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2019/20 Taught Postgraduate Module Catalogue

LUBS5952M Database and Data Management

15 creditsClass Size: 30

Module manager: Peng Li
Email: p.li@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

This module is not approved as an Elective

Module summary

This module covers the workflow of empirical research in accounting and finance. It equips students with key applied econometric skills using Stata and SAS software. Students will gain necessary skills required to download data from A&F research databases, clean and transform the extracted data, and to conduct data analysis (including variable construction, summary statistics, univariate test and regression models). By the end of this module, students should have the skills required to produce a formally assessed 3000-word empirical project and the knowledge required to apply the learnt skills to academic papers.

Objectives

This module aims to provide students with knowledge of popular accounting and finance databases and develop skills of computer software (Stata and SAS) to manipulate data and analyse data efficiently.

Learning outcomes
Upon completion of this module, students will be able to:
-- Demonstrate understanding through classes and by formal examination, of how to use key research databases and extract data for empirical analysis
-- Apply data management techniques to extracted data, including: knowledge of how to clean and transform data using the statistical software programs Stata and SAS
-- Critically evaluate econometric techniques relevant for finance and accounting research in order to produce an independent, and formally assessed, 3000-word empirical project


Syllabus

1. Introduction to databases
- Overview of accounting and financial databases, learn how to access to WRDS
- Use Stata and SAS to retrieve data from WRDS
2. CRSP and Compustat
- Understand CRSP and Compustat: universe, data availability, variables, sample selection
- Learn data cleaning to remove irrelevant and incorrect data
3. I/B/E/S and Thomson Reuters 13F
- Understand I/B/E/S and Thomson Reuters 13F: universe, variables, sample selection, data errors
- Learn Stata commands – data distribution, summary statistics, sort, variable generation
4. Datastream and Thomson One
- Learn how to download data from Datastream and Thomson One
- Use Stata and SAS to transpose data
5. Compustat Global and Bloomberg
- Learn how to extract data from Compustat Global and Bloomberg
- Learn Stata programming – logical operators, conditional statements, loop, macro, t-test
6. Execucomp and Corporate Governance
- Understand Execucomp: universe, variables, sample selection
- Learn how to run regression in Stata – OLS, panel data regression, standard error correction
7. Merge Databases
- Learn how to merge multiple datasets
- Use Stata and SAS to merge different datasets
8. Asset Pricing
- Stata application: capital asset pricing model (CAPM), Fama-French 3-factor model and Fama-French 5-factor model
9. Merge and Acquisition
- Stata application: long-term stock price performance of M&A
10. International Study
- Stata application: post-earnings-announcement-drift (PEAD)

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture102.0020.00
Private study hours130.00
Total Contact hours20.00
Total hours (100hr per 10 credits)150.00

Private study

A mixture of a core test (below) and carefully selected research/journal articles.
Core textbook:
- The Econometrics of Financial Markets, John Y. Campbell, Andrew W. Lo, & A. Craig MacKinlay, 1996
- Stata user guide: https://www.stata.com/manuals14/u.pdf

Research articles:
- A list of journal articles will be given in the first class.

Opportunities for Formative Feedback

The small group size means that students can ask questions and get immediate feedback in lectures.

Progress will be monitored by lecture activities and individual interaction.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Report3000 words empirical research in any of the topics: asset pricing, merge and acquisition and event study. It should include data source, sample description, summary statistics, univariate test and regression analysis.100.00
Total percentage (Assessment Coursework)100.00

The resit for this module will be by 100% coursework

Reading list

There is no reading list for this module

Last updated: 30/04/2018

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