Bayes London Summer School

About the programme

This short summer module is designed to give students exposure to the key issues in modern finance. It begins by introducing the language of finance, describing the structure of financial markets and detailing the roles played by financial intermediaries.

The introductory finance course proceeds to analyse how companies make their financing choices (for example, how they decide which investment projects to undertake) before going on to discuss how financial securities (e.g. bonds and stocks) may be valued.

The course is intensive yet rewarding and the ideal introduction to finance for people from a wide range of professional experience or academic backgrounds. The course will cover fundamental financial terminology as well as providing students with the skills to outline and discuss the basic concepts of financial security, financial markets and financial valuations.

The course is also suitable for first year undergraduate students who seek to transfer from a non-finance major to a finance concentration route from their second year.

Course Content

• The fundamental terminology of finance
• The essential structure of the financial system and the role of financial inter-mediation
• The characteristics of different financial securities
• The role and functioning of various financial markets

On successful completion of this module, you will be expected to be able to:

Knowledge & Understanding

• Demonstrate the key considerations affecting the decisions of financial market participants.
• Explain the structure and the institutions making up the financial markets.
• Be equipped for further study of finance-related courses at postgraduate level.

Skills

• Outline the role and functioning of financial intermediaries.
• Discuss the role of financial markets in processing and incorporating information into the prices of securities
• Master the concept of the time value of money for making informed and carefully evaluated financial decisions
• Outline basic concepts of valuation of securities and firms.

Values & Attitudes

• Discuss the wider social context of financial markets
• Demonstrate the importance of regulatory regimes on financial market participants

Module Leaders: Dr Aneel Keswani and Dr Sonia Falconieri

Dr Aneel Keswani worked as an economist for a commodities research company prior to completing his PhD at London Business School. He taught at both LSE and Lancaster University before coming to Bayes, and has also consulted for various investment banks. His main research area is fund management and he is a Director of the Centre for Asset Management Research at Bayes.

Dr Sonia Falconieri joined the faculty of Finance at Bayes in September 2009. Her research interests are in Corporate Finance; specifically she has been working on Initial Public Offerings, Takeovers and the financing side of Public Private Partnerships among others. Her articles have been published in some major journals such as the JEEA, the Review of Finance and Financial Management.

About the programme

Finance, both academic and practical, is a quantitative subject. Risk managers use data and statistical techniques to evaluate portfolios, investors must estimate the expected returns and risk contributions of potential investments and traders wish to forecast future movements in the levels of stock, bond and foreign exchange markets.

Understanding the fundamentals of statistics, mathematics and econometrics is thus vital to a successful career in finance and is also necessary for all students wishing to study for an MSc level award in finance.

This module is designed to equip students with essential statistical and mathematical tools.

Students will become familiar with the language of mathematics and statistics, and will cover important fields such as linear algebra, calculus, probability, inference and linear regression and be able to apply the concepts to practical problems in business and finance.

Students who have taken this course would, for example, be fully prepared to undertake the quantitative elements of a finance-related MSc at Bayes (and at other top UK universities).

An undergraduate who has completed a degree in a non-quantitative subject can use this course as a primer that would help them to switch to studying finance at MSc level.

Course Content

• Key terms and notation used in financial applications of mathematics and statistics
• The fundamentals of algebra and of calculus
• Basic statistics - for example, the notations of random variable, expectation, estimation and testing
• How to apply statistics, and mathematics, in the analysis of financial and accounting data.

On successful completion of this module, you will be expected to be able to:

Knowledge & Understanding

• Use the basic mathematics required to undertake the fundamental types of calculation that will be required in the study of finance.
• Demonstrate the principles of statistical analysis and their applications to financial data
• Describe the different statistical methods which can be used to summarise and interpret financial data
• Discuss the underlying assumptions in statistical modelling and the dangers of ignoring them.

Skills

• Develop skills to enable statistical analysis of data.
• Apply statistical methods to facilitate answers to real world problems in a variety of practical settings
• Provide critical assessment of empirical research in the field.
• Develop and interpret empirical models that capture the stylized behaviour of financial data
• Develop and interpret empirical tests to assess the validity of finance theories.

