Event detail

Name: 2nd Machine Learning & AI in Quantitative Finance Conference USA

From: 14 Nov 2018

To: 16 Nov 2018

Address: Downtown Conference Center
157 William Street
New York, NY 10038

Organizer: WBS Training

Key speakers: Peter Carr, Managing Director, MORGAN STANLEY Michael Beal, CEO, Data Capital Management Gordon Ritter, Senior Portfolio Manager, GSA Capital Miquel Alonso, Executive Director, UBS Richard Rothenberg, Executive Director, Global AI Corporation Igor Halperin, Research Professor of Financial Machine Learning, NYU Tandon School of Engineering ShengQuan Zhou, Quantitative Researcher, Bloomberg LP Sol Steinberg, Founding Principle, OTC Partners Ksenia Shnyra, Senior Advisor, Deloitte Marcelo Labre, Executive Director, Morgan Stanley Terry Benzschawel, Founder and Principal, Benzschawel Scientific Amit Srivastav, Executive Director of Quantitative Analytics Group, Morgan Stanley Cristian Homescu, Director of Portfolio Analytics, Bank of America Merrill Lynch Bernhard Hientzsch, Head of Model Development for Corporate Model Risk, Wells Fargo Joseph Simonian, Director of Quantitative Research, Natixis Investment Managers Arik Dor, Managing Director and Head of Quantitative Equity Research, Barclays

Web page: https://www.wbstraining.com/events/nyc-machine-learning-conference/

Pricing: - variable, starting at $2159.10

Description: Module 1
Modern Market structure looking beyond 2020: The rise of alternative technology, marketplaces, and products such as exchange traded derivatives, and crypto currencies.
Exchanges, Clearing houses, and Collateral
Exchange traded & OTC derivative landscape
Big Data, AI, and machine learning in trading, finance, and operations

Module 2
HFT, Connectivity, & AI in Trading- Have we hit a wall? How competitors have reached critical mass
Combating HFT? IEX launches HFT proof exchange, reviewing the offering and why it works and why it doesn’t matter anymore.
Case Study: No more traders? How market leader JPM is automating almost their entire worldwide trading business – eventually
Case Study: Hedge Fund Renaissance & Artificial Intelligence greatest success story in the Markets - How Renaissance’s Medallion Fund Became Finance’s Blackest Box

Module 3
Big Data in the financial eco-system: Financial modelling, data governance, integration, NoSQL, batch and real-time computing and storage
Infrastructure and technology
New data sources
Modern data analysis: Structured / Unstructured data and new models

Module 4
Machine Learning Models: what is your best fit use in your business?
Asymmetric Trading Strategies
Non Linear Multi-Factor Models
High Frequency Trading
Advanced Machine Learning

Module 5
Machine learning in finance - Opportunities and challenges
Algo-Grading 101, Interpretation
Data mining biases: overfitting, survivorship and data-snooping
Robust trading strategies: The future of machine learning in finance


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