Smart Beta vs. Quant Active: Playing the Same Tune?

Arguing over smart beta vs. quant is like arguing whether a band falls under alternative rock or indie rock. In the same way that people just want to listen to music that they enjoy and don’t care what it’s called, investors want to earn high returns at low risk and don’t care about labels.

Let’s start with the basics. Smart beta strategies tilt towards particular factors in a transparent way and oftentimes with a low turnover. Quantitative strategies use clever quantitative and statistical methods to earn higher returns, involve more complex data, and generally generate greater turnover. We’ll talk about these differences more explicitly shortly, but first, let’s discuss why anyone would ever care about this distinction.

Three (cynical) considerations go into an asset management firm’s decision to label its products as quant or smart beta: fees, allocations, and asymmetric explanations, each of which we’ll discuss. First, quantitative active strategies normally command higher fees than smart beta strategies. Second, many asset owners have specific and often large allocations for smart beta, so calling yourself smart beta may generate greater assets under management for your firm. Lastly, if a smart beta product underperforms, the asset manager can simply say, “Well, the factor underperformed last quarter.” Quant analysts can’t say this; quant strategies are just meant to perform well. If you sell a quant product to a client and subsequently underperform, you can’t simply say, “Well, the factors I used underperformed.” Their response would be, “Well, why did you choose those factors, you fool?”

How to tell them apart?

Cynicism aside, where can we draw the line between smart beta and quant?

If you use standard financial and market data, you might be a smart beta strategy. If you use sophisticated alternative data, you might be a quant strategy. Just use return on equity and size as measured by market capitalization—that sounds like a smart beta strategy. Scrape comments from social media on public companies, run a sentiment algorithm powered by natural language processing, and trade based on those signals—that sounds like a quant strategy.

If you use cuts and tilts to select stocks and adjust weights respectively, you might be a smart beta strategy. If you predict returns and covariance and optimize weights under a particular utility function, you might be a quant strategy. Cutting stocks might involve removing growth stocks from your portfolio while tilting stocks means reducing weights to growth stocks slightly and increasing weights to value stocks slightly. If you instead predict returns by feeding hundreds of firm characteristics into a random forest model with regularization to avoid overfitting and only predict out-of-sample because who gives a damn about in-sample performance—then that’s a quant model.

If your one-way turnover is low to moderate, say, below 50% per year, you might be a smart beta strategy. If it’s much higher, then you might be a quant strategy. In a similar vein, if you rebalance quarterly or less frequently, you might be a smart beta strategy. If you rebalance more frequently especially intraday, daily, or weekly, you might be a quant strategy.

If you put your strategy in an index for all to see, you might be a smart beta strategy. If you keep your weights and precise methods secret, you might be a quant strategy. This one is a bit iffy. I can create an index out of a strategy that rebalances daily and uses the discount from block trades and satellite imagery of the number of cars parked outside stores as signals to predict return and then call it a smart beta strategy. However, practically, I’d probably want to keep that a bit close to the vest. Some strategies like value are so well-known, and likely more able to absorb large amounts of capital, that we have absolutely no problem letting the public see them. Some are so clever and a bit more liquidity-constrained such that we hold them close.

Perhaps we could say that if your strategy has low tracking error, it’s more likely to be smart beta, but this is not strictly true. Quant strategies that optimize information ratio will often have tight tracking errors.

Of course, everything lies on a continuum from a single factor strategy on one end to a pure quant black box on the other, generating weights and trading faster than the eye can see.

What should you buy?

Obviously, there isn’t going to be a cut and dry answer for the question of which one is superior. Otherwise, this article would be titled, “Why _____ should die”, with quant or smart beta filling the blank.

Your choice should be made based on your own situation. If you are trying to maximize risk-adjusted return and you trust the quantitative abilities of the money manager, a quantitative product is better. With good implementation, it can contain all the expected return information in a smart beta product and more.

If you are uncertain how “quant” your asset manager is—this is understandable because the knowledge that helps you evaluate a quant is the exact same knowledge as being a quant—then again you should lean towards smart beta. It’s possible to trust someone to understand factors but not to trust them to understand expected returns, risk, and portfolio optimization.

Smart beta or quant—ultimately, these are just semantics. At the end of the day, we and many other investment managers aspire to build strategies that make money for investors. That means understanding investors’ needs and delivering the appropriate product. Whatever that product ends up being, we’re happy as long as investors earn great returns from the strategy.