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.
WatersTechnology looks at some of the major projects coming out of Asia that are leading the way for firms around the globe.
This year, natural language processing came to the fore in capital markets, helping firms of all kinds parse huge, unstructured datasets.
The firm is working with different machine-learning methods for portfolio construction, and expects its AI system to go live early next year.
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 data provider is using natural language generation to summarize news articles and write automated stories.
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.
Anthony says that while machine-learning models have been hit-and-miss during the pandemic, NLP is taking on greater importance. He also looks at how exchanges are looking to move their core matching engines to the cloud.
Quants wrestle with how far into the past their machine learning models should peer.
The exchange's ESG Footprint converts ESG data into everyday metrics to show investors the impact of their portfolios.
A look at some of the key people moves from this week, including Steven Nichols (pictured) who has been appointed head of NLP and unstructured data at Liquidnet.
Wei-Shen thinks about how the battle for supremacy in AI will evolve in the near future, and what the implications of China’s advances in the field might mean for Wall Street technology.
Anthony takes a look at some new alternative data offerings coming to market, and also explains why there’s so little election coverage on this website.
The news sentiment and analysis specialist wants to help banks tap into the datasets they sit on every day, but don't yet possess the capabilities to use.
Anthony explores changing concept of a trading platform, and what that might mean for the future of tech development.
Once the bot is in production, the D10X team will start scaling it beyond the oil trading team to other trading desks.
Victor explains how Citi Ventures—Citibank’s corporate venture arm—and the D10X program approaches challenges.
In minutes, JEFQuants compiles information from multiple sources into a unique data package based on traders' queries.
What do Liquidnet and Trading Technologies (and others) have in common? Anthony explains. He also discusses advancement—and disillusionment—in the quantum space.
The bank will look to enhance existing capabilities and potentially introduce new solutions with Google’s help.
The asset manager has adopted materiality tools, industry handbooks, and NLP techniques to help navigate ESG data limitations.
Quant funds are striving to adjust their ESG models to take into account changes in corporate behavior during the pandemic.
Anthony looks at an interesting project using causal inference by IBM and Refinitiv, and what this latest evolution of machine learning could mean for innovation in the capital markets in the future.