Generate alpha. Manage Risk. Optimize Portfolios.
Conference Day One: March 15th 2021
KEYNOTES & OPENING PLENARY SESSIONS
8:30 am - 8:40 am
Opening remarks
Welcome and start of conference
8:45 am - 9:25 am
Fireside Chat – AI, Data Science and Financial Services – What's Next?
Jeff Wecker -
Chief Technology Officer,
Two Sigma
After the excitement of alternative data, new AI and ML techniques and cloud elastic computing, what's actually been achieved in using these advancements in the financial services industry? A senior industry technologist shares his thoughts on the current state of play for data science and data engineering, what's been effective and what is over-hyped, and how to attract the right talent to solve these problems.
9:35 am - 10:25 am
Panel – Adapting ML strategies before, during and after a pandemic
Rajesh Krishnamachari -
Head of Data Science, Data and Innovation Group,
Bank of America
Edward Mitby - Senior AI Engineer, Vanguard
Yevgeniy Vahlis - Head of AI Capabilities, Bank of Montreal
Darko Matovski - Co-Founder and CEO, CausaLens
· Improving models to decrease the chances of false predictions
· Automation technology being utilised as part of crisis solution
· Opportunities opening up for investment strategies post-Covid
10:35 am - 11:05 am
Networking break
11:15 am - 11:35 am
A New Approach to AI: How a Self-Evolving Market Neural Strategy Defended Against the Pandemic and Quant Crash
Alex Bogdan -
Chief Scientific Officer, Castle Ridge Asset Management,
Castle Ridge Asset Management
Adrian de Valois-Franklin - CEO, Castle Ridge
· While many firms experience massive losses as the market crashed earlier this year, a unique approach to AI allowed Castle Ridge Asset Management to continually deliver steady returns for their clients
- Geno-Synthetic Algorithms (GSAs), a new approach to self-evolving AI that constantly adapts to changing market conditions.
- Why a departure from traditional static ML techniques may make sense for your firm
Alex Bogdan
Chief Scientific Officer, Castle Ridge Asset ManagementCastle Ridge Asset Management
11:45 am - 12:00 pm
Understanding risk through the data mosaic
Peter Hafez -
Chief Data Scientist,
RavenPack
Uncovering future sources of risk requires an understanding of how the world is connected through data. Leveraging data mosaics allows investors to glean new insights about real-time business risk. The future of alternative data is not found off-the-shelf. Curated creation of new insights is becoming the norm.
12:10 pm - 1:10 pm
Networking Lunch Break
Birds of a feather session: Sentiment Scoring and Event Detection Using Neural Networks
There are numerous variations in deep learning-based methodologies used to perform sentiment analysis and event detection. In this workshop, we’ll demonstrate a relatively straightforward approach that is still quite effective. After briefly reviewing the deep learning basics, we’ll create a 4-layer neural network based on Long Short-Term Memory (LSTM) networks using the TensorFlow library and its Keras interface. With the help of publicly available pre-trained word embeddings, we will fine tune the model and train it to output a sentiment score and detected events.
Speaker: Marko Kangrga, Senior Data Scientist, RavenPack
Birds of a Feather Session: The Value and Certainty of Transaction Data During Uncertain Times
The COVID-19 pandemic turned the economy upside down and inside-out in 2929. Investment and business models built on years of regular, expected economic data have been thrown out the window and investors are grappling with ‘what’s the new normal?’ This session will highlight how transaction data from Facteus became a key ‘source of truth’ during the pandemic and beyond to enable investors to understand the consumer economy in real-time and make strategic investments. Examples of data sourcing, compliance, normalization, and industry/merchant analysis will be shared in this session.
Speaker: Don Wood, Data Strategist, Facteus
Birds of a Feather Session: Avoid spurious correlations and optimize your portfolio with Causal AI
The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future. This approach can work in static environments and for closed problems with fixed rules. However it does not work for financial time-series and other dynamic systems. In order to make consistently accurate predictions about the future, and to achieve true artificial intelligence, the development of new science that enables machines to understand cause and effect is required. This talk will present the power of Causal AI and some practical applications that are already being used in the field.