Values & Attitudes

• Learn from the data but at the same time be self-critical and aware of the limitations of empirical analyses
• Support statements on the basis of empirical evidence

Module Leaders: Dr Malvina Marchese & Professor Anh L. Tran

Dr Malvina Marchese holds an M.Sc. in Econometrics and Mathematical Economics and a Ph.D. in Statistics (Econometrics) from the London School of Economics and Political Science. She has been a lecturer at City, University of London and at the University of Genova, Italy. Malvina has extensive industry experience in quantitative risk management, having held full time and consultancy positions since 2008 in the industry. She is currently a research advisor to Maersk Broker for shipping econometrics and forecasting. Her research interests include long memory time series , multivariate fractionally integrated GARCH models, long memory in realized volatility, forecasting measures and applied econometrics.

Professor Anh L. Tran is the Director of the Summer School, Academic Director of the Bayes M&A Research Centre, and a Fellow of the Gupta Governance Institute. He has taught corporate finance classes at both MSc and undergraduate levels as well as executive education. His research interests are in empirical corporate finance, including mergers and acquisitions, institutional investors, executive compensation, and corporate governance. Anh has published many research articles in world leading journals including Journal of Financial Economics, Journal of Accounting and Economics, Journal of Financial and Quantitative Analysis, and Management Science. He has received the City University Staff Prize for outstanding research and the Bayes Business School Excellent Research Publication Awards. His research has been mentioned in various media outlets including the Wall Street Journal, the Financial Times, the Economist, Bloomberg, the New York Times, the Times, Le Monde, Les Echos, etc.

About the programme

The aim of this module is to provide students with understanding of the principles and practices of accounting, the characteristics and limitations of the accounting data in an international context.

Students will learn how to apply accounting principles in businesses in the context of international financial reporting standards (IFRS), which is the world’s most widely applied accounting standard. Students will acquire knowledge on preparing and interpreting financial statements, applying and commenting on accounting policies.

Students will learn how to appreciate and discuss the implications of accounting principles on the financial performance of the business.

This module aims to give students the skills to compete effectively in today’s global business environment. A good understanding of IFRS helps to distinguish students from other accounting and finance professionals and expand their global career opportunities.

Course Content

• Presentation of financial statements
• Recognising revenue, profit, cash and accrual accounting
• Inventory transactions, construction contracts
• Plant property and equipment, borrowing costs, intangible assets, impairment of assets
• Long-term liabilities, provisions, contingent liabilities, bonds and leases
• Accounting for equity, share-based payments
• Statement of cash flows
• Business combinations

On successful completion of this module, you will be expected to be able to:

Knowledge & Understanding

• Have a critical knowledge of the accounting principles and methods underlying financial statements
• Read and prepare financial statements
• Interpret and analyze accounting numbers

Skills

• Develop practical, analytical, problem-solving and decision-making skills by marshaling financial and accounting data
• Communicate effectively complex information in relation to international reporting standards.
• Evaluate current accounting practices, their motivation and economic impact
• Appreciate implications of alternative accounting policies

Values & Attitudes

• Appreciate of the variety of accounting methods and their individual economic repercussions
• Be responsive to firm-specific or country-specific demands in terms of accounting practices and methods.
• Be critically aware of ethical issues in accounting and financial reporting

Module Leader: Dr Ivana Raonic

Dr Ivana Raonic has more than fifteen years of experience in teaching financial reporting, interpretation of financial statements, security analysis and equity valuation. In addition to teaching at the business school, she has held visiting positions at EDHEC, Nice, HEC, Paris, University of Belgrade, and University of Siena where she taught masters and MBA students.

Her research is focused on studying the role of financial reporting in capital markets and other settings, the economic effects of corporate disclosure and transparency, corporate governance and corporate financing. She regularly publishes in leading international journals such as Journal of Business Finance and Accounting, European Accounting Review, Abacus, The International Journal of Accounting.

She has served as a member of editorial boards and as an ad-hoc referee in a number of journals such as Accounting and Business Research, European Accounting Review, Accounting in Europe, Journal of Business Finance and Accounting, British Accounting Review and Journal of Accounting and Public Policy.

She earned her first degree in Economics at the University of Belgrade, master’s in finance at the Brunel University, London and her doctoral degree in Accounting and Finance at the University of Wales in Bangor.

About the programme

This module offers an overview of three complementary elements future leaders will seek to master over their career. In the first set of sessions on how to effectively lead their teams, students will learn about the fundamentals of drivers of human behaviour: motivation and (self-)perception.

Some leaders have intuitive knowledge of these concepts and capabilities, often implementing these concepts, subconsciously. However, an explicit understanding of these concepts and skills opens up a set of possibilities to the leader to knowingly tweak, adapt, train and improve the best concepts and skills for each situation.

