Conference Day Two
The key discussion points will include:
- Using AI to quantify unstructured data on ESG/SDG factors and associated non-financial risks
- The use of Natural Language Processing and ESG/SDG taxonomies to quantify textual data in multiple languages
- Ranking and benchmarking stocks based on ESG/SDG factors to implement Thematic, Long-Short and ESG/SDG-Tilted investment strategies
- The relevance of SDG and non-financial risk factors for Alpha Research, Fiduciary Duty, Materiality Assessments, Country Risk and Risk Management
Richard V. Rothenberg:
Global AI Corporation & Research Affiliate, Lawrence Berkeley National Laboratory
Richard V. Rothenberg: Executive Director, Global AI Corporation, New York, NY and Research Affiliate, Lawrence Berkeley National Laboratory, Berkeley, CA
- Why artificial intelligence for capital markets investing?
- Challenge 1: Data acquisition, integration, processing power
- Challenge 2: Artificial intelligence and its subcomponents
- Where should financial services professionals focus their effort?
CEO, Data Capital Management
Michael Beal: CEO, Data Capital Management
Michael M. Beal is Chief Executive Officer of Data Capital Management. Previously he was Co-Founder and Head of Strategy & Finance at JPMorgan Intelligent Solutions, Deal Associate at TPG Capital and M&A Investment Banking Analyst at Morgan Stanley. Mr. Beal earned a B.A from Harvard College with honors in Economics and an M.B.A from Harvard Business School with distinction.
Abstract: Machine learning is rapidly transforming the field of quantitative finance. In this talk, we discuss how two distinct subfields of machine learning, namely reinforcement learning and supervised learning, can be combined into a single model that harvests the power of reinforcement learning in handling multi-period problems with delayed rewards and costs, and simultaneously harvests the power of supervised-learning to learn the structure of a non-linear model with interactions. Our technique fuses the two within the framework of generalized policy iteration by generating training sets which are then used by the supervised learner to learn a better representation of the action-value function, which is then used to generate a better training set for the next iteration. We show that our method outperforms tabular Q-learning in a simulation involving trading a very illiquid asset, and can handle discrete as well as continuous predictors.
Senior Portfolio Manager, GSA Capital
Gordon Ritter: Senior Portfolio Manager, GSA Capital
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry and abstract algebra.
Prior to Harvard he earned his Bachelor’s degree with honours in Mathematics from the University of Chicago. Gordon is currently a senior portfolio manager at GSA Capital, and leader of a team trading a range of high-Sharpe absolute return strategies across geographies and asset classes. GSA Capital has won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times, with numerous other awards including in the long-term performance category.
Prior to joining GSA, Gordon was a Vice President of Highbridge Capital and a core member of the firm’s statistical arbitrage group, which although less than 20 people, was one of the most successful quantitative trading groups in history, responsible for billions in pro_t and trillions of dollars of trades across equities, futures and options.
Concurrently with his positions in industry, Gordon teaches courses ranging from portfolio management to econometrics, continuous-time finance, and market microstructure in the Department of Statistics at Rutgers University, and also in the MFE programs at Baruch College (CUNY) and New York University (both ranked in the top 5 MFE programs).
He has published several articles on modern portfolio theory in top practitioner journals including Risk, and academic journals including European Journal of Operational Research.
This talk will provide an introduction to the theory and practice of convex optimization for financial applications. We will discuss the geometric intuition for convex objectives and constraints, demonstrate applications of convex optimization for portfolio construction, and discuss techniques for evaluating portfolio construction techniques from generative pricing models.
Software Engineer, Quantopian
Scott Sanderson: Software Engineer, Quantopian
Scott Sanderson is a senior software engineer at Quantopian, where he is responsible for the design and implementation of Quantopian’s backtesting and research APIs. Outside of work, Scott is a contributor to several open source projects in the Python data science ecosystem, and he is a regular conference speaker on topics in numerical programming.
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.
Peter Decrem: Director, Citigroup
- Equity factors
- Volatility surface
- Style investing
Quantitative Researcher, Bloomberg LP
ShengQuan Zhou: Quantitative Researcher, Bloomberg LP
Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.
Presenter: To be Confirmed