Short-term dynamics in auction-based Day-Ahead electricity markets remain insufficiently studied. This paper investigates two such aspects in the Irish Day-Ahead market. First, we address an empirical gap by examining the extent of speculator (financial trader) participation and its evolution over time in a European Day-Ahead setting. Using granular participant-level order and trade data, we quantify speculators’ share of overall market activity and assess how often they are marginal in the auction. Although their share of orders and trades is relatively small, speculators are marginal in a substantial proportion of trading periods and their behaviour changes significantly following a Brexit-related structural shift in market coupling. Second, we introduce a sensitivity-based measure of price inertia defined as the resistance of prices to small changes in demand or supply, adapted to the Day-Ahead auction context, a dimension of market behaviour that has received little prior attention. We find that inertia levels vary considerably and also shift following the structural change. Taken together, these analyses provide empirical evidence that speculators play a non-negligible role in a European auction-based Day-Ahead market, while price inertia offers an additional lens through which to examine short-term market dynamics and their evolution under different market conditions.

Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing market is the closest to real-time and the most volatile among them. Its price forecasting literature is limited, inconsistent and outdated, with few deep learning attempts and no public dataset. This work applies to the Irish balancing market a variety of price prediction techniques proven successful in the widely studied day-ahead market. We compare statistical, machine learning, and deep learning models using a framework that investigates the impact of different training sizes. The framework defines hyperparameters and calibration settings; the dataset and models are made public to ensure reproducibility and to be used as benchmarks for future works. An extensive numerical study shows that well-performing models in the day-ahead market do not perform well in the balancing one, highlighting that these markets are fundamentally different constructs. The best model is LEAR, a statistical approach based on LASSO, which outperforms more complex and computationally demanding approaches.

This paper presents two distinct but related empirical analyses of the Irish Day-Ahead electricity market, which underwent a structural change during the study horizon due to a Brexit-related shift in market coupling. First, we examine the activity of speculators (i.e. financial traders), measuring their overall market share in terms of order volumes and executed trades. We also assess the proportion of trading periods in which speculators are marginal, that is when their bid or offer prices equal the market-clearing price. Despite their relatively small share of total market activity, we find that speculators are marginal in a significant percentage of trading periods. We also identify a clear shift in their behavioural patterns following the structural market change. The second analysis introduces an intuitive method to quantify price inertia, the extent to which market prices resist small changes in demand or supply. While conceptually similar to liquidity or order book depth in continuous trading environments, this approach is applied within the context of a Day-Ahead auction market. We observe that price inertia levels also change following the structural shift. The Irish market offers a representative and transparent setting for short-term European electricity markets, allowing for clear identification of participant types. A clearer understanding of both aspects is essential for regulators evaluating market design as well as for market participants managing risk in Day-Ahead markets.