A Multivariate Analysis Of Earnings Forecasts Generated By Financial Analysts And Univariate Time Series Models
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A Multivariate Analysis of Earnings Forecasts Generated by Financial Analysts and Univariate Time Series Models
The study provides evidence on the relative accuracy of forecasts of earnings generated from five sources including statistical models and financial analysts. The statistical models were chosen on the basis of their usage in recent studies in the literature. The results indicate that the five types of forecasts are not significantly different using a multivariate testing procedure.
A Multivariate Analysis of Annual Earnings Forecasts Generated from Quarterly Forecasts of Financial Analysts and Univariate Time Series Models
The study compares the forecast accuracy of financial analysts, ARIMA models, and various permier models considered in the literature in the predicting of annual earnings per share. Various refinements were made of previously used methodologies. The results of the multivariate analysis indicated that financial analysts provide the most accurate forecasts. In addition, the divergence in accuracy between the various sources of forecasts tend to decrease as the end of the year approaches, while at the same time there is a general increase in accuracy. Also specific results are provided for individual model performance.
ACRN Proceedings in Finance and Risk Series ‘13
Author: Dr. Othmar M. Lehner
language: en
Publisher: ACRN Publishing House
Release Date: 2014-03-06
Proceedings of the 14th FRAP Finance, Risk and Accounting Perspectives conference taking place in Cambridge UK.