mlsanityData to 14 Oct 2026

Deep Learning in Finance

arXiv 1602.06561 ↗First posted 21 Feb 2016cs.LGcited by 18

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Abstract (arXiv)

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems -- such as those presented in designing and pricing securities, constructing portfolios, and risk management -- often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Feb 2016.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 2; 2024: 2; 2025: 5; 2026: 1.

18 papers.

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  1. 2022WOODS: Benchmarks for Out-of-Distribution Generalization in Time Seriescited by 30
  2. 2021ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Tablecited by 12
  3. 2021On the Robustness of Domain Constraintscited by 11
  4. 2021Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlookcited by 8
  5. 2022Long Short-Term Memory Neural Network for Financial Time Seriescited by 8
  6. 2021Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlookcited by 4
  7. 2022Deep Learning for Systemic Risk Measurescited by 3
  8. 2024Generative Modeling: A Reviewcited by 2
  9. 2025Condition Numbers and Eigenvalue Spectra of Shallow Networks on Spherescited by 2
  10. 2021Deep Calibration of Interest Rates Modelcited by 1
  11. 2025Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifierscited by 1
  12. 2023Modelling Concurrency Bugs Using Machine Learningcited by 0
6 more
  1. 2023Reinforcement Learning for Financial Index Trackingcited by 0
  2. 2024Adaptive Class Emergence Training: Enhancing Neural Network Stability and Generalization through Progressive Target Evolutioncited by 0
  3. 2025Convolutional Attention in Betting Exchange Marketscited by 0
  4. 2025Deep Learning-Enhanced Calibration of the Heston Model: A Unified Frameworkcited by 0
  5. 2025Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modelingcited by 0
  6. 2026Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Constructioncited by 0