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Chapter 12: 74E (page 756)

Buy-side vs. sell-side analysts鈥� earnings forecasts. Refer to the Financial Analysts Journal (July/August 2008) comparison of earnings forecasts of buy-side and sell-side analysts, Exercise 2.86 (p. 112). The Harvard Business School professors used regression to model the relative optimism (y) of the analysts鈥� 3-month horizon forecasts. One of the independent variables used to model forecast optimism was the dummy variable x = {1 if the analyst worked for a buy-side firm, 0 if the analyst worked for a sell-side firm}.

a) Write the equation of the model for E(y) as a function of type of firm.

b) Interpret the value of尾0in the model, part a.

c) The professors write that the value of尾1in the model, part a, 鈥渞epresents the mean difference in relative forecast optimism between buy-side and sell-side analysts.鈥� Do you agree?

d) The professors also argue that 鈥渋f buy-side analysts make less optimistic forecasts than their sell-side counterparts, the [estimated value of尾1] will be negative.鈥� Do you agree?

Short Answer

Expert verified

a) A dummy variable model with one qualitative independent variable can be written as Ey=尾0+尾1x1where x1represents the type of firm.

b) 尾0represents the value of relative optimism (y) at the base level which here is if the analysts worked for a sell-side firm. 尾1represents the changes in the value of relative optimism (y) due to analysts working in the buy-side firms.

c) The value of 尾1 represents the mean difference in relative forecast optimism between buy-side and sell-side analysts. The base-level here is analyst representing sell-side firms hence for x1= 1 the coefficient for buy-side firms will be (尾0+尾1). Therefore, 尾1essentially represents the mean difference between the two levels.

d) If buy-side analysts, make less optimistic forecasts than the sell-side analysts then the mean difference between the尾0 and 尾1will be less and the value of 尾1will be negative.

Step by step solution

01

Dummy variable model

A dummy variable model with one qualitative independent variable can be written as Ey=尾0+尾1x1where x1represents the type of firm.

02

Interpretation of β

尾0represents the value of relative optimism (y) at the base level which here is if the analysts worked for a sell-side firm

尾1represents the changes in the value of relative optimism (y) due to analysts working in the buy-side firms.

03

Interpretation of β1

The value of 尾1 represents the mean difference in relative forecast optimism between buy-side and sell-side analysts. The base-level here is analyst representing sell-side firms hence for x1= 1the coefficient for buy-side firms will be (尾0+尾1 ). Therefore,尾1essentially represents the mean difference between the two levels.

04

Analysis of β1

If buy-side analysts, make less optimistic forecasts than the sell-side analysts then the mean difference between the尾0and 尾1will be less and the value of 尾1will be negative

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Most popular questions from this chapter

Suppose you used Minitab to fit the model y=尾0+尾1x1+尾2x2+蔚

to n = 15 data points and obtained the printout shown below.

  1. What is the least squares prediction equation?

  2. Find R2and interpret its value.

  3. Is there sufficient evidence to indicate that the model is useful for predicting y? Conduct an F-test using 伪 = .05.

  4. Test the null hypothesis H0: 尾1= 0 against the alternative hypothesis Ha: 尾1鈮� 0. Test using 伪 = .05. Draw the appropriate conclusions.

  5. Find the standard deviation of the regression model and interpret it.

Question: Reality TV and cosmetic surgery. Refer to the Body Image: An International Journal of Research (March 2010) study of the impact of reality TV shows on one鈥檚 desire to undergo cosmetic surgery, Exercise 12.17 (p. 725). Recall that psychologists used multiple regression to model desire to have cosmetic surgery (y) as a function of gender(x1) , self-esteem(x2) , body satisfaction(x3) , and impression of reality TV (x4). The SPSS printout below shows a confidence interval for E(y) for each of the first five students in the study.

  1. Interpret the confidence interval for E(y) for student 1.
  2. Interpret the confidence interval for E(y) for student 4

Suppose you fit the second-order model y=尾0+尾1x+尾2x2+蔚to n = 25 data points. Your estimate of尾2is尾^2= 0.47, and the estimated standard error of the estimate is 0.15.

