Examines financial analytical methods for the analysis of cross-section and time-series data on stock returns.
Financial analytics is the in-depth analysis of large financial datasets using statistical methods and programming. It has a wide range of applications in investments and corporate finance, including assisting with the construction and evaluation of investment opportunities. Financial analytics is essential for students who wish to enter the investment banking and corporate finance professions. It also provides a strong base for research in finance.
|Paper title||Special Topic: Financial Analytics|
|Teaching period||Second Semester|
|Domestic Tuition Fees (NZD)||$1,101.55|
|International Tuition Fees (NZD)||$5,026.17|
- Recommended Preparation
- FINC 302 or FINC 308
- May not be credited with FINC 499 taken in 2019.
- Teaching staff
Lecturer: Edwin Ruan
- Paper Structure
The paper covers two key themes:
- Cross-sectional analysis
- Time-series analysis
- Teaching Arrangements
Stata software will be available for the students via Student Desktop platform to use during the lectures.
Empirical Asset Pricing: The Cross Section of Stock Returns by T. G. Bali, R. F. Engle, S. Murray, John Wiley & Sons, 2016.
E-book is available in library at
- Graduate Attributes Emphasised
Information Literacy, Independent Learning, Critical Thinking, Research, Written Communication, Oral Communication
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete the paper will be able to:
- Identify the sources of financial data (in particular, CRSP and Compustat via WRDS)
- Acquire the programming skills in a particular statistical software (e.g., Stata)
- Demonstrate the knowledge on cross-sectional analysis, such as portfolio analysis and Fama-Macbeth regression analysis
- Apply the knowledge on time-series analysis, such as in-sample analysis, out-of-sample analysis and asset allocation analysis
- Demonstrate appropriate financial analytics in solving financial investment problems in academic research and in practice (e.g. investment banks) by using a statistical software
- Acquire the ability to report, interpret and present the results