An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F25%3A43925113" target="_blank" >RIV/62156489:43110/25:43925113 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s10614-024-10605-5" target="_blank" >https://doi.org/10.1007/s10614-024-10605-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10614-024-10605-5" target="_blank" >10.1007/s10614-024-10605-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

Popis výsledku v původním jazyce

In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard &amp; Poor&apos;s 500 (S&amp;P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student&apos;s distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.Byla vydána korekce článku pod UT WoS 001320224300001 a EID 2-s2.0-105002042651.

Název v anglickém jazyce

An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

Popis výsledku anglicky

In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard &amp; Poor&apos;s 500 (S&amp;P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student&apos;s distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.Byla vydána korekce článku pod UT WoS 001320224300001 a EID 2-s2.0-105002042651.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Computational Economics

ISSN

0927-7099

e-ISSN

1572-9974

Svazek periodika

65

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

35

Strana od-do

1819-1853

Kód UT WoS článku

001220298900002

EID výsledku v databázi Scopus

2-s2.0-105001569882