FIN-525 / 3 credits

Teacher: Challet Damien

Language: English

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.

Remark: MA3 only


Summary

The course's first part introduces modern methods to acquire, clean, and analyze large quantities of financial data efficiently. The second part expands on how to apply these techniques to financial analysis, in particular to intraday data and investment strategies.

Content

Keywords

Big Data, stylized facts, data wrangling, dimension reduction, tick-by-tick data, trading strategy, strategy selection.

Learning Prerequisites

Required courses

  • Very good programming skills (required) and a first experience with R or/and Python.
  • Good knowledge of the probability and statistics concepts taught in the first (two) year(s) at EPFL. This includes the Central Limit Theorem and its important applications in statistics.

Recommended courses

  • Advanced statistics
  • Econometrics
  • Investments
  • Programming with R, or Python.

Important concepts to start the course

See above

Learning Outcomes

By the end of the course, the student must be able to:

  • Choose appropriate methods and tools to manipulate and analyze complex financial data.
  • Conduct efficient data cleaning.
  • Implement financial big data analysis using R and Python
  • Implement proper computationally intensive strategy backtests
  • Plan computing resource usage time
  • Infer financial measurables with robust estimates

Transversal skills

  • Collect data.
  • Write a scientific or technical report.
  • Demonstrate a capacity for creativity.
  • Access and evaluate appropriate sources of information.
  • Continue to work through difficulties or initial failure to find optimal solutions.

Teaching methods

3 hours of ex-cathedra lectures and supervised applications for 14 weeks

Expected student activities

  • Actively participating at lectures
  • Completing theoretical and practical exercices during the lectures.
  • Writing up of a report which uses the concepts and tools of this course and which contains three parts: data wrangling, dimensionality reduction, backtest of machine learning-based trading strategies.

Assessment methods

  • Group projects 100%

Supervision

Office hours No
Assistants Yes
Forum Yes
Others

Resources

Bibliography

  • Empirical properties of asset returns: stylized facts and statistical issues - Cont (2001) 
  • An Introduction to Statistical Learning - James, Witten, Hastie, Tibshirani (2013)
  • Analysis of Financial Times Series - Tsay (2005)
  • Financial Applications of Random Matrix Theory: A short review - Potters and Bouchaud (2009)
  • Python for Finance: Mastering Data-Driven Finance - Hilpisch (2019)
  • Ressources en bibliothèque

    Moodle Link

    In the programs

    • Semester: Fall
    • Exam form: During the semester (winter session)
    • Subject examined: Financial big data
    • Lecture: 3 Hour(s) per week x 14 weeks
    • Semester: Fall
    • Exam form: During the semester (winter session)
    • Subject examined: Financial big data
    • Lecture: 3 Hour(s) per week x 14 weeks
    • Semester: Fall
    • Exam form: During the semester (winter session)
    • Subject examined: Financial big data
    • Lecture: 3 Hour(s) per week x 14 weeks

    Reference week

     MoTuWeThFr
    8-9     
    9-10     
    10-11     
    11-12     
    12-13     
    13-14     
    14-15     
    15-16     
    16-17     
    17-18     
    18-19     
    19-20     
    20-21     
    21-22