FIN 3400 Digital tools and financial analysis
FIN 3400 Digital tools and financial analysis
The modern world generates data at an incredible rate. This has profound impact on how businesses are run, investors make their decisions, and how we apply financial theory to navigate the world. This course provides a first introduction to essential quantitative tools to take advantage of data available and to analyze data through the lenses of financial theory.
To be ready for today’s competitive labor market and being able to quickly and precisely analyze and visualize data, students should master quantitative tools. Everyone uses software in the modern world, but we would be kidding yourself if we think that this only means spreadsheets such as Excel, OpenOffice or Google Sheets. Students give themselves a huge advantage if they learn to write programs -- to code.
In this course, students will therefore learn both to use spreadsheets (such as Excel, OpenOffice or Google Sheets), and elementary programming using R. R is today widely used in business, in general, and in the financial industry, in particular. It is a general programming language that also allows statistical analysis and handling of large datasets. R is open source, which has, at least, three advantages: it is a dynamic and evolving project with a huge amount of contributors around the world, there are extensive, freely available online resources, and it is free. R is a market leading programming language, and is used by e.g. NBIM (“Oljefondet”, Norges Bank Investment Management) and many financial institutions.
Students will learn to use spreadsheets and elementary coding through analyzing a set of topical questions in finance using data and visualization techniques. This course will be "hands-on" and prepare students to provide quantitative answers to question facing the organization they are working for. This course is intended to be both fun and interesting. Students will learn to work together. Several teaching methods will be utilized in the course.
Students will develop basic skills that will be useful in their studies of finance, and later, in professional life. In most entry-level positions, ability to efficiently gather and process data is a crucial skill. Spreadsheets are widely used and ability to use spreadsheets fast and efficiently are expected by almost all employers. Programming skills are also expected by more and more employers. Combining programming skills with financial insight is important in the financial industry, in business in general, and in public administration. Below you find examples where programming is useful:
- Analysis of companies’ investment and finance decisions (this also applies to non-profit organizations)
- Data based analysis for portfolio and investment strategies in financial markets
- Analysis of big data sets in order to provide insight before making important decisions
- Automating processes relevant for buying and selling stocks, foreign exchange, and other securities
- Financial advice
During the course students shall:
- Learn about the financial system
- Get to know financial aggregates for the macro economy and major financial variables such as return and turnover
- Learn about financial instruments, such as stocks, bonds and exchange rates
After completed course students will be able to:
- Be able to retrieve, process, and do simple data analysis to solve a specific problem
- Describe and present data in graphs and tables using R and spreadsheets
Throughout the course, students will develop important basic skills that are in high demand by employers. In this course, emphasis will be placed on learning a set of immediately practical and applicable skills. These skills will also be expected in other bachelor of finance courses.
- Introduce interesting issues that are highlighted in finance
- Understand the financial system and how different participants must relate to finance
- Introduction to R and spreadsheets (Excel/OpenOffice/Google sheets)
- Retrieve data for Excel and R. Use data for macroeconomics and financial markets
- Process data in Excel and R. "Clean data", merge data, create new variables (e.g. returns on boxes)
- Create figures and tables of data. Make simple summaries, such as averages and standard deviations
- Apply Excel and R to problems. For example:
- Compare wealth in different countries
- Compare returns on various financial instruments, such as equities and bonds
The course consists of lectures and task solving in class. There will be use of R and spreadsheets (Excel / Open Office / Google sheets).
The final grade in the course is a result of continuous evaluation of several examination components, process evaluation, during the semester. Each exam component is assessed on a scale of 0-100. The components are then weighted together according to the information in the course description, which then leads to final letter grade. Students who do not achieve points in one or more of the examination components will receive a lower grade or fail. Detailed information about the points system, and which letter grades they give, will be informed at the start of the course. In the case of continuation, all the examination components, as a general rule, must be re-executed at the next course completion.
The process evaluation includes one assignment that must be solved in groups, which are organized by the lecturer. In addition, there is a final individual exam. Specific evaluation information will be provided in class. It is the student's responsibility to obtain such information. Please note that while attendance is not mandatory, it is the student's responsibility to obtain information given in class.
The course uses R which is free (open source). Details regarding the installation of various packages and additional tools are given at the beginning of the course. In addition to R, spreadsheets (Excel, Open Office and Google sheets) are used. During the first two weeks of the course, students are expected to acquire skills similar to the online intro course, which is offered for free on the Portal.
Students will have access to web resources (R and Excel / Open Office / Google sheets) that will enable them to be well prepared when the course starts.
The course may be taught in English or Norwegian, depending on the available teaching skills. The exam paper will be given in both languages.
Higher Education Entrance Qualification
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
No specific prerequisites required.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 30 Grouping: Group/Individual (2 - 5) Duration: 3 Week(s) Comment: Assignment 1 Exam code: FIN 34001 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 70 Grouping: Individual Support materials:
Duration: 2 Hour(s) Comment: Final Exam Exam code: FIN 34001 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 30 Hour(s) | |
Seminar groups | 12 Hour(s) | |
Prepare for teaching | 83 Hour(s) | |
Group work / Assignments | 30 Hour(s) | |
Examination | 45 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.