| Unit name | Empirical Finance for Financial Technology |
|---|---|
| Unit code | ACFIM0004 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Sapre |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Finance; Quantitative Methods, Big Data and Machine Learning |
| Units you must take alongside this one (co-requisite units) |
Algorithmic Trading |
| Units you may not take alongside this one |
None |
| School/department | School of Accounting and Finance - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
This unit introduces students to conducting quantitative empirical research in finance, teaching core skills for research that are essential to produce a good dissertation and in high demand from employers in financial technology. The unit also examines several families of empirical approaches to data analysis that form the cornerstone of academic research in asset pricing and corporate finance.
How does this unit fit into your programme of study? This unit picks up where Quantitative Methods for Financial Technology left off, providing further coverage of a wide range of statistical techniques and implementing some of the asset pricing and corporate finance models presented during the Finance unit undertaken in Teaching Block 1. Empirical Finance for Financial Technology also provides an important precursor to the Dissertation, developing the research skills described below.
An overview of content
The unit begins by introducing the Wharton Research database, a vast resource of financial information available for student use, explaining how to identify and download data from the system. Topics in asset pricing and corporate finance are discussed, including how to: model the cross-section of asset returns; evaluate mutual fund performance; examine corporate capital structure; forecast stock returns and model long-run relationships between variables. The unit also discusses the identification of research literature and how to draft a literature review. How will students personally be different as a result of the unitStudents will develop valuable industry-relevant research skills that they will then put into practice in their dissertations. These include time management, critical thinking, report writing, and statistical analysis.
Intended learning outcomes
Upon successful completion, students will be able to:
The core material will be covered in weekly two-hour lectures, which will cover a mixture of ‘theory’ and applications to relevant areas of finance. Students will also attend weekly computer lab sessions where they will learn how to implement the models presented in the lectures using a statistical package (such as Stata) with real financial data. The lab-sessions will be in small groups to facilitate interaction. A weekly resource list will be provided via Blackboard and Talis Aspire comprising recommended readings and YouTube videos for self-study.
In line with the Learning Outcomes, the unit takes an inquiry-based learning approach. Lectures will typically summarise the research frontier in terms of theory, empirical methods, and empirical results of a research topic within finance. Computer labs are used to replicate some of the established findings discussed during the lecture as well as extending themin various directions (e.g., investigating whether results hold during a more recent period). By doing so students will (i) develop an understanding ofwhat quantitative methods work best in different settings, which is assessed with the group coursework assignment and the exam,and (ii) how to work with the financial databases frequently used by the research community,which assessed with the group coursework assignment.
During the computer lab sessions,students learn the use of a statistical package (such asStata),which is essential when working with large quantitative datasets. With the help of pre-written code distributed to students, they willdevelop an understanding of best practices and standardapproaches when working with financial data. While individual tests focus on assessing students’ lower-level knowledge of the statistical package(e.g., understanding what different commands do within the statistical package), the group coursework assignment and the exam assess students’ higher-level understanding of the statistical package (how to interpret its results).
Tasks which help you to learn and prepare you for summative tasks (formative)
Students will participate in weekly tutorials where non-assessed problem sets that they have prepared in advance will be discussed and generic oral feedback will be offered. There will also be weekly computer lab classes where students will work individually and in small groups to implement models using a statistical package (e.g., Stata).They will be provided with oral feedback on their progress and solution code will be distributed on problems not covered during the tutorials. Practice tests using the statistical package will also be made available via Blackboard.They will be distributed during the first few weeks of the teaching block so that students understand the format and requirements before weekly marked (summative) tests are administered, with feedback given in terms of a video explaining the solutions.
Clinics prior to the group coursework submission deadline are dedicated to address student questions arising while working on their group assignments. Clinics provide an opportunity to discuss various aspects of a quantitative empirical report so that expectations are clarified. Guidance will also be given for working in groups and preparing joint reports and support will continue throughout the period while the group assignment is live. Tasks could include identifying best practices in reporting quantitative resultsusing sample journal articles; identifying the relevant academic literature with the help of tools such as Google Scholar; the discussion of quantitative reportsand the feedback submitted by previous cohort students (anonymised). Students will receive oral feedback during clinics.
Tasks which count towards your unit mark
The summative assessment for this unit is comprised of three components:
Each broad research topic or quantitative method discussed will be accompanied by a set ofmultiple-choice test questions administered through Blackboard to be submitted individually. These tests will be distributed during the first half of the teaching block, each of them with a one-week deadline. The group coursework assignment will be due towards the end of the teaching block and is distributed at least four weeks prior to the submission deadline. Students are given the choice of several research topics from those discussed during the unit and are required to narrow down to a research question from their chosen topic and address it quantitatively in the form of a group report. This assignment requires the students to use at least one of the databases that the School subscribes to (e.g., Thomson Eikon or databases within the Wharton Research Data Services) as well as a statistical package (e.g, Stata).
When assessment does not go to plan
Students must resubmit all failed assessment components in case they fail to pass the unit. In the case of individual tests, they will be given an additional set of tests. In the case of the group coursework assignment, resit students will receive an individual assignmentthat will include a reflection on how collaborative processes can contribute to the activity. In the case of the exam, students will have to take a resit exam that has an identical structure to the first sitexam.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ACFIM0004).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
