Empirical Evaluation of Large Language Models for Asset‑Return Prediction

Bingxing Wang

doi:10.70393/616a736d.333035

OPEN ACCESS |Research Article ||13 July 2025

Empirical Evaluation of Large Language Models for Asset‑Return Prediction

Bingxing Wang

Shanghai Jingzhuo Investment Management Co., Ltd.

stellawang6262@foxmail.com

Shanghai Jingzhuo Investment Management Co., Ltd., China.

* Corresponding Author¹: Bingxing Wang, E-Mail: stellawang6262@foxmail.com

Publication

Accepted 2025 July 11 ; Published 2025 July 13

Academic Journal of Sociology and Management, 2025, 3(4), 3005-5040.

Abstract

In an era of exploding financial‐market information and rapid algorithmic iteration, traditional asset‑return forecasting models struggle to exploit unstructured text. Using cross‑asset data—equities, Treasuries and commodity futures—from 2004 to 2024, we build an integrated prediction framework that fuses semantic factors extracted by Large Language Models (LLMs) with price‑volume and macro‑numerical factors. We benchmark it against Logit, Random Forest, LightGBM and bidirectional LSTM. A comprehensive evaluation with weighted F₁, ROC‑AUC, Information Ratio and Sharpe Ratio shows that (i) LLM‑based semantic factors significantly improve directional accuracy (F₁ + 20.5 %, ROC‑AUC + 11.9 %); (ii) after a 3 bp transaction cost, the LLM‑driven long–short portfolio achieves annualised information and Sharpe ratios of 0.96 and 1.17, markedly outperforming all baselines; (iii) robustness checks confirm this edge across high‑volatility regimes, asset classes and text‑lag scenarios; and (iv) the combination of SHAP and attention visualisation traces keyword‑level contributions, enhancing interpretability. Our results provide reproducible, quantifiable evidence for large‑scale LLM deployment in quantitative investing and point to future work on model compression, slippage estimation and multimodal extension.

Keywords

Large Language Models , Asset‑return Prediction , Textual‑sentiment Factor , Machine Learning , Information Ratio , Interpretability .

Metadata

DOI:

10.70393/616a736d.333035

ARK:

ark:/40704/AJSM.v3n4a03

Pages: 18-25

References: 47

Disciplines: Economics

Subjects: Behavioral Economics

Cite This Article

APA Style

Wang, B. (2025). Empirical evaluation of large language models for asset‑return prediction. Academic Journal of Sociology and Management, 3(4), 18-25. https://doi.org/10.70393/616a736d.333035

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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