Assessment Brief
|
Module Level:
|
Level 7
|
|
Module Code:
|
FINM070
|
|
Credit Value:
|
10
|
|
Module Name:
|
Quantitative Methods
|
|
Assessment Code:
|
TC1
|
|
Assessment Type:
|
Time Constrained Assessment
|
|
Assessment Deliverable(s) as stated in the Module Specification:
|
5 days to produce a 1000-word report on a statistical model
|
|
Weighting (%):
|
100%
|
|
Submission dates:
|
Please access the module NILE (Northampton Integrated Learning Environment) site, and check submission dates under the Assessment and submission item within the Course Content section.
|
|
Feedback and Grades due:
|
Please see the Assessment and submission section of the module NILE site.
|
Please read the whole assessment brief before starting work on the Assessment Task.
Assessment Task Guidance Description
Learning Outcomes aligned to this assessment:
On successful completion of this assessment, you will be able to:
|
Subject-Specific Knowledge, Understanding & Application
|
|
a) Select and apply mathematical and statistical methods for financial analysis.
|
|
b) Critically analyse, and interpret numerical and graphical data.
|
|
c) Evaluate and justify the use of regression techniques in finance.
|
|
Employability & Changemaker Skills
|
|
d) Communicate complex numerical information effectively and in a manner suitable to specialist audiences.
|
Task:
Students will grow in their knowledge and understanding of the subject material as we progress through this module. For this assessment, students are expected to provide a detailed and informative financial analysis of the given task, use graphical techniques to present and interpret data, apply appropriate numerical techniques to process data, and conclude results to generate useful information for supporting decision making.
This is a time constrained assessment. Data will only be available when the assessment starts, and students will have 5 days to complete this assessment.
Scenario:
You have recently begun your job as an analyst at an asset management firm. Your team has just received an exciting new project from a client who is keen to invest in companies listed on the main stock exchanges. The client wishes to focus on companies with high market capitalisation while also considering their share price movements. Your manager has entrusted you with a critical task of evaluating the sensitivity of a security to TWO key economic variables.
Students are required to:
|
1. Write an overview of the price movement of the given target by using primary measures of descriptive statistics to process, interpret, and analyse the given data. Appropriate diagrams are used to graphically illustrate and interpret data.
2. Briefly explain the potential impacts of economic variables on stock markets by using existing literature. Based on your argument, formulate a research question and corresponding hypotheses for multiple regression.
3. Build multiple regression model, ensure the selected variables are appropriate, examine the correlation of data, and test the model assumptions.
4. Carry out Multiple Regression Analysis, present and interpret results, assess the model validity and usefulness, and calculate and interpret a predicted value.
5. Explanation, interpretation, discussion, and analysis are justified by linking to existing literature.
|
Report Structure and Instructions
Please note that assessment presentation is assessed. Poor presentation will be penalized. The required format is given below:
Title Page
Table of Contents
Chapter 1. Introduction
Chapter 2. Regression Analysis
Chapter 3. Conclusion
Reference List
• This assessment MUST be typed and submitted in Microsoft Word.
• Format - letter in Calibri font with letter size 12 and 1.5 line spacing.
• A title page, detailed table of contents and list of appendices (if any) should be included.
• The title page should contain the topic of the report, assessment title, module code and module title, world counts, and date of submission.
• All supporting documentation (tables, figures, diagrams) should be included in the main content, not as part of the appendices.
• All tables and diagrams should have a number and a title.
• All pages should be numbered.
Note:
• Appendices should only be used for non-essential supplementary information. The document should be able to stand without appendices. Content in the appendices will not be marked.
• Please use tables and graphs in the text where data is discussed. Please do not include any large tables or complete financial reports in the text or appendices. Avoid descriptive writing, for instance merely repeating figures in the paragraphs.
• Please use a wide collection of reliable and relevant references and avoid using Investopedia/Wikipedia.
Word Limits:
The maximum word limit for this assessment is 1000 words.
Please note, a submission exceeds the stipulated word limit by more than 10%, the submission will only be marked up to and including the additional 10%. Anything over this will not be included in the final grade for the assessment item. Abstracts, bibliographies, reference lists, appendices and footnotes are excluded from any word limit requirements. A submission is notably under the word limit, the full submission will be marked on the extent to which the learning objectives have been met.
Use of Generative AI (Artificial Intelligence) within this assessment:
Some uses of Generative AI may be deemed as unethical in your assessment. Further guidance on the conditions for allowable use of Generative AI will be given by the module team.
