responsible for more than 70% of the drop in the fund's active return.
Longton is currently reviewing the investments in question in order to report to Coreblue whether any warning signs were evident from the financial statements. Longton hopes this report will lead to a much bigger project for BKJE involving redesign of Coreblue's screening and analysis process.
The first company Longton is reviewing, Reddyfast, Inc., rose to prominence in 2012 when it promised to deliver custom built kitchen/dining room extensions in customer backyards, which could be built onsite in a day. Longton intends to include in her report the following extracts from Reddyfast's financial statements shown in Exhibit 1. Exhibit 1 Reddyfast Financial Statements and Notes (Extracts, $ '000s)
2012 2013 2014
Revenue 14,000 13,720 15,915
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Gross Profit 2,660 2,744 3,024
Accounts Receivable (note 1) 1,789 1,907 2,610
Note 1 Accounts Receivable Securitization
In 2014, the company received $400,000 from the sale of accounts receivable. These balances are not shown in the accounts receivable figure in the balance sheet. An associated finance charge has been disclosed in operating profit for the year.
Longton has isolated these figures as she believes that analysis of the relationship between receivables and revenue should have revealed cause for concern over the period 20122014. She intends to restate the financial statements for accounts receivables sold, and then compute the trend in days of sales outstanding using endof year receivables.
Longton plans to make the following statements concerning inventory management trends that Coreblue should be on the lookout for: Statement 1
A substantial and unexpected increase in sales in the final quarter may be a sign that a company is using billandhold transactions to give a oneoff boost to revenue and cash flows toward the end of a period.
Statement 2
Earnings are made up of a cash earnings component and an accrual earning component. The cash component of earnings is more persistent. A firm with a higher proportion of cash earnings will have a higher β in the following expression of earnings persistence:
Earnings = α + β(Earnings ) + ε
The second company in question is Ervington Boddan, Inc. (EB), a provider of heating solutions for recreational vehicles across the United States. Three board members of EB have also served as board members for Reddyfast since its inception in 2009.
Longton is concerned that EB's growth is fueled largely by income from associates, and, as a result, Longton intends to prepare a report showing the core ROE without including the results on such investments. In order to illustrate the driving forces behind ROE, she intends to perform a classic DuPont analysis that excludes the impact of associates from the margin and turnover ratios. One of Longton's interns has prepared the extracts shown in Exhibit 2 to assist with the analysis.
Exhibit 2 EB Financial Statements (Extracts, $ millions)
2014 2015 2016
Revenue 11,719 12,071 12,795
Cost of Goods Sold 9,243 9,502 10,357
Research and Development 80 78 76
Depreciation and Amortization 831 839 864
Other Operating Expenses 590 625 675
Total Expenses 10,744 11,045 11,973
Operating Profit 975 1,026 822
Finance Costs (178) (183) (194)
Finance Income 23 23 23
Income From Associates 56 63 94
Profit Before Tax 876 929 745
Tax (157) (159) (160)
Profit After Tax 719 770 585
NonControlling Interest (16) (16) (16)
Net Income Attributable to
Shareholders of Parent 703 754 568
Balance Sheet
NonCurrent Assets
PPE 8,120 8,193 8,203
Intangibles 982 980 992
Investment in Associates 1,733 1,890 2,014 Other NonCurrent Assets 1,013 1,102 1,712
Total NonCurrent Assets 11,848 12,165 12,921
Total Current Assets 3,245 3,345 3,354
Total Assets 15,093 15,510 16,275
Total Liabilities 10,678 10,899 11,010
Shareholders' Equity 4,415 4,611 5,265
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A) B) C)
Question #10 of 60
Question ID: 692278Shareholders' Equity 4,415 4,611 5,265
The third company under review is Yopatta Solutions, Inc. The company provides marketing and advertising services to a variety of clients, promising to deliver a "one stop shop" solution for all client customer communication needs.
