3 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
Fusing Narrative Semantics for Financial Volatility Forecasting
Yaxuan Kong, Yoontae Hwang, Marcus Kaiser +3
We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured ne…
Signature-Informed Transformer for Asset Allocation
Yoontae Hwang, Stefan Zohren
Modern deep learning for asset allocation typically separates forecasting from optimization. We argue this creates a fundamental mismatch where minimizing prediction errors fails t…
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
Yaxuan Kong, Yiyuan Yang, Yoontae Hwang +5
Time series data are foundational in finance, healthcare, and energy domains. However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as fore…
CAFO: Feature-Centric Explanation on Time Series Classification
Jaeho Kim, Seok-Ju Hahn, Yoontae Hwang +2
In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimension…