预测公司利润:从法马-麦克白到梯度提升

Murray Z. Frank, Keer Yang
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引用次数: 1

摘要

本文利用Fama-MacBeth回归和梯度提升方法研究了企业利润的可预测性。梯度增强可以使用更多相关因素,预测效果更好。大型、投资级、低研发、低市净率、低现金流波动性的公司利润更可预测。对融资决策的影响和股票收益的横截面进行了研究。在经济衰退期间,利润难以预测——尤其是非投资级公司。这两种算法产生的估计,就像那些在文献中被解释为人类在繁荣时期过度乐观、在衰退时期过度悲观的证据一样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Firm Profits: From Fama-MacBeth to Gradient Boosting
This paper studies the predictability of firm profits using Fama-MacBeth regressions and gradient boosting. Gradient boosting can use more relevant factors and it predicts better. Profits are more predictable at firms that are large, investment grade, low R&D, low market-to-book, low cash flow volatility. Effects on financing decisions, and cross-section of stock returns are studied. During recessions profits are less predictable - particularly non-investment grade firms. Both algorithms produce estimates like those interpreted in the literature as evidence of excessive human optimism during booms and excessive pessimism during recessions.
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