Earlier this month, I moderated a panel of leaders in the alternative data space on the topic of the booming data landscape. While alternative data was a field once dominated by hedge funds, it is quickly being adopted by more traditional asset managers who are looking to new data sources, such as satellite imagery, shipping feeds, and news sentiment scores to feed their investment strategies.
As the financial industry accelerates its use of new data sources, experts including Leigh Drogen, CEO of Estimize; Ruggero Gramatica, CEO and Co-Founder of Yewno; Stephen Malinak, Chief Data and Analytics Officer of TruValue Labs; and Ben Chu, Manager of Equity Projects at Genscape discussed the how investment professionals can best navigate an increasingly crowded marketplace.
Here are three key takeaways from that discussion:
Find the Right Fit—It’s All in How You Tailor
While alternative data can provide enlightening insights, it is also can be cumbersome, voluminous, unstructured, and complex. These challenges are why roles such as Data Scientist and Data Engineer are becoming increasingly important, and why taking a methodical approach to incorporating alternative data into your workflow is critical.
A new alternative data source should not be used in a silo; it must be paired with other financial data to generate value. The key is to identify what types of feeds will supplement and improve what you already have in your investment process.
“There are so many data sources available, which can make it intimidating even to begin,” said Gramatica. “The benefit of integrating alternative data starts from looking at the universe of your dataset holistically. It is about surfacing the unforeseen insights new data can provide together with traditional financial data, leading to capturing better trend and performance predictions. Is there a particular blind spot I can focus on? Can I discover hidden relationships to help devise investment strategies? What is it I don't know?”
“It’s all in how you tailor,” said Drogen. “For many of our clients, the value in our feeds comes from seeing the spread between us and the overall Street consensus.”
Look to Generate Deeper Insights
Alternative data offers novel sources of data (images, credit card transactions, heat maps, social media interactions), often at a higher frequency than traditional filing and price data. Satellite data, for example, can offer real-time insight on the parking lot traffic a specific corporate location is attracting.
However, to warrant wider adoption, alternative datasets need to add value to the existing foundation of fundamentals and estimates data that firms are already using.
Stephen Malinak argued that alternative data increases transparency, which can help clients evaluate existing positions on companies they cover.
“Data from external parties can help investors adapt their investment strategies at a more granular and in some ways more meaningful level,” said Malinak. “They are no longer solely reliant on what a company tells them. Our clients can generate greater confidence in a position thanks to third-party information, or alternatively, shift a position in a timelier manner. It’s about adding depth to your understanding of a company, not replacing traditional information channels.”
Today's alternative data users should identify complimentary datasets to their existing core of Fundamentals, Estimates, etc. By having a strong symbology foundation in place, implementing these complimentary datasets can be easy.
Pace Yourself on the Alpha Treadmill
Today, we're seeing the adoption of alternative data not just for the alpha generation use case but also for the risk management use case. Deeper company insights are helping investors spot opportunities as well as risks. However, as investment managers are evaluating new exciting sources of data they also must make sure the same strong vetting and due diligence processes are in place. The best adoption practices are those that have a steady pace.
“Walk before you run on the alpha treadmill” was a further piece of advice from Leigh Drogen. “The most successful data users refine their performance over time,” he said, which increases effectiveness and generates stronger ROI.
These returns are not limited to alpha generation, and setting the right pace from the beginning can help you identify additional areas where new datasets can provide value before you ramp up.
According to Ben Chu, Manager of Equity Projects at Genscape, “Alpha generation generates a lot of headlines, but new data resources are equally applicable to risk management. Looking at energy usage, for example, can be extremely helpful when it comes to identifying risks ahead of the market.”
One recent example of this is Tesla. Many investors were caught unhappily surprised by its missed production targets, but low energy consumption levels were an early signifier for others.
“They were able to see a discrepancy between energy use and company targets and adapt their positions accordingly,” said Chu.
Overall, panelists foresaw a growing pool of new data sources on the horizon and a financial services industry that is quickly adapting to make use of it.
Lauren joined FactSet in 2006 as a Consultant. She is responsible for analyzing market research and determining the direction of the FactSet content, cloud, and technology strategy. In 2013, she joined the Content & Technology Solutions team. Lauren earned a B.S. in Policy Analysis from Cornell University.