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.

中文翻译：

---

回归模型中的损失函数：对日前电力交易利润和风险的影响

我们研究了用于估计回归型模型参数的损失函数对提前一天电力交易中利润和风险的影响。为了提供实用的见解，我们考虑了一种包含电池存储并在收入计算中包括实际运营成本的策略。以德国市场的 2021-2024 年数据为测试场地，我们提供的证据证明，最小化将绝对误差与相反符号的价格价差预测的二次惩罚相结合的损失函数是首选选项。在大多数情况下，基于引入的定向损失函数的预测结果优于基于使用平方、绝对、百分比或不对称损失（以夏普比率和每笔交易利润衡量）估计的模型预测的交易决策。