The second set of sessions asks students to apply this knowledge of the self and of others to work processes and organisational structure: how have organisations been designed in the past to maximise different outcomes?

Students will learn about how different formal decision-making rules impact creativity, accuracy, and engagement. Finally, in the third and last set of sessions, students will learn about the high forecasted rates of change (due to disruptive technology and the innate qualities of data) and how organisations strive to adapt to these.

In this last set of sessions, students will explore why, despite all this information being available to managers, and despite forecasts of increased industry and market dynamism, it is still so difficult for organisations to change.

Course Content

• Emotional intelligence as a foundation for organising
• Human behaviour: extrinsic motivation and homo economicus
• Human behaviour: intrinsic motivation, relationships and (self-)perception
• Human behaviour and decision-making: bias and heuristics
• Guiding behavior with decision-making rules and organisational design
• Understanding trade-offs of centralisation and hierarchy for omission and commission errors, creativity, engagement and identification (e.g., Wikipedia, Valve, Pixar)
• Internal sources of organisational change (Greiner Model)
• External sources of organisational change (Porter’s Five Forces, VRIO, regulation, Moore’s Law, the knowledge economy)
• Basics of organisational resilience and adaptability (e.g., managing slack, experimental culture, organisational identification, psychological safety)

On successful completion of this module, you will be expected to be able to:

Knowledge & Understanding

• Recognise the theorised drivers of deliberate and subconscious human behavior include a mix of intrinsic and extrinsic motivation
• Reflect on how intrinsic and extrinsic incentives differ depending on self-perception
• Distinguish between useful heuristics, competitive specialisation and counterproductive biases
• Recall how decision-rules and organisational design have been previously applied to incentivise specific behavior and organisational outcomes
• Anticipate internal sources of change, the problems they pose, and their solutions

Skills

• Apply appropriate decision-making rules to different types of problems and organisational goals
• Analyse short-term drivers of organisational change in due to market forces
• Hypothesise the unique impact of technological change on the parameters of competitive behavior
• Set personal objectives and design an action plan to reach those objectives
• Assess progress against the plan, and adapt the plan as appropriate
• Assess one's own level of skill acquisition, and plan their on-going learning goals
• Collect and analyse primary and secondary data for a written case study

Values & Attitudes

• Take account of the social and human dimensions of management science
• Demonstrate a capacity for creativity and critical thinking

Module Leaders: Dr Nettra Pan

Dr Nettra Pan is a Lecturer in Entrepreneurship.  She received her doctorate in Management of Technology from EPFL (Switzerland). Nettra's expertise is at the intersection of entrepreneurship and social change. Her research interests include how firms resolve tensions between commercial and seemingly non-commercial imperatives, tensions she has examined in a number of empirical settings, including technology startups and venture capital-like firms with a social impact mandate.

She has taught executive training sessions for founders, scientists, non-profit leaders and corporate executives interested in introducing innovative, revenue-generating products or services. In these sessions, Nettra helps project leaders develop and articulate their vision and metrics for success; a fundamental north star for understanding whom project leaders should serve over time, and how project leaders should engage with new markets.

Finally, Nettra also enjoys working with startup investors on how to understand their biases and tweak biased decision-making processes to achieve desired investment outcomes (for founders and investors). Nettra is an active member of startup communities, a frequent speaker at startup conferences and a member of selection juries of top startup acceleration programs.

The theories taught in this course draw from research in motivation, organisational design, entrepreneurship and innovation management. In addition to completing a personal project, students will be expected to participate in online and offline class discussions.

About the programme

Entrepreneurship has grown into one of the most attractive and potentially rewarding career choices for creative and business minded individuals. Coming up with a venture idea, understanding the industry and market, engaging with customers, managing the team, attracting financial resources; all this and more is required of the entrepreneur and offers extensive opportunity to work creatively and practice innovation.

The aim of this module is to give students an understanding of fundamental aspects in entrepreneurship and to provide the knowledge and skills to become an entrepreneur or to act entrepreneurially within existing organizations.

Over the course of ten sessions, students will be familiarized with tools and frameworks related to various stages in the process of starting a new company. This is an action-oriented module, meaning that it will go beyond providing students with theoretical knowledge. Students will obtain a hands-on experience and apply practical tools that are used by people worldwide to start taking the first steps towards founding a new venture.

