FA691 Deep Learning for Finance

Course Catalog Description


This course focuses on neural network models and their applications to finance. Building on fundamental statistical learning theory, the course covers advanced topics in deep learning and big-data analytics for classification and prediction. Learning and building from financial data sets, the lectures will introduce machine learning models in quantitative investing, portfolio management, algorithmic trading, risk management, client-relationship management, and beyond. A final project on related topics is required
Prerequisite: Students must have taken FA590 or comparable introduction to machine learning methods

Campus Fall Spring Summer
On Campus X
Online X


Professor Email Office
Zachary Feinstein
zfeinste@stevens.edu Babbio Center 628

More Information

Course Objectives

This is an advanced course in the FINTECH and Machine Learning concentration of the Financial Analytics program.

Course Outcomes

  1. Create and apply neural network models for regression and classification tasks in finance.
  2. Evaluate performance of trained machine learning models
  3. Understand applicability of diverse techniques in deep learning

Course Resources


  1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
  2. Dixon, M. F., Halperin, I., Bilokon, P. (2020). Machine Learning in Finance: From Theory to Practice. Germany: Springer International Publishing.


Grading Policies

HW 35%
Projects 30%
Weekly Quizzes 20%
Presentation 15%

Lecture Outline

Week Topics Readings HW
1 Review of Supervised Machine Learning Dixon et al.: Chapter 1
Goodfellow et al.: Chapter 5
Homework 1: Review of Python (assigned)
2 Feed-forward neural networks with applications in finance Dixon et al.: Chapter 4
Goodfellow et al.: Chapter 6
Homework 1 (due)
Homework 2: Application of feed-forward neural networks (assigned)
Project (assigned)
3 Convolutional neural networks with applications in finance Dixon et al.: Chapter 8.5
Goodfellow et al.: Chapter 9
Homework 2 (due)
Homework 3: Application of neural networks for financial data (assigned)
Project proposal due
Applications to Finance
Research papers; Examples:
Clark et al. (2020): A machine learning efficient frontier
Golbayani et al. (2020): Application of deep neural networks to assess corporate credit rating
5 Recurrent neural networks & long short-term memory (LSTM) networks with applications to financial time series Dixon et al.: Chapter 8.2-8.4
Goodfellow et al.: Chapter 10
Homework 3 (due)
Homework 4: Application of neural networks for time series (assigned)
6 Special topics (ex: generative adversarial networks (GANs) or reinforcement learning for deep hedging) Dependent on topic area chosen Homework 4 (due)
7 Presentations Project (due)