Financial Econometrics and Forecasting
Draft Assessment Brief – Postgraduate
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| Programme: | MSc International Finance and Investment and MSc Finance |
| Module Code: | AF7004 |
| Module Title: | Financial Econometrics and Forecasting I |
| Distributed on: | First Teaching Week |
| Submission Time and Date: |
Tuesday 17th May 2022. Time: 11am |
| Word Limit: | 3,000 Words |
| Weighting | This coursework accounts for 100% of the total mark for this module |
| Submission of Assessment |
Electronic Management of Assessment (EMA): This assignment should be submitted online via Turnitin by the given deadline. You will find a Turnitin link on the module’s eLP site. It is your responsibility to ensure that your assignment arrives before the submission deadline stated above. See the University policy on late submission of work (the relevant extract is set out below). |
AssignmentTutorOnline
Instructions on AF7004 coursework assignment:
There are three sections in this coursework assignment and you are required to
answer all questions.
Section A (30 marks)
Construct a portfolio of stocks from the US market and using EViews analyse its
performance based on the estimates from the Fama – French (1992, 1993) and
Carhart (1997) models.
You are required to answer the two questions listed below. Please follow the
instructions in these questions.
Requirements:
1) Select 10 stocks listed on the US market and explain your choice. You may
apply such selection criteria as market indicators (P/E, P/BV, DY ratios etc.) or
any other selection rule, which you can reasonably justify. The period of
analysis should be the last full 10 calendar years from January 2011 to
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December 2020 and the data frequency should be monthly. Download the
stock price data from Bloomberg and construct a portfolio from your stocks.
Calculate the monthly returns of this portfolio in the entire 10-year period:
2011-2020.
Using Eviews perform estimation of parameters of Fama – French and Carhart
models in the entire sample (10 years) covering 120 monthly observations.
The data for the Fama – French and Carhart models factors for the US market
can be found at Prof Kenneth French’s website using the following link:
https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
The three Fama and French (1992,1996) factors RmRf, SMB, HML can be
found in the .csv file under the “Fama/French 3 Factors” and the Carhart’s
(1997) MOM factor can be found in the .csv file under “Sorts Involving Prior
Returns” “Momentum Factor Mom”.
Divide the whole sample into 10 annual periods and investigate how the
estimation results change in these sub-samples in case of your portfolio.
Discuss the econometric issues, which are related to your estimations.
(15 marks, 300 words approx.)
2) Discuss your estimation results in terms of the evaluation of your portfolio
performance in the whole period and in all 10 annual sub-periods. Analyse the
existence of small stocks effect, value premium effect and momentum effect in
your portfolio and explain your results in line with the asset pricing theories.
(15 marks, 700 words approx.)
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Section B (30 marks)
Using the data from the US stock market, analyse the extent of the intervalling effect
in the estimation of stocks’ beta (β) parameters.
You are required to answer the two questions listed below. Please follow the
instructions in these questions.
Requirements:
3) Select 15 companies from the US market and download their stock price data
from Bloomberg. The sample period should include the last full 5 calendar
years: from 2016 to 2020. Your selection should cover companies of different
sizes, i.e. 5 large stocks, 5 medium size stocks and 5 small stocks. Discuss
briefly the stocks which you have chosen and then use the CAPM model (in
the market model version, i.e. without a deduction of the risk free rate) to
estimate in EViews their betas (β) for daily and monthly frequency data.
Discuss the econometric issues, which are related to your estimations.
(20 marks, 200 words approx.)
4) Summarize and discuss your findings about the extent of the intervalling effect
in the estimation of the beta (β) parameter in case of all your 15 stocks and
discuss any patterns that you found across your results from the point of view
of the differences in daily and monthly beta (β) estimates and the stocks size.
(10 marks, 800 words approx.)
Section C (40 marks, 1000 words)
Using the data in the excel file “Panel Data” uploaded on the modules Blackboard
site, identify and discuss the determinants of capital structure of the selected firms.
The file is uploaded under the “Assessment and Submission folder of the module’s
Blackboard Site”. The file consists of following firm-specific factors: liquidity (nwc),
risk (beta), dividend payment- pay dividend or not (dividend), firm size(size), and
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capital structure (lev). The “dividend” variable is a binary variable which takes a value
of 1 if the firm pays dividend and 0 if the firm does not pay the dividend. The data is
already in the panel data format.
Requirements:
1) Upload the data in Eviews and estimate the appropriate panel data model by
performing the required tests to identify the determinants of the capital
structure of firms.
2) Clearly explain and discuss the model results
Late submission of work
Where coursework is submitted without approval, after the published hand-in deadline, the following
penalties will apply.
For coursework submitted up to 1 working day (24 hours) after the published hand-in deadline without
approval, 10% of the total marks available for the assessment shall be deducted from the
assessment mark.
For clarity: a late piece of work that would have scored 65%, 55% or 45% had it been handed in on
time will be awarded 55%, 45% or 35% respectively as 10% of the total available marks will have been
deducted.
Coursework submitted more than 1 working day (24 hours) after the published hand-in deadline
without approval will be regarded as not having been completed. A mark of zero will be awarded for
the assessment and the module will be failed, irrespective of the overall module mark.
For clarity: if the original hand-in time on working day A is 12noon the 24 hour late submission
allowance will end at 12noon on working day B.
The Penalty does not apply to Pass/Fail Modules, i.e. there will be no penalty for late submission if
assessments on Pass/Fail are submitted up to 1 working day (24 hours) after the published hand-in
deadline.
These provisions apply to all assessments, including those assessed on a Pass/Fail basis.
Word limits and penalties
If the assignment is within +10% of the stated word limit no penalty will apply.
