Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
Jaskaran Singh Walia, Aarush Sinha, Srinitish Srinivasan, Srihari Unnikrishnan
TL;DR
The paper tackles bond yield forecasting under data scarcity and nonlinear macro dependencies by integrating CausalGAN-based time-series synthesis with Soft Actor-Critic reinforcement learning to generate high-fidelity synthetic yields conditioned on 12 macro variables. A fine-tuned LLM (Qwen2.5-7B) then produces trading signals, risk, and volatility analytics from the augmented data, with evaluation via both automated and human assessments. Key results show the RL-enhanced synthetic data achieving a mean absolute error as low as $0.103$, a $60\%$ profit-day rate for certain bonds, and superior LLM judge and expert scores compared to GANs and actual data. This framework closes the loop between synthetic data generation, LLM-driven forecasting, and domain-specific evaluation, enabling scalable, AI-assisted decision-making in fixed-income markets.
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.