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 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:

To be confirmed

11.00 - 11.45
Stream One: Trading / Investment Strategies
Machine Learning Hedging
  • Dos-Don’ts of Strategies for Accurate Prediction

Inder Singh:

Vice President, CitiBank

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
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.

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.

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.

15.00 - 15.15
Afternoon Break and Networking Opportunities
15.15 - 16.00
Closing Talk: Both Streams
To be confirmed

Presenter to be confirmed

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 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

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: Deep Learning
Deep Learning in Finance: Prediction of Stock Return with Long Short-Term Memory Networks

Miquel Noguer Alonso:

Co-Founder at Artificial Intelligence Finance Institute – AIFI

Miquel Noguer Alonso: Co-Founder at Artificial Intelligence Finance Institute – AIFI

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.

11.45 - 12.30
Stream Two: Deep Learning
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 finace. 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.

12.30 - 13.30
Lunch
13.30 - 14.15
Stream Two: Deep Learning
Topic to be confirmed
Presenter to be confirmed
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
Topic to be confirmed

Presenter to be confirmed

  • Discount Structure
  • Super early bird discount
    25% until February 8th 2019

  • Early bird discount
    10% until March 1st 2019

  • 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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