In this project, students are required to show competence in the following areas:
- Identification of and summary of relevant existing literature
- Data extraction and preparation
- Estimation of econometric models
- Interpretation of results from econometric tests
- Clear and concise report writing
The project also incentivizes students to self-teach themselves basic data science and analytics skills.
To appreciate the recent demand for such skills by employers, refer to the articles below:
- JPMorgan’s requirement for new staff_ coding lessons_8Oct2018FT.pdf
- EY hiring Data Analytics and Decision Science Professional in London LinkedIn.pdf
- Contact the library to obtain the username and password for accessing databases on WRDS (Wharton Research Data Services).
- Download the data items for the requirements of this project from databases on WRDS. For example,
o Compustat Historical Segments data:
North America Fundamental Annual data:
- Detailed documentation on the databases and brief descriptions of the Compustat variables can be found in the Manuals and Overviews tab and the Variable Descriptions tab on the download pages above. Additional details on the variable definitions can be found with the Search tab at:
o Can also use the variable list in the Index tab to find variables; e.g., AT as Assets – Total.
- Replicate to the best you can the analysis for the whole panel B of Table II and also columns (1),(2), (5) and (6) of Tables III in Custodio (2014) using both the data of the paper’s sample period and of an expanded sample period up to and including Compustat fyear = 2018.
- The replication above should be done for all firms available in Compustat (as in the original study)and also for firms in your choice of a first-digit SIC industry.
- In case of difficulties, you may simplify the analysis and to the best you can explain (i) the difficulties, (ii) your constraints, and (iii) why the simplifications give the closest approximation feasible for implementation given your constraints.
- If a student wants, s/he may additionally replicate some of the analysis in Tables IV and V so long as the report is within the acceptable length (see below). Such voluntary additional work, if properly done, can earn bonus points to compensate for points lost due to mistakes in the required replications.
- You may use any software tools to complete this project. If no preference, you might want to invest time in a self-taught process to learn a data science tool such as Python or R. (Demo codes are provided to incentivize such students to get started in learning to use R in the RStudio Cloud environment.)
- Prepare a report containing around 10-17 numbered pages of main text in Times Roman 12pt,double-spaced that summarises and critically evaluates your method and findings. The main text should be constituted of sections equivalent to the below:
o Brief review of related literature (need to understand the key article in concern and closely related studies cited therein, such as Berger and Ofek 1995 and Campa and Kedia 2002)
o Data and descriptive statistics
o Replication of the required analysis (comprising of the description of the methodology and the findings)
The main text should be followed by the reference list, figures (if any), and result tables, in this sequence, and covered by an unnumbered title page with the report title, your name and student ID on it. These components following the main text have no page limit but are expected to be as concise as possible.
- You also need to provide a zip folder containing programming codes, Excel files, or the like as evidence of independent individual work for the project.
- The ARP is an individual-based independent self-learning project.
- Discussions with other students are encouraged. However, the execution of the project and the writing of the report must be performed by each student individually and independently.
- When running into difficulties, a student should make the best effort to resolve them independently.Usually a search over the internet for related discussions is the starting point of a journey toward finding the solutions. If solutions cannot be identified after serious attempts, the student should raise the issues for discussion in the ARP meetings.
References (more materials in a shared folder on OneDrive):
Berger, P.G., Ofek, E., 1995. Diversification’s effect on firm value. Journal of Financial Economics 37,39–65. https://doi.org/10.1016/0304-405X(94)00798-6
Campa, J.M., Kedia, S., 2002. Explaining the Diversification Discount. The Journal of Finance 57,1731–1762. https://doi.org/10.1111/1540-6261.00476
Custodio, C., 2014. Mergers and Acquisitions Accounting and the Diversification Discount. The Journal of Finance 69, 219–240. https://doi.org/10.1111/jofi.12108