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Quantitative Investing Group Meeting

March 14, 2023 | 5:00 PM - 6:30 PM

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Group Description

The Quantitative Investing Group brings together professionals seeking to incorporate cutting edge quantitative investment techniques and alternative data sets in their investing and risk processes. Members include (but are not limited to) discretionary and systematic portfolio managers, risk managers, traders and fundamental analysts, data strategists, quantitative researchers, and others. The topic covered range from quantitative alpha generation, big data as well as alternative datasets, quantamental signals i.e. the intersection of fundamental analysis and quantitative decision making, mathematical and statistical aspects of modern quantitative analysis, use of programming languages or quant tools, Natural Language Processing, machine learning for investing and risk management, theory and implementation of AI in finance and more.

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Guest Speaker

Miquel Noguer I Alonso, Ph.D.

Miquel Noguer i Alonso is a financial markets practitioner with more than 30 years of experience in quantitative finance he is the Founder of Artificial Intelligence Finance Institute. He is a Professor at NYU Courant Institute of Mathematical Sciences, NYU Tandon and the CQF instituteHe has been Adjunct Professor at Columbia University.  He is also the Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance. He serves on the advisory board of Financial Data Professional Institute FDPI and the CFA New York Quant Investing Group.

He worked for UBS AG (Switzerland) as Executive Director. He has been a member of the European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.  He started his career at KPMG.
He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a Ph.D. in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).

Overview

Topic: Generative Models for Time Series in Finance

A generative model is a statistical model of the joint probability distribution. We built a generative model for univariate time series in finance using a Variational Autoencoder (VAE) neural network architecture. We test the model in SP500 and the Heston Model widely used for option pricing and hedging in the financial industry. In both contexts the generative model is able to correctly learn the density according to our qualitative and quantitative tests . Users have to consider the potential non-stationarity in this context and potentially enrich the learning process.Applications of these generative models include risk management, data augmentation to be used in supervised learning applications and can alleviate the problem of small data in finance. In future research we intend to investigate the multivariate case extremely important for portfolio and risk management as well as testing other generative architectures.

Quantitative Group

Guest Speaker

Miquel Noguer I Alonso, Ph.D.

Miquel Noguer i Alonso is a financial markets practitioner with more than 30 years of experience in quantitative finance he is the Founder of Artificial Intelligence Finance Institute. He is a Professor at NYU Courant Institute of Mathematical Sciences, NYU Tandon and the CQF instituteHe has been Adjunct Professor at Columbia University.  He is also the Head of Development at Global AI and co-Editor of the Journal of Machine Learning in Finance. He serves on the advisory board of Financial Data Professional Institute FDPI and the CFA New York Quant Investing Group.

He worked for UBS AG (Switzerland) as Executive Director. He has been a member of the European Investment Committee for the last 10 years. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006.  He started his career at KPMG.
He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a Ph.D. in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain).

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