Machine Learning in Finance, London
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.
This two-day training course will provide attendees with a deep understanding of machine learning applications within finance.
The sessions offer a technical look at machine learning and provide suggestions and strategies for integrating it within your organisation. You will learn about key theories, models and more advanced tools in machine learning, using a quantitative approach presented by top practitioners from leading firms in the financial industry. Further sessions will delve into portfolio construction, trading, risk management and other business areas.
How to properly and efficiently manage data for machine learning
A new or improved understanding of the relationship between machine learning and risk management
Methods for portfolio construction with machine learning
Insight into machine learning models and their application
How to address the human element in machine learning and banking
Best practice approaches to applying machine learning models
Machine learning in finance: opportunities and limitations
Data management for machine learning
Machine learning in risk management
Machine learning for portfolio construction
Machine learning models
Natural language processing: a deep dive
Managing the human factor and biases in machine learning
Applying machine learning in practice
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.
Bas works as Technology Lead in the financial services domain, focusing on building data-driven solutions for customers. His academic background is in Artificial Intelligence and Informatics. His research on reference architectures for big data solutions was published at the IEEE conference ICITST 2013. Bas has a background in software development, design and architecture with a broad technical view from C++ to Prolog to Scala. He occasionally teaches programming/architecture courses and is a regular speaker on conferences and informal meetings, where he brings a mixture of market context, his own vision, business cases, architecture and source code towards his audience.
Quantitative researcher - machine learning
BestX (State Street)
Sachapon Tungsong leads machine learning research and implementation for pre- and post-trade analytics at BestX, a multi-award winning Transaction Cost Analytics (TCA) provider which is now a part of State Street. BestX is an industry leader providing TCA (FX, fixed income and, in the near future, equities) using cutting-edge technology and rigorous statistical/machine learning methods.
Prior to BestX, Sachapon was an alpha quantitative researcher at Jetstone Asset Management, a visiting research scholar at the Wharton School of the University of Pennsylvania, and a lecturer in finance (with tenure) at Thammasat University.
Sachapon received her PhD in Computer Science from University College London (UCL) where she focused on computational finance and machine learning and her B.Com. from McGill University, Canada. Her areas of expertise include machine learning, financial time series, and financial econometrics.
Head of AI - financial services, risk advisory
Alexander Denev has more than 15 years of experience in finance, financial modelling and machine learning and he is the former lead of the Advanced Analytics & Quantitative Research at IHS Markit. He has written several papers and two books on topics ranging from stress testing and scenario analysis to asset allocation. He is currently writing his third book on Alternative Data in Trading&Investing. Alexander Denev attained his Master of Science degree in Physics with a focus on Artificial Intelligence from the University of Rome, and he holds a degree in Mathematical Finance from the University of Oxford, where he continues as a visiting lecturer.
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.
Dr. Manuel Proissl
Head of predictive analytics in banking products
Dr. Proissl is currently Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a quant, senior advisor and machine learning cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for global organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Control Systems and Particle Physics.
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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.
Your essential Python toolkit for machine learning implementation