Blending traditional research and ML processes - There is a lot of overlap

Systematic investing is process driven and not just implementing a single model. There is a quant investment pipeline which starts with data, modeling of decisions for alpha generation, portfolio optimization, execution, and performance review. This same model pipeline is used with any machine learning investment process. There is commonality between old and new quant processes. Yes, machine learning is different, but the process is the same.

• There is first data analysis and feature extraction - What are the inputs for any model? Features can be simple price-based systems or include exogenous variables like fundamental macro or firm specific data.
• From the feature selected there is alpha modeling, the method of stacking or ranking the set of opportunities. Alpha modeling can look at times series or cross-sectional data to assess each investment or pick high and low performance deciles.
• The set of alpha choices then have to be placed through a portfolio optimization process which determines size and distribution of allocations. The optimization process finds the mix of alpha choices from which to target return and risk.
• Output then has to be converted into executable orders. A model has to be deployed through specific order processes that signal what is to be bought and sold.
• Performance has to be assessed and learning generated to adapt or change models. An effective system has to include a feedback loop to learn from mistakes.

The process of alpha generation is the core difference between traditional and ML approaches. This difference may not be trivial but preparing data, optimizing, executing, and feedback are the same.