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PLoS One. 2017 May 12;12(5):e0177071. doi: 10.1371/journal.pone.0177071. eCollection 2017.

Dynamics of analyst forecasts and emergence of complexity: Role of information disparity.

Author information

1
Computational Economics Laboratory, Computational Science Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.
2
Department of Economics, Seoul National University, Seoul 08826, Republic of Korea.
3
HSBC Business School, Peking University, University Town, Nanshan District, Shenzhen 518055, P.R. China.
4
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
5
Department of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, Seoul 08826, Republic of Korea.

Abstract

We report complex phenomena arising among financial analysts, who gather information and generate investment advice, and elucidate them with the help of a theoretical model. Understanding how analysts form their forecasts is important in better understanding the financial market. Carrying out big-data analysis of the analyst forecast data from I/B/E/S for nearly thirty years, we find skew distributions as evidence for emergence of complexity, and show how information asymmetry or disparity affects financial analysts' forming their forecasts. Here regulations, information dissemination throughout a fiscal year, and interactions among financial analysts are regarded as the proxy for a lower level of information disparity. It is found that financial analysts with better access to information display contrasting behaviors: a few analysts become bolder and issue forecasts independent of other forecasts while the majority of analysts issue more accurate forecasts and flock to each other. Main body of our sample of optimistic forecasts fits a log-normal distribution, with the tail displaying a power law. Based on the Yule process, we propose a model for the dynamics of issuing forecasts, incorporating interactions between analysts. Explaining nicely empirical data on analyst forecasts, this provides an appealing instance of understanding social phenomena in the perspective of complex systems.

PMID:
28498831
PMCID:
PMC5428939
DOI:
10.1371/journal.pone.0177071
[Indexed for MEDLINE]
Free PMC Article

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