3 papers
cs.LG2026
BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop
Chao Chen, Xujia Li, Dongsheng Hong +4
The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient du…
q-fin.ST2026
Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization
Hao Wang, Jingshu Peng, Yanyan Shen +4
Stock recommendation is critical in Fintech applications, which leverage price series and alternative information to estimate future stock performance. Traditional time-series fore…
q-fin.PM2025
Automate Strategy Finding with LLM in Quant Investment
Zhizhuo Kou, Holam Yu, Junyu Luo +7
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our a…