MIFID II’s research unbundling requirements begin to take full effect in 2018 and global asset management firms are responding by standardizing their approach across regions. As these global firms continue to adapt to the ever changing regulatory environment; Greenwich Associates recent report covered by The Trade “found large buy-side firms with a presence in multiple regions are looking to ‘lessen the burden’ of maintaining distinct research management processes in different parts of the world.” By insuring a standardized approach across all regions these firms will be “importing MiFID standards into the United States – despite the lack of a regulatory directive.” according to associate director at Greenwich, William Llamas. This provides opportunities for asset management firms as well as independent research providers to utilize and create innovative new research data to drive alpha or manage risk within a firm.
As leading industry journalist Ivy Schmerken wrote in her article Impact of MiFID II: Unbundling, the Sell Side and Research Trends, “While unbundling is stirring up uncertainty around the research payment model, the nature of research itself is also evolving.” Research historically may have been primarily analyst coverage of companies, markets or sectors but is evolving into a highly data driven resource. Chris Tiscornia, president and CEO of Westminster Research states in Impact of MiFID II: Unbundling, the Sell Side and Research Trends – “A lot of the new innovation that we’ve seen in the research space over the last 10 years has come from independents, technology companies and from groups that are employing data scientists to scrape data, whether it’s from credit card information, satellite data, or web scraping.”
These new techniques and technologies are creating even more powerful actionable insights and signals in this evolution of research data. For asset management firms, institutional investors, hedge funds and individuals we believe that research unbundling and access to quantified news using NLP and ML techniques can be a crucial component of remaining competitive in the rapidly changing research and alternate data markets.