The role of AI recommendations in extending the Black-Litterman portfolio
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The role of AI recommendations in extending the Black-Litterman portfolio
Original language description
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&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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Intelligent Computing and Cybernetics
ISSN
1756-378X
e-ISSN
1756-3798
Volume of the periodical
19
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
24
Pages from-to
"115–138"
UT code for WoS article
001631405700001
EID of the result in the Scopus database
2-s2.0-105025414260