Accounting and Finance

The course covers key theoretical areas of financial accounting research. It provides a crucial link to the literature for students seeking to strengthen the theoretical foundations of their empirical research. The underlying perspective is an economic one and implicitly assumes a stock market setting defines the corporate financial information environment.

By the end of the course, students will be able to understand four fundamental theoretical areas that underpin research in financial accounting, namely mandatory financial disclosure, voluntary financial disclosure, the politics of accounting regulation, and agency and contracting theory.

The course covers four topics as follows:

• The social value of (mandatory) financial disclosure
• Voluntary financial disclosure
• The role of regulation in accounting research
• Agency and contracting theory

This module is designed to introduce students to some of the core issues associated with empirical accounting research through the review of recent trends in accounting research. The module will discuss the importance of incremental contribution, basic research design issues, issues with analysing data and tabulating results, and extending extant research. The module will comprise a mixture of faculty-led lectures, interactive student-led presentations and discussions.

Topics

• Accounting conservatism
• Recognition vs. disclosure
• IFRS adoption
• Bank accounting
• Financial instruments and fair value accounting
• Economic consequences of financial reporting and disclosure

This is a module in accounting research for first-year doctoral students. The module comprises nine weekly sessions, each based on a published academic paper (or suite of papers). Each session involves a student-led discussion of the paper(s), with faculty providing a facilitating role for the discussion. The aim is to understand key research contributions, methods, and opportunities for further research.

Topics:

• The determinants and consequences of accounting choice
• Various measures of earnings quality and their determinants and consequences
• Accounting anomalies and the use of accounting information in fundamental analysis
• The role of auditors as a corporate governance mechanism
• • The application of experimental methods to examine accounting issues
• • Understanding of behavioural theory in accounting research

The course covers various aspects of equilibrium asset prices in dynamic economies. The aims of this course are to introduce students to the main asset pricing theories that are fundamental to research development in finance and to provide an understanding of how asset pricing models are formally constructed.

On completion of this unit successful students will be able to have

1. an understanding of key elements in the mainstream asset pricing theories,
2. systematic knowledge in both implications of asset pricing models and empirical implementations.

Topics covered

• Utility preferences 2
• Mean-variance theory and the CAPM
• Stochastic discount factor
• Arrow-Debreu economy and state prices
• Dynamic programming
• Consumption-based asset pricing
• Production-based asset pricing.

This course takes place in the second term and builds upon the Manchester taught module AcF853, which is an advanced dominantly theoretical asset pricing course. Spillover effects from AcF851 Advanced Quantitative Methods are also anticipated.

In the first part of the module recent advances in cross-sectional asset pricing will be covered. This will include an empirical treatment of the classical asset pricing model and its extensions and related testing and model specification approaches. This part will cover the critical assessment of existing models in the light of risk-based and behaviour explanations as well as multi-factor models related to profitability and investment patterns across firms.

In the second part of the module will focus and the consumption-based model both from a theoretical and empirical perspective.

In the third part of the module asset pricing with option implied information will be covered. This includes the theoretical coverage, and understanding of the options data structure as well as the empirical implementation in advanced software packages.

The last part of the module will focus on mutual and hedge funds. This includes their performance evaluation and performance attribution from a mainly empirical perspective. Unique characteristics of hedge funds and various hedge fund strategies will be covered.

The topics covered in this module will be fine-tuned to the specific decomposition of each year's PhD student cohort.

This course is designed to help students understand how to operationalize empirical research on topics that are central to corporate finance. The overarching goal is to expose students to “state-of-the-art” empirical research methods and prepare them to conduct their own work using new tools. All students with an interest in finance, economics, accounting, human resources, and related disciplines are welcome to take the course.

The course will include:

• Endogeneity and Basic Identification Strategies
• Difference Estimators
• LATE and Regression Discontinuity Design
• Matching Estimators

This course introduces doctoral students to the knowledge and skills in empirical research on corporate finance. Topics include capital structure, corporate liquidity management, payout policies, and executive compensation. Selected papers in relevant literature will be discussed, with a focus on research ideas, data sources, and empirical methodologies. This course aims to help students identify and formulate research questions that may lead to dissertation topics.

After successfully completing the course students are expected to have:

• A broader knowledge of empirical research in corporate finance;
• A better understanding of how empirical techniques can be used to answer research questions in the areas covered in the course;
• More confidence in presenting and defending their own research;
• Formulate one research idea with concrete research agenda.

This course will cover econometric techniques on an advanced level that form the basis for quantitative and empirical research in Finance and Accounting. It will also cover the related necessary programming skills in advanced econometrics and statistical software packages such as MatLab and GAUSS. The course will start with covering two key areas: i) Generalized Methods of Moments and ii) Maximum Likelihood Techniques in a) time series models, b) multiple equations models and c) panel frameworks.

Depending on the composition of the students’ cohort additional advanced methods that will be covered in the course may include: Simulated Maximum Likelihood, Efficient Important Sampling, Indirect Inference, Bayesian Methods, High-Frequency Financial Econometrics, Point Processes, Count Data Models, Limited Dependent Variable models.

The success of developing and publishing accounting research critically depends on the knowledge of the data and programming skills to manage large datasets. The course will introduce students to key databases in accounting research, will train students how to use these data, and will develop students’ programming skills and applied econometrics. This module is designed to introduce students to the main research design choices and methods in empirical accounting research. The areas covered by the module include data collection and main financial databases, handling large datasets using STATA, analysing data and tabulating results. The module provides a broad overview of major data operations and statistical techniques used in accounting research and shows how to effectively program using STATA and other software packages. Further the module, introduces students to the Python and R programming languages.

This module covers the econometric theory and application of selected econometric methods. The first part (Weeks 1-5) covers Time Series Econometrics, Panel Data Econometrics and Financial Econometrics. The second part (Weeks 6-10) covers Panel Data extensions, Discrete choice models and Tobit models.

Topics:

• Time Series Econometrics: Statistical Inference, Time Series Modelling, Modes of Convergence, Spurious regression, Nonparametric Methods
• Panel Data Econometrics: Pooled Regression, Fixed Effects/Random Effects, Dynamic Panel Models
• Introduction to Financial Econometrics: Asset Returns, Expectations, Volatility, ICAM, Realized Volatility, High-Frequency Methods
• Panel Data extensions: Endogeneity, IV estimation, Treatment effects, Diff-in-Diff estimators
• Discrete Choice Models: Binary models (Probit/Logit), Ordered Probit/Logit, Multinominal Probit/Logit
• Tobit and Selection Models

The Pilot study/Research proposal for the PhD scheme continues on from the research undertaken in the literature review and is the final preparation before their initial paper. It is intended to help provide the student to expand their knowledge of the appropriate theoretical and empirical literature and attempt to plan a project that would make an incremental contribution to the research area. This may consist of collecting an appropriate data set that will be analysed at a later date, or constructing a theoretical framework that can be built on at a later date.

Our research seminars with external speakers attract leading academics that present their current research. Attending such seminars exposes participants to current research trends and allows them to expand their network. We encourage active student participation in seminars and their contact with seminar speakers. To facilitate better understanding of seminar papers, we will conduct regular pre-seminar meetings that are going to discuss in detail presented papers. The objective of pre-seminar meetings are:

• Facilitate better understanding of current research streams and presented papers;
• Provide a platform for exchanging ideas and opinions on current research streams;
• Help you provide informed feedback on presented research;
• Improve your writing skills and critical thinking;
• Further connect PhD students and our faculty by conducting regular meetings with different faculty members.

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 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.

To enable students 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.