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2020-11-25 12:12
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-中国稻米

2020年11月25日发(作者:纪政)




School of

of Southampton Mathematics


University












DIPLOMA/MSc IN
OPERATIONAL
RESEARCH AND FINANCE




2006/2007


CONTENTS
1. Introduction

2. Staff

3. Structure of the Programme

4. Industrial Liaison Committee

5. MSc Courses
5.1 List of Courses
5.2 Core Courses
5.3 Compulsory Courses
5.4 Options in the School of Mathematics
5.5 Options in the School of Management

6. Assessment scheme
6.1 Diploma Assessment
6.2 Project Assessment
6.3 Examinations
6.4 Part-time Students
6.5 Illness/Personal Circumstances
6.6 Examination Timetables
6.7 Anonymous Marking
6.8 Results
6.9 Graduation
6.10 Appeals
6.11 External Examiner
6.12 Assignments

7. Project
7.1 General Arrangements
7.2 Project Supervision
7.2 Writing Dissertations
8. Facilities for Students
8.1 MSc Room and Computing Facilities
8.2 Photocopying

9. Personal Tutors

10. Safety Information
10.1 Safety at the University
10.2 Safety within the School of Mathematics
Appendix I Staff for OR and Finance Courses
Appendix II Calendar
Appendix III Industrial Committee
Appendix IV Examination Structure
Appendix V Examination Rubrics
Appendix VI Assignment Schedule
Appendix VII Guidelines on Writing Dissertations
Appendix VIII Plagiarism
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1. INTRODUCTION

This booklet attempts to answer queries you may have about the structure and organisation of the
course and the way in which it is assessed.

Please report any errors or omissions to Dr Huifu Xu.


2. STAFF

A list of the staff teaching on the programme, together with email addresses is given in Appendix I.
For 2006/7 the following positions will be held.
Room

Director of MSc Prof Russell Cheng 10021

Industrial Liaison Officer Mrs Gillian Groom

Timetable Organiser & Examinations Officer Dr Huifu Xu 11007

Computer Workshops Organiser Dr Jonathan Whitehead 10023

Seminar Arranger Dr Ian Rowley 10017

Benson Librarian Dr Jonathan Whitehead 10023

Postgraduate Administrator, School Office Mrs Debbie Hunter 5015


You will be allocated a member of the OR staff as a personal tutor (see Section 9).



Head of School Prof Alistair Fitt 6017

Director of the Graduate School: Dr Chris Howls 7011

School International Student Tutor Dr Wei Liu 6013

School Manager Miss Frances Hubbold 5019

School Computing Officer Miss Patricia Foderingham 8001

School Safety Officer Miss Patricia Foderingham 8001

2
3. STRUCTURE OF THE PROGRAMME

There will be an introductory talk on Friday 6 October at 2.00pm in Mathematics Lecture Room 5A.
This will be preceded at 12.30pm by lunch in the Staff Reading Room on level 4 for all new members
of the Graduate School including students on the MSc in Operational Research & Finance.

The taught part of the programme consists of core, compulsory courses and options in the first
semester. These are taught either in conventional mode (two or three lectures a week) or as three or six
half days. A timetable for the first semester will be available at the introductory talk. There will be
workshops in Week 11.

The book Operations Research: Applications and Algorithms by Wayne Winston published by
Duxbury is a good general work covering some of the topics taught in the course.

The second semester contains parts of three compulsory courses but consists mainly of options.
Students must choose 30 CATS points of options from the list of Operational Research and
Management options given on the next page. Options will be taught in whole day or half day mode, a
7.5 CATS point option will occupy time equivalent to one and a half or two days. A timetable for the
second semester will be issued towards the end of the first semester but you should be aware that some
of the options may take place in evenings or in the Easter vacation.

Other special events may be arranged during the year. Recruitment visits from various organisations
will also take place during the year.

It is anticipated that students will need to do a minimum of 40 hours work per week including
timetabled lectures, workshops, seminars and private study during the instructional part of the course.

During the taught part of the course, students should only have holidays during the Christmas and
Easter vacations.

Students should not take holidays during the project. There is usually time for a short break after the
last examination and before the project allocation “Open Day” (see 7.1), otherwise holidays should be
deferred until after the project has been submitted.



4. INDUSTRIAL LIAISON COMMITTEE

The MSc programme is supported by an Industrial Liaison Committee whose names are set out in
Appendix III. It is expected that you will have the opportunity to meet members of this committee on
several occasions during the year.

The first meeting of the Committee is planned for Tuesday 24 October. You will be invited to an
informal get together over a buffet lunch to meet them and members of the teaching staff from other
schools. After one of the meetings during the year, members of the Committee will probably run mock
job interviews for those students who wish to take part.


3
5. MSC COURSES

5.1 List of Courses SEMESTER Credit Points

CORE COURSES (these courses must be passed before a qualification can be obtained, see Section
6.1)
MATH6002 Deterministic OR Methods 1 15
MATH6004 Stochastic OR Methods 1 15
MANG6022 Corporate Finance 1 1 15

COMPULSORY COURSES (some compensation is possible if some of these courses are not passed,
see 6.1)
MATH6006 Statistical Methods 1/2 15
MATH6010 Spreadsheet and Database Modelling 1 4
MATH6003 Presenting Reports 1/2 6

MATH6005 Visual Basic for Applications 2 7.5
MANG6023 Corporate Finance 2 1 15

Total 92.5


OPTIONAL COURSES (30 Credit Points to be selected) (some compensation is possible of some of
your chosen courses are not passed, see 6.1)

Options in the School of Mathematics
MATH6011 Forecasting 2 7.5
MATH6017 Financial Portfolio Theory 2 7.5
MATH6018 Current Trends in IT 2 7.5
MATH6112 Computer Analysis of Data and Models 2 7.5
MATH6113 Constraint Satisfaction 2 7.5
MATH6116 Economics for OR/MS 2 7.5

