The Journal of Machine Learning in Finance is the definitive source of the latest approaches, innovations and applications in machine learning in institutional investment management.

The journal will cover cross-asset markets, across learning and data types, covering areas including risk management, execution and allocation frameworks, among other areas. The first edition will be published in Q1, 2020.

The journal’s editorial board will include quantitative finance professionals, data scientists, machine-learning professionals as well as academics specializing in finance and artificial intelligence. Tony Guida, author of Big Data and Machine Learning in Quantitative Investment, and Executive Director, Senior Quant Researcher, is Editorial Chair of The Journal of Machine Learning for Finance.

1st Edition: The Journal of Machine Learning in Finance

Submissions have opened

Submission Guidelines

Submissions & Contact

Please contact our Head of Content, Chantal Hermans and Editor-in-Chief, Tony Guida, at for submissions and more information.

Editorial Board:

Editor-in-Chief: Tony Guida, Author of Big Data and Machine Learning in Quantitative Investment /  Executive Director, Senior Quant Researcher / Chair of ThinkTank EMEA

Deputy Editors:

  • Matthew Dixon, Assistant Professor of Applied Math, Illinois Institute of Technology
  • Miquel Noguer Alonso , Co-Founder, Artificial Intelligence Finance Institute – AIFI / Chief Development Officer, Global AI / Adjunct Professor, Columbia University in the City of New York 

Board members:

  • Eric Bouyé, Head, Quantitative Strategies and Asset Allocation / Lead Investment Officer, The World Bank / Chair of ThinkTank US
  • Guillaume Coqueret, Associate Professor of Finance, Emlyon Business School
  • Carmine de Franco, Head of Fundamental Research, OSSIAM
  • Giuliano De Rossi, Head of European Quantitative Research, Macquarie Securities
  • David Fellah, Quantitative Research, ITG
  • Nick Firoozye, Honorary Senior Lecturer – Computer Science (Computational Finance), University College London
  • Ahcene Gareche, Head of Quantitative Strategies, Axa IM Chorus / Chair of ThinkTank ASIA
  • Yves Hilpisch, Chief Executive Officer, The AI Machine / Chief Executive Officer, The Python Quants
  • Rheia Khalaf, M.Sc., FSA, FCIA, CERA, Directrice, Recherche Collaborative et Partenariats – Réseau Fin-ML / Director, Collaborative Research & Partnerships – Fin-ML Network / CREATE Program on Machine Learning in Quantitative Finance and Business Analytics, Université de Montréal / IVADO
  • Michael G. Kollo, PhD, General Manager – Quantitative Solutions and Risk, HESTA
  • Petter Kolm, Clinical Professor of Mathematics, Courant Institute of Mathematical Sciences / Director, Mathematics in Finance Master Program, NYU Courant’s Quantitative Finance Program
  • Dr. Mingzhu Lu, R&D Lead / Data Science, Accenture
  • Yin Luo, CFA, CPA, PStat, Vice Chairman / Quantitative Research, Economics, and Strategy (QES), Wolfe Research
  • Mark Nebelung, Portfolio Manager, Co-Head of Global Systematic Solutions, Principal Global Equities
  • Sandhya Prabhakaran, Research Fellow, MMoffitt Cancer Center
  • Michael Recce, Chief Data Scientist, Neuberger Berman
  • Yaz Romahi, Chief Investment Officer, Quantitative Beta Strategies, J.P. Morgan Asset Management
  • Julien Turc, Head of the QIS Lab, BNP Paribas
  • Sandrine Ungari, Head of Cross Asset Quantitative Research, Société Générale
  • Maurits van der Meer, Portfolio Manager Systematic Equity Strategies, PGGM
  • Kari Vatanen, CFA, FRM, Head of Cross Assets and Allocation, Varma Mutual Pension Insurance Company

Potential topics include:

  • ML techniques to model risk
  • Empirical asset pricing using machine learning
  • CTA strategies using ML to make predictions on market direction
  • ML in compliance, fraud detection
  • Investment/securities selection using ML across main asset classes
  • Investment/new signals — NLP, ESG
  • Portfolio construction techniques using deep learning
  • New trading techniques using agent reinforcement learning
  • ML used in derivatives, pricing options
  • Creating synthetic data using GAN for stress testing
  • Use of machine learning in execution, reinforcement learning for better trade routing