Conference Day One: March 19 2019
7:45 am - 8:50 am Welcoming tea, coffee and registration
KEYNOTES & OPENING PLENARY SESSIONS
8:50 am - 9:00 am Chair's opening remarks
9:00 am - 9:30 am Opening keynote
Outlook for the Hedge Fund industry: survival of the leanest or the most tech savvy?
9:30 am - 10:10 am Panel discussion: The asset allocator's view
- Evaluation: What does a buy side investor look for in quantitative strategies
- Analysis: Finding risk, uniqueness and maintaining a diverse yet unbiased holding: the challenges of asset allocation
- Risk: Meeting your mandate while incorporating technologically forward investment strategies
10:10 am - 10:40 am Interview
10:40 am - 11:20 am Networking refreshment break in the exhibition area
11:20 am - 11:50 am Keynote: A new breed of investor: The development and disruption of asset management through machine learning techniques
- The new model: What do you really need to launch a new age hedge fund?
- Practical recent examples and insights into what it takes to succeed and common pitfalls to avoid
- The best and worst attributes of AI: Where do funds go wrong?
11:50 am - 12:30 pm Panel discussion: The talent war: How to attract and retain the top quant and data science minds
- How to reach the best people and the top minds
- Understanding that all data scientists and quants are not created equal: what looks like a data scientist may not be!
- How to create a culture that will entice them to join and stay with you: understand your value proposition as an employer
- Motivating your workforce to share their best ideas, and building a culture of collaboration in a typically combative and siloed industry
- Considering the factors that will affect retaining and motivating top talent
12:30 pm - 1:00 pm Technology keynote: Applications of AI and machine learning to finance
- Listen and learn from the advances and innovations by this tech giant
- Evaluate how these concepts can be applied into financial markets and into your tech and systems
1:00 pm - 2:00 pm Networking lunch in the exhibition area
STREAM A2:00 pm - 2:05 pm ALT DATA
STREAM A2:05 pm - 2:45 pm Panel discussion: Understanding and navigating the ‘Wild Wild West’ of alt data vendors and suppliers Lisa Schirf - Former COO Data Strategies Group and AI Research Large Global Hedgefund
Robert Morse - Head of Data Strategy and Sourcing PDT Partners
- Usefulness of data – evaluating the landscape and the trends
- Filtering and working with vendors: Excessive demand for newer/more interesting data sources places the onus of data prep firmly on the purchaser whilst low quality/inconsistent data gives a low conversion rate to prospective sources. How can you work with your supplier to provide the product you need?
- Evaluating the pros and cons of searching for the foundations of specific trade ideas vs ‘playing’ with new and interesting concepts
- Best data sourcing practices
Lisa SchirfFormer COO Data Strategies Group and AI Research
Large Global Hedgefund
Robert MorseHead of Data Strategy and Sourcing
STREAM A2:45 pm - 3:05 pm What are the next hot data sources/ regions? What are the different business models that are emerging?
- We’ve had sentiment analysis on social media, car park image processing, mobile positioning, video, credit card transactions… Where or what is the next big alt data source?
- Evaluating the global demand for data
STREAM A3:05 pm - 3:25 pm Case study: Practical uses of cutting edge NLP usage: what is the current state of play, and how can you benefit from incorporating it into your investment strategy? Mike Chen - Portfolio Manager PanAgora Asset Management
- Defining the value you can add from overcoming language barriers and maximizing your translation services to processing unstructured data from around the world in a variety of formats
- Enhancing your systems abilities: overcoming the translation barriers: an insight into unsupervised learning of rap, slang and unusual language structure
- Other practical NLP hints and tricks to set you on the path to alpha generation
Mike ChenPortfolio Manager
PanAgora Asset Management
STREAM B2:00 pm - 2:05 pm QUANT FUNDAMENTAL
STREAM B2:05 pm - 2:25 pm Case study: Introducing quant methods into your fundamental approach to enhance alpha capture
- Sharing the journey: How this fundamental house took a considered approach to introducing systematic methods
- Presenting the business case, investment required and time frame for returns
- A culture change: embracing the value of the opportunities that technology can add to your portfolio
-Efficiency gains, processing and compute power
-The talent factor: How do you compete for talent with quant houses and technology pure plays?
- Using algorithms to optimize your position sizing: can you eliminate the human bias?
STREAM B2:25 pm - 2:45 pm Panel discussion: The outlook for a pure fundamental approach in an increasingly data driven world
- Measuring effectiveness: comparing the overall industry outlook for fundamental vs quantitative strategies
- Can the value of human instinct and knowledge override the trend for technology and quant strategies?
- Understanding the risks of a market saturated with the systematic approach: when will the models fail?
STREAM B2:45 pm - 3:25 pm Presidential debate: This house believes a blended approach will consistently outperform a purely systematic approach
Empirical evidence: In 2016, the quant space pulled in $13bn as the HF industry lost $70bn investment overall. However in H1 2018 the discretionary outperformed the systematic.
Watch the action as two heavyweights battle it out. You decide who wins the argument. We will have a vote on the motion prior to the arguments being set out, and a vote after to see who has swung the votes. The debate is worth your ticket price alone!
- Can systematic investment strategies cope with market regime change?
