World Business StrategiesServing the Global Financial Community since 2000

Friday 18th October

08.30 - 09.00
Morning Welcome Coffee
Stream Chair:

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.

09.00 - 09.45
Machine Learning & Quantum Computing Techniques Stream
Quantifying Model Uncertainty with Artificial Intelligence
  • Defining model risk and model uncertainty
  • Overview of relevant regulatory frameworks
  • Measuring uncertainty with ML
  • Model risk of AI

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.

09.45 - 10.30
Machine Learning & Quantum Computing Techniques Stream
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.

 

 

 

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
Machine Learning & Quantum Computing Techniques Stream
P Pricing by Q Learning

Andrey Chirikhin:

Founder, Quantitative Recipes

Andrey Chirikhin: Founder, Quantitative Recipes

Andrey was formerly Head of Modelling and Quantitative Analytics for L1 Treasury, part of a USD 25bn privately held investment vehicle LetterOne. Prior to LetterOne, Andrey was MD and Head of CVA and CCR quantitative Analytics at RBS. There he has created and run the front office cross asset CVA quant team. He also restructured and led the risk-side quant team charged with delivering a new Basel III compliant internal CCR methodology. The system utilizing the newly delivered methodology has won the 2013 Internal System of the year Risk award. In his 20 year career in investment banking, Andrey held several leadership and senior quant positions at Goldman Sachs, HSBC and Dresdner Kleinwort. Andrey Chirikhin holds PhD in Theoretical Statistics from Warwick University (UK), MBA from INSDEAD and MSc in Applied Mathematics from Moscow Institute for Physics and Technology (Phystech).

Since 2018 Andrey runs his own company, Quantitative Recipes, that advises on wide rage of XVA, long-term market modelling for risk and quant infrastructure.

11.45 - 12.30
Machine Learning & Quantum Computing Techniques Stream
Validating / Auditing ML Models

Machine Learning Models are like any other models, but different. You probably had presentation or experiences on how to use them in order to get good value out of their relative complexity.

This presentation will be about what, from a Model Audit point of view, should be set in place in order to avoid them failing despite all the efforts you put in building them.

But also incidentally how some can exploit their specific vulnerability to make them fail.

It will cover :

  • Model Risk : Machine Learning models are like any other model.
    • So what’s required for non-ML models?
  • Artificial Intelligence and ML : Machine Learning models are not exactly like any other model.
    • What could make them fail ?
    • How could they fail
  • Tricks or treats? Application to Neuron networks or Random Forest

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 - 15.00
Machine Learning & Quantum Computing Techniques Stream
Extended Talk: Deep Analytics

Abstract:

We apply deep learning to resolve the conundrum of revaluation of large, diverse trading books in the context of regulatory simulations and also offer a solution to MVA.

Antoine Savine:

Quantitative Research, Danske Bank

Antoine Savine: Quantitative Research, Danske Bank

Antoine Savine has worked for various Investment Banks since 1995, along Bruno Dupire, Leif Andersen and Marek Musiela. He was Global Head of Quantitative Research for Fixed Income, Currency and Credit Derivatives for BNP-Paribas 1999-2009, and currently works in Copenhagen for Danske Bank, where his work with Jesper Andreasen earned the In-House System of the Year 2015 Risk Award. His upcoming publications in Wiley’s Computational Finance series are dedicated to teaching the technologies implemented in those award-winning systems.

Antoine also teaches Volatility Modeling and Numerical Finance in the University of Copenhagen’s Masters of Science in Mathematics-Economics. The curriculum for his Numerical Finance lectures is being published by Wiley under the name “AAD and Parallel Simulations”.

Antoine holds a Masters from the University of Paris (Jussieu) and a PhD from the University of Copenhagen, both in Mathematics.

Brian Norsk Huge:

Chief Quantitative Analyst, Danske Markets

Brian Norsk Huge: Chief Quantitative Analyst, Danske Markets

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
All Streams
Closing Presentation: NLP and Quant Investing: Finding Signals in the Noise
At it’s most basic, Natural Language Processing can be seen as a way for a computer to understand human language. Given that the vast majority of data comes in unstructured form, the potential opportunities for structuring, modeling and implementing text-based investment strategies are huge. For all those on the buy-side, understanding NLP – and the data, tools and processes needed to make it a success – is essential.
  • How NLP has developed alongside the explosion of text-based content.
  • Use-cases today for NLP within investment processes.
  • Factors to consider when building NLP into a quantitative strategy

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.

