World Business StrategiesServing the Global Financial Community since 2000

Friday 26th March 2021

Stream One: Volatility & Options
Smiles Without Tears: Semi-Analytic Short Rate Modelling with Smile and Skew

EDT: 09.00
GMT: 13.00
CET: 14.00

  • Comparatively few analytic formulae exist in relation to short rate models.
  • Analytic tractability is sacrificed to fit volatility smile and skew.
  • Idea: instead of using LSV, generate smile and skew through configurable functional dependence of short rate on underlying.
  • Consider polynomial representations of the short rate with reference to mean-reverting normal or lognormal underlyings.
  • Using perturbation methods, deduce accurate analytic representation of forward rate evolution.

Colin Turfus:

Quantitative Analyst, Deutsche Bank

Colin Turfus: Quantitative Analyst, Deutsche Bank

Colin Turfus has worked for the last twelve years as a financial engineer, mainly analysing model risk for credit derivatives and hybrids. More recently his interest has been in the application of perturbation methods to risk management, finding efficient analytic methods for computing, e.g., CVA, VaR and model risk. He is currently working in Global Model Validation and Governance at Deutsche Bank. He also taught evening courses on C++ and Financial Engineering at City University for seven years. Prior to that Colin worked as a developer consultant in the mobile phone industry after an extended period in academia, teaching applied maths and researching in fluid dynamics and turbulent dispersion.

Aurelio Romero-Bermudez:

Quantitative Analyst, Deutsche Bank

Aurelio Romero-Bermudez: Quantitative Analyst, Deutsche Bank

Analyst in the IMM group with  research interests spanning from perturbative and semi-analytic approaches to risk analysis to the application of Deep Learning and adjoint differentiation in risk management. Over 8 years research experience in Theory of Condensed Matter and Quantum Gravity with a PhD in Theoretical Physics from the University of Cambridge.

Stream One: Volatility & Options
Effective Markovian Projections for General Stochastic Volatility Models

EDT: 10.00
GMT: 14.00
CET: 15.00

In this talk we consider the class of Generalized Stochastic Volatility (see [1] M. Felpel, J. Kienitz, T. McWalter, Effective Stochastic Volatility Models of ZABR type – Quantitative Finance 2020x) which includes all well known Stochastic Volatility models inclding the SABR, ZABR and free SABR models. In this talk we apply the results of [1] to the pricing of basket options. To this end we make use of the new effective Markovian Projection technique.

We apply this new technique to the pricing of CMS Spread and Midcurve options. However, it can readily be generalized to other instruments such as options on CMS baskets.

  • General Stochastic Volatility Models
  • Markovian Projection
  • Projection Methods
  • CMS Spread options
  • Midcurve options
  • Numerical examples

Jörg Kienitz:

Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town

Jörg Kienitz: Finciraptor, AcadiaSoft, University of Wuppertal and Cape Town

Jörg Kienitz works in Quantitative Finance and Machine Learning  at Acadiasoft and the owner of the Finciraptor website (finciraptor.de). He is primarily involved in consulting on the development, implementation and validation of models. Jörg lectures at the University of Wuppertal as an Assistant Professor and is an Adjunct Associate Professor at UCT. He has addressed major conferences including Quant Minds and WBS Quant Conference. Jörg has authored four books “Monte Carlo Object Oriented Frameworks in C++” (with Daniel J. Duffy), “Financial Modelling” (with Daniel Wetterau), “Interest Rate Derivatives Explained I” and “Interest Rate Derivatives Explained II” (with Peter Caspers).

His SSRN author page is https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=744396″

Stream One: Volatility & Options
Black Baskets for Spread Options

EDT: 11.00
GMT: 15.00
CET: 16.00

Alexandre Antonov:

Chief Analyst, Danske Bank

Alexandre Antonov: Chief Analyst, Danske Bank

Alexandre Antonov received his PhD degree from the Landau Institute for Theoretical Physics in 1997. He worked for Numerix during 1998-2017 and recently he has joined Danske Bank as the Chief Analyst in Copenhagen.

His activity is concentrated on modeling and numerical methods for interest rates, cross currency, hybrid, credit and CVA/FVA/MVA. AA is a published author for multiple publications in mathematical finance and a frequent speaker at financial conferences.

