A discussion with Airbnb Co-founder and CEO Brian Chesky on leadership in a crisis, how the coronavirus pandemic has changed the hospitality and travel industries, and what that means for the future of the home-sharing unicorn.

In recent years, we have seen an unprecedented explosion of interest in applying artificial intelligence and machine learning to a variety of quantitative finance problems, ranging from derivatives pricing and risk management to market forecasting and algo trading. In fact, Artificial Intelligence and Machine Learning are now seen as the greatest enablers of competitive advantage in the finance sector. 

In this paper, Svetlana Borovkova, Probability & Partners, Margot Dijkstra, VU Amsterdam, and Rossy Nguyen, VU Amsterdam use Refinitiv News Analytics, Refinitiv's natural language processing engine in two applications of machine learning with sentiment: 

This week we look at how the Chinese equity market is echoing the market of 2015, which eventually reached bubble territory before the inevitable crash back down to earth. Investors should avoid being seduced by a breakout. In the Chatter, we look at the build-up in government and corporate debt that makes it almost impossible for central banks to extract themselves from the markets. In the Whisper, we highlight a few technical indicators which together may now warrant some caution on equities in general. 

Roger Hirst and Daniel Buttino, Senior Account Director at Refinitiv, talk about how the COVID pandemic has set back Brazil’s prospects after the local equity market dropped nearly 50%. Is there light at the end of the tunnel for the newly reformist nation of Brazil?

In today’s world of globalization and interdependence and in times of financial crisis, issues such as climate change, biodiversity, human rights, “licence to operate,” business ethics and corporate governance are at the forefront of public and political attention.How companies respond to these issues is becoming as important as traditional financial metrics when evaluating corporate performance, therefore playing a more central role in investors’ decision-making efforts to identify long-term opportunities and risks for companies. ESG factors can be easily integrated into portfolio analysis, equity research, screening or quantitative analysis.Information is collected by our ESG specialists based on publicly available sources such as company websites, annual reports, and corporate social responsibility reports or contributed by firms then audited and standardized.

Access a cloud-hosted development environment for Python scripting, enabling you to leverage the Refinitiv Data Platform APIs to rapidly build and deploy models, apps and analytics that fit your workflow needs.

In January, the growth of UK fintech looked unstoppable. 2019 saw huge levels of investment. We were set for another record-breaking year in 2020. That was until Covid-19 threw the world into disarray. So what does Covid-19 mean for UK fintech in the long term? Is it a fatal? A bump in the road? Can the sectors’ strengths see it through?

We see plenty of reasons for optimism. While fintech has taken a few hits, it’ll take more than Covid-19 to damage the UK’s lead. Here are five ways in which fintech will bounce back.

In recent years, we have seen an unprecedented explosion of interest in applying artificial intelligence and machine learning to a variety of quantitative finance problems, ranging from derivatives pricing and risk management to market forecasting and algo trading. In fact, Artificial Intelligence and Machine Learning are now seen as the greatest enablers of competitive advantage in the finance sector. 

In this paper, Svetlana Borovkova, Probability & Partners, Margot Dijkstra, VU Amsterdam, and Rossy Nguyen, VU Amsterdam use Refinitiv News Analytics, Refinitiv's natural language processing engine in two applications of machine learning with sentiment: 

Investors are increasingly demanding that ESG criteria are factored into their portfolios. To help satisfy this demand, fund ESG scores enable investors to objectively assess the ESG performance of a company or a fund. But how is it possible to achieve a consistent, reliable and unbiased score system?

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MIT adjunct professor Michael Stonebraker, a computer scientist, database research pioneer, and Turing award winner, said he sees several things companies should do to build their data enterprises — and just as importantly, mistakes companies should cease or avoid.

In a talk last fall as part of the 2019 MIT Citi Conference, Stonebraker borrowed a page from David Letterman to offer 10 big data blunders he’s seen in the last decade or so. His (sometimes opinionated!) advice comes from discussions with tech and data executives during more than decades in the field as well as his work with several data startups.  

Here’s Stonebraker’s list, including a bonus tip.

Blunder #1: Not moving everything to the cloud

Companies should be moving their data out of the building and into a public cloud, or purchase a private cloud, Stonebraker said. Why? Firms like Amazon offer cloud storage at a fraction of the cost and with better infrastructure,...

The biggest concern for any machine learning developer is to figure if their models work outside their labs, in the real world, and in the wild. The emergence of pragmatic ML as a domain coincides with the increasing adoption of AI. But, when can one call their system to be pragmatic?

To build more robust learning systems, it is essential to design benchmarks for the attributes of these sub-tasks. Even before that, we have to define what these subtasks and their attributes are. According to the researchers at the University of Washington, there are 5 key attributes, accounting for which can make machine learning work in the real world.

Here are the 5 desired attributes of a pragmatic machine learning system:

Fintech has dominated the news cycle this year. Bold headlines celebrated large exits (Plaid, Credit Karma, Personal Capital) and venture-capital investment poured in.

While the category has heated up quickly, the sheer size of the fintech opportunity suggests that these exits are just the tip of the iceberg. In the next five years, fintech will drive some of the biggest VC exits.

Automating tasks previously done by hand to simplify and enhance production is nothing new. Humans have been doing it since 350 BCE when the first waterwheels for processing grain were recorded in Syria and Egypt.

Today, many businesses are embracing technology to automate manual processes, generating a 30-200 per cent return of investment in the first year. With 74 per cent of organisations actively looking for new use cases for automation it’s no surprise that by 2022 it’s estimated that 42 per cent of total task hours will be completed by machines.