# Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation

arXiv: https://arxiv.org/abs/2502.17011 · HTML: https://mlsanity.com/paper/2502.17011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance](https://mlsanity.com/paper/2306.05443/) (2023)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Quant GANs: Deep Generation of Financial Time Series](https://mlsanity.com/paper/1907.06673/) (2019)
- [Deep Reinforcement Learning for Trading](https://mlsanity.com/paper/1911.10107/) (2019)
- [LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction](https://mlsanity.com/paper/2406.10811/) (2024)
- [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://mlsanity.com/paper/2004.06627/) (2020)
- [Generating Realistic Stock Market Order Streams](https://mlsanity.com/paper/2006.04212/) (2020)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [Deep Hedging: Learning to Simulate Equity Option Markets](https://mlsanity.com/paper/1911.01700/) (2019)
- [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.02787/) (2018)
- [Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization](https://mlsanity.com/paper/1901.08740/) (2019)
- [Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction](https://mlsanity.com/paper/2310.05627/) (2023)
- [Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context](https://mlsanity.com/paper/2309.07708/) (2023)
- [SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications](https://mlsanity.com/paper/2404.19063/) (2024)
- [The Evolution of Reinforcement Learning in Quantitative Finance: A Survey](https://mlsanity.com/paper/2408.10932/) (2024)
- [Using generative adversarial networks to synthesize artificial financial datasets](https://mlsanity.com/paper/2002.02271/) (2020)
- [FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance](https://mlsanity.com/paper/2111.05188/) (2021)
- [Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow](https://mlsanity.com/paper/2407.18103/) (2024)
- [Evaluating Financial Relational Graphs: Interpretation Before Prediction](https://mlsanity.com/paper/2410.07216/) (2024)
- [LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data](https://mlsanity.com/paper/2502.09172/) (2025)
- [Optimal Market Making by Reinforcement Learning](https://mlsanity.com/paper/2104.04036/) (2021)
- [Simulating financial time series using attention](https://mlsanity.com/paper/2207.00493/) (2022)

## Built on by (most cited first, 2 of 2)

- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)
- [CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market](https://mlsanity.com/paper/2606.31461/) (2026)

## Abstract (arXiv)

Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In this paper, we propose a novel framework that leverages Causal Generative Adversarial Networks (CausalGANs) and Soft Actor-Critic (SAC) reinforcement learning (RL) to generate high-fidelity synthetic bond yield data for four major bond categories (AAA, BAA, US10Y, Junk). By incorporating 12 key macroeconomic variables, we ensure statistical fidelity by preserving essential market properties. To transform this market dependent synthetic data into actionable insights, we employ a finetuned Large Language Model (LLM) Qwen2.5-7B that generates trading signals (BUY/HOLD/SELL), risk assessments, and volatility projections. We use automated, human and LLM evaluations, all of which demonstrate that our framework improves forecasting performance over existing methods, with statistical validation via predictive accuracy, MAE evaluation(0.103%), profit/loss evaluation (60% profit rate), LLM evaluation (3.37/5) and expert assessments scoring 4.67 out of 5. The reinforcement learning-enhanced synthetic data generation achieves the least Mean Absolute Error of 0.103, demonstrating its effectiveness in replicating real-world bond market dynamics. We not only enhance data-driven trading strategies but also provides a scalable, high-fidelity synthetic financial data pipeline for risk & volatility management and investment decision-making. This work establishes a bridge between synthetic data generation, LLM driven financial forecasting, and language model evaluation, contributing to AI-driven financial decision-making.