Systematic Strategies and Machine Learning

A Talk by
Kevin Noel

Wednesday, October 16

5:45 PM Registration
6:00 PM Seminar Begins
7:30 PM Reception

Abstract

Systematic strategies have a long history in the field of investment area, encompassing the high-frequency ones as well as low-frequency strategies. Over the last decade, the rise of ETF, Robo-allocator made them a popular choice compared to discretionary strategies. More recently, progresses in machine learning renew the theoretical development in that field as well as highlight new perspectives.

Here, we focus on low-frequency strategies and first recall briefly the history of such strategies through a common statistical framework (dynamic basket allocation): Markowitz, CPPI, Buy-Write, Vol. Control, Risk Budgeting, Factor-based, Arbitrage based,… We illustrate those strategies through actual use cases and highlight the importance of underlying risk framework.

In the second part, we focus on the various machine learning methods available to develop or optimize systematic strategies. Especially, we underline the paradigm difference with traditional statistical/stochastic methods by deepening on the fundamental concept of learning vs calibration, as well as the role of prior knowledge.

In the final part, we will evoke some potential future research to go beyond the paradigm of covariance matrix: neural control, graph representation learning.

Biography

Kevin Noel is graduated from Ecole Centrale, in financial mathematics and data mining. From 2007, He worked at BNP Paribas and then at US bank Merrill Lynch on developing advanced statistical framework and risk solutions for Institutional Investor systematic strategies in Asia/Japan. Among those solutions: volatility based, arbitrage Premium, dynamic replication of mutual/ hedge funds, long short… Then, at ING Japan, he co-leads in Re-Insurance hedging/valuation of large scale Japanese Variable Annuities, modeling complex insurance derivatives product, as well as complex modeling of optimal end-user decision process. For the latter, he started to develop machine learning and data analytics for semi-structured, unstructured data, decided to pursue research in Machine Learning/Deep learning applied to optimality or in information processing. He joined Rakuten as Principal Data Scientist and is working on solutions for unstructured or semi-structured Big Data.

Acknowledgments
Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.

About the Series
The IAQF’s Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com). The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.

Registration Fees:

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Thalesians Members can register for $25

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