20 - 22 April, 2020 | Twickenham Stadium, London

Conference Themes

Below are the conference themes and initial guest speakers, details of the full program will be available soon.

Plenary conversations
- Effective risk management (paradigm shifts to models, regulatory, exogenous factors)
- The talent war: creating a culture to attract and retain top minds
- The risks of Quantum Computing

Risk Management & Regulation Ready
- Effective risk management (paradigm shifts to models, regulatory, exogenous factors)
- Regulation: Who is responsible for ensuring good practice in the use of data?
- Reacting and coping with market regime change and extreme events eg Flash Crashes, election outcomes, unpredictable news
- Age and depth of reliable data: are models factoring in unusual/unseen market conditions?

Quant for Fundamentals
- Embracing and processing quant concepts/cultures in a fundamental house
- The blended approach: using fundamental research with algorithmic optimisers
- Using an optimiser to suggest trade ideas and comparing to fundamental research
- Concerns about the future of fundamentals in a quant world

Quantitative Methods
- Purely systematic trading: data scientists/trading team watch, analyse and execute
- Interpretability issues: where is the regulation and the trust in model selected statistics
- Over defined models that are led to give an output
- When a model can’t cope with a market regime change
- Coping with sudden extreme exogenous factors: is your model prepared?

Use of Alt Data
- Sourcing the newer and fresher forms of alternative data
- Coping with a labour intensive cleaning process
- Signal mining: Finding the balance between big insights in unique/exclusive data or finding weaker hidden  levels of insight from widely available data
- Encouraging suppliers to follow standards and invest in the opportunities of selling data
- Monitoring the direction of Alt Data: where will privacy/compliance go?

Artificial Intelligence
- Applications of different types of AI (NLP and language barriers/translation, machine learning, genetic algorithms)
- Can your system truly teach itself new skills or is it operating in the method and world you define?
- Uses in various stages of investment process (eg cleaning/labelling, optimising trade ideas, executing)
- Signal hunting: Working in blackbox style with own statistics (interpretability above)
- Open source concepts: working with a community to share ideas and concepts to help towards the next big breakthrough
- Is it truly AI that can regenerate, self learn and adapt or is it a defined algorithm working in a set universe that is open to tail risk and market regime change?
- Going back to the proven fundamentals: AI as a science

The Future
- AI advance: mini breakthroughs and where to find them (the biggest impact is processing power)
- How can you quantify a non-standard universe
- What is the impact of quantum computing? How far is it really and will you be able to get new systems in whilst there is still trust in the safety of capital value?
- Blockchain: the benefits and the fallacies

Technology, Infrastructure & Innovation
- Data usage platforms 
- High Performance Computing
- Speed of processing data
- In house systems vs vendor built

Guest speakers include:

Robert Morse, Head of Data Strategy and Sourcing, PDT Partners
Zach Lipton, Assistant Professor, Machine Learning Department, Carnegie Mellon University
Brice Lemke, Senior Researcher, Octic Capital
Dr Alex Bogden, Chief Scientific Officer, Castle Ridge
Kathryn Kaminski, Chief Research Strategist & Portfolio Manager, AlphaSimplex Group
Keywan Rasekhschaffe, Senior Quantitative Strategist, Gresham Investment Management
Olga Kokareva, Director of Business Development, Quantstellation
Aaron Brown, Author, eraider & former Chief Risk Officer, AQR Capital Management
Kevin Lalande, Managing Director, Santé Capital