Author Peter Simon

Discover insights on the specific conditions that make machine learning effective in certain financial applications, such as high-frequency trading. Read more.

Financial markets are the one area of life where it’s reasonable to expect different results from doing the same thing over and over again, and it’s not considered a definition of madness. So, it shouldn’t be a surprise that there is no such thing as a market-predicting magic machine learning box that ingests markets data and spits out lots of…

The response from COVID-19's gradual recovery will be top of mind for nearly every firm and industry in 2021. Some businesses may become stagnant or never recover. Others will view the shakeup as an unprecedented opportunity to understand and improve their data and analytical assets, operationalize and update their model production process, and reassure customers that their AI can be trusted. Everyone is looking…

This was going to be a four-part blog series. I figured that I’d covered most of what financial markets participants might be interested in hearing about when it comes to AI in the markets (and had probably bored the audience for long enough, all things considered). Then we did a webinar on the topic together with FactSet, had a great…

So far in this series, we’ve discussed how the securities industry would use AI in general (part one), compared machine learning in the front office to “traditional” quant (part two), and described how automated machine learning in particular can bring enormous power and efficiency gains to the quantitative investment process (part three). In this installment, we’ll describe our recommendations for…

The advantages of automated machine learning in the front office, the importance of problem framing, and how automated machine learning techniques expand the problem space that can be investigated. Plus, why the choice of machine learning algorithm should be seen as just another parameter to search on.

Check out all of the blog series: part 1, part 3, part 4, part 5. “I define nothing. Not beauty, not patriotism. I take each thing as it is, without prior rules about what it should be.” — Bob Dylan It’s entirely possible that someone, somewhere, has successfully built one of the amazing market predicting magic boxes from the previous…

This series of blog posts is based on a talk the author presented at ODSC Europe 2019 alongside Ayub Hanif, VP at JPMorgan’s Quantitative and Derivatives Strategy. We’ll look at how recent developments in automated machine learning and interpretability can help market participants build, test, and understand powerful AI models that support and enhance their investment processes. But first, some context is needed. Part 1: Beyond the Market-Predicting Magic Box In which we conclude that the existence of this article is strong evidence that we don’t know how to use AI to predict asset prices, either, and try to get real.