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

ArXiv ID: 2502.17011 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Causal Generative Adversarial Networks (CausalGAN), Soft Actor-Critic (SAC), Large Language Models (LLM), Synthetic data generation, Bond yield forecasting, Fixed Income (Bonds)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including Causal GANs and Soft Actor-Critic RL, but provides extensive empirical validation with specific metrics like MAE (0.103%), profit rates (60%), LLM evaluation scores, and a clear experimental pipeline, indicating high implementation readiness.

  flowchart TD
    A["Research Goal: Predict Liquidity-Aware Bond Yields via CausalGAN & DRL"] --> B["Data Input: 4 Bond Categories & 12 Macroeconomic Variables"]
    B --> C["Synthetic Data Generation: CausalGAN & Soft Actor-Critic SAC"]
    C --> D["LLM Processing: Qwen2.5-7B Finetuning for Signals/Risk/Volatility"]
    D --> E["Automated & Human Evaluation"]
    E --> F["Key Outcomes: 0.103% MAE, 60% Profit, 4.67/5 Expert Score"]