The Barclays Liquidity Cost Score (LCS®) is an objective, quantitative, bond-level liquidity metric developed and produced by Barclays’ Quantitative Portfolio Strategy team (QPS). LCS is expressed as a percent of the bond’s price and measures the cost of an immediate, institutional-size, round-trip transaction. It is consistent and comparable across asset classes and over time. LCS relies on simultaneous bid-ask quotes issued by Barclays traders and is computed at the end of each month, incorporating quotes data collected over the course of that month.
Besides Monthly LCS, Daily LCS aggregated to a bucket-level (e.g. sector, rating, region etc.) are available for actively-quoted subsets of the USD and EUR investment grade and high yield credit, USD emerging markets, and USD convertible bonds.
Along with LCS, which is an absolute measure of liquidity, a companion relative, intra-market metric is included in the feed: Trade Efficiency Score (TES). This measure combines LCS and trading flow to produce a liquidity rank from 1 (most liquid) to 10 (least liquid). Incorporating trading volume helps reflect market’s capacity to absorb large or numerous trades. TES facilitates cross-sectional comparison of bonds within the same market, unaffected by secular liquidity trends.
As of December 2019, this feed contains monthly LCS for more than 22,000 global fixed-income securities with a total amount outstanding exceeding $50 trillion.
Asset Class: Government, Agency, and Public Companies’ debt
Data Frequency: Daily: Sector and Quality Aggregates, Monthly: Bond-level detail
Delivery Frequency: Weekly: Sector and Quality Aggregates, Monthly: Bond-level detail
History: The inception date varies by asset class. The longest historical time series are USD IG and HY Credit (from January 2007).
The inputs into LCS calculation are collected daily and include Barclays traders’ bid-ask quotes, TRACE and TRAX bond trading volume, bond analytics, and indicative data.
The LCS methodology includes an algorithm to estimate the likelihood of a particular quote to be an indication rather than a firm, executable quote. In the former case, the model adds a certain degree of conservatism by widening the bid-ask spread.
LCS for bonds not quoted in a particular month are estimated by intuitive and transparent econometric models specific for each market and based on the universe of quoted bonds. The process performs a cross-sectional regression analysis that estimates a statistical relationship between the observed LCS of quoted bonds and various observable bond attributes which investors would be looking at to assess liquidity of a bond (e.g. amount outstanding, age, spread etc.) It is logical to assume that the same relationship holds for non-quoted bonds, so their LCS are calculated accordingly. In the spirit of making LCS a conservative measure, the estimated values are adjusted upwards, assuming that a non-quoted bond is likely to be less liquid than a quoted bond with similar attributes.
Trade Efficiency Score (TES) is calculated as the second step, blending LCS and trading volume into a single rank.
Analysis of Market Liquidity Trends
The consistent methodology ensures LCS comparability over time. The fact that LCS is a bond-level measure means that investors can aggregate it to any desirable market segment, with infinite flexibility. These two properties of LCS allow customized analysis of liquidity trends over time, for example, focusing on particular periods of interest such as the financial crisis of 2008.
Liquidity-constrained portfolio/benchmark construction and optimization
Passive, benchmark-tracking investment vehicles are particularly difficult to implement in the fixed income space because of very large numbers of diverse instruments, by far exceeding the number of bonds in the portfolio. A bond-level liquidity measure helps narrow down the investable universe while holding down rebalancing costs.
Quantifying the impact of transaction costs on alpha strategies
Back-tested performance of alpha-generating strategies sometimes underestimates high transaction costs that can make or break such strategies. Given the highly-customized nature of most alpha strategies, having a bond-level metric allows to introduce a liquidity dimension making such back testing more realistic.
Credit spread decomposition into the risk premium, default, and liquidity components
Credit debt investors are compensated for three main risks: market volatility, possibility of default, and illiquidity. Any spread decomposition methodology needs quantitative factors representing each of these risks. Having an objective, quantitative, bond-level liquidity metric greatly facilitates such analysis.
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