The word count is to be declared on the front page of your assignment and the assignment cover
sheet. The word count does not include:
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| • Title and Contents page |
• Reference list | • Appendices | • Appropriate tables, figures and illustrations |
| • Glossary | • Bibliography | • Quotes from interviews and focus groups. |
Please note, in text citations [e.g. (Smith, 2011)] and direct secondary quotations [e.g. “dib-dab
nonsense analysis” (Smith, 2011 p.123)] are INCLUDED in the word count.
If this word count is falsified, students are reminded that under ARNA page 30 Section 3.4 this will be
regarded as academic misconduct.
If the word limit of the full assignment exceeds the +10% limit, 10% of the mark provisionally awarded
to the assignment will be deducted. For example: if the assignment is worth 70 marks but is above the
word limit by more than 10%, a penalty of 7 marks will be imposed, giving a final mark of 63.
Students must retain an electronic copy of this assignment (including ALL appendices) and it
must be made available within 24hours of them requesting it be submitted.
Note: For those assessments or partial assessments based on calculation, multiple choice etc.,
marks will be gained on an accumulative basis. In these cases, marks allocated to each section will
be made clear.
Time limits and penalties for presentations
The time allocated for the presentation must be adhered. At the end of this time, the presentation will
be stopped and will be marked based on what has been delivered within the time limit.
Academic Misconduct
The Assessment Regulations for Northumbria Awards (ARNA) contain the Regulations and
procedures applying to cheating, plagiarism and other forms of academic misconduct.
The full policy is available at: http://www.northumbria.ac.uk/sd/central/ar/qualitysupport/asspolicies/
You are reminded that plagiarism, collusion and other forms of academic misconduct as referred to in
the Academic Misconduct procedure of the assessment regulations are taken very seriously by
Newcastle Business School. Assignments in which evidence of plagiarism or other forms of academic
misconduct is found may receive a mark of zero.
| Note: | Students must retain an electronic copy of this assignment and it must be made available within 24 hours of them requesting it be submitted. |
Mapping to Programme Goals and Objectives:
This assessment will contribute directly to the following Postgraduate programme goals and
objectives.
Goal One: Be independent, reflective critical thinkers
| x | 1. | Demonstrate awareness of their personal strengths and weaknesses through critical reflective practice. |
| x | 2. | Understand and challenge personal patterns of thinking and behaving. |
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Goal Two: Be culturally and ethically aware
| 1. | Demonstrate their ability to work in diverse groups and teams. | |
| x | 2. | Reflect on their own ethical values. |
Goal Three: Have developed leadership and management capability
| 1. | Demonstrate their personal contribution to team effectiveness. | |
| x | 2. | Communicate complex issues effectively. |
| x | 3. | Demonstrate decision making and problem solving skills. |
| 4. | Carry out presentations and lead discussions. |
Goal Four: Have developed and applied knowledge of international business and management
theory
| x | 1. | Acquire, interpret and apply knowledge of international business, management and organisational functions. |
Goal Five: Have developed a range of research skills and project capabilities
| x | 1. | Plan and complete a major individual piece of research on a contemporary business, management or leadership topic of their choice. |
| x | 2. | Demonstrate skills of analysis and synthesis in the application of research methods to the exploration of contemporary business issues. |
Goal Six: Have developed specialist knowledge about the theory and practice of your
programme of study
| x | 1. | Demonstrate specialist functional knowledge in relation to your programme of study. |
Draft Assessment Brief – Postgraduate
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Module Specific Assessment Criteria
| Scales | ||||||||
| Q # |
Prgm goals and Objectives |
0-39% Standard Not Met 1 |
40-49% Standard Not Met 2 |
50-59% Meets Standard 1 |
60-69% Meets Standard 2 |
70-79% Exceeds Standard 1 |
80-89% Exceeds Standard 2 |
90-100% Exceeds Standard 3 |
| 1 | 1.1,1.2,2.2,3.2,3.3 4.1 5.1,5.2 |
Lack or very weak understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Insufficient understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Reasonable understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Good understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Very good understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Excellent understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
Outstanding understanding of the econometric issues related to the estimation of parameters of the Fama-French and Carhart models. |
| 2 | 1.1,1.2,2.2,3.2,3.3 4.1 5.1,5.2 |
Lack of any presentation of estimation results or very limited / inadequate presentation of some estimations of models parameters. Very poor description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Insufficient presentation of estimation results. Weak description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Sufficient presentation of estimation results. Reasonably good description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Good and detailed presentation of estimation results. Good description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Very good and detailed presentation of estimation results. Detailed and comprehensive description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Excellent, comprehensive and detailed presentation of estimation results. Detailed and comprehensive description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
Outstanding, comprehensive and very detailed presentation of estimation results. Very detailed and comprehensive description of empirical findings from the application of the Fama-French and Carhart models as econometric methods. |
| 3 | 1.1,1.2,2.2,3.2,3.3 4.1 5.1,5.2 |
Very poor or no explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Weak explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Good explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Good and clear explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Very good and clear explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Excellent and very clear explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Outstanding and very clear explanation of econometric issues (such as heteroscedasticity of the error term etc.) based on the empirical work carried out. |
Draft Assessment Brief – Postgraduate
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| 4 | 1.1,1.2,2.2,3.2,3.3 4.1 5.1,5.2 |
Lack of any interpretation or very limited interpretation of the presented results about the extent of the beta intervalling effects and in panel data models. |
Insufficient interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |
Sufficient interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |
Good interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |
Very good interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |
Excellent interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |
Outstanding and fully complete interpretation of the presented results about the extent of the beta intervalling effects and in panel data models |