Full time 12 Month(s)
This one-year interdisciplinary MSc programme delivered by the Management School and the Faculty of Science and Technology is designed to give you in-depth knowledge of the problems and issues in the financial sector, and enables you to develop advanced analytical, problem-solving and technical skills.
The programme gives you access to expertise and facilities in different but related areas, and offers a wide range of potential topics for your summer dissertation. Optional modules also allow you to develop particular specialisms.
You will acquire skills in data and financial analysis, forecasting, optimisation, and computer programming. You’ll also become proficient in various statistical and econometrics packages.
You will study a range of modules as part of your course, some examples of which are listed below.
Due to their inherent randomness, it is natural to model financial and economic systems using probability models and stochastic processes. Analysis of appropriate stochastic models has become extremely important in recent years, such as for accurately pricing options. This module gives a thorough (but not too rigorous) introduction to stochastic processes in general and their use in modeling in business, finance and economic applications. Students will gain understanding about how both simulation and mathematical techniques can be used to learn about stochastic processes. Specifically the course will include
There is substantial amounts of data collected which relates to business, financial or economic applications. Examples include data on stock returns and survey data used for credit scoring.
This course will cover how such data can be modelled, how inferences about the models can be made, and how statistical models can be used for predicting future outcomes and behaviour.
Specifically, the course will cover
Implementation of analyses using R will be covered throughout the course. Statistical methods will be illustrated on applications including stochastic volatility and risk management.
This module will cover the following topics:
This module will initially introduce students to core concepts in finance like time value of money, net present value analysis and alternative investment rules to assess investment decisions taken by firms and then moves on to the introduction of basic concepts related to financial markets, including definitions of key assets and market types as well as an understanding of the economics of financial markets with a focus on their functions, participants and organisational forms.
This module will then provide students with a good understanding of fundamental theories and techniques in finance that are of concern to all financial market participants, such as bond markets and term structure of interest rates, economics of derivatives markets, forward and future contracts, swap agreements. The module will place a particular emphasis on understanding how quantitative methods and techniques are used in financial markets.
From May to July you will be working on your Masters dissertation with guidance from your academic supervisor.
The dissertation will be submitted at the beginning of September, at the end of the Masters Programme.
There is a wide choice of dissertation topics provided by all 4 departments.
The dissertation gives you the opportunity to apply all the theories and concepts you have seen during the year to a relevant research topic.
The aim of the module is to develop students' communication/presentation skills (oral and written), and generic skills which are needed in applied research in quantitative
finance when communicating/collaborating with colleagues from their own discipline and from other disciplines. It will provide students with an understanding of the structure of a scientific paper or a consultancy report, and equip them with an understanding of how a quantitative argument is presented in written form. It will give students a good knowledge of how to summarise finance/quantitative finance papers (newspaper articles or academic papers) both verbally and in written form. It will equip students with skills to analyse, interpret and conduct a small quantitative investigation.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
This module explains how econometric methods can be used to learn about the future behaviour of the prices of financial assets by using the information in the history of asset prices and in the prices of derivative securities. It also gives you practical experience of analysing market prices.
It will help you to understand the important features of time series of market prices, appreciate the relevance of efficient market theory to predicting prices, and make you familiar with appropriate methods for forecasting price volatility. You will also learn how to use option prices to make statements about the distributions of future asset prices, gain experience of applying computational methods in Excel to stock market and currency prices, and develop your knowledge of a broad range of econometric methods that are applied in finance research.
This module develops modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning.
The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour.
The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.
Beginning with the basic international parity relationships, this module examines the nature of business exposure to foreign exchange risk and the techniques available for hedging these risks. In addition to reviewing forward and futures contracts, several sessions are devoted to the theory and application of options contracts in the context of forex risk hedging.
Assessment of financial risk requires accurate estimates of the probability of rare events. For example, in the next day of trading what is the risk of a share portfolio losing half of its value, or equivalently what is the value of the portfolio at risk of being lost with a specified probability...? Estimating the probability of such "extreme" events is challenging, as by nature they are sufficiently rare that there is little direct empirical evidence on which to base inference. Instead we have to extrapolate based on the past frequency of the occurrence of less extreme events. This module covers ideas from Extreme Value Theory which give a sound mathematical basis to such extrapolation, and shows practically how it can be used to give accurate assessments of financial risk in a range of scenarios.
Note for non-accounting students:The specific study of the code of conduct for CFA can be replaced with self-studymaterials looking at the code of conduct for the Institute of Directors (IoD). The IFACcode is compulsory for all students as it represents one of the most advanced andinternationally accepted codes of conduct in existence for any profession, andtherefore acts as a useful exemplar for any student who needs to understand anyother codes, later in their professional life.
This module focuses on how financial theories are applied to investment management decisions. It will also critically analyse various portfolio management approaches used by professional investors in order to understand the strengths and weaknesses of these approaches.
Although not intended to track the Chartered Financial Analysts syllabus, the module should prove useful to those intending to take this qualification or looking to enter the investment management industry as a portfolio manager or security analyst.
This module contributes to the following CFA syllabus areas:
This module provides extensive coverage of methods used for valuing derivative securities in the investment banking industry, and includes an introduction to stochastic calculus.
Topics covered include:
This module contributes to the following CFA syllabus areas:
The aim of the module is to provide students with the hands-on time-series skills to competently estimate, test and interpret market-risk forecasting and control models & techniques which are required in the current regulatory environment: Value-at-Risk, Expected Shortfall, backtesting, extreme-value distributions, and copula models.
Stochastic Calculus is a theory that enables the calculation of integrals with respect to stochastic processes. It has wide-ranging applications, which have been particularly fruitful in mathematical finance. This module begins with the study of continuous-time stochastic processes, focusing on Brownian motion. Along the way, key concepts such as martingales and stopping times are encountered. The module then explores how to construct an integral with respect to Brownian motion. This leads on to the derivation of Ito's formula, a stochastic analogue of the chain rule, which is then used in the definition and solution of stochastic differential equations (SDEs), the stochastic analogue to ordinary differential equations (ODEs). The theory is then used to rigorously derive the Black-Scholes Formula for pricing financial options.
This module extends the analytical tools used for evaluating strategic and investment decisions learnt in other modules by deviating from the paradigm of rational decision making. It focuses on the implications of investor behaviour and capital market imperfections (such as limits to arbitrage) for investment management. The concepts you will cover on this module provide a foundation for value investing, arbitrage, asset management and opportunistic corporate finance. Insights from psychology and behavioural finance are used to complement traditional market frictions and explain the behaviour of capital markets.
Python is a simple, yet very powerful, high level computer programming language that becomes immensely popular in our days. It is widely used in many scientific areas for data exploration and at the same time it is the preferred programming language among a wide range of modern organisations.
This course is an introduction to computer programming using Python for students without any prior programming experience. It introduces the basic principles of computer programming but is doing it with an emphasis on examples from the areas of business analytics and finance.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
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