Articles & Video
New research, new breakthroughs, and new opportunities
What are the latest innovations by leading quants?
Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks
How powerful are graph neural networks?
Differential machine learning
Unreasonably effective pricing and risk approximation by automatic differentiation (AAD) combined with machine learning (ML)
A conversation with “Quant of the Year 2019” Marcos Lopez de Prado
Marcos Lopez De Prado opens up about the issues of econometric investments and how asset management needs new technological innovations.
Unexpected bug in your machine learning: how can you recover?
What went wrong when ML doesn’t work as expected, and how can you mitigate the model risk?
Machine learning with quantum annealing
2018 Quant of the Year Alexei Kondratyev shares his passion for machine learning and quantum computing.
What does 2020 have in store for quant finance?
Understanding new trends, challenges, and solutions
What’s the one thing that will disrupt quant finance the most?
QuantMinds International thought leaders talk about the biggest game-changers in the industry!
Artificial intelligence, machine learning, and data in quant finance
In this compilation of articles, FutureQuantMinds explore AI, ML, and the impact of data on quant finance
What’s the best language for machine learning?
Python vs R – Erdem Ultanir, Quantitative Credit Risk Analytics Lead at Barclays, evaluates the two languages
10 ways AI can supercharge trading
To what extent will artificial intelligence and machine learning alter the fundamentals of investing?
Accessing new trends in quant finance
How will alternative finance and QuantTech development change the quant finance landscape?
The next evolutionary step for quant finance
Computational intelligence, cutting-edge data science, and complex machine learning
Quant finance’s machine learning journey: Are we there yet?
In which practitioners and academics weigh in on machine learning applications and its development in finance.
Computational intelligence: a principled approach for the era of data exploration
De-noising the AI hype is an exercise of intellectual honesty and recognising computational intelligence as a more realistic representation of the current progress achieved in the field of machine intelligence is part of it.
A neural network approach to understanding implied volatility movements
Presentation by John Hull, Maple Financial Professor Of Derivatives & Risk Management, Joseph L. Rotman School of Management at University Of Toronto, from QuantMinds International 2019
Data challenges in applications of machine learning to quant finance problems
Is machine learning made for quant finance? Dr. Svetlana Borovkova (Vrije Universiteit Amsterdam and Probablity & Partners) shares some of the biggest breakthroughs in machine learning application and evaluates the uses and limitations of ML in various scenarios.
How to survive the new challenges in quant finance?
The latest QuantMinds eMagazine delves into the use of machine learning, alternative data, blockchain applications, diversity, and more!
Machine learning in quant finance – what now?
Machine learning is one of the biggest game-changers in quant finance, so how will ML keep transforming this industry?
What are the latest trends in quantitative finance?
Industry thought leaders share where they see the most room for innovation.