Stock market alphas help predict macroeconomic innovations

被引:0
|
作者
Hung, Mao-Wei [1 ]
Yeh, Andy Jia-Yuh [2 ,3 ]
机构
[1] Natl Taiwan Univ, Grad Inst Int Business, Taipei City, Taiwan
[2] Natl Taiwan Univ, Grad Inst Finance, Taipei City, Taiwan
[3] Brass Ring Int Dens Enterprise, Hong Kong, Peoples R China
关键词
Fama-French multi-factor models; vector autoregressions; Granger causation tests; dynamic conditional alphas; macroeconomic innovations; asset return anomalies; BOOK-TO-MARKET; ASSET PRICE DYNAMICS; CROSS-SECTION; RISK-FACTORS; CORPORATE-INVESTMENT; CONDITIONAL CAPM; GROWTH OPTIONS; LONG-RUN; MOMENTUM; RETURNS;
D O I
10.1017/S1365100523000184
中图分类号
F [经济];
学科分类号
02 ;
摘要
We extract dynamic conditional factor premiums from the Fama-French factor model and find that most anomalies disappear after one accounts for time variation in these premiums. Vector autoregression evidence shows that mutual causation between dynamic conditional alphas and macroeconomic surprises serves as a core qualifying condition for fundamental factor selection. This economic insight is an incremental step toward drawing a distinction between rational risk and behavioral mispricing models. To the extent that dynamic conditional alphas can reveal the marginal investor's fundamental news and expectations about the cross-section of average asset returns, our economic insight helps enrich macroeconomic asset return prediction.
引用
收藏
页码:612 / 646
页数:35
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