This module builds your personal and interpersonal skills alongside technical content. There are four elements to the module:

• Communication skills: How to present yourself to employers, build rapport and make a lasting positive impression, whether you are communicating face-to-face or in writing.
• Client Management skills: You will learn how to build relationships with different types of clients, how to interact with different types of personalities and how to manage difficult situations. These skills will enable you to provide excellent client service.
• Management and Leadership: How to manage and lead others.
• Technical content on the topic of ESG and how you can apply your communication skills to present this technical topic effectively.

The financial markets and the world of investment management have undergone significant change since the global financial crisis. Investment managers need to keep abreast of these developments and take advantage of such changes. This module introduces the key changes in market conditions, including high-frequency trading, dark pools and the move to bring off-exchange instruments such as credit derivatives, on exchange. Within the world of investment management, we look at developments in liability-driven investment, alternative investments and new approaches to diversification.

The key objectives are to:

• Endow students with an understanding of today’s financial markets, trading processes and decision-making (also on a high-frequency level)

• Endow students with the concepts and ideas of advanced portfolio analysis

• Endow students with an understanding of the subtleties of investment decisions and dealing with clients

• Endow students with the relevant quantitative skills in this area for further advanced studies

This module looks at the core tool set of the buy-side analyst, and emphasises that the key to adding value is in understanding where to find the ‘raw materials’ to include within the calculations, and the impact accounting policy choices have on the outcome. Estimation of intrinsic valuation is obtained by the application of valuation techniques. These techniques will determine the value based on assumptions of future performance and several other judgements. The base of these forecasts will be derived from the current financial position of a company. As what happened in the past is not necessarily what will happen in the future, an integrated financial model will allow research analysts to predict key indicators of performance and value by forecasting key financial drivers. It will further their ability to explore the potential developments of a company in the future. In addition to basic valuation techniques, insights are provided into how to use best practice to construct a good, professional model.

The module aims to address advanced financial reporting issues from both conceptual and technical angles within the context of International Financial Reporting Standards and US GAAP. It assesses the impact of accounting theory in resolving major reporting issues and examines the importance of relevant academic research to the area. Additionally, it examines the importance of financial reporting for business analysis.

The aim of the module is to provide a thorough understanding of the issues that are relevant to the asset management industry with special emphasis on understanding and measuring investment performance.

Our discussions will focus on mutual funds, hedge funds and exchange-traded funds with less emphasis given to other fund management vehicles. The course will not cover security analysis.

The purpose of this module is to give students a very solid grounding in the quantitative empirical research skills that are necessary in producing high-quality research, both in the academic as well as in the applied context, in the areas of empirical asset pricing and portfolio allocation.

The module aims to equip students with a good background in software packages and programming languages commonly used in the finance industry, such as Excel/VBA and R. In addition, it will provide training in advanced features of Bloomberg to retrieve and manage financial data and create reports. Participants are advised to use a Windows laptop for this course. MacBooks are not supported in this class.

The first part of the module (CFA Level II training) aims to prepare students 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 students to demonstrate critical thinking and analytical skills;
• develop in students the ability to write an extended report based on empirical analysis;
• prepare students 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 students 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 students learn to use “backtesting” to evaluate a strategy. The class is highly quantitative. It requires the students to work independently, analyse and manipulate large datasets, and use mathematical modelling.

During the five weeks (Week 1-5) of the Summer Term, it is compulsory for students to attend all the lectures and training sessions designed to provide an introduction to the key literature and research methods. Both during and following this taught component, students are required 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. This document provides details of the taught element of AcF 701.

Python and MATLAB will be introduced and used extensively for chosen investment strategy. Admitted students need to achieve at least 65% on average for their 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 students to take the CFA Level I examination.