Friday 18th October

08.30 - 09.00
Morning Welcome Coffee
Stream Chairs:

Helyette Geman:

Professor of Mathematical Finance, Birkbeck – University of London & Johns Hopkins

Helyette Geman, PhD, PhD: Professor of Mathematical Finance, Birkbeck – University of London & Johns Hopkins

Helyette GEMAN is a Professor of Mathematical Finance at Birkbeck – University of London and at Johns Hopkins University. She is a Graduate of Ecole Normale Supérieure in Mathematics, holds a Masters degree in Theoretical Physics, a PhD in Probability from the University Pierre et Marie Curie and a PhD in Finance from the University Pantheon Sorbonne.
She has been a scientific advisor to a number of major energy and mining companies for the last 20 years, covering the trading of crude oil, natural gas, electricity as well as metals in companies such as EDF Trading, Louis Dreyfus or BHP Billiton and was named in 2004 in the Hall of Fame of Energy Risk.
Prof Geman was previously the head of Research and Development at Caisse des Depots. She has published more than 140 papers in major finance journals including the Journal of Finance, Mathematical Finance, Journal of Financial Economics, Journal of Banking and Finance and Journal of Business. She has also written the book entitled Insurance and Weather Derivatives and is a Member of Honor of the French Society of Actuaries.
Her research includes exotic option pricing for which she got the first prize of the Merrill Lynch awards, asset price modeling through the introduction of transaction time (JOF, 2000); she is one of the authors of the CGMY pure jump Levy model (2002). Prof Geman had organized in 2000 at College de France the first meeting of the Bachelier Finance Society, with Paul Samuelson, Robert Merton and Henry McKean as keynote speakers.
Her book, ‘Commodities and Commodity Derivatives’ is the reference in the field. She was a Scientific Expert on Agriculture for the European Commission and is on the Board of the Bloomberg Commodity Index.
She counts among her numerous PhD students Nassim Taleb, author of the Black Swan

Rita Laura D’Ecclesia

Professor: Università degli Studi di Roma “La Sapienza”

Rita Laura D’Ecclesia Professor: Università degli Studi di Roma “La Sapienza”

09.00 - 09.45
Volatility & Modelling Techniques Stream
Transforming Financial Markets through Smart Contracts and Blockchains
  • How is it being used as part of operations within organisations?

Massimo Morini:

Head of Interest Rate and Credit Models, Banca IMI

Massimo Morini: Head of Interest Rate and Credit Models, Banca IMI

Massimo Morini is also Coordinator of Model Research. Massimo is Professor at Bocconi University and MSc Director at Milan Polytechnic, and he was Research Fellow at Cass Business School, London. He has published papers in journals including Risk Magazine, Mathematical Finance, and the Journal of Derivatives, and is the author of “Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators” and other books on credit, funding and interest rate modelling. Massimo holds a PhD in Mathematics.

09.45 - 10.30
Volatility & Modelling Techniques Stream
Smart Derivative Contracts

Christian Fries: 

Head of Model Development, DZ Bank

Christian Fries: Head of Model Development, DZ Bank

Christian Fries is head of model development at DZ Bank’s risk control and Professor for Applied Mathematical Finance at Department of Mathematics, LMU Munich.

His current research interests are hybrid interest rate models, Monte Carlo methods, and valuation under funding and counterparty risk. His papers and lecture notes may be downloaded from http://www.christian-fries.de/finmath

He is the author of “Mathematical Finance: Theory, Modeling, Implementation”, Wiley, 2007 and runs www.finmath.net.

