Economics, Finance and Data Analysis

The dissertation is a substantial piece of independent work where you can apply research techniques and relevant economic theory to a research topic. This can be an area which has attracted your attention in the course of your studies, or may be linked to an aspect of your professional working experience. You choose your topic during the second term, in consultation with the MSc Director. You are then assigned to an appropriate member of teaching staff who acts as a supervisor and gives you guidance on the structure and content of the research.

This module introduces you to the main techniques that are used for empirical analysis in fields ranging from microeconomics to macroeconomics and finance. The main goal is to teach you how to become both a producer and a critical consumer of empirical research. This is achieved by focusing on the practical implementation of econometric techniques.

The credit crunch and subsequent events have challenged modern financial theory, emphasising the need to develop better pricing and hedging models for financial products. They have also sparked renewed interest in how financial markets and institutions function. Striking a balance between theory and practice, this module gives you a firm grounding in the economics of financial markets, and covers a variety of finance topics, focusing on the valuation of stocks, bonds, derivatives (futures and forwards, options, swaps), portfolio management and hedging strategies.

Providing you with a strong foundation for understanding both the economics of financial markets and the main types of securities traded in these markets, this module focuses on bonds, futures, swaps and options. It strikes a balance between the theory and practice as well as making important links between models and the real world. The emphasis is on both principles and problem solving.

Topics covered include bonds, the economics of derivatives markets, futures and forwards, swaps, and options.

This module contributes to the following CFA syllabus areas:

• Securities Markets (CFA level I)

• Derivative Investments (CFA levels I and II)

• Debt Investments (CFA levels I, II and III)

The aim of this course is to provide students with an overview of the methods and models used in modern macroeconomics. The course introduces students to intertemporal decisions and presents workhorse models in modern quantitative macroeconomics, including the overlapping-generations model, economic growth theory in closed and open economies, the Real-Business-Cycle approach, and the Classical Monetary and New-Keynesian models.

The course will put emphasis on the link between the theory and data, and how to solve the model analytically and/or numerically (by using Matlab and Dynare software). By the end of this course, students should be able to choose the appropriate model and solve it to address a macroeconomic question. Within the course, we tackle some key macroeconomic questions such as the size of the fiscal multiplier in modern macroeconomics, the impact of technological change on labour, the cause of international differences in hours worked, and the impact of monetary policy, among others.

The aim of this course is to equip students with the tools necessary to enable them to make the core investment management decisions that managers face on a daily basis as well as the knowledge as to where they can find the information necessary to apply those tools. This course covers fundamental concepts and key issues in factor investing; equilibrium theories of asset pricing; mutual funds, ETFs and hedge funds; Environmental, Social and Governance; textual analysis in empirical asset pricing.

This module looks at what can happen to asset pricing in situations where market imperfections coincide with imperfections in investor rationality. It, therefore, explores the boundary between mispricing which can be exploited and that which cannot be exploited profitably.

The module lays the foundations for arbitrage, investment and wealth management, investment banking, and corporate finance. The material covered is at the frontier of academic and industry research, forming a conceptually advanced body of knowledge (CFA level III) which is of relevance for theory, research and practice.

Topics covered include:

• The efficient markets hypothesis and competing theories
• Limits to arbitrage
• Heuristics, biases and prospect theory: mental accounts and evidence in market prices
• Myopic loss aversion, disposition effect and overtrading
• Professional investors and analysts: over- and under-reaction
• Bubbles: observational and experimental, rational and non-rational
• Closed-end fund discounts, co-movement and sentiment
• The equity premium puzzle and the volatility puzzle
• Herding
• Behavioural portfolio theory

Turbulent decades of economic crises and increased volatilities across all asset classes have brought about innovative and strategic derivatives solutions to manage financial risk and create value. Their failure has typically been due to a lack of understanding of how to use and price derivatives. Understanding derivatives’ dynamics, risks, valuations and uses has become more important than ever.

Although there is a plethora of derivatives structures, the key to understanding derivatives is that all financial products, no matter how complex, are portfolios of just two fundamental building blocks: a swap (forward) and an option. This course provides this understanding in a rigorous, consistent and coherent framework.

This course provides an introduction to key concepts, tools, and methods in environmental economics, as well as to several key policy applications of these concepts, tools, and methods. You will learn the foundations that cut across environmental issues but you will also be exposed to some in-depth assessments of specific sectors where governments deal with complex decisions to address the market failures at the origin of environmental concerns.

Many policy discussions about the environment are plagued by conceptual shortcuts, lack of data or appropriate data analysis. The course will help you master the tools and methods that are relevant for solving market failures in environmental contexts and apply them to provide rigorous foundations to ground your views on current environmental issues. While the course emphasises public interventions (as is traditional in environmental economics), it also discusses the potential contributions of the private sector and individuals.

This module equips you with the latest skills in data science, financial econometrics and quantitative finance to analyse and model asset price dynamics using techniques at the research frontier in these areas, including the work with high-frequency data, relevant for the job market (especially in Quants) and the MSc dissertation streams.

It will teach you important features of financial time series, key modelling approaches in the field, how to use high-frequency data to construct the latest volatility estimators and the most appropriate methods for forecasting price volatility and risk. It will also give you practical experience of analysing market prices, constructing volatility estimators and designing forecasting analyses through empirical projects implemented with MatLab.

Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore, forecasting plays a vital role in enhancing managerial decision-making.

After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.

The course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the course is classification and uses the R programming language.

The aim of the module is to provide students with the hands-on time-series skills to competently estimate, test and interpret market-risk forecasting and control models & techniques which are required in the current regulatory environment: Value-at-Risk, Expected Shortfall, backtesting, extreme-value distributions, and copula models.

Government interventions in the economy are ubiquitous, ranging from direct and indirect provision of goods and services to regulations and standards. In this module, we explore two key questions.

The first question concerns how and when should governments intervene. The most obvious reason is that governments enter the economy to correct market failures and restore efficiency. This is particularly relevant in cases where goods generate positive externalities, such as education or culture, or negative externalities, such as pollution. However, restoring efficiency in the market often results in creating new distortions by taxing individuals and companies. Optimal policies need to take both costs into account, as well as problems such as tax evasion and tax avoidance. Another reason for government intervention is to address perceived economic inequalities resulting from free market outcomes. To look at this question, we need to understand how to formalize and measure preference for redistribution.

The second question we explore is where government decisions originate from in practice and what limitations exist on the implementation of optimal policies. This requires an examination of the incentives of voters and politicians. We also investigate the influence of media, special interests, and lobbying in politics.

Overall, this module combines classic economic models and contemporary empirical techniques to offer a comprehensive view of different economic approaches to the role of collective action. It is an essential resource for anyone interested in studying public policy and working in government, academia, international organisations, or regulated industries.

This module is designed to provide students who have no or little programming experience with introduction to Python programming in the context of academic research and real-life problem solving in accounting and finance. This module aims to develop students’ interests and confidence in financial programming and analysing big financial data, and to equip students with programming skills and data-driven problem-solving abilities.