World Business StrategiesServing the Global Financial Community since 2000

Conference Day One

08.00 - 09.00
Registration and Morning Welcome Coffee
09.00 - 10.00
Keynote Speech: Machine Learning and AI in Finance: Applications, Cases and Research
  • Machine learning and deep learning applications in quantitative finance and risk management
  • Practitioners’ case studies
  • Research and development in deep learning

Marcelo Labre:

Executive Director, Morgan Stanley

Marcelo Labre: Executive Director, Morgan Stanley

10.00 - 10.45
Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics

We review some new approaches from research and literature and Wells Fargo’s work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions

Bernhard Hientzsch:

Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo

Bernhard Hientzsch: Managing Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo

Bernhard Hientzsch is the Head of Model, Library, and Tool Development in the Corporate Model Risk Management Group at Wells Fargo. His group is responsible for the implementation of models, libraries, components, and tools for the validation, benchmarking, and oversight of models at Wells Fargo. Prior to joining Wells Fargo, he was a postdoctoral scientist at New York University in several DoE supported projects and consulting on mathematical, financial, and computer modelling in the USA and Germany. Bernhard received his PhD in applied mathematics from the Courant Institute at New York University.

10.45 - 11.15
Morning Break and Networking Opportunities
11.15 - 12.00
Deep Learning in Finance – LSTN’s 
  • Modern Data Analysis
  • Times Series Models Univariate
  • Linear Factor Models
  • Multivariate Time Series
  • Modern Financial Engineering
  • Long Short Term Memory Networks
    • Results
    • Conclusions

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

12.00 - 12.45
Model-free Option Pricing and Hedging by Reinforcement Learning

In discrete time, option hedging and pricing amount to sequential risk minimization. In particular, a discrete-time version of the Black-Scholes-Merton (BSM) option pricing model can be formulated as a problem of dynamic Markowitz optimization of an option replicating (hedge) portfolio made of an underlying stock and cash. This talk shows how this problem can be approached using Reinforcement Learning (RL). Once the problem is posed as an RL problem, option pricing and hedging can be done without any model for the underlying stock dynamics, using instead model-free, data-driven RL methods such as Q-learning and Fitted Q Iteration. As a result, both option price and hedge are obtained by a well-defined and converging maximization problem that uses only market prices and option trading data (inter-temporal re-hedges and hedge losses in the replicating portfolio) to find the optimal option hedge and price. The model can learn when re-hedges in data are suboptimal/noisy, or even purely random. This means, in particular, that our RL model can learn the BSM model itself, if the world is according to BSM.

Computationally, the RL-based option pricing model is very simple, as it uses only basic linear algebra and linear regressions to compute the option price and hedge. The only tunable parameters in this approach are parameters defining the optimal hedge and price themselves. This approach does not need any model calibration (as there is no model anymore), and it automatically solves the volatility smile problem of the BSM model. We also discuss some extensions of this approach, including in particular an Inverse Reinforcement Learning setting, where inter-temporal losses from re-hedges are unobservable. 

Igor Halperin:

Research Professor of Financial Machine Learning, NYU Tandon School of Engineering

Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering

Igor Halperin is currently an Adjunct Professor of Financial Machine Learning at the NYU Tandon School of Engineering. Prior to that, he was an Executive Director of Quantitative Research at JPMorgan Chase where he focused on the research and development of predictive and statistical models and machine learning methods for modeling risk of financial portfolios.

He has authored a number of publications on quantitative finance, and is a frequent speaker at financial conferences. Dr. Halperin has a Ph.D. in theoretical physics from Tel Aviv University, and M.Sc. in nuclear physics from St. Petersburg State Technical.

12.45 - 14.00
Lunch
14.00 - 14.45
Machine Learning Models for Corporate Bond Default, Recovery in Default, and Relative Value

Machine Learning Models for Corporate Bond Default, Recovery in Default, and Relative Value

Terry Benzschawel:

Founder and Principal, Benzschawel Scientific, LLC

Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC

Terry Benzschawel is the Founder and Principal of Benzschawel Scientific, LLC. The former Managing Director in Citigroup’s Institutional Clients Business. Terry headed the Credit Trading Analysis group which develops and implements quantitative tools and strategies for credit market trading and risk management, both for Citi’s clients and for in-house applications. Some sample tools include models of corporate default and recovery values, relative value of corporate bonds, loans, and credit default swaps, credit portfolio optimization, credit derivative trades, capital structure arbitrage, measuring and hedging liquidity risk, and cross-credit-sector asset allocation.

