Multi-Horizon Equity Returns Predictability via Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F24%3A10480567" target="_blank" >RIV/00216208:11230/24:10480567 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=yOBbXjBxPL" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=yOBbXjBxPL</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.32065/CJEF.2024.02.01" target="_blank" >10.32065/CJEF.2024.02.01</a>
Alternative languages
Result language
angličtina
Original language name
Multi-Horizon Equity Returns Predictability via Machine Learning
Original language description
We investigate the predictability of global expected stock returns across various forecasting horizons using machine learning techniques. We find that the predictability of returns decreases with longer forecasting horizons both in the U.S. and internationally. Despite this, we provide evidence that using firm -specific characteristics can remain profitable even after accounting for transaction costs, especially when we consider longer forecasting horizons. Studying the profitability of long -short portfolios, we highlight a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. Increasing the forecasting horizon while matching the rebalancing period increases risk -adjusted returns after transaction costs for the U.S. We combine predictions of expected returns at multiple horizons using double -sorting and a turnover reducing strategy, buy/hold spread. Double sorting on different horizons significantly increases profitability in the U.S. market, while buy/hold spread portfolios exhibit better risk -adjusted profitability.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50201 - Economic Theory
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Finance a úvěr
ISSN
0015-1920
e-ISSN
2464-7683
Volume of the periodical
74
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
49
Pages from-to
142-190
UT code for WoS article
001231631500002
EID of the result in the Scopus database
2-s2.0-85194413663