Machine Learning New York
This two day training course will provide delegates with a comprehensive understanding of machine learning applications. Sessions will cover in-depth the technical aspects of machine learning and provide suggestions and strategies for integrating it within your organization.
This two day training course will build a strong foundation in machine learning by examining its theory, technical approaches, solutions and how to make better decisions and apply machine learning methods in your organization.
Led by seven top practitioners from the leading firms in the financial industry the course will provide delegates with best capabilities of machine learning techniques and tools in portfolio construction, trading, risk management and beyond.
- 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
- 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
Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead
Steve Yalovitser is a Director of Quantitative Strategies Group, Global Banking and Markets, within Bank of America Merrill Lynch's Counterparty Portfolio Management (CPM) Group. Yalovitser has been a lead architect for the bank, with exposure to a wide variety of asset classes, for the past 13 years. He has delivered multiple innovation-driven technology solutions for the bank, including its first equity exotics booking platform, its first equity back-testing platform and its first ad hoc scenario platform for Capital Calculations.
Yalovitser founded, led and delivered the Quartz Equity Derivatives Risk eco-system, currently running a portion of Bank of America Merrill Lynch's end-of-day risk reporting function. He is currently working on building out a Strategy Platform for CPM, covering Counterparty Valuation Adjustment (CVA), Capital Valuation Adjustment (KVA), Funding Valuation Adjustment (FVA), and Initial Margin (IM) posting.
Before joining Bank of America Merrill Lynch, Yalovitser founded Integrasoft LLC, creating the first product to address data aspects of grid computing and implementing it for use in derivative pricing applications for potential client sites. Prior to that, he held lead architect roles at several firms, including DoubleClick, Morgan Stanley and Dow Jones.
Professor & Director of the Mathematics in Finance, Courant Institute
New York University
Petter Kolm, Director of the Mathematics in Finance Masters Program and Clinical Professor, Courant Institute of Mathematical Sciences, NEW YORK UNIVERSITY
Petter Kolm is the Director of the Mathematics in Finance Masters Program and Clinical Associate Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group's hedge fund. Petter coauthored the books Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in mathematics from Yale, an M.Phil. in applied mathematics from Royal Institute of Technology, and an M.S. in mathematics from ETH Zurich.
Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Investment Strategies (JOIS), Journal of Portfolio Management (JPM), and the board of directors of the International Association for Quantitative Finance (IAQF). As a consultant and expert witness, he has provided his services in areas such as algorithmic and quantitative trading strategies, econometrics, forecasting models, portfolio construction methodologies incorporating transaction costs, and risk management procedures.
OCH-ZIFF Capital Management
Ken created the Risk Management department at Och Ziff and served as Chief Risk Officer for over 13 years. He led the Firm through a five-fold increase in AUM and headcount, the transition from private to public company, and managed major and minor financial/business crises and the introduction of new strategies and products.
He pioneered the use of quantitative techniques for portfolio construction and analysis at a fundamentally oriented firm, anticipating the “quantamental” revolution. Most recent activities have focused on how Artificial Intelligence may adapted to Finance.
Founder and Principal
Terry Benzschawel recently started his own firm after 30 years as a quant on Wall Street. The firm specializes in financial education, advanced model development, and systematic trading. Before that, Terry was a Managing Director in Citigroup's Institutional Clients Business, heading the Quantitative Credit Trading group.
Terry received a Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry, Ophthalmology, and engineering prior to embarking on a career in finance. Terry began his financial career in 1988 at Chase Manhattan Bank, building genetic algorithms to predict corporate bankruptcy. In 1990, he moved to Citibank and trained a neural network to detect fraud on credit card transactions. In 1992 he was hired by Salomon's Fixed Income Arbitrage Group to build models for proprietary fixed income trading. In 1998, he moved to Citi’s Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets where he worked in related roles since then. Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities and government institutions. In addition, he has published over a dozen articles in refereed journals and has authored two books: CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS and CREDIT MODELING: ADVANCED TOPICS.
