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.

翻译：金融债券收益率预测因数据稀缺、宏观经济非线性依赖关系及市场条件动态演变而极具挑战性。本文提出一种创新框架，结合因果生成对抗网络（CausalGAN）与软演员-评论家（SAC）强化学习，为四大债券类别（AAA、BAA、美国10年期国债、高收益债）生成高保真合成债券收益率数据。通过纳入12个关键宏观经济变量，我们在保留核心市场属性的前提下确保统计保真度。为将依赖市场特征的合成数据转化为可操作洞见，我们采用微调后的大语言模型（LLM）Qwen2.5-7B生成交易信号（买入/持有/卖出）、风险评估与波动率预测。通过自动化评估、人工评估及大语言模型评估，我们的框架在预测性能上均优于现有方法：预测准确率、平均绝对误差（MAE）达0.103%、盈亏评估（60%盈利率）、大语言模型评估得分3.37/5、专家评估得分4.67/5。强化学习增强的合成数据生成方法实现了最低平均绝对误差0.103，充分证明其复刻真实债券市场动态的有效性。本研究不仅强化了数据驱动型交易策略，还为风险与波动率管理及投资决策构建了可扩展的高保真合成金融数据管道。该工作架起了合成数据生成、大语言模型驱动的金融预测与语言模型评估之间的桥梁，为人工智能驱动的金融决策作出贡献。