After six years of post-doctoral research in academia and industry and two years in consumer banking, Terry began his investment banking career in at Salomon Brothers in 1992. Terry built models for proprietary arbitrage trading in bonds, currencies and derivative securities in Salomon’s Fixed Income Arbitrage Group. In 1998, he moved to the Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets and has worked in related roles since then. Terry was promoted to Managing Director at Citi in 2008.

Terry received his Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine and was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering Programs at the University of California at Berkeley and the University of California at Los Angeles and Carnegie Mellon University’s Computational Finance Program.

Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities. In addition, he has published over a dozen articles in refereed journals and is author of CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS. In addition, Terry has been the instructor for courses in credit modelling for Incisive Media and the Centre for Finance Professionals. Finally, Terry has taught a course on credit modelling at Russia’s Sberbank in Moscow.

14.45 - 15.30
"Is Information Extracted from Earning Calls Transcripts using NLP Predictive of Future Stock Returns?"

Abstract: We examine whether sentiment extracted from earnings call transcripts using an advanced NLP (Natural Language Processing) technique is predictive of subsequent stock returns. We show that controlling for commonly used measures of informational surprise, stocks’ performance following the earning calls was significantly positively correlated with the sentiment level extracted from their transcripts. The outperformance was persistent over time and across all sectors, and was higher for smaller stocks with limited coverage by equity analysts, consistent with the effect of investors’ inattention. A daily rebalanced L-S strategy we construct using the NLP-extracted signal delivered inf. ratio close to 2 and an annualized alpha over 12% in the past decade. Furthermore, a strategy based on combining the NLP signal with traditional earnings measures resulted in an annualized alpha that was higher by 50% compared with just using the traditional measures alone.

Arik Ben Dor:

Managing Director and Head of Quantitative Equity Research, Barclays

Arik Ben Dor: Managing Director and Head of Quantitative Equity Research, Barclays

Over the past 15 years, Dr. Ben Dor oversaw large scale research projects in rates, credit, equities, and hedge funds used by the largest institutional investors globally, including central banks, Sovereign wealth funds, asset managers, insurance companies, pensions and hedge funds. He co-authored two books on quantitative investing in credit securities and over a dozen articles in leading industry journals such as the Journal of Portfolio Management, Journal of Fixed Income, Journal of Investment Management, and Journal of Alternative Investments. One of his articles received the Martello award for the 2007 best practitioner paper.

Dr. Ben Dor research on ‘DTS (Duration Times Spread)’, a new approach to measuring the spread risk of corporate bonds and credit default swaps changed industry practices and was widely adopted by credit investors globally. In 2018, he was ranked 1st in the II All-America Fixed Income Research survey in the Quantitative Analysis category. Dr. Ben Dor also conducted research on ‘cloning’ hedge funds was the basis for several products and was awarded a U.S. patent.

His work on exploring the cross-asset relation between stocks and bonds was the basis for constructing systematic equity strategies such as momentum and ‘value’ based on credit signals, and the usage of equity derivatives for hedging high-yield bonds. His systematic strategies were adopted by some of the most prominent quantitative hedge funds and ‘long-only’ asset managers and were presented in leading industry conferences.

Prior to Barclays, Dr. Ben Dor worked at Lehman Brothers and Morgan Stanley. He holds a PhD in Finance from the Kellogg Business School at Northwestern University, and completed his B.A. and M.A. in Economics from Tel Aviv University, Cum Laude.

15.30 - 16.00
Afternoon Break and Networking Opportunities
16.00 - 16.30
Using Machine Learning to Forecast Realized Volatility

Using Machine Learning to Forecast Realized Volatility

Peter Carr:

Professor and Dept. Chair of FRE Tandon, New York University

Peter Carr: Professor and Dept. Chair of FRE Tandon, New York University

Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.

