The Use of Earnings Forecasts in Stock Recommendations: Are Accurate Analysts More Consistent?
研究了分析师在准确与乐观的激励冲突下，如何选择使用严谨估值模型或增长启发法生成股票推荐，发现最准确的分析师更少依赖乐观增长模型，其推荐与估值模型更一致，且能带来更高未来回报。
Abstract: We examine how analysts’ conflicting incentives to be either accurate or optimistic affect their choice to generate stock recommendations with rigorous valuation models or growth‐based heuristics. Consistent with prior research the average analyst recommendation is negatively associated with rigorous valuation models and positively associated with growth‐based heuristics, we document that these associations are weakest for the most accurate analysts and strongest for the least accurate analysts. We also find evidence consistent with consistency between recommendations and valuation models underlying the positive future returns from trading on the most accurate analysts’ recommendations. Our results are consistent with reputation incentives to be accurate mitigating the use of optimistic growth‐based models in generating stock recommendations.