Finance, both academic and practical, is a quantitative subject. Risk managers use data and statistical techniques to evaluate portfolios, investors must estimate the expected returns and risk contributions of potential investments and traders wish to forecast future movements in the levels of stock, bond and foreign exchange markets.
Understanding the fundamentals of statistics, mathematics and econometrics is thus vital to a successful career in finance and is also necessary for all students wishing to study for an MSc level award in finance.*
The Introduction to Quantitative Methods course is designed to equip students with essential statistical and mathematical tools. You will become familiar with the language of mathematics and statistics, and will cover important fields such as linear algebra, calculus, probability, inference and linear regression. The module is designed to enable you to understand the core language of mathematics and statistics, and to be able to apply the concepts to practical problems in business and finance. Thus this is not just a theory course; it also provides students with hands on experience of working with, manipulating and understanding financial data.
Introduction to Quantitative methods is taught by Dr Lorenzo Trapani, a researcher in financial econometrics and the Associated Dean for Teaching and Learning at Cass, and by Professor Richard Payne, an empirical finance researcher with a background in quantitative research, both in academia and as a quantitative equity trader.
*Students who have taken this course would, for example, be fully prepared to undertake the quantitative elements of a finance-related MSc at Cass (and at other top UK universities). Thus an undergraduate who has completed a degree in a non-quantitative subject can use this course as a primer that would help them to switch to studying finance at MSc level.
- Key terms and notation used in financial applications of mathematics and statistics
- The fundamentals of algebra and of calculus
- Basic statistics - for example, the notations of random variable, expectation, estimation and testing
- How to apply statistics, and mathematics, in the analysis of financial and accounting data.
On successful completion of this module, you will be expected to be able to:
Knowledge & Understanding
- Use the basic mathematics required to undertake the fundamental types of calculation that will be required in the study of finance.
- Demonstrate the principles of statistical analysis and their applications to financial data
- Describe the different statistical methods which can be used to summarise and interpret financial data
- Discuss the underlying assumptions in statistical modelling and the dangers of ignoring them.
- Develop skills to enable statistical analysis of data.
- Apply statistical methods to facilitate answers to real world problems in a variety of practical settings
- Provide critical assessment of empirical research in the field.
- Develop and interpret empirical models that capture the stylized behaviour of financial data
- Develop and interpret empirical tests to assess the validity of finance theories.
Values & Attitudes
- Learn from the data but at the same time be self-critical and aware of the limitations of empirical analyses
- Support statements on the basis of empirical evidence
Module Leader: Professor Richard Payne
Richard holds a PhD in Economics from the London School of Economics and has worked at the LSE, the University of Bristol and Warwick Business School. He has also worked in the asset management industry and actively consults for industry clients.
Richard's research interests are in the areas of market microstructure, investment management, international finance and financial econometrics. He has worked on topics including the relationship between order flows and exchange rates, the effects of macroeconomic news on exchange rates and FX market activity and the operation of hybrid equity markets. At present he is working on topics including the impact of short-selling on stock markets and the effects of high-frequency trading on the quality of UK equity markets.
See Richard Payne discussing Computer Based Trading on a recent episode of Cass Talks.
The course is open to current undergraduates and recent graduates of any discipline. Students on this course are not expected to have previously studied
econometrics to advanced undergraduate level. It is recommended that students wishing to enrol on this course have mathematical skills to the equivalent of a UK A-level in Mathematics.
Students who have not studied a degree programme taught in English before are required to have an overall IELTS score of at least 6.5 (with a minimum of 6.5 in writing).