In the 2 years Dr. Carr been FRE dept. chair, applications increased from 1,300 per year to 1,900 per year. The number of FRE Masters students in residence was the highest in any 2-year period. For the incoming 2018 class, current verbal GRE is 169/170 and GPA is 3.82. FRE moved up in Quantnet rankings both years. An online summer course was initiated last summer and an on-campus bootcamp will be initiated this summer. Six electives on machine learning in finance were introduced. The distance learning room will become operational this summer.

16.30 - 17.30
Machine Learning & Ai in Quantitative Finance Panel

Topics:

  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What’s the evolution of a team using machine learning in terms of day to day operations?
    What is a typical front office ‘Quant’ skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
    What applications can you list among its successes?
    How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
    What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
    What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
    Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
    If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for.
  • Discuss quantum computing in quant finance:
    • Breakthroughs
    • Applications
    • Future uses

Panellists:

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer i Alonso is a financial markets practitioner with more than 20 years of experience in asset management, he is currently working for UBS AG (Switzerland). He worked as a CFO and CIO for a European bank from 2000 to 2006. He started his career at KPMG.

He is Adjunct Assistant Professor at Columbia University teaching Asset Allocation, Big Data in Finance, Fintech and Hedge Fund Professor at ESADE. He received an MBA and a Degree in business administration and economics in ESADE in 1993. In 2010 he earned a PhD in quantitative finance with a Summa Cum Laude distinction (UNED – Madrid Spain). He also holds the Certified European Financial Analyst diploma ( 2000 ).

His research interests range from asset allocation, big data to algorithmic trading and fintech. His academic collaborations include a visiting scholarship in Columbia University in 2013 in the Finance and Economics Department, in Fribourg University in 2010 in the mathematics department, and presentations in Indiana University, ESADE, London Business School, CAIA Association, AFI and several industry seminars.

Terry Benzschawel:

Founder and Principal, Benzschawel Scientific, LLC

Terry Benzschawel: Founder and Principal, Benzschawel Scientific, LLC

Terry Benzschawel is the Founder and Principal of Benzschawel Scientific, LLC. The former Managing Director in Citigroup’s Institutional Clients Business. Terry headed the Credit Trading Analysis group which develops and implements quantitative tools and strategies for credit market trading and risk management, both for Citi’s clients and for in-house applications. Some sample tools include models of corporate default and recovery values, relative value of corporate bonds, loans, and credit default swaps, credit portfolio optimization, credit derivative trades, capital structure arbitrage, measuring and hedging liquidity risk, and cross-credit-sector asset allocation.

After six years of post-doctoral research in academia and industry and two years in consumer banking, Terry began his investment banking career in at Salomon Brothers in 1992. Terry built models for proprietary arbitrage trading in bonds, currencies and derivative securities in Salomon’s Fixed Income Arbitrage Group. In 1998, he moved to the Fixed Income Strategy department as a credit strategist with a focus on client-oriented solutions across all credit markets and has worked in related roles since then. Terry was promoted to Managing Director at Citi in 2008.

Terry received his Ph.D. in Experimental Psychology from Indiana University (1980) and his B.A. (with Distinction) from the University of Wisconsin (1975). Terry has done post-doctoral fellowships in Optometry at the University of California at Berkeley and in Ophthalmology at the Johns Hopkins University School of Medicine and was a visiting scientist at the IBM Thomas J. Watson Research Center prior to embarking on a career in finance. He currently serves on the steering committees of the Masters of Financial Engineering Programs at the University of California at Berkeley and the University of California at Los Angeles and Carnegie Mellon University’s Computational Finance Program.

Terry is a frequent speaker at industry conferences and events and has lectured on credit modelling at major universities. In addition, he has published over a dozen articles in refereed journals and is author of CREDIT MODELING: FACTS, THEORIES AND APPLICATIONS. In addition, Terry has been the instructor for courses in credit modelling for Incisive Media and the Centre for Finance Professionals. Finally, Terry has taught a course on credit modelling at Russia’s Sberbank in Moscow.