He has received a Quant of Year Award of Risk magazine in 2016.

Both Streams
Panel: Machine Learning in Quantitative Finance

EDT: 12.00
GMT: 16.00
CET: 17.00

  • Unique challenges of using ML in finance
    • Dealing with scarce and non-stationary data
    • Best practices to overcome these challenges
  • How do we deal with model risk in ML?
    • Is there a way to make ML more explainable?
    • Should we trust ML models more than traditional stochastic models chosen because they have an analytical solution?
    • Will the regulators allow ML models for capital or accounting P&L?
  • How much value is ML adding?
    • Classical machine learning (RBMs, SVMs, etc.) vs. classical statistics (linear regression, convex optimization)
    • Deep learning vs. classical machine learning
    • What are the most important machine learning techniques for the future?
  • Where is the greatest potential for ML in systematic trading:
    • What styles of systematic trading can benefit from ML
    • How can ML be applied to systematic trading: sentiment analysis, time series analysis, alternative data use
  • What applications can you list among ML successes in finance?
    • Time series analysis, market generators
    • Derivative valuation
    • Capital and margin optimization
  • What are the global trends in financial services using AI/ML technologies?

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Igor Halperin:

AI Research Associate, Fidelity Investments

Igor Halperin: AI Research Associate, Fidelity Investments

Igor Halperin is an AI Research Associate at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering.  Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the books “Machine Learning in Finance: From Theory to Practice” (Springer 2020) and “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University.

Marko Kangrga:

Head of Quantitative Research, RavenPack

Marko Kangrga: Head of Quantitative Research, RavenPack

Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies and trading structured derivative instruments.

Ryan Ferguson:

Founder & CEO, Riskfuel

Ryan Ferguson: Founder & CEO, Riskfuel

Ryan is Founder and CEO at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies.Previously, Ryan was Managing Director and Head of Securitization, Credit Derivatives and XVA at Scotiabank. Prior roles have included credit correlation trading and managing the equity derivatives trading desk. Ryan began his career with positions in risk management and financial engineering. Ryan has a PhD in Physics from Imperial College, and a BASc and MASc in Electrical Engineering from the University of Waterloo.

David Jessop:

Head of Investment Risk, Columbia Threadneedle Investments EMEA APAC

David Jessop: Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC

David is the Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC. Previously the Global Head of Equities Quantitative Research at UBS. His areas of research include portfolio analysis and construction, style analysis and risk modelling. He also helps clients understand, use and implement the quantitative tools available from UBS. David joined UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund.

David graduated from Trinity College, Cambridge with an MA in Mathematics.

Artur Sepp:

Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp: Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank’s Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).

Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.

Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

Matthew Dixon:

Stuart School of Business, Illinois Institute of Technology

Matthew Dixon: Stuart School of Business, Illinois Institute of Technology

Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF.  Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets.  He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.

Friday 26th March 2021

Stream Two: Machine Learning
A Class of Mesh-free Algorithms for Mathematical Finance, Machine Learning and Fluid Dynamics

EDT: 09.00
GMT: 13.00
CET: 14.00

We introduce a numerical methodology that applies to a broad class of partial differential equations and discrete models, which we refer to as the transport-based mesh-free method. We combine together finite discretization techniques based on the (RKHS) theory of reproducing kernels and the theory of transport mappings, in a way that is reminiscent of the Lagrangian methods classically used in computational fluid dynamics. The proposed methodology leads us to several numerical algorithms (implemented in a Python library, called CodPy) which are particularly relevant when a large number of dimensions or degrees of freedom are present, as is the case in machine learning, mathematical finance, and fluid dynamics. We demonstrate the efficiency of our approach by presenting numerical results for neural networks based on support vector machines and for the Fokker-Planck-Kolmogorov system of mathematical finance. Importantly, the proposed algorithms enjoy quantitative error estimates based on the notion of discrepancy error and allows us to evaluate the relevance and accuracy of given data and numerical solutions, while kernel engineering techniques allow us to adapt the kernels to a variety of problems.

