Postgraduate Induction Programme

We are pleased that you have carefully selected to come and study with us and are delighted to welcome you as a new student at School of Economics and Finance (SEF), Queen Mary University of London. All joint-programme students are welcome to attend our welcome week programme as scheduled by your home school. Outlined below is important information about programmes and induction.

*Please note this information is subject to change and additions.

Welcome week and induction timetable

Download the updated timetable - SEF PGT Welcome Week Schedule 2024-25 [PDF 274KB]

*Please note edits and updates may be made to the schedule, so please do keep an eye out on this web page. Please find details below and link to the Mile End Campus Map.

Welcome Week overview

• Induction Week begins Monday 16th September 2024 until Friday 27th September 2024. During this time, you will attend welcome talks, social events, pre-sessional lectures and classes and above all, you will have the opportunity to connect with your peers.
• Teaching of modules in the School of Economics and Finance (SEF) begins Monday 30th September 2024. The teaching timetable will be made available during or shortly after the induction weeks.
• Enrolment is the process of officially registering with the University, and it is compulsory that you do this before you start your course. You can find further details on the QMUL Enrolment website.
• Student ID Collection In order to collect your card you will need to have completed Pre-Enrolment and be Fully or Temporary Enrolled and be able to show/tell us your student ID number. See Collecting your Student ID Card for further details.
• Welcome Talk will be in the format of a live face-to-face session. Live streaming will not be available. The talk will cover information regarding the pre-sessional programme, module registration, the school handbook and facilities available to students. You will have the opportunity to ask us any question or if you need any further clarity.
• Welcome, orientation and social events: for all students such as a walking tour some of which are ticketed. Keep an eye on the above Induction table for registration links.
• University-wide welcome events for all students are also available on the QMSU website.

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Important Dates

View all important dates.

Programme Structure

Please refer to your individual programme page for information on the structure of modules you will undertake during the academic year.

Further details about individual module teaching structure shall be updated on the module page on QMplus. You can ask more about the teaching style during the Programme Welcome Talk scheduled during Welcome Week.

The MSc programmes involve self-study outside of your lectures and tutorials, such as working in the library and having informal discussions with other students. Overall, you should plan on lectures, tutorials and independent study taking up at least 35-40 hours a week.

E-Learning Training

The E-Learning Unit will be running a live induction session to introduce you to the e-learning technologies in use at QMUL. The training will offer students an insight how best to use QMPlus, Q-Review and other e-learning systems for their studies. A version of the training will be recorded and available after to students unable to attend or to revisit later. Please note - you are only required to attend one of the sessions.

Pre-sessional Maths and Stats Programme

We are providing you with a Pre-sessional Maths and Statistics programme before you start your main degree. We would recommend that you watch the lectures and tutorials and aim to complete the syllabus before the Induction week. During the Induction week, we will run Q&A sessions with the module leader and the tutors, which will be your opportunity to discuss the module.

Please visit QMPlus and QMPlus Hub to start your Pre-sessional programme.

This course provides you with the opportunity to:

Review key mathematical and statistical concepts and tools. Show examples of how these tools are used in Economics and Finance. Ensure a solid foundation for your study in the Msc program. The course is divided into 6 main topics. Each topic is introduced through recorded video lectures with supporting slides, followed by problem sets enabling you to test your knowledge and prepare for your chosen postgraduate programme.

Additionally, throughout this course we will blend in real world examples and applications of mathematical and statistical concepts within Economics and Finance.

Please visit QMPlus to start your Pre-sessional programme.

Professional Development Modules

In addition to the modules you take as part of your MSc programme, the School also provides the following courses to help you build upon your professional skills. These courses are not intended to provide academic credit towards your MSc but some of them would be recognised on your HEAR transcript. These extra-curricular opportunities are there to support your transition into the Finance sector and develop skills employers are looking for.

Previously we have run programmes such as The Queen Mary MSc Investment Fund (QUMMIF), AmplifyME Experience Finance, C++ for Finance, VBA/Excel for Finance, MATLAB for Finance, Financial Trading Programme, and Applied Portfolio Management. The list for 24/25 will be confirmed as soon as possible.

You will need to pre-book onto these sessions. The links to register will be available on QMPlus and we will send you reminders so that you do not miss these sessions. We strongly encourage you to attend if you can.

Below are two extremely popular Professional Development modules which you may wish to join at the beginning of the year.

Queen Mary MSc Investment Fund (QUMMIF)

Queen Mary MSc Investment Fund (QUMMIF) is a student-managed investment fund that currently has responsibility for over £40,000 of investments!

It is one of the only investment clubs of its kind in London, established for postgraduate students interested in obtaining relevant, practical work experience in trading and portfolio analysis.

A pre-recorded introductory video to this largely attended optional module will be available for you and in addition, we have organised a live Q&A session to provide you with an opportunity to ask us any questions or if you need any further clarity (please see the schedule below).

Read more about QUMMIF.

Financial Trading Programme

Finance Trading Programme (FTP) provides a working theoretical knowledge and a practical understanding of financial markets, trading strategies, risk and money management and trader analytics at the highest level.

A pre-recorded introductory video to this largely attended optional module will be available for you and in addition, we have organised a live Q&A session to provide you an opportunity to ask us any question or if you need any further clarity (please refer to the Welcome Week timetable).

Read more about the Financial Trading Programme.

Read more about all the Professional Development Modules taught throughout the year.

Reading List

For many programmes, the school will be providing access to e-books for compulsory modules.

Corporate Finance

• Berk, J. and Demarzo, P. (2016). Corporate Finance. (Global 3^rd/4^th/5^th edition). Pearson Education.

Investment Management & Primary Markets and Securities

• Zvi Bodie, Alex Kane & Alan Marcus Investments, McGraw-Hill Education; 11 edition (15 Oct. 2017).

Quantitative Methods in Finance & Financial Modelling in Excel

• Benninga, S. (2014). Financial Modeling. (4th edn). The MIT Press, Cambridge, Massachusetts.

Valuation

• Koller, T, Goedhart, M and D. Wessels (2015), Valuation: Measuring and Managing the Value of Companies, Wiley (6th Edition)

Practical Valuation

• Hillier, D., Ross, S. A., Westerfield, R., Jaffe, J. F. and Jordan, B. D. (2016). Corporate Finance. (3rd edition). McGraw-Hill Education, New York. 

Asset Pricing, Trading, and Portfolio Construction

• John Y. Campbell, Financial Decisions and Markets: A Course in Asset Pricing. Published: 10/31/2017

Introduction to Machine Learning

• Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, Inc.

Your Postgraduate Support Team

If you have any further questions please do not hesitate to contact the School: pgsefsupport@qmul.ac.uk

 

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