Forecasting stock prices: Do forecasters herd?

Pierdzioch, Christian; Rülke, Jan-Christoph

doi:10.1016/j.econlet.2012.03.019

Cited by 26 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly to Pierdzioch and Rülke (2012), we have found that anti-herding has a negative influence on forecast accuracy indicating that anti-herding in the banking industry may reflect strategic interactions among forecasters not necessarily targeting towards the best outcome. Similarly to Pierdzioch and Rülke (2012), we have found that anti-herding has a negative influence on forecast accuracy indicating that anti-herding in the banking industry may reflect strategic interactions among forecasters not necessarily targeting towards the best outcome.…”

Section: Con Clus Ionsupporting

confidence: 59%

Effects of supplying additional information: Experimental evidence on the behavior of capital market experts

Hommel

Prokesch

Wohlenberg

et al. 2019

Futures & Foresight Science

View full text Add to dashboard Cite

Herding is extensively discussed in the literature, yet the results are so far inconclusive. The question of how additional information is processed remains unclear. This article presents the outcome of a novel two‐stage forecasting experiment that examines how capital market experts process ‘additional’ information. In the first stage, we test whether experts are engaging in herding or anti‐herding. In the second stage, we analyze how additional information is reflected in the experts' forecasting behavior in terms of hit rate, miscalibration, width of forecasting interval and confidence level. The paper delivers evidence on the presence of anti‐herding, which also tends to impact forecasting accuracy negatively. Moreover, additional information decreases the width of the forecast interval, leading to a lower hit rate. As a consequence, the level of overconfidence increases, while the level of forecasting confidence remains stable.

show abstract

Section: Con Clus Ionsupporting

confidence: 59%

Effects of supplying additional information: Experimental evidence on the behavior of capital market experts

Hommel

Prokesch

Wohlenberg

et al. 2019

Futures & Foresight Science

View full text Add to dashboard Cite

show abstract

“…Such differences may arise in terms of the rationality of forecasts, but also in terms of, for example, (rational) forecaster anti-herding (Pierdzioch & Rülke, 2012) and the shape of the loss function (Aretz et al, 2011). Although aggregate stock market forecasts allow important aspects of the REH to be analysed in terms of a "consensus forecast," aggregate stock market forecasts may also cloud important differences across individual forecasters.…”

Section: Discussionmentioning

confidence: 99%

“…Lakonishok (1980) reports that stock market forecasts from the Livingston Survey are not efficient and that forecasts do not outperform naive benchmark models. Pierdzioch and Rülke (2012) report evidence of forecaster anti-herding, and Aretz, Bartram, and Pope (2011) find that assuming an asymmetric loss function weakens evidence against rationality of stock market forecasts. In more recent research, Söderlind (2010) reports that forecasts do not outperform the historical mean or small-scale regression models using the dividend yield or the T-bill rate as predictors.…”

Section: Introductionmentioning

confidence: 99%

A machine‐learning analysis of the rationality of aggregate stock market forecasts

Pierdzioch

Risse

2018

Int. J Fin Econ

Self Cite

View full text Add to dashboard Cite

We use a machine‐learning algorithm known as boosted regression trees (BRT) to implement an orthogonality test of the rationality of aggregate stock market forecasts. The BRT algorithm endogenously selects the predictor variables used to proxy the information set of forecasters so as to maximize the predictive power for the forecast error. The BRT algorithm also accounts for a potential non‐linear dependence of the forecast error on the predictor variables and for interdependencies between the predictor variables. Our main finding is that, given our set of predictor variables, the rational expectations hypothesis (REH) cannot be rejected for short‐term forecasts and that there is evidence against the REH for longer term forecasts. Results for three different groups of forecasters corroborate our main finding.

show abstract

“…We use the test for herding of Bernhardt et al (2006), which was originally applied to assess the behaviour of professional financial analysts, but has recently been applied more widely, including to macroeconomic forecasting (see, among others, Pierdzioch et al, 2010;Pierdzioch and Rülke, 2012). This approach often suggests anti-herding: that forecasters exaggerate their differences.…”

Section: Discussionmentioning

confidence: 99%

Do US Macroeconomic Forecasters Exaggerate their Differences?

Clements

2015

Journal of Forecasting

View full text Add to dashboard Cite

Application of the Bernhardt et al. (Journal of Financial Economics 2006; 80(3): 657–675) test of herding to the calendar‐year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon, when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Forecasting stock prices: Do forecasters herd?

Cited by 26 publications

References 14 publications

Effects of supplying additional information: Experimental evidence on the behavior of capital market experts

Effects of supplying additional information: Experimental evidence on the behavior of capital market experts

A machine‐learning analysis of the rationality of aggregate stock market forecasts

Do US Macroeconomic Forecasters Exaggerate their Differences?

Contact Info

Product

Resources

About