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The purpose of this module is to provide you with the necessary skills to undertake quantitative research in finance. In particular, we focus on analysing financial markets and firms’ investment and financing decisions. Lectures will introduce a broad range of topics (e.g. ARCH/GARCH). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. The module is a compulsory module on the MSc Finance. The module introduces empirical methods used in finance and is a prerequisite for Advanced Time Series Modelling in the 2nd semester. In particular, cross-sectional, panel and time series methods are introduced and applied to financial data. The module will introduce methods developed in econometrics and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will only be offered on the MSc Finance. The module provides an introduction to time series modelling, which will be extended in the optional module Advance Time Series Modelling (MANG6297).

The purpose of this module is to provide you with the necessary skills to undertake quantitative research in finance. In particular, we focus on analysing financial markets and firms’ investment and financing decisions. Lectures will introduce a broad range of topics (e.g. ARCH/GARCH). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. The module is a compulsory module on the MSc Finance. The module introduces empirical methods used in finance and is a prerequisite for Advanced Time Series Modelling in the 2nd semester. In particular, cross-sectional, panel and time series methods are introduced and applied to financial data. The module will introduce methods developed in econometrics and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will only be offered on the MSc Finance. The module provides an introduction to time series modelling, which will be extended in the optional module Advance Time Series Modelling (MANG6297).

Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).

You will be introduced to a number of key statistical concepts and data presentation formats. Beginning with exposure to a variety of data types defining the nature and properties of data you are likely to encounter. Emphasis is placed on distinguishing between population parameters and sample statistics and exploring the nature of distributions. Aided via the introduction of R Studio, a dedicated statistical software, you will become familiar with the concept of central tendency and the measurement of variation, and how these may be presented graphically. Emphasis is placed on information transfer to aid presentations, essays, reports and dissertation.

A significant portion of the unit is given to developing your understanding of a variety of common statistical procedures including establishing the presence and strength of a relationships and standard approaches for determining if significant differences exist between groups within a variety of experimental designs. Central to this is the concept of hypotheses testing.

MANG6003 aims to develop statistical reasoning. Via a series of examples and activities, students are introduced to the idea of probability modelling and how it can be applied to aid decision making in uncertain situations, which are frequently encountered in organisations. On successful completion of this module, students should be able to collect relevant data and summarise the main features of an uncertain situation, to identify standard problems and analyse them with the correct statistical tools, to process and analyse data in a statistical computer package, to understand the risks involved in a decision which involves uncertainty, and quantify such risks. Students should also develop problem solving skills, modelling skills, become familiar with a standard statistical computer package (SPSS), and be able to interpret and critically evaluate statistical results.

Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).

Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).

MANG6003 aims to develop statistical reasoning. Via a series of examples and activities, students are introduced to the idea of probability modelling and how it can be applied to aid decision making in uncertain situations, which are frequently encountered in organisations. On successful completion of this module, students should be able to collect relevant data and summarise the main features of an uncertain situation, to identify standard problems and analyse them with the correct statistical tools, to process and analyse data in a statistical computer package, to understand the risks involved in a decision which involves uncertainty, and quantify such risks. Students should also develop problem solving skills, modelling skills, become familiar with a standard statistical computer package (SPSS), and be able to interpret and critically evaluate statistical results.

You will be introduced to a number of key statistical concepts and data presentation formats. Beginning with exposure to a variety of data types defining the nature and properties of data you are likely to encounter. Emphasis is placed on distinguishing between population parameters and sample statistics and exploring the nature of distributions. Aided via the introduction of R Studio, a dedicated statistical software, you will become familiar with the concept of central tendency and the measurement of variation, and how these may be presented graphically. Emphasis is placed on information transfer to aid presentations, essays, reports and dissertation.

A significant portion of the unit is given to developing your understanding of a variety of common statistical procedures including establishing the presence and strength of a relationships and standard approaches for determining if significant differences exist between groups within a variety of experimental designs. Central to this is the concept of hypotheses testing.

Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).