BST Awards 2019: Best Buy-Side Data Analytics Tool—FactSet

In a category whose past winners are as diverse as each winning company’s definition of analytics, collecting this award more than once represents an achievement. FactSet won this category in 2017, and its repeat this year emphasizes the growing importance of analytics to the buy side, especially around risk—and not just managing it, but exploiting it.

“Historically, we think of analytics as traditional portfolio- or security-level characteristics. But now, the question is, how do you combine analytics with more data and new content, ranging from structured and unstructured alternative data available through Open:FactSet to third-party data and models that can now be integrated with FactSet’s content and analytics via its open API,” says Rob Robie, executive vice president of analytics and trading at FactSet. “We see clients leveraging analytics in traditional and new ways. So we’ve invested heavily in risk capabilities for wealth, asset management clients, and asset owners around how they construct portfolios.”

The vendor has beefed up its multi-asset class risk model, and has expanded its coverage, and now offers different versions—both linear and Monte Carlo models—to cater to clients’ different risk needs. FactSet plans to expand its risk capabilities further, adding fat-tail and regional versions, and incorporating third-party models.

Earlier this year, the firm also rolled out its portfolio management platform, which allows portfolio managers to simultaneously manage their portfolios, asset allocation, and risk in one place.

According to Robie, one reason for the increased importance of risk models is that FactSet anticipates a shift in the dynamics of client portfolios over the next 12 months to incorporate more private company and debt investments, driven by the search for greater returns and because the number of publicly traded companies is lower than in the past. But these more opaque markets carry greater risk, and therefore require specialist tools, creating demand for data on private assets and the tools to analyze these in familiar ways, alongside “traditional” and public market data on an investor’s other holdings.