Risk.net Machine Learning Forum

Risk.net Machine Learning Forum

March 5th 2019: Machine Learning Forum: The latest applications, data &  interpretability

Risk.net Quant Summit Europe is pleased present the 2nd edition of Machine Learning Forum, a workshop-style event designed to provide in-depth analysis on the latest ML/AI applications, challenges and limitations these tools pose, and showcase ideas of how can industry practitioners go about them.

At the Risk.net Machine Learning Forum, we will host multiple senior speakers sharing their experiences, research and opening up a debate about new ML applications in finance, this year, with particular focus on practical use cases, NLP tools, data access and aggregation, limitations and interpretability.

 

Why attend Risk.net Machine Learning Forum:

  • Unique multi speaker format featuring sessions from key industry practitioners;
  • Gain insights into the latest industry applications and what progress has been achieved since the ML boom;
  • Get insights into how to access Big Data and alternative data in financial markets  and how to work with it efficiently when building your models;
  • NLP applications: How are quants successfully harnessing data?
  • Discuss ML/AI limitations and how industry view interpretability issues;
  • Join the Harvest Session with our speakers  at the end of the day and have your questions answered!


Led by:

CHAIRMAN: Alexander Denev, Former Head of Advanced Analytics & Quantitative Research, IHS MARKIT & Lecturer in Mathematical Finance at, UNIVERSITY OF OXFORD
David Jessop, Managing Director, Global Head of Equities Quantitative Research, UBS
Christian Schwarz, Executive Director, Head of Quant Research, Focus on Machine Learning and Algo Trading, MIZUHO INTERNATIONAL
Giuliano De Rossi,  Head of European Quantitative Strategy
Saeed Amen, Founder, CUEMARCO
Peter Hafez, Chief Data Scientist, RAVENPACK
Flavia Poma, Head of Machine Learning Quantitative Research, LLOYDS BANK COMMERCIAL

08:20

REGISTRATION & REFRESHMENTS 
9.00

CHAIRMAN’S OPENING REMARKS: Alexander Denev , Former Head of Advanced Analytics & Quantitative Research, IHS MARKIT & Lecturer in Mathematical Finance at, UNIVERSITY OF OXFORD

09:10

I. Machine Learning in Finance: Limits and Potentials

  • Artificial intelligence (AI), in particular deep learning, has become a subject of intense media hype
  • Strengths and limitations of machine learning, deep learning and AI more generally
  • Applying deep learning techniques to the investment process
  • A stock selection model that uses neural networks and compare its performance to "classical" machine learning models, such as decision trees and regularised regression
  • Results and contribution to the long-term investment strategies

David Jessop, Managing Director, Global Head of Equities Quantitative Research, UBS

11:00

MORNING BREAK

11:30

News sentiment: Insights from top investment banks

  • Alternative data has become a “must-have” for Quants and Fundamental investors to stand out in an incredibly competitive market
  • The latest use-cases on RavenPack Sentiment
  • New ways of constructing alpha signals around alternative data

Peter Hafez, Chief Data Scientist, RAVENPACK

II.  Harnessing information through NLP and machine learning

Flavia Poma, Head of Machine Learning Quantitative Research, LLOYDS BANK COMMERCIAL BANKING

1:00

LUNCH & OPPORTUNITY TO NETWORK

2:00

I. Machine learning in algorithmic credit trading

  • Practical ML use cases
  • Data access and aggregation
  • Limitations & interpretability
  • How to build political capital and a positive feedback loop

Christian Schwarz, Executive Director, Head of Quant Research, MIZUHO INTERNATIONAL
 

II. Building a multifactor signal: The machine learning approach

  • Building multifactor signals from a large set of stock characteristics
  • Potential of artificial intelligence (AI) techniques in this field  of application
  • Stimulating a nonlinear data generating process for stock returns
  • Our finding and conclusions

Giuliano De Rossi, Former Head of European Quantitative Strategy

3:30

AFTERNOON BREAK

4:00

Big Data and using machine readable news to trade FX

  • The concept of Big Data and alternative data in finance
  • Generating  trading signals for FX spot
  • Relationship between news and FX volatility
  • Case study on using  news to forecast volatility around ECB and FOMC meetings
  • Python tools for data analysis, with a short demo

Saeed Amen, Founder, CUEMARCO
5:00

HARVEST SESSION & Q&A

Each presenter will summarize key takeaways form their presentations and will open up for the Q&A and final thoughts on the industry’s adoption of ML tools!
 

5:30

END OF THE SEMINAR