Multi-Horizon Equity Returns Predictability via Machine Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00587009" target="_blank" >RIV/67985556:_____/24:00587009 - isvavai.cz</a>

Výsledek na webu

<a href="https://journal.fsv.cuni.cz/mag/article/show/id/1531" target="_blank" >https://journal.fsv.cuni.cz/mag/article/show/id/1531</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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Horizon Equity Returns Predictability via Machine Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-Horizon Equity Returns Predictability via Machine Learning

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GX19-28231X" target="_blank" >GX19-28231X: Dynamické modely pro digitální finance</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FINANCE A UVER-CZECH JOURNAL OF ECONOMICS AND FINANCE

ISSN

2464-7683

e-ISSN

2464-7683

Svazek periodika

74

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

48

Strana od-do

142-190

Kód UT WoS článku

001231631500002

EID výsledku v databázi Scopus

2-s2.0-85194413663