Arik Ben Dor
Head of Quantitative Equity Research
Over the past 15 years, Dr. Ben Dor oversaw large scale research projects in rates, credit, equities, and hedge funds used by the largest institutional investors globally, including central banks, Sovereign wealth funds, asset managers, insurance companies, pensions and hedge funds. His research focused on asset allocation, smart beta, alpha generation, portfolio optimization, risk management, cost of investment constraints and hedging.
He published two books on quantitative investing in credit securities and over a dozen articles in leading industry journals such as the Journal of Portfolio Management, Journal of Fixed Income, Journal of Investment Management, and Journal of Alternative Investments.
He co-authored the influential articles on ‘DTS (Duration Times Spread)', a new approach to measuring the spread risk of corporate bonds and credit default swaps. It changed industry practices and was widely adopted by credit investors globally. One of his articles received the Martello award for the 2007 best practitioner paper, and his research on ‘cloning' hedge funds was the basis for several products and was awarded a U.S. patent.
His recent work on exploring the cross-asset relation between stocks and bonds was the basis for constructing systematic equity strategies such as momentum and ‘value' based on credit signals, and the usage of equity derivatives for hedging high-yield bonds. His systematic strategies were adopted by some of the largest global asset managers and were presented in leading industry conferences.
Prior to his current role, Dr. Ben Dor worked at Lehman Brothers and Morgan Stanley. He holds a PhD in Finance from the Kellogg Business School at Northwestern University, and completed his B.A. and M.A. in Economics from Tel Aviv University, Cum Laude.
Machine Learning Engineer Consultant
Federal Reserve Bank of New York
Elliot Noma has worked in both finance and data science. He is a consultant on machine learning and natural language processing solutions at the Federal Reserve Bank of New York. He also runs a proprietary trading company, Garrett Asset Management.
Dr. Noma taught machine learning at Columbia University and currently teaches quantitative risk management in the Masters in Mathematical Finance program at Rutgers University.
Previously, Dr. Noma was a portfolio manager running a fund of hedge funds and was the Chief Risk Officer at Asset Alliance, a $3 billion seeder of hedge funds.
Prior to working at Asset Alliance, Elliot was a risk manager at both Merrill Lynch Investment Advisers and Deutsche Bank, was an analyst at both JP Morgan and Salomon Brother, and was a investment banker at Chase.
He has patented a levered ETF structure, has written a book on psychometrics, and has published scholarly articles in behavioral finance, psychology, information science, sociology, math, and finance. His most recent article in 2018 describes a new method of measuring financial risk aversion using hypothetical investment preferences.
Dr. Noma has a Ph.D. in the mathematical modeling of psychological processes from the University of Michigan along with an M.A. in Mathematics. He graduated from Dartmouth College with a B.A. in Mathematics.
Head of Multi-Assets and Quantitative Solutions
BNP Paribas Asset Management
Patrick Dugnolle, PhD, U.S. Head of Multi-Asset and Quantitative Solutions
Patrick is the U.S. Head of Multi-Asset and Quantitative Solutions at BNP Paribas Asset Management. In this role he is responsible for developing the strategy and products for our Multi-Asset and Quantitative Solutions business in the U.S. He is based in the firm’s New York office.
He joined BNP Paribas more than 16 years ago and his contribution to quantitative factor investing dates back to 2004 when he joined the Risk-Managed-Funds Research Team of CooperNeff Advisors in King of Prussia, PA. Patrick moved back to Paris in 2006 in order to manage a $100 million long-short portfolio of U.S. equities, and later he became Head of Financial Engineering for Fixed Income for BNP Paribas Asset Management. Since 2013, Patrick has largely contributed to the reengineering of the Multi-Factor Quantitative Equity Investment process, which currently has over $2 billion under management. Prior to moving to New York City in September 2018, he was managing more than $1.5 billion of assets invested in U.S., European and global equities.
Patrick holds a PhD in Computer Science applied to Theoretical Biology from the University Joseph Fourier of Grenoble and an Engineer Degree in Electronics from ICPI Lyon.
55 Broad Street
55 Broad Street, 22nd Floor
New York, NY 10004
Model Risk Management New York course has been designed to delve into best practice approaches to building a model risk framework. You will be equipped with a thorough understanding of model risk now and into the future, including the impact of machine learning.
Learn about the effect IBOR ending is going to have on the industry and how the market will deal with the transition to risk free rates.