Course Content

• Entrepreneurship with Impact
• Identifying and Evaluating Entrepreneurial Opportunities
• Prototyping and Protecting your Ideas
• Growth, Unicorns, and Hypes
• Understanding Business Models
• Conducting Market Research
• Targeting and Attracting the Right Customers
• Navigating the Investor Landscape and Pitch Perfect
• Group Presentations
• Living the Entrepreneurial Life

On successful completion of this module, you will be expected to be able to:

Knowledge & Understanding

• Evaluate the fundamentals of entrepreneurship
• Evaluate new venture opportunities by conducting a feasibility analysis
• Provide data driven insights into the development of an entrepreneurial opportunity
• Apply relevant theories, frameworks and models in different phases of the entrepreneurial process

Skills

• Develop critical thinking skills to recognise business opportunities
• Apply tested methods for setting up a new venture
• Critically assess business opportunities
• Develop a compelling elevator pitch

Values & Attitudes

• Learn how to work in a team with people from various backgrounds
• Consider societal and environmental impact when exploring a business opportunity
• Support statements on the basis of empirical evidence

Module Leader: Dr Ruben van Werven

Dr Ruben van Werven is a lecturer in entrepreneurship. He takes a hands-on approach to teaching by giving students the practical tools they need to turn an untested business idea into a validated prototype. When doing so, he draws on the experience he gained by joining an incubator programme as part of his research on entrepreneurial communication. This research has also allowed him to reflect on the startup scene. These reflections form the second component of Ruben's teaching style and are input for discussions on unicorns, hypes, and the impact startups have on society.

About the Programme

The last two decades have seen a dramatic increase in investor interest in alternative investments and chief amongst these have been hedge funds. The purpose of this module is to provide an in-depth study of the structure of the asset management industry and the strategies that funds use to generate returns.

We will begin with issues of industrial structure, ultimately investigating why the fund industry has come to look the way it does. Then the coverage will proceed through an exhaustive study of the major fund strategies paying particular attention to the risks underlying these strategies. Students will be introduced to the key issues involved in constructing portfolios of funds as well as issues that one faces when incorporating funds into a traditional portfolio.

For all topics students will be provided with both the academic and practitioner perspectives.

Course content

• An overview of the hedge fund industry, history, organisation, issues and current trends
• A Review of the 10 major hedge fund strategies
• Analysis of hedge fund performance, performance metrics and factor models
• Hedge fund data, availability, biases and statistical properties
• Case studies of five major hedge fund failures

On successful completion of this module, you will be expected to be able to:

Knowledge and understanding

• Demonstrate an understanding of the global market for hedge funds
• Explain the structure of hedge funds
• Describe and compare the investment strategies of hedge funds
• Demonstrate an understanding of hedge fund diversification and what to expect when investing in portfolios of hedge funds or funds of funds
• Assess the shortcomings of standard performance measurement tools such as the Sharpe ratio and Mean-Variance analysis when applied to hedge funds

Skills

• Appraise the global market for hedge funds
• Assess the risk of hedge fund investments
• Compare the drivers of return of the 10 major hedge fund strategies
• Assess the pros and cons of investing in funds of hedge funds
• Articulate the shortcomings of standard performance measurement tools such as the Sharpe ratio and Mean-Variance analysis when applied to hedge funds

Values and attitudes

• Show awareness of the ever changing landscape of the financial services industry and of emerging trends
• Explain how pursuing the wrong strategy can lead to failure

Module Leader: Dr Nick Motson

Dr Nick Motson joined the faculty of finance at Bayes in 2008 following a 13 year career as a proprietary trader of interest rate derivatives in the City of London for various banks including First National Bank of Chicago, Industrial Bank of Japan and Wachovia Bank.

Nick's research interests include asset management, particularly hedge funds, alternative assets and structured products. In 2009 he was awarded the Sciens Capital Award for Best Academic Article, in The Journal of Alternative Investments for his paper Locking in the Profits or Putting It All on Black? An Empirical Investigation into the Risk-Taking Behaviour of Hedge Fund Managers.

Nick teaches extensively at masters level on alternative investments, derivatives and structured products and in recognition of the quality of his teaching he was nominated for the Economist Intelligence Unit Business Professor of the Year Award in 2012.

As well as teaching and researching at Bayes, Nick actively consults for numerous banks and hedge funds and has provided research or training clients including ABN Amro, Aon Hewitt, Barclays Wealth, BNP Paribas, FM Capital Partners, NewEdge, Societe Generale and Rosbank.

