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Related Experiment Video

Updated: Jul 19, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Model-based reinforcement learning with non-Gaussian environment dynamics and its application to portfolio

Huifang Huang¹, Ting Gao², Pengbo Li²

¹School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China.

Chaos (Woodbury, N.Y.)

|August 10, 2023

Summary

This study introduces a novel AI trading strategy using heavy-tailed preserving normalizing flows for complex financial markets. The method enhances portfolio optimization and reduces risk, outperforming existing approaches during market volatility.

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Area of Science:

Quantitative Finance
Artificial Intelligence
Financial Engineering

Background:

Financial markets exhibit complex dynamics, including abrupt transitions and hidden causal factors, challenging traditional AI-based algorithmic trading.
Accurate simulation of high-dimensional joint probabilities is crucial for robust portfolio optimization.

Purpose of the Study:

To develop an AI-driven algorithmic trading strategy that effectively simulates complex financial markets.
To enhance portfolio optimization by employing heavy-tailed preserving normalizing flows within a model-based reinforcement learning framework.

Main Methods:

Utilized heavy-tailed preserving normalizing flows for simulating high-dimensional joint probabilities.
Implemented a model-based reinforcement learning framework for algorithmic trading.
Conducted experiments on stocks from Dow, NASDAQ, and S&P markets.

Main Results:

The proposed method demonstrated superior performance in portfolio optimization across tested markets.
The approach effectively mitigated losses during the COVID-19 pandemic, showing a lower maximum drawdown.
Analysis confirmed the algorithm's convergence and identified effective patterns for optimization.

Conclusions:

Heavy-tailed preserving normalizing flows offer a robust solution for simulating complex financial environments in AI trading.
The model-based reinforcement learning framework provides a powerful tool for adaptive and resilient portfolio optimization.
The study highlights the potential of advanced AI techniques to navigate financial market uncertainties.