Modular Machine Learning - Best Of Both Worlds?

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A Webinar By Joseph Simonian Abstract: After reviewing some differences between traditional statistics & data science, we present a modular machine learning framework for model validation which blends the two paradigms. Model validation is set up as a sequence of procedures, in which the output from one procedure serves as the input to another procedure within a single validation framework. An econometric model is used in the first module to classify data in an economically intuitive way. Proceeding modules apply data science techniques to evaluate the predictive characteristics of the model components. We apply the framework to the fundamental law of active management, a well-known formal characterization of portfolio managers alpha generation process.

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