Papers, Presentations, and Full-Length Session Videos
Policy Sessions
How Do Machines Learn Finance?
Is machine learning (ML) a fundamentally new set of tools? Or does it represent an accelerated innovation of tools already long in use? What are the major strengths, weaknesses, and limitations of ML? This session examines how human experts and decision makers interact with ML.
Panelists:
Ryan Adams, Professor of Computer Science, Princeton University [Presentation
]
John P. Cunningham, Associate Professor, Department of Statistics, Columbia University [Presentation]
Francis X. Diebold, Paul F. and Warren S. Miller Professor of Economics and Professor of Finance and Statistics, University of Pennsylvania
Gideon Mann, Head of Data Science, Bloomberg [Presentation | Interview 
]
Is machine learning (ML) a fundamentally new set of tools? Or does it represent an accelerated innovation of tools already long in use? What are the major strengths, weaknesses, and limitations of ML? This session examines how human experts and decision makers interact with ML.
Panelists:
Ryan Adams, Professor of Computer Science, Princeton University [Presentation
]John P. Cunningham, Associate Professor, Department of Statistics, Columbia University [Presentation
]Francis X. Diebold, Paul F. and Warren S. Miller Professor of Economics and Professor of Finance and Statistics, University of Pennsylvania
Gideon Mann, Head of Data Science, Bloomberg [Presentation
| Interview ]Machines Learning Regulation (and Vice Versa)
Machine learning (ML) has the potential to change significantly the regulatory environment—for better and worse. This session discusses how ML is teaching financial firms to optimize their risk management and comply with regulation more effectively and at lower cost. The session also examines ways ML helps supervisors enforce existing regulation more effectively.
Scott Bauguess, Deputy Director, Division of Economic and Risk Analysis, Securities and Exchange Commission
William Lang, Managing Director, Promontory Financial Group [Presentation]
John Schindler, Associate Director, Program Direction Section, Financial Stability, Board of Governors of the Federal Reserve System
Stacey Schreft, Deputy Director for Research and Analysis, Office of Financial Research, U.S. Department of the Treasury
Machine learning (ML) has the potential to change significantly the regulatory environment—for better and worse. This session discusses how ML is teaching financial firms to optimize their risk management and comply with regulation more effectively and at lower cost. The session also examines ways ML helps supervisors enforce existing regulation more effectively.
Scott Bauguess, Deputy Director, Division of Economic and Risk Analysis, Securities and Exchange Commission
William Lang, Managing Director, Promontory Financial Group [Presentation
]John Schindler, Associate Director, Program Direction Section, Financial Stability, Board of Governors of the Federal Reserve System
Stacey Schreft, Deputy Director for Research and Analysis, Office of Financial Research, U.S. Department of the Treasury
Learning about an ML-Driven Economy
This session examines the size of the likely impact of machine learning (ML) on the economy. Will changes to business models affect statistical measures of macroeconomic performance? If macroeconomic policymakers—especially monetary policymakers—conclude that ML is significantly affecting the overall economy, how should they respond?
Carolyn Evans, Head Economist and Senior Data Scientist, Intel Corporation
Charles Evans, President and Chief Executive Officer, Federal Reserve Bank of Chicago [Presentation]
Rob Kaplan, President and Chief Executive Officer, Federal Reserve Bank of Dallas
Vincent Reinhart, Chief Economist, Standish Mellon Asset Management LLC
This session examines the size of the likely impact of machine learning (ML) on the economy. Will changes to business models affect statistical measures of macroeconomic performance? If macroeconomic policymakers—especially monetary policymakers—conclude that ML is significantly affecting the overall economy, how should they respond?
Carolyn Evans, Head Economist and Senior Data Scientist, Intel Corporation
Charles Evans, President and Chief Executive Officer, Federal Reserve Bank of Chicago [Presentation
]Rob Kaplan, President and Chief Executive Officer, Federal Reserve Bank of Dallas
Vincent Reinhart, Chief Economist, Standish Mellon Asset Management LLC
Machines Learning Investments
Machines have been learning finance for decades, and algorithmic, high-frequency trading has received much attention. However, recent developments in data availability and storage, computer speed, and learning techniques might dramatically change and accelerate technology's participation in the financial system. This session will explore the strengths and weaknesses of using ML to design and execute portfolio strategies.
