Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading

Tomasz Serafin and Rafał Weron

Energy Economics, 2025, vol. 148, issue C

Abstract: We study the impact of the loss function used to estimate the parameters of a regression-type model on profits and risk in day-ahead electricity trading. To provide practical insights, we consider a strategy that incorporates battery storage and includes realistic operating costs in the calculation of revenues. Using 2021-2024 data from the German market as the testing ground, we provide evidence that minimizing a loss function that combines absolute errors with a quadratic penalty for price spread predictions of the opposite sign is the preferred option. Forecasts based on the introduced directional loss function repeatedly and in the majority of cases yield trading decisions that outperform those based on predictions from models estimated using squared, absolute, percentage, or asymmetric losses, as measured by the Sharpe ratio and profits per trade.

Keywords: Electricity price forecast; Day-ahead market; Loss function; Trading strategy; Battery storage; Sharpe ratio (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 G17 Q41 Q47 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004207

DOI: 10.1016/j.eneco.2025.108596

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