Paper: arXiv 2510.15883

Authors: Yang Li, Zhi Chen

Abstract

Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics including flow matching, stochastic differential equations, and ODE solvers, while demonstrating rigorous empirical validation through high-frequency trading backtests with specific performance metrics and comparisons.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop adaptive stochastic control<br>for financial markets overcoming<br>traditional simplifying assumptions"] --> B["FinFlowRL Framework Architecture"]
  
  B --> C["Imitation Learning: Pretrain on multiple expert strategies<br>to create a meta-policy"]
  B --> D["Reinforcement Learning: Fine-tune meta-policy<br>in stochastic noise space"]
  
  C --> E{"Action Chunking Mechanism"}
  D --> E
  
  E --> F["Generative action sequences<br>instead of single decisions"]
  F --> G["Computational Process:<br>Handles non-Markovian market dynamics"]
  
  G --> H["Key Findings:<br>Outperforms individually optimized experts<br>across diverse market conditions"]

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4 of 151 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.