Machine Learning Toronto

Sessions will cover machine learning models, recent trends in machine learning and its application to quantitative finance, risk and portfolio construction.

Thumbnail

Machine Learning in Finance: A Quantitative Approach

28-29 March, Toronto

View the Agenda    Early Bird Pricing

This two day training course will provide delegates with an in-depth understanding of machine learning applications. This course will be a technical look at machine learning and provide suggestions and strategies for integrating it within your organization.

The multi-tutor format will provide attendees with an understanding of key theory, models, and more advanced tools in machine learning solutions through a quantitative approach that will also consider portfolio construction, trading, risk management and other business areas.

What Will You Learn?
  • In-depth understanding about how machine learning is changing the financial industry
  • The theory behind machine learning, earlier and latest applications of machine learning and how main methods can be applied in your firm
  • Latest approaches to machine learning applications in finance from a quantitative viewpoint
  • Machine learning in risk management and trading 
  • Best practices and the emerging techniques applicable to quantum computing and its applications
  • How to apply NLP in equity and credit markets
  • Challenges and opportunities of machine learning tools on portfolio construction and optimization 
  • Understanding the relationship between Deep Learning and Big Data
  • ML and AI capabilities- how they can help you solve problems more effectively and drive your business forward

More information

 

Who Should Attend

Relevant departments may include:

  • Risk Management 

  • Machine Learning

  • Portfolio Management

  • Wealth Management 

  • Asset Allocation

  • Data Science

  • Financial Engineering

  • Quantitative Analytics

  • Quantitative Modeling

  • Innovation

  • Forecasting

  • Infrastructure and Technology

More information

Course Highlights
  • Introduction to Machine Learning and Early Financial Applications
  • Recent Applications of Machine Learning in Finance
  • Machine learning in trading and portfolio optimization     
  • AI Interpretability
  • A deeper dive on Natural Language Processing  
  • Applying NLP to earning calls transcripts in equity and credit markets
  • Machine Learning and Robust Portfolio Construction
  • Quantum Machine Learning 

More information

Thumbnail
Joseph Simonian

Director of Quantitative Research

Natixis Investment Management

Thumbnail
Amit Srivastav

Executive Director, Quantitative Analytics Group

Morgan Stanley

Amit Srivastav, is an Executive Director in Morgan Stanley and manages the Quantitative Analytics Group (QAG) in Internal Audit which is responsible for independent assessment of model risk across the Firm including continuous monitoring, risk assessments, testing and reporting of model risk. Prior to Morgan Stanley, Amit was at Bank of America for 12 year career where he spent different roles as the Head of the Market and Counterparty Credit Risk audit functions, Model Risk audit team and in model validation. Amit has a MS in Mechanical Engineering and MBA from CUNY, NY. He also has a certificate in Statistics from Carnegie Mellon and is a CFA Charterholder.

Thumbnail
Jesús Calderón

Managing Director

Gravito

Thumbnail
Johannes van de Wetering

Head of Quantitative Risk, Capital Markets Risk Management

CIBC

Johannes van de Wetering is Head of Quantitative Risk for Capital Markets Risk Management, responsible for providing CIBC with the development, implementation, and maintenance of financial models to ensure effective pricing and risk measurement in meeting the demands of changing global and domestic regulations.

On top of his regular duties Johannes is tasked with building new capabilities in Risk Management, utilizing Artificial Intelligence and Machine Learning, to leverage data and analytics. In a previous role he was Head of Data Science, providing AI/ML solutions to the entire enterprise ranging from Capital Markets to Retail. He is experienced in unlocking value in customer data as well as trading data.

Before CIBC Johannes spent a decade in the London hedge fund industry as a fund manager, risk manager and options trader. Most recently he was fund manager for Partner Capital in Mayfair, trading systematic FX. His first role on the buy-side was as a Senior Portfolio Manager trading Volatility in all asset classes for ABP/APG, one of the world's  largest pension funds. Before that he was Head Quant on the Swaps desk for Deutsche Bank in Tokyo.

Johannes started his finance career in New York in Swaps, Credit Derivatives and MBS, worked on the Mortgage desk at Salomon Brothers and was Head Quant for Sanwa New York. He earned a Ph.D. in Physics from Princeton University.

Armando Benitez

VP Trading Products, Data Science Lead

BMO

Thumbnail
Arthur Berrill

Head of Content and Location Services, DNA

RBC

Arthur Berrill is the Head of Content and Location Services at the Royal Bank of Canada. He has a charter to research, design and guide the use of content and specifically location intelligence across all departments of the bank.

Arthur is a founding board member of the Location Forum that develops best practices and guidelines in the use of location data and technology. Arthur is also the chair of the CRCSI Research Investment Committee, a board responsible for guiding research directions and funding in spatial analytics and applications of spatial technology. And Arthur is a member of the TECTERRA Advisory Committee providing evaluations and guidance on the direction of funded geomatics research and innovation projects.

Arthur has almost 40 years of experience in the architecture, design and development of enterprise spatial systems including WILDMAP (a GIS before the term existed), SYSTEM 9, SpatialWare®, MapInfo products and Location Hub®. He holds numerous key patents in the location intelligence and spatial systems domain.

Fascinated by the use of new technology to solve business challenges, Arthur is working on new or improved algorithms and methods borrowed from other disciplines (such as genetics, deep learning), reinvented for modern architectures (such as massively parallel, share nothing) or imposed on the spatial disciplines by the evolution of technology (such as big data and spatial federation).

Prior to RBC, Arthur led the location intelligence initiative at Scotiabank and before then was president of DMTI Spatial. Arthur was Inventor of the Year for 2008 at Pitney Bowes (MapInfo) and won the TechAmerica 50th Anniversary Innovation Award in 2009.

Arthur is a graduate with Honours from the University of Queensland and did his postgraduate work at the International Institute of Aerial Survey and Earth Sciences in the Netherlands.

Thumbnail
Jos Gheerardyn

Co-Founder and CEO

YIELDS.IO

Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques to imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven (Belgium).

Funds Transfer Pricing

This training course is designed to provide attendees with all the tools needed to properly implement and govern FTP strategy. Sessions will cover the main areas of funds, liquidity & capital transfer pricing as well as looking at the business implications of FTP.

  • Singapore

Energy Risk Regulatory Update London

Learn how to comply with MiFID II, EMIR, REMIT & MAR and the operational implications they have. Additional sessions include renewable energy trading and sustainable development and best practice approaches to staying compliant with evolving technologies in energy markets.

  • London

ALM and Balance Sheet Optimisation Toronto

Come and learn how to optimise your balance sheet and implement and improve ALM strategies whilst focusing on the changing regulatory environment. Sessions will include insight on FTP and liquidity reporting, behavioural modelling and interest rate risk, capital management, Basel III/Basel IV and machine learning in balance sheet management.

  • Toronto, Canada