Peter Kohl-Landgraf

XVA Management, DZ Bank

Peter Kohl-Landgraf, XVA Management, DZ BANK

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
Volatility & Modelling Techniques Stream
Futures and Options on Bitcoins: A Tentative Arbitrage Approach

Helyette Geman:

Professor of Mathematical Finance, Birkbeck – University of London & Johns Hopkins

Helyette Geman, PhD, PhD: Professor of Mathematical Finance, Birkbeck – University of London & Johns Hopkins

Helyette GEMAN is a Professor of Mathematical Finance at Birkbeck – University of London and at Johns Hopkins University. She is a Graduate of Ecole Normale Supérieure in Mathematics, holds a Masters degree in Theoretical Physics, a PhD in Probability from the University Pierre et Marie Curie and a PhD in Finance from the University Pantheon Sorbonne.
She has been a scientific advisor to a number of major energy and mining companies for the last 20 years, covering the trading of crude oil, natural gas, electricity as well as metals in companies such as EDF Trading, Louis Dreyfus or BHP Billiton and was named in 2004 in the Hall of Fame of Energy Risk.
Prof Geman was previously the head of Research and Development at Caisse des Depots. She has published more than 140 papers in major finance journals including the Journal of Finance, Mathematical Finance, Journal of Financial Economics, Journal of Banking and Finance and Journal of Business. She has also written the book entitled Insurance and Weather Derivatives and is a Member of Honor of the French Society of Actuaries.
Her research includes exotic option pricing for which she got the first prize of the Merrill Lynch awards, asset price modeling through the introduction of transaction time (JOF, 2000); she is one of the authors of the CGMY pure jump Levy model (2002). Prof Geman had organized in 2000 at College de France the first meeting of the Bachelier Finance Society, with Paul Samuelson, Robert Merton and Henry McKean as keynote speakers.
Her book, ‘Commodities and Commodity Derivatives’ is the reference in the field. She was a Scientific Expert on Agriculture for the European Commission and is on the Board of the Bloomberg Commodity Index.
She counts among her numerous PhD students Nassim Taleb, author of the Black Swan

11.45 - 12.30
Volatility & Modelling Techniques Stream
Do Diamonds Shine in Investor Portfolios

Diamonds are emerging as a new investment asset, providing great opportunities for trading, investing and diversification. Hedge funds and financial intermediaries have shown increased interest in the market and recent available data allow us to study its features and dynamics. The lack of a standardization system for the diamond commodity prevented the existence of an exchange regulated trading platform for diamonds which is being created and is starting to play an important role. Over the last decade trading diamonds has been advertised by banks and financial intermediaries as a hedge even if not enough evidence was provided. Diamond stocks have also been considered as a promising diversification asset for investors’ portfolios (McKeough, 2015; Neil, 2014; Wilson and England, 2014; Cameron, 2014), though, to our best knowledge, neither academic scholars nor industry professionals have tested this hypothesis.
In this paper we test if diamonds represent an hedge or safe haven in the investment worlds and if diamond stocks represent an alternative to investing in diamonds. We address two practical investment questions: Can an investment in diamonds represent a hedge for an investment portfolio? Is diamond equity sensitive to diamond prices? We use Polished Prices data set to build a Diamond basket index (D’Ecclesia Jotanovic 2017) and the diamond mining stock prices traded at the main stock markets to investigate the safe haven or hedge hypothesis and to study the relationship existing between diamond prices ad diamond stocks. Our results show that Diamonds can represent a hedge in investor’s portfolios, however the market of diamond-mining stocks does not represent a valid investment alternative to the diamond commodity market.

Rita Laura D’Ecclesia

Professor: Università degli Studi di Roma “La Sapienza”

Rita Laura D’Ecclesia Professor: Università degli Studi di Roma “La Sapienza”

12.30 - 13.30
Lunch
13.30 - 15.00
Volatility & Modelling Techniques Stream
Extended Talk: “Default and correlation model for issuer risk (FRTB-DRC internal model)”
  • Calibrating market Asset and Default correlation
  • Correlated default timing in structural model
  • Hedging extreme losses

Francois Bergeaud:

FRTB Lead Quantitative Analyst, BNP Paribas

Francois Bergeaud: FRTB Lead Quantitative Analyst, BNP Paribas

FRTB Lead Quantitative Analyst at BNP Paribas

Previously , Head of the XVA quantitative analytics, Modelling and development of the XVA engine and analytics used globally by the XVA desk and corporate sales.

