The role of AI recommendations in extending the Black-Litterman portfolio

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63599800" target="_blank" >RIV/70883521:28120/25:63599800 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.emerald.com/ijicc/article/doi/10.1108/IJICC-03-2025-0137/1323389/The-role-of-AI-recommendations-in-extending-the" target="_blank" >https://www.emerald.com/ijicc/article/doi/10.1108/IJICC-03-2025-0137/1323389/The-role-of-AI-recommendations-in-extending-the</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1108/IJICC-03-2025-0137" target="_blank" >10.1108/IJICC-03-2025-0137</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The role of AI recommendations in extending the Black-Litterman portfolio

Popis výsledku v původním jazyce

Purpose – This study explores the role of artificial intelligence (AI) recommendations in portfolio optimization by extending the Black-Litterman (BL) model using consensus analyst opinions generated by ChatGPT. The aim is to assess if AI recommendations can improve portfolio diversification and risk-adjusted returns compared to traditional investment strategies. Design/methodology/approach – We conducted a quantitative analysis using weekly historical price data across equities, commodities, fixed-income securities and cryptocurrencies from January 2018 to May 2023. Portfolios constructed with the extended BL model were tested against standard benchmarks, including the S&amp;P500 index and various mean-variance portfolios. Out-of-sample performance and robustness were evaluated through 100 random resampling procedures. Findings – Results indicate that integrating AI-generated analyst consensus significantly improves the BL portfolio’s risk-adjusted returns. The AI-enhanced model consistently outperformed traditional mean-variance portfolios, the unadjusted BL model and market benchmarks. Robustness tests confirmed the method’s stability and practical feasibility in real-world investing. Practical implications – Portfolio managers and individual investors can apply this enhanced BL model for more effective asset allocation decisions. Using AI-generated recommendations simplifies the integration of broad analyst perspectives, reduces reliance on subjective human judgments and leads to portfolios that deliver stronger and more consistent risk-adjusted performance. Originality/value – This research is the first to integrate ChatGPT-generated analyst recommendations directly into the BL framework. It addresses critical limitations of modern portfolio theory, particularly estimation errors, offering a practical solution leveraging AI advancements for portfolio optimization.

Název v anglickém jazyce

The role of AI recommendations in extending the Black-Litterman portfolio

Popis výsledku anglicky

Purpose – This study explores the role of artificial intelligence (AI) recommendations in portfolio optimization by extending the Black-Litterman (BL) model using consensus analyst opinions generated by ChatGPT. The aim is to assess if AI recommendations can improve portfolio diversification and risk-adjusted returns compared to traditional investment strategies. Design/methodology/approach – We conducted a quantitative analysis using weekly historical price data across equities, commodities, fixed-income securities and cryptocurrencies from January 2018 to May 2023. Portfolios constructed with the extended BL model were tested against standard benchmarks, including the S&amp;P500 index and various mean-variance portfolios. Out-of-sample performance and robustness were evaluated through 100 random resampling procedures. Findings – Results indicate that integrating AI-generated analyst consensus significantly improves the BL portfolio’s risk-adjusted returns. The AI-enhanced model consistently outperformed traditional mean-variance portfolios, the unadjusted BL model and market benchmarks. Robustness tests confirmed the method’s stability and practical feasibility in real-world investing. Practical implications – Portfolio managers and individual investors can apply this enhanced BL model for more effective asset allocation decisions. Using AI-generated recommendations simplifies the integration of broad analyst perspectives, reduces reliance on subjective human judgments and leads to portfolios that deliver stronger and more consistent risk-adjusted performance. Originality/value – This research is the first to integrate ChatGPT-generated analyst recommendations directly into the BL framework. It addresses critical limitations of modern portfolio theory, particularly estimation errors, offering a practical solution leveraging AI advancements for portfolio optimization.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

International Journal of Intelligent Computing and Cybernetics

ISSN

1756-378X

e-ISSN

1756-3798

Svazek periodika

19

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

24

Strana od-do

"115–138"

Kód UT WoS článku

001631405700001

EID výsledku v databázi Scopus

2-s2.0-105025414260