Options in the School of Management
MANG6008 Quantitative Research in Finance 2 15
MANG6020 Financial Risk Management 2 15
MANG6045 Consultancy Skills 1/2 7.5
MANG6054 Credit Scoring and Data Mining 2 7.5
MANG6065 Project Management 2 7.5
MANG6100 Game Theory in Business 2 7.5
MANG6133 Managing Resources and Operations 2 15

Overall Total 122.5

4
5.2 CORE COURSES

MATH6002 – DETERMINISTIC OR METHODS
PROF CHRIS POTTS, DR JONATHAN WHITEHEAD AND DR HUIFU XU

Aims
This course aims to introduce the student to the main deterministic techniques that are used in operational
research, namely linear and integer programming, dynamic programming, machine scheduling, project
networks, and heuristics. The process of modelling problems of a practical nature as a linear or integer
program will be developed. Following an explanation of a standard version of the simplex method, some
of its variants will be introduced. The main ideas of linear programming duality will also be explained. A
computer workshop session trains students in the use of commercial linear programming software. The
branch and bound approach for solving integer programming problems will also be developed. Dynamic
programming will be introduced as a technique for tackling problems in which decisions can be made
sequentially. For machine scheduling, the main focus will be to introduce the main problem types, and
develop solution procedures for selected models. For project networks, the representation of projects as
networks and methods for analysing such networks will be covered. Following a discussion of the reasons
for using heuristic methods for complex problems, a discussion of the properties of good heuristics will be
given. Some of the design principles for heuristics will be explained, and local search heuristics will be
discussed.

Syllabus
Linear Programming. Model construction and modelling issues. Simplex method: two-phase algorithm,
dual simplex method, network simplex method. Duality: motivation and definitions, duality theorem,
complementary slackness, optimality testing. Sensitivity analysis: ranging for objective function
coefficients and right hand-side constraints, economic interpretation and applications. Parametric
programming.

Integer Programming. Modelling techniques using zero-one variables. Branch and bound algorithm for
integer programming. Applications of integer programming.

Dynamic Programming. Terminology including stages, states, recursion, principle of optimality.
Examples involving resource allocation, equipment replacement. Probabilistic dynamic programming:
illustrated by example in production planning. Computational considerations.

Machine Scheduling. Introduction to scheduling models: machine environment, constraints on jobs,
objective functions. Use of adjacent job interchange argument to derive SPT and EDD rules. Moore's
algorithm for minimising the number of late jobs. Scheduling with precedence constraints. Dispatching
rules for job shop scheduling: no idle and active schedule generation.

Project Networks. Drawing project networks. Analysing project networks: earliest and latest event times,
floats. PERT: assumptions, finding the probability of completion by a given time, drawbacks. Time/cost
trade-offs: linear programming solution, direct solution.

Heuristics. Design of heuristics: construction, relaxation, restriction, hierarchical decomposition,
improvement. Heuristics for the travelling salesman problem: nearest neighbour, savings, insertion,
greedy. Local search: neighbourhoods, simple descent, multi-start descent, iterated descent, simulated
annealing, tabu search. Genetic algorithms: crossover, mutation.

Reading
Chvatal, V. Linear Programming (Freeman)
Williams, H.P. Model Building in Mathematical Programming (Wiley)
Williams, H.P. Model Solving in Mathematical Programming (Wiley)
Hillier, F.S. & Lieberman, G .J. Introduction to Mathematical Programming (McGraw Hill)
Winston, W.L. Operations Research: Applications and Algorithms (Duxbury)
Pinedo, M. Scheduling: Theory, Algorithms and Systems (Prentice Hall)
French, S. Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop (Ellis

5
Horwood)

Assessment: 70% Closed Book Examination (2? hours); 30% Coursework (1 piece) on Linear/Integer
Programming, requiring development of a computer model in Xpress-MP and a written report.

MATH6004 – STOCHASTIC OR METHODS
DR CHRISTINE CURRIE AND DR PAUL HARPER
Aims
The main aim of this course is to provide the students with a grounding in the stochastic elements of
operations research. The course is divided into two parts, Stochastic OR Techniques and Simulation.

The Stochastic OR Techniques part introduces the concepts and applications of the following four
topics: queuing systems, inventory systems, reliability theory and decision theory. Models and
examples are also given to demonstrate applications of the topics.

The Simulation part introduces the concepts of, and approaches to, simulation modelling (including
Monte Carlo, discrete event and continuous simulation). Real-life applications are discussed and
computer workshops introduce the students to Simul8 and @Risk computer packages. The assignment
allows the students to develop and run simulation models of their own.

Syllabus
Queuing Systems. Basic elements of a queue, continuous time Markov process, exponential
distribution, and applications of queuing systems.

Inventory System. EOQ models, newsboy models, inventory model with stochastic demand.

Reliability Theory. Reliability of a component, reliability of a system, maintenance models, reliability
and economics.

Decision Theory. Bayes’ rule, value of information, Decision trees, and utility.

Simulation Methodology. Basic concepts of the modelling approach, simulation approaches
(stochastic/ deterministic, static/dynamic, continuous/discrete issues), Monte-Carlo and Discrete-Event
simulation (event, activity, process and three-phase methods), Systems Dynamics, @Risk computer and
Simul8 computer packages, applications.

The Modelling Process. Activity flow diagrams, validation and verification, experimental design,
analysis and interpretation of results.

Sampling Methods. Generation of random variables and random variates.

Reading
Winston, W.L. Operations Research: Applications and Algorithms (Duxbury)
Hillier, F. Introduction to Operations Research (McGraw-Hill)
Schaum. Outline of Operations Research
Ross, S.M. Applied Probability Models with Optimization Applications (Dover)
Nelson, B.L. Stochastic Modeling: Analysis & Simulation (Dover)
Ramakumar, R. Engineering Reliability (Prentice Hall)
Pidd, M. Computer Simulation in Management Science (Wiley)
Law, A.M. & Kelton, W.D. Simulation Modelling and Analysis (McGraw-Hill)

A
ssessment: 70% Closed Book Examination (2? hours) on all aspects of the course and 30%
Coursework on Simulation (an individual assignment) .



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MANG6022 – CORPORATE FINANCE 1
DR SIMON WOLFE
Aims
The aim of this course is for you to gain a sound understanding of corporate finance from a banker’s
perspective.

