4 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 4.6/10, 3 with linked code. Quadrant mix: Holy Grail ×2, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Reinforcement Learning for Trading, Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting.
Frequent co-authors: Yang She (2), Xiao-Yang Liu (2), Yanxin Zhang (1), Yue Xiao (1), Hao Wu (1), Yiyang Zhang (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing invest
Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor’s 500). In this paper, we propose a practical scheme that recommends stocks from S
We present FinRL-X, a modular and deployment-consistent trading architecture that unifies data processing, strategy construction, backtesting, and broker execution under a weight-centric interface. While existing open-source platforms are often backtesting- or model-centric, they rarely provide syst
Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reas