World Business StrategiesServing the Global Financial Community since 2000

Friday 22nd March

08.30 - 09.00
Morning Welcome Coffee
09.00 - 09.45
Opening Talk: Both Streams
Machine Learning for Trade Strategies
  • Finding alpha – value investing
  • Factor investment
  • Reinforcement Learning
  • AI for ESG
  • Sentiment Analysis

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

09.45 - 10.30
Opening Talk: Both Streams
Machine Learning Enhanced Trading

Georgios Papaioannou:

Trading Strategist, Bank of America Merrill Lynch

Georgios Papaioannou: Trading Strategist, Bank of America Merrill Lynch

George Papaioannou, is a VP Trading Strategist within the Scientific Implementation Group of Bank of America Merrill Lynch. A Global quantitative team employing systematic, quantitative and scientifically informed methodologies around execution, portfolio management, and risk management, with emphasis on development of client solutions. George joined BAML in May 2018, following 12 years in energy major Shell, where he worked on a variety of functions. His latest role was in a team of computational science specialists, advising on machine learning, data, cloud, and high performance computing projects. He has previously worked in production operations, oil and gas forecasting, production optimization, reservoir management, development and project execution, for offshore fields in Brunei. The first 5 years of his industry career he worked in R&D as a scientific software developer focusing on scalable solvers and high performance computing.  George holds a PhD in Computational Fluid Mechanics from the Massachusetts Institute of Technology, where he also completed two MSc degrees and worked as a post-doctoral associate for a year. He has authored academic articles and acted as referee for several scientific journals.

10.30 - 11.00
Morning Break and Networking Opportunities
Stream Chair:

Jos Gheerardyn:

Co-founder and CEO, Yields.io

Jos Gheerardyn: Co-founder and CEO of Yields.io

Jos is the co-founder and CEO of Yields.io. Prior to his current role he has been active in quantitative finance both as a manager and as an analyst. Over the past 15 years he has been working with leading international investment banks as well as with award winning start-up companies. He is the author of multiple patents applying quantitative risk management techniques on imbalance markets. Jos holds a PhD in superstring theory from the University of Leuven.

11.00 - 11.45
Stream One: Trading / Investment Strategies
Big Data and Machine Readable News to Trade Markets

We give a brief overview of using Big Data and alternative data in financial markets, as well as some use cases for machine learning. We present a case study, examining how machine readable news can be used to trade FX systematically. We also show how news can help understand the market volatility around FOMC and ECB meetings.

Saeed Amen

Founder: Cuemacro

Saeed Amen: Founder: Cuemacro

Saeed has a decade of experience creating and successfully running systematic trading models at Lehman Brothers and Nomura. He is the founder of Cuemacro, Cuemacro is a company focused on understanding macro markets from a quantitative perspective. He is the author of ‘Trading Thalesians – What the ancient world can teach us about trading today’ (Palgrave Macmillan), and graduated with a first class honours master’s degree from Imperial College in Mathematics& Computer Science.

11.45 - 12.30
Stream One: Trading / Investment Strategies
Digital Transformation to Optimise the Trading Floor: Automatic Booking and Smart Algorithms

• Unpack the reality of AI uses on the trading floor
• Explore machine learning models used to generate electronic
trading signals
• Examine how AI is already helping traders perform better in some
of the leading banks
• Supporting traders with automated systems freeing human
talent for more complex tasks

Jan Novotny:

Front Office Quant

Jan Novotny: 

Jan is a former front office quant at HSBC in the eFX markets working on predictive analytics and alpha signals. Prior to joining HSBC team, he was working in the Centre for Econometric Analysis on the high-frequency time series econometric models and was visiting lecturer at Cass Business Group, Warwick Business School and Politecnico di Milano. He co-authored number of papers in peer-reviewed journals in Finance and Physics, contributed to several books, and presented at numerous conferences and workshops all over the world. During his PhD studies, he co-founded Quantum Finance CZ.

12.30 - 13.30
Lunch
13.30 - 14.15
Stream One: Big Data
Non-negative Matrix Factorization for Analysing High-Dimensional Datasets

Abstract:

Non-negative matrix factorization (NMF) is a widely-used tool for analysing high-dimensional datasets. Its popularity stems from its ability to extract meaningful factors from the data. Applications include image processing, text mining and bioinformatics. In this talk we will give an overview of NMF and demonstrate our implementations of recent NMF algorithms by automatically classifying a series of websites based on their content. We will then briefly discuss applications of NMF in finance.

