The data vendor’s product is its first that aims to sort what it believes to be truly innovative companies from the pack.
Researchers from a Paris university are using the provider’s data and coding environment to build models for more efficient regulatory approaches.
A summary of some of the past week’s financial technology news.
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.
Algorithm development specialist BestEx Research is making a play to address inefficiencies in futures trading algorithms.
ThemeBot uses textual relevance and revenue attribution to construct a list of stocks, which is then verified by JPMAM’s active equity analysts.
Buy-side firms are using patent and other types of data to identify trailblazer companies from phonies.
WatersTechnology looks at how 10 different firms are embedding machine learning algorithms into their platforms and tools.
Technologists are working to automate indications of interest from trading desks, according to UBS’s head of machine learning.
Agent-based modeling has taken root seemingly everywhere throughout the last decade, from theoretical physics, to military operations, to public health, to ride-sharing apps like Uber, and to a much lesser extent, finance. However, a year such as 2020…
Anthony provides some of his initial questions and thoughts following the S&P-IHS Markit deal. He also takes a second look at AML technology after getting some sage feedback.
Sell-side firms and data providers are increasingly experimenting with natural-language generation to create new forms of automatically curated reports, emails and alerts, but the technique comes with significant challenges.
The QED team within the asset manager is aligning its focus next year to deliver a holistic AI platform to its investment professionals—including a recommendation engine.
Quants wrestle with how far into the past their machine learning models should peer.
Anthony wonders if AML platforms are being scrutinized enough by banks and regulators, then looks at Wells Fargo's tapping of HPR for its quant division and Northern Trust’s blockchain plans.
Already well established as an alpha-enhancing input to equities trading, sentiment data is now being applied to other asset classes, starting with foreign exchange.
Wells Fargo’s Quantitative Prime Services division has tapped HPR’s Unimus platform, starting with its market access gateway and risk management tool.
The bank’s recent moves signal what could become a managed services offering, as Goldman further embraces cloud, open source, and APIs.
In minutes, JEFQuants compiles information from multiple sources into a unique data package based on traders' queries.
Quants and data scientists can now access five years of Level 3 data through the vendor's Data Lab platform for use in alpha generation.
Ratings toolkit includes features to help investment teams grade securities and funds to meet sustainability mandates.
Later this year the vendor is looking to allow users to clip together various components of an algorithmic trading strategy, making it easier for users with limited programming skills to build their own trading strategies.
Quant funds are striving to adjust their ESG models to take into account changes in corporate behavior during the pandemic.
The two companies are in the early stages of using causal inference to help firms build machine learning models that are better able to handle disruption from events like the Covid-19 pandemic.