The following compulsory courses are for the 2018/19 programme. Courses are subject to change.
Financial Markets and Investment
Semester 1
On this course you will become familiar with the structure of equity, bond and derivative markets and the main instruments traded in these markets.
The course is designed to introduce you to key financial markets and methods used to make portfolio investment decisions in these markets, by also focusing on specific topics that include: modern portfolio theory, asset pricing factor models, behavioral finance, fixed income valuation, the term structure of interest rates and risk management using derivative securities.
Financial Valuation
Semester 1
This course will give you an overview of the valuation of financial assets in the Information age, introducing you to the concept of measurement and how accounting information is used to assess the value of financial instruments.
You will learn to distinguish between relative and absolute methods to determine the value of financial assets, explore a range of tools and techniques, and gain understanding of the role of information and its effect on the dynamic relationship among assets, attitudes and behavior towards valuation.
Python Programming
Semester 1
The practical use of computing to support mathematics real-world problems.
This course will introduce you to modern programming concepts and practice using the computer language Python. In particular, the course will start with a presentation of programming basics including data types and structures as they exist in Python. Following this loops and conditional execution will be introduced and discussed - this will lead to a presentation of vectorisation and efficiency.
Object oriented programming will then be briefly introduced by working with predefined objects. Discussions will then follow on how to create objects and methods, along with a more in-depth debate on structured programming and ways to reuse code.
Data Value Chains and Constellations
Semester 1
In the digital economy the complex networks of people, artefacts and bots that feedback data allow for the co-creation of new forms of value. These dynamic relationships can be described not as value chains, but as value constellations.
This compulsory course will introduce you to how value is co-created in data value constellations and the implications for new business models in the digital economy. You will gain understanding of historical and contemporary concepts of value creation; learn methods in the mapping of value constellation from existing contexts; and develop skills in the design of new economic models for the digital economy.
Markets Design Policy
Semester 1
Markets are often reified – taken as given, with unspecified origins – in current policy and academic debates. This course will argue against this common misperception, therefore allowing you to understand the social anthropology and political economy of meanings and representations within which markets are embedded.
Through this course you will develop an interdisciplinary understanding of the formation of markets and regulation, and gain a historical perspective for understanding new financial instruments and technologies. You will examine the factors that lead to the creation and the scope of markets, highlighting the importance of the socio economic and institutional context of market behaviour.
Modelling, High Frequency Trading and the Sociology of Finance
Semester 2
Financial markets have been transformed radically since the 1970s, first by the widespread use of mathematical models, and then by fully automated, ultrafast ‘high-frequency trading’ or HFT. This course examines modelling and HFT from an economic sociology viewpoint.
On this course you will be introduced to major perspectives in economic sociology, especially those that also draw on the sociology of science and technology. Initially you will learn about high-frequency trading, followed by mathematical modelling.
Modern Financial Market Microstructure
Semester 2
Financial markets have undergone transformational changes over the past few decades; however, the last decade has witnessed the most significant changes in the way financial trading platforms operate. The changes induced by the declining costs of technology and changes in policy, hold significant implications for market structure in several respects. For example the growth of alternative platforms, as with algorithmic trading and high frequency trading, has been largely driven by advances in technology.
This course provides an advanced level microstructure analysis of modern financial markets as viewed through the lens of the technological transformation markets have been subjected to over the past decade. It will help you to gain an understanding of the nature of modern financial markets and how prices are formed within them as well as the impact that technology and policies have on named financial markets.
Introductory Applied Machine Learning
Semester 2
Organisations seek to make better decisions by examining their data with an aim to discovering and/or drawing conclusions and ultimately making informed business decisions.
On this course you will learn the principled application of machine learning techniques to extracting information from data. Providing you with a set of practical tools that can be applied to solve real - world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
Dissertation (Finance, Technology and Policy)
Semester 2
Undertake a critical review of literature or a piece of empirical research on a Finance, Technology and Policy related topic during which you will use the skills you have developed and acquired during the programme. Dissertations are to be submitted by the end of August.