英文标题：
《News Co-Occurrence, Attention Spillover and Return Predictability》
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作者：
Li Guo and Lin Peng and Yubo Tao and Jun Tu
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最新提交年份：
2018
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英文摘要：
We examine the effect of investor attention spillover on stock return predictability. Using a novel measure, the News Network Triggered Attention index (NNTA), we find that NNTA negatively predicts market returns with a monthly in(out)-of-sample R-square of 5.97% (5.80%). In the cross-section, a long-short portfolio based on news co-occurrence generates a significant monthly alpha of 68 basis points. The results are robust to the inclusion of alternative attention proxies, sentiment measures, other news- and information-based predictors, across recession and expansion periods. We further validate the attention spillover effect by showing that news co-mentioning leads to greater increases in Google and Bloomberg search volumes than unconditional news coverage. Our findings suggest that attention spillover in a news-based network can lead to significant stock market overvaluations, and especially when arbitrage is limited.
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中文摘要：
我们检验了投资者注意力溢出对股票收益可预测性的影响。使用一种新的测量方法，即新闻网触发注意力指数（NNTA），我们发现NNTA对市场回报率的预测为负，每月样本内（外）R平方为5.97%（5.80%）。在横截面上，基于新闻共现的多空投资组合每月产生68个基点的显著阿尔法。在经济衰退和经济扩张期间，研究结果对替代性注意力指标、情绪测量、其他基于新闻和信息的预测指标的纳入非常可靠。我们进一步验证了注意力溢出效应，表明新闻联名比无条件新闻报道更能增加谷歌和彭博社的搜索量。我们的研究结果表明，在一个以新闻为基础的网络中，注意力溢出会导致股市大幅高估，尤其是在套利有限的情况下。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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