Beyond Diversification Series: Part One Continued

“Some will find that finance is not easy. We don’t know the outcomes in advance. The information we use is always incomplete, and we can’t control the variables. Still, we must make decisions because, often, the absence of decision is worse.” ?

–Jean-Paul Page ?

(This is a quote from my father’s finance textbook.) _________________________________________________

A few years ago, before I joined T. Rowe Price, I was in a meeting with several senior investors when I put my foot in my mouth. I was the “newbie” in the room—I had just joined the firm. The topic of discussion was our long-term return forecasts. We wanted to build a new set of capital markets assumptions to be used in Solutions studies. The goal of these studies was to advise institutional investors on their strategic asset allocation decisions.

As the debate on one particular estimate intensified (I don’t remember which estimate), a self-confident investor, who had such a successful career that he was clearly enormously wealthy and didn’t feel the need to sit in long meetings to build consensus, said:

“Just use 2%.”????

I jumped in with a basic question:

“How did you arrive at this estimate?”

With a smile and a hint of sarcasm, he responded:

“I made it up.”

I was taken aback. I couldn’t help it; I had to say:

“But you can’t make stuff up like that. We need a robust estimate.”

In the introduction to my book I talk about the “GIGO” (garbage-in, garbage-out) critique. I explain that quants tend to get annoyed with the GIGO critique. Another, a perhaps even more irritating critique, and a favorite of academic journal referees, is to question an approach’s “robustness.”

He responded:

“This firm has been ‘making stuff up’ for decades! In fact, we ‘make stuff up’ every day. And now, we’re one of the world’s leading asset managers.”

The subtext was: “Welcome, newbie, you need to learn how we do things here. We use judgment.” One of the reasons I was taken aback by his “I made it up” response was that he was a well-known quant investor. He used econometrics, statistics, and systematic strategies as core parts of his process. Had he become disillusioned with mathematical approaches?

Probably not. He simply recognized when and how to use judgment as part of a process that also relies on quantitative models. And, importantly, he didn’t think we should use just anybody’s judgment—he recognized that some investors could forecast better than others. His main point, I suppose, was that his judgment, given his experience and track record, was probably better than any mathematically derived estimate. ?

One of the great misconceptions on portfolio theory is that it precludes the use of judgment and experience. It doesn’t. It’s right there in Markowitz’s 1952 seminal paper, for everyone to see, in the first paragraph:

“The process of selecting a portfolio may be divided into two stages. The first stage starts with observation and experience and ends with beliefs about the future performances of available securities. The second stage starts with the relevant beliefs about future performances and ends with the choice of a portfolio. This paper is concerned with the second stage.”

With his “I made it up” comment, my new colleague meant that he had used “observation and experience” to form “beliefs about the future performance” of the asset class under debate. Nothing wrong with that. (Of course, it would have helped if he had walked us through his rationale so that Solutions team members could explain the estimate to clients.)

Legendary investor Ray Dalio, in his recent book “Principles,” explains the evolution of his approach to marry quantitative analysis with judgment: ?

Rather than blindly following the computer’s recommendations, I would have the computer work in parallel with my own analysis and then compare the two. When the computer’s decision was different from mine, I would examine why. Most of the time, it was because I had overlooked something. In those cases, the computer taught me. But sometimes I would think about some new criteria my system would’ve missed, so I would teach the computer. We helped each other.

He adds that over time, the computer became more effective than him, but the investor’s role remained to evaluate the “decision-making criteria” used by the computer. ?

My view is that those who hope for a consistently effective return forecasting algorithm may suffer from what MIT Professors Andrew Lo and Mark Mueller call “physics envy.” In their 2010 paper titled “Warning: Physics Envy May Be Hazardous to Your Wealth,” they warn that finance and physics are different:

“The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems—and financial markets in particular—that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, ‘physics envy’ has also create a false sense of mathematical precision in some cases.” ?

At the top of their paper, they picked a brilliant quote. It gets at the heart of the issue, which is that asset prices are set by humans. The quote is from the famous physicist and Nobel Laureate Richard Feynman: ?

“Imagine how much harder physics would be if electrons had feelings!”

I keep a related reminder on my office wall: a cartoon of two scientists in front of a blackboard full of complicated equations, with the following quote, spoken by one of the scientists: “Oh, if only it were so simple!” That cartoon has a bit of sentimental value for me. For the 10 years I worked with my mentor Mark Kritzman and learned most of what I know about quantitative investment research, he had the same cartoon on his office wall.

Rules of thumb for asset class-level return forecasting

To forecast returns, a key takeaway from my book is that it helps to keep it simple. As a summary, here are my top 20 rules of thumb on how to build return forecasts. Ultimately, there are no precise cookbook instructions to forecast returns because the future is always uncertain.

Long-term forecasts:

1. When in doubt, assume that returns will be proportional to risk (beta). Free lunches are rare.
2. For stocks, check that your estimate is not too far from the inverse of the P/E ratio plus inflation.
3. For earnings (the “E” in P/E), don’t rely on optimistic, short-run, or noisy sell-side estimates.
4. For more flexibility, break down returns into two building blocks: income and long-term growth.
5. Valuation changes are harder to model. When in doubt, assume valuation ratios mean-revert.
6. For bonds, make sure your estimate is not too far from the asset class’s yield to maturity.
7. Beware of large credit and currency risk exposures. Apply a default haircut when needed.
8. If possible, ask experienced investors for their forecasts of earnings growth, rates, spreads, etc.
9. Use these forecasts as the key inputs to transparent building-block models that can be debated.

Tactical forecasts:

10. For shorter-term equity forecasts, focus on valuation changes more than on income and growth.

11. Use valuation ratios (P/E, P/CF, P/B) to evaluate whether an asset class is cheap or expensive.

12. Pay close attention to P/CF for relative bets and to P/E for absolute (market timing) decisions.

13. Use 6-and 12-month momentum as secondary factors to better time valuation-based trades.

14. With macro factors, account for current conditions and how macro factors affect asset prices.

15. Don’t blindly assume that rising rates are “bad” for risk assets such as stocks and credit bonds.

16. When fundamentals (such as margins) reach extreme levels, assume they might mean-revert.

17. For bonds, use yield-to-maturity ratios to forecast one-year relative returns between asset classes.

18. Don’t expect return momentum to work in bond markets, except weakly in the very short run.

Two general rules of thumb:

19. Use data and models to estimate factor relationships, assess signal quality, and remove biases. ?

20. Use judgment to account for current conditions across monetary, fiscal, and geopolitical factors.

This is an abstract from my book “Beyond Diversification: What Every Investor Needs to Know About Asset Allocation,” McGraw Hill, 2020.

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.

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