Online, part-time
Global Finance Analytics MScUnlock the future of finance with data-driven insights and knowledge.
Key information
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Study mode: |
100% online |
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Duration: |
2 years, part time |
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Next start date: |
7 January 2025 |
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Application deadline: |
19 November 2024 |
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Intakes: |
January, May and September |
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Tuition fees: |
£26,076 * These tuition fees may be subject to increases in subsequent years of study, in line with King's terms and conditions. (funding options and instalments are available) |
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.
Apply nowWhy choose this online MSc?
Versatile skills
Develop highly transferable skills that prepare you for a wide range of specialisms, from standard financial analysis to leveraging advanced analytics tools.
Global perspectives
By connecting with an international cohort of like-minded learners, you’ll gain the breadth of insight required to navigate today’s intrinsically global financial landscape.
New career paths
This degree opens doors to many different opportunities, including risk management, investment analysis, financial modelling, and strategic decision-making.
What you'll study
This MSc course is grounded in practice and focuses on applying cutting-edge theories to contemporary industry.
As an MSc student, you’ll:
- Delve into the logic behind finance theory and translate your knowledge into real-world financial solutions.
- Gain an in-depth understanding of statistical programming, big data analytics, and deep learning through industry-standard applications and methods.
- Apply portfolio management concepts and methods to real business problems.
- Develop the acumen required to analyse models and decisions in a critical light.
Module overview
This online Global Finance Analytics MSc course is comprised of seven core modules, and ten optional modules. Each module counts as 15 credits apart from the Research Project, which is a 30-credit module. You’ll need to take a total of 180 credits to complete the course. The optional modules have been specifically designed to be studied in a non-linear order, with the order in which you’ll study the modules depending on when you begin the course.
All core modules on the course are compulsory and you must pass all your chosen modules to complete the award.
Core modules
The following modules are compulsory for MSc students.
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.
Optional modules
The following modules are optional. You’ll be required to select 75 credits in total to supplement the core modules above.
This module goes beyond standard accounting practices, focusing on the practical application of financial statement analysis. You'll explore its use in evaluating investments, assessing ongoing business value, and gauging corporate performance.
Additionally, you'll learn how it is used in critical operations like risk identification and management. You’ll become proficient in interpreting key financial statements, ratios, and recognising potential pitfalls.
This module introduces financial derivatives to students at all levels, covering key contracts like Futures, CDOs, CDS, and some Exotic Options. Focusing on practical use, pricing methods, and arbitrage concepts, you’ll learn why and how derivatives are used in industry for hedging and speculation.
You'll delve into various pricing approaches and applications, including the Binomial Tree Approach, the Black Scholes Model and the Monte Carlo Method for pricing financial derivatives.
This module focuses on practical risk management in the financial industry, exploring types of risks and effective mitigation tools and processes used by institutions.
It goes beyond risk identification, covering analyses of the financial system, key portfolio risks for banks, methods for managing diverse risk types, and an overview of main risk models for managers. By the end of this module, you'll have a solid understanding of contemporary risks in the financial sector.
This module introduces Corporate Finance and Mergers and Acquisitions, led by an experienced industry practitioner in M&A advisory and Merger Arbitrage Trading. It covers Corporate Finance professionals' responsibilities, focusing on modelling tools for valuing projects and companies.
You’ll also explore the roles of traders and portfolio managers in markets, particularly at hedge fund and asset management firms.
This module introduces econometric techniques in finance. You’ll explore empirical research on asset returns, market efficiency, and pricing models like CAPM, APT, and consumption-based CAPM. You'll focus on applying the methodology in practical empirical analyses of real finance issues. By the end of this module, you’ll have enhanced your analytical, report-writing, and critical research evaluation skills.
This module delves into the regulatory framework of Wealth Management for both companies and individuals in modern banking. Topics include client rights, complaints, money laundering, and best execution. You'll explore client profiling for building tailored wealth portfolios, and how they translate into investment guidelines. The module covers major asset classes, investment approaches, and wealth solutions such as insurance and pensions within the broader wealth portfolio context.
This module enhances investment decision-making and portfolio management, with practical insights and cutting-edge methodologies used by professional portfolio managers. It covers modelling asset price procedures, understanding empirical research findings, and addresses diverse issues relevant to portfolio management. By the end of this module, you'll master portfolio management and risk concepts. Consequently, you’ll be adept at constructing advanced portfolios, navigating business challenges, critically assessing global investment selections, and using various asset pricing models.
In this module, you’ll gain both theoretical and practical insights into the financial decision-making process for investors and traders, with a focus on electronic finance. Recognising the recent developments in financial markets, you’ll develop your understanding of the psychology influencing markets, investors, and traders when analysing and evaluating market dynamics.
This module introduces high dimensional inference and modern techniques used in text analysis. You’ll cover industry-standard methods for organisations that use big data. The module will provide a clear summary of the theory for each method, but the main focus will be on applying these methods to real-world scenarios.
This module aims to provide thorough training in applying data analytics to economics, banking, and finance problems. It covers a wide variety of cutting-edge techniques, including Classical OLS, Time Series Analysis, Machine Learning, and Volatility estimation.
Through real research examples, you’ll gain a clear understanding of writing a research project. You’ll explore a wide variety of topics ranging from investments, portfolio construction and corporate finance to big data analytics and cryptocurrencies. The module also presents comprehensive information on data availability in King’s Business School.
Meet your Programme Director
Dr Fotis Papailias
Programme Director, Senior Lecturer in Banking and Finance
Dr Fotis Papailias specialises in time series econometrics. His research focuses on analysing and forecasting financial and macroeconomic series, dependent data resampling procedures, portfolio selection, and technical trading.
His work has been published in reputable academic journals and has practical applications in investment strategies.
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
Projects
Group presentations
Group presentations
Written coursework
Written coursework
Entry requirements
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.
If you have a lower degree classification, or a degree in an unrelated subject, your application may be considered if you can demonstrate significant relevant work experience, or offer a related graduate qualification (such as a master's or PGDip).
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.
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 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.
For information on our English language requirements and whether you need to complete an English language test, please see our English Language requirements page.
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.
Not sure if you meet the requirements, or if the course is right for you? Speak to our team to get tailored support:
Discuss my optionsCareer options in Global Finance Analytics
The multidisciplinary nature of our MSc Global Finance Analytics course makes it perfect for a wide range of different professionals. Are you a finance professional looking to progress your career, specialise in the analytical areas of your discipline, or make a move into academia? You might also be a programming buff seeking to change your career trajectory towards financial services. As a student on our course, you’ll be able to achieve all these goals. As a graduate, you’ll also be well-positioned to enter a wide range of potential career paths, including:
- Risk Analyst
- Quantitative Risk Manager
- Investment Analyst
- Portfolio Manager
- Asset Manager
- Financial Data Analyst
- FinTech Analyst
- Data Scientist
- Quantitative Analyst
- Financial Engineer
- Compliance Analyst
- Regulatory Consultant
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