2026/02/27 by Nabeel Ahmad Saidd · 1 voice
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Engineering · #Asset allocation #Bayesian inference #Bayesian probability #Deep learning #Energy Load and Power Forecasting #Equity (law) #Financial market #Forecasting Techniques and Applications #Hyperparameter #Portfolio #Randomness #Stock Market Forecasting Methods #cs.LG #q-fin.GN #q-fin.ST
paper · pdf · doi:10.48550/arxiv.2603.16886
openalex publication_date 2026/02/27 · arxiv published 2026/02/27 · arxiv updated 2026/02/27 · openalex created_date 2026/03/20 · openalex updated_date 2026/07/28
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer) spanning Transformer, MLP, CNN, and RNN families across cryptocurrency, forex, and equity index markets at 4-hour and 24-hour horizons. A total of 918 experiments were conducted under a strict five-stage protocol including fixed-seed Bayesian hyperparameter optimization, configuration freezing per asset class, multi-seed retraining, uncertainty aggregation, and statistical validation. ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate, followed by PatchTST (2.000). Results reveal a clear three-tier ranking structure and show that architecture explains nearly all performance variance, while seed randomness is negligible. Rankings remain stable across horizons despite 2 to 2.5 times error amplification. Directional accuracy remains near 50 percent across all configurations, indicating that MSE-trained models lack directional skill at hourly resolution. The findings highlight the importance of architectural inductive bias over raw parameter count and provide reproducible guidance for multi-step financial forecasting.