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

Tomasz Serafin and Rafał Weron

No WORMS/24/03, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

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 Q41 Q47 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2024
New Economics Papers: this item is included in nep-ene and nep-mac
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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_24_03.pdf Revised version, 15.02.2025 (application/pdf)

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