The Journal of Financial Data Science

Machine Learning for Econometricians: The Readme Manual
Marcos López de Prado
An Artificial Intelligence–Based Industry Peer Grouping System
George Bonne, Andrew W. Lo, Abilash Prabhakaran, Kien Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari and Howard Zhang
Alternative Data in Investment Management: Usage, Challenges, and Valuation
Gene Ekster and Petter N. Kolm
Machine Learning for Active Portfolio Management
Söhnke M. Bartram, Jürgen Branke, Giuliano De Rossi and Mehrshad Motahari
The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach
Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz and Joseph H. Davis
Deep Hedging of Derivatives Using Reinforcement Learning
Jay Cao, Jacky Chen, John Hull and Zissis Poulos
Deep Learning for Portfolio Optimization
Zihao Zhang, Stefan Zohren and Stephen Roberts

Latest Articles

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Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation
Fernando de Meer Pardo, Peter Schwendner and Marcus Wunsch

The Journal of Financial Data Science Fall 2022, jfds.2022.1.109; DOI: https://doi.org/10.3905/jfds.2022.1.109
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Meta-Labeling Architecture
Michael Meyer, Jacques Francois Joubert and Mesias Alfeus

The Journal of Financial Data Science Fall 2022, jfds.2022.1.108; DOI: https://doi.org/10.3905/jfds.2022.1.108
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The Benefit of Narratives for Prediction of the S&P 500 Index
Pascal Blanqué, Mohamed Ben Slimane, Amina Cherief, Théo Le Guenedal, Takaya Sekine and Lauren Stagnol

The Journal of Financial Data Science Fall 2022, jfds.2022.1.107; DOI: https://doi.org/10.3905/jfds.2022.1.107
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Corrigendum
The Journal of Financial Data Science Fall 2022, jfds.2022.1.106; DOI: https://doi.org/10.3905/jfds.2022.1.106
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Gains and Losses Revisited: Skill Detection and Similarity Assessment
Sid Browne

The Journal of Financial Data Science Fall 2022, jfds.2022.1.105; DOI: https://doi.org/10.3905/jfds.2022.1.105
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Deep Learning Meets Statistical Arbitrage: An Application of Long Short-Term Memory Networks to Algorithmic Trading
Yijun Zhao, Shengjian Xu and Jacek Ossowski

The Journal of Financial Data Science Fall 2022, jfds.2022.1.103; DOI: https://doi.org/10.3905/jfds.2022.1.103

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In our series of videos, the authors of research published in The Journal of Financial Data Science, discuss the findings of their article, offering more in-depth analysis around it and explain how the conclusions can be implemented in practice.