AI in Financial Markets Part 1 It Looks like Magic But Its Got to Be Real hero banner

AI in Financial Markets

Part 1: It Looks like Magic, But It’s Got to Be Real.

Machine learning techniques cannot magically make “signal” appear out of thin air, nor can they make unstable factors more stable. Data science typically succeeds where there are complex behaviors that can be described in data and where consistent inputs lead to consistent, predictable outcomes.

Nevertheless, there are a number of useful aspects that set apart machine learning techniques from the traditional quantitative toolbox, and modern automated machine learning is a compelling proposition for modern quantitative traders, analysts, and investors.

Join Peter Simon, DataRobot’s lead data scientist for financial markets, on part one of the podcast, AI in Financial Markets.

He’ll discuss:

  • Why AI black boxes are no longer a thing
  • Use cases for machine learning in financial markets — in the front office and beyond
  • 6 key characteristics that set apart machine learning techniques from the traditional quantitative and “quantamental” investing toolbox
  • Limitations of machine learning to keep in mind
  • Why financial markets should embrace AI now, or be left behind


Peter Simon
Peter Simon

Lead Data Scientist, DataRobot

  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.