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

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