arXiv:2606.30037v1 Announce Type: new Abstract: In a deep forecasting pipeline for fat-tailed financial returns at short horizons, which matters more - the backbone architecture or the output head? We compare four modern backbones (TimesNet, DLinear, N-BEATS, iTransformer) under three output heads: a point head, a single-Gaussian density head, and a Gaussian mixture density head with K=4 components. On S and P 500 monthly log-returns (1871-2023) under anchored walk-forward validation, the three heads form a strict gradient: switching from point to Gaussian improves CRPS by about 1.3 percent; s
The proliferation of advanced AI/ML techniques in finance creates a continuous need to optimize forecasting models for higher accuracy and robustness, especially for volatile, fat-tailed returns.
This research provides crucial insights for practitioners building deep forecasting pipelines in finance, highlighting that output head architecture contributes more to accuracy than backbone choice for fat-tailed returns.
The focus for improving deep forecasting models for financial returns shifts towards selecting and optimizing output heads over simply using the newest backbone architectures.
- · Financial quantitative analysts
- · Hedge funds and asset managers utilizing AI for trading
- · Developers of specialized financial AI models
- · Generic AI model developers ignoring financial domain specificities
- · Academic groups focusing solely on backbone innovation for financial forecasting
- · Users of simpler statistical models for fat-tailed returns
Improved financial forecasting models lead to more efficient and potentially profitable trading strategies.
Increased adoption of sophisticated density-based forecasting could alter market dynamics by better pricing risk, especially in volatile assets.
The demonstrated impact of 'heads' over 'backbones' in a specific fat-tailed domain may generalize to other complex, high-stakes forecasting applications, influencing broader AI development priorities.
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Read at arXiv cs.LG