When considering the future performance of a company, financial analysts often use forecasts for earnings per...

 

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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 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 u 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 ztest 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? 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 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 u 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 ztest 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?

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1 Answer Explanation The null hypothesis is defined as the mean forecast error is zero and the alternative hypothesis tests the claim that the mean fo... View the full answer