43 citations · 95 across the 31 of their papers we have counts for
10 papers · 1 filter
When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents
Lingfei Qian, Xueqing Peng, Yan Wang +14
Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies t…
From Text to Alpha: Can LLMs Track Evolving Signals in Corporate Disclosures?
Chanyeol Choi, Yoon Kim, Yu Yu +10
Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving…
FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering
Chanyeol Choi, Jihoon Kwon, Alejandro Lopez-Lira +8
Accurate information retrieval (IR) is critical in the financial domain, where investors must identify relevant information from large collections of documents. Traditional IR meth…
Your AI, Not Your View: The Bias of LLMs in Investment Analysis
Hoyoung Lee, Junhyuk Seo, Suhwan Park +5
In finance, Large Language Models (LLMs) face frequent knowledge conflicts arising from discrepancies between their pre-trained parametric knowledge and real-time market data. Thes…
Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance
Chanyeol Choi, Alejandro Lopez-Lira, Yongjae Lee +8
Extracting structured and quantitative insights from unstructured financial filings is essential in investment research, yet remains time-consuming and resource-intensive. Conventi…
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application
Xueqing Peng, Lingfei Qian, Yan Wang +44
Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…
Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations
Alejandro Lopez-Lira
This paper presents a realistic simulated stock market where large language models (LLMs) act as heterogeneous competing trading agents. The open-source framework incorporates a pe…
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
Chanyeol Choi, Jihoon Kwon, Jaeseon Ha +5
In the fast-paced financial domain, accurate and up-to-date information is critical to addressing ever-evolving market conditions. Retrieving this information correctly is essentia…
The Memorization Problem: Can We Trust LLMs' Economic Forecasts?
Alejandro Lopez-Lira, Yuehua Tang, Mingyin Zhu
Large language models (LLMs) cannot be trusted for economic forecasts during periods covered by their training data. Counterfactual forecasting ability is non-identified when the m…
Bridging Language Models and Financial Analysis
Alejandro Lopez-Lira, Jihoon Kwon, Sangwoon Yoon +2
The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financia…