NDLI: Financial Analysts' Forecast Accuracy and Dispersion: High-Tech versus Low-Tech Stocks

Content Provider	Springer Nature Link
Author	Kwon, Sung S.
Copyright Year	2002
Abstract	This study focuses on systematic differences in security analysts' forecast accuracy and dispersion between high- and low-tech firms. In line with the recent development in theoretical models and empirical findings, it posits that security analysts' unsigned forecast error and forecast dispersion are expected to differ between high-tech and low-tech firms. The results of this study provide evidence of lower unsigned error and dispersion for high-tech firms vis-à-vis low-tech firms. The higher forecast accuracy and forecast convergence for high-tech firms relative to low-tech firms in financial analysts' forecasts of earnings can be attributed to the information effect prevailing over the noise effect. Given the lack of empirical studies that compare analysts' forecast accuracy and dispersion between high-tech and low-tech sectors, the results of this paper provide a fresh basis for assessing how market participants vary in their treatment of New Economy stocks and factors that affect such decisions. In the light of the fact that the 1990s is a period characterized by the start of the Information Revolution through the Internet, the results of this study shed light on the usefulness of examining factors that differentiate between high-tech firms (New Economy stocks) and low-tech firms (Old Economy stocks) in financial analysts' forecasting earnings.
Starting Page	65
Ending Page	91
Page Count	27
File Format	PDF
ISSN	0924865X
Journal	Review of Quantitative Finance and Accounting
Volume Number	19
Issue Number	1
e-ISSN	15737179
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2002-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Econometrics Accounting/Auditing Finance /Banking Operation Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Finance Accounting

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