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Topic: Modular Machine Learning for Model Validation
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.
Joseph Simonian is the Founder and CEO of Autonomous Investing Solutions. Prior to that, Joseph was an Investment Strategist at Acadian Asset Management. Before joining Acadian, Joseph was the director of Quantitative Research for the Portfolio Research and Consulting Group at Natixis Investment Managers. Prior to that, he was a principal research analyst in the Global Institutional Solutions Group at Fidelity Investments. He was also previously a vice president at JPMorgan Asset Management and PIMCO.
Joseph is currently the co-editor of the Journal of Financial Data Science and Advisory Board member for the Financial Data Professional Institute.
Joseph holds a Ph.D. from the University of California, Santa Barbara; an M.A. from Columbia University; as well as a B.A. from the University of California, Los Angeles.
Sri Krishnamurthy, CFA is the Founder and CEO of QuantUniversity. Prior to that, Sri has worked at Citigroup, Endeca, MathWorks and with more than 25 customers in the financial services. Sri is the creator of QuSandbox, a platform for experimenting analytical and machine learning solutions for enterprises prior to adoption.
Sri teaches classes at QuAcademy (www.qu.academy) and teaches graduate courses in Machine Learning and AI at Northeastern University.
Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA from Babson College.
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