Please access the following position guidance from University of Northampton on the use of Generative AI within assessments.
Assessment Submission
To submit your work electronically, please go to the ‘Assessment and submission’ area on the NILE site and use the relevant submission point to upload the assignment deliverable. The deadline for this is 11.59pm (UK local time) on the date of submission. Please note that Essays and text-based reports should be submitted as Microsoft Word documents (.doc or .docx), or as guided within the assignment. Please access the following guide to submitting assessments.
Written work submitted to Turnitin will be subject to anti-plagiarism detection software. Turnitin checks student work for possible textual matches against internet available resources and its own proprietary database. Please access the University of Northampton’s Plagiarism Avoidance Course (UNPAC) to learn more.
When you upload your work correctly to Turnitin you will receive a receipt which is your record and proof of submission. If your assessment is not submitted to Turnitin, rather than a receipt, you will see a green banner at the top of the screen that denotes successful submission.
N.B Work emailed directly to your tutor will not be marked.
Marking:
Your mark will depend on the extent to which you meet the learning outcomes in the way relevant for this assessment.
The marking criteria is included in a table called the rubric that has different statements for how well each learning outcome has been met. This table is used by anyone marking the module to ensure consistency in the marking of assignment. Please see the marking criteria / rubric on NILE or see the final page of this document for further details of the marking criteria for this assessment.
Further Assessment Guidance:
Please access the following document for more general information about the assessment process, including anonymous marking, submissions, and where to find feedback and grades.
UON Standard Assessment Guidance.
Marking Criteria for FINM070 (Rubric)
|
Learning Outcomes addressed through this assignment
|
No submission / no evidence Work submitted is of no academic value / nothing submitted.
|
Fail
Evidence included or provided but missing some very important aspects.
|
Pass
Of satisfactory quality, demonstrating evidence of achieving the requirements of the learning outcomes.
|
Merit
Of high quality, demonstrating evidence which is rigorous and convincing, appropriate to the task or activity.
|
Distinction
Of very high quality, demonstrating evidence, which is strong, robust, and consistent, appropriate to the task or activity.
|
|
LO A & B & D
Introduction 1/2
10%
|
No attempt to address the learning outcome
|
Poor overview of the price movement of the given target, as the primary measures of descriptive statistics haven’t been used appropriately to process, interpret, and analyse the given data. Diagrams have not been appropriately used to illustrate/interpret data. Explanation, interpretation, and analysis are limited/incomplete and not linked to existing literature.
|
Fair overview of the price movement of the given target by using primary measures of descriptive statistics to process, interpret, and analyse the given data. Diagrams are used to graphically illustrate and interpret data but have a lack of clarity. Some diagrams are labelled with limited explanation. Explanation, interpretation, and analysis are adequate but descriptive. There is a lack of focus and clarity, and not clearly linked to existing literature.
|
Good overview of the price movement of the given target by using primary measures of descriptive statistics to process, interpret, and analyse the given data. Diagrams are used to graphically illustrate and interpret data. Some diagrams are labelled and explained. Explanation, interpretation, and analysis are reasonable and partially linked to existing literature.
|
Excellent overview of the price movement of the given target by using primary measures of descriptive statistics to process, interpret, and analyse the given data. Appropriate diagrams are used to graphically illustrate and interpret data. All diagrams are properly labelled and explained. All explanation, interpretation, and analysis are detailed, informed, and linked to existing literature.
|
|
LO C
Introduction 2/2
10%
|
No attempt to address the learning outcome
|
Insufficient/Poor explanation on the potential impacts of economic variables on stock markets. A research question and hypothesis/hypotheses have been formulated for multiple regression, with mistakes.
|
Adequate explanation on the potential impacts of economic variables on stock markets. The explanation is partially linked to existing literature. Based on the explanation, a research question and hypothesis/hypotheses have been formulated for multiple regression, with minor mistakes.
|
Good explanation on the potential impacts of economic variables on stock markets. The explanation links to existing literature. Based on the explanation, a research question and hypothesis/hypotheses have been formulated appropriately for multiple regression.
|
Detailed and informative explanation on the potential impacts of economic variables on stock markets. Based on the explanation, a research question and hypothesis/hypotheses have been formulated appropriately for multiple regression. The explanation is well supported by using existing literature and forms an excellent justification of the research question and hypothesis/hypotheses.
