Postgraduate Course: Financial Networks (INFR11283)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | What if you could predict how a financial crisis unfolds before it even begins? In this course, you will dive into the networks that shape the financial world, discovering how connections between institutions, markets, and people drive it. Rather than focusing on individual entities, you will explore the web of relationships influencing everything from financial contagions to the rise and fall of cryptocurrencies, and spending behaviour. Through practical examples, you will build a toolkit for analysing complex financial systems. Whether you are interested in banking, digital finance, or economic policy, this course offers fresh insights into finance.
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| Course description |
Financial Networks will be delivered through a combination of lectures and hands-on group tutorials. Students will be expected to complete both pen-and-pencil and programming-based exercises in their own time as well as during tutorials.
Topics covered in this course will broadly cover these three topics:
1) Network analytics
¿ Introduction to network science
¿ Different types of financial networks
¿ Network metrics and communities
¿ Programming tools for network analysis
2) Network visualisation
¿ Complexity reduction through visualisation
¿ Reading financial networks
¿ Tools to visualise financial networks
3) Applications*:
¿ Banking
¿ Transactions
¿ Cryptocurrencies and digital finance
¿ Risk contagion and economic policy
¿ Investing
*Applications and datasets might change every year to reflect the current financial landscape. This list includes example topics that could be covered.
During tutorials, students will learn how to use the most popular Python packages to analyse and visualise financial networks. They will also work in groups to discuss the financial and economic implications of their results, equipping them with a practical skillset which can be immediately transferred to industry. These tutorials also prepare students for the coursework, in which students will be assigned a real-world dataset and will be asked to analyse it to provide practical and actionable insights, mimicking a work environment.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically analyse and explain complex financial systems based on empirical observations.
- Select and justify appropriate network analytics techniques for specific financial network tasks.
- Implement network analytics methods to uncover insights into financial behaviours and dynamics.
- Evaluate how network structures influence financial performance and risk, interpreting the implications for various financial scenarios.
- Present highly interdisciplinary work in an understandable and comprehensive manner to people with different backgrounds.
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Reading List
Menczer et al A first course in network science - Cambridge University Press (2020)
Delli Gatti et al. Agent-based models in economics: a toolkit - Cambridge University Press (2018) |
Additional Information
| Graduate Attributes and Skills |
During this course, students will develop a number of personal attributes/generic transferrable skills, including, but not necessarily limited to:
- Problem solving
- Critical thinking
- Analytical thinking
- Information elicitation
- Information filtering
- Decision making
- Independent learning
- Teamwork
- Written communication
- Cross-disciplinary communication |
| Keywords | Human behaviour,Social networks,Agent-based modelling,Data Science |
Contacts
| Course organiser | Dr Valerio Restocchi
Tel:
Email: V.Restocchi@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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