  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 in
OTC markets. We apply machine learning methods to model and predict the trading
behavior of OTC dealers for US corporate bonds. We create sequences of daily
historical transaction reports for each dealer over a vocabulary of US
corporate bonds. Using this history of dealer activity, we predict the future
trading decisions of the dealer. We consider a range of neural network-based
prediction models. We propose an extension, the Pointwise-Product ReZero (PPRZ)
Transformer model, and demonstrate the improved performance of our model. We
show that individual history provides the best predictive model for the most
active dealers. For less active dealers, a collective model provides improved
performance. Further, clustering dealers based on their similarity can improve
performance. Finally, prediction accuracy varies based on the activity level of
both the bond and the dealer.
