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Trading in Over-The-Counter (OTC) markets is facilitated by broker-dealers,in comparison to public exchanges, e.g., the New York Stock Exchange (NYSE).Dealers play an important role in stabilizing prices and providing liquidity inOTC markets. We apply machine learning methods to model and predict the tradingbehavior of OTC dealers for US corporate bonds. We create sequences of dailyhistorical transaction reports for each dealer over a vocabulary of UScorporate bonds. Using this history of dealer activity, we predict the futuretrading decisions of the dealer. We consider a range of neural network-basedprediction models. We propose an extension, the Pointwise-Product ReZero (PPRZ)Transformer model, and demonstrate the improved performance of our model. Weshow that individual history provides the best predictive model for the mostactive dealers. For less active dealers, a collective model provides improvedperformance. Further, clustering dealers based on their similarity can improveperformance. Finally, prediction accuracy varies based on the activity level ofboth the bond and the dealer.

Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets