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Implementing model validation through a set of interdependent modules that utilizes both traditional econometrics and data science techniques can produce robust assessments of the predictive effectiveness of investment signals in an economically intuitive manner.
The proposed methodology, modular machine learning, also answers a number of practical questions that arise when applying block time series cross-validation such as what number of folds to use and what block size to use between folds.
It is possible to re-interpret the Fundamental Law of Active Management into a model validation framework by expressing its fundamental concepts, information coefficient and breadth, using the formal language of data science.
In this talk, we introduce an approach towards model validation which we call modular machine learning (MML) and use it to build a methodology that can be applied to the evaluation of investment signals within the conceptual scheme provided by the FL. Our framework is modular in two respects: (1) It is comprised of independent computational components, each using the output of another as its input, and (2) It is characterized by the distinct role played by traditional econometric and date science methodologies.
