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IB93F0 Research Methodology
Creation date：2024-06-19 15:35:51
Belonging category：商业分析business analysis
Tag IB93F0
Research Methodology

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 Business and Finance

IB93F0 Research Methodology
Individual Assignment Question and Guidance
Maximum length: 1500 words. This is a strict word limit for the written report,
not a guideline. However, please note that codes, tables, figures and appendices do
not count against the word limit.
Assessment weighting: 70%
Submission deadline: Tuesday, 25 June 2024 before 12:00 (UK time)
Marks released by: Tuesday, 23 July 2024 (we will aim to release marks by
this date, but in the event of an unavoidable delay a message will be posted on the
module page)
Instructions start overleaf.
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IB93F0
INSTRUCTIONS
Please read all instructions carefully.
The objective of the project is to allow students to apply skills in gathering, clean-
ing, analysing and presenting data; in formulating and testing hypotheses; and in
interpreting results through the lens of empirical and statistical analysis.
As such, the project is designed to help you acquire transferable skills relevant for
writing your dissertation. An important aspect of this exercise is the economic
interpretation of results and their discussion in the context of the strengths and
weaknesses of the tests themselves. The quality of the interpretation is as impor-
tant as the empirical analysis and the results. Please note that the task is very
open-ended: there is no guarantee that you end up having the same or similar em-
pirical results compared to what has been documented previously. While you should
check the accuracy of your analysis, it is very likely that your results will differ from
the existing literature and you should proceed and try to interpret your findings by
relating them to the concepts covered in class.
There are three research topics provided and you must choose ONLY
ONE topic to analyse and write a report on .
Format
The report should follow the format of academic research papers, i.e. it should con-
tain abstract, introduction, literature review, methodology, results, conclusion, and
references.
Figures and tables must be self-explanatory (again, following the format or academic
research papers). That is, they should have a title and a descriptive caption.
Software and good programming practice
You are free to use any software to solve the assignments.
MATLAB is available to students to install via WBS. If you have issues
please contact the programme team.
When coding, please remember to follow good programming practices, including:
• Use a consistent indentation style.
• Use descriptive variable and function names, with appropriate prefixes.
• Comment all function inputs and outputs and comment other parts of your
code where appropriate.
• Consider writing little functions for pieces of code that are used repeatedly.
Submitting your work
Before you submit your assessment, you should ensure you are familiar with the
guidance and rules in the “Your Assessments” section of your Student Handbook,
paying particular attention to:
Page 2 of 7
IB93F0
• Inserting a completed assignment coversheet as the first page of your submis-
sion.
• Academic integrity (including plagiarism).
• Referencing.
• Word count policy and formatting.
• Confidentiality of your work.
• Extensions and late submission of work.
• Guidelines for online submission.
In addition, please adhere to the following instructions:
• Upload a single PDF to ‘Individual Project’ for the written answer.
• Put all coding-related work files into one single folder without any subfolders,
and upload it as a .zip file to ‘Data File.’ The folder name must be your
student ID. The folder needs to contain all relevant files including files with
data.
• Within the folder, use one single file that produces all results once the folder
is unzipped. The file name must be your student ID. Check that the file runs
without error.
Mitigating circumstances
Mitigating circumstances MUST be submitted within 20 working days following the
submission deadline. Mitigating circumstances not submitted by the relevant dead-
line are not required to be considered by the School/Department and may have to be
considered by an Academic Appeals Committee as part of an academic appeal—for
further information, please see:
Use of artificial intelligence
For this assessment, AI is: PERMITTED
The University recognises an increasing number of technologies such as Artificial
Intelligence and that they may be applicable in your completing this assessment.
The assessment brief sets out specific requirements or restrictions, and your student
handbook has further guidance and advice.
You are reminded that the inappropriate use of such a technology may constitute a
breach of University policy, such as the Proofreading Policy or Regulation 11 (Aca-
demic Integrity). If you breach these policies, it may have significant consequences
for your studies. Please make sure you read and understand the assessment brief
and how AI may or may not be used.
