43 citations · 95 across the 31 of their papers we have counts for
32 papers
FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents
Hoyoung Lee, Suyeol Yun, Jack Haverty +17
Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on…
Making Alternative Data Work: Context-Augmented LLMs for Financial Forecasting
Jihoon Kwon, Lawrence Liu, Daekyung Park +15
When forecasting a firm's future financial performance, alternative data - data collected from non-traditional sources such as consumer transactions, web traffic, and prediction ma…
Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems
CheolWon Na, Hao Ni, Lukasz Szpruch +7
LLM-based multi-agent trading systems, in which specialized agents collaborate through structured communication to produce trading decisions, are moving rapidly from research proto…
Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron
Sahong Park, Suhwan Park, Hoyoung Lee +8
Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific investment preferences. We study…
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee +15
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…
Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data
Suhwan Park, Hoyoung Lee, Zhangyang Wang +5
Demand for personalized financial advice is growing, yet current LLM-based advisors often fail to provide consistent and specialized guidance. Simple persona prompts rarely specify…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
Concordia: Self-Improving Synthetic Tables for Federated LLMs
Jimin Huang, Duanyu Feng, Nuo Chen +8
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remai…
ChatGPT as a Time Capsule: The Limits of Price Discovery
Sebastian Lehner, Alejandro Lopez-Lira
Frozen large language model (LLM) checkpoints extract information from pre-cutoff public text that is associated with future fundamentals and equity returns beyond standard contemp…
Forecasting Future Language: Context Design for Mention Markets
Sumin Kim, Jihoon Kwon, Yoon Kim +9
Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilist…
LLM as a Risk Manager: LLM Semantic Filtering for Lead-Lag Trading in Prediction Markets
Sumin Kim, Minjae Kim, Jihoon Kwon +7
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains…
Evaluating LLMs in Finance Requires Explicit Bias Consideration
Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon +8
Asset retrieval (finding similar assets in a financial universe) is central to quantitative investment decision-making. Existing approaches define similarity through historical pri…
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
Yuechen Jiang, Zhiwei Liu, Yupeng Cao +10
We introduce RFC Bench, a benchmark for evaluating large language models on financial misinformation under realistic news. RFC Bench operates at the paragraph level and captures th…
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
Zhiwei Liu, Yupen Cao, Yuechen Jiang +22
Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit…
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…