Philippe G. LeFloch:

Research Professor
Sorbonne University and CNRS

Philippe G. LeFloch: Research Professor, Sorbonne University

Philippe G. LeFloch holds a permanent position at Sorbonne University, as a Research Professor of the Centre National de la Recherche Scientifique. He graduated from the École Normale Supérieure (Saint-Cloud, France) and obtained a Ph.D. in Mathematics in 1988 from the Ecole Polytechnique (Palaiseau, France). In 1995, he received a Faculty Early Career Development award from the National Science Foundation. He worked at the Courant Institute of Mathematical Sciences (New York) and the University of Southern California (Los Angeles). He has published more than 200 research papers with about 100 different co-authors, and has written several textbooks, including the graduate course “Hyperbolic Systems of Conservation Laws”, Birkhäuser (2002) and a monograph establishing the “Global nonlinear stability of Minkowski space for self-gravitating massive fields’’.

Jean-Marc Mercier:

Senior researcher
MPG-Partner, Paris

Jean-Marc Mercier: Senior researcher, MPG-Partner, Paris

Jean-Marc Mercier is the head of the research and development team at MPG-Partners, a consulting firm for the financial services industry. He graduated from the University of Bordeaux (France) with a Ph.D. in applied mathematics obtained in 1996. He started his career as an Academic researcher, then moved to engineering in the finance industry. He is now sharing his time between various challenging industrial problems which he tackles with fundamental research tools.

Stream Two: Machine Learning
Future of Forecasting the Future in Finance

EDT: 10.00
GMT: 14.00
CET: 15.00

Abstract: In this talk, we cover various aspects of forecasting methods used in finance. A smart consensus of various economists, brokers, analysts and forecasters is a good way to think about forecasts and we illustrate the methodologies needed to score, rank and aggregate forecasts from multiple sources. Another important area for consensus forecasting is error & regime change detection. We also talk about the challenges of calibrating machine learning models to partial ground truth data on errors and how an ensemble methodology is needed to precisely identify different classes of errors accurately to allow for automation of the entire process from error detection & removal to scoring/ranking of forecasters to the final smart consensus forecast.

We will demonstrate use cases in company financials estimates, economic indicator and FX/Commodity spot rates forecasting.

Arun Verma:

Quantitative Research Solutions, Bloomberg, LP

Arun Verma: Quantitative Research Solutions, Bloomberg, LP

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing and hedging. More recently, he has enjoyed working at the intersection of diverse areas such as data science (for structured & unstructured data), innovative quantitative & machine learning methods and finally interactive visualizations to help reveal embedded signals in financial data.

Stream Two: Machine Learning
Alternative Sentiment Data for Managed Futures

EDT: 11.00
GMT: 15.00
CET: 16.00

  • Does macro sentiment data work for managed futures?
  • Application of Alexandria sentiment data and macro features engineering
  • ML methods for feature selection and model training
  • Simulation of systematic futures strategies
  • The defensive risk-profile of simulated sentiment-driven strategies

Artur Sepp:

Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp: Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank’s Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).

Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.

Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

Both Streams
Panel: Machine Learning in Quantitative Finance

EDT: 12.00
GMT: 16.00
CET: 17.00

  • Unique challenges of using ML in finance
    • Dealing with scarce and non-stationary data
    • Best practices to overcome these challenges
  • How do we deal with model risk in ML?
    • Is there a way to make ML more explainable?
    • Should we trust ML models more than traditional stochastic models chosen because they have an analytical solution?
    • Will the regulators allow ML models for capital or accounting P&L?
  • How much value is ML adding?
    • Classical machine learning (RBMs, SVMs, etc.) vs. classical statistics (linear regression, convex optimization)
    • Deep learning vs. classical machine learning
    • What are the most important machine learning techniques for the future?
  • Where is the greatest potential for ML in systematic trading:
    • What styles of systematic trading can benefit from ML
    • How can ML be applied to systematic trading: sentiment analysis, time series analysis, alternative data use
  • What applications can you list among ML successes in finance?
    • Time series analysis, market generators
    • Derivative valuation
    • Capital and margin optimization
  • What are the global trends in financial services using AI/ML technologies?

Moderator:

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

Igor Halperin:

AI Research Associate, Fidelity Investments

Igor Halperin: AI Research Associate, Fidelity Investments

Igor Halperin is an AI Research Associate at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering.  Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the books “Machine Learning in Finance: From Theory to Practice” (Springer 2020) and “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University.

