Uncertainty-Aware Financial Forecasting: Leveraging Conformal Prediction for Risk-Adjusted Models

Financial forecasting presents a critical challenge in risk management, where traditional point estimation approaches fail to capture the inherent uncertainty and volatility of financial markets. While deep learning models have demonstrated remarkable predictive capabilities, they typically provide deterministic predictions without quantifying prediction confidence or risk boundaries. This paper introduces a practical uncertainty-aware forecasting framework that integrates Conformal Prediction (CP) with deep recurrent neural networks to generate statistically valid prediction intervals alongside point estimates. Our approach leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures as base learners, enhanced with adaptive conformal inference mechanisms that provide approximate marginal coverage under realistic market dynamics. We propose three key design contributions: (1) a volatility-adaptive nonconformity measure that adjusts prediction intervals based on market regime detection, (2) a temporal split conformal framework specifically designed for financial time series with autocorrelation structures, and (3) an extreme event awareness mechanism that dynamically widens intervals during high-uncertainty periods. Comprehensive experiments on multiple stock market datasets spanning multiple years demonstrate that our framework achieves superior calibration (95.2% empirical coverage at 95% target level) while maintaining competitive predictive accuracy (RMSE reduction of 8.3% compared to baseline models). Notably, our method exhibits enhanced early warning capabilities for extreme market events, achieving 87.6% detection rate for significant price movements with controlled false positive rates. We additionally evaluate the framework on cryptocurrency data and with multiple modern backbone families across the same 10-stock subset, providing empirical evidence that VAC-FF is compatible with diverse base predictor architectures and asset classes. The proposed framework offers financial practitioners a transparent, statistically rigorous tool for risk-adjusted decision-making, bridging the gap between predictive accuracy and uncertainty quantification in high-stakes financial applications.