Learning Outcomes
On successful completion of the course, you will be able to:
? understand companies and their sources of finance
? understand present and future values
? understand convertible bonds and their uses
? understand capital structure and the associated cost of capital
? understand the techniques of asset-backed securitisation
? analyse critically theoretical material and case study evidence on the basis of an informed
knowledge
? develop logical and concise arguments and engage in independent enquiry grounded in appropriate
source material

Syllabus
1. An introduction to Business Finance and Interest rate mathematics
2. Sources of long- term finance
3. Convertible Bonds
4. Cost of Capital and Capital structure I and II
5. Asset Backed Securitisation

This course will involve 24 contact hours over a six-week period. Lectures are used to introduce and
develop knowledge of individual topics. Lectures may involve formal lecture using OHP, case studies,
with the remaining time being devoted to class exercises.
Reading
Brealey, R.A.& Myers, S.C. Principles of Corporate Finance, 7th Edition (McGraw-Hill). Core Text
Buckley, A., Ross, S., Westfield, J. and Jaffe, J. Corporate Finance Europe 1st Edition (McGraw Hill)
Elton, E.J. and Gruber, M.J. Modern Portfolio Theory and Investment Analysis, 6th Edition (Wiley)
Pike, R. and Neale, B. Corporate Finance and Investment (decisions and strategies), 4th Edition
(Prentice Hall)

Assessment: 70% Unseen written examination, 2hrs; 30% written piece of (group) course work, 4000
words. In addition, students should expect to participate in class presentations on selected material and
from time to time there will be spot tests.



7
5.3 COMPULSORY COURSES

MATH6006 – STATISTICAL METHODS
DR CHRISTINE CURRIE AND DR VESNA PERISIC

Aims
The main aim of the course is to provide the students, who may have no previous knowledge of
statistics or stochastic processes, with sufficient knowledge of these subjects to carry out simple
statistical procedures, and to develop simple stochastic models. The course is split in to three parts:
Statistics, Stochastic Processes and Multivariate Methods.

Syllabus
Statistics
1. Probability and distributions: random events and frequencies; probability axioms; random variables
and their distributions; measures of centre and spread.
2. Discrete random variables: Binomial distribution; Poisson distribution; Geometric and Negative
Binomial distributions; Chi-square test for goodness of fit.
3. Continuous random variables: revision of distribution theory, Exponential distribution; Gamma and
Normal distributions; lifetime distributions, hazard functions; Weibull distribution, component
failure example; QQ-plot.
4. Estimation and confidence intervals: Central Limit Theorem; parameter estimation (method of
moments); confidence interval for mean (large sample size); confidence interval for mean (small
sample size using student t); comparison of two means; introduction to hypothesis testing.
Stochastic Processes
1. Discrete time Markov chains: determining the state space; classification of states; finite absorbing
chains; finite ergodic chains; general finite chains.
2. Semi-Markov chains: Poisson processes; elementary renewal theorem; regenerative processes; semi-
Markov chains.
3. Continuous time Markov chains: global and detailed balance; forward and backward equations; birth-
death chains.
Multivariate Methods
1. Data and correlation matrices; multivariate hypothesis testing.
2. Principal component analysis.
3. Clustering and discriminant analysis.
4. Linear and multiple regression.

Reading
Statistics
Freund, J.E. Mathematical Statistics (Prentice Hall)
Hogg, R.V & Craig, A.G. Introduction to Mathematical Statistics (Prentice Hall)
Walpole, R .E., Myers, R.H., Myers, S.L. & Ye, K. Probability and Statistics for Engineers and
Scientists (Prentice Hall)
Stochastic Processes
Minh, D.L.P. Applied Probability Models (Duxbury). Core Text
Jones, P.W & Smith P. Stochastic Processes an Introduction (Arnold)
Grimmett, G.R. & Stirzaker, D.R. Probability and Random Processes (Oxford University Press)
Multivariate Methods
Bartholomew, D.J., Steele, F., Moustaki, J. & Galbraith, J.I. The Analysis and Interpretation of
Multivariate Data for Social Scientists (Chapman and Hall)
Flury, B. & Riedwyl, H. Multivariate Statistics: A Practical Approach (Cambridge Universty Press)
Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. Multivariate Data Analysis with Readings
(Macmillan)
Manly, B.F.J. Multivariate Statistical Methods (Chapman and Hall)

Assessment: 70% Closed Book Examination (2? hours), 30% Coursework (1 piece). This takes the form
of a poster and a short technical report on Multivariate Methods.)

8
MATH6010 – SPREADSHEET AND DATABASE MODELLING
DR JONATHAN WHITEHEAD

Aims
This course aims to:
? introduce students to the use of spreadsheets in OR modelling and analysis;
? show how complex data sets can be manipulated using a database package;
? convey the value of such spreadsheet methods for handling data sets and solving realistic practical
problems;
? enable students to carry out a project using spreadsheets.

Syllabus
Introduction to Excel spreadsheet as an integrated modelling package for data manipulation and
presentation; and exploration of mathematical, statistical and investigative
functions and tools available in Excel for carrying out such analyses will be introduced.

Reading
Lehmann, M. and Zeitz, P. Statistical Explorations with Microsoft? Excel (Duxbury)
Winston, W.L. Operations Research: Applications and Algorithms (Duxbury)
Barlow, J.F. Excel Model for Business and Operations Management (Wiley)
Liengme, B.V. A Guide to Microsoft Excel for Business and Management (Butterworth-Heinemann)
Weida, N., Richardson, R. & Vazsonyi, A. Operations Analysis Using Microsoft? Excel (Duxbury)
Rolland, F D. The Essence of Database (Prentice Hall)
Ritchie, C. Relational Database Principles (Letts Educational)

Assessment: Coursework (1 piece, requiring development of a spreadsheet model in Excel and written
documentation).


MATH6003 – PRESENTING REPORTS
CHRIS VEYSEY/DR CHRISTINE CURRIE

Aims
The course aims to develop the skills required for researching, writing and presenting a report in
operational research (OR). Students undertake a literature study on a topic in OR of their own choice.
They are required to produce a written report and to give a short oral presentation of their work. Lectures
to assist with this work are given on writing reports, presenting them orally and producing poster
sessions. Students are also given a session introducing them to the library facilities.

