2 citations · 2 across the 3 of their papers we have counts for
6 papers
Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning
Yan Liu, Ye Luo, Zigan Wang +1
Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We p…
AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systems
Yang Li, Siqi Ping, Xiyu Chen +4
With the rapid progress of large language models (LLMs), LLM-powered multi-agent systems (MAS) are drawing increasing interest across academia and industry. However, many current M…
Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market
Chujun He, Zhonghao Huang, Xiangguo Li +5
We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity…
Meta-Policy Reflexion: Reusable Reflective Memory and Rule Admissibility for Resource-Efficient LLM Agent
Chunlong Wu, Ye Luo, Zhibo Qu +1
Large language model (LLM) agents achieve impressive single-task performance but commonly exhibit repeated failures, inefficient exploration, and limited cross-task adaptability. E…
Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks
Qiang Chen, Tianyang Han, Jin Li +5
Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates AI agents' capability to master econometrics, focusing on empi…
OpenAI-o1 AB Testing: Does the o1 model really do good reasoning in math problem solving?
Leo Li, Ye Luo, Tingyou Pan
The Orion-1 model by OpenAI is claimed to have more robust logical reasoning capabilities than previous large language models. However, some suggest the excellence might be partial…