Portfolio optimization with asset preselection using data envelopment analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F23%3A10250898" target="_blank" >RIV/61989100:27510/23:10250898 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10100-022-00808-2#citeas" target="_blank" >https://link.springer.com/article/10.1007/s10100-022-00808-2#citeas</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10100-022-00808-2" target="_blank" >10.1007/s10100-022-00808-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Portfolio optimization with asset preselection using data envelopment analysis

Popis výsledku v původním jazyce

This paper uses data envelopment analysis (DEA) approach as a nonparametric efficiency analysis tool to preselect efficient assets in large-scale portfolio problems. Thus, we reduce the dimensionality of portfolio problems, considering multiple asset performance criteria in a linear DEA model. We first introduce several reward/risk criteria that are typically used in portfolio literature to identify features of financial returns. Secondly, we suggest some DEA input/output sets for preselecting efficient assets in a large-scale portfolio framework. Then, we evaluate the impact of the preselected assets in different portfolio optimization strategies. In particular, we propose an ex-post empirical analysis based on two alternative datasets: the components of S &amp;P500 and the Fama and French 100 portfolio formed on size and book to market. According to this empirical analysis we observe better performances of the DEA preselection than the classic PCA factor models for large scale portfolio selection problems. Moreover, the proposed model outperform the S &amp;P500 index and the strategy based on the fully diversified portfolio.

Název v anglickém jazyce

Portfolio optimization with asset preselection using data envelopment analysis

Popis výsledku anglicky

This paper uses data envelopment analysis (DEA) approach as a nonparametric efficiency analysis tool to preselect efficient assets in large-scale portfolio problems. Thus, we reduce the dimensionality of portfolio problems, considering multiple asset performance criteria in a linear DEA model. We first introduce several reward/risk criteria that are typically used in portfolio literature to identify features of financial returns. Secondly, we suggest some DEA input/output sets for preselecting efficient assets in a large-scale portfolio framework. Then, we evaluate the impact of the preselected assets in different portfolio optimization strategies. In particular, we propose an ex-post empirical analysis based on two alternative datasets: the components of S &amp;P500 and the Fama and French 100 portfolio formed on size and book to market. According to this empirical analysis we observe better performances of the DEA preselection than the classic PCA factor models for large scale portfolio selection problems. Moreover, the proposed model outperform the S &amp;P500 index and the strategy based on the fully diversified portfolio.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50200 - Economics and Business

Projekt

<a href="/cs/project/GA19-11965S" target="_blank" >GA19-11965S: Teorie sítí při problému optimalizace a trackování portfolia</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Central European Journal of Operations Research

ISSN

1435-246X

e-ISSN

1613-9178

Svazek periodika

31

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

287-310

Kód UT WoS článku

000824988100001

EID výsledku v databázi Scopus

2-s2.0-85133624088