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Datascience for Finance

  • Enseignant(s):   Z.Zhao  
  • Titre en français: Datascience pour la finance
  • Cours donné en: anglais
  • Crédits ECTS: 6 crédits
  • Horaire: Semestre d'automne 2020-2021, 4.0h. de cours + 1.0h. d'exercices (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en finance, Orientation gestion des actifs et des risques

    Maîtrise universitaire ès Sciences en finance, Orientation finance d'entreprise

    Maîtrise universitaire ès Sciences en finance : Entrepreneuriat financier et science des données

 

Objectifs

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. It will be composed of two parts, in the first part of each session, we will go over econometric models; in the second part, we will use software such as STATA, python, etc to analyze related examples.

Contenus

1. Introduction

Introduction to Econometrics

Tools used for economic analysis

2. The Linear Regression Model

The Linear Regression Model

Least Squares Regression

Hypothesis Tests and Model Selection

3. Estimation Methodology

4. Cross Sections, Time Series and Panel Data

* Important: I might adjust the pace of the class and this syllabus according to our progress throughout the semester. Please pay close attention to the Moodle system.

Références

Required Textbook:

W. Greene (2018), "Econometric Analysis" (8th edition, Prentice Hall).

Other (read if interested):

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

W. McKinney (2013), "Python for Data Analysis" (O'Reilly Media Inc)

Evaluation

1ère tentative

Examen:
Ecrit 1h30 heures
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

Grading will be based on

Homework Assignments (30%) + Group Projects (Details in syllabus, 50%) + Final Exam (Open book, 20%).

Grading is done on a relative (not absolute) basis.

Final Exam will be online.

Rattrapage

Examen:
Ecrit 1h30 heures
Documentation:
Autorisée
Calculatrice:
Autorisée
Evaluation:

The retake will include several questions related to the class materials.

Other elements (homework, projects, etc.) remain acquired



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