Due to the nature of its business, Yopatta (like its peers) has relatively few tangible assets on its balance sheet. However, on reviewing the notes to the balance sheet, Longton identifies that Yopatta has a significant operating lease commitment. Using endofyear reported balance sheet data, Longton calculated Yopatta's debtto equity ratio to be 48%. She now intends to restate the ratio after capitalizing operating lease commitments using the information in Exhibit 3.
Exhibit 3 Yopatta Leverage
Balance Sheet 31 Dec 2015 As Reported
NonCurrent Assets 7,892
Current Assets 6,422
Total Assets 14,314
Debt 2,367
Other Liabilities 7,011 Total Liabilities 9,378 Shareholders' Equity 4,936
Notes:
Operating Lease Commitments
Yopatta is committed to making the following payments under noncancellable operating leases:
$ millions
Year ended 31 December 2016 148 Year ended 31 December 2017 148 Year ended 31 December 2018 148 Year ended 31 December 2019 148 Year ended 31 December 2020 98 Years ending 31 December 202125 490
Longton assumes that all lease payments occur at the end of each year, and that the payments from 202125 are all equal. Yopatta recently went to the market and issued senior unsecured debt at a yield of 4%; Longton intends to apply this rate to capitalize the operating lease.
Longton believes that this recalculation is essential for all companies with operating leases as she believes that U.S. GAAP will very soon be updated to require the capitalization of all operating leases longer than one year. As a result, Longton will add the following comment on the impact on the income statement in her report:
Potential Accounting Policy Change
The requirement to capitalize operating leases will impact not only leverage ratios, but also coverage ratios based on the income statement. This lease capitalization will result in a decrease in operating profit, a decrease in interest expense, and a decrease in interest coverage ratios.
... Statement 2 by Longton is most likely to be:
correct.
incorrect, because accruals component of earnings is more persistent. incorrect, because in for formula given, a high α (not β) represents persistence.
Kay Longton, CFA, works as an equity analyst for BKJE Services, a small advisory firm Longton founded with three colleagues she previously worked with. BKJE offers a range of services to both institutional and retail investors and prides itself on its ability to service both clients with relatively shallow knowledge of the markets as well as experienced veterans.
9/29/2016
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19/69
As recently as 2014, Coreblue was featured in lists highlighting the best performing funds, but recent poor performance has resulted in a 24% drop in assets under management. A thorough inhouse review revealed that several of the historically bestperforming investments in one of Coreblue's biggest funds had not been subject to the mandatory screening process. Three of these investments subsequently saw decreases in market capitalization of more than 40% and were responsible for more than 70% of the drop in the fund's active return.
Longton is currently reviewing the investments in question in order to report to Coreblue whether any warning signs were evident from the financial statements. Longton hopes this report will lead to a much bigger project for BKJE involving redesign of Coreblue's screening and analysis process.
The first company Longton is reviewing, Reddyfast, Inc., rose to prominence in 2012 when it promised to deliver custom built kitchen/dining room extensions in customer backyards, which could be built onsite in a day. Longton intends to include in her report the following extracts from Reddyfast's financial statements shown in Exhibit 1. Exhibit 1 Reddyfast Financial Statements and Notes (Extracts, $ '000s)
2012 2013 2014
Revenue 14,000 13,720 15,915
Cost of Goods Sold (11,340) (10,976) (12,891)
Gross Profit 2,660 2,744 3,024
Accounts Receivable (note 1) 1,789 1,907 2,610
Note 1 Accounts Receivable Securitization
In 2014, the company received $400,000 from the sale of accounts receivable. These balances are not shown in the accounts receivable figure in the balance sheet. An associated finance charge has been disclosed in operating profit for the year.
Longton has isolated these figures as she believes that analysis of the relationship between receivables and revenue should have revealed cause for concern over the period 20122014. She intends to restate the financial statements for accounts receivables sold, and then compute the trend in days of sales outstanding using endof year receivables.
Longton plans to make the following statements concerning inventory management trends that Coreblue should be on the lookout for: Statement 1
A substantial and unexpected increase in sales in the final quarter may be a sign that a company is using billandhold transactions to give a oneoff boost to revenue and cash flows toward the end of a period.