|
|
LO A & B & C & D
Build Regression Model
25%
|
No attempt to address the learning outcome
|
Irrelevant/No multiple regression model is proposed and formulated. Insufficient/No explanation on the choice of variables. The assumptions of the multiple regression haven’t been addressed properly. Most key aspects have been omitted. Diagrams have not been appropriately used to illustrate/interpret data.
|
A multiple regression model is proposed and formulated, but with minor mistakes. The choice of variables is explained, but unclear. The assumptions of the multiple regression have been addressed. However, some key aspects have been omitted. Explanation and interpretation are partially justified by linking to existing literature. Diagrams are used to graphically illustrate and interpret data but have a lack of clarity.
|
A multiple regression model is proposed with formulation. The explanation on the choice of variables is appropriate. The assumptions of the multiple regression have been addressed, including testing the correlation of variables. However, a few key aspects have been omitted. Most explanation and interpretation are justified by linking to existing literature. Diagrams are used to graphically illustrate and interpret data. Some diagrams are labelled and explained.
|
A multiple regression model is proposed and presented with correct formulation. Detailed and supportive explanation on the choice of variables. The assumptions of the multiple regression have been properly explained and addressed, including testing the correlation of variables. All explanation and interpretation are well justified by linking to existing literature. Diagrams are used to graphically illustrate and interpret data appropriately. All diagrams are properly labelled and explained
|
|
LO A & B & C & D
Regression Analysis
25%
|
No attempt to address the learning outcome
|
Poor/Insufficient knowledge of multiple regression analysis. Relevant techniques have not been used and results are interpreted incorrectly. Insufficient/Incorrect evaluation on the validity and usefulness of the predicted model. Diagrams have not been appropriately used to illustrate/interpret data.
|
Fair knowledge of multiple regression analysis. Relevant techniques have been used and results are interpreted, with mistakes. The results from ANOVA table are used to reflect the validity and usefulness of the predicted model. However, some key aspects have been omitted. Few references are used to support the discussion and analysis. Diagrams are used to graphically illustrate and interpret data but have a lack of clarity.
|
Good knowledge of multiple regression analysis. Relevant techniques have been used and results are interpreted, with few mistakes. The results from ANOVA table are used to evaluate the validity and usefulness of the predicted model. Some references are used to support the discussion and analysis. Diagrams are used to graphically illustrate and interpret data. Some diagrams are labelled and explained.
|
Excellent knowledge of multiple regression analysis evidenced by exceptional application of techniques and excellent interpretation of the results, with supportive justification that links to existing literature. The results from ANOVA table are presented and evaluated appropriately to test the validity and usefulness of the predicted model. Diagrams are used to graphically illustrate and interpret data appropriately. All diagrams are properly labelled and explained.
|
|
LO C & D
Conclusion
10%
|
No attempt to address the learning outcome
|
Poor conclusion on multiple regression analysis, where most key points have been omitted.
|
Fair conclusion on multiple regression analysis, where few key points have been omitted.
|
Good conclusion on multiple regression analysis, which summarizes the results and explains the impacts of model misspecification. The predicted value is calculated and interpreted with minor mistakes. Most explanation, interpretation, and discussion link to existing literature.
|
Excellent conclusion on multiple regression analysis, which summarizes the results and explains how model misspecification could affect the results of a regression analysis. The predicted value is calculated and interpreted correctly with appropriate assumed values for the independent variables. All explanation, interpretation, and discussion link to existing literature.
|
|
Sources and referencing.
10%
|
No attempt to address the learning outcome
|
Limited academic and practical sources are used to support the analysis, discussions and arguments. Some attempts at referencing but mostly do not conform. to Harvard referencing style.
|
Sufficient academic and practical sources are used to support the analysis, discussions and arguments. Referencing is partially accurate and conforms to Harvard referencing style.
|
A very good collection of academic and practical sources is used to support the analysis, discussions and arguments. Referencing is most consistently accurate and conforms to Harvard referencing style.
|
Extensive academic and practical sources are used to support the analysis, discussions and arguments. Referencing is accurate and conforms to Harvard referencing style.
|
|
Academic / Professional quality
10%
|
Unsatisfactory command of academic / professional conventions appropriate to the discipline.
|
Poor command of academic / professional conventions appropriate to the discipline.
|
Satisfactory command of academic / professional conventions appropriate to the discipline.
|
Rigorous command of academic / professional conventions appropriate to the discipline.
|
Authoritative
command of academic / professional conventions appropriate to the discipline.
|