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α: calibrated so average coauthorship-adjusted count equals average raw count
Real-time information has the potential to improve market outcomes in wholesale electricity markets. However, transparency can also facilitate coordination between firms, raising questions over the appropriate extent of information disclosure. Despite this ongoing debate, there is a lack of understanding of the information employed by firms when bidding in wholesale electricity markets. Using data from Alberta, we leverage machine learning techniques to evaluate the real-time information firms use when forming their bidding decisions. We find that aggregate market-level variables emerge as important predictors, while detailed firm-specific information does not lead to a material improvement in predicting firms’ bidding decisions. These results suggest that firm-specific information, which has raised concerns because of its potential use in facilitating coordinated behavior, may not be required to promote efficient market outcomes.