MSc Global Finance Analytics

Unlock the future of finance with data-driven insights and knowledge.
  • via Kings College London
  • Program Fee: £26,076

Course Overview

With today’s global financial landscape rapidly shaped by AI technology and analytics, it’s more important than ever for professionals to stay ahead of the curve. Have you got what it takes to meet this demand, and develop the skills sought after by employers worldwide?

Our 100% online Global Finance Analytics MSc is specially designed to help you achieve this. Combining core finance principles with cutting-edge analytics, you’ll be challenged to delve into the industry’s most pressing topics: artificial intelligence, investment analysis, big data and machine learning.

Under the guidance of leading academics, you’ll gain practical skills that can be applied to both current and future financial scenarios. The holistic knowledge you acquire will set you up for long-term success, and unlock rewarding career opportunities across the globe as the financial industry evolves. Embark on this transformative journey with us to invest in your future and stay ahead of the game.

Course details

Mode:100% online
Length:2 years (part-time)
Fees:£26,076
Start dates:January, April and September
Next welcome week:
Next start date:3 September 2024
Application deadline:6 August 2024

How you're assessed

Assessments are designed to test your knowledge, understanding and critical awareness of the topics discussed during the course. We’ll also look at your ability to analyse and apply specialist knowledge to practice. While these may vary between modules, they are likely to include one or more of the following: projects, group presentations, and written coursework, including essays, dissertations and a research project.

Projects

Group presentations

Written coursework (including essays, dissertations and a research project)

What are the entry requirements?

Standard Requirement: You should have programming knowledge and meet one of the following criteria:

  • A 2:1 honours degree (or above) in a business, finance or other quantitative subject area or international equivalent.
  • A 2:1 honours degree (or above) in any subject area or international equivalents and at least two years’ relevant professional experience.

Degree certificates or transcripts (including evidence of quantitative subject) will be required when submitting your application. If you’re required to provide evidence of your professional experience, you should also include a CV detailing your professional experience in the finance sector.

Non-Standard Entry Requirements: If you don’t meet the standard entry requirements, your application may still be considered and will be assessed on a case-by-case basis. This includes situations where you are applying based on professional experience and qualifications.

Non-standard applications will need to be supported by degree certificates or transcripts (where relevant). You’ll also need to provide a CV detailing your professional experience in the finance sector and your familiarity with (or knowledge of) coding and programming.

English Language Requirements: English language band: B 

To study at King’s, it is essential that you can communicate in English effectively in an academic environment. You’re usually required to provide certification of your competence in English before starting your studies.

Nationals of majority English speaking countries (as defined by the UKVI) who have permanently resided in this country are not usually required to complete an additional English language test. This is also the case for applicants who have successfully completed:

  • An undergraduate degree (at least three years duration) within five years of the course start date.
  • A postgraduate taught degree (at least one year) within five years of the course start date.
  • A PhD in a majority English-speaking country (as defined by the UKVI) within five years of the course start date.

Personal Statement and Supporting Information: Depending on your previous qualifications, you may need to submit a personal statement and a reference letter as part of your application.

You’ll need to submit a copy (or copies) of your official academic transcript(s), showing the subjects studied and marks obtained. If you have already completed your degree, copies of your official degree certificate will also be required. Applicants with academic documents issued in a language other than English, will need to submit both the original and official translation of their documents.

You’ll need to submit your CV as part of your application to highlight your experience.

Course modules

This module introduces the quantitative methods and financial econometrics used in banking and finance.

You’ll cover industry-standard techniques like Ordinary Least Squares estimation, Instrumental Variable estimation, Probability models, and more. Rather than simply learning about the methodologies, you’ll go further and focus on how they can be applied practically in empirical analyses.

In this module, you’ll gain a thorough understanding of investment management processes using industry-standard methods employed by banks and financial institutions.

Focusing on the practical application of finance theory, you’ll explore decision-making aspects such as investment rules, diversification, financial market theories, and risk management. By applying these to real-world scenarios, you’ll develop a profound awareness of how contemporary investment techniques drive progress in finance.

This module will introduce you to the basics of statistical programming. You’ll mostly use R and be introduced to Python. You’ll focus on the basic tasks of data loading, data preparation, data cleaning, basic statistical analysis and output visualisation.

Throughout this process, you’ll learn to write loops, simulate and analyse data, which will be useful in many subsequent modules.

This module is split into two parts. For the first half, you’ll compare financial theories (e.g., efficient markets, CAPM) with real-world data, analysing price and return behaviour in markets using statistical techniques.

The second half explores deviations, discussing popular investment strategies and assessing the potential for market outperformance. You’ll also briefly be introduced to and evaluate the relevance of modern digital assets like cryptocurrencies to traditional investors.

This module focuses on using traditional and modern computational methods for pricing and managing risk in financial derivatives. It covers simulation methods like Monte Carlo, and grid methods such as trees and Finite Differences. There is also a substantial programming component using Matlab and Python.

This module is a must for students aspiring to work with quant libraries in financial institutions, providing hands-on experience in pricing and risk management calculations.

Here, you’ll use the Statistical Programming module as a springboard for diving into more complex topics like high-dimensional regression, model selection, and forecasting. You’ll delve into state-of-the-art methodologies such as ridge regression, lasso, elastic net, and others, discussing their implications for analysing and forecasting high-dimensional data.

You’ll develop a comprehensive understanding of modern techniques, applications, and possibilities arising from recent advances in econometric theory. This knowledge will prove vital, should you opt for the research project module.

This module introduces high-dimensional inference, machine learning, and contemporary techniques. These include a variety of industry-standard methods, from lasso and ridge regression to neural nets and support vector machines. With an emphasis on practical applications, you’ll gain an in-depth understanding of each method’s theory.

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