Quantitative Methods for Finance and Business

• Credit value: 15 credits at Level 7
• Convenor: Professor Emmanuel Mamatzakis
• Assessment: a two-hour examination (100%)

In this module you will gain skills in statistical modelling techniques and explore the application of these techniques in finance-related topics and research applications. The aim is to provide you with the analytical and programming skills to pursue empirical studies in finance.

Indicative syllabus

• Introduction to quantitative methods for finance and business
• Inferential statistics based on the relationship between two variables, regression analysis
• Multivariate regression analysis, time series and financial forecasting models
• Introduction to panel data analysis, pooled regression, cross-section times series
• Panel regressions analysis: fixed effects, random effects models, Hausman test, instrumental variable estimation, GMM, 2SLS, 3SLS
• Advanced financial econometrics VAR model, panel VAR models, dynamic panel models (DPD)
• Panel models of frontier analysis: DEA, SFA (parametric vs nonparametric estimation)
• Artificial neural network, deep learning, artificial neural networks, support vector machine fuzzy logic

By the end of this module, you should be able to:

• identify and understand the basic statistical principles
• apply existing models of financial data
• use technical and research skills to tackle specific problems in the area of empirical finance
• apply statistical and computing tools in the analysis of financial data
• critically analyse relevant issues in finance and investment
• apply econometrics analysis in analysing financial data.