  1. TestH0:尾2=0againstHa:尾2鈮�0. Use伪=0.05.
  2. Suppose you want to determine only whether the quadratic curve opens upward; that is, as x increases, the slope of the curve increases. Give the test statistic and the rejection region for the test for伪=0.05. Do the data support the theory that the slope of the curve increases as x increases? Explain.

Question: Shared leadership in airplane crews. Refer to the Human Factors (March 2014) study of shared leadership by the cockpit and cabin crews of a commercial airplane, Exercise 8.14 (p. 466). Recall that simulated flights were taken by 84 six-person crews, where each crew consisted of a 2-person cockpit (captain and first officer) and a 4-person cabin team (three flight attendants and a purser.) During the simulation, smoke appeared in the cabin and the reactions of the crew were monitored for teamwork. One key variable in the study was the team goal attainment score, measured on a 0 to 60-point scale. Multiple regression analysis was used to model team goal attainment (y) as a function of the independent variables job experience of purser (x1), job experience of head flight attendant (x2), gender of purser (x3), gender of head flight attendant (x4), leadership score of purser (x5), and leadership score of head flight attendant (x6).

a. Write a complete, first-order model for E(y) as a function of the six independent variables.

b. Consider a test of whether the leadership score of either the purser or the head flight attendant (or both) is statistically useful for predicting team goal attainment. Give the null and alternative hypotheses as well as the reduced model for this test.

c. The two models were fit to the data for the n = 60 successful cabin crews with the following results: R2 = .02 for reduced model, R2 = .25 for complete model. On the basis of this information only, give your opinion regarding the null hypothesis for successful cabin crews.

d. The p-value of the subset F-test for comparing the two models for successful cabin crews was reported in the article as p 6 .05. Formally test the null hypothesis using 伪 = .05. What do you conclude?

e. The two models were also fit to the data for the n = 24 unsuccessful cabin crews with the following results: R2 = .14 for reduced model, R2 = .15 for complete model. On the basis of this information only, give your opinion regarding the null hypothesis for unsuccessful cabin crews.

f. The p-value of the subset F-test for comparing the two models for unsuccessful cabin crews was reported in the article as p < .10. Formally test the null hypothesis using 伪 = .05. What do you conclude?

Question: Manipulating rates of return with stock splits. Some firms have been accused of using stock splits to manipulate their stock prices before being acquired by another firm. An article in Financial Management (Winter 2008) investigated the impact of stock splits on long-run stock performance for acquiring firms. A simplified version of the model fit by the researchers follows:

E(y)=尾0+尾1x1+尾2x2+尾3x1x2

where

y = Firm鈥檚 3-year buy-and-hold return rate (%)

x1 = {1 if stock split prior to acquisition, 0 if not}

x2 = {1 if firm鈥檚 discretionary accrual is high, 0 if discretionary accrual is low}

a. In terms of the 尾鈥檚 in the model, what is the mean buy and- hold return rate (BAR) for a firm with no stock split and a high discretionary accrual (DA)?

b. In terms of the 尾鈥檚 in the model, what is the mean BAR for a firm with no stock split and a low DA?

c. For firms with no stock split, find the difference between the mean BAR for firms with high and low DA. (Hint: Use your answers to parts a and b.)

d. Repeat part c for firms with a stock split.

e. Note that the differences, parts c and d, are not the same. Explain why this illustrates the notion of interaction between x1 and x2.

f. A test for H0: 尾3 = 0 yielded a p-value of 0.027. Using 伪 = .05, interpret this result.

g. The researchers reported that the estimated values of both 尾2 and 尾3 are negative. Consequently, they conclude that 鈥渉igh-DA acquirers perform worse compared with low-DA acquirers. Moreover, the underperformance is even greater if high-DA acquirers have a stock split before acquisition.鈥� Do you agree?
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