Quantitative Analysis / Library development / Solution, XVA , Interest Rates, Fixed Income, Credit Derivatives, Inflation, FX, DRC, FRTB, PhD Mathematics ECP and Courant Institute (NYU) ; Graduated from Ecole Centrale Paris

Icarus Gupta:

Quantitative Analyst, BNP Paribas

Icarus Gupta: Quantitative Analyst, BNP Paribas

Experienced Quantitative Analyst with a demonstrated history of working in the banking industry. Strongresearch professional skilled in Risk Analytics, Derivatives, Statistical Modelling, Machine Learning.Extensive coding experience in C#, python, R and C++

 

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
All Streams
Closing Presentation: NLP and Quant Investing: Finding Signals in the Noise
At it’s most basic, Natural Language Processing can be seen as a way for a computer to understand human language. Given that the vast majority of data comes in unstructured form, the potential opportunities for structuring, modeling and implementing text-based investment strategies are huge. For all those on the buy-side, understanding NLP – and the data, tools and processes needed to make it a success – is essential.
  • How NLP has developed alongside the explosion of text-based content.
  • Use-cases today for NLP within investment processes.
  • Factors to consider when building NLP into a quantitative strategy

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.

Friday 18th October

08.30 - 09.00
Morning Welcome Coffee
Stream Chair:

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.

09.00 - 09.45
XVA, AAD, MVA & Initial Margin Stream
"Approximation techniques for risk calculations, strengths and weaknesses. From Chebyshev to Machine Learning"

Abstract:

The computation of risk metrics poses a huge computational challenge to banks. Many different techniques have been developed and implemented in the last few years to try and tackle the problem. We focus on Chebyshev tensors, and compare it to increasingly popular Deep Neural Networks, as approximating tools in risk metric calculations. We will give special attention on how to side-step the curse of dimensionality.

After presenting some results we make a comparison of some of the main techniques.

  • How to use Chebyshev tensors in risk calculations and the curse of dimensionality
  • Solutions via Deep Neural Networks
  • Three ways to side-step the curse of dimensionality
  • Results and comparisons between different techniques: from Chebyshev to Deep Neural Networks
  • Options for free software available for in-house testing and implementation

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.

09.45 - 10.30
XVA, AAD, MVA & Initial Margin Stream
KVA Under IMM and Advanced Approaches

The two largest components of Capital Valuation Adjustment (KVA) are the costs of Counterparty Credit Risk (CCR) and CVA capital. For a bank using the most advanced capital models – Internal Models Method for CCR and the incoming SA-CVA capital –an accurate KVA involves forward simulating expected exposures (EE) over the lifetime of the portfolio – potentially a Monte Carlo in a Monte Carlo. We present a practical regression-based solution.

  • Simulating EE: from regulatory stressed real-world measure to market implied measure
  • A comparative study of regression vs brute force nested Monte Carlo
  • SA-CVA: extending from simulating forward EE to simulating forward CVA sensitivities

Justin Chan:

Quantitative Strategy, Adaptiv, FIS

Justin Chan: Quantitative Strategy, Adaptiv, FIS

Justin Chan has over 11 years of experience in financial risk management and capital markets. Mr. Chan has a deep focus on quantitative modelling in areas such as xVA, credit exposure, and collateral simulations. He is currently responsible for the Risk Quantitative Strategy and Innovation program at FIS. Prior to FIS, Mr. Chan worked at Manulife Financial as a manager in corporate risk management.

Mr. Chan studied engineering science (BASc), and theoretical physics (MSc) at University of Toronto, where he also holds a Master of Mathematical Finance (MMF) degree.

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
XVA, AAD, MVA & Initial Margin Stream
Efficient Calculation Techniques for Credit Exposure in the Presence of Initial Margin
  • Modeling collateralized exposure
  • Producing exposure on a daily simulation time grid without daily revaluations or daily IM calculations
  • Reducing simulation noise in the presence of IM
  • Alternatives to calculating IM along simulation paths

Michael Pykhtin:

Manager, Quantitative Risk, U.S. Federal Reserve Board

Michael Pykhtin: Manager, Quantitative Risk, U.S. Federal Reserve Board

Michael Pykhtin is a manager in the Quantitative Risk section at the U.S. Federal Reserve Board. Prior to joining the Board in 2009 as a senior economist, he had a successful nine-year career as a quantitative researcher at Bank of America and KeyCorp. Michael has edited “Counterparty Risk Management” (Risk Books, 2014) and “Counterparty Credit Risk Modelling” (Risk Books, 2005). He is also a contributing author to several recent edited collections. Michael has published extensively in the leading industry journals; he has been an Associate Editor of the Journal of Credit Risk since 2007. Michael is a two-time recipient of Risk Magazine’s Quant of the Year award (for 2014 and 2018). Michael holds a Ph.D. degree in Physics from the University of Pennsylvania and an M.S. degree in Physics and Applied Mathematics from Moscow Institute of Physics and Technology.

