Last October, I had the privilege to attend Santa Fe Institute and Morgan Stanley (NYSE:MS)’s Risk Conference, and it was one of my most inspiring learning experiences of the year (read last year’s post on the conference, and separately, my writeup of Ed Thorp’s talk about the Kelly Criterion). It’s hard not to marvel at the brainpower concentrated in a room with some of the best practitioners from a variety of multi-disciplinary fields ranging from finance to physics to computer science and beyond and I would like to thank Casey Cox and Chris Wood for inviting me to these special events.
I first learned about the Santa Fe Institute (SFI) from Justin Fox’s The Myth of the Rational Market. Fox concludes his historical narrative of economics and the role the efficient market hypothesis played in leading the field astray with a note of optimism about the SFI’s application of physics to financial markets. Fox highlights the initial resistance of economists to the idea of physics-based models (including Paul Krugman’s lament about “Santa Fe Syndrome”) before explaining how the profession has in fact taken a tangible shift towards thinking about markets in a complex, adaptive way. As Fox explains:
These models tend to be populated by rational but half-informed actors who make flawed decisions, but are capable of learning and adapting. The result is a market that never settles down into a calmly perfect equilibrium, but is constantly seeking and changing and occasionally going bonkers. To name just a few such market models…: “adaptive rational equilibrium,” “efficient learning,” “adaptive markets hypothesis,” “rational belief equilibria.” That, and Bill Sharpe now runs agent-based market simulations…to see how they play out.
The fact that Bill Sharpe has evolved to a dynamic, in contrast to equilibrium-based perspective on markets and that now Morgan Stanley hosts a conference in conjunction with SFI is telling as to how far this amazing multi-disciplinary organization has pushed the field of economics (and importantly, SFI’s contributions extend well beyond the domain of economics to areas including anthropology, biology, linguistics, data analytics, and much more).
Last year’s focus on behavioral economics provided a nice foundation upon which to learn about the “limits to forecasting and prediction.” The conference once again commenced with John Rundle, a physics professor at UC-Davis with a specialty in earthquake prediction, speaking about some successful and some wrong natural disaster forecasts (Rundle operates a great site called OpenHazards). Rundle first offered a distinction between forecasting and prediction. Whereas prediction is a statement validated by a single observation, forecasting is a statement for which multiple observations are required for a confidence level.
He then offered a permutation of risk into its two subcomponents. Risk = Hazard x exposure. The hazard component relates to your forecast (ie the potential for being wrong) while the exposure relates to the magnitude of your risk (ie how much you stand to lose should your forecast be wrong). I find this a particularly meaningful breakdown considering how many colloquially conflate hazard with risk, while ignoring the multiplier effect of exposure.
As I did last year, I’ll share my notes from the presentations below. Again, I want to make clear that my notes are geared towards my practical needs and are not meant as a comprehensive summation of each presentation. I will also look to do a second post which sums up some of the questions and thoughts that have been inspired by my attendance at the conference, for the truly great learning experiences tend to raise even more questions than they do offer answers.
Antti Ilmanen, AQR Capital
With Forecasting, Strategic Beats Tactical, and Many Beats Few
Small, but persistent edges can be magnified by diversification (and to a lesser extent, time). The bad news is that near-term predictability is limited (and humility is needed) and long-term forecasts which are right might not setup for good trades. I interpret this to mean that the short-term is the domain of randomness, while in the long-term even when we can make an accurate prediction, the market most likely has priced this in.
Intuitive predictions inherently take longer time-frames. Further, there is performance decay whereby good strategies fade over time. In order to properly diversify, investors must combine some degree of leverage with shorting. Ilmanen likes to combine momentum and contrarian strategies, and prefers forecasting cross-sectional trades rather than directional ones.
When we make long-term forecasts for financial markets, we have three main anchors upon which to build: history, theory, and, current conditions. For history, we can use average returns over time, for theory, we can use CAPM, and for current conditions we can apply the DDM. Such forecasts are as much art as they are science and the relative weights of each input depend on your time-horizon (ie the longer your timeframe, the less current conditions matter for the inevitable accuracy of your forecast).
Historically the Equity Risk Premium (ERP) has averaged approximately 5%, and today’s environment the inverse Schiller CAPE (aka the cyclically adjusted earnings yield) is approximately 5%, meaning that 4-5% long run returns in equity markets are justifiable, though ERPs have varied over time. Another way to look at projected returns is through the expected return of a 60/40 (60% equities / 40% bonds) portfolio. This is Ilmanen’s preferred methodology and in today’s low-rate environment the prospects are for a 2.6% long-run return.
In forecasting and market positioning, “strategic beats tactical.” People are attracted to contrarian signals, though the reality of contrarian forecasting is disappointing. The key is to try and get the long-term right, while humbly approaching the tactical part of it. Value signals like the CAPE tend to be very useful for forecasting. To highlight this, Ilmanen shared a chart of the 1/CAPE vs. the next five year real return.
Market timing strategies have “sucked” in recent decades. In equity, bond and commodity markets alike, Sharpe Ratios have been negative for timing strategies. In contrast, value + momentum strategies have exhibited success in timing US equities in particular, though most of the returns happened early in the sample and were driven more by the momentum coefficient than value. Cheap starting valuations have resulted in better long-run returns due to the dual forces of yield capture (getting the earnings yield) and mean reversion (value reverting to longer-term averages).
Since the 1980s, trend-following strategies have exhibited positive long-run returns. Such strategies work best over 1-12 month periods, but not longer. Cliff Asness of AQR says one of the biggest problems with momentum strategies is how people don’t embrace them until too late in each investment cycle, at which point they are least likely to succeed. However, even in down market cycles, momentum strategies provided better tail-risk protection than did other theoretically safe assets like gold or Treasuries. This was true in eight of the past 10 “tail-risk periods,” including the Great Recession.
In an ode to diversification, Ilmanen suggested that investors “harvest many premia you believe in,” including alternative asset classes