Papers by Jingshu Peng

2 papers
Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

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Challenge: Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach: They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome: The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.
FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation (2025.acl-long)

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Challenge: Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years, but there is a notable lack of effective and specialized multimodal evaluation datasets in the financial domain.
Approach: They introduce FinMME, a multimodal large language model with 11,000 financial research samples and 20 annotators.
Outcome: The proposed model performs better than state-of-the-art models, highlighting its challenging nature.

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