ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Empirical Corporate Finance and Accounting
Code ULMR802
Coordinator Dr M Kim
Finance and Accounting
Minjoo.Kim@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 8 FHEQ Second Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
ACFI901 Financial Econometrics 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

 12

         36
Timetable (if known)              
Private Study 114
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual research essay Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Assessment: Yes  0    100       

Aims

This module aims to introduce PhD students to econometric and statistical methods, essential for conducting advanced empirical research in corporate finance and accounting.

Learning Outcomes

(LO1) Students will be able to design and implement econometric analysis for research questions.

(LO2) Students will be able to understand the pros and cons of each econometric method.

(LO3) Students will be able to collect data from various data sources and manage them efficiently.

(LO4) Students will be able to understand the statistical characteristics of the data used in empirical analysis.

(LO5) Students will be able to identify a suitable econometric method that can control the statistical characteristics of the data used in empirical analysis.

(LO6) Students will be able to interpret the statistical implication of results by econometric analysis.

(LO7) Students will be able to critically evaluate research papers in the field of empirical corporate finance and accounting.

(S1) Research skills.
Students will develop research skills during lectures and seminars and through reading journal articles.

(S2) Problem solving skills.
Students will develop problem solving skills through problem sets and the coursework assignment.

(S3) IT skills.
Students will develop IT skills through learning STATA programming and data management with Excel.

(S4) Numeracy.
Students will develop numeracy through computation in the coursework assignment and the seminar tasks.

(S5) Written communication skills.
Students will develop written communication skills in the coursework assignment.

(S6) Lifelong learning.
Students will develop lifelong learning through the development of research capacity and critical thinking.

Teaching and Learning Strategies

Lectures x 24 hours (2 hours per week)

Seminars x 12 hours (1 hour per week)

Self-directed learning x 114 hours
Self-directed learning hours will be spent on reading the module textbooks and journal articles, working through examples, and solving problems and exercises additional to those covered in learning materials.

Syllabus
 

Basic Regression Analysis:
Linear regression models
Limitations: Omitted variables, endogeneity, sample selection
Other issues

Panel Data Analysis:
Fixed effects (FE) and random effects (RE)
Robust standard errors

Instrumental Variables:
Selection principles
Validity of instrumental variables
Other methods to address endogeneity

Matching Methods & Natural Experiments:
Treatment vs. control
Difference-in-Difference estimator
Propensity-score matching and entropy balancing.
Triple difference etc.

Regression Discontinuity Design

Nonlinear & Dynamic Models:
Limited dependent variables: Probit/Logit
Censored dependent variables: Tobit
Lagged dependent variables: GMM

Event Studies:
Corporate events: M&As, dividend announcement
Short-term and long-term event studies

Advanced Topics: Textual Analysis

Advanced Topics: Network Analysis

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.