• Media type: E-Book
  • Title: Stock Market Alphas Help Predict Macroeconomic Innovations
  • Contributor: Yeh, Andy [Author]
  • Published: [S.l.]: SSRN, [2021]
  • Extent: 1 Online-Ressource (73 p)
  • Language: English
  • DOI: 10.2139/ssrn.3938357
  • Identifier:
  • Keywords: fama-french factor models ; vector auto-regressions ; granger causation tests ; dynamic conditional alphas ; macroeconomic innovations ; and asset return anomalies
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 9, 2021 erstellt
  • Description: 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
  • Access State: Open Access