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Business Perspectives - Enhancing portfolio optimization with multi-LLM sentiment aggregation: A Black-Litterman integration approach

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Investment Management and Financial Innovations

Issue #3

Enhancing portfolio optimization with multi-LLM sentiment aggregation: A Black-Litterman integration approach

Enhancing portfolio optimization with multi-LLM sentiment aggregation: A Black-Litterman integration approach

Received
March 11, 2025;

Accepted
July 10, 2025;

Published
August 14, 2025

Author(s)

Link to ORCID Index
:
https://orcid.org/0000-0002-8565-0760

Lamukanyani Alson Mantshimuli

,

Link to ORCID Index
:
https://orcid.org/0000-0002-8002-1156

John Weirstrass Muteba Mwamba

DOI
http://dx.doi.org/10.21511/imfi.22(3).2025.16

Article Info
Volume 22 2025, Issue #3, pp. 213-226

TO CITE

АНОТАЦІЯ

Cited by

2 articles

Journal title:

Discover Artificial Intelligence

Article title:

Benchmarking deep reinforcement learning and classical models for portfolio optimization across market efficiency regimes

DOI:

10.1007/s44163-026-01869-x

Volume:

6

/

Issue:

1

/

First page:

/

Year:

2026

Contributors:

Hitesh Kumar Sahu, Avishek Bhandari

Journal title:

Article title:

DOI:

Volume:

/

Issue:

/

First page:

/

Year:

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2965 Views

1018 Downloads

This work is licensed under a

Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Sentiment analysis of financial text data plays a crucial role in investment decision-making, yet existing approaches often rely on single-model sentiment scores that may suffer from biases or hallucinations. This study aims to enhance portfolio optimization by integrating sentiment signals from multiple Large Language Models (LLMs) into the Black-Litterman framework. The proposed method aggregates sentiment scores from three finance-domain fine-tuned LLMs using a Long Short-Term Memory network, which captures non-linear relationships and temporal dependencies to produce a robust Meta-LLM sentiment score. This score is then incorporated into the Black-Litterman model as investor views to derive optimal portfolio weights. The methodology is tested on a portfolio of S&P 500 stocks. The results show that the proposed approach significantly improves portfolio performance, achieving an annualized return of 31.22%, compared to 24.57% for the market capital-weighted portfolio. Additionally, the model attains a Sharpe Ratio of 3.02, an Omega Ratio of 2.48, and a Jensen’s Alpha of 1.95%, outperforming both the benchmark portfolios and portfolios based on single-LLM sentiment. The findings demonstrate that aggregating sentiment from multiple LLMs enhances risk-adjusted returns while mitigating model-specific limitations. Future research could explore the integration of LLMs with different architectures to further refine sentiment-aware portfolio strategies.

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PAPER PROFILE

AUTHORS CONTRIBUTIONS

FIGURES

TABLES

REFERENCES

Keywords
Black-Litterman model
,
financial text data
,
large language models
,
long short-term memory
,
portfolio optimization
,
sentiment analysis

JEL Classification (Paper profile tab)
G11

References
30

Tables
7

Figures
2

Figure 1. Transformer model architecture
Figure 2. LSTM cell and its operations

Table 1. Configured parameters for LSTM sentiment aggregation
Table 2. Machine learning models’ predictive performance
Table 3. LLMs’ performance metrics on sentiment analysis
Table 4. Equity returns descriptive statistics
Table 5. Portfolio performance comparison
Table 6. Portfolio performance comparison: Varying τ parameter
Table 7. Portfolio performance: Meta-LLM vs individual LLMs

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Conceptualization

Lamukanyani Alson Mantshimuli, John Weirstrass Muteba Mwamba

Data curation

Lamukanyani Alson Mantshimuli

Formal Analysis

Lamukanyani Alson Mantshimuli

Investigation

Lamukanyani Alson Mantshimuli, John Weirstrass Muteba Mwamba

Methodology

Lamukanyani Alson Mantshimuli, John Weirstrass Muteba Mwamba

Project administration

Lamukanyani Alson Mantshimuli

Resources

Lamukanyani Alson Mantshimuli

Software

Lamukanyani Alson Mantshimuli

Validation

Lamukanyani Alson Mantshimuli

Visualization

Lamukanyani Alson Mantshimuli

Writing – original draft

Lamukanyani Alson Mantshimuli

Supervision

John Weirstrass Muteba Mwamba

Writing – review & editing

John Weirstrass Muteba Mwamba

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Acknowledgment
The authors acknowledge everyone who contributed to the study, particularly Goa Business School and Goa University.

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</reference>

<statements>
1. Multi-LLM sentiment aggregation to BL (Business Perspectives 2025): LSTM-aggregated Meta-LLM sentiment as views achieved approximately 31.22% annualized return versus a 24.57% baseline.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.