Howard Marks on bubbles - thoughtful advice

It is good to get some perspective on the current bubble discussion across assets. The following comments from Howard Marks provide useful insights that are evenhanded yet focus thinking on the problem. These excerpts are from a longer letter with sage advice on how to think through the AI bubble and associated thinking on financing data centers.

I’ve concluded there are two different but interrelated bubble possibilities to think about: one in the behavior of companies within the industry, and the other in how investors are behaving with regard to the industry.

Newness plays a huge part in this. Because there’s no history to restrain the imagination, the future can appear limitless for the new thing. And futures that are perceived to be limitless can justify valuations that go well beyond past norms – leading to asset prices that aren’t justified on the basis of predictable earning power.

The key thing to note here is that the new thing understandably inspires great enthusiasm, but bubbles are what happen when the enthusiasm reaches irrational proportions.

Struggling with whether to apply the “bubble” label can bog you down and interfere with proper judgment; we can accomplish a great deal by merely assessing what’s going on around us and drawing inferences with regard to proper behavior. 

The key realization seems to be that if people remained patient, prudent, analytical, and value-insistent, novel technologies would take many years and perhaps decades to be built out. Instead, the hysteria of the bubble causes the process to be compressed into a very short period – with some of the money going into life-changing investment in the winners but a lot of it being incinerated.

Debt is neither a good thing nor a bad thing per se. Likewise, the use of leverage in the AI industry shouldn’t be applauded or feared. It all comes down to the proportion of debt in the capital structure; the quality of the assets or cash flows you’re lending against; the borrowers’ alternative sources of liquidity for repayment; and the adequacy of the safety margin obtained by lenders. We’ll see which lenders maintain discipline in today’s heady environment.

I know I don’t know enough to opine on AI. But I do know something about debt, and it’s this:

• It’s okay to supply debt financing for a venture where the outcome is uncertain.

• It’s not okay where the outcome is purely a matter of conjecture.

• Those who understand the difference still have to make the distinction correctly.

from Howard Marks Is It a Bubble? Oaktree Client Letter 

 

 

Nonlinear momentum - Increase positions with signal strength

 

Many firms are using trend-following. Their distinctions are often based on the length of trend signals, the set of markets used, and the risk management techniques employed. A recent paper, Nonlinear Time Series Momentum, measures the nonlinear relationship between trend and risk-adjusted returns using machine learning techniques. Using techniques to exploit nonlinear relationships within momentum outperforms simple linear methods. This nonlinear value-added is observed across all asset classes, frequencies, and horizons and lookback periods. This is especially true during market downturns. 

For modelers, this research concludes that simple nonlinear transformation of momentum signals will improve strategy performance, and signal strength interacts with predictability. However, as signals move to extremes, the extra risk from adding to position sizing diminishes, and at some point is not worth taking the extra risk. Hence, there is a complex nonlinear function between signal strength, sizing, and optimal risk-adjusted returns. Increase position exposure when signals are stronger, but reduce the signal exposure as you move to extremes.  

The foundation for decision-making - measurement

“Measure what is measurable, and make measurable what is not so.” - Galileo Galilei

"If you cannot measure it, you cannot manage it"  - Peter Drucker 

The one-two combination for quant decision-making is: first, measure everything because when something is placed on a scale, it can be compared, and second, remember that if it cannot be measured, it cannot be managed. There are no feelings in investing. There is no place for comments like, "I feel rates are going higher..." There is only a place for precision that can be compared with past predictions and future views. Yes, this is hard, but it is the only way to judge the quality of your decisions. 

Chaos and machine learning

 

"Chaos theory -the qualitative study of unstable aperiodic behavior in deterministic nonlinear dynamical systems" - Stephen Kellert 

That definition of chaos theory is all-inclusive, yet in a world before ML, it would be hard to model using simple linear regression techniques. ML is helpful because it can address the characteristics of chaotic systems. It also helps define the type of ML necessary to employ when faced with a chaotic system. Foremost, ML learning can address nonlinear relationships. All neural network ML can address nonlinear relationships. ML can also work with dynamic systems that have strong cross-asset relationships. ML can also address aperiodic behavior by examining time-series relationships using techniques such as long short-term memory (LSTM) recurrent neural networks (RNNs). What takes more work is dealing with unstable systems that change over time. This requires ML models that are compact and can be retrained regularly.