About the Programme

Mergers and acquisitions are a major form of corporate activity with important and wide-ranging implications for firm managers, employees, customers and investors.

The M&A module will provide students with a detailed understanding of the financial issues surrounding M&A activity, within a strategic context and from an international perspective. Students will complete the module with not only an understanding of the blend of strategic and financial implications thrown up by M&A activity, but more importantly with a full recognition of the impact of corporate restructurings on organisations and people.

The course will cover topics related to the motivation for deals, determinants of the success of deals, deal valuation and post-merge integration.

Course content

This course will provide you with a detailed understanding of the financial issues within a strategic context regarding mergers and acquisitions from an international perspective. At the end of the module you should have the ability to form your own views about M&A, and should be prepared to make your own creatively strategic and analytically supportable recommendations regarding potential M&A transactions.

• Corporate motives for M&A
• Strategic alternatives to a merger or acquisition
• Why so many acquisitions fail; value creation and value destruction
• Commonly used takeover defences and tactics
• Deal valuation and financing
• Due diligence
• Role of outside advisors and company management
• Regulators and regulatory and tax environment (focus on the UK)
• Post merger integration and other impacts of the M&A process
• Surviving an M&A deal

On successful completion of this module, you will be expected to be able to:

Knowledge and understanding

• Articulate your understanding of the role of M&A activity in its wider economic context
• Illustrate relevant company valuation concepts
• Assess strategic implications of M&A activity

Skills

• Demonstrate team working skills
• Understand the implications of current finance theories for practical M&A issues
• Evaluate the value-creating potential of an M&A proposition
• Evaluate complex M&A propositions
• Apply understanding of the building blocks of M&A transactions (e.g. sources of finance, accounting implications)
• Demonstrate presentation and report writing skills

Values and attitudes

• Demonstrate confidence in applying financial and strategic concepts to M&A
• Demonstrate awareness of the wider business context of M&A activity

Module Leader: Professor Scott Moeller

Professor Scott Moeller is the Director of the M&A Research Centre at Bayes Business School. He teaches Mergers & Acquisitions on the MBA and MSc programmes at Bayes. During his six years at Deutsche Bank, Scott was Global Head of the bank’s corporate venture capital unit, Managing Director of the Investment Bank’s Global eBusiness Division and Managing Director of the department responsible for world-wide strategy and new business acquisitions.

Scott worked first at Booz Allen & Hamilton Management Consultants for over 5 years and then at Morgan Stanley for over 12 years in New York, Japan, and most recently as co-manager and then member of the board of Morgan Stanley Bank AG in Germany. Scott has held a number of other board seats throughout Europe, Africa, Asia and the Americas and is currently a non-executive director on several boards.

About the Programme

Shipping is a very important sector of the world economy. The aim of the module is to provide an overview of the fundamentals of shipping markets and describe the operating and investment practices of modern shipping companies.

Students will examine in depth the fundamentals of shipping investment and will be equipped with the analytical tools and skills for making shipping investment and finance decisions. You will also be taught to understand how revenue is earned by shipping companies, the importance of the industry's cost structure and the necessity for cost minimisation and the risks involved in a shipping project and how these can be managed.

Course content

At the end of the module you should be able to appreciate the specific details of operating and investing in shipping, to form your own views about shipping investment and to evaluate the potential of operating and investment in shipping:

• The importance and position of the shipping industry in the world economy.
• Analysis and features of various shipping sectors: dry-bulk; tanker; container and specialised sectors.
• Analysis of the four shipping markets: freight; new-building; second-hand and demolition.
• Supply and demand factors in shipping.
• Market equilibrium and freight rate determination.
• Contracting and cost and revenue responsibilities in different shipping contracts.
• Stylized features of freight rates: analysis of volatility, term structure and seasonality.
• Analysis of shipping risks; risk management of shipping revenues and costs.
• Freight risks and the use of derivative contracts.
• Project evaluation and cash flow analysis of a shipping project.
• Financing a shipping project.
• Sources of capital for shipping companies; bank loans; bonds; private and public equity

On successful completion of this module, you will be expected to be able to:

Knowledge and understanding

• Understand the fundamental principles of shipping markets.
• Comprehend the economics of shipping and its inter-related markets, including freight, new-building, second hand and demolition.
• Comprehend the key parameters involved in shipping investment decisions and the tools used in a shipping investment feasibility study.
• Assess and evaluate the major financial risks involved in a shipping project.

