# FINANCIAL MODELLING - 2023/4

Module code: MANM525

## Module Overview

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.

DIAS Fabio (SBS)

### Module cap (Maximum number of students): N/A

Independent Learning Hours: 90

Lecture Hours: 10

Seminar Hours: 20

Guided Learning: 20

Captured Content: 10

Semester 2

None

## Module content

Indicative content includes, but not exhausted to:

• Introduction to Python

• Time-series modelling

• Panel data modelling

• Value at Risk

• Portfolio optimization

## Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Individual case study 30
Project (Group/Individual/Dissertation) Individual written project 70

Not applicable

## Assessment Strategy

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

Formative assessment

- 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

Feedback

- Individual one-to-one feedback on the case study

## Module aims

• 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

## Learning outcomes

 Attributes Developed 001 Evaluate the characteristics of financial data and their statistical properties KPT 002 Measure and compare the cost of capital for a company CKPT 003 Calibrate the variance-covariance matrix of returns and the risk to derive the optimal active and passive portfolio of securities CKPT 004 Apply simulation models to estimate the unknown distribution of financial security returns CKPT 005 Analyze and appraise dynamic properties of the volatility of financial assets CKPT

Attributes Developed

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

## Other information

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.

## Programmes this module appears in

Programme Semester Classification Qualifying conditions
Accounting and Finance MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Business Analytics MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
Investment Management MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
FinTech and Policy MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
International Financial Management MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module
International Corporate Finance MSc 2 Optional A weighted aggregate mark of 50% is required to pass the 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.