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Quantitative Methods for Finance and Business


  • Credit value: 15 credits at Level 7
  • Convenor and tutor: Dr Christine Guo
  • Assessment: a two-hour examination (100%)

Module description

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

Learning objectives

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.