- The impact of flash crashes and market blips
For: Fundamental house
Against: Pure systematic house
STREAM C2:00 pm - 2:05 pm AI/ML CUTTING EDGE ACADEMIC RESEARCH
STREAM C2:05 pm - 2:25 pm Academic presentation: Cutting edge AI
Hear this leading academic present cutting edge research into the field of AI and machine learning
STREAM C2:25 pm - 2:45 pm Academic presentation: Cutting edge AI
Hear this leading academic present cutting edge research into the field of AI and machine learning
STREAM C3:05 pm - 3:25 pm Neo-cybernetics: New and developing AI techniques
- Extending the ideas and principles of classical finance to change the foundations of the use of AI in modern finance
- Practical examples: How to recognize and avert catastrophic phenomena like market crashes
- Understanding the applications to finance and beyond
3:25 pm - 3:55 pm Networking refreshment break in the exhibition area
STREAM A3:55 pm - 4:00 pm DATA
STREAM A4:00 pm - 4:40 pm Spotlight on data management
What are the different tools that are emerging to analyse the data? What are their benefits?
- Should you buy vs design in house?
- Data linkage: Overlaying and connecting disparate data sets
- Effective algo choice for effective optimization
- Successful noise removal: How can you avoid the red herrings that are unique to your data?
- Is it possible to enrich data without overfitting or removing signals?
- Backfilling without adding bias
STREAM A4:40 pm - 5:00 pm Presentation: What do good data management processes look like?
- A lot of work is required before a data set is fit for purpose, with months of analysis, tidying, infrastructure set up and support, fitting into algorithms and strategies whilst avoiding overfitting. What are the biggest stumbling blocks and how can they be avoided?
- Achieving good internal communication between engineers and data scientists
- Effective data processing software: Building internally vs vendor selection
- Using machine learning in data processing and cleanup effectively
STREAM A5:00 pm - 5:20 pm Panel discussion: Effectively navigating the potential legal and regulatory risk associated with alt data sets
- Consumer views are changing: as the availability of data increases exponentially, people are becoming more aware of data privacy issues, and regulation is likely to follow suit. What are the ethics of good data practice and who is responsible for ensuring these are followed as data hungry algorithms process larger and larger quantities from more and more diverse sources?
- Best data practices: where does the responsibility for the data you are using lie?
- Is it with the vendor, the purchaser, the data scientist or the trader?
- Evaluating the insider risk: has your purchased data set offered jigsaw pieces or the whole picture?
- The impact of material non-public information
- GDPR and regulatory risk: Has your data been anonymised? Does it meet data privacy requirements?
STREAM B3:55 pm - 4:00 pm RISK
STREAM B4:00 pm - 4:40 pm Panel discussion: Understanding the most salient factors in effective risk management today
- Quant or not: is there value in using different risk methodology depending on your investment approach?
- Interpretability: Can you or should you trust the black box effect?
- Paradigm shifts to models: can you predict the end variances of the distribution shift? What solutions can you use: is it possible to teach your existing model to accommodate and adapt?
- Evaluating macroeconomic conditions with a multi-asset risk assessment
- Operational risk management: modelling and allocating capital for low frequency events
STREAM B4:40 pm - 5:00 pm Utilizing a blended man and machine approach to risk management:
- Integrating data, machine learning and fundamental insights to manage risk
- Finding patterns in data to aid risk management
- Automatic feed: immediately processing news and live market updates to your portfolio to accurately maintain risk
STREAM B5:00 pm - 5:20 pm Aligning your risk modelling, investment strategy & portfolio optimization techniques
- Understanding the challenges of using machine learning to build your alpha generation strategies
- Ongoing and effective monitoring of risk levels when using machine learning strategies to make your decisions
- Can you ever model all circumstances in an undefinable and constantly moving market? How can AI measure a risk it hasn’t seen yet?
STREAM C3:55 pm - 4:00 pm PRACTICAL APPLICATIONS OF AI/ML
STREAM C4:00 pm - 4:20 pm When AI goes wrong
- Setting up a self-learning system can have many benefits, enabling smart tech to produce output that the human mind struggles to compete with. Here we evaluate some of the outcomes when AI has not performed intelligently
- Can this be avoidable through stronger first principles?
STREAM C4:20 pm - 4:40 pm Machine Learning for Security Selection and the Dangers of Overfitting Mr Keywan Rasekhschaffe - Senior Quantitative Strategist and Portfolio Manager Gresham Investment Management
- Data prep and feature engineering: Is the AI built over the data based on solid foundations?
- Issues of overfitting and maximizing the signal to noise ratio
- Evaluating your algorithm choice: what do you want to achieve?
- Understanding fake signals: when machine learning fails
Mr Keywan RasekhschaffeSenior Quantitative Strategist and Portfolio Manager
Gresham Investment Management
STREAM C4:40 pm - 5:00 pm Case Study: WALLACE: The self-taught fully functional investment system Adrian de Valois-Franklin - CEO Castle Ridge
WALLACE is a genetic algorithm based on the concept of survival of the fittest. Constantly growing and developing, it is currently analysing about 10,000 securities across 42 dimension space features. As a self-learning system, the team at Castle Ridge offer it new features and data sources, and WALLACE decides what will add value from these, adopting and learning itself to build a highly liquid market neutral strategy.
- Survival of the fittest: An insight into the process and journey of creating WALLACE
- The burden of unsupervised learning: enabling the machine to decide what inputs are more or less useful
- Becoming social: How WALLACE explains its decisions and exposes its thought process to overcome interpretability issues
Adrian de Valois-FranklinCEO
STREAM C5:00 pm - 5:20 pm Case study: Developing MindRank: a self-learning and automated natural language processing web crawler
MindRank was initially developed to process unstructured web information for the venture capital team. Early testing and development showed greater success as a tool for finding institutional mispricing, and so Santé Capital was created as an offshoot of Santé Ventures in order to benefit from the outputs of this NLP web crawler. Analysing over 4,300 features across approximately 5,000 companies, MindRank is self-generating a monthly long/short highly liquid strategy
- The journey of developing the tech
- The importance of correct functionality and defined marketspace
- How a journey to support venture capital data processing turned into an institutional investment solution