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Analyst network centrality, forecast accuracy, and persistent influence

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  • Yang Bai
  • Zhehao Zhang
  • Tingting Chen
  • Wenyan Peng

Abstract

This paper explores how analysts’ forecasting behaviour is related to their centrality within a dynamic information network. In this network, analysts who issued coverage reports on the same listed firms in clusters are connected. The social learning hypothesis and social capital theory suggest that financial analysts could learn from other analyst forecasts and obtain information from analyst reports. Employing a dynamic complex network methodology, we focus on analysts’ network centrality – degree, betweenness, and closeness – to represent their information access based on a sample of 819,539 analyst forecasts in the Chinese A-share market from 2018 to 2022. Our findings suggest that analysts with more central positions in the network produce more accurate earnings-per-share forecasts and have a longer persistent influence on other analysts. Our results support the perspective that the diffusion of information among analysts affects their forecasts and reporting behaviour.

Suggested Citation

  • Yang Bai & Zhehao Zhang & Tingting Chen & Wenyan Peng, 2024. "Analyst network centrality, forecast accuracy, and persistent influence," Applied Economics, Taylor & Francis Journals, vol. 56(52), pages 6667-6689, November.
    • Handle: RePEc:taf:applec:v:56:y:2024:i:52:p:6667-6689
    DOI: 10.1080/00036846.2024.2394702
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