Skills

• Undertake a shipping feasibility study.
• Carry out a cash-flow analysis for a shipping project and critically evaluate a shipping investment appraisal.
• Understand and assess different sources of funding for a shipping project.
• Identify different sources of risk in shipping operations and measure exposure to such risks.
• Measure and compare the effectiveness of different derivatives instruments in the management of financial risks in shipping.

Values and attitudes

• Demonstrate confidence in applying financial concepts for shipping projects.
• Demonstrate the use of judgement in the comparison and evaluation of projects and clarity and non-bias in describing the relative merits of investments to others.

Module Leaders: Professor Nikos Nomikos and Dr Nikos C. Papapostolou

Professor Nikos Nomikos is Professor of Shipping Risk Management at Bayes Business School. He commenced his career at the Baltic Exchange as Senior Market Analyst where he was responsible for the development of the shipping indices that are currently used in the market as pricing benchmarks. For the last 10 years he has been with the Faculty of Finance at Bayes Business School, where he is also the Director of the MSc course in Shipping, Trade and Finance. His area of expertise is Ship Finance and Risk Management. As such, he particularly enjoys lecturing on the topics of shipping economics, ship finance and shipping risk management as well as quantitative finance and risk management in financial and commodity markets.

Dr Nikos Papapostolou is a Reader in Shipping Finance at the Costas Grammenos Centre for Shipping, Trade and Finance. He holds a BSc in Money, Banking and Finance from the University of Birmingham, an MSc in Shipping, Trade and Finance and a PhD in Finance from City, University of London. He is involved in Shipping Finance Executive training in collaboration with the Baltic Exchange and acts as a consultant to industry clients.

His research interests are in the field of shipping investment and finance, with a focus on capital markets as a source of finance for shipping companies, investors’ sentiment and behavior in the shipping industry, freight options pricing and vessel valuation, technical analysis trading rules, and commodity derivatives.

About the Programme

Financial engineering is an integrative field in the practice of finance that involves financial theory, mathematical models, quantitative methods and programming for the design and analysis of financial markets, products and strategies.

Through this module, you will be equipped with valuable skills in these directions that will help students build a finance career. In more detail, this module covers a wide range of topics and tools of modern finance including optimal portfolio construction, calculation of the efficient frontier, dynamic portfolio management, asset price modelling, optimised volatility and correlation estimation, risk measurement and forecasting, Monte Carlo simulation and scenario generation, and pricing of financial derivatives.

Students will gain an insight into financial data and will also learn how to implement the various techniques using suitable programming and computing platforms by means of instructor-led demonstrations. Students will develop hands-on experience on related exercise-solving, learn how to interpret results, and perform model validation.

Course content

• Practical portfolio analysis: efficient frontier calculation techniques, optimal risky portfolios and optimization using different risk measures.
• Asset price and volatility modelling: definitions, stylized facts and distributional properties.
• Volatility and correlation: estimation, forecasting implementations and interpretation.
• Density and tail forecasting, management of adverse price movements, dynamic portfolio management and examples of these techniques in Excel.
• One-dimensional and multi-dimensional models. Analysis of price trajectories: tranquillity and / or existence of jumps.
• Inverse problems: model calibration and implied volatility profiles based on basic financial contracts.
• Numerical implementation in Excel.
• Monte Carlo simulation. Applications in Excel in asset price path generation, investments, pricing of one-asset and multi-asset contracts, expected exposure for defaultable contracts.

On successful completion of this module, you will be expected to be able to:

Knowledge and understanding

• Formulate financial and investment objectives mathematically and apply analytical skills to evaluate portfolio construction and solve portfolio management problems
• Investigate different types of market price data and their observed properties
• Select appropriate asset price and volatility models, estimate and validate them using relevant techniques in Excel
• Understand the principles of Monte Carlo methods and their application in price generation, investments, portfolio management and valuation of financial contracts
• Make decisions via relevant practical case studies: e.g., the amount of assets required for construction of well-diversified portfolios, estimation of loss from investing in a portfolio over a given time horizon, study of the impact of uncertainty on the investment decision

Skills

• Build on theory, formulate and understand different financial models and methods, as opposed to simply treating them as black boxes
• Develop an understanding of the model building process
• Implement and calibrate models using real data
• Provide assessment of empirical results
• Develop important applied software skills in financial modelling for use in a quantitative finance professional environment or further related studies

