2nd Annual Machine Learning in Quantitative Finance

New York, NY, USA

This GFMI conference will enable buy side and sell side to discuss the latest strategies for Machine Learning (ML) within quantitative finance and identify the best Return on Investment (ROI) and use cases. The event will discuss the utility of reinforcement learning and Natural Language Processing (NLP) and how best to adapt these models to the industry. Delegates will also explore the importance of data quality and how to approach the high noise-to-signal ratios to guarantee models can produce accurate results. As well as this, discussions will be held about how to derive actionable insights from alternative data to maximize ROI. Methodologies to overcome the issues of interpretability and explainability will also be assessed to ensure that firms can satisfy regulators, clients and investors. Finally, the future for ML will be discussed, including an assessment of how best to bridge the gap between quantitative finance professionals and machine learning experts. Key Topics: • Benchmark the best use cases for ML in quantitative finance • Overcome issues surrounding data quality in ML models • Examine the best utility of alternative data within quantitative strategies • Develop new methods to explain and interpret ML results to stakeholders • Compare recruitments approaches to attract and hire ML experts Practical Insights from: • Alexander Fleiss, Chief Executive Officer, RebellionResearch.com • Michael Recce, Chief Data Scientist, Neuberger Berman • Arik Ben Dor, Head of Quantitative Equity Research, Barclays • Tucker Balch, Managing Director, JP Morgan AI Research Group, JP Morgan & Chase • Ritesh Singh, Vice President, Data Scientist, Goldman Sachs • Boris Lerner, Global Head of Quantitative Equity Research, Morgan Stanley • Kevin Slader, Senior Data Scientist, Societe Generale