Estimating analyst's forecast accuracy using behavioural measures (Herding) in the United Kingdom

Purpose - The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts.Design methodology approach - Two alternative measures of herding behaviour, on analysts' earnings forecasts ar...

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Published in:Managerial finance Vol. 36; no. 3; pp. 234 - 256
Main Authors: Salamouris, Ioannis S, Gulnur Muradoglu, Yaz
Format: Journal Article
Language:English
Published: Patrington Emerald Group Publishing Limited 23-02-2010
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Abstract Purpose - The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts.Design methodology approach - Two alternative measures of herding behaviour, on analysts' earnings forecasts are proposed. The first measure identifies herding as the tendency of analysts to forecast near the consensus. The second measure identifies herding as the tendency of analysts to follow the most accurate forecaster. This paper employs the method of The Generalised Method of Moments in order to relax any possible biases.Findings - In both measures employed, a positive and significant relation is found between the accuracy of analysts' earnings forecasts and herding behaviour. According to the first measure analysts exhibit herding behaviour by forecasting close to the consensus estimates. According the second herding measure, it is found that analysts tend to herd towards the best forecaster at the time. Finally, it is concluded that the accuracy of analysts' forecasts increases as herding increases.Research limitations implications - The present study triggers concerns for further research in the modelling of analysts' forecasting behaviour.Originality value - This paper proposes that a measure based on human biases is the best way to estimate and predict the analysts' earnings forecast future accuracy.
AbstractList The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts. Two alternative measures of herding behaviour, on analysts' earnings forecasts are proposed. The first measure identifies herding as the tendency of analysts to forecast near the consensus. The second measure identifies herding as the tendency of analysts to follow the most accurate forecaster. This paper employs the method of The Generalised Method of Moments in order to relax any possible biases. In both measures employed, a positive and significant relation is found between the accuracy of analysts' earnings forecasts and herding behaviour. According to the first measure analysts exhibit herding behaviour by forecasting close to the consensus estimates. According the second herding measure, it is found that analysts tend to herd towards the best forecaster at the time. Finally, it is concluded that the accuracy of analysts' forecasts increases as herding increases. The present study triggers concerns for further research in the modelling of analysts' forecasting behaviour. This paper proposes that a measure based on human biases is the best way to estimate and predict the analysts' earnings forecast future accuracy.
Purpose - The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings forecasts.Design methodology approach - Two alternative measures of herding behaviour, on analysts' earnings forecasts are proposed. The first measure identifies herding as the tendency of analysts to forecast near the consensus. The second measure identifies herding as the tendency of analysts to follow the most accurate forecaster. This paper employs the method of The Generalised Method of Moments in order to relax any possible biases.Findings - In both measures employed, a positive and significant relation is found between the accuracy of analysts' earnings forecasts and herding behaviour. According to the first measure analysts exhibit herding behaviour by forecasting close to the consensus estimates. According the second herding measure, it is found that analysts tend to herd towards the best forecaster at the time. Finally, it is concluded that the accuracy of analysts' forecasts increases as herding increases.Research limitations implications - The present study triggers concerns for further research in the modelling of analysts' forecasting behaviour.Originality value - This paper proposes that a measure based on human biases is the best way to estimate and predict the analysts' earnings forecast future accuracy.
Author Salamouris, Ioannis S
Gulnur Muradoglu, Yaz
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  organization: Cass Business School, City University, London, UK
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Snippet Purpose - The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts'...
The purpose of this paper is to identify herding behaviour on financial markets and measure the herding behaviour impact on the accuracy of analysts' earnings...
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StartPage 234
SubjectTerms Accuracy
Behavior
Decision making
Earnings
Earnings forecasting
Financial analysis
Peers
Reputations
Studies
Title Estimating analyst's forecast accuracy using behavioural measures (Herding) in the United Kingdom
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