The goal of this paper is to establish a benchmark for transaction cost analysis in bond trading for retail investors. Investors can use this benchmark to improve decisions when requesting quotes from dealers on electronic platforms . This benchmark is constructed in two steps. The first step is  to identify abnormal trades by approximating the market liquidity using different statistical regression methods: OLS, two-step LASSO and Elastic Net  . The second step is to  estimate the amplitude and the decay pattern of price impact using non-parametric methods . A key discovery is the price impact asymmetry between customer-buy orders and costumer sell orders: customer buy orders have a larger price impact than customer sell orders. We show that this asymmetry is statistically significant .
With the rise of the electronic trading, corporate bond traders have access to data information of past trades. As a first step to automation, they have to start monitoring their own trades, and using past data to build a benchmark for the expected transaction costs with given bond characteristics and market conditions. Given the limited liquidity of corporate bonds which are traded few times daily, a statistical model is the only way to benchmark effective costs. It brings focused attention of the dealing desk of an institutional investor on the most costly trades, and enables identifying and improving business practices such as the market timing for selection counterparties. Unlike existing literature which focuses on general measurements using OLS, this paper takes the viewpoint of a given investor, and provides an analytical approach to establish a benchmark for transaction cost analysis in corporate bond tradings. Regularized methods are used to improve the selection of explanatory variables, as fewer variables provide easier analytics from a business perspective . This benchmark is constructed in two steps. The first step is the regression analysis with cross validation to identify abnormal trades . Three regression approaches, OLS, two-step Lasso and Elastic Net , are adopted to identify key features for the bid-ask spread of corporate bonds . The second step is to use the non-parametric approach to estimate the amplitude and decay pattern of price impact . A key discovery is the price impact asymmetry between customer-buy orders and consumer-sell orders. This benchmark can aid decision makings for retail investors when requesting quotes on the electronic platform .
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The goal of this paper is
With the rise of the electronic trading, corporate bond traders have access to data information of past trades. As a first step to automation, they have to start monitoring their own trades, and using past data to build a benchmark for the expected transaction costs with given bond characteristics and market conditions. Given the limited liquidity of corporate bonds which are traded few times daily, a statistical model is the only way to benchmark effective costs. It brings focused attention of the dealing desk of an institutional investor on the most costly trades, and enables identifying and improving business practices such as the market timing for selection counterparties. Unlike existing literature which focuses on general measurements using OLS, this paper takes the viewpoint of a given investor, and provides an analytical approach
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bond trading for retail investors. Investors can use this benchmark to improve decisions when requesting quotes from dealers on electronic platforms
corporate bond tradings. Regularized methods are used to improve the selection of explanatory variables, as fewer variables provide easier analytics from a business perspective
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the regression analysis with cross validation
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by approximating the market liquidity using different statistical regression methods:
. Three regression approaches,
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LASSO
Lasso
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, are adopted to identify key features for the bid-ask spread of corporate bonds
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use the non-parametric approach to
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the
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using non-parametric methods
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costumer sell orders: customer buy orders have a larger price impact than customer sell orders. We show that this asymmetry is statistically significant
consumer-sell orders. This benchmark can aid decision makings for retail investors when requesting quotes on the electronic platform