1. Deep Learning for Business and Finance
Brian Freeman, PhD, PE

2. Machine Learning vs Artificial Intelligence
Data
Images
Speech
Sounds
Measurements
RNNs & CNNs

3. Perceptron model

4. Different types of architecture, Different types of learning

5. Machine learning model
Data
Pre-processing
Model selection
Training
Testing

6. Business & Finance Application
Investments
Identification/Discovery
Timing (Market Entrance/Exit)
Portfolio optimization
Credit risk
Fraud/Compliance
Market classification

7. Data Unsupervised Deep Learning Supervised Deep Learning
<10,000 observations
>10,000 observations
Unsupervised Deep Learning
Supervised Deep Learning
Self-assembling
Target objective

8. Natural Language Processing
M. Kraus and S. Feuerriegel, Decision support from financial disclosures with deep neural networks and transfer learning, Decision Support Systems 104 (2017) 38–48, doi: /j.dss

9. Results
L. Troiano, E. Mejuto, and P. Kriplani (2017), On Feature Reduction using Deep Learning for Trend Prediction in Finance, arXiv: v1

10. Results Profitability (%)
W. Bao, J. Yue, and Y. Rao (2017), A deep learning framework for financial time series using stacked autoencoders and long short term memory, PLoS ONE 12(7): e doi: /journal.pone

11. It is unlikely that any theoretical models built from existing axiomatic foundations will be able to compete with the predictive performance of deep learning models.
J. B. Heaton, N. G. Polson, and J. H. Witte, (2018) Deep Learning in Finance, arXiv: v3