7 citations · 10 across the 7 of their papers we have counts for
7 papers
The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
Yuxuan Ye, Jun Han, Ao Hu +7
End-to-end LLM trading agents have moved quickly from research curiosity to a small ecosystem of named systems, including FinCon, FinMem, TradingAgents, FinAgent, QuantAgent, and F…
FactorMiner: A Self-Evolving Agent with Skills and Experience Memory for Financial Alpha Discovery
Yanlong Wang, Jian Xu, Hongkang Zhang +3
Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable si…
FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model
Yanlong Wang, Jian Xu, Fei Ma +8
Financial time series forecasting is both highly significant and challenging. Previous approaches typically standardized time series data before feeding it into forecasting models,…
Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method
Yanlong Wang, Jian Xu, Shao-Lun Huang +2
This study seeks to advance the understanding and prediction of stock market return uncertainty through the application of advanced deep learning techniques. We introduce a novel d…
FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
Yanlong Wang, Jian Xu, Tiantian Gao +4
Despite the growing attention to time series forecasting in recent years, many studies have proposed various solutions to address the challenges encountered in time series predicti…
PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
Yanlong Wang, Jian Xu, Fei Ma +3
Time series forecasting remains a critical challenge across various domains, often complicated by high-dimensional data and long-term dependencies. This paper presents a novel tran…
Serverless inferencing on Kubernetes
Clive Cox, Dan Sun, Ellis Tarn +3
Organisations are increasingly putting machine learning models into production at scale. The increasing popularity of serverless scale-to-zero paradigms presents an opportunity for…