11.45 - 12.30
XVA, AAD, MVA & Initial Margin Stream
Topic to be confirmed

Presenter to be confirmed.

12.30 - 13.30
Lunch
13.30 - 15.00
XVA, AAD, MVA & Initial Margin Stream
Extended Talk: Balance Sheet XVA by Deep Learning and GPU

(joint work S Crépey, Univ Evry, France, and Rodney Hoskinson, ANZ Bank, Singapore)

ABSTRACT:

Two competing XVA paradigms are a semi-replication framework and a cost-of-capital, incomplete market approach. Burgard and Kjaer once dismissed an earlier incarnation of the Albanese and Crépey holistic, incomplete market XVA model as being elegant but difficult to solve explicitly. We show that the model (set on a forward/backward SDE formulation) is not only elegant, but also able to be solved efficiently using GPU computing combined with AI methods in a whole bank balance sheet context. We calculate the Mark-to-Market process cube (or its increment, in the context of trade incremental XVA computations) using GPU computing and the XVA process cube using Deep Learning (including joint ES and VaR) Regression methods.

Stéphane Crépey:

Professor of Mathematics, University Of Evry

Stéphane Crépey: Professor of Mathematics, University Of Evry

Stéphane Crépey is professor at the Mathematics Department of University of Evry (France), head of Probability and Mathematical Finance and head of the Engineering and Finance branch (M2IF) of the Paris-Saclay Master Program in Financial Mathematics. His research interests are financial modeling, counterparty and credit risk, numerical finance, as well as related mathematical topics in the fields of backward stochastic differential equations and partial differential equations. He is the author of numerous research papers and two books: “Financial Modeling: A Backward Stochastic Differential Equations Perspective” (S. Crépey, Springer Finance Textbook Series, 2013) and “Counterparty Risk and Funding, a Tale of Two Puzzles” (S. Crépey, T. Bielecki and D. Brigo, Chapman & Hall/CRC Financial Mathematics Series, 2014).

He is an associate editor of SIAM Journal on Financial Mathematics, International Journal of Theoretical and Applied Finance, and a member of the scientific council of the French financial markets authority (AMF). Stéphane Crépey graduated from ENSAE and he holds a PhD in applied mathematics from Ecole Polytechnique and INRIA Sophia Antipolis.

Rodney Hoskinson:

Director, Quantitative Support (Strategic Trading and Funding), ANZ Banking Group

Rodney Hoskinson: Director, Quantitative Support (Strategic Trading and Funding), ANZ Banking Group

Rodney Hoskinson is a Director at ANZ Banking Group having joined in 2017 in the front office quantitative team for the Group’s Global Markets business. In this role based in Singapore, he is responsible for XVA support, primarily focussed on the strategic and quantitative development of the KVA (capital valuation adjustment). Before joining ANZ he was manager, KVA desk quantitative analysis in Fixed Income, Currencies and Commodities at National Australia Bank. His past experience includes a Director role at PwC Australia focussed on financial services consulting and audit support in market risk, implementation of economic capital models for several large insurers, and senior actuarial management roles at QBE Insurance Group. Rodney holds a PhD in Finance from France’s EDHEC Business School and is also a Chartered Enterprise Risk Actuary.

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
All Streams
Closing Presentation: NLP and Quant Investing: Finding Signals in the Noise
At it’s most basic, Natural Language Processing can be seen as a way for a computer to understand human language. Given that the vast majority of data comes in unstructured form, the potential opportunities for structuring, modeling and implementing text-based investment strategies are huge. For all those on the buy-side, understanding NLP – and the data, tools and processes needed to make it a success – is essential.
  • How NLP has developed alongside the explosion of text-based content.
  • Use-cases today for NLP within investment processes.
  • Factors to consider when building NLP into a quantitative strategy

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.

  • Discount Structure
  • Early bird discount
    10% until 20th September 2019

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

  • Conference + Workshop
    £300 Discount

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

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