MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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When considering the future performance of a company, financial analysts often use forecasts for earnings per share (EPS). Naturally, we are interested in the quality of these forecasts. We can define a forecasting error as follows:

  • Forecasting Error = Predicted Value of the Variable – The Actual Value of the Variable.

The optimal forecast would have a mean forecasting error of zero. This suggests that, on average, the predicted value is equal to the actual value. Therefore, we construct a hypothesis test to see if the mean forecasting error is equal to zero. You have collected data, as shown in the picture below, for two analysts covering different industries. Analyst A covers the pharmaceutical sector; Analyst B covers the retail sector.

Please complete the following tasks (1–3) and provide an answer to the following question (4):

  1. Define μ as the population mean forecasting error and formulate the null and alternative hypothesis for a zero mean test of forecasting quality.
  2. For Analyst A, use both a t-test and a z-test to determine whether to reject the null hypothesis at the 0.05 and 0.01 levels of significance.
  3. For Analyst B, use both a t-test and a z-test to determine whether to reject the null at the 0.05 and 0.01 levels of significance.
  4. What conclusions do you reach about the forecasts for Analyst A and the forecasts for Analyst B?
Financial Analyst A
Financial Analyst B
Number of Forecasts
201
121
Mean Forecast Error
(Predicted - Actual)
0.06
0.01
Standard Deviations of
Forecast Errors
0.12
0.11
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Transcribed Image Text:Financial Analyst A Financial Analyst B Number of Forecasts 201 121 Mean Forecast Error (Predicted - Actual) 0.06 0.01 Standard Deviations of Forecast Errors 0.12 0.11
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