collaborators

6 papers

cs.AI2026

Can Agentic Trading Systems Pay for Their Own Intelligence?

Qiqi Duan, Changlun Li, Chen Wang +10

Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce tradin…

cs.LG2026

The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting

Chen-Hui Song, Shuoling Liu, Liyuan Chen

While deep learning has revolutionized financial forecasting through sophisticated architectures, the design of the supervision signal itself is rarely scrutinized. We challenge th…

cs.LG2026

From Intent to Evidence: A Categorical Approach for Structural Evaluation of Deep Research Agents

Shuoling Liu, Zhiquan Tan, Kun Yi +6

Deep Research Agents (DRAs) aim to answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneous sources. We introduce a categ…

cs.LG2026

CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News

Liyuan Chen, Shilong Li, Jiangpeng Yan +3

Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic de…

q-fin.CP2025

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges

Liyuan Chen, Shuoling Liu, Jiangpeng Yan +8

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-pur…

q-fin.ST2025

Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment

Shuoling Liu, Gaoguo Jia, Yuhang Jiang +2

Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been util…