Syllabus
Introduction to the library and its facilities. Techniques for writing a technical report. Presenting reports
orally. Preparing a poster session.

Reading
Material relevant to the chosen topic from a variety of sources.

Assessment: Coursework (a report and an oral presentation on a chosen topic in OR).


9
MATH6005 – VISUAL BASIC FOR APPLICATIONS
DR JONATHAN WHITEHEAD

Aims
This course aims to teach students the fundamentals of writing structured computer programs,
applicable using any high level programming language. However, students will be shown the special
event driven features of Visual Basic for Application (VBA) that makes it especially versatile. The
course uses software engineering techniques to enforce the importance of good programming manners
and will review traditional computing algorithm analysis, design and implementation using VBA.

Syllabus
No prior programming experience is required. The course will cover the basic principles of
programming in a high level language.

The main focus will however be in developing a working facility of Visual Basic. The course will
cover a range of the most commonly used techniques and algorithms including technical calculations as
well as data manipulation, graphical users interface, file handling, object-oriented programming (OOP),
and integration with other packages such as Excel and Access.

Practical exercises are used to reinforce the ideas taught in the course, which will enable the students to
build up their own library of algorithms for use in other courses or in project work.

Reading
Albright, S.C. VBA for Modelers: Developing Decision Support Systems Using Microsoft Excel
(Duxbury)
Jones, P. Visual Basic: A Complete Course (Letts Educational)
Kerman, M.C. & and Brown, R.L. Computer Programming Fundamentals with Applications in Visual
Basic? (Addison Wesley)
Stephens, R. Ready-To-Run Visual Basic Algorithms (Wiley)
Knuth, D.E. The Art of Computer Programming, Volume 1 (Addison-Wesley)

Assessment: Coursework (1 piece) and weekly exercise sheets.


MANG6023 – CORPORATE FINANCE 2
DR IAN McMANUS
Aims
This unit aims to enhance your understanding of several key issues in corporate finance.

Learning Outcomes
On successful completion of the course, you will be able to:
? demonstrate a rigorous understanding of key concepts in investment appraisal, managing foreign
exchange exposure, corporate governance, managerial compensation and the market for corporate
control
? demonstrate qualitative and quantitative skills in evaluating alternative investments
? demonstrate teamwork skills in analysing a contemporary issue

Syllabus
1. Investment Appraisal
The capital budgeting process. Methods of investment appraisal. Identification of relevant cash
flows. Net present value. Discount rate. Internal rate of return. Capital rationing. Real options.
International investment decisions.
2. Managing Foreign Exchange Exposure
Foreign exchange exposure and the euro. Hedging and the value of the firm. Types of foreign
exchange exposure and methods of management. Transaction exposure. Accounting exposure.
Operating exposure.


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3. Corporate Governance
Corporate governance systems. Separation of ownership and control. Agency problems.
4. Managerial Compensation
Compensation systems. Types of incentive structure. Executive share options.

5. Mergers and Acquisitions
The market for corporate control.

Reading
Brealey, R.A. & Myers, S.C. Principles of Corporate Finance, 7th edition, (McGraw-Hill). Main
Course Text
Buckley, A., Ross, S.A., Westerfield, R.W. & Jaffe, J.F. Corporate Finance Europe, 1st edition
(McGraw-Hill)
Eiteman, D., Stonehill, A. & Moffett, M. Multinational Business Finance, 8th edition

Assessment: 70% examination (2hrs), 30% group project.





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5.4 OPTIONS IN THE SCHOOL OF MATHEMATICS

MATH6011 – FORECASTING
PROFESSOR RUSSELL CHENG AND HONORA SMITH

Aims
This course aims to:
? introduce students to time series models and associated forecasting methods
? show how such models and methods can be implemented on a spreadsheet to analyse time series data
? give an appreciation of the different fields of application of time series analysis and forecasting
? convey the value of such quantitatively based methods for solving realistic practical problems.

Syllabus
Time Series Models: decomposition, analysis and removal of trends and seasonality.
Exponential Smoothing Methods: single exponential, Holt and Holt-Winters methods.
Simple and Multiple Regression Techniques. Simple ARIMA Models.

Reading
Anderson, R.A., Sweeney, D.J. & Williams, T.A. An Introduction to Management Science (West
Publishing)
Makridakis, S., Wheelwright, S.C. & Hyndman, R.J. Forecasting: Methods and Applications (Wiley)
Robinson, A. Bath University. Forecasting Notes
/~masar/math0118/forecasting/
Gilchrist, W.G. Statistical Forecasting (Wiley)

Assessment : 100% Coursework. There will be one individual assignment. This will typically involve
the analysis of a data set using spreadsheet tools. The data will be taken from a real-life context and the
students will be required to present the results as if to a senior management board as well as providing a
technical report of the analysis used.


MATH6017 – FINANCIAL PORTFOLIO THEORY


PROFESSOR ALISTAIR FITT

Aims
The course aims to introduce the students to the basics of portfolio theory. Beginning with a summary of
the reasons why both private investors and large institutional investors might wish to own share
portfolios, the course progresses to consider how risk and return vary as share prices move and
introduces the student to the basics of Markowitz portfolio theory. Illustrative two-asset cases will then
be considered before the risk/reward diagram for an N asset portfolio is examined. The notions of short
selling and riskless assets will then be introduced to the student and incorporated into the theory. Finally,
the student will learn how to solve the general Markowitz portfolio problem to determine the Optimum
portfolio, the Capital Market Line and the Market Price of Risk. If time permits, discussion will also take
place of more advanced models of portfolio theory.