Speaker: Darko Matovski, CEO and Co-Founder, CausalLens
AFTERNOON STREAMS
STREAM A
1:15 pm - 1:20 pm Stream A: Machine Learning Case StudiesSTREAM A
1:20 pm - 1:40 pm Reinforcement Learning for optimal execution of future hedging in a limit order book Matteo Rolle - Head of Cross Market Trading, Banca Sella Holding· Building a model that can optimally hedge an option while reducing risk
· Examining the feasibility of using RL to hedge basis risk in a realistic setting
STREAM A
1:50 pm - 2:40 pm Panel - Utilizing advanced ML techniques – Reinforcement learning, NLP, Deep Learning Benoit Mondoloni - DIrector, Bank of America Merrill LynchAlejandra Litterio - Co-founder & Chief Research Officer, Eye Capital
Stuart Kozola - Global Head of Finance, MathWorks
Simon Chao - Head of Asset Management Emerging Technologies,, Fidelity Investments
· How to personalize, maintain and monitor your models
· Using AI for complex and dynamic decision-making
· Building models to optimize and predict investment outcomes
STREAM A
2:50 pm - 3:10 pm Modelling uncertainty with Bayesian ML Brandon Da Silva - Associate Portfolio Manager, OPTrust· Bayesian interpretation of Q-learning
· Applications of Baysian Q-Learning to Finance
· Broader applications of Bayesian Deep Learning
STREAM B
1:15 pm - 1:20 pm Stream B: Data quality, infrastructure and engineeringSTREAM B
1:35 pm - 1:55 pm Building data teams and data engineering· Mapping the recruitment landscape and how to ensure you remain competitive in hiring and retaining data professionals
· Where to find the best talent in the industry
· Outlining the infrastructural data strategy and a leading fund
STREAM B
1:50 pm - 2:40 pm Panel: Building an infrastructural data strategyRavi Trivedi - Assistant VP, Innovation and Decision Sciences, Nuveen
Joshua Packwood - Senior Analyst, Balyasny Asset Management
Bill Dague - Head of Alternative Data Research, Quandl
· How to reliably build a data strategy from scratch
· Defining your data strategy and investing in the infrastructural elements to support it
· Building models to optimize and predict investment outcomes
STREAM B
2:50 pm - 3:10 pm Commoditization of data engineering Tom Taylor - Head of Alpha Technology, Man Numeric- The future for the commoditization of data engineering
- Utilising cloud providers and open source tools to streamline your optimise your investment workflow
- Defining key aspects from whole investment workflow
3:15 pm - 3:45 pm
Networking break
STREAM A
3:50 pm - 3:55 pm Stream A: Machine Learning Case StudiesSTREAM A
3:55 pm - 4:45 pm Panel – Navigating data challenges and common pitfalls with latest ML/AI Applications Peng Cheng - Head of Machine Learning Strategies, JP MorganBrandon Da Silva - Associate Portfolio Manager, OPTrust
Stan Yakoff - Head of Americas Supervision, Citadel
Melissa Lin - AI Investing Data Scientist & Quantitative Strategist, NN Investment Partners
Maximillian Stroh - Co-Head of Forecasts, Quoniam Asset Management
· Identifying at what point human intervention is necessary
· Avoiding building models on spurious relationships
STREAM A
4:50 pm - 5:10 pm Utilizing ML for prediction in private markets Francesco Filia - CEO, Fasanara· Overcoming structural challenges and training models differently
· Driving value through cost reduction
· How to organize institutional data to scale
STREAM B
3:50 pm - 3:55 pm Stream B: Data quality, infrastructure and engineeringSTREAM B
4:00 pm - 4:50 pm Panel: Effective integration and implementation of cloud services to maintain a competitive advantage Christopher Thomas - Vice President, Director, Content and Technology Solutions, Delivery Platform Strategy, FactSetSameer Gupta - Head of Data, Fusion, Point72
Vijay Devadiga - Director, Credit Suisse
· Assessing the best cloud deployment models for your fund
· Challenges in switching to cloud computing
· Leveraging cloud services in a scalable, streamlined way
Christopher Thomas
Vice President, Director, Content and Technology Solutions, Delivery Platform StrategyFactSet
STREAM B
4:50 pm - 5:10 pm The strategy for accumulating knowledge in ML and Data Sylvain Champonnois - Quantitative Researcher, CFM· Articulating a new strategy – testing new alternative datasets, new open-source techniques and machine learning processes
· Importance of organisations to allow for the development of creativity and innovation
