World Business StrategiesServing the Global Financial Community since 2000

“Practical Machine Learning for Quantitative Finance”

Post-Conference Workshop: “Practical Machine Learning for Quantitative Finance”

Monday 29th & Tuesday 30th March 2021

EDT: 09.00
BST: 14.00
CEST: 15.00

We will focus on two concepts in machine learning that hold great promise for quant finance: regression models on Day 1, and generative models on Day 2. Each day, we will discuss theory during the first hour, and then use what we learned to solve practical quant finance problems during the second hour.

During the practice sessions, we will review, update and run code on Colab and AWS. Participants are welcome to just observe, or if desired also participate in writing or running the code (participation is strictly optional). The code will be available on GitHub after the workshop.

Day 1 – Regression Models

  1. Regression model types
  2. Feedforward networks
  3. Autoencoders

Practice session: Estimation of Drift in Real-World Measure

Day 2 – Generative Models

  1. Generative model types
  2. Joint and Conditional Probabilities
  3. Gibbs sampling

Practice session: Estimation of Probability in Real-World Measure

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder of Duality Group, where he served as CTO from 2017 to 2020.

Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin, for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, and the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).

Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
    (FULL-TIME Students Only)

Event Email Reminder