Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance

TL;DR

This paper benchmarks various deep learning architectures for financial time series prediction, focusing on risk-adjusted performance metrics like the Sharpe ratio across diverse assets from 2010 to 2025.

Contribution

It provides a comprehensive large-scale evaluation of modern deep learning models for financial forecasting, highlighting the effectiveness of hybrid models and robustness considerations.

Findings

Hybrid models like VSN with LSTM outperform others in Sharpe ratio.

Models explicitly designed for temporal representations excel over generic models.

xLSTM shows superior robustness to trading frictions.

Abstract

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 find that models explicitly designed to learn rich temporal representations consistently outperform linear benchmarks and generic deep learning models, which often lead the ranking in standard time series benchmarks. Hybrid models such as VSN with LSTM, a combination of Variable Selection Networks (VSN) and LSTMs, achieves the highest overall Sharpe ratio, while VSN with xLSTM and LSTM with PatchTST exhibit superior downside adjusted characteristics. xLSTM demonstrates the largest breakeven transaction cost buffer, indicating improved robustness to trading frictions.