Evaluating the information content of earnings forecasts
提出一个框架比较不同盈利预测方法捕捉市场预期的能力，发现基于回归的模型满足充分性条件，并利用该模型识别市场过度悲观的股票，买入持有可获得显著超额收益。
This study develops a framework to compare the ability of alternative earnings forecast approaches to capture the market expectation of future earnings. Given prior evidence of analysts’ systematic optimistic bias, we decompose earnings surprises into analysts’ earnings surprises and adjustments based on alternative forecasting models. An equal market response to these two components indicates that the associated earnings forecast is a sufficient estimate of the market expectation of future earnings. To apply our framework, we examine four recent regression-based earnings forecasting models, alongside a simple earnings-based random walk model and analysts’ forecasts. Using the earnings forecasts of the model that satisfies our sufficiency condition, we identify a set of stocks for which the market is unduly pessimistic about future earnings. The investment strategy of buying and holding these stocks generates statistically significant abnormal returns. We offer an explanation as to why this and similar strategies might be successful.