Marcelo Labre:

Executive Director, Morgan Stanley

Marcelo Labre: Executive Director, Morgan Stanley

Igor Halperin:

Research Professor of Financial Machine Learning, NYU Tandon School of Engineering

Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering

Igor Halperin is currently an Adjunct Professor of Financial Machine Learning at the NYU Tandon School of Engineering. Prior to that, he was an Executive Director of Quantitative Research at JPMorgan Chase where he focused on the research and development of predictive and statistical models and machine learning methods for modeling risk of financial portfolios.

He has authored a number of publications on quantitative finance, and is a frequent speaker at financial conferences. Dr. Halperin has a Ph.D. in theoretical physics from Tel Aviv University, and M.Sc. in nuclear physics from St. Petersburg State Technical.

Bernhard Hientzsch:

Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo

Bernhard Hientzsch: Managing Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo

Bernhard Hientzsch is the Head of Model, Library, and Tool Development in the Corporate Model Risk Management Group at Wells Fargo. His group is responsible for the implementation of models, libraries, components, and tools for the validation, benchmarking, and oversight of models at Wells Fargo. Prior to joining Wells Fargo, he was a postdoctoral scientist at New York University in several DoE supported projects and consulting on mathematical, financial, and computer modelling in the USA and Germany. Bernhard received his PhD in applied mathematics from the Courant Institute at New York University.

Arik Ben Dor:

Managing Director and Head of Quantitative Equity Research, Barclays

Arik Ben Dor: Managing Director and Head of Quantitative Equity Research, Barclays

Over the past 15 years, Dr. Ben Dor oversaw large scale research projects in rates, credit, equities, and hedge funds used by the largest institutional investors globally, including central banks, Sovereign wealth funds, asset managers, insurance companies, pensions and hedge funds. He co-authored two books on quantitative investing in credit securities and over a dozen articles in leading industry journals such as the Journal of Portfolio Management, Journal of Fixed Income, Journal of Investment Management, and Journal of Alternative Investments. One of his articles received the Martello award for the 2007 best practitioner paper.

Dr. Ben Dor research on ‘DTS (Duration Times Spread)’, a new approach to measuring the spread risk of corporate bonds and credit default swaps changed industry practices and was widely adopted by credit investors globally. In 2018, he was ranked 1st in the II All-America Fixed Income Research survey in the Quantitative Analysis category. Dr. Ben Dor also conducted research on ‘cloning’ hedge funds was the basis for several products and was awarded a U.S. patent.

His work on exploring the cross-asset relation between stocks and bonds was the basis for constructing systematic equity strategies such as momentum and ‘value’ based on credit signals, and the usage of equity derivatives for hedging high-yield bonds. His systematic strategies were adopted by some of the most prominent quantitative hedge funds and ‘long-only’ asset managers and were presented in leading industry conferences.

Prior to Barclays, Dr. Ben Dor worked at Lehman Brothers and Morgan Stanley. He holds a PhD in Finance from the Kellogg Business School at Northwestern University, and completed his B.A. and M.A. in Economics from Tel Aviv University, Cum Laude.

Joseph Simonian:

Director of Quantitative Research, Natixis Investment Managers

Joseph Simonian: Director of Quantitative Research, Portfolio Research & Consulting Group, Natixis Investment Managers

Joseph Simonian is the Director of Quantitative Research in the Portfolio Research and Consulting Group at Natixis
Investment Managers. In this role, he leads quantitative research and portfolio strategy for the team’s model portfolio program, as well as customized solutions for the firm’s institutional and advisory clients. Dr. Simonian also leads thought leadership efforts for the team.
Dr. Simonian has over 12 years of investment industry experience and has served previously at Lehman Brothers,
PIMCO, JP Morgan, and Fidelity’s Global Institutional Solutions group. He has been widely published in leading industry journals and is the co-editor of the Journal of Financial Data Science.

  • Discount Structure
  • Early bird discount
    10% until November 2nd 2018

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

  • Conference + Workshop
    $250 Discount

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

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