arXiv 9 Jan 2026 · Econometrics
arXiv:2601.07852 · PDF · DOI · OpenAlex · Extracted main text
Forecasting accuracy is routinely optimised in financial prediction tasks even though investment and risk-management decisions are executed under transaction costs, market impact, capacity limits, and binding risk constraints. This paper treats forecasting as an econometric input to a constrained decision problem. A predictive distribution induces a decision rule through a utility objective combined with an explicit friction operator consisting of both a cost functional and a feasible-set constraint system. The econometric target becomes minimisation of expected decision loss net of costs rather than minimisation of prediction error. The paper develops a utility-weighted calibration criterion aligned to the decision loss and establishes sufficient conditions under which calibrated predictive distributions weakly dominate uncalibrated alternatives. An empirical study using a pre-committed nested walk-forward protocol on liquid equity index futures confirms the theory: the proposed utility-weighted calibration reduces realised decision loss by over 30% relative to an uncalibrated baseline ($t$-stat -30.31) for loss differential and improves the Sharpe ratio from -3.62 to -2.29 during a drawdown regime. The mechanism is identified as a structural reduction in the frequency of binding constraints (from 16.0% to 5.1%), preventing the "corner solution" failures that characterize overconfident forecasts in high-friction environments.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bessembinder, Hendrik (2003) Issues in Assessing Trade Execution Costs | 1.000 | 16 | 3 | 100% |
| 2 | Gu, Shihao and Kelly, Bryan and Xiu, Dacheng (2020) Empirical Asset Pricing via Machine Learning | 1.000 | 5 | 3 | 100% |
| 3 | Israel, Ronen and Kelly, Bryan and Moskowitz, Tobias (2020) Can Machines 'Learn' Finance? | 1.000 | 5 | 3 | 100% |
| 4 | Goyenko, Ruslan Y. and Holden, Craig W. and Trzcinka, Charles A (2009) Do Liquidity Measures Measure Liquidity? | 0.874 | 8 | 2 | 100% |
| 5 | Bessembinder, Hendrik and Seguin, Paul J (1993) Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets | 0.874 | 7 | 2 | 100% |
| 6 | Allen, Sam and Koh, Jonathan and Segers, Johan and Ziegel, Johanna F (2025) Tail calibration of probabilistic forecasts | 0.811 | 4 | 2 | 100% |
| 7 | Cheng, Jie (2024) Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context | 0.644 | 2 | 2 | 100% |
| 8 | Gârleanu, Nicolae and Pedersen, Lasse Heje (2013) Dynamic Trading with Predictable Returns and Transaction Costs | 0.644 | 2 | 2 | 100% |
| 9 | Holzmann, Hajo and Eulert, M (2014) The Role of the Information Set for Forecasting Functionals | 0.644 | 2 | 2 | 100% |
| 10 | Agrawal, Akshay and Amos, Brandon and Barratt, Shane and Boyd, Steph… (2019) Differentiable Convex Optimization Layers | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 45 scored citations.