Syllabus
Investment in shares from the point of view of private investors, speculators and large investment
companies. The role of investment in pension funds. Definitions of mean and variance. The mean and
variance for sums of variables. How shares move relative to each other: covariance and correlation
coefficients. The advantages of portfolio diversification. The differences between negative, zero and
positive correlation and examples of shares that display these properties. Risk and reward for shares,
definition of the risk/reward diagram. Drawing risk/reward diagrams for portfolios with a small number of
assets. Particular two-asset cases. Generalisation to portfolios with N assets. The effect of short selling in
risk/reward diagrams. Including riskless assets in the analysis. Generation of portfolio possibilities region.
Analysis of a combination of risky and riskless assets: arbitrage. The general Markowitz portfolio
problem. Finding the CML, MPOR and optimal portfolio. (If time permits) asset risk and reward in the
presence of uncertainty, strong and weak efficient market hypotheses.

12

Reading
Elton, E.J. & Gruber, M.J. Modern Portfolio Theory and Investment Analysis (Wiley)
Blake, D. Financial Market Analysis (McGraw- Hill)
Merton, R.C. Continuous Time Finance (Blackwell)

Assessment: 100% Closed Book Examination (1? hours)


MATH6018 – CURRENT TRENDS IN IT
PROFESSOR PAUL LEWIS

Aims
This course aims to develop a knowledge of some broad issues in computing and information technology
(IT) which are of current interest and of potential benefit and to enhance the students’ skill in applying
some of the latest technology.

Syllabus
This course is an awareness course covering a variety of issues in IT including such topics as:
1. the world wide web;
2. artificial intelligence and knowledge processing;
3. agents and agent technology;
4. object oriented approaches to software engineering;
5. multimedia information handling;
6. communications and networks.

Reading
A web site with notes and links to articles and other web-based material will be provided.

Assessment: 100% Coursework (2 pieces: the first piece is to develop a modest but dynamic web site
relevant to some other aspect of the degree programme; the second piece is to write an essay on a
particular topic in IT).

MATH6112 – COMPUTER ANALYSIS OF DATA AND MODELS
PROFESSOR RUSSELL CHENG

Aims
The overarching aim of the course is to provide the student with a modern view of computer model
analysis and data analysis under uncertainty. The course gives a unified and comprehensive approach
to one of the most commonly occurring aspects in the use of OR in modelling systems – the assessment
of the validity of results and their sensitivity to uncertainty.

In particular the course will:
? show how the behaviour of models of systems is dependent on the probabilistic properties of data;
? introduce the student to a range of resampling methods including bootstrap and Bayesian
resampling methods for analysing data sets and computer models:
? convey the value of such quantitatively based methods for solving realistic practical problems.


Syllabus
Representational models and metamodels; bootstrap sampling and analysis techniques; Bayesian
sampling and analysis techniques; Specific applications of resampling techniques to (i) estimation and
confidence interval construction, (ii) model selection, (iii) model validation, (iv) time series analysis.

13
Reading
Urban Hjorth, J.S. Computer Intensive Statistical Methods. (Chapman & Hall).
Chernick, M.R. Bootstrap Methods, A Practioner’s Guide (Wiley)
Daviso, A.C. & Hinkley D.V. Bootstrap Methods and their Application (Cambridge University Press)

Assessment: 100% Coursework comprising one individual assignment. This will typically involve the
analysis of a data set using spreadsheet tools. The data will be taken from a real-life context and the
students will be required to present the results as if to a senior management board as well as providing a
technical report of the analysis used.


MATH6113 – CONSTRAINT SATISFACTION
PROFESSOR CHRIS POTTS

Aims
Whereas branch and bound is the standard operational research technique for solving integer
programming and combinatorial optimization problems, constraint satisfaction approaches are more
widely used within the computer science community. Both approaches have their advantages. This
course provides an introduction to constraint satisfaction, and covers modelling techniques and solution
approaches. Students will use a software package for solving constraint satisfaction problems.

Syllabus
The basics of constraint satisfaction: problem definition and terminology. Arc consistency. Search
algorithms: backtracking, forward checking and maintaining arc consistency (MAC). Applications in
scheduling and car sequencing. Modelling. Use of a commercial constraint satisfaction software
package.

Reading
Brailsford, S.C., Potts, C.N. & Smith, B.M. (1999). Constraint satisfaction problems: Algorithms and
applications. European Journal of Operational Research 119, 557-581

Assessment: 100% Coursework: Classwork 20% (1 piece), Assignment 80% (1 piece).

MATH6116 – ECONOMICS FOR OR/MS
DR MAX KWIEK

Aims
This course aims to introduce the students to the cognate discipline of economics, that is needed to
critically evaluate business and management problems. The way in which markets operate will be
described, with particular emphasis on the use of game theory to analyse decisions on prices and product
specification. Following a discussion of the criteria by which performance may be judged, situations
that result in market failure will be introduced. The issue of government intervention will also be
discussed.

Syllabus
The nature of economics. Perfect competition: the nature of perfect competition and measuring the degree
of competition. Monopoly: analysis of monopoly and policies for dealing with monopolies. Oligopoly
with homogeneous goods: game theory and Nash equilibrium, Cournot, Bertrand and Stackelberg models,
cartels and collusion. Entry and strategic entry deterrence: entry in the Cournot model, blockaded,
deterred and accommodated entry, and contestability.

Reading
Carlton, D & Perloff, J. Modern Industrial Organization (Scott Foresman)
Gibbons, R. A Primer in Game Theory (Prentice Hall)

Assessment requirements: 100% Coursework (several short pieces).

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5.5 OPTIONS IN THE SCHOOL OF MANAGEMENT

MANG6008 – QUANTITATIVE RESEARCH IN FINANCE
DR FRANK McGROARTY and DR IAN McMANUS
Aims
To provide you with the necessary skills to undertake quantitative research into the structure of
financial markets.

Learning outcomes
By the end of this course, you should be able to:
? Demonstrate understanding of the basic theory of financial econometrics.
? Demonstrate understanding of some specific applications of such theory.
? Apply such understanding to a specific empirical project.
? Demonstrate competence in using a basic econometrics software package.
? Demonstrate quantitative skills in evaluating numerical data.

Syllabus
Part 1: Introduction to the Theory of Econometrics
? Introduction to simple and multiple regression.
? Hypothesis testing and the use of diagnostic statistics.
? ARCH and GARCH models.
? Introduction to Microfit.

