Transaction Cost Analytics for Corporate Bonds

TL;DR

This paper develops an analytical framework for Transaction Cost Analysis of corporate bonds, estimating transaction costs, price impact, and decay patterns using TRACE data, with implications for benchmarking and trade evaluation.

Contribution

It introduces a novel methodology combining regression models and impact analysis to assess transaction costs and price impacts in corporate bond trading.

Findings

Identifies key features affecting trading costs.

Estimates price impact and decay patterns of bond trades.

Discovers asymmetry in price impact between buy and sell orders.

Abstract

The electronic platform has been increasingly popular for executing large corporate bond orders by asset managers, who in turn have to assess the quality of their executions via Transaction Cost Analysis (TCA). One of the challenges in TCA is to build a realistic benchmark for the expected transaction cost and to characterize the price impact of each individual trade with given bond characteristics and market conditions. Taking the viewpoint of retail investors, this paper presents an analytical methodology for TCA of corporate bond trading. Our analysis is based on the TRACE Enhanced dataset; and starts with estimating the initiator of a bond transaction, followed by estimating the bid-ask spread and the mid-price dynamics. With these estimations, the first part of our study is to identify key features for corporate bonds and to compute the expected average trading cost. This part is on the time scale of weekly transactions, and is by applying and comparing several regularized regression models. The second part of our study is using the estimated mid-price dynamics to investigate the amplitude of its price impact and the decay pattern of individual bond transaction. This part is on the time scale of each transaction of liquid corporate bonds, and is by applying a transient impact model to estimate the price impact kernel using a non-parametric method. Our benchmark model allows for identifying abnormal transactions and for enhancing counter-party selections. A key discovery of our study is the price impact asymmetry between customer-buy orders and consumer-sell orders.