Course Agenda

Agenda

Agenda: Machine Learning in Finance - London

Day one: Tuesday, February 25, 2020

08:3009:00

Registration and refreshments

08:30 - 09:00

09:0010:30

Machine learning in finance: opportunities and limitations

09:00 - 10:30

  • Introduction to machine learning in finance

  • Machine learning in banking, risk management & modelling

  • Early financial applications: detecting credit card fraud, selecting mutual funds, trading treasury bonds

  • Practical examples of machine learning successes 

Alex Adranghi

VP, quantitative analyst & front office AI lead

MUFG

Alex Adranghi is a Vice President, Quantitative Analyst and front office AI lead at MUFG. He is a founding member of the MUFG European Innovation Labs. He previously worked at WestLB and Bloomberg. Alex pop has a background in Applied Mathematics and Computer Science.

Chris Kenyon

Head of XVA quant modelling, and AI innovation lead

MUFG Securities EMEA plc

Chris Kenyon is head of XVA Quant Modelling, and AI Innovation lead at MUFG Securities EMEA plc.  Previously he was Head of XVA Quantitative Research at Lloyds Banking Group, head quant for Counterparty Credit Risk at Credit Suisse, and (post-crisis) Head of Structured Credit Valuation at DEPFA Bank Plc.  He is active in XVA research, introducing KVA and MVA, with Andrew Green, in Risk papers 2014-15 and their accounting treatment in 2016-17, as well as PFL as the replacement for PFE (2019).  He publishes mostly in the Cutting Edge section of Risk magazine (5th most published 1988-2018, and 3rd most cited in 2017), co-wrote “Discounting, LIBOR, CVA and Funding” (Palgrave 2012) and co-edited “Landmarks in XVA” (Risk 2016). He has a Ph.D. from Cambridge University and is an author of the open source software QuantLib.

10:3010:45

Morning break

10:30 - 11:00

10:4512:00

Recent advances in autoencoders and latent constraints

10:45 - 12:00

  • Deep neural networks

  • Unsupervised vs supervised learning

  • Dimension reduction and autoencoders

  • Latent space constraints

  • Clustering

Juan Acevedo Valle

Machine learning specialist

ABN AMRO

12:0013:00

Lunch

12:30 - 13:30

13:0014:30

Unsupervised learning applied to transaction data

13:00 - 14:30

  • Anomaly detection

  • Problem description

  • Recent advances in finance and other fields

  • Autoencoders, reconstruction error and other approaches

  • Production deployment

  • Final remarks

Juan Acevedo Valle

Machine learning specialist

ABN AMRO

14:3014:45

Afternoon break

15:00 - 15:30

14:4516:15

Building trading strategies using machine learning

14:45 - 16:15

  • Introduction to momentum trading

  • Evolution of momentum trading strategies from manual to rule based and to machine learning based

  • Feature selection & model selection

  • Tuning of hyperparameters

  • Training the machine learning models

  • Backtesting & summary

Praneeth Maganti

London Business School

16:1516:15

End of day one

17:00 - 17:30

Day two: Wednesday, February 26, 2020

08:3009:00

Morning Refreshments

08:30 - 09:00

09:0010:30

Alternative data for traders and natural language processing

09:00 - 10:00

  • Alternative data and the challenges associated with using it

  • Approached to structuring alternative datasets, in particular text using natural language processing

  • Practical use cases for investors based on alternative data

Saeed Amen

Founder

Cuemacro

Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura.

He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley), due in 2020.

Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians. 

10:3010:45

Morning break

10:00 - 10:30

10:4512:00

Machine learning in investment risk management

10:30 - 12:00

  • Common problems 

  • Practical approaches

  • Machine learning in action 

  • Further resources

Dr Richard Saldanha

Founder & Co-Head

Oxquant

Dr Richard Saldanha is Founder and Co-Head of Oxquant, a consultancy firm that provides expertise and advice on risk management, investments and the impact of artificial intelligence. He is also an Independent Adviser to Oxford Portfolio Advisers.

Richard has over 20 years’ experience in asset management and investment banking in the areas of risk, trading and investments. In particular, he was Global Head of Risk at Investec Asset Management (2010–15), headed a quantitative global macro initiative for the same firm (2006–09) and founded and ran his own hedge fund Oxquant Capital Partners (2004–06).

In addition to his consulting and advisory activities, Richard lectures on statistical machine learning and its applications in finance at Queen Mary University of London. He attended Oriel College, University of Oxford, and holds a doctorate (DPhil) in statistics.

12:0013:00

Lunch

12:00 - 13:00

13:0014:30

Validating machine learning models in the enterprise

13:00 - 14:30

  • Machine learning in risk management

  • Machine learning in banking, risk management and modelling

  • Analysis of rare events;

    • Labelled, unbalanced data

    • Anomaly detection

  • Network analysis

  • Time series spikes and breakouts

  • ML applications in equities vs fixed income

Mike Taylor

Associate director

Deloitte

14:3014:45

Afternoon break

14:30 - 15:00

14:4516:05

Applying machine learning in practice

15:00 - 16:30

  • Pros and cons of applying ML to investing 

  • Focusing on use cases which add business value

  • Importance of features selection 

  • Subtleties of applying ML to investing 

  • Case study: code in practice

  • Where to start?

Gilles Artaud

Head of model risk audit

Crédit Agricole Group

Gilles Artaud has been working in investment banking for the last 25 years, where he held various positions within Quant, Front Office, IT and Risk Departments, working all along on many underlying types, pricing, validation, regulatory and economic capital, market risk and counterparty credit risk topics. 

After managing IT and Quant teams in the 1st line of defence (Front Office), he moved to 2nd line of defence (Risk and Validation) to lead topics around CCR, XVA, IM and many regulatory topics.

Gilles is now in charge for the 3rd line of defence of Model Audit for Group Inspection in Group Crédit Agricole SA, managing the transformation of Model Audit induced by new models, framework, market and business evolutions, new fields of application and compliance with ever-increasing regulations. 

 

 

16:1516:15

End of course

16:30 - 17:00