A blind principal bid (BPB) is one of the mechanisms for simultaneously trading a basket of stocks at a pre-determined execution price. In a BPB, asset managers auction a basket of stocks directly to liquidity providers who do not know the identities of the individual stocks in the basket. Unlike other methods of trading, the cost and composition of the BPB basket are not reported in a standard and timely manner. Complete basket data are available only to the asset manager and the broker who won the auction. The current literature contains very little information on the BPB phenomenon, largely due to a lack of public data for research. This paper analyses a unique dataset of 140 executed baskets, building on the seminal papers of Kavajecz and Keim (2005) and Stoll (1978a, b) to develop empirical and structural models of BPB trading costs. Our research provides novel insights into the dynamics of pricing BPB trading costs, a topic that has rarely been examined in the literature. The research reported here also has significant practical applications. Asset managers obtain a benchmark for evaluating the lowest bid, and brokers obtain qualitative insights that can aid them in formulating their bids.