Information and Transparency: Using Machine Learning to Detect Communication

Information and data transparency have been shown to have an important impact on competitive behavior and market outcomes. Market transparency can enhance competition by allowing firms to respond efficiently to a changing market environment. However, a high degree of information can facilitate coordination by enhancing communication and the monitoring of rival behavior. A recent example highlighting concerns over the use of publicly available information to communicate across firms involves the Alberta wholesale electricity market. This market used to release anonymized information on firms’ pricing strategies in near real-time. Allegations were raised that firms were using unique patterns in their prices to reveal their identities to rival firms and coordinate on higher prices. This paper uses machine learning techniques to investigate how firms could use anonymized publicly available information to communicate with their rivals. These techniques can be employed as a possible screen to evaluate whether publicly available information can be used to identify rival behavior and facilitate coordination. Based on these results, regulators can determine if the degree of market transparency is detrimental to market competition.