Andrei Kirilenko, Director of the Centre for Global Finance and Technology, Imperial College London
Rishi Narang, Founding Principal, T2AM
Christina Qi, Partner, Domeyard [Interview]
Chester Spatt, Pamela R. and Kenneth B. Dunn Professor of Finance, Tepper School of Business, Carnegie Mellon University
Machines have been learning finance for decades, and algorithmic, high-frequency trading has received much attention. However, recent developments in data availability and storage, computer speed, and learning techniques might dramatically change and accelerate technology's participation in the financial system. This session will explore the strengths and weaknesses of using ML to design and execute portfolio strategies.
Andrei Kirilenko, Director of the Centre for Global Finance and Technology, Imperial College London
Rishi Narang, Founding Principal, T2AM
Christina Qi, Partner, Domeyard [Interview
] Chester Spatt, Pamela R. and Kenneth B. Dunn Professor of Finance, Tepper School of Business, Carnegie Mellon University
Keynotes
A Conversation on Machine Learning in Financial Regulation
Randal K. Quarles, a member of the Federal Reserve System's Board of Governors and the Board's vice chairman for supervision, discuss the potential impact of machine learning on the supervision and regulation of financial institutions.
Randal Quarles, Vice Chairman of Supervision, Board of Governors of the Federal Reserve System
Randal K. Quarles, a member of the Federal Reserve System's Board of Governors and the Board's vice chairman for supervision, discuss the potential impact of machine learning on the supervision and regulation of financial institutions.
Randal Quarles, Vice Chairman of Supervision, Board of Governors of the Federal Reserve System
Design, Data and Development: How AI Ethics Is Transforming Engineering, Identity, and Economics
John C. Havens, executive director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, talks about the transformative possibilities inherent in artificial intelligence and related technology.
John Havens, Executive Director, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems [Presentation]
John C. Havens, executive director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, talks about the transformative possibilities inherent in artificial intelligence and related technology.
John Havens, Executive Director, IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems [Presentation
]Digitization of Money and Finance: Challenges and Opportunities
Tao Zhang, deputy managing director of the International Monetary Fund, discusses the digital revolution represented by the digitization of money. Its impact holds a nearly unimaginable combination of technological benefits—and potential dislocations.
Tao Zhang, Deputy Managing Director, International Monetary Fund
Tao Zhang, deputy managing director of the International Monetary Fund, discusses the digital revolution represented by the digitization of money. Its impact holds a nearly unimaginable combination of technological benefits—and potential dislocations.
Tao Zhang, Deputy Managing Director, International Monetary Fund
Research Papers
Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Presenter: Chad Syverson, Eli B. and Harriet B. Williams Professor of Economics, University of Chicago [Presentation]
Discussant: Dave Altig, Executive Vice President and Research Director, Federal Reserve Bank of Atlanta
Moderator: Patrick Harker, President and Chief Executive Officer, Federal Reserve Bank of Philadelphia
Presenter: Chad Syverson, Eli B. and Harriet B. Williams Professor of Economics, University of Chicago [Presentation
]Discussant: Dave Altig, Executive Vice President and Research Director, Federal Reserve Bank of Atlanta
Moderator: Patrick Harker, President and Chief Executive Officer, Federal Reserve Bank of Philadelphia
Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies
Presenter: Matt Scherer, Associate, Littler Mendelson [Presentation]
Discussant: Greg Scopino, Special Counsel, Division of Swap Dealer and Intermediary Oversight, U.S. Commodity Futures Trading [Presentation]
Moderator: Larry Wall, Director of the Center for Financial Innovation and Stability, Federal Reserve Bank of Atlanta
Presenter: Matt Scherer, Associate, Littler Mendelson [Presentation
]Discussant: Greg Scopino, Special Counsel, Division of Swap Dealer and Intermediary Oversight, U.S. Commodity Futures Trading [Presentation
]Moderator: Larry Wall, Director of the Center for Financial Innovation and Stability, Federal Reserve Bank of Atlanta