JOHN C. HULLUNIVERSITY OF TORONTO

John Hull is an internationally recognized authority on derivatives and risk management and has many publications in this area. His work has an applied focus. In 1999 he was voted Financial Engineer of the Year by the International Association of Financial Engineers.

He has acted as consultant to many North American, Japanese, and European financial institutions. He has won many teaching awards, including University of Toronto’s prestigious Northrop Frye award.

COURSE DESCRIPTION

This one-day workshop is designed for participants who are new to machine learning and want to acquire skills in this area.

TOPICS

1. Introduction a) Types of machine learning. b) Why ML is suddenly so popular in finance. c) Training, validation and test sets. d) Linear regression with many features: ridge, lasso, elastic regression. Case study. e) Bayes classification. f) Principal components.2. Supervised Learning a) Logistic regression. Case study. b) Support vector machines. c) Neural networks. d) Decision trees and random forests. e) Bagging and boosting; ensemble. f) The variance-bias trade-off.3. Unsupervised and Reinforcement Learning a) Clustering. Case study. b) Reinforcement learning. c) Biases and data cleaning. d) Image recognition. e) Limitations of ML.4. Other Financial Innovations a) The pattern of innovation. b) Blockchain and hashing. c) Cryptocurrencies and ICOs. d) Roboadvisors, insurtech, and regtech. e) Kodak vs. IBM.

MACHINE LEARNING

In Finance

MARCOS LÓPEZ DE PRADOADJUNCT PROFESSOR, FINANCIAL MACHINE LEARNINGCORNELL UNIVERSITY

Marcos López de Prado has over 20 years of experience developing investment strategies with the help of machine learning algorithms and supercomputers. He has recently sold his patents to AQR Capital Management, where he was a principal and AQR’s first head of machine learning. Marcos also founded and led Guggenheim Partners’ Quantitative Investment Strategies business, where he developed high-capacity investment algorithms that consistently delivered superior risk- adjusted returns, receiving up to $13 billion in assets. Concurrently with the management of

investments, between 2011 and 2018 Marcos was a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). He has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals, and SSRN ranks him as the most-read author in economics.

Among several monographs, Marcos is the author of the graduate textbook Advances in Financial Machine Learning (Wiley, 2018). Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain´s National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a financial machine learning course at the School of Engineering. In 2019, he received the ‘Quant of the Year Award’ from The Journal of Portfolio Management.

TOPICS

1. De-noising and de-toning of covariance matrices2. Entropy metrics: Moving beyond correlations3. Clustering methods4. Caveats of Markowitz’s Efficient Frontier5. The Hierarchical Risk Parity method6. The Dual-Clustered Optimization Method

COURSE DESCRIPTION:

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform.

As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Attendees will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives.

REQUIREMENTS– Graduated from an economic and/or administrative career.

– Preferably working in Financial Institutions.

– Participants should bring a laptop.

VENUE: Westin Santa Fe HotelJavier Barros Sierra 540 Col. Lomas de Santa Fe,

Mexico City.

Price: $30,000.00 M.N. + IVADuration: 3 Sessions (24 Hours)

10:00 pm - 6:00 pm

REGISTRATIONE-mail: derivatives@riskmathics.comTelephone: +52 (55) 5638 0367 +52 (55) 5669 4729

PAYMENT METHODS:

1. Bank Transfer and Cash Deposits (for Local Institutions)NAME: RiskMathics, S.C.BANK: BBVA BancomerCLABE: 012180001105829640BANK ACCOUNT: 0110582964

2. Bank Transfer in US Dollars (Foreign Institutions)Transferencia Bancaria en DólaresBANK: BBVA BancomerBRANCH NUNBER: 0956SWIFT: BCMRMXMMBENEFICIARY: RiskMathics, S.C.ACCOUNT: 0121 8000 11 0583 0066

3. Credit Card: VISA, MASTERCARD or AMERICAN EXPRESS

IMPORTANT NOTICE: There will be no reimbursements.

Agenda 2019JunE

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