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. 

Wet streets cause rain problem - the need for causal thinking

The "wet streets cause rain" problems, and the need for causal thinking. Reading the news causes noise in thinking about markets because newspapers can get causality wrong. If "A"  happens in the news, then that must have caused "B". Or, if I see the event "A", it is likely to result in "B", not because of past history, but just because they happen at the same time. It is vital to get the causality right before the narrative. Narrative can be easily written when you accept correlation as the driver, not causality. Causality is connected with what is probable by what may have happened in the past. 

Causality provides constraints on thinking. It can lead us to conclude that we don't know the reason for some events. It is harder to write news stories when you are forced to use reason and causality. It is harder to be an investor if you are constrained by the limits of what is possible. If you focus on causality and logic, you may have to say, "I don't know why the market moved." 

China tech sector showing surge

 

The Chinese stock indices have been strong performers, matching those of other major countries around the world. This increase is despite an overall economy that is not doing very well. The real estate markets remain morbid, but sentiment has changed regarding trade wars and geopolitical risks.

The strong China tech sector may be about relative valuation. Chinese tech is cheap relative to similar US firms. It is also associated with a government focus on growing the tech sector, both to avoid dependence on the US and to increase the opportunity to dominate this industry globally. Since the launch of Deepseek AI, there has clearly been more attention to this sector. Clearly, Chinese tech is being pulled along by the strong US moves.