AF7004 - Financial Econometrics and Forecasting I

What will I learn on this module?

This module will provide you the knowledge and skills of empirical finance that are important in the field of banking, finance and investment. The module will deliver you the knowledge of econometrics that is required to understand and analyse the real world financial data. You will learn about classical econometric models such as linear regression involving Ordinary Least Squares (OLS) method, time series regressions, hypothesis testing. Furthermore, you will be introduced to forecasting methods and their applications. You will also learn panel data analysis.

The content of the module that you will study comprises four key blocks, which are listed below.

Block 1 - Properties of financial data, Classical Linear Regression Methods, Time series models and Hypothesis testing
Block 2 – Introduction to forecasting methods in Finance and their applications such as forecasting stock market returns and trading strategies.
Block 3 – Application of Classical Linear Regression models
Block 4 – Panel Data Analysis

How will I learn on this module?

The module is supported by a teaching and learning plan which outlines the formal sessions, workshops in IT lab, together with tutor-directed study and independent reading. The emphasis will be on you having high levels of engagement in understanding theory, collecting and analysing real world data and interpreting results for forecasting financial markets. Independent learning will use both theory in finance and application of statistical software. You can therefore expect the reflective-practitioner approaches to learning that are both knowledge and skills based which are embedded in all delivery sessions.

The assessment requirements will engage you with a wide range of scholarly sources to evaluate your effectiveness and currency and subsequently communicate them in exam and assignment.

Your directed learning will centre upon a range of activities including pre-reading, preparation for interactive activities, practice in IT lab and use of the discussion board to learn and share knowledge.

Your independent learning will centre upon you identifying and pursuing areas of interest in relation to econometrics and financial forecasting and by providing deeper/broader knowledge and understanding of the subject through a range of learning activities that will include extended reading, reflection, research, statistical calculations etc.

Critical reflection on knowledge, experience and practice underpins the learning and teaching philosophy that you will be exposed to along with the explicit development of competence.

How will I be supported academically on this module?

The module will comprise lectures and IT lab based workshop activities where you will have access to real world financial data. IT lab workshops will teach you the use of the econometric package (EViews) in which you will be provided the data and information on key financial forecasting exercises.

You will also have access to Northumbria University’s library and databases. Academic content and delivery will be enhanced through opportunity for guest practitioner expert input. In addition, there will be an induction programme to introduce you to the university and the course. You will also be assigned a personal tutor to provide pastoral support and guidance throughout the course. Additionally, you will be supported by Academic Skills Unit (ASK), the feedback process of summative and formative assessments. At the start of the module, you will also be provided the module teaching and learning plan which details the delivery structure and activities.

The eLP will house lecture and workshop materials relating to the module. Additionally, you will find recorded lecture videos after each class that would be uploaded in the eLP to support you in your independent study. Where available you will also be given individual login access to use Bloomberg and training will be provided to learn various Bloomberg commands. Moreover, the use of Bloomberg is fully supported by a comprehensive range of materials which are housed on the eLP for your independent learning.

You will be provided with a wide-ranging electronic reading list that comprises of academic journal articles, web pages and youtube videos that showcase the application of various quantitative techniques presented in the module.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with.

The reading list for this module can be found at:

(Reading List service online guide for academic staff, this contains contact details for the Reading List team –

What will I be expected to achieve?

Knowledge & Understanding:
• To understand and critique important theories in financial forecasting. [MLO1]
• To be able to formulate, analyse and test various financial models. [MLO2]
• To be able to appreciate the relevance of theory for empirical applications. [MLO3]

Intellectual / Professional skills & abilities:
• To be able to understand and analyse the nature of real world financial data. [MLO4]
• To be able to use econometric software for the statistical analysis of data. [MLO5]

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):

How will I be assessed?

The module will be assessed by one summative assessment of 3000 words which will have 100% weightage.
(MLOs 1-5).
The assignment needs to be submitted electronically via electronic learning platform.
In this summative assignment there will be three sections. Each section will assess different topics within the module , which will require you to collect and clean data, perform preliminary data analysis, perform necessary econometric analysis, present results of the econometric models and discuss them critically within the context of existing literature.
Summative feedback mechanism will ensure that you obtain timely and adequate feedback on their works. In the case coursework, feedback will be provided in writing for each section of the questions.





Module abstract

You will acquire the knowledge and skills of econometrics that is required to understand and analyse the real world financial data. The module consists of topics such as linear regression and ARCH models (Nobel Prize was awarded in 2003 to Robert F. Engle for his contribution and research in this area) that are very useful in forecasting prices of financial markets. As such you will learn about the common empirical approaches in finance used by practitioners, alongside important skills of using econometric software. You will work in the IT lab each week for all your workshop sessions where you will be guided by qualified tutors. Additionally, you will learn to estimate profitability of your chosen trading strategy and also assess the stability of your returns for which you will make extensive use of Bloomberg terminals, Microsoft Excel and econometric software, Eviews.

Course info

Credits 20

Level of Study Postgraduate

Mode of Study 1 year full time
2 other options available

Department Newcastle Business School

Location City Campus, Northumbria University

City Newcastle

Start September 2024

Fee Information

Module Information

All information is accurate at the time of sharing. 

Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.  

Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.


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