- Department: The York Management School
- Module co-ordinator: Prof. John Ashton
- Credit value: 60 credits
- Credit level: M
- Academic year of delivery: 2023-24
- Notes: This is an independent study module
Finance and Investment Management
Statistics and Mathematics for Finance
|A||Summer Semester 2023-24|
This module acts as the final element of Type 1 finance postgraduate programmes and for the Accounting and Finance MSc. The module enables learners to undertake an individual research project on a topic related to their MSc. programme. This is to be undertaken for all type 1 programmes including the MSc. Accounting and Finance (MSc. Finance, MSc, Finance and Investments, MSc. Corporate Finance, MSc. Accounting and Finance, MSc Finance and Management and MSc Investments, Finance and Economics.. Learners from the type 2 finance postgraduate programmes (including MSc. Financial Economics, MSc Financial Engineering and MSc. Mathematical Finance will not take this module – rather they will undertake the dissertation module.
Within this project the learners are expected to produce a substantial piece of discussion, analysis and reflection reflecting the key skills underlying a dissertation module. This module is split into two parts.
Initially, we provide a refresher and revision course as to the research methods embedded in the finance programme taken by the learner. This part of the module will focus on developing econometric analytical skills, revisiting methods for accessing, organising and developing data sets, how to develop a critical literature review and considering how we use research, write conclusions and derive the implications of our research. This element for the module will be assessed through the development of an individual project proposal and data collection as to how the learner will undertake their individual research project.
The second part of the module involves the completion of the individual research project. The learner will attend and engage in a series of lectures as to the purpose and use of appropriate research designs and apposite empirical testing frameworks to assess issues of concern or debate with contemporary finance. This instruction will introduce project areas for students to follow and to focus on when developing their individual research project. The project areas considered will reflect the programme goals and subject matter in the different programmes.
Lastly, we will offer three online surgeries and writing workshops to support learners over this module.
After successful completion of the module students will be able to:
Independently author an extended piece of focused writing, longer than a module assessment, using a contemporary research design and methods drawn from modern finance and financial economics.
Synthesise complex information to develop original findings and critically assess assumptions necessary to draw certain conclusions.
Demonstrate a mastery of information literacy skills by critically engaging with a range of sources
Understand how to draw upon secondary data to make original conclusions
Comprehend the requirements and expectations needed to produce robust research work in a cogent area of finance.
Interpret and analyse financial data by selecting appropriate theoretical and statistical concepts and tools
Academic and graduate skills
Advanced subject specific knowledge and understanding
Cognitive (thinking) skills: through self-study and assessment
Problem solving and analytical skills required to undertake finance analysis and/or estimations.
Ability to conduct research into financial issues using data collection from Refinitiv, WRDS and other financial databases and platforms made available by the university.
|Task||Length||% of module mark|
Individual research project
Individual research project - proposal
Feedback will be given in accordance with the University Policy on feedback in the Guide to Assessment as well as in line with the School policy.
Brooks, C., (2019) Introductory Econometrics for Finance, 4th ed, Cambridge.
Indicative readings will be provided for each of the following topics for a contemporary issue of debate identified for further discussion and evaluation.
Manifest Content Analysis and Testing – developing new data sets for the quantitative analysis of qualitative data sets. This will be developed using the example of quantifying the effects of corporate misconduct.
Analyse the behaviour of returns in a particular market or set of markets using a suitable dataset. This may involve the assessment of investment strategies and market anomalies using appropriate econometric methods. It may employ event studies which assess the impact of external changes in regime on asset performance or natural experiments which measure the outcomes from different policies and interventions.
Economics and Finance
Analyse the interaction between the behaviour of the macro economy and financial markets using a suitable dataset. It may employ event studies which assess the impact of external changes in regime on asset performance or natural experiments which measure the outcomes from different policies and interventions.