Panel: Matthew Dixon, Igor Halperin, and Paul Bilokon: ML in Finance
Webinar
Thursday, August 13, 2020 - 06:30 PM
Until: Thursday, August 13, 2020 - 08:30 PM
(Adjusted for timezone: Europe/London)
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Details
PLEASE NOTE THAT THIS WEBINAR WILL START ON THURSDAY, 13 AUGUST, 2020, AT 6:30 PM ***BST (LONDON TIME)*** (1.30 PM ***EDT (NEW YORK TIME)***)
TITLE: Machine Learning in Finance: From Theory to Practice
ABSTRACT:
The three coauthors will present their latest book, Machine Learning in Finance: From Theory to Practice.
With the trend towards increasing computational resources and larger datasets, machine learning has grown into a central computational engineering field, with an emphasis placed on plug-and-play algorithms made available through open-source machine learning toolkits. Algorithm focused areas of finance, such as algorithmic trading have been the primary adopters of this technology. But outside of engineering-based research groups and business activities, much of the field remains a mystery. A key barrier to understanding machine learning for non-engineering students and practitioners is the absence of the well-established theories and concepts that financial time series analysis equips us with. These serve as the basis for the development of financial modeling intuition and scientific reasoning. Moreover, machine learning is heavily entrenched in engineering ontology, which makes developments in the field somewhat intellectually inaccessible for students, academics, and finance practitioners from the quantitative disciplines such as mathematics, statistics, physics, and economics. Consequently, there is a great deal of misconception and limited understanding of the capacity of this field. How do we place key concepts in the field of machine learning in the context of more foundational theory in time series analysis, econometrics, and mathematical statistics? Under which simplifying conditions are advanced machine learning techniques such as deep neural networks mathematically equivalent to well-known statistical models such as linear regression? How should we reason about the perceived benefits of using advanced machine learning methods over more traditional econometrics methods, for different financial applications? What theory supports the application of machine learning to problems in financial modeling? How does reinforcement learning provide a model-free approach to the Black–Scholes–Merton model for derivative pricing? How does Q-learning generalize discrete-time stochastic control problems in finance?
BIOGRAPHY
MATTHEW F. DIXON is an Assistant Professor of Applied Math at the Illinois Institute of Technology. His research in computational methods for finance is funded by Intel. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis, respectively. He has published over 20 peer-reviewed publications on machine learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker in Silicon Valley and on Wall Street. He has published R packages, served as a Google Summer of Code mentor, and is the co-founder of the Thalesians Ltd.
IGOR HALPERIN is a Research Professor in Financial Engineering at NYU and an AI Research Associate at Fidelity Investments. He was previously an Executive Director of Quantitative Research at JPMorgan for nearly 15 years. Igor holds a Ph.D. in Theoretical Physics from Tel Aviv University (1994). Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.
PAUL BILOKON is CEO and Founder of Thalesians Ltd. and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup.
For more information, visit https://www.meetup.com/thalesians/events/272279293/
PLEASE NOTE THAT THIS WEBINAR WILL START ON THURSDAY, 13 AUGUST, 2020, AT 6:30 PM ***BST (LONDON TIME)*** (1.30 PM ***EDT (NEW YORK TIME)***)
TITLE: Machine Learning in Finance: From Theory to Practice
ABSTRACT:
The three coauthors will present their latest book, Machine Learning in Finance: From Theory to Practice.
With the trend towards increasing computational resources and larger datasets, machine learning has grown into a central computational engineering field, with an emphasis placed on plug-and-play algorithms made available through open-source machine learning toolkits. Algorithm focused areas of finance, such as algorithmic trading have been the primary adopters of this technology. But outside of engineering-based research groups and business activities, much of the field remains a mystery. A key barrier to understanding machine learning for non-engineering students and practitioners is the absence of the well-established theories and concepts that financial time series analysis equips us with. These serve as the basis for the development of financial modeling intuition and scientific reasoning. Moreover, machine learning is heavily entrenched in engineering ontology, which makes developments in the field somewhat intellectually inaccessible for students, academics, and finance practitioners from the quantitative disciplines such as mathematics, statistics, physics, and economics. Consequently, there is a great deal of misconception and limited understanding of the capacity of this field. How do we place key concepts in the field of machine learning in the context of more foundational theory in time series analysis, econometrics, and mathematical statistics? Under which simplifying conditions are advanced machine learning techniques such as deep neural networks mathematically equivalent to well-known statistical models such as linear regression? How should we reason about the perceived benefits of using advanced machine learning methods over more traditional econometrics methods, for different financial applications? What theory supports the application of machine learning to problems in financial modeling? How does reinforcement learning provide a model-free approach to the Black–Scholes–Merton model for derivative pricing? How does Q-learning generalize discrete-time stochastic control problems in finance?
BIOGRAPHY
MATTHEW F. DIXON is an Assistant Professor of Applied Math at the Illinois Institute of Technology. His research in computational methods for finance is funded by Intel. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis, respectively. He has published over 20 peer-reviewed publications on machine learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker in Silicon Valley and on Wall Street. He has published R packages, served as a Google Summer of Code mentor, and is the co-founder of the Thalesians Ltd.
IGOR HALPERIN is a Research Professor in Financial Engineering at NYU and an AI Research Associate at Fidelity Investments. He was previously an Executive Director of Quantitative Research at JPMorgan for nearly 15 years. Igor holds a Ph.D. in Theoretical Physics from Tel Aviv University (1994). Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.
PAUL BILOKON is CEO and Founder of Thalesians Ltd. and an expert in electronic and algorithmic trading across multiple asset classes, having helped build such businesses at Deutsche Bank and Citigroup.
For more information, visit https://www.meetup.com/thalesians/events/272279293/