Financialization and the Sociology of Finance
Course content
“It’s the economy, stupid,” is a slogan famously associated with Bill Clinton’s 1992 presidential campaign. But how does the economy actually work and how does it affect individuals and society?
In this course, designed for both undergraduate and master’s students, we address these questions by focusing on a critically important domain of the economy—namely, financial markets. Overall, the ambition is to provide a sociological understanding of why we have financial markets, who the key financial actors are, how financial markets are evolving, as well as how finance and financial logics affect individuals and society.
More specifically, the aim of the course is to introduce students to recent sociological discussions of two aspects of finance.
The first concerns “financialization,” that is, the growing use and importance of financial logics in otherwise non-financial fields. One example of this is when production companies generate more revenue from finance operations than from their core production activities. Another dimension of financialization relates to the everyday life of ordinary citizens who are increasingly embedded in financial logics. For example, we will discuss inequalities pertaining to the ability to obtain loans (credit).
The second aspect of the course focuses on the inner workings of contemporary financial markets. In addition to discussing the backdrop to the 2008 financial crisis, particular emphasis will be given to understanding how financial markets have transformed in light of automated trading, that is, fully automated algorithms acting in markets without direct human involvement. We will cover the main elements in present-day markets, including trading firms, financial exchanges, so-called dark pools, and financial regulation, as well as discuss the kinds of financial crashes automated trading might give rise to.
The course draws upon a combination of (1) classical sociological analyses and theories of financial markets (from Max Weber to Wayne Baker); (2) more recent sociological discussions of performativity in markets; (3) financialization literature; (4) and social studies of science-inspired analyses of automation.
Full-degree students enrolled at the Faculty of Social Science, UCPH
- Bachelor and Master Programmes in Political Science
- Master Programme in Social Science
Knowledge
At the end of the course, students will be able to:
- Understand fundamental aspects of financial markets;
- Identify key sociological theories and concepts that can help to explain how financialization and financial markets operate and affect everyday lives and societies;
- Examine how financial markets have evolved over time.
Skills
At the end of the course, students will be able to:
- Describe how finance contributes to inequality;
- Identify key actors within financial markets and how they operate;
- Analyze the role of technology in contemporary markets;
- Understand the mechanism behind select financial crises and crashes.
Competencies
At the end of the course, students will be able to:
- Discuss issues of finance and financialization, including in their historical evolution;
- Apply relevant sociological theories or concepts to understand the role of finance and financializaton in today’s society.
Teaching is based on a combination of lectures and discussion in class, where students are expected to participate actively. Students are also required to do one presentation in class. In addition, an excursion to a major financial institution is foreseen.
Readings will consist of journal papers and book chapters which will be made available on Absalon, including:
Baker, W. (1984) “The Social Structure of a National Securities Market,” American Journal of Sociology 89(4): 775–811.
De Goede, M. (2005) “Finance, Gambling, and Speculation,” pp. 47–85 in Virtue, Fortune, and Faith: A Genealogy of Finance. Minneapolis and London: University of Minnesota Press.
Granovetter, M. (1985) “Economic Action and Social Structure: The Problem of Embeddedness,” American Journal of Sociology 91(3): 481–510.
Knorr Cetina, K. and Bruegger, U. (2002) “Traders’ Engagement with Markets: A Post-social Relationship,” Theory, Culture & Society 19: 161–85.
Lin, K.-H. & Neely, M. T. (2020) “American Life in Debt,” pp. 111–36 in Divested: Inequality in the Age of Finance. Oxford: Oxford University Press.
MacKenzie, D. (2015) “Dark Markets,” London Review of Books 37(11): 29–32.
MacKenzie, D. (2018) “Material Signals: A Historical Sociology of High-Frequency Trading,” American Journal of Sociology 123(6): 1635–83.
MacKenzie, D. and Millo, Y. (2003) “Constructing a Market, Performing Theory: The Historical Sociology of a Financial Derivatives Exchange,” American Journal of Sociology 109(1): 107–45.
Mattli, W. (2019) “Bad Governance in Fragmented Markets,” pp. 107–54 in Darkness by Design: The Hidden Power in Global Capital Markets. Princeton and Oxford: Princeton University Press.
van der Zwan, N. (2014) “Making sense of financialization,” Socio-Economic Review 12(1): 99–129.
Weber, M. (2000 [1894]) “Stock and Commodity Exchanges,” Theory and Society 29(3): 305–38.
Zaloom, C. (2003) “Ambiguous Numbers: Trading Technologies and Interpretation in Financial Markets,” American Ethnologist 30(2): 258–72.
Peer feedback. Students will receive feedback on their presentations.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment
- Type of assessment details
- The students are required to formulate their own exam questions
based on pre-defined guidelines provided by the teacher. Students
will receive the exam guidelines for formulating exam questions
during the ongoing semester. The teacher is required to provide at
least two exemplary exam questions that adhere to the guidelines.
The exam can be written individually or in groups of max. 4 students.
Length of the exam is 10 pages + 5 pages pr. extra group member. - Aid
- All aids allowed
Policy on the Use of Generative AI Software and Large Language Models in Exams
The Department of Sociology prohibits the use of generative AI software and large language models (AI/LLMs), such as ChatGPT, for generating novel and creative content in written exams. However, students may use AI/LLMs to enhance the presentation of their own original work, such as text editing, argument validation, or improving statistical programming code. Students must disclose if and how AI/LLMs have been used in an appendix, which will not count toward the page limit of the exam. This policy is in place to ensure that students’ written exams accurately reflect their own knowledge and understanding of the material.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Re-exam
-
Students have to write a new essay using the guidelines provided by the teacher.
NOTE!
This is an elective course. We can only guarantee that the exam can be taken during the 3 immediate exam periods after the course has ended.
Criteria for exam assessment
See learning outcome
- Category
- Hours
- Class Instruction
- 42
- English
- 42
Kursusinformation
- Language
- English
- Course number
- ASOA22101U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
Full Degree Master
- Duration
-
1 semester
- Placement
- Autumn
- Studyboard
- Department of Sociology, Study Council
Contracting department
- Department of Sociology
- Department of Political Science
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Christian Borch (3-676c6644777367326f7932686f)
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