As institutional participation in cryptocurrency markets increases, traditional data vendors and new specialist crypto data providers are taking different approaches to supplying necessary data to financial firms.
Building a startup is hard. Building a blockchain startup is harder. More than 10 current and former financial blockchain builders and users detail their experiences of trying to cut their teeth on a once-darling tech, and the lessons they’re still learning from it.
From crypto and Web3 to Robinhood and Reddit, democratization underscores it all. While it’s a largely benign concept that aims to level the playing field between institutions and individuals, it’s also really hard to get right.
The next iteration of the internet is upon us, with the potential to deliver radical shifts to every industry, including banking. The movement, which is currently buoyed by the prospects of blockchain and virtual reality, has implications for computing, data protection, networking, collaborating, and the very definition of a bank as a trusted intermediary and institution.
A look at some of the key people moves from this week, including Michelle Neal (pictured), who joins the Federal Reserve Bank of New York as head of the markets group.
While some alternative data providers are jumping in on the meme-stock craze by producing new datasets and analyses geared toward risk management and alpha generation, others—perhaps rightly so—are staying cautious.
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 evolution of natural language processing is rapidly progressing. Jo Wright takes a look at BERT, one of the more game-changing innovations that is helping to transform the field of machine learning in the capital markets.
As the spread of false information online threatens our view of the world, Josephine Gallagher examines how this phenomenon has evolved with technology.
WatersTechnology looks at 16 projects in the capital markets that involve machine learning to show where the industry is heading.
Assuming that automated artificial intelligence holds the key to unlocking fragmented datasets, the absence of standardized models coupled with regulatory concerns remain barriers to adoption.
Mazing is well-versed in using Twitter from his time running and promoting his own investment businesses and theses.
The interoperability platform will enable clients to have access to real-time data sharing and improve workflow collaboration.
Including support for concept-based cryptocurrency searches in the Selerity Context engine will provide more focused and relevant search results for institutional investors monitoring the crypto markets, officials say.
The data provider uses machine learning and natural language processing techniques to identify relevant topics.
Technology can’t solve all of the market’s problems. But sometimes events can conspire to make it seem like it can. James warns on the propensity among technologists to believe in false prophets.
Morse, a former speaker on social media usage and alternative data at IMD's North American Financial Information Summit, will help Finn.ai grow its "strategic" accounts business.
Carroll has over 30 years of experience, most recently serving as tech lead of Twitter’s NFL live-streaming project
Stephen Morse gives a presentation on how traders are using information created via Twitter to derive trading insights.
The new offering identifies unusual trading activity and correlates it to news events on Twitter.
Max Bowie explores the opportunities arising from new, alternative datasets, and the challenges of managing them.
The vendor and its clients see tweets relating to political events as a potential source of alpha.
Waters examines how natural language processing and natural language generation tools are being used on Wall Street
The social media company's stock isn't the only thing that's improving.