Data science has brought investors powerful new tools to help generate returns, so is there still need for a human touch? Franklin Templeton Fixed Income CIO Sonal Desai weighs in on the role of quantitative science within the active-passive investment debate.

As data science has made its way into investment management, it has generated a polarized debate—as fierce as the one between passive and active investment and, in my view, a lot more interesting and consequential.

Before I get to it, though, let me point out one thing on the active versus passive choice: in the fixed income world, active investment has historically beaten passive hands down. Over the past 10 years, the median active fixed income investment manager has beaten the median passive strategy by 65 basis points1 (bps) and median exchange-traded fund (ETF) strategy by 63 bps on an annualized basis (as of September 30, 2019, with manager performances measured net of expenses).

So, in fixed income, active investment historically has been the winning strategy. The question is what kind of active investment works best.

Quantitative Science—Actively Adding to Fixed Income Decisions

Advocates of the quantitative (or “quant”) approach argue that it beats traditional, fundamentals-based investing. They say their algorithms can identify rules-based factors that generate alpha2 more effectively and reliably. Supporters of a more traditional active approach counter that experienced economists and credit analysts will always have the edge over algorithms that mine data without context.

I think the entire active versus quant debate is a false dichotomy.

At Franklin Templeton Fixed Income Group, we bring the two approaches together. We believe the future of fixed income investing lies in marrying quantitative science with fundamentals-based active management.

We have just released a white paper outlining how we do it, and why we believe it’s by far the best strategy for us.