Buy-side research sources are changing as asset managers embrace alternative data and Artificial Intelligence (AI). How are these emerging technologies being used to deliver smarter investments?
- Emerging technologies, such as alternative data and AI, mean that asset managers are increasingly able to use their own proprietary research.
- A recent Investment Association event discussed AI and Natural Language Processing as tools for asset managers to diversify portfolios and find new investments.
- Examples of alternative data in buy-side research include a StarMine model of credit risk, which has the potential to pre-date ratings changes from agencies.
“Making predictions is hard, especially about the future” – Niehls Bohr, the Nobel Prize winning Dutch scientist, was witty as he was insightful. For firms on the buy side, this is proving a testing truism.
Macro-political instability, question marks over the tail-end of the market cycle, and the ever-present threat of margin compression from passive funds are all posing challenging questions for active managers.
What is not hard to predict is that the investment management industry will look vastly different over the next five years compared with the previous five.
The landscape for how investment decisions are considered, analyzed, executed and reviewed is being indelibly impacted by new technologies and data sources.
Undoubtedly, MiFID II unbundling has shaken up the traditional model of research consumption.
But it also reaffirmed a pre-existing trend within the buy side: away from reliance on the sell-side to guide their investment decisions and towards their own proprietary research and models.
Results from a Refinitiv/Greenwich survey published in June — based on in-depth interviews with 30 CIOs, portfolio managers and analysts — confirmed this, with 90 percent of respondents stating that their primary source of investment research was internal.
Demand for alternative data
At a recent Investment Association event in London, held in partnership with Refinitiv, speakers from the buy side discussed the emergence of technologies such as artificial intelligence (AI) and unstructured data sources, including text mined through Natural Language Processing.
These are the tools that will provide a crystal ball for keen-eyed asset managers to manage risk, diversify portfolios and discover new investment opportunities.
Refinitiv’s Joe Rothermich, Senior Director of Quantitative Science, and Tim Gaumer, Director of Fundamental Research, told the audience of CIOs, portfolio managers and technology officers how quickly interest has grown in new technologies to support proprietary research.
In 2012, an AI-focused CFA event being organized by Joe was canceled because of a lack of interest. Today, artificial intelligence is firmly embedded in the investment zeitgeist.
Alternative data seems to be riding a similar crest, and here there is an obvious uptake among the buy side.
Half of the respondents to the Refinitiv/Greenwich survey were using alternative data in their investment processes, with a further 20 percent expecting to do so over the coming 12 months.
Knowledge Graph
Tim told the event how his StarMine team at Refinitiv had developed a model of credit risk by using the alternative and unstructured data available in Eikon, which could pre-date ratings changes from the agencies. Sears’ recent struggles were clearly signposted by the model.
Tim and Joe explained how this approach was being taken one step further by using Refinitiv’s Knowledge Graph, which maps links between entities such as organizations, people, financial instruments, and supply chains to identify opportunities or mitigate risks.
While making predictions can be a thankless task, what is clear is the direction of travel for the buy side.
As asset managers wake up to the power of these technologies and seek guidance from industry bodies on the future, Refinitiv will be a key partner to provide practical insights on how they can be applied.