Edvin Hopkins:

Technical Consultant, NAG

Edvin Hopkins: Technical Consultant, NAG

Edvin first worked with NAG between 2010 and 2013, as part of a Knowledge Transfer Partnership with the University of Manchester. Long-time NAG collaborator Professor Nick Higham and his team had developed many new algorithms to compute matrix functions. Edvin’s role was to convert these algorithms into code for the NAG Library.

After the successful collaboration, Edvin worked with Professor Higham as a post-doctoral research associate, before finally joining NAG in 2015. He is based in our Manchester Office.

Edvin gained a PhD in Numerical Relativity from the University of Cambridge in 2009. His supervisor was Dr John Stewart. This followed a first class honours degree in Mathematics and a “Certificate of Advanced Study in Mathematics” from the same institution.

 

 

 

14.15 - 15.00
Stream One: Big Data
Quantitative Factor Investing Using Alternative Data and Machine Learning

Abstract: To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.

This session will cover the following topics:

  • The broad application of machine learning in finance
  • Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms
  • Construction of scoring models and factors from complex data sets such as supply chain graph, options (implied volatility skew, term structure) and ESG (Environmental, Social and Governance)
  • Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five-factor model.

Sandrine Foldvari:

Product Manager BQUANT/BQL, Bloomberg LP

Sandrine Foldvari: Product Manager BQUANT/BQL, Bloomberg LP

Dr Sandrine Foldvari joined Bloomberg as a BQL/BQUANT Product manager in 2009.

Prior to this, Sandrine earned her PhD from London School of Economics in portfolio allocation. She has 15+ years experience as a quantitative trader / researcher in proprietary trading desks at major investment banks (Goldman Sachs and Credit Suisse) as well as in Hedge Funds (AHL- Man Group and Taranis-BlueQuant). She has joined Bloomberg to develop their Equity Back-testing tool leveraging on Bloomberg data integration (through the in house query language BQL) to provide analytics tools through the BQUANT platform.

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
Closing Talk: Both Streams
From Artificial Intelligence to Machine Learning, From Logic to Probability

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finance. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. 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. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

Friday 22nd March

08.30 - 09.00
Morning Welcome Coffee
09.00 - 09.45
Opening Talk: Both Streams
Machine Learning for Trade Strategies
  • Finding alpha – value investing
  • Factor investment
  • Reinforcement Learning
  • AI for ESG
  • Sentiment Analysis

Harsh Prasad:

Vice President, Morgan Stanley

Harsh Prasad: Vice President, Morgan Stanley 

Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to the financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.

09.45 - 10.30
Opening Talk: Both Streams
Machine Learning Enhanced Trading

Georgios Papaioannou:

Trading Strategist, Bank of America Merrill Lynch

Georgios Papaioannou: Trading Strategist, Bank of America Merrill Lynch

George Papaioannou, is a VP Trading Strategist within the Scientific Implementation Group of Bank of America Merrill Lynch. A Global quantitative team employing systematic, quantitative and scientifically informed methodologies around execution, portfolio management, and risk management, with emphasis on development of client solutions. George joined BAML in May 2018, following 12 years in energy major Shell, where he worked on a variety of functions. His latest role was in a team of computational science specialists, advising on machine learning, data, cloud, and high performance computing projects. He has previously worked in production operations, oil and gas forecasting, production optimization, reservoir management, development and project execution, for offshore fields in Brunei. The first 5 years of his industry career he worked in R&D as a scientific software developer focusing on scalable solvers and high performance computing.  George holds a PhD in Computational Fluid Mechanics from the Massachusetts Institute of Technology, where he also completed two MSc degrees and worked as a post-doctoral associate for a year. He has authored academic articles and acted as referee for several scientific journals.

