The MSc in Finance Analytics at King’s Business School is designed predominantly for graduates pursuing careers in financial data & analytics, either in large incumbent financial firms or one of the disruptors focused on financial innovation.
You’ll cover the cutting-edge financial principles and technologies utilised in the industry through the following selection of core modules:
Introduction to Analytics in Finance – Become familiar with the industry standard quantitative methods used to analyse financial data.
Principles of Finance – Discover key fundamental topics in finance, including fixed income, asset pricing, equities, derivatives and portfolio management.
Big Data Analytics & Visualisation – Learn state-of-the-art methodologies and explore complex topics in financial analytics, such as high-dimensional regression, model selection and forecasting.
Business Risk Analytics – Study how recent developments in analytics have transformed financial firms’ ability to manage new and complex business risk types.
You will also have the chance to deepen your knowledge in analytics through studying two of the following modules:
Big Data, Text Business & Financial Analytics - Understand the key methods used in machine learning, text analysis, and high dimensional inference, applied to real-world problems in finance.
FinTech Analytics & Robo Trading – Gain a detailed understanding of the FinTech, RegTech & Robo-trading landscape with coverage across all main areas of recent development, including Retail, Commercial and Capital market use-cases.
Conduct Risk Management – Learn how recent advancements in technology can facilitate efficient conduct risk management, against a backdrop of global regulation and the increased digitalisation of financial services.
You will also benefit from our range of optional modules, which will allow you to apply your learnings to wider topics in finance:
Empirical Finance – Compare of the effectiveness of alternative financial theories about stock prices and returns with real world financial data.
Corporate Finance – Learn the methods used to value investments, make corporate financing decisions and evaluate policies corporates undertake to make cash returns or dividend decisions to stakeholders.
Financial Econometrics – Examine the relevant econometric techniques applied to key problems in finance.
Behavioural Finance – Examine the behavioural biases that financial market participants exhibit and study the resulting asset pricing anomalies.
Portfolio Management – Explore investment decision making and learn improved portfolio management techniques, specifically within an investment banking & asset management context.
You’ll also undertake a 15,000 word Research Project (Dissertation), where you will conduct your own in-depth research and apply the knowledge & methods gained from studying our taught modules.
Our MSc in Finance Analytics offers a unique combination of a rigorous academic programme that teaches the recent developments in data analytics and a unique practitioners’ view of how these developments are shaping the practical aspects of finance.
For example, the programme teaches big data methods with applications to risk management, product origination, and trading; text mining techniques with applications to forecasting; automation techniques with applications to robo-advice/trading; and FinTech developments in banking.
Graduates will thus gain knowledge and skills that are highly sought after by financial industry firms, including incumbent large financial institutions as well as disruptor/start-ups focusing on innovation. In addition, given the importance of analytics in a broader context, the course provides skills that can be used across a wide range of industries outside Finance, including e-commerce, hospitality, retail, pharmaceutical, and media.
Student employability is one of the main focuses of the Programme. Firstly, the Programme has been designed in close collaboration with leading financial industry practitioners. Second, active senior finance industry practitioners participate in the teaching of a large proportion of the modules. Third, the Programme offers additional ungraded modules that enhance students’ programming skills as well as their understanding of financial markets.
This course is best suited for students with strong mathematical skills and quantitative backgrounds, such as finance, mathematics, engineering, computer science, and economics.
As part of the course, you will also have the chance to take ungraded modules alongside the above modules. These will allow you to gain practical, real world financial skills geared towards maximising your employability once you graduate.
We currently offer ungraded modules in the key programming languages, software and environments for finance, including Matlab, Python, C++, R, VBA and Stata.