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An investigation on impacts of structural changes in stocks’ past returns on financial analysts’ earnings forecasting rationality

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  • Zhixin Kang

Abstract

Purpose - The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns. Design/methodology/approach - By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes. Findings - The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period. Originality/value - This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.

Suggested Citation

  • Zhixin Kang, 2019. "An investigation on impacts of structural changes in stocks’ past returns on financial analysts’ earnings forecasting rationality," Journal of Capital Markets Studies, Emerald Group Publishing Limited, vol. 3(2), pages 188-202, October.
  • Handle: RePEc:eme:jcmspp:jcms-06-2019-0033
    DOI: 10.1108/JCMS-06-2019-0033
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