“检查超额收益可预测性的来源:随机波动还是市场无效?”

Kevin J. Lansing, Stephen F. LeRoy, Jun Ma
{"title":"“检查超额收益可预测性的来源:随机波动还是市场无效?”","authors":"Kevin J. Lansing, Stephen F. LeRoy, Jun Ma","doi":"10.24148/WP2018-14","DOIUrl":null,"url":null,"abstract":"We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of model variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. From an empirical perspective, we investigate whether 1-month ahead excess returns on stocks can be predicted using measures of consumer sentiment and excess return momentum, while controlling directly and indirectly for the presence of stochastic volatility. A variable that interacts the 12-month sentiment change with recent return momentum is a robust predictor of excess stock returns both in-sample and out-of-sample. The predictive power of this variable derives mainly from periods when sentiment has been declining and return momentum is negative, forecasting a further decline in the excess stock return. We show that the sentiment-momentum variable is positively correlated with fluctuations in Google searches for the term ?stock market,? suggesting that the sentiment-momentum variable helps to predict excess returns because it captures shifts in investor attention, particularly during stock market declines.","PeriodicalId":250744,"journal":{"name":"Federal Reserve Bank of San Francisco, Working Paper Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"\\\"Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?\\\"\",\"authors\":\"Kevin J. Lansing, Stephen F. LeRoy, Jun Ma\",\"doi\":\"10.24148/WP2018-14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of model variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. From an empirical perspective, we investigate whether 1-month ahead excess returns on stocks can be predicted using measures of consumer sentiment and excess return momentum, while controlling directly and indirectly for the presence of stochastic volatility. A variable that interacts the 12-month sentiment change with recent return momentum is a robust predictor of excess stock returns both in-sample and out-of-sample. The predictive power of this variable derives mainly from periods when sentiment has been declining and return momentum is negative, forecasting a further decline in the excess stock return. We show that the sentiment-momentum variable is positively correlated with fluctuations in Google searches for the term ?stock market,? suggesting that the sentiment-momentum variable helps to predict excess returns because it captures shifts in investor attention, particularly during stock market declines.\",\"PeriodicalId\":250744,\"journal\":{\"name\":\"Federal Reserve Bank of San Francisco, Working Paper Series\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Federal Reserve Bank of San Francisco, Working Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24148/WP2018-14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Federal Reserve Bank of San Francisco, Working Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24148/WP2018-14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

我们使用基于消费的资产定价模型来表明,风险资产超额回报的可预测性只能来自两个来源:(1)模型变量的随机波动,或(2)偏离理性预期,从而导致可预测的投资者预测误差和市场效率低下。从实证的角度来看,我们研究了在直接和间接控制随机波动的情况下,是否可以使用消费者情绪和超额回报动量来预测股票1个月前的超额回报。一个影响12个月情绪变化与近期回报动量的变量是样本内和样本外股票超额回报的有力预测指标。这一变量的预测能力主要来自于市场情绪下降和回报动量为负的时期,预测超额股票回报将进一步下降。我们表明,情绪动量变量与谷歌搜索“股票市场”一词的波动呈正相关。这表明情绪动量变量有助于预测超额回报,因为它捕捉到了投资者注意力的变化,尤其是在股市下跌期间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?"
We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of model variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. From an empirical perspective, we investigate whether 1-month ahead excess returns on stocks can be predicted using measures of consumer sentiment and excess return momentum, while controlling directly and indirectly for the presence of stochastic volatility. A variable that interacts the 12-month sentiment change with recent return momentum is a robust predictor of excess stock returns both in-sample and out-of-sample. The predictive power of this variable derives mainly from periods when sentiment has been declining and return momentum is negative, forecasting a further decline in the excess stock return. We show that the sentiment-momentum variable is positively correlated with fluctuations in Google searches for the term ?stock market,? suggesting that the sentiment-momentum variable helps to predict excess returns because it captures shifts in investor attention, particularly during stock market declines.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信