AI-Based Financial Forecasting

Abstract

Financial forecasting has long resisted automation -- markets are noisy, non-stationary, and adversarially adaptive in ways that make yesterday's winning model tomorrow's underperformer. Recent advances in deep learning, particularly transformer-based sequence models and graph neural networks, have reopened the question of whether AI can deliver reliable, actionable forecasts across asset classes. This study evaluates nine AI-based forecasting architectures - spanning LSTM, Temporal Fusion Transformer (TFT), Informer, PatchTST, N-BEATS, DeepAR, TCN, GNN-based market-graph models, and a hybrid ensemble -- on five financial forecasting tasks: equity return prediction, volatility forecasting, credit default probability estimation, FX rate forecasting, and commodity price prediction. Evaluation covers six years of out-of-sample data (2018-2023) across 14 markets. We find that transformer-based models (TFT, PatchTST) outperform LSTM baselines on equity return prediction by 8.4-11.2% in directional accuracy. Volatility forecasting sees the largest gains from hybrid ensembles (RMSE reduction of 14.6% vs. GARCH baseline). Credit default models achieve AUC = 0.891 with GNN-based architectures leveraging counterparty network structure. Across all tasks, model performance degrades significantly during market stress regimes (Sharpe ratio drops of 0.38-0.61), revealing a persistent regime-sensitivity limitation. An adaptive regime-detection wrapper that switches model selection based on volatility state is proposed and validated.