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Empirical Methods for Finance

Objectives

The primary objective of this course (*) is to introduce students to empirical research methods and data analysis in finance . As the availability and complexity of financial data continue to grow rapidly, there is an increasing demand in the financial industry for individuals with quantitative skills who can effectively analyze such data. Through a combination of face-to-face and online lectures, this course aims to equip students with the necessary knowledge and skills to excel in this field.

(*Fall Semester 2023-2024. The updated course’s syllabus will be available to students at the beginning of each academic term)

General characterization

Code

2269

Credits

3.5

Responsible teacher

Robert Anthony Hill

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

n/a


Bibliography

Wooldridge, J.M. (2013). Introductory econometrics: A modern approach (5th edition). Mason, OH: South-Western, Cengage Learning

Brooks, C. (2019) Introductory Econometrics for Finance, 4th edition. Cambridge University Press, New York 


Teaching method

This is a blended course. During the in-person lectures we will cover the core material and discuss examples. The asynchronous videos go through the Stata applications providing a step-by-step explanation of the code (data and code are always provided). You are encouraged to have Stata open on your computer and run the code yourself while watching the video: the asynchronous format is very well suited for this purpose. By looking at several examples of common empirical problems you will develop an intuition for what methods are most suited to overcome specific challenges faced when working with data. By the end of the course, you will be able to write simple codes, to assess the validity of the approach used and to discuss the results of linear regression models. Pre-class preparation and in-class participation is expected.


Evaluation method

Students will be assessed on one group assignment (30%) and a final exam (70%).

Subject matter

Univariate and Multivariate Regression Analysis

Linear regression model

Ordinary Least Square (OLS)

Inference

Special regressors (dummy variables and interaction terms)

Specification issues

Regression diagnostics

Heteroskedasticity

Methods for Panel Data

Event Studies

Models with Fixed Effects