dorsal/arxiv
View SchemaUtility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions
| Authors | Craig S Wright |
|---|---|
| Categories | |
| ArXiv ID | 2601.07852vv1 |
| URL | https://arxiv.org/abs/2601.07852 |
| License | http://creativecommons.org/licenses/by-sa/4.0/ |
Abstract
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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"abstract": "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.",
"arxiv_id": "2601.07852",
"authors": [
"Craig S Wright"
],
"categories": [
"econ.EM",
"q-fin.CP",
"q-fin.PM",
"q-fin.TR"
],
"license": "http://creativecommons.org/licenses/by-sa/4.0/",
"title": "Utility-Weighted Forecasting and Calibration for Risk-Adjusted Decisions under Trading Frictions",
"url": "https://arxiv.org/abs/2601.07852",
"version": "v1"
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