3 papers in the archive (2023), average empirical rigor 7.2/10, average math complexity 4.8/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Reinforcement Learning for Trading, Options & Derivatives Pricing, NLP & LLMs in Finance, Statistical Arbitrage & Pairs Trading.
Frequent co-authors: Weiguang Han (3), Jimin Huang (3), Yanzhao Lai (3), Min Peng (3), Boyi Zhang (2).
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%).
Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can
Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousands of correct actions that nevertheless have no obv
Recently, large language models (LLMs) like ChatGPT have demonstrated remarkable performance across a variety of natural language processing tasks. However, their effectiveness in the financial domain, specifically in predicting stock market movements, remains to be explored. In this paper, we condu