Part 2: Applications
? Non- Synchronous / Thin Trading
? Prices, Dividends and Returns
? Market Microstructure
? Volatility Forecasting

In addition, students will undertake an empirical project involving research design and the estimation
and testing of hypotheses using the menu- driven software package MicroFit.

Reading
Gujarati, C.N. (1995), Basic Econometrics, McGraw-Hill, 3
rd
Edition (International). Main text.
Campbell, J.Y., A.W. Lo and A.C. MacKinlay (1997), The Econometrics of Financial Markets,
Princeton University Press.
Cuthbertson, K. (1996), Quantitative Financial Economics, Wiley. Pesaran and Pesaran, Microfit,
Oxford Press.

Assessment: 70% Examination (2hrs). 30% Empirical project


MANG6020 – FINANCIAL RISK MANAGEMENT
DR GITA PERSAND


Aims
To explain the practical aspects of the risk management techniques employed in the financial services
industry. To compare the regulators’ demands with the needs of the investment banking world. To
provide sufficient theoretical and practical knowledge of data modelling techniques to enable you to
implement the fundamental market, credit and operational risk measurement models that are used in the
industry today.

This course will introduce you to the current debate concerning the appropriate level of capital required
for securities firms, before briefly describing the main tools available for financial risk management
and the types of risks associated with these instruments. We first look at the concept of hedging before
focusing in detail on risk measurement for financial firms, with particular emphasis on investment

15
banks and securities firms. This will include a discussion of the regulatory requirements for capital
adequacy and recent developments in the area of Value-at-Risk, with particular regard to J.P Morgan’s
RiskMetrics and CreditMetrics. More generally, the course will consider the trend towards the use of
internal models for risk management. We also review how these models are backtested (checking for
their adequacy) and stress tested (looking at worst case scenarios). Lastly, the modelling of operational
risk is examined.

Learning Outcomes
At the end of the course, you should be able to:
? understand regulatory issues concerning investment banks and securities firms
? understand how banks/firms try to control market risk through Value-at-Risk models
? apply internal risk management models to credit risk
? understand control for operational risk through regulation
? understand how banks/firms allocate capital among their departments in respect of both volatile and
calm market conditions.
? calculate a bank’s market, credit and operational risks based on regulatory principles and actual
data.

The course will consist of 10 weekly 2-hour lectures. In addition, questions will be posted on
BlackBoard. You are encouraged to attempt these questions. Answers will be posted after
approximately a week. Discussion threads will be set up for each of the syllabus topics, to which you
should post any questions that you have concerning the material.

Syllabus
1. Introduction to Financial Risk Management
2. A Brief Review of Derivatives
3. Hedging
4. Regulatory Capital Risk Requirements
5. Value-at-Risk Models
6. Value-at-Risk Models (cont’d)
7. Backtesting and Stress testing Value-at-Risk models
8. Credit Risk
9. Operational Risk
ations of Risk Management Systems

Reading

There is no single textbook that covers the course completely, but the following relate to much of the
materials covered in lectures and classes. More detailed reading lists will be given at the end of each
topic of the course.

Jorion, P. Value-at-Risk: The New Benchmark for Managing Financial Risk, 2nd Edition (McGraw-
Hill)
Dowd, K. Beyond Value-at-Risk: The New Science of Risk Management (Wiley).
Dowd, K. An Introduction to Market Risk Measurement (Wiley)
Allen, L. Boudoukh, J. & Saunders, A. Understanding Market, Credit and Operational Risk: The
Value-at-Risk Approach (Blackwell)
J.P Morgan.. RiskMetrics Technical Document (1996).
Saunders, A. Credit Risk Measurement: New Approaches to Value-at-Risk and Other Paradigms,
(Wiley)
King, J.L. Operational Risk: Measurement and Modelling (Wiley)
Alexander, C. Operational Risk: Regulation, Analysis and Management (Prentice Hall)
Joel Bessis, Risk Management in Banking, John Wiley and Sons, Second Edition, (2002).
J.P Morgan. CreditMetrics Technical Document (1997).
Ong, M. Internal Credit Risk Models (Risk Publications)
Alexander, C. Risk Management and Analysis: Measuring and Modelling Financial Risk (Wiley)

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Basle Committee on Banking Supervision, 1999b
,
Credit Risk Modelling: Current Practices and
Applications, BIS, Basle, Switzerland
Basle Committee on Banking Supervision, 1998d, Operational Risk Management, BIS, Basle,
Switzerland
Basle Committee on Banking Supervision, 1999c, A New Capital Adequacy Framework, BIS, Basle,
Switzerland

The Basle Committee on Banking Supervision papers can be downloaded from

Assessment: 70% unseen examination (2 hours); 30% coursework (1 essay, 5000 words)


MANG6045 – CONSULTANCY SKILLS
DR MIRELA SCHWARZ

Aims
This course aims to introduce a range of skills required to successfully engage in management
consultancy and to provide opportunities to apply particular skills.

Learning Outcomes
By the end of the course students will be able to
? understand and evaluate the range of skills required to successfully engage in management
consultancy
? apply certain skills in group work.
? critically analyse the different consultancy stages
? develop a consultancy proposal

Syllabus
1. Introduction & Trends in Consultancy
2. Marketing & Selling/ Developing a Proposal
3. Conducting Consultancy Assignment

The course will be taught through a range of methods such as lectures, class discussions, guided
background reading, small group work followed by group presentations and discussions, exploration of
case studies/papers and videos.

Reading
Cope, M. The Seven Cs of Consulting: the Definitive Guide to the Consulting Process, (Pearson). Core
Text

Block, P. Flawless Consulting (Pfeiffer).
Cockman, P., Evans, B. & Reynolds, P. Consulting for real people - a client-centred approach for
change agents and leaders (McGraw Hill)
Czerniawska, F. Management Consultancy in the 21st Century (MacMillan)
Gray, D.A. Profitable Consulting Business (Kogan Page)

Assessment: 100% Coursework comprising a 4000 word assignment.


