Beyond Diversification Series: Part One

[An excerpt from my upcoming book, Beyond Diversification – What Every Investor Needs to Know About Asset Allocation, 2020, McGraw Hill]

Several years ago, I was half-asleep at a small quantitative research conference, when an interesting discussion took place. A fundamental portfolio manager (i.e. a heretic amongst quants1) asked the presenter, somewhat rudely, whether all portfolio optimization models were in fact completely worthless, considering the “GIGO” critique. This question brought an immediate silence to the room. Perhaps the easiest way to annoy a quant is to bring up the GIGO critique. It stands for “Garbage In, Garbage Out”. The questioner was arguing that because we can’t estimate expected returns with any reasonable level of confidence (Garbage In), the output of portfolio optimization models will always be wrong (Garbage Out). There is validity to this critique in that portfolio optimization models often give a false sense of precision. And ultimately, there’s no such thing as financial alchemy – a calculator can’t turn bad inputs into the right answer (portfolio optimization models are, broadly speaking, sophisticated calculators). 

The presenter was Dr. Bernd Scherer, a highly regarded thought leader who has straddled the worlds of academia and investment practice for many years. Dr. Scherer was jet lagged. He had just flown in from overseas, and he was not in the mood for a philosophical discussion on the GIGO critique. In one short sentence, he gave the most remarkable rebuttal of the critique I had ever heard. His answer ended the debate before it started, and it has stayed with me throughout the years. I must admit, I’ve used it a few times myself. He said: 

“If you don’t think you can estimate expected returns, you shouldn’t be in the investment business.” 

Investing is mostly about forecasting returns. Even investors who don’t formulate expected returns as a precise number make implicit forecasts when they pick stocks, bonds, or allocate assets. These implicit forecasts aren’t precise, but they require a view on directionality (up or down? outperform or underperform?) and to a certain extent, magnitude, as reflected in position sizing. Even proponents of risk parity2 make implicit return assumptions when they equalize risk contributions in their portfolio. Of course, there’s more to the story. We must balance our return expectations against risk. And we must think about correlations, goals, time horizon, risk tolerance, liabilities, and so on. But to echo Dr. Scherer, it’s hard to call yourself an investor if you don’t think you have insights about expected returns. 

There are many ways to estimate expected returns, from fundamental to quantitative approaches, and everything in between. Over the years, I’ve worked with fundamental investors who broadly dismiss quantitative models as na?ve, usually on the basis that they rely on past data. Also, I’ve worked with quants who think fundamental approaches lack rigor, and amount to a collection of made-up stories. Most successful investors are less dogmatic, and their views lie somewhere between these two extremes. They believe that a combination of powerful quantitative data analysis and fundamental, forward-looking judgment leads to the best outcomes. 

The challenge, of course, is how to marry fundamental and quantitative approaches.  But first, the criticism that quantitative expected returns are flawed because they use historical (as opposed to “forward-looking”) information merits a rebuttal. As for the GIGO critique it used to make my blood pressure spike. During the first few years of my career a significant part of my responsibilities was to help develop quantitative expected returns. Sometimes clients would question the models. They would say a significant part of my responsibilities was to help develop quantitative expected returns. Sometimes clients would question the models. They would say: 

“This approach doesn’t work, because it’s not forward-looking – it’s based on past data, and the current environment is different.” 

I loved my job at the time, but the global travel schedule was grueling. So like Dr. Scherer, one day I was jet-lagged and gave a client a curt answer. I said: 

“I’m sorry. I’ve looked everywhere for future data, but I can’t find any. They’re not on Bloomberg.” 

The salesperson who had invited me to present to this client was not impressed. The point is that historical data is all we have. It’s useful to the extent it helps formulate a view about the future. 

It’s in this context that in my book “Beyond Diversification”, I review various approaches to forecasting returns, which will be available at major book retailers in November.  

1A “quant” is an industry term used to describe those involved in quantitative research and investment management. Quants use computer models and mathematics to predict markets and manage risk. 

2The goal of risk parity is to equalize the contribution to risk from the different component assets. 

Important Information

The views contained herein are as of the date noted on the material; My views are my own and may differ from those of other T. Rowe Price investment professionals, portfolio managers and associates. 

T. Rowe Price Associates, Inc.