Today’s quantitative investors seem split between innovation on one hand and engineering on the other. The prior group is constantly looking for new factors that predict returns in areas like alternative data and machine learning - yet often fail to find them. The latter camp is focused on investing in the more established factors such as value, momentum and quality, where long-term patience, conviction and execution seem to matter much more than innovation. So how did we end up here?

To explore the intriguing yet paradoxical interaction between “innovation factors” and “risk-premia factors”, let us turn again to the risk vs return dimensions of factor investing (mentioned last week here). This time, through the eye’s of the Barra risk model (for those unfamiliar, Barra makes one of the most well known risk models in quantitative finance).

PART I - The Simple Years (pre-2005)

Since the early 80s, traditional quantitative investing looked like this:

Step 1:

Find ‘Alpha Factors’ - characteristics that predicted a cross-section of stock returns and reflected some fundamental logic or perhaps slightly technical aspects like price momentum, combined into an overall proprietary stock ranking model.

Step 2:

Build an Optimal Portfolio - by feeding the final rank from step 1, perhaps after some adjustments, as expected returns into an optimizer with a risk model, which was typically provided by a third party like Barra, Northfield or Axioma.

The first time I ran a Barra optimization backtest was in 2004 on a newly constructed model and as I looked at the performance attribution report, I didn’t know what I was looking for. What were the success parameters for the optimized strategy beyond the obvious one of information ratio?

But my boss knew what to look for. She ran many of these reports before, and this time around she was very excited. “Look at the size of the ‘Asset Selection’ return!” - she said. “This is great.”

‘Asset Selection’ is Barra’s terminology for alpha - the residual part of the return that could not be explained by their risk model.

At first, I was confused. Barra’s list of risk factors, which it calls ‘Risk Indices,’ included some of the same names as our ‘alpha factors’, like valuation and momentum. Didn’t we want the optimized portfolios to load on these? Why was it a good thing that most of our return came from ‘Asset Selection’ and not the ‘Risk Indices’?

My boss explained: “No. All those Barra factors are just risk. Clients don’t want them. They want uncorrelated alpha and there is no alpha in risk indices. You want to clean them out of your portfolio, which is what the optimizer tries to do anyway”. (Over time, this view proved to be both right and wrong. She, as most other traditional quants, were wrong in thinking that client's wouldn’t want to invest in risk factors directly. This caused many traditional quants to completely miss the current factor investing boom [but more on that later]. However, she was right that the uncorrelated alpha lives beyond those risk indices.)

The image below is a typical summary view of the Barra attribution report that we would look at. Maximizing row #9, while minimizing all other rows, was the objective of the original quant game.