The first part of the module (CFA Level II training) aims to prepare you to take the CFA Level II examination.
The second part of the module (Capstone project) is aimed at providing the knowledge and skills necessary to conduct an empirical investigation of key current financial issues. The main aims are to:
- allow you to demonstrate critical thinking and analytical skills;
- develop your ability to write an extended report based on empirical analysis;
- prepare you for a career in particular specialist roles;
- provide a sound basis for future professional development and/or qualifications;
- relate cutting-edge research to recent and prospective developments in practice;
- give you the ability to address applied financial problems using quantitative methods of analysis;
- the ability to present written work, and to proceed to doctoral research.
Empirical Asset Pricing and Investment Strategies
The class introduces the fundamentals of investment strategies and some of the main strategies used by hedge funds and proprietary traders. In class and through reading exercises and discussions, the strategies are illustrated using real data, and you will learn to use “backtesting” to evaluate a strategy or fund performance. The class is highly quantitative. It requires you to work independently, analyse and manipulate large datasets, and use mathematical modelling.
During the five weeks (Week 1-5) of the Summer Term, you must attend all the lectures and training sessions designed to provide an introduction to the key literature and research methods. During and following this taught component, you will need to undertake independent research that (i) reviews the academic literature relating to investment strategies and market anomalies and (ii) reports empirical evidence of the effectiveness of a chosen investment strategy or asset pricing anomaly.
SAS, Python, and MATLAB will be introduced and used extensively for the chosen investment strategy. To undertake this module, you will need to achieve at least 65% on average for your first-semester coursework. High achievement in econometrics-related subjects, previous knowledge in at least one of the following programming languages or econometric packs (e.g. EVIEWS, VBA, STATA, MATLAB, SAS, Python, R, C++, Fortran, Gauss, Mathematica), or strong desire to input hard work to learn programming will be critical for high achievement in this dissertation topic.
This module will enable you to understand the key concepts and methods in data science, econometrics, and quantitative finance to carry out independent empirical work required for the job market, more advanced modules in accounting and finance and the MSc dissertation streams.
This module aims to prepare you to take the CFA Level I examination.