Symphony decided to first migrate client data to Google from AWS, and then focus on applications.
As interest rates rise and house prices fall after a steady period of the opposite dynamic, investors are looking for more accurate ways to price these factors into the value of mortgage-backed securities.
The vendor is drawing on its vast collection of data to develop predictive signals for credit ratings, shareholder activism and more.
Researchers at NYU’s Courant Institute of Mathematical Sciences are using granular futures data from BMLL for research on less-covered futures markets.
For years, the mantra of the market data world has been ‘content is king.’ But with trading strategies now more dependent on being able to see the big picture, the value of context could quickly overtake the data itself.
Anthony loves when his opinions spark debate. Following responses to a recent column on consolidation among mid-market data technology vendors, he provides something of a case study, which looks at how Exegy is evolving after its acquisition of Vela.
The strategic partnership will involve a three-part integration including system connectivity, combined visualization and the creation of client feedback loops.
APG has improved prediction accuracy for G10 currency movements after adopting decision tree-based machine learning.
Creighton AI is using a regression-based approach to machine learning to help make predictions about the excess return of a stock relative to the market.
A new tool that helps business users test and validate their own POCs is set to join the bank’s ranks alongside its other AI projects implemented over the last two years: Linc, Guardrail, and Ants.
The Frankfurt-based asset manager is using machine learning to look at the performance of stocks with low returns, high-growth.
How the Covid-19 pandemic accelerated the digital transformation and the move to cloud-based services for capital markets firms, and the extent to which such offerings will continue to find traction across the industry.
Waters Wavelength Podcast Interview Series: PanAgora’s George Mussalli and Mike Chen hit on topics including building predictive models using point-in-time data, and balancing ESG portfolios.
Mary-Catherine Lader says that the asset manager is building out new modeling tools to help users better understand how the decisions a company makes today can affect their performance in the future.
The firm is working with different machine-learning methods for portfolio construction, and expects its AI system to go live early next year.
The trading platform is working to develop its pre-trade automation capabilities to predict a bond’s likelihood of execution, and helping buy-side clients navigate fixed income trading protocols.
What do Liquidnet and Trading Technologies (and others) have in common? Anthony explains. He also discusses advancement—and disillusionment—in the quantum space.
Advancements in modeling and the rise of alt data have made the process of prepping for the US presidential election more complex, but hopefully more accurate.
The modules, which use machine learning to derive predictive insights, are scheduled to go live in Q1 2021.
The unit is combining foot-traffic data and proprietary datasets derived from hospitals to develop a better understanding of outbreaks and predict a timeline for recovery.
Wei-Shen and Tony talk about alt datasets relating to the pandemic.
Investment firms and vendors are searching for signals in healthcare and pharmaceutical data in a bid to get a leg up on a Covid-19 vaccine.
Originally conceived to serve the needs of financial professionals, Predictive Insights has a wide range of predictive applications, even beyond capital markets.
The product combines emerging technologies to offer speed, scale, and new perspectives on credit spreads and portfolio performance by overlaying financial data with alt data.