Values and attitudes

• Demonstrate awareness of the assumptions and ideas underlying different financial models
• Demonstrate an appreciation of the strength and limitation of financial models and methods
• Learn from the data and support statements on the basis of empirical evidence
• Have an attitude of careful documentation and communication of analysis results

Module Leaders: Dr Ioannis Kyriakou and Dr Panos Pouliasis

Dr Ioannis Kyriakou is a Senior Lecturer at Bayes. He holds a PhD in Finance. Previously he worked for Lloyd’s Treasury and Investment Management on Lloyd’s Investment Risk Model for measuring the market and credit risks under the Solvency II Directive. His teaching duties relate to Numerical Methods in Finance, Financial Derivatives, and Probability and Statistics. His research agenda encompasses stochastic asset modelling and development of efficient methodologies for valuation of exotic derivatives in freight and commodity markets.

Dr Panos Pouliasis is a Senior Lecturer in Energy/Commodities and Finance at Bayes. He holds a PhD in Finance. He joined Bayes originally as a researcher at The Costas Grammenos International Centre for Shipping, Trade and Finance. Currently, he is in the Faculty of Finance where he lectures on Finance, International Business and Financial Markets, Quantitative Methods, Commodity Derivatives and Structured Equity/Energy Derivatives. His research interests relate to commodity and shipping risk management, volatility-correlation modelling and forecasting, and financial econometrics.

About the Programme

Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. Its primary objective is that of turning information into knowledge and value by “letting the data speak”. To this purpose, machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures, and graphical inspection more than tight assumptions, algebraic development, and analytical solutions.

This module is a primer to machine learning techniques. After the course, participants are expected to have a good understanding of big data and machine learning methods and to perform some of the most used marching learning techniques, thus becoming able to master research tasks including, among others: (i) signal-from-noise extraction, (ii) correct model specification, (iii) model-free classification, both from a data-mining and a causal perspective.

The teaching approach will be mainly based on the graphical language and intuition more than on algebra. The training will make use of instructional as well as real-world examples, and will balance evenly theory and practical sessions.

Course content

• Supervised vs. unsupervised learning
• Regression vs. classification problems
• The trade-off between prediction accuracy and model interpretability
• Model selection as a correct specification procedure with Lasso and Ridge Regression
• How to apply machine learning methods in the analysis of financial data

On successful completion of this module, you will be expected to be able to:

Knowledge and understanding

• Demonstrate the principles of machine learning analysis and their applications to financial data
• Describe the different machine learning methods which can be used to interpret financial data
• Provide data driven forecasts of financial variables
• Demonstrate ability to let the data speak

Skills

• Develop skills to enable big data analysis
• Apply machine learning methods to facilitate answers to real world problems in a variety of practical settings
• Critically assess empirical research in the field
• Develop and interpret empirical models that capture the stylised behaviour of financial data
• Develop and interpret empirical tests to assess the validity of finance theories
• Write appropriately in a coding language

Values and attitudes

• Learn from the data but at the same time be self-critical and aware of the limitations of empirical analyses
• Support statements on the basis of empirical evidence

Eligibility

The course is open to current undergraduates and recent graduates with some prior finance knowledge, basic mathematical skills and some familiarity with probability distributions. Basic understanding of Excel is also recommended. Building on these, you will learn how to implement various financial models and become competent in using Excel to this end.

The module is well-suited for prospective entrants to the finance industry in areas such as quantitative analysis, investment analysis, financial and real asset research, portfolio construction and management, credit risk research, but also students with an interest in further studies in quantitative finance, financial engineering and mathematical finance.

Students who have not studied a degree programme taught in English before are required to have an overall IELTS score of at least 6.5 (with a minimum of 6.5 in writing).

Module Leader: Dr Malvina Marchese

Dr Malvina Marchese holds an M.Sc. in Econometrics and Mathematical Economics and a Ph.D. in Statistics (Econometrics) from the London School of Economics and Political Science. She has been a lecturer at City, University of London and at the University of Genova, Italy.

Malvina has extensive industry experience in quantitative risk management, having held full time and consultancy positions since 2008 in the industry. She is currently a research advisor to Maersk Broker for shipping econometrics and forecasting. Her research interests include long memory time series, multivariate fractionally integrated GARCH models, long memory in realized volatility, forecasting measures and applied econometrics.