FINANCIAL MODELLING - 2023/4
Module code: MANM525
The module equips students with the knowledge and tools to implement financial models using Python. The course introduces students to the general principles of building financial models, as well as a number of specific financial modelling tools, including matrix calculations, optimization, regression analysis (both time-series modelling and panel data modelling), out-of-sample forecasting and simulation. These methods are applied to a range of practical problems in finance, including passive and active portfolio management, risk management and currency valuation. The emphasis of the course is on practical application of the theory, with lectures on each topic followed by in-depth practical classes, in which students work through real world problems using Python.
Surrey Business School
DIAS Fabio (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 90
Lecture Hours: 10
Seminar Hours: 20
Guided Learning: 20
Captured Content: 10
Prerequisites / Co-requisites
Indicative content includes, but not exhausted to:
- Introduction to Python
- Time-series modelling
- Panel data modelling
- Value at Risk
- Portfolio optimization
|Unit of assessment
|Individual case study
|Individual written project
The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to select, download and manipulate data; plan and undertake independent research, and interpret and present their findings in a professional manner.
Thus, the summative assessment for this module consists of:
- A practical based individual written case study to be completed by the end of the mid-term assessment week.
- Individual written final project
- Weekly online quizzes
- Informal advice and discussion during lectures, set activities, tutorials and weekly student feedback and consultation hours; timely response to student emails and questions prior to submission of class test and the exam
- Suggested solutions to weekly tutorial questions
- Individual one-to-one feedback on the case study
- Undertake a range of financial and statistical calculations (the OLS, Fixed Effects, Vector Autoregression (VAR) based Granger Causality tests) for a range of securities (including stocks, bonds, derivatives and currencies) in Python
- Estimate the optimal investment portfolio of securities (with and without investment constraints)
- Evaluate risk using Value at Risk (VaR) approach and forecast volatility of financial assets using the univariate GARCH model
|Evaluate the characteristics of financial data and their statistical properties
|Measure and compare the cost of capital for a company
|Calibrate the variance-covariance matrix of returns and the risk to derive the optimal active and passive portfolio of securities
|Apply simulation models to estimate the unknown distribution of financial security returns
|Analyze and appraise dynamic properties of the volatility of financial assets
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Facilitate students' understanding and application of financial data.
- Engage students with appropriate models to estimate return and risk to optimize portfolio allocation.
Lectures will establish key principles, tutorials to discuss concepts/theories and focus on practical application using Python. The teaching and learning methods include lectures, seminars, independent learning, and self-reflection. Specifically, the lectures intend to introduce key principles with support materials and reinforcement covering the topics in the above. In seminars, students will undertake interactive exercises in order to demonstrate the application of key principles using Python. It is expected that students will conduct wide readings, work with peers, conduct individual research, and perform reflective review in their independent learning. Formative feedback will be provided to students during these interactive sessions.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MANM525
Global and Cultural Capabilities: The content of this module is relevant to the finance industry in an international setting, with examples drawn from a number of global financial markets.
Digital Capabilities: Throughout the Programme students learn to navigate and utilise the Virtual Learning Environment @ Surrey (SurreyLearn) and develop proficiency in a plethora of digital resources and online databases, including Excel, Python, Bloomberg, Datastream, WRDS, Compustat, World Bank.
Employability: This is an applied module that covers the implementation of quantitative financial models that are widely used in financial institutions such as investment banks, mutual funds and hedge funds, and in non-financial corporations.
Sustainability: The sustainable operation of financial markets relies on the fact that security prices reflect the fair value of the future cash flows that they promise to pay. The fair valuation of security prices is maintained by the actions of investors, who exploit deviations from fair value through the implementation of financial models such as those covered in this module.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.