Nasdaq's managing director on the use of AI in data management
Its latest platform Quandl identifies data sets from firms and builds investment models.
What is crucial for success in financial markets today is being able to find data sets that are unique. Alternative data is increasingly popular. This is the undiscovered data from non-traditional data sources that gives investors information and unique insights that help them to evaluate investment opportunities. The alternative data market, expected to become a US$1.7b industry in the next few years, can encompass a range of sources. These include natively digital information like web traffic, online buying habits and social media activity as well as more granular indicators of financial performance, such as ocean cargo and automobile registration information.
In the capital markets space, alternative data is viewed as increasingly important. According to a 2019 survey by Greenwich Associates, 95% of trading professionals believe alternative data will become more valuable to the trading process, and 85% of banks, investors and capital markets service providers plan to increase spending on data management.
As this new data becomes a powerful differentiator in the search for alpha, a rapidly growing community of buy side firms are using it to add power to quantitative and fundamental investment models with the aim of outperforming the market.
For example, Nasdaq’s platform Quandl, which identifies data sets from local firms to build investment models, is partnering with large insurance companies in the U.S. to access insurance policies on new car purchases. This enables users to accurately measure car sales before auto manufacturers report them. This data is extremely important, say, to hedge fund investors who need investment insights into the auto sector.
In Asia, Nasdaq is building regional-specific data products and is partnering with local fintechs and other innovative local vendors that have both core data and alternative data that could migrate to the Quandl platform.
“Finding” data is not enough
More sophisticated technologies mean we can create datasets that support managers with short-term trading strategies as well as those with a long-term approach, such as institutional investors. Another report by Greenwich Associates last year found that 74% of firms surveyed agreed that alternative data is starting to have a big impact on institutional investing, and nearly 30% of quantitative funds attribute at least 20% of their alpha to alternative data.
But simply “finding” data is not enough. It can only work if it is properly interpreted and analyzed. By its nature, alternative data is harder to consume than financial data; it is often unstructured, does not follow patterns, and is created at a very fast rate. Hence, investors’ growing need for talent and technology, including analytics platforms, testing tools, fluid data architecture and data science teams, to help them with their data management.
Advanced analytics and artificial intelligence (AI), such as machine learning and natural language processing, can be crucial to analyzing data. Machines can process events at roughly 2,000 times the speed of humans, digest vast data sets and work around the clock. During the investment process, AI-enabled data processing can increase the volume and quality of idea generation, and this increase in data, including alternative data, as well as computing power can help investment managers develop a long-lasting competitive advantage.
Whilst some organizations are well on their way to introducing AI-based models, the industry is still understanding and identifying the operational, regulatory and technological risks that come with the race for data-driven insights and predictive capabilities. Effective risk management practices will be key for the successful adoption of AI.
Future fuel for investors
Data providers have an opportunity to assist the asset management industry as alternative data and AI look set to drive the future of investment research. We may see active portfolio managers look less to the sell-side for their research needs and instead develop their own research, invest in data experts and technology, and partner with vendors to supply the information and analytical tools they need.
Written by: Tomas Franczyk, Managing Director and Head of Investment Intelligence, Asia Pacific at Nasdaq
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