Introductory description

The module aims to equip students with the necessary tools to conduct independent empirical research in finance. In particular, the module touches on various topics that are relevant for writing the final dissertation. This includes a) an in-depth look at the relevant research methodologies in empirical finance, b) training in programming and quantitative analysis and c) introduction to various data sources available at or through WBS.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module contains two main parts. First, we focus on basic data collection and computer programming skills, including:

• Logic of programming
• Use of matrices, arrays, data types, structures and manipulation.
• Good programming practice.
• Algorithmic principles in applied research.
• Simple numerical methods for finance.
• Data storage and retrieval.

In order to deliver the basic insights to programming, we cover the following topics to illustrate applications:

• Statistical properties of asset prices.
• Volatility modelling and forecasting in finance.
• Risk measures and risk measurement using statistical methods.
  Later in the module, the focus shifts on applications relevant for research in in students' dissertation subject areas. The topics include empirical methods used in the fields of asset pricing as well as corporate finance such as:
• Asset pricing: Tests of asset pricing models, Fama-Macbeth regressions, constructing and optimizing portfolios.
• Corporate finance: Instrumental variables, quasi-experimental methods, diff-in-diff methods and event studies.

Prerequisites:

• Basic knowledge of statistics and mathematics is essential.
• However, no prior programming experience is necessary.
• Students need to ensure they have access to WRDS via WBS.

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate an in-depth comprehension of the current literature in the topic area, including the boundaries of current knowledge and where these may be extended.
• Understand the limitations of knowledge and current technologies in their chosen area.
• Critically review relevant literature leading to the formation of testable hypotheses.
• Synthesise and apply research methods and tools to real and practical examples.
• Develop, apply and interpret the outcome of appropriate tests of the hypotheses.
• Fully appreciate and thoroughly explain the limitations of the research project; be realistic in what conclusions can safely be drawn because of the research carried out.

Indicative reading list

Campbell, John Y., Andrew W. Lo, and A. Craig MacKinlay. The econometrics of financial markets. Princeton University press, 1997.
Hunt, Andrew, and David Thomas. The Pragmatic Programmer: From Journeyman to Master. 2nd ed. Addison-Wesley Professional, 2019.

Research element

The title implies a research element - the final project is a simplified research project.

International

Data used in the project can be international.

Subject specific skills

Recognise, critically analyse and discuss the merits of various approaches to finance, accounting and management research.
Understand the logic of programming, use of matrices, arrays, and data manipulation, including good programming practices.
Apply computer programming techniques to process financial and accounting databases and perform statistical tests.
Write a research project and report with an appropriate consideration of terminology, argument development and referencing.

Transferable skills

Use university/library resources to generate information/datasets for use in the project.