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Datascience for Finance

  • Teacher(s):  
  • Course given in: English
  • ECTS Credits:
  • Schedule: Autumn Semester 2019-2020, 4.0h. course + 1.0h exercices (weekly average)
  •  sessions
  • site web du cours course website
  • Related programmes:
    Master of Science (MSc) in Finance, Orientation Asset and Risk Management

    Master of Science (MSc) in Finance, Orientation Corporate Finance

    Master of Science (MSc) in Finance : Financial Entrepreneurship and Data Science



The objective of the class is to provide students with:

  • an understanding the basic concepts of statistics and estimation
  • an understanding of how to design an estimation strategy
  • an understanding of how to efficiently implement it and then reach an interpretation of the results.

This class should be a "hands-on" class on dealing with data.


1. From moment estimation to maximum likelihood estimation

  • Moment estimation
  • Generalized method of moments
  • Maximum likelihood estimation
  • Goodness of fit tests
  • Numerical optimization

2. The linear model

  • Ordinary Least Square for the univariate linear model
  • Multivariate linear model
  • Instrumental variables
  • Introduction to time series analysis
  • Forecasting

3. Big data and non-linear models

  • Dealing with large datasets
  • From non parametric estimation to machine learning.


J. Hamilton (1994), “Time Series Analysis” (Princeton University Press).

F. Hayashi (2000), “Econometrics” (Princeton University Press).

W. Greene (2011), “Econometric Analysis” (7th edition, Prentice Hall).


First attempt

Without exam (cf. terms)  

Grade will be determined based on

  • Assignments (A)
  • Project (P)

according to the rule:

a group project will be accounting for 50% of the grade and 50% for the weekly assignments


Oral 0h20 minutes
Not allowed
Not allowed

Retake exam is an oral exam with a set of questions to prepare and answer. Closed book exam, no calculator permitted.

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