QF301 Advanced Time Series Analytics and Machine Learning

Course Catalog Description


This course introduces the main concepts of data analysis applied to financial problems. The course explores data analysis techniques; multivariate, and factor models applied to risk management, portfolio optimization, forecasting or asset pricing. Students will work with historical databases, conduct their analysis, and test their financial strategies based on the techniques reviewed during the class.

Prerequisites: QF201, and intermediate statistics (MA331 or BT221 or MGT620)

Campus Fall Spring Summer
On Campus x x
Web Campus


Professor Email Office
Zachary Feinstein
zfeinste@stevens.edu Babbio 628
Anthony Diaco adiaco@stevens.edu Babbio 4405

More Information

Course Description

The significant amount of information available in any field requires a systematic and analytical approach to select the most important information and anticipate major events. Machine learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major physical, biological, economics or social variables.

This course introduces advanced time series methods and statistical and graphical (machine learning) models used for inference and prediction in finance. The emphasis of the course is in the learning capability of the algorithms and their application in finance or in marketing related to financial companies.

Relationship of Course to Rest of Curriculum Students will have the opportunity to formalize the concepts of quantitative finance in machine learning algorithms that can be applied to risk management or trading.

Course Outcomes

By the end of this course, the students will be able to:

  • Learn the fundamental concepts of machine learning algorithms.
  • Explore existent and new applications of statistical learning methods such as forecasting or classification.
  • Apply advanced time series models and machine learning methods for analysis of financial time series (return and volatility)
  • Recognize the value and also the limits of analytical methods in financial machine learning algorithms.

Course Resources


Required Text(s):

Foster Provost and Tom Fawcett, Data Science for Business, O’Reilly, 2013 (to get a discount on oreilly.com use this code: AUTHD)

Marco López de Prado, Advances in Financial Machine Learning, Wiley, 2018.

Optional Readings:

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013 (link) (ISLR).


Grading Policies

Assignments To understand the course material and get a good grade, it is necessary (though not sufficient) to invest a substantial amount of time working on the assignments. Homeworks will be posted on Canvas (approximately) every other week. These assignments will be due on the specified due date at the specified time. No late assignments, without prior approval, will be accepted.

Grades and Evaluation:

  • Participation and Short Quizzes: 10%
  • In-Class Assignments: 20%
  • Homework Assignments: 40%
  • Final Project: 30%

Exam policy: There will be no examinations in this course.

Re-grades: Regrade requests are due no later than one (1) week after the material is returned. All requests must be accompanied with a clearly written reason as to what is incorrect. Regrades are for correcting grading errors, not for negotiating higher grades.

Lecture Outline

Session Topic
Week 1 Introduction to data science; Review of probability and statistics
Week 2 Overview of machine learning; Review of R/Python
Week 3 Linear models for time series I
Week 4 Linear models for time series II
Week 5 Neural networks
Week 6 Decision trees and random forest
Week 7 Model performance & backtesting revisited
Week 8 Classification I: Linear models
Week 9 Classification II: Nonlinear models
Week 10 Clustering analysis
Week 11 Feature importance & model calibration
Week 12 Financial Applications (TBD)
Week 13 Financial Applications (TBD)
Week 14 Presentations