Although many banks and asset managers still prefer to build models in-house, off-the-shelf products are maturing.
After Redditors staged an epic short squeeze against a handful of hedge funds, some in the industry are left wondering whether today’s models and data techniques are prepared for world where online often equals real life.
The share of electronic trading in the market remains low, but a host of factors promise to change that for good.
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.
ThemeBot uses textual relevance and revenue attribution to construct a list of stocks, which is then verified by JPMAM’s active equity analysts.
The company is consulting with buy-side and sell-side clients on how its newly developed GK Research Bot can best solve their research and information overload woes.
The system monitors annual reports for issuer compliance with listing rules, speeding up a formerly manual job.
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.