Peter Simon
Managing Director, Financial Markets Data Science
Peter leads DataRobot’s financial markets data science practice and works closely with fintech, banking, and asset management clients on numerous high-ROI use cases for the DataRobot AI Platform.
Prior to joining DataRobot, he gained twenty-five years’ experience in senior quantitative research, portfolio management, trading, risk management and data science roles at investment banks and asset managers including Morgan Stanley, Warburg Pincus, Goldman Sachs, Credit Suisse, Lansdowne Partners and Invesco, as well as spending several years as a partner at a start-up global equities hedge fund. Peter has an M.Sc. in Data Science from City, University of London, an MBA from Cranfield University School of Management, and a B.Sc. in Accounting and Financial Analysis from the University of Warwick. His paper, “Hunting High and Low: Visualising Shifting Correlations in Financial Markets”, was published in the July 2018 issue of Computer Graphics Forum.
When Does Machine Learning Work Well in Financial Markets Blog
A DATA SCIENTIST EXPLAINS: WHEN DOES MACHINE LEARNING WORK WELL IN FINANCIAL MARKETS?
January 17, 2023
by
Peter Simon
As a data scientist, one of the best things about working with DataRobot customers is the sheer variety of highly interesting questions that come up. Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. Peek into our conversation to learn when machine learning does—and doesn’t—work well in financial markets use cases.
Yield Book Analytics
Empowering financial markets with the best-in-class models, robust analytics, and related services.
Our brand is changing to LSEG Yield Book
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https://solutions.yieldbook.com/en
Machine learning-aided modeling of fixed income instruments
Daniel Martin, Barnabás Póczos
Machine Learning Department
Carnegie Mellon University
Pittsburgh, PA 15213
Burton Hollifield
Tepper School of Business
Carnegie Mellon University
Pittsburgh, PA 15213
https://www.ml.cmu.edu/research/dap-papers/f18/dap-martin-daniel.pdf
Supervised similarity learning for corporate bonds using
Random Forest proximities
Jerinsh Jeyapaulraj
2022
BondGPT
2023
https://fortune.com/2023/06/13/bondgpt-artificial-intelligence-finance-bond-kings-bill-gross-jeffrey-gundlach/
PIMCO - UNDERSTANDING INVESTING in BONDS
https://europe.pimco.com/en-eu/resources/education/everything-you-need-to-know-about-bonds
data science in bond trading - Google search interesting results
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