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08:30: Registration and Morning Welcome Coffee


To be confirmed

09.00 - 10.00:  Keynote Speech: Machine Learning and AI in Finance: Applications, Cases and Research

  • Machine learning and deep learning applications in quantitative finance and risk management
  • Practitioners’ case studies
  • Research and development in deep learning

Presenter: Marcelo Labre: Executive Director, Morgan Stanley

09.45 – 10.30Deep Learning in Finance – LSTN’s 

  • Modern Data Analysis
  • Times Series Models Univariate
  • Linear Factor Models
  • Multivariate Time Series
  • Modern Financial Engineering
  • Long Short Term Memory Networks
    • Results
    • Conclusions 

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University 

10.30 – 11.00: Morning Break and Networking Opportunities

11.00 - 11.45: Model-free Option Pricing and Hedging by Reinforcement Learning

In discrete time, option hedging and pricing amount to sequential risk minimization. In particular, a discrete-time version of the Black-Scholes-Merton (BSM) option pricing model can be formulated as a problem of dynamic Markowitz optimization of an option replicating (hedge) portfolio made of an underlying stock and cash. This talk shows how this problem can be approached using Reinforcement Learning (RL). Once the problem is posed as an RL problem, option pricing and hedging can be done without any model for the underlying stock dynamics, using instead model-free, data-driven RL methods such as Q-learning and Fitted Q Iteration. As a result, both option price and hedge are obtained by a well-defined and converging maximization problem that uses only market prices and option trading data (inter-temporal re-hedges and hedge losses in the replicating portfolio) to find the optimal option hedge and price. The model can learn when re-hedges in data are suboptimal/noisy, or even purely random. This means, in particular, that our RL model can learn the BSM model itself, if the world is according to BSM.  

Computationally, the RL-based option pricing model is very simple, as it uses only basic linear algebra and linear regressions to compute the option price and hedge. The only tunable parameters in this approach are parameters defining the optimal hedge and price themselves. This approach does not need any model calibration (as there is no model anymore), and it automatically solves the volatility smile problem of the BSM model. We also discuss some extensions of this approach, including in particular an Inverse Reinforcement Learning setting, where inter-temporal losses from re-hedges are unobservable.        

Presenter: Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering

11.45 - 12.30: Machine Learning - Recent Trends and Applicability to Risk and Related Areas 

  • Supervised, unsupervised, Reinforcement
  • Deep learning, feature Learning, incremental learning
  • Predictive power and robustness 

Presenter: To be confirmed

12.30- 13.30: Lunch

13.30 – 14.15: Application of Natural Language Processing and Related Machine Learning Techniques at Large Commercial Banks

Increased digitalization of communication and recent advances in natural language processing allow us to satisfy new regulatory requirements and to advance automation in the financial industry.  But our industry has its own quirks and challenges – a unique, highly formalized parlance coupled with a lack of large sets of labeled data. We use neural nets and a variety of tools from statistical machine learning to help us solve these evolving problems.  Even more exciting, these methods can now be applied to pricing and risk management methods; fields that have largely stagnated over the last few decades, and that have not adapted to the reduced holding periods of risk by liquidity providers.  Comprehensive data policies and the ability to integrate probabilistic models on this data are preconditions for successful deployment of machine learning in capital markets.

 Presenter: Peter Decrem: Director, Citigroup

14.15 – 15.00: A Worked Example of Using Neural Networks for Time Series Prediction

Many publicly available examples of time series prediction with neural networks use fake or
random data. Other examples, particularly in finance, present poorly performing models. It is very hard to learn good practices when only presented with toy examples. Instead, this talk aims to teach the full process of using a neural network for time series prediction by walking through a real problem from start to finish.  

We will begin by explaining the concrete problem we would like to solve and how to frame our problem in a way that we can model. Once we understand our problem, we will discuss how to collect the needed data. We will discuss the process of reducing our input data into important features for the model to consume. We will then learn how to use Keras to implement our neural network. Once we have a working model, we will cover some tricks to improve its performance.

At every step, we will cover problems faced while working on this model. We will show how to use data visualization to aid in model development and catch problems early. We will also cover tips for using numpy to work with time series data efficiently.  

By the end of the talk, audience members will:  

  • Know how to frame a problem in a way that a neural network can model
  • Know how to think about feature selection
  • Be familiar with the Keras API for time series predictions
  • Understand that the hardest problems come before you even get to Keras

Presenter: To be confirmed

15.00 - 15.30: Afternoon Break and Networking Opportunities

15.30 - 16.15: Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs

Presenter: Ksenia Shnyra: Senior Advisor, Deloitte   

16.15 - 17.15: Machine Learning & Ai in Quantitative Finance Panel


  • Luis Cota: Data Scientist, Thalesians


  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering 
  • Marcelo Labre: Executive Director, Morgan Stanley


  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What's the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office 'Quant' skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for