Coursebooks 2017-2018

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Financial big data

FIN-525

Lecturer(s) :

Challet Damien

Language:

English

Withdrawal

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

Remarque

Only for MA3.

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 investment strategy backtesting.

Content

Big Data

  1. A brief history of technology: storage, computing power, efficiency
  2. Multicore/GPU and cluster computing in R and Python
  3. Financial data sources and acquisition
  4. Data cleaning and formatting
  5. Visualization techniques

Application to financial data

  1. Dimensionality reduction
    Correlation matrix cleaning with random matrix theory
    Random Factors
    Clustering of assets and days
  2. Investment strategies
    Backtesting and non-stationarity
    Machine learning and trading
    Portfolios of strategies 

Keywords

Big Data, stylized facts, data wrangling, dimension reduction, statistical learning, portfolio optimization, realized risk and profits

Learning Prerequisites

Required courses

Recommended courses

Important concepts to start the course

See above

Learning Outcomes

Transversal skills

Teaching methods

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

Expected student activities

Assessment methods

Supervision

Office hours No
Forum Yes
Others

Assistant support envisioned depending on attendance

Online (Skype) hours

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)
  • Honey, I shrunk the sample covariance matrix - Ledoit and Wolf (2004)
  • Algorithms of maximum likelihood data clustering with applications - Giada and Marsili (2002)
  • The Art of R Programming: A Tour of Statistical Software Design - Matloff (2011)
  •  

    Ressources en bibliothèque
    Moodle Link

    In the programs

    Reference week

     MoTuWeThFr
    8-9     
    9-10     
    10-11     
    11-12     
    12-13     
    13-14  EXTRANEF_126  
    14-15    
    15-16    
    16-17     
    17-18     
    18-19     
    19-20     
    20-21     
    21-22     
     
          Lecture
          Exercise, TP
          Project, other

    legend

    • Autumn semester
    • Winter sessions
    • Spring semester
    • Summer sessions
    • Lecture in French
    • Lecture in English
    • Lecture in German