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arXiv:2608. 14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation.
The paper investigates how the choice of loss function versus the choice of forecasting model affects cryptocurrency volatility predictions. By comparing seven loss functions and five models, and aligning forecast levels before evaluation, the study finds that after level adjustment model choice dominates performance differences, while loss-induced variations largely disappear. The work clarifies that apparent loss effects in raw comparisons are largely due to forecast level differences rather than intrinsic model performance.
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
arXiv:2609.37255v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are pretrained on heterogeneous collections containing billions of observations, yet their training windows are...
The paper proposes a method for controlling downside risk when adjusting forecasts from frozen models, such as foundation models, by combining a static corrector and an online corrector on the simplex. Using only post‑horizon losses, the approach achieves minimal deterioration (0.15%) and up to 11.5% gains across 28 forecast pairs, and consistently reduces mean MSE in day‑ahead load forecasts for seven European bidding zones. The method’s applicability is bounded by three empirical conditions related to expert speed, stream length, and outcome alignment.