This work analyzes the volatility connectedness of the prices of ethanol and gasoline sold to
consumers in fifteen Brazilian capitals. Our data cover the period from 2012 to 2022 and …

Wholesale electricity markets have several features that increase the likelihood of collusion,
including frequent interaction, multimarket contact, and a high degree of information …

We propose a data-driven machine learning approach to flag bid-rigging cartels in the
Brazilian road maintenance sector. First, we apply a clustering algorithm to group the …

In railway infrastructure, construction and maintenance is typically procured using
competitive procedures such as auctions. However, these procedures only fulfill their …

The escalating threat of natural disasters to public safety worldwide underlines the crucial
role of effective environmental risk management tools, such as insurance. This is particularly …

We propose an original application of screening methods using machine learning to detect
collusive groups of firms in procurement auctions. As a methodical innovation, we calculate …

We propose a detection method for flagging bid-rigging cartels, particularly useful when
cartels are incomplete. Our approach combines screens, ie, statistics derived from the …

Adding to the literature on the data-driven detection of bid-rigging cartels, we propose a
novel approach based on deep learning (a subfield of artificial intelligence) that flags cartel …

Cartels and other anti-competitive behaviour by companies have a tremendously negative
impact on the economy and, ultimately, on consumers. To detect such anti-competitive …

The oil and gas industries are crucial to global economies, influencing geopolitics, driving
technological advancements, employing millions, and impacting financial markets. The …