AMS 520, Machine Learning in Quantitative Finance
2) Feedforward neural networks: Feedforward architecture; Stochastic gradient descent and backpropagation algorithm; Non-Linear Factor Modeling and applications in asset pricing; Convolutional neural networks; Autoencoders.
3) Sequential Learning: Linear time series models; Probabilistic sequence modeling – Hidden Markov Models and particle filtering; Recurrent Neural Networks; Applications in finance.
4) Reinforcement Learning: Markov decision process and dynamic programming methods (Bellman equations and Bellman optimality); Reinforcement learning methods (Monte-Carlo methods, policy-based learning, TD-learning, SARSA, and Q-learning); Deep reinforcement learning; Applications of reinforcement learning in finance.
Offered in fall semester; 3 credits; ABCF grading
"Machine Learning in Finance: From Theory to Practice" by Paul Bilokon, Matthew F. Cixon, and Igor Halperin; 2020, Springer Publishing; ISBN: 978-3030410674 (eBook rental)
Recommended Additional Readings:
"Machine Learning for Algorithmic Trading" by Stefan Jansen; 2nd edition, 2020, Packt Publishing; ISBN: 978-1-83921-771-5
"The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (available at : http://statweb.stanford.edu/~tibs/ElemStatLearn/ )
"Machine Learning for Factor Investing" by Guillaume Coqueret and Tony Gida, R Version, CRC Press (available at: http://www.mlfactor.com/)
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto, 2nd edition, 1998, The MIT Press; ISBN: 978-0262193986
"Python for Finance: Analyze Big Finanical Data" by Yves Hilpish, 1st edition; ISBN: 978-1491945285
Demonstrate understanding of probabilistic modeling:
- Bayesian vs. frequentist estimation, bias-variance tradeoff, sequential Bayesian updates, model selection and model averaging;
- Probabilistic graphical models and mixture models;
- Multiplicative Weights Update Method
- Bayesian regression and Gaussian processes.
Demonstrate understanding of feedforward neural networks:
- Feedforward architecture;
- Stochastic gradient descent and backpropagation algorithm;
- Non-Linear Factor Modeling and applications in asset pricing;
- Convolutional neural networks.
Demonstrate understanding of methods for sequential learning:
- Linear time series models;
- Probabilistic sequence modeling – Hidden Markov Models and particle filtering;
- Recurrent Neural Networks;
- Applications in finance.
Demonstrate understanding of dynamic programming and reinforcement learning algorithms:
- Markov decision process and dynamic programming methods (Bellman equations and Bellman optimality);
- Reinforcement learning methods (Monte-Carlo methods, policy-based learning, TD-learning, SARSA and Q-learning);
- Deep reinforcement learning;
- Applications of reinforcement learning in finance.