Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
We present a large scale benchmark of modern deep learning architectures for a financial time series prediction and position sizing task, with a primary focus on Sharpe ratio optimization. Evaluating linear models, recurrent networks, transformer based architectures, state space models, and recent sequence representation approaches, we assess out of sample performance on a daily futures dataset spanning commodities, equity indices, bonds, and FX spanning 2010 to 2025. Our evaluation goes beyond average returns and includes statistical significance, downside and tail risk measures, breakeven transaction cost analysis, robustness to random seed selection, and computational efficiency. We fi...
| Authors | Adir Saly-Kaufmann, Kieran Wood, Jan Peter-Calliess et al. |
|---|---|
| Primary category | q-fin.TR |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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