Instructor information:

Clinton Watkins Ph.D   Professor, Global Business Program, Akita International University

Web-site   Office: A4-7

Office Hours: Tuesday 14:00-15:00 or by appointment

Course information

Mode: On-line   Credits: 1

Lecture schedule: 12:30 – 15:15 on 5, 7, 12, 14 and 17 January.

Description

This is an intensive 1 credit workshop-style course that gives students hands-on experience in analysing and modelling financial data, and interpreting the results of analyses. Students will work with financial data from the Nikkei NEEDS database that is used by analysts in the finance industry. The course focuses on the analysis of Japanese stock market and listed corporation data. Analyses will be conducted using the statistical programing language R, and will include the estimation of financial econometric models. Students will conduct analyses during the workshop sessions on their own computers, and use R Notebooks to produce HTLM documents containing their code, results and interpretation. This process develops the understanding of basic concepts related to producing verifiable and reproducible empirical analysis. GB students planning to conduct research in Financial Economics or Financial Management in their Capstone are strongly encourage to take this course to explore data and learn how to construct and interpret empirical models.

Objectives

1. Develop students’ abilities to do empirical analyses and interpret results.
2. Gain an understanding of stock market data and selected financial models.
3. Gain familiarity with the methodology of empirical research and coding in R.

AILA Elements

The course connects the disciplines of accounting, economics, econometrics, finance and statistics while also broadly relating to the analysis of data and empirical methods and methodology across the fields of business and other areas. In particular, the course aims to provide students with an understanding of data, empirical methods, methodology and practical coding skills that will be useful in their research on a broad range of topics in the ECN401 Capstone Seminar (2008 Curriculum students) and AILA III and IV (2021 Curriculum students).

Reading & references

Reading materials will be provided in class.

Finance glossary: A good on-line reference for financial terms is Investopedia. Make use of Investopedia to look up finance words. Finance textbooks such as Madura (2018) and Pilbeam (2010) contain lists of financial terms.

References for data analysis and econometric methods include: Baltagi (2005), Gujarati (2011), Kleiber and Zeileis (2008), Wooldridge (2012).

AIU students can access Nikkei Telecom, a professional financial markets newswire service, via the Nakajima Library website. Nikkei Telecom has articles in Japanese and English, including some of the top stories each day from the Wall Street Journal. I recommend you check a few articles on Nikkei Telecom each day to familiarise yourself with developments in the financial markets and the terminology used. Access the service from 日経テレコン on the Nakajima Library Digital Resources site.

Assessment

Homework based on coding and interpreting financial analyses 60%, quiz on interpreting empirical results 30%, active contribution in class 10%.

Academic background

Ideally, students will have completed some or all of the following courses. MAT150 College Algebra or MAT250 Calculus, MAT200 Statistics and ECN210 Principles of Microeconomics. An introductory finance course, such as ECN230 International Financial Management, ECN301 Financial Theories and Applications, ECN330 Corporate Finance or similar finance course at another institution during Study Abroad. ECN220 Analysis of Economic Data would be helpful background.

Preparation

Students will need their own computer running Windows, Mac OS, or Linux. A second screen would be helpful during the workshop sessions. Students must download and install R and RStudio Desktop on their computer at least one week prior to the course following the instructions on my website.

Students should work through a set of simple preliminary exercises to familiarize themselves with R prior to the first class session. The exercises will be posted on the AIMS course page. Do this at least one week before the class begins to understand and iron out any difficulties with R.

Join the course Slack workspace prior to the first class session and use this to seek guidance regarding any difficulties with setting up R, the pre-course exercises or any other questions. An invite link to the Slack workspace is provided. Students are encouraged to collaborate with each other on setting up R, working through the pre-course exercises and throughout the course, but students should submit their own homework write-ups.

Integrity

Acts of Academic Dishonesty: In accord with AIU policies and good practices in higher education, acts of academic dishonesty such as plagiarism, cheating, forgery (on a paper, examination, test, or other assignment) will result in the failure of the course at a minimum. An act of academic dishonesty during the final examination or assignment in lieu of the final examination will result in failure of all courses registered in the relevant academic term. Cases of academic dishonesty will be reported to the Dean of Academic Affairs for relevant action.

Academic misconduct during on-line exams is taken very seriously and will be dealt with strictly. Remember that your good reputation takes a lifetime to build, but it can be destroyed with one stupid act.

Please participate in this course to the fullest extent of your ability and with your highest levels of personal and professional integrity.

Class schedule by day

1. Introduction to financial terms and concepts, R and RStudio, Nikkei NEEDS database.
2. Workshop Topic (1)
3. Workshop Topic (2)
4. Workshop Topic (3)
5. Workshop Topic (3), wrap-up and quiz.

The three Workshop Topics will be selected from the following topic list:

1. Linear regression models for finance.
2. Case studies of financial time series.
3. Factor analysis, including fundamental and principal components models.
4. Modelling and forecasting risk and volatility.
5. Panel data analyses in finance.
6. Screening by valuation ratios and forming quantile portfolios.
7. A simple corporate financial event study.
8. Analysis of corporate governance (data availability permitting).