Postgraduate Course: Research Methods in Financial Computing (INFR11216)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||The aim of this course is to equip students on the MSc in Advanced Technology in Financial Computing (ATFC) with the necessary theoretical, conceptual and applied skills to critically and effectively read,
evaluate, design, propose and ultimately carry out relevant and effective research projects in the realm of financial computing. This course will provide students with an opportunity to develop the necessary theoretical and practical foundational skills that are specifically relevant to conduct financial computing oriented research that can be deployed in academic and commercial environments.
This course introduces the necessary elements to support research in the realm of financial computing, such as providing a holistic understanding of some of the theoretical, conceptual, and applied skills to be able to read, evaluate, design, propose and undertake research in financial computing critically and effectively.
By allowing students to experience and practice the different aspects of the research journey, students will be able to develop a good understanding of research and practical skills to support effective research design in financial computing, such as what kind of data should be collected and analysed, about how inferences should be drawn if the aims of the research are to be realised, as well as appreciating the importance of demonstrating that conclusions drawn from the research are robust and well-grounded epistemologically, theoretically and methodologically.
Topics covered will include:
- What makes research piece interesting, relevant, and how to critically assess its contribution to knowledge
- The role of theory and key theoretical frameworks of financial computing research
- Identifying relevant research questions in financial computing
- Identification, contrasting and combining methodological approaches
- Identification of research contexts and datasets in financial computing
- Developing effective research strategy and structure
- Critical revision of relevant academic literature
- The strengths and weaknesses of different kinds of research design as they relate to the aims, objectives and theoretical underpinnings of any piece of research in financial computing
- Ethics, fairness and diversity, appreciation of good research practice and key practical considerations when proposing research in financial computing
The delivery of this course will vary between lectures and tutorials. Lectures are aimed at providing an overarching guidance on the key aspects to master the learning outcomes of the course and foundations to undertake the written coursework, while tutorials are organised to facilitate in-depth and interactive discussion with peers and tutors, to allow the opportunity to explore areas of interest with respect to specific angles of the topics covered in the course.
By the end of this course, students will produce a research proposal, where they will have the opportunity to exercise and practice the key elements of a financial computing research journey and
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Informatics Research Review (INFR11136)
||Other requirements|| None
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 5,
Seminar/Tutorial Hours 6,
Dissertation/Project Supervision Hours 3,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||The summative coursework (100% of marks) will involve the delivery of an at most 8 pages project proposal, excluding the cover page and references, in a financial computing topic, including a critical discussion on research background, motivation, and the identification of appropriate methodological and theoretical frameworks, data requirements, expected contribution to the body of knowledge and / or practical implications, and a research feasibility review assessment. A good structure of the project proposal may include:
1. Relevant title of the project being proposed, that is descriptive, concise, and that uses the most appropriate language of the target audience
2. Abstract on the cover page (up to 250 words), containing a description of context and purpose of the research, methods and data to be utilised, and potential implication to knowledge/significance of research
3. Introduction, containing: Research Context, Motivation, Problem Statement, Aims / Objectives, Tentative Hypotheses and Potential Contribution of the Work
4. Literature Survey: Summary of relevant previous research and theories, clearly identifying a research gap and substantiating the research questions
5. Methodology: Research strategy and design, data requirements, availability and strategy, methods to be utilised to process/analyse data, and ethical considerations
6. Feasibility assessment: Tentative research timeline / gantt chart, resource and skills mapping to undertake the project, and reflection on potential limitations/challenges to undertake the project and tentative risk management strategy to address identified limitations/challenges
By completing this assessment, students will have learned essential thesis writing skills, prepared their data strategy and collection, have a good overview of relevant financial computing literature within the area of research being proposed, become familiar with appropriate financial computing methodological and ethical considerations to support their research, and have a robust plan in place to undertake their thesis work related to financial computing specific domains.
There will also be a formative coursework, aimed at allowing students to read and critically evaluate key research articles in financial computing. This formative coursework (and the written feedback provided) is designed to support students developing early in the course, the essential skills to master the summative coursework. This coursework will be based on a brief presentation with the key characteristics as follows:
- Students will work in teams of 3-4 students
- Each team will receive a journal article on a financial computing related topic to review
- Teams will be asked to prepare a brief presentation, containing:
1. Elevator pitch of the article (Summary and why the research is important / research gap it fills)
2. Description of research context
3. Description of data and methods used
4. Key findings
5. Reflection on strengths and weaknesses of the article
This formative assessment will help students getting familiar with key literature in the financial computing domain, thus providing a good benchmark of good research practice, structure, and design, as well as allowing students to exercise critical reflection when analysing academic literature. These are essential foundational skills required to pass the summative assessment and master the learning outcomes of this course. This coursework will also give students the opportunity to engage with other peers and potentially instigate relevant collaborations that may last throughout the dissertation project.
||Written feedback will be provided on formative and summative coursework; and oral feedback on tutorial sessions
|No Exam Information
On completion of this course, the student will be able to:
- critically evaluate academic literature on various financial computing issues or other prior work appropriate for their chosen research subject
- use existing literature or other prior work to select and justify choices on the most appropriate methodological and philosophical frameworks, experimental designs, and theoretical goals to carry out their own research in a financial computing related subject
- critically apply project management skills to assess, direct, manage and deliver a research project, such as developing timely plans with a clear breakdown of the relevant activities and tasks required to achieve the project¿s goal, identify links and dependencies between required activities and tasks and determine risk mitigation strategies
- develop skills on relevant project implementation issues, such as recognising the availability, strengths, and limitations associated with primary and secondary data, identify the required databases, instruments, tools, and test environments to process the necessary data to conduct research, understand the core quantitative, qualitative and mixed methods approaches used to conduct research in the realm of financial computing, and recognise potential legal, social, ethical and professional issues
- develop a structured research project proposal in the domain of financial computing
|Graduate Attributes and Skills
||Research, enquiry, and drive: Develop original and creative responses to problems and issues, and identification of best practices to ensure the achievement of goals.
Critical thinking: Critically review, consolidate, and extend knowledge, skills, practices and critical thinking in financial computing and its sub-disciplines.
Communication: Communicate with peers, more senior colleagues, specialists, both at the written and verbal levels.
Personal effectiveness: Take responsibility for own work, collaboration and teamwork skills across disciplines and cultures, leadership skills, and critical reflection on own and others' roles and work.
|Course organiser|| Luis Costa Sperb
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701