Abstract
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
Summary
- 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
Access the full video and slides
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