World Business StrategiesServing the Global Financial Community since 2000

Wednesday 26th September

Outline
  • Using machine learning in the new financial markets big data landscape
  • Big Data in Finance Landscape
  • Infrastructure and technologyData sources
  • Modern data analysis – Structured and Unstructured Data & New Models
  • Classical and advanced models
  • Machine Learning models in practice
  • Machine learning robust modeling
  • The future of machine learning in finance
Big Data in Finance Landscape
  • Big data in finance landscape: Financial modeling, data governance, integration, NoSQL, batch and real-time computing and storage
  • Infrastructure and technology
  • New data sources
  • Modern data analysis: Structured / Unstructured data and new models
Machine Learning Models
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Advanced machine learning models
Machine learning in finance - Practice
  • Momentum and Mean Reversion
  • Sentiment Analysis
  • Asymmetric Trading Strategies
  • Non Linear Multi-Factor Models
  • High Frequency Trading
  • Advanced Machine Learning
Machine learning in finance - Opportunities and challenges
  • Algo-Grading 101
  • Interpretation
  • Data mining biases: overfitting, survivorship and data-snooping
  • Robust trading strategies
  • The future of machine learning in finance

Speaker

Miquel Noguer Alonso:

Adjunct Assistant Professor, COLUMBIA UNIVERSITY

Miquel Noguer Alonso: Adjunct Assistant Professor, COLUMBIA UNIVERSITY

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.

Workshop Schedule: 09:00 – 17:30

Break: 10:30 – 11:00
Lunch: 12:30 – 13:30
Break: 15:15 – 15:30

Wednesday 26th September

Foundations: CVA, DVA and FVA 
  • CVA & DVA by Replication
  • Credit Mitigants
  • FVA by Replication
  • FVA & DVA (overlaps)
  • FVA in pricing and accounting
  • A brief tour of the XVA Monte Carlo engine
MVA 
  • Initial Margin and XVA
  • MVA by Replication
  • MVA for VaR-type IM (CCPs)
  • MVA for SIMM-IM
KVA 
  • What is Capital?
  • KVA by Replication
  • KVA vs Hurdle Rates
  • Which measure?
  • The Cost of Capital
Introducing Machine Learning 
  • Types of machine learning problem:Motivation I: Multivariate Linear Regression
    • Supervised learning
      • Regression problems
      • Classifier problems
    • Unsupervised learning
  • Motivation II: Logistic Regression
  • Bias and Variance
  • Regularization
  • Neural Networks
    • Activation functions
    • Geometry
    • Forward and backpropagation
    • Initialization
  • Deep Learning
Credit Curve Mapping as a Machine Learning Problem 
  • Dealing with Illiquid counterparties in XVA
  • Key Requirements of the Map
    • Trading Requirements
    • Regulatory Requirements: Basel III & FRTB-CVA
  • Classical Methods
  • Classifier Approach
  • Regression Approach
A Tour of XVA ML Applications 
  • Optimising MVA
  • Dimension Reduction: PCA & Autoencoders

Speaker

Andrew Green: 

Managing Director and XVA Lead Quant, Scotiabank

Andrew Green: Managing Director and XVA Lead Quant, Scotiabank

Andrew Green is a Managing Director and lead XVA Quant at Scotiabank in London. Prior to joining Scotiabank, Andrew held roles as a quantitative analysis in several different banks in London. He is the author of XVA: Credit, Funding and Capital Valuation Adjustments, published by Wiley. 

Workshop Schedule: 09:00 – 17:30

Break: 10:30 – 11:00
Lunch: 12:30 – 13:30
Break: 15:15 – 15:30

Wednesday 26th September

1. Motivation and Basic AAD
  • Tangents and Adjoints: Story in a Nutshell
  • Implementation (Code Generation Rules vs. Overloading)
  • Case Study: Black-Scholes SDE (Euler-Maruyama Scheme)
  • Case Study: Black-Scholes PDE (Explicit Scheme)
2. AAD of Linear Algebra and Implicit Functions
  • Nonlinear Equations
  • Basic Linear Algebra Subprograms
  • Systems of Linear Equations
  • Systems of Nonlinear Equations
  • Convex Optimization
  • Ordinary Differential Equations
  • Case Study: Black-Scholes PDE (Implicit Scheme)
3. Checkpointing in AAD
  • Adjoint Ensembles
  • Adjoint Evolutions
  • Multi-Level Checkpointing
  • DAG and Call Tree Reversal Problems
  • Case Study: Black-Scholes SDE and PDE
4. Second- and Higher-Order AAD
  • Tangents and Adjoints
  • Implementation (Code Generation Rules vs. Overloading)
  • Case Study: Black-Scholes PDE

Speaker

Uwe Naumann:

Professor of Computer Science, RWTH Aachen University

Uwe Naumann: Professor of Computer Science, RWTH Aachen University

Uwe Naumann is the author of the popular text book on (Adjoint) Algorithmic Differentiation (AAD) titled “The Art of Differentiating Computer Programs” and published by SIAM in 2012. He holds a Ph.D. in Applied Mathematics / Scientific Computing from the Technical University Dresden, Germany.

Following post-doctoral appointments in France, the UK and the US, he has been a professor for Computer Science at RWTH Aachen University, Germany, since 2004. As a Technical Consultant for the Numerical Algorithms Group (NAG) Ltd. Uwe has been playing a leading role in the delivery of AAD software and services to a growing number of tier-1 investment banks since 2008.

Workshop Schedule: 09:00 – 17:30

Break: 10:30 – 11:00
Lunch: 12:30 – 13:30
Break: 15:15 – 15:30

  • Discount Structure
  • Early bird discount
    20% until July 20th 2018

  • Early bird discount
    10% until September 7th 2018

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

  • Conference + Workshop
    £150 Discount

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

Only 22 hours left to claim this discount!

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