Statement 2
Earnings are made up of a cash earnings component and an accrual earning component. The cash component of earnings is more persistent. A firm with a higher proportion of cash earnings will have a higher β in the following expression of earnings persistence:
Earnings = α + β(Earnings ) + ε
The second company in question is Ervington Boddan, Inc. (EB), a provider of heating solutions for recreational vehicles across the United States. Three board members of EB have also served as board members for Reddyfast since its inception in 2009.
Longton is concerned that EB's growth is fueled largely by income from associates, and, as a result, Longton intends to prepare a report showing the core ROE without including the results on such investments. In order to illustrate the driving forces behind ROE, she intends to perform a classic DuPont analysis that excludes the impact of associates from the margin and turnover ratios. One of Longton's interns has prepared the extracts shown in Exhibit 2 to assist with the analysis.
Exhibit 2 EB Financial Statements (Extracts, $ millions)
2014 2015 2016
Revenue 11,719 12,071 12,795
Cost of Goods Sold 9,243 9,502 10,357
Research and Development 80 78 76
Depreciation and Amortization 831 839 864
Other Operating Expenses 590 625 675
Total Expenses 10,744 11,045 11,973
Operating Profit 975 1,026 822
Finance Costs (178) (183) (194)
Finance Income 23 23 23
Income From Associates 56 63 94
Profit Before Tax 876 929 745
Tax (157) (159) (160)
Profit After Tax 719 770 585
NonControlling Interest (16) (16) (16)
Net Income Attributable to
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Net Income Attributable to
Shareholders of Parent 703 754 568
Balance Sheet
NonCurrent Assets
PPE 8,120 8,193 8,203
Intangibles 982 980 992
Investment in Associates 1,733 1,890 2,014 Other NonCurrent Assets 1,013 1,102 1,712
Total NonCurrent Assets 11,848 12,165 12,921
Total Current Assets 3,245 3,345 3,354
Total Assets 15,093 15,510 16,275
Total Liabilities 10,678 10,899 11,010
Shareholders' Equity 4,415 4,611 5,265
The third company under review is Yopatta Solutions, Inc. The company provides marketing and advertising services to a variety of clients, promising to deliver a "one stop shop" solution for all client customer communication needs.
Due to the nature of its business, Yopatta (like its peers) has relatively few tangible assets on its balance sheet. However, on reviewing the notes to the balance sheet, Longton identifies that Yopatta has a significant operating lease commitment. Using endofyear reported balance sheet data, Longton calculated Yopatta's debtto equity ratio to be 48%. She now intends to restate the ratio after capitalizing operating lease commitments using the information in Exhibit 3.
Exhibit 3 Yopatta Leverage
Balance Sheet 31 Dec 2015 As Reported
NonCurrent Assets 7,892
Current Assets 6,422
Total Assets 14,314
Debt 2,367
Other Liabilities 7,011 Total Liabilities 9,378 Shareholders' Equity 4,936
Notes:
Operating Lease Commitments
Yopatta is committed to making the following payments under noncancellable operating leases:
$ millions
Year ended 31 December 2016 148 Year ended 31 December 2017 148 Year ended 31 December 2018 148 Year ended 31 December 2019 148 Year ended 31 December 2020 98 Years ending 31 December 202125 490
Longton assumes that all lease payments occur at the end of each year, and that the payments from 202125 are all equal. Yopatta recently went to the market and issued senior unsecured debt at a yield of 4%; Longton intends to apply this rate to capitalize the operating lease.
Longton believes that this recalculation is essential for all companies with operating leases as she believes that U.S. GAAP will very soon be updated to require the capitalization of all operating leases longer than one year. As a result, Longton will add the following comment on the impact on the income statement in her report:
Potential Accounting Policy Change
The requirement to capitalize operating leases will impact not only leverage ratios, but also coverage ratios based on the income statement. This lease capitalization will result in a decrease in operating profit, a decrease in interest expense, and a decrease in interest coverage ratios.