MANG6054 – CREDIT SCORING AND DATA MINING
DR BART BAESENS

Aims
The course will start by defining the concept of Knowledge Discovery in Data (KDD) as consisting of
three steps: data preprocessing, data mining and post- processing. Next, we will zoom into the data
mining step and distinguish two types of data mining: descriptive data mining (e.g. clustering,
association and sequence rules) and predictive data mining (e.g. regression and classification). The

17
course will then illustrate how KDD can be successfully used to develop credit scoring applications,
where the aim is to distinguish good customers from bad customers (defaulters) given their
characteristics. The importance of developing good credit scoring models will be highlighted in the
context of the recently put forward Basel II guidelines. The theoretical concepts will be illustrated
using real-life credit scoring cases and the SAS Enterprise Miner software.

Learning outcomes
By the end of this course, students will be able to:
?
?
understand the potential of KDD and data mining for developing scorecards
?

?

work with the SAS Enterprise miner software to develop credit scoring solution
develop a scorecard using very advanced data mining techniques (e.g. neural networks)
?


understand the practical difficulties that arise when implementing scorecards
understand the cross- fertilisation potential to other business contexts (e.g. fraud detection,
bankruptcy prediction)

Syllabus
Introduction
1. Knowledge Discovery in Data
2. The KDD process model
3. Descriptive versus predictive data mining
4. Credit scoring: problem statement, origins and objectives
5. The Basle II regulation
6. Risk management
7. Consumer credit scoring, Behavioural Scoring, Collection Scoring, Bankruptcy Prediction
8. Risk Based Pricing (customization of credit products)
9. Customized scorecards versus generic scorecards
10. Developing scorecards
Data preprocessing
1. Selecting the sample
2. Segmentation
3. Example variables needed for application and behavioural scoring
4. Oversampling versus Undersampling
5. Credit scoring characteristics
(a) Application form characteristics
(b) Credit bureau characteristics
6. Reject inference
7. Definitions of good and bad
8. Binary versus three-way classification (good, bad, and indeterminate)
9. Outlier detection
10. Missing values
11. Nominal variables versus Ordinal variables
Data mining
1. Basic concepts of classification
2. Classification techniques
3. Overfitting versus generalisation
4. Input selection
5. Setting the cut-off
6. Measuring scorecard performance
Post processing
1. Reporting
2. Strategy curve
3. Profit scoring
4. Recalibrating scorecards
5. Tracking scorecards
Post processing
1. Reporting
2. Strategy curve

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3. Profit scoring
4. Recalibrating scorecards
5. Tracking scorecards

The course is delivered through pre-course reading and lectures. The various concepts will be
illustrated using real-life credit scoring data and the SAS Enterprise miner software.
Reading
Baesens B., Van Gestel T., Viaene S., Stepanova M., Suykens J. & Vanthienen J.

(2003).

Benchmarking State of the Art Classification Algorithms for Credit Scoring, Journal of the Operational
Research Society, 54, 627-635
Jacka S. & Hand D.J., Statistics in Finance (Edward Arnold)
SAS Institute, Data Mining using Enterprise Miner Software, A Case Study Approach.
Thomas L.C., Crook J.N. & Edelman D.B., Credit Scoring and Its Applications (SIAM Press)

Assessment: 100% Coursework.


MANG6065 – PROJECT MANAGEMENT
PROF TERRY WILLIAMS

Aims
This course aims to develop a competence in the core elements of project management. This includes the
technical aspects of managing operations; and the nature of project management including planning,
scheduling and control.

Learning Outcomes
By the end of the course, students will be able to
? organise resources and establish work plans and cost budgets for a project
? evaluate and report performance against project plans and budgets

Syllabus
Project management: planning and scheduling, organising resources, preparing cost budgets, evaluating
performance against project plans and budgets.

Reading
Maylor, H. Project Management (Media Edition with MS Project CD), 3rd edition (Pearson)

Assessment: 100% Coursework


MANG6100- GAME THEORY IN BUSINESS
PROFESSOR LYN THOMAS

Aims
The course will introduce you to the use of game theory in business applications. It will ensure an
understanding of the solution concepts used in game theory and how to calculate such solutions. It will
examine a number of different business and management problems and develop game theory models of
these problems and indicate how the solutions of these models are used in developing strategies to
address these problems


19

Learning outcomes
At the end of this course, students will be able to:
? understand the basic solution concepts of two person zero sum games, two person non-zero sum
games and n-person games
? apply methods for obtaining the solution of such games
? model a wide range of business problems as games, and how to solve and interpret the solutions of
such models
? analyse business problems and build game theory models of them
? Solve and interpret the solutions of game theory models
? understand the strengths and weaknesses of game theory as a modelling tool
? evaluate the appropriateness of game theory models in a number of areas of business

Syllabus
1. Introduction to two person zero sum games and their solution; simple applications to motivate ideas
of mixed strategies, maximum solution, equilibrium pairs. Methods of calculating solutions.
2. Introduction to non-cooperative non-zero-sum games and their solutions; introduction to
cooperative two person non- zero sum games and their solution.
3. Applications of non-cooperative games in business including auditing; pricing and marketing;
principal and agent situations; analysis of conflict in strategy analysis.
4. Auctions: design and bidding; Contract bidding; multi-item auctions including examples from
electricity supply telecommunications bandwidth and internet auctions like eBay.
5. Introduction and solution of n-person cooperative and non- cooperative games.
6. Applications of n-player games in business including cost allocation, congestion pricing and
network games.

Reading
Thomas, L.C. Game Theory and its Applications (Dover). Core Text
Luce R.D. & Raiffa H, Games and Decisions (Wiley)
Von Neumann J, & Morgenstern Theory of Games and Economic Behaviour (Princeton University
Press)
Fudenberg & Triole, Game Theory (MIT Press)
McMillan J., Games, Strategies and Managers (Wiley)
Davies, Game Theory: A Nontechnical Introduction (Wiley)
Rasmussen, Games and Information (Blackwell)

Assessment: 90% Examination. 10% performance on three teaching games.