Marko Kangrga:

Head of Quantitative Research, RavenPack

Marko Kangrga: Head of Quantitative Research, RavenPack

Marko is the Head of Quantitative Research for the Americas at RavenPack with over 10 years of experience in the finance industry. He focuses on exploring novel approaches and techniques for combining fundamental drivers with big data quantitative frameworks to identify alpha opportunities from a wide universe of securities across multiple asset classes. Previously, as the head trader/investment analyst at an event-driven hedge fund in New York, he was responsible for macro research, idea generation and risk management. Marko has experience in utilizing quantitative methods in portfolio construction, developing hedging strategies and trading structured derivative instruments.

Ryan Ferguson:

Founder & CEO, Riskfuel

Ryan Ferguson: Founder & CEO, Riskfuel

Ryan is Founder and CEO at Riskfuel, a capital markets focused startup that is developing ultra-fast AI-based valuation technologies.Previously, Ryan was Managing Director and Head of Securitization, Credit Derivatives and XVA at Scotiabank. Prior roles have included credit correlation trading and managing the equity derivatives trading desk. Ryan began his career with positions in risk management and financial engineering. Ryan has a PhD in Physics from Imperial College, and a BASc and MASc in Electrical Engineering from the University of Waterloo.

David Jessop:

Head of Investment Risk, Columbia Threadneedle Investments EMEA APAC

David Jessop: Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC

David is the Head of Investment Risk at Columbia Threadneedle Investments EMEA APAC. Previously the Global Head of Equities Quantitative Research at UBS. His areas of research include portfolio analysis and construction, style analysis and risk modelling. He also helps clients understand, use and implement the quantitative tools available from UBS. David joined UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund.

David graduated from Trinity College, Cambridge with an MA in Mathematics.

Artur Sepp:

Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp: Head Systematic Solutions and Portfolio Construction, Sygnum Bank

Artur Sepp is Head Systematic Solutions and Portfolio Construction at Sygnum Bank’s Asset Management in Zurich, specializing in crypto assets and decentralized finance. Prior, Artur led quantitative research at a systematic hedge fund (Quantica Capital) focusing on data-driven investment strategies and asset allocation in global managed futures. In previous roles, Artur worked as front office Quant Strategist on the implementation of systematic solutions in private banking (Julius Baer), and on the full-cycle development of quantitative solutions and derivatives in investment banking (Merrill Lynch/BofA).

Artur is dedicated to connecting financial applications with science and technology. His expertise covers quantitative investing and asset allocation, modeling of financial markets and instruments, statistical and Machine Learning methods, modern computational and programming tools. His 14 years professional experience includes performing in leading roles at top quant teams in New-York, London, and Zurich.

Artur has a PhD in Mathematical Statistics from University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. He is the author and co-author of several research articles on quantitative finance published in key journals. Artur is known for contributions to stochastic volatility and credit risk modelling with an H-index of 16. He is a member of the editorial board of the Journal of Computational Finance. Artur loves martial arts, water, and mountain sports.

Matthew Dixon:

Stuart School of Business, Illinois Institute of Technology

Matthew Dixon: Stuart School of Business, Illinois Institute of Technology

Matthew Dixon, Ph.D, FRM, began his career as a quant in structured credit trading at Lehman Brothers. He has consulted for numerous investment management, trading and financial technology firms in machine learning and risk analytics. He is the author of the 2020 textbook “Machine Learning in Finance: From Theory to Practice” and has written over 20 peer reviewed papers on machine learning and computational finance, including SIAM J. Financial Mathematics and the Journal of Computational Finance. He is the recipient of an Illinois Tech innovation award, and his research has been funded by Intel and the NSF.  Matthew has recently contributed to the CFA syllabus on machine learning and he currently serves on the CFA advisory committee for quantitative trading. He has been invited internationally to give talks at prestigious seminars organized by investment banks and universities in addition to being quoted in the Financial Times and Bloomberg Markets.  He holds a Ph.D. in Applied Math from Imperial College, has held visiting academic appointments at Stanford and UC Davis, and is a tenure-track Assistant Professor at Illinois Tech.

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
    (FULL-TIME Students Only)

Event Email Reminder

Error