Machine Learning in Finance, London
Sessions include recent advances in autoencoders and latent constraints, building trading strategies and alternative data.
This training course will address in-depth the opportunities and limitations of machine learning in quantitative finance with practical guidance from a variety of expert tutors.
Sessions will cover key theories, models and more advanced tools in machine learning using a quantitative approach. The course will examine what impact machine learning has on trading, portfolio construction and optimisation as well as focus on deep neural networks, applications of natural language processing , trading strategies and more.
The theory behind machine learning, latest applications and how these methods can be applied in your firm
How to properly and efficiently manage data for machine learning
Machine learning in risk management and trading
Insight into machine learning models and their application
How to apply natural language processing
Building trading strategies using machine learning
Machine learning in finance: opportunities and limitations
Recent advances in autoencoders and latent constraints
Unsupervised learning applied to transaction data
Alternative data for traders and natural language processing
ML in investment risk management
Validating machine learning models in the enterprise
Applying ML in practice
VP, quantitative analyst & front office AI lead
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.
Juan Acevedo Valle
Machine learning specialist
London Business School
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.
Dr Richard Saldanha
Founder & Co-Head
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.
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.
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.
CPD / CPE Accreditation
This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.