MANG6133 – MANAGING RESOURCES AND OPERATIONS
PROF TERRY WILLIAMS AND PROF STEVE BROWN (External)

Aims
This course aims to provide and understanding of the role and practice of operations management for both
service and manufacturing organisations, and develop a competence in the core elements of project
management. This includes the study of; the strategic role of operations and the interface between
operations and the other functions of the organisation; behavioural considerations; the technical aspects of
managing operations; and the nature of project management including planning, scheduling and control.

Learning Outcomes
By the end of the course, students will be able to
? an appreciation of role of operations management both at a strategic level and for the effective
production of goods and services
? gained knowledge and insight into the various tools of operations management
? understand of the behavioural aspects of managing operations
? appreciate of the need and appropriate application of project management methodologies.
? understand of framework, theories and techniques of project management
? identify stakeholders and appreciate the various perceptions of project success or failure

20
? analyse time series data, use software to produce forecasts and evaluate the quality of the forecasts.
? identify and apply appropriate quality control statistical analysis.
? evaluate production and inventory control methodologies
? articulate the theories and concepts of supply chain management
? organise resources and establish work plans and cost budgets for a project
? evaluate and report performance against project plans and budgets

Syllabus
1. Strategic operations management
2. Supply chain management
3. Inventory and production control
4. Quality management
5. Project management

The sessions will consist of lectures, case studies, computer workshops, discussions and a business game.
The course is supported with assigned reading and practice example sheets.

Reading
Brown, S., Lamming, R., Bessant, J. & and Jones, P. Strategic Operations Management, 2nd edition
(Elsevier). Core Text
Maylor, H. Project Management (Media Edition with MS Project CD), 3rd edition (Pearson)

Assessment: 50% coursework (3000 words); 50% examination (2hrs).

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6 ASSESSMENT SCHEME

6.1 DIPLOMA ASSESSMENT

The Assessment for the MSc/Diploma will consist of both examination papers and assignments (until
June, the Diploma) and a project (June onwards) which converts the Diploma to an MSc.

In June, the Board of Examiners which consists of the Internal Examiners and the External Examiner
(see Section 6.12) will meet and recommend one of the following Diploma categories for each student:-

Pass with distinction. Proceed to project work.

Flag for distinction. Proceed to project work. (Note this category will not appear on pass lists.)

Pass. Proceed to project work.

Pass at Diploma level.

Fail.

Candidates who Pass with distinction or who are flagged for distinction are candidates for the MSc
with Distinction (see Section 6.2).

The pass mark for each course is 40%.

Core courses (see Section 5.1) must be passed. Students will be able to retake failed core courses in
June. If they fail any of these retakes, they will need to resit in the following year.

It is possible to compensate for failure in up to 30 Credit Points of non-core courses by obtaining
“good” marks in courses with at least an equivalent number of Credit Points. At the Diploma level this
will mean marks of at least 50%; to proceed to the MSc marks must be at least 60%.

A weighted average will be computed from all assignment and examination marks. In this average all
courses will be weighted in accordance with their Credit Points.

The conditions for each classification are as follows:

Weighted average at least

Pass at Diploma level 40

Pass. Proceed to project work. 50

Flag for distinction. Proceed to project work. 68

Pass with distinction. Proceed to project work. 70

Students who fail to achieve the required average by 3% or less, or who achieve the required average
but fail non- core courses for which they cannot be compensated, are allowed one referral. Referrals for
non- core courses will take place at the corresponding times during the next academic year. The
decision on which parts of the assessment are to be retaken will be decided by the Programme Director
in consultation with the candidate.

22
Candidates not being referred have an automatic right of resit. Resit assessments will take place at the
corresponding times during the next academic year. The decision on which parts of the assessment are
to be retaken will be decided by the Programme Director in consultation with the candidate.

A candidate who successfully completes courses totalling 60 Credit Points and achieves 40% or more
on aggregate on these courses, and who does not proceed for any reason to qualify for the Diploma is
eligible for the award of the Postgraduate Certificate.

After the January examinations, tutors (or the Programme Director) will inform you, either verbally or
in writing, if your progress is not satisfactory and advise on the action to be taken.


6.2 PROJECT ASSESSMENT

Projects will be assessed on a five-point scale; A*, A, B, C (pass) and F (fail). Candidates who pass
the Diploma with distinction will obtain an MSc with distinction if they submit a project graded A* or
A; those who are flagged for distinction will obtain an MSc with Distinction if they submit a project
graded A*.

School regulations require projects to be submitted by 30 September. If you submit your project after
30 September, you will fail unless in very exceptional circumstances you have obtained permission to
submit later. Applications (well in advance of the deadline) should be made in the first instance to
your project supervisor or the course director and will then, if supported, have to be approved by the
Programme Director (Prof Cheng). A project will be deemed to be submitted if it has been handed in
to the bindery on or before the due date and a receipt presented to the School Office.

The project is assessed by two internal examiners, normally the first and second supervisors. A report
is written by the first internal examiner and the recommendation discussed with the second internal
examiner. All the internal examiners' reports and recommendations are then sent to the External
Examiner and he also sees a selection of the dissertations. These will normally include:

All those graded F
All those graded C/F
Those where the two internal examiners disagree about the grade
Those of candidates flagged for distinction graded A* or A
Those of distinction candidates graded B or less
One representative A or A* and one representative B or C
Any others requested by the External Examiner


6.3 EXAMINATIONS

The examinations will be held in two sessions; the first 4 papers in January (in the third or fourth
weeks of the Spring term) and the final papers in May/June (in the sixth or later weeks of the Summer
term). For 2006/7 the examination periods will start on Monday 22 January 2007 and Tuesday 29 May
2007 (see Appendix II for the relationship between University weeks and calendar dates).

In the first semester, the three MATH papers (MATH6002, MATH6004 and MATH6006) will be of
2? hours duration and will be closed book (conventional) examinations (that is one where notes and
books may not be taken into the examination room). See Appendix IV for further details. Lecturers
may provide formula sheets for individual courses, and these will be specified in the rubric. The
rubrics on these papers are contained in Appendix V. Rubrics for other papers will be advised later.


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-青春不败


-馒头记


-独立人格


-评价量规


-高中英语教学设计


-戴望舒的诗



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