Equity‐premium prediction: Attention is all you need
Luiz Renato Lima and
Lucas Lúcio Godeiro
Journal of Applied Econometrics, 2023, vol. 38, issue 1, 105-122
Abstract:
Predictions of stock returns are greatly improved relative to low‐dimensional forecasting regressions when the forecasts are based on the estimated factor of large data sets, also known as the diffusion index (DI) model. However, when applied to text data, DI models do not perform well. This paper shows that by simply using text data in a DI model does not improve equity‐premium forecasts over the naive historical‐average model, but substantial gains are obtained when one selects the most predictive words before computing the factors and allows the dictionary to be updated over time.
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1002/jae.2939
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:38:y:2023:i:1:p:105-122
Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252
Access Statistics for this article
Journal of Applied Econometrics is currently edited by M. Hashem Pesaran
More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().