Predictability in Financial Analyst Forecast Errors: Learning or Irrationality?
提出理性学习解释分析师盈利预测误差的可预测性，通过模拟和真实数据表明序列相关模式更符合理性学习而非非理性行为。
ABSTRACT In this paper, we propose a rational learning‐based explanation for the predictability in financial analysts' earnings forecast errors documented in prior literature. In particular, we argue that the serial correlation pattern in analysts' quarterly earnings forecast errors is consistent with an environment in which analysts face parameter uncertainty and learn rationally about the parameters over time. Using simulations and real data, we show that the predictability evidence is more consistent with rational learning than with irrationality (fixation on a seasonal random walk model or some other dogmatic belief).