10.30 - 11.00
Morning Break and Networking Opportunities
Stream Chair:

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. 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. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

11.00 - 11.45
Stream Two: Fintech Collaboration
Hybrid Chebyshev Neural Networks
  • Neural networks training challenges
  • Neural networks as parametrising algorithms
  • The strength of Chebyshev Spectral Decomposition and interpolations
  • How to combine classic neural networks with Chebyshev to optimise computational load
  • Impact in performance and usability of neural networks
  • Numerical examples

Ignacio Ruiz:

Founder & CEO, MoCaX Intelligence

Ignacio Ruiz: Founder & CEO, MoCaX Intelligence

Ignacio Ruiz has been the head strategist for Counterparty Credit Risk, exposure measurement, for Credit Suisse, as well as the Head of Risk Methodology, equities, for BNP Paribas. In 2010, Ignacio set up iRuiz Consulting as an independent advisory business in this field. In 2014, Ignacio founded iRuiz Technologies to develop and commercialise MoCaX Intelligence.

Ignacio has several publications in the space of quantitative risk management and pricing. He has also published a comprehensive guide to the subject of XVA Desks and Risk Management.

He holds a PhD in nano-physics from Cambridge University.

11.45 - 12.30
Stream Two: Module Validation
Validation and Audit of Machine Learning Models

Gilles Artaud: 

Head of Model Internal Audit, Group Crédit Agricole

Gilles Artaud: Head of Model Internal Audit, Group Crédit Agricole

Gilles Artaud has been working in investment banking for the last 20 years, where he held various positions within Quant, Front Office and Risk Department, working all along on many underlying types, pricing, validation, regulatory and economic capital, market risk and counterparty credit risk topics.

After setting in place the methodology and library for CCR and CVA, he lead XVA, initial margins on non-cleared transactions, and many regulatory topics.

His current “hot” topics are XVAs (CVA DVA FVA AVA MVA…) and impact of new regulatory requirements on derivatives, among which SA-CCR, NSFR, FRTB and FRTB-CVA and Artificial Intelligence technologies in Risk Management.

12.30 - 13.30
Lunch
13.30 - 14.15
Stream Two: Deep Learning
Deep Learning Volatility

Abstract

We present a consistent neural network based calibration method for a number of volatility models-including the rough volatility family-that performs the calibration task within a few milliseconds for the full implied volatility surface.

The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data.

Blanka Horvath:

Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka Horvath: Honorary Lecturer, Department of Mathematics, Imperial College London

Blanka is a Honorary Lecturer in the Department of Mathematics at Imperial College London and a Lecturer at King’s College London. Her research interests are in the area of Stochastic Analysis and Mathematical Finance.

Her interests include asymptotic and numerical methods for option pricing, smile asymptotics for local- and stochastic volatility models (the SABR model and fractional volatility models in particular), Laplace methods on Wiener space and heat kernel expansions.

Blanka completed her PhD in Financial Mathematics at ETHZürich with Josef Teichmann and Johannes Muhle-Karbe. She holds a Diploma in Mathematics from the University of Bonn and an MSc in Economics from the University of Hong Kong.

14.15 - 15.00
Stream Two: Deep Learning
Deep learning of Investor Behaviour and Risk Appetite

Grant Fuller:

Co-founder of Irithmics

Grant Fuller: Co-founder of Irithmics

Grant Fuller is co-founder of Fintech applying artificial intelligence to gain greater insight and analysis of hedge funds. Grant was previously part of the hedge fund risk practice of Ernst & Young in London, and prior to that he helped start and develop Bloomberg’s successful hedge fund trading and analytics AIM platform, leading the firm’s European and Asian business. Before joining Bloomberg, he was part of RiskMetrics where we was responsible for helping build the European asset management technologies and consulting capabilites. Grant Fuller holds a BSc in Chemistry from the University of St Andrews. He remained at St Andrews to undertake a PhD applying neural networks within carbohydrate chemistry, after which he joined academic research at Cambridge University.

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
Closing Talk: Both Streams
From Artificial Intelligence to Machine Learning, From Logic to Probability

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finance. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.

Paul Bilokon:

Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Paul Bilokon: Founder, CEO, Thalesians & Senior Quantitative Consultant, BNP Paribas

Dr. 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. Before focussing on electronic trading, Paul worked on derivatives and has served in quantitative roles at Nomura, Lehman Brothers, and Morgan Stanley. Paul has been educated at Christ Church College, Oxford, and Imperial College. Apart from mathematical and computational finance, his academic interests include machine learning and mathematical logic.

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

  • Conference + Workshop
    £150 Discount

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

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