Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets
This addresses the need for better modeling of dealer behavior in OTC markets to enhance liquidity and price stability, but it is incremental as it builds on existing neural network methods with specific extensions.
The paper tackled the problem of predicting trading behavior of dealers in Over-The-Counter corporate bond markets using machine learning, resulting in improved performance with a proposed PPRZ Transformer model and showing that individual history works best for active dealers while collective models help for less active ones.
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.