FINN43115: Financial Data Analysis and Econometrics Methods (Fudan DBA)

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• provide students with the necessary training to undertake advanced level research in the area of Finance and Economics;
• provide students with an advanced understanding of the relevance and importance of alternative epistemological positions in the social sciences and the nature of both qualitative and quantitative approaches to research;
• provide students with opportunities to be familiar with the frontier empirical and theoretical research in finance;
• build upon students' knowledge of econometric methods and provide them with the specific advanced technical skills necessary to pursue empirical research in finance;
• provide students with the tools required to model, analyse and predict financial markets.

• Part I The research process 1. How to write a research paper 2. Using statistical software for financial data analysis 2.1 Data management 2.2 Estimation issues 3. Making use of and managing library facilities, databases and other learning resources
• Examples of how to conduct research in different fields 1. Capital Structures 2. Collective behavior in capital markets 3. Foreign reserve management 4. Mergers and acquisitions
• Part II 1. The statistical properties of univariate time series models and their application in Finance 2. Models of nonstationary time series 3. Cointegration and error-correction model 4. Cointegration in multivariate system 5. Modelling volatility 6. Further topics on ARCH 7. Forecasting in financial econometrics

Subject-specific Knowledge:

• By the end of this module, students should:
• be aware of the significance of alternative epistemological positions when designing and undertaking research
• be aware of, and familiar with, the facilities available for conducting literature searches and obtaining relevant data to facilitate empirical investigation
• have an advanced knowledge of the principles and methods of modern financial econometrics
• have extended and deepened their understanding of econometric methods, their application, and the interpretation of results at an advanced level
• have improved their critical judgement and discrimination in the choice of techniques applicable to complex situations

Subject-specific Skills:

• By the end of this module, students should:
• be able to apply the core mathematical and statistical skills that underpin econometric analysis
• be able to effectively organise, structure and manage a research project at an advanced level, including undertaking critical appraisal of relevant literature, and apply critical judgement and discrimination
• have further developed the skills of inquiry, quantitative and qualitative research design, experimental research, data collection and information retrieval, bibliographic search, measurement and analysis, interpretation and presentation, self-discipline and time-management and the ability to work autonomously
• have further practised problem solving skills at an advanced level and the use of econometric software

Key Skills:

• Ability to make an initial formulation and articulation of a potential scheme of research
• Ability to understand and resolve the problems and issues in undertaking doctoral research
• Computer literacy
• Transferring academic knowledge into practice

• The module will be delivered in a workshop format over two intensive two-day teaching blocks. Workshops will comprise a balanced mix of lecture- and seminar-type delivery combined with small group discussions and other activities as appropriate to the nature of the material.
• Learning will also occur through tutor-supported, as well as self-supported learning groups. In addition, guided reading will address key topics. A reading list will be provided consisting of current published articles relevant to module content, available within library.
• This range of methods will ensure that students will acquire the advanced skills and knowledge to enable them to develop a thorough understanding of this specialist field of study.
• The summative assessment is a 3000-word written assignment, designed to prepare students for subsequent stages of the programme ultimately the doctoral thesis/viva.

Individual oral examination, designed to test students' knowledge and understanding of the subject matter and their ability to articulate a researchable issue.

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