Equity-premium prediction: Attention is all you need
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AKZSIXUUX" target="_blank" >RIV/00216208:11320/23:KZSIXUUX - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138695644&doi=10.1002%2fjae.2939&partnerID=40&md5=529ff81ac37609c3fb4444360ae17fa2" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138695644&doi=10.1002%2fjae.2939&partnerID=40&md5=529ff81ac37609c3fb4444360ae17fa2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/jae.2939" target="_blank" >10.1002/jae.2939</a>
Alternative languages
Result language
angličtina
Original language name
Equity-premium prediction: Attention is all you need
Original language description
"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. © 2022 John Wiley & Sons, Ltd."
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"Journal of Applied Econometrics"
ISSN
0883-7252
e-ISSN
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Volume of the periodical
38
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
105-122
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85138695644