arXiv:2606.27688v1 Announce Type: new Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed. We study this issue in multi-horizon volatility forecasting. Our starting point is that a trained multi-output (MIMO) forecaster does not define a single deployable predictor: by changing the inference-time rollout rule, the same trained model induces a family of forecasts with different accuracy and cost profiles. Across 20 stock-volatility series, three forecast horizons, and architectures ranging from line
The increasing sophistication of AI models and the urgent need for robust financial forecasting in volatile markets are driving research into deployment-side adaptiveness.
This research highlights that model deployment strategy significantly impacts predictive performance in financial forecasting, moving beyond just model architecture to operational execution.
The focus of financial forecasting AI shifts from solely model training to include dynamic, inference-time rollout rules, optimizing accuracy and cost profiles post-training.
- · Quantitative hedge funds
- · Financial AI developers
- · High-frequency trading firms
- · Risk management departments
- · Static model deployment strategies
- · Firms not adapting to deployment-side optimizations
Financial institutions will invest more in optimizing model deployment pipelines rather than just model development.
New financial AI products will emerge that offer dynamic deployment configurations for improved real-time performance.
The competitive landscape in financial services could be reshaped as firms with superior deployment adaptiveness gain an edge in predictive accuracy and operational efficiency.
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Read at arXiv cs.LG