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Trumid Launches New Adaptive Model for Continuous Intraday Corporate Bond Pricing
  • USA - English


News provided by

Trumid

Sep 19, 2017, 09:00 ET

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FVMP automatically uses both endogenous and exogenous market data to produce a single, defensible price per bond.

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New York, NY (PRWEB) September 19, 2017 -- Trumid, a financial technology company that brings trading efficiency and market intelligence to credit market professionals, today launched its new service for continuous bond pricing, known as the Fair Value Model Price (FVMP). FVMP is an adaptive model that produces a price for approximately 22,000 corporate bonds every five minutes based on price movements from multiple data sources, including the Trumid Market Center. Trumid believes the new service will improve liquidity by providing a constant source of pre-trade transparency, empowering traders to execute trades with confidence.

“Like the prices of other securities, bond prices are affected by numerous factors,” said Jason Quinn, Head of Product Development at Trumid. “FVMP automatically uses both endogenous and exogenous market data to produce a single, defensible price per bond. Our Trumid Labs team has built a pricing model that is able to be more consistent, objective and adaptive than human-evaluated prices.”

In FVMP’s nine-month testing period, Trumid Labs, Trumid’s internal quantitative analytics group, found that FVMP-produced bond prices were consistently in line with the subsequent TRACE(TM) data. For example, investment grade bonds scored by FVMP were within 0.55 points of the following TRACE print 95% of the time, and high-yield bonds were within 0.82 points. At the 80th percentile, the absolute error of the overall FVMP model is 0.28 points.

“In addition to using novel model-based approaches to achieve consistent pricing performance across the large corporate universe, Trumid Labs has also developed a framework for which services such as these can be evaluated,” said Jason Quinn. By comparing and measuring the difference between any recent TRACE print and the prior FVMP value of that bond, Trumid Labs is able to calculate the absolute error of each estimate. Trumid calls this approach the regularized next trade metric (RNTM).

For the first time in the industry, Trumid is making comprehensive FVMP pricing publicly available on its website, alongside the trailing three months of FVMP performance reports and a complete whitepaper detailing methodology. Trumid aims to inform any institution seeking to trade corporate bonds by posting this information in a public forum.

To access the FVMP model in real time, download FVMP performance reports or view the model whitepaper, please visit https://www.trumid.com/labs.html

About Trumid
Trumid is a financial technology company bringing efficiency to credit trading through data, technology, and beautifully simple products. Trumid Market Center, the company’s all-to-all electronic trading and market intelligence platform, provides corporate bond market professionals with direct access to anonymous liquidity. Trumid’s products leverage the network effect and data science to empower all credit market participants to make more informed decisions. Trumid was founded in 2014 by credit professionals with over 100 years of trading experience and is headquartered in New York City. Learn more at http://www.trumid.com

Information included in this message does not constitute a trade confirmation or an offer or solicitation of an offer to buy/sell securities or any other products. There is no intention to offer products and services in countries or jurisdictions where such offer would be unlawful under the relevant domestic law.

Raanan Loew, Trumid, http://www.trumid.com, +1 (347) 897-9276, [email protected]

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FVMP provides transparent pre-trade information. Download whitepaper at https://www.trumid.com/labs.html
FVMP provides transparent pre-trade information. Download whitepaper at https://www.trumid.com/labs.html
FVMP provides transparent pre-trade information. Download whitepaper at https://www.trumid.com/labs.html

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