美国投资级公司债券定价bit like navigating your way to a friend’s summer house for the weekend. They give you directions, and mile markers along the way, but your journey is invariably marked by traffic, potential detours, and other factors holding up your holiday. Fortunately, GPS technology and community driven platforms like Google Maps and Waze have transformed how we travel by providing live reference data to find the fastest and easiest route to our destinations.
如果信用交易员以前利用其关系确定定价和流动性，他们开始受益于类似的技术来导航市场，并在没有陷入栅格捕获的情况下实现最佳执行。
映射公司债券景观
In many marketplaces, there are enough visible prices to help discern where the market is trading before an investor seeks to process an order or execute. This level of pre-trade transparency is relatively common in treasuries, equities, FX, or futures, but not in more bespoke markets like U.S. corporate bonds.
“这部分是一个精确的问题，但我会争辩到这一点，”美国学报队长Chris Bruner说TradeWeb.。“这也是一个时间问题。例如，拍摄公共跟踪数据并确定最后一次贸易传播是不难的。但是，如果上次贸易在公司债券上蔓延到一个月前，那么现在债券的价格是多少？在当前的环境中，产量可以在几周内转移30或40个基点。“
这种缺乏普遍同意的贸易前基准价格让市场参与者以零碎的方式进行，这使得这一工作很困难。“你甚至不能做一个不完善transaction cost analysis if you don’t have some sense of what the benchmark price was at the time of execution,” says Bruner.
This is why institutional credit trading is frequently described as part art and part science. But as is the case with GPS and traffic apps, Bruner and his team at Tradeweb are adding a heavier dose of data science to improve the equation for pricing in the U.S. corporate bond market.
一个新模式不仅仅是更多信息
利用其在固定收益方面的专业知识，TradeWeb制定了更尖锐的复合定价模式，利用了债券之间的关系;根据流动性，成熟，自发行以来的时间等因素，在其他方面。因此，数据告诉您债券类似于其“最近邻居”的程度，并且交易商出现了市场的更清晰的图片。该模型远远超出了更多信息的聚合，以更复杂的，数据驱动的方法，使得赋予交易者更精确，并专注于他们理解债券交易的地方。它将数据与前所未有的规模融为一体，提供市场参与者，以依据以相对方式做出决策，而不是通过电话征求多项价格。
“其他资产类别中使用的典型方法，例如引导曲线，不要捕获公司债券价格变动的动态。我们的数据科学模式与我们的域名专业知识结婚，以获得债券之间的关系以及这些关系如何通过时间演变，“布鲁纳说。“随着世界上每一项公司债券贸易的数据，我们采取所有这些信息和这些证券之间的协会，并说'正确的价格如果it were going to trade again right now?’ That’s basically our approach.”
创新whose time has come
TradeWeb的复合模型的组成部分在过去几年中沿着独立的轨道演变;但是，解锁了将它们的创新集中在一起是一个相当近来的发展。
“我们目前从利率掉期和国债和国债的高质量数据一起使用，以及定价分析和参考数据，以捕捉所有这些债券的特征。本质上，它是一种在实时处理信息的发动机，并在10,000多个债券上提供市场数据。我们正在使用我们知道提供价值和强大的输出的来源和绑定宇宙。在未来，将在适当的情况下添加额外的数据来源和债券覆盖范围，其中有形益处可以证明，“布鲁纳说。
随着对信用市场的这种定价基准的需求，该模型的潜在用途众多。它已经在TradeWeb的自动智能执行（AIEX）功能中具有差异，它使用预编程规则直接从OM直接执行大量的交易。
“Our clients who use AiEX need a price quality check to make sure a price isn’t too far out of bounds, and they’re already using our reference price as that check,” says Bruner. “That is strong confirmation we are headed in the right direction so we will continue to refine. It’s important to note we are not suggesting the model is an executable price. Rather, having a robust reference prices with broad coverage allows new workflows that didn’t exist before. In this case, combining AiEX with the real-time pricing helps fully automate trades within a tolerance, but allows clients to manually inspect those that need closer attention before executing.”
Yet another change the reference price model could bring about is in transaction cost analysis (TCA). In the near future, the model will power transaction cost analytics for Tradeweb credit clients. “We have an opportunity to offer a differentiated solution; the model delivers advanced metrics such as liquidity scores, price confidence values, and flow metrics, along with price estimates. They’ll easily be able to look at whole portfolios of trades and have an efficient route to figure out pricing, and finally be able to move away from the current trade-by-trade process. That’s the value of having a better benchmark.”
随着模型不断成熟，购买和销售方面的参与者将获得利益。在买边，该模型将有助于构建投资组合，了解投资组合中的流动性，并使执行更有效。在销售方面，胃口比比变得更加有效的系统性市场制造商，高质量的贸易前信贷投入来源将不可避免地被视为可信赖和较差的复合材料。信用市场尚未实现这一创新的影响，但贸易队的价格将来自贸易网络。