The Neural Networks Survival Kit for Quants

Matthew Dixon, Assistant Professor of Finance and Statistics, Illinois Institute of Technology | October 08, 2018

View an extract of this session held at the Generation AI: The New Data-Driven Investor event in September 2018.You can also access the full video and slides.

Using examples ranging from portfolio construction to algorithmic trading, this talk explains neural networks as a non-parametric econometrics technique. Matthew also provides various examples illustrating the tradeoffs between using Deep Q-learning versus supervised deep learning for predictive modeling with signals such as news sentiment.

Important Considerations

  • Traditional Statistical Modeling
  • Stats vs Machine Learning
  • What Does a Network Classifier Output
  • Taxonomy of Most Popular Neural Network Architectures
  • Geometric Interpretation of Neural Networks
  • Half-Moon Dataset
  • Why Deep Learning


  • Neural networks aren't themselves “black-boxes”, although they do treat the data generation process as a black-box The output from neural network classifiers are only probabilities if the features are conditionally independent (or there are enough layers)
  • One layer is typically sufficient to capture the non-linearity in most financial applications (but multiple layers are needed for probabilistic output)
  • Recurrent neural networks are non-parametric, non-linear, extensions of classical time series methods
  • TensorFlow doesn’t check that fitted Recurrent Neural Networks are stationary

By providing your personal information and submitting your details, you acknowledge that you have read, understood, and agreed to our Privacy Statement and you accept our Terms and Conditions. We will handle your personal information in compliance with our Privacy Statement. You can exercise your rights of access, rectification, erasure, restriction of processing, data portability, and objection by emailing us at in accordance with the GDPRs. You also are agreeing to receive occasional updates and communications from RavenPack about resources, events, products, or services that may be of interest to you.

Data Insights

Read More