arXiv:2602.03395v4 Announce Type: replace Abstract: While deep learning has revolutionized financial forecasting through sophisticated architectures, the design of the supervision signal itself is rarely scrutinized. We challenge the canonical assumption that training labels must strictly mirror inference targets, uncovering the Label Horizon Paradox: the optimal supervision signal often deviates from the prediction goal, shifting across intermediate horizons governed by market dynamics. We theoretically ground this phenomenon in a dynamic signal-noise trade-off, demonstrating that generalizat
The proliferation of deep learning in financial forecasting necessitates a deeper examination of fundamental assumptions, as initial advancements plateau and subtle optimizations become critical for further gains.
This research challenges a core assumption in AI model development for finance, potentially leading to more robust and accurate forecasting models, impacting investment strategies and risk management.
The understanding of optimal supervision signals in AI training for financial applications changes, moving away from a direct mirroring of inference targets to a more dynamic, market-driven approach.
- · Hedge Funds
- · Quantitative Trading Firms
- · Financial AI Researchers
- · Risk Management Firms
- · Traditional Econometric Models
- · Naive AI Implementations
Financial institutions adopting AI for forecasting will begin to re-evaluate and re-engineer their training label strategies.
Improved forecasting accuracy could lead to shifts in capital allocation and increased volatility due to more efficient market responses.
The 'Label Horizon Paradox' principle might extend beyond finance, influencing optimal supervision design in other complex, dynamic systems.
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Read at arXiv cs.LG