新消息就是坏消息

Paul Glasserman, Harry Mamaysky, Jimmy Qin
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引用次数: 0

摘要

新闻新颖性的增加预示着明年股市的负回报和负面的宏观经济结果。我们量化新闻新颖性-新闻文本分布的变化-通过熵度量,使用应用于大型新闻语料库的递归神经网络计算。熵比一组标准指标更能预测市场回报。横截面熵暴露具有负风险溢价,表明与熵正协变的资产对冲了与新闻语言变化相关的总风险。熵风险不能用现有的多空因素来解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New News is Bad News
An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors.
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