Course description

Analyses of Financial Data

Introduction

The course aims at taking advantage of the information contained in data for decision-making. We are all prone to different kind of biases such as “overconfidence” or “recency.” Quantitative empirical methods help us to discipline decision making process. More specifically, they allow to test the existence of a relation between variables (e.g., does inflation affect nominal interest rates?), quantify this relation (e.g., a one percent increase in inflation should lead to how much increase in nominal interest rate?) and forecast the evolution of variables (e.g, which interest rate should we expect in six month from now?).

Course content

This course introduces students to empirical techniques that are relevant for finance and business in general. More specifically, the outline of the course is as follows:

Foundations for empirical methods in finance.

  • Probability basics
  • When and why econometric can work
  • Econometric basics

Introduction to programming for data analysis

  • Data and computer basics: data in finance, what is a programming language
  • Introduction to R: Basic data manipulation with R
  • Introduction to programming with R: Control structures in R, Monte Carlo simulation

Linear regression analysis

  • Simple regression analysis
  • Regression analysis with multiple explanatory variables
  • Limits and assumptions of regression analysis

Learning outcome knowledge

The aims of this course are to:

  1. Introduce students to important empirical quantitative techniques that are used in finance and more generally in business.
  2. Make students able to apply them appropriately.
  3. Prepare students for subsequent course work in finance and business.

More specifically, on completion of the course the students’ acquired knowledge and skills should be as follows:

  • Understand basic probability theory
  • Understand basic measures of location, tendency and dispersion such as the expectation, median, variance, standard deviation, skewness, kurtosis.
  • Understand what is meant by correlation and regression analysis - and the difference between them
  • Understand what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model.
  • Limits and assumptions of regression analysis
  • Be familiar with basic R syntax.

 

Exam organisation

  • Written assignment: 10%
  • Written assignment: 20%
  • Written exam: 70%