Synthetic Data Generation in Finance

2 min readNov 19, 2020


With Stefan Jansen

QuantUniversity partnered with PRMIA for QuantUniversity’s fall school in Machine Learning and AI in Finance. We had more than 1000 participants from more than 20 countries including India, China, Australia, UK, Turkey, South Africa etc. attend the our courses.

This fall, we are offering 3 courses in Data Science, Machine Learning, and Model Risk Management:

  1. Just Enough Python for Data Science
  2. Machine Learning and AI for Financial Professionals
  3. Model Risk Management for Machine Learning Models

In addition, we are hosting guest lectures from eminent quants, innovators, and thinkers on various topics in AI/ML and Fintech related topics.

Lecture 4

In Week 4, we Stefan Jansen, and Sri Krishnamurthy from QuantUniversity discuss Synthetic Data Generation in Finance. Here is a summary of the workshop.


In this master class, Stefan showed how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data. The goal is to yield a generative model capable of producing synthetic samples representative of this class. While most popular with image data, GANs have also been used to generate synthetic time-series data in the medical domain. Subsequent experiments with financial data explored whether GANs can produce alternative price trajectories useful for ML training or strategy backtests.

Slides, demos, and videos at

Missed this event and want to attend the next?

Next week’s event (11/25) is joined by Yaacov Weinstock, CFA and Sri Krishnamurthy, CFA where they will present about Alternative Data and the API Jungle

Register here!