The Efficient Market Hypothesis… has two components that I like to refer to with the terms No Free Lunch and The Price Is Right. The No Free Lunch component says that it is impossible to predict future stock prices and earn excess returns except by bearing more risk. The Price Is Right component says that asset prices are equal to their “intrinsic value,” somehow defined.
– Richard Thaler, “From Cashews to Nudges:The Evolution of Behavioral Economics”, American Economic Review 2018
An important problem in finance is trying to properly incorporate risk preferences when forming portfolios. This is especially true if risk preferences are not stable. Yet, we have increasing evidence that risk preferences do change over time. (See article “Are risk preferences stable?” by Hannah Schildberg-Horisch in the Journal of Economic Perspectives Spring 2018 and illustration below.)
The four biggest hedge fund launches of 2018 have attracted more than $17bn, according to figures compiled by the FT. That compares with the $13.7bn investors have put in existing funds, according to data from eVestment. from FT Hedge fund stars rake in billions for new funds
Historian Deirdre McCloskey says, “For reasons I have never understood, people like to hear that the world is going to hell.”
John Stuart Mill wrote in the 1840s: “I have observed that not the man who hopes when others despair, but the man who despairs when others hope, is admired by a large class of persons as a sage.” from Morgan Housel “The Psychology of Money”
I have always thought that the simple physics analogy that a market at rest will stay at rest and a market in motion will stay in motion is apt for trend-following. Trends will change when there is a shock or catalyst that will change the underlying fundamentals. Trend-following does not require knowing all of the reasons for why a trend is happening or why it may stop. Trend-following only requires that a signal is extracted and followed until price dynamics tell you otherwise. The success with trend-following is driven by the fact that trends last longer than expected. They last longer because most new information is trend reinforcing. Fundamentals do not generally change quickly. Nevertheless, loses will occur when new information causes an expectations reversal. Expectations may change more frequently than fundamentals.
Managed futures performance for May was driven by one sector, global bonds. The surprise events in Italian politics led to a flight to quality move into safe bonds around the world. This sharp reversal caught most short trend-follower flat-footed. The commitment of traders reports have shown a strong short tilt in managed money. The size of the move over less than 10 trading-days ensured stops would be hit and positions changed. The question was just how much pain managers took in this sector. Notably, the markets sold-off on the good economic employments numbers to further hurt managers who switched to longs earlier in the week. A similar set of events followed the rates markets. Expectations for fewer Fed hikes given the political turmoil only reversed again after the US employment number.
May saw a set of return reversals with bonds posting gains on flight to quality while international markets saw strong return declines. Selected country equity declines were very strong based on increased political risks. It was a good month for those cautious and focused on US smaller cap names.
One way to measure market uncertainty is to run a simple thought experiment. A well-behaved market should match performance with events in a well-defined manner. An uncertain complex market environment would behave in an ill-defined manner. Close your eyes and assume you have knowledge of the news highlights for the month of May. For example:
Political turmoil in Italy and the EU
Off-again/on-again North Korea talks
Good economic data albeit with lower momentum
EM problems in Turkey and Argentina
Trade war discussions
“The hardest thing to explain is the glaringly evident which everybody has decided not to see.” Ayn Rand
“We have too much emphasis on bias and not enough emphasis on random noise”
– Dan Kahneman Speaking at the Kahneman-Treisman Center for Behavioral Science and Public Policy
The benefits from using algorithms are well documented, yet they are still not used for many decision-making situations. The reasons for this lack of use are varied. It could be self-interest. It could be algorithms anxiety. It could be a lack of confidence in the modeling process. If there is a high level of uncertainty concerning the most effective model, there may be fear of being wrong.
“An algorithm could really do better than humans, because it filters out noise. If you present an algorithm the same problem twice, you’ll get the same output. That’s just not true of people.”
“But humans are not very good at integrating information in a reliable and robust way. And that’s what algorithms are designed to do.”
An investor may want to increase his commodity beta exposure to meet his strategic allocation target for this asset class. Unfortunately, all betas are not created equal in the commodity space. There is a wide difference in the choices that are available and this chasm is much greater than anything found in other asset classes.