Old School / New School Analysis - Feeling your way across a data stream

“Machine learning can be a very powerful tool and can be used well by those who respect markets, but unsupervised machine learning techniques that find patterns without first forming any hypothesis can also pick up spurious correlations. We retain an old school quantitative approach of forming a hypothesis and testing it. Our use of machine learning is zero.”

https://thehedgefundjournal.com/transtrends-harold-de-boer/

There is an old school versus new school battle for model building within managed futures and other hedge styles. It is a battle for hearts and minds of quantitative management and investors. Do you need a reason for a model or is successful performance enough? 

The "old school" or classic quantitative approach to investment management is steeped in the scientific method of hypothesis testing. Pose a hypothesis based on a model, and test whether it works at prediction and can forecast future prices. This old school comes from the classic foundations of statistics, econometrics, and finance. It is not about just predicting but offering explanations for why a model may work. A theory drives decision-making.

The "new school" focuses on data analysis such as machine learning and not hypothesis testing. There is a tremendous focus on successful prediction and the data will speak for itself. The emphasis is centered on practicality. Does it predict? Yes. Use it.  No. Scrap it. Data are complex and messy, and we accept our limitations at forming the right hypothesis or understanding market dynamics. If you are looking at large data sets, there may not be clarity concerning what is the cause for behavior even if there are repeatable patterns. 

Who is winning the battle between these two quant schools? If current usage is the driver of success, the new school of machine learning is dominating interest. Perhaps all of the hypotheses are exhausted. Is there a machine learning a fad? Perhaps. 

We have seen fads in techniques and hypotheses about markets. Market efficiency may have been the greatest fad on how to view market behavior. Econometric techniques have also risen and fallen in popularity only to be displaced by new ideas. However, what machine learning does have is a deeper focus on non-linear behavior which is often difficult to tease using traditional hypothesis testing. 

Nevertheless, return performance has ultimately been the driver of technique usage. If an investor does not make money with a data tool, it is thrown into the dust bin of history. Right now, the new school is still in ascent and the old school of hypothesis testing seems so conventional and yesterday. These techniques could face a form of market efficiency. As more investors use machine learning, earlier successes will diminish. 

Still, for many, there is a feeling surrounding new techniques that should err on the side of caution and conservatism. Use a new approach on old and new data, but still look for an explanation consistent with theory. Form a new hypothesis, but incorporate new data science to find proof. This does not have to be data mining but a search for deeper truth as more information is made available.