Bidding models for bond market auctions

Abstract: In this study, we explore models for optimal bidding in auctions on the bond market using data gathered from the Bloomberg Fixed Income Trading platform and MIFID II reporting. We define models that aim to fulfill two purposes. The first is to hit the best competitor price, such that a dealer can win the trade with the lowest possible margin. This model should also take into account the phenomenon of the Winner's Curse, which states that the winner of a common value auction tends to be the bidder who overestimated the value. We want to avoid this since setting a too aggressive bid could be unprofitable even when the dealer wins. The second aim is to define a model that estimates a quote that allows the dealer to win a certain target ratio of trades. We define three novel models for these purposes that are based on the best competitor prices for each trade, modeled by a Skew Exponential Power distribution. Further, we define a proxy for the Winner's Curse, represented by the distance of the estimated price from a reference price for the trade calculated by Bloomberg which is available when the request for quote (RFQ) arrives. Relevant covariates for the trades are also included in the models to increase the specificity for each trade. The novel models are compared to a linear regression and a random forest regression method using the same covariates. When trying to hit the best competitor price, the regression models have approximately equal performance to the expected price method defined in the study. However, when incorporating the Winner's Curse proxy, our Winner's Curse adjusted models are able to reduce the effect of the Winner's Curse as we define it, which the regression methods cannot. The results of the models for hitting a target ratio show that the actual hit ratio falls within an interval of 5% of the desired target ratio when running the model on the test data. The inclusion of covariates in the models does not impact the results as much as expected, but still provide improvements with respect to some measures. In summary, the novel methods show promise as a first step towards building algorithmic trading for bonds, but more research is needed and should incorporate more of the growing data set of RFQs and MIFID II recorded transaction prices.