Tomas Franczyk, Managing Director, Head of Global Information Services, Asia Pacific, Nasdaq
Alternative data has increasingly become more popular. This is the undiscovered data from non-traditional data sources that give investors information and unique insights to help them evaluate investment opportunities.
The alternative data market, expected to become a $1.7 billion 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 have started 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 datasets from local firms to build investment models, has partnered with large insurance companies in the United States to access insurance policies on new car purchases. This enables users to accurately measure car sales before automotive manufacturers report them. This data would be extremely important, say, to hedge fund investors who need investment insights into the automotive sector.
Meanwhile, in Asia, Nasdaq is building regional-specific data products and has partnered with local fintechs and other innovative local vendors to migrate their core data and alternative data to the Quandl platform.
'Finding' data is not enough
More sophisticated technologies mean organisations 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 has started to have a big impact on institutional investing, while 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 analysed.
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 now have a 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 analysing data. Machines can process events at roughly 2,000 times the speed of humans, digest vast datasets, and work around the clock. During the investment process, AI-enabled data processing can increase the volume and quality of idea generation; this increase in data, including alternative data, when combined with computing power can help investment managers develop a long-lasting competitive advantage.
While some organisations 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 to gain 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 by making alternative data and AI the drivers of future investment research. In fact, 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.
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