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09.00 - 09.10: Welcome Address. Chairman’s Opening Remarks

08:30: Morning Welcome Coffee

Chair:

To be confirmed


09.00 - 10.00: Keynote Speech 

Topics in Self-Learning Agents and Traditional Quantitative Models in Finance   

  • What can we draw from our experience of training and running an industry first self-learning agent for electronic order execution?
  • Will traditional hand-crafted heuristic- and quant-based execution algorithms go extinct within 10 years?
  • Does the success of ML and AI agents in finance indicate the eventual demise of traditional quantitative models?
  • Practical aspects of using feeder models and heuristics in AI agents for trading applications.
  • Do we have practical solutions for the equivalence puzzle in Neural Nets

Presenter: To be confirmed


10.00 – 10.45: Delivering Alpha: Artificial Intelligence in Capital Markers Investing 

  • 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?

Presenter: Michael Beal: CEO, Data Capital Management


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 - 12.00: Trading Strategies Using a Mixture of Supervised and Reinforcement Learning 

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. 

Presenter: Gordon Ritter: Senior Portfolio Manager, GSA Capital  


12.00 - 12.45Topic to be Confirmed 

Presenter: Leigh Drogen: Founder and CEO, Estimize


 12.45- 13.45: Lunch


13.45 - 14.30: Topic to be Confirmed

Presenter: Jared Broad: CEO, QuantConnect


14.30 – 15.15: Factor Investing Using Volatility Data & Machine Learning

 

  • Equity factors
  • Volatility surface
  • Style investing

 

Presenter: ShengQuan Zhou: Quantitative Researcher, Bloomberg LP 


15.15 - 15.30: Afternoon Break


15.30 - 16.15: From Artificial Intelligence to Machine Learning, from Logic to Probability   

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