If a generative AI or similar is permitted and has been used you MUST make clear
why you used such a tool or service, what you used it for and you will be obliged
to confirm that you take sole intellectual ownership of any submitted work. As
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IB93F0
appendices, and as part of your submitted work, you must provide screenshots of the
question and the AI-generated response, alongside an explanation of how the content
has been utilised. You should note the relevant reference alongside each screenshot.
When you submit you must complete (physically or electronically) a declaration.
This requires you to explain the use of any AI. Failure to disclose at the point of
submission may be prejudicial in any later investigations should they arise.
If you use a generative Artificial Intelligence (AI) in the process of completing this
assessment you MUST set out clearly the following:
• WHY you used a generative AI
• WHAT it was used for
• WHICH AI was used; and
• If any generated content has been used directly in this submission, if so where.
Note that this declaration does NOT contribute towards the word count for the as-
sessment.
You will also have to confirm in your declaration that the work remains yours and
you have intellectual ownership of it. You may be called for viva or other interview
to demonstrate such intellectual ownership. A failure to disclose the use of AI, or the
use of a misleading description of its use may have significant consequences for your
studies. As a result, keeping good records of your interactions is strongly advised.
Your assessment starts overleaf.
Page 4 of 7
IB93F0
Topic I
Momentum in currencies.
Background: A large body of literature documents evidence of stock return pre-
dictability based on a variety of firm-specific variables. Among these anomalies, the
price momentum effect is probably the most difficult to explain within the context
of the traditional risk-based asset pricing paradigm (see, e.g., Jegadeesh and Titman
(2001) ). Menkhoff, Sarno, Schmeling and Schrimpf (2012) document momentum
returns in currency markets that, however, are not easily exploitable.
Research objective: Examine whether momentum effects are still present in currency
markets. Discuss potential issues with regards to exploiting momentum returns.
Data: A suitable cross-section of currency returns for the period January 2000 to
December 2022.
Potential empirical methods: Portfolio sorts, Fama-MacBeth regressions.
Potential references (incomplete):
• Jegadeesh, N., and S. Titman (2001): “Profitability of momentum strategies:
An evaluation of alternative explanations,” The Journal of Finance, 56, 699–
720.
• Menkhoff, L., L. Sarno, M. Schmeling and A. Schrimpf (2012), “Currency
momentum strategies,” Journal of Financial Economics, 106(3), 660–684.
Continued . . . /
Page 5 of 7
IB93F0
Topic II
Post-earnings announcement drift PEAD.
Background: No other single event has been found to explain more of the cross-
sectional variation in stock returns than the earnings announcement. Earnings an-
nouncements are the primary mechanism through which public companies provide
periodic financial performance updates to investors. It is therefore not surprising
that a considerable body of academic research examines the relation between stock
prices and earnings. PEAD refers to the phenomenon of abnormal stock returns’ ten-
dency to be positive (negative) in the months following positive (negative) surprise
earnings announcements. Ball and Brown (1968) first documented this phenomenon
using annual earnings announcements. More recently, Daniel, Hirshleifer, and Sun
(2020) and Bryzgalova, Huang, and Julliard (2023) show the importance for PEAD
to price the cross-section of stock returns.
Research objective: Examine whether the PEAD is still an observable phenomenon,
discuss the potential explanations, and evaluate whether it is a violation of market
efficiency.
Data: Pick 50 US firm and collect stock returns and accounting information from
the CRSP and COMPUSTAT for the period of January 2000 to December 2022.
Potential empirical methods: Event study.
Potential references (incomplete):
• Ball, R., and P. Brown (1968): “An empirical evaluation of accounting income
numbers.” Journal of Accounting Research, 6, 159–177.
• Bryzgalova, S., J. Huang, and C. Julliard (2023): “Bayesian solutions for the
factor zoo: We just ran two quadrillion models,” The Journal of Finance,
78(1), 487–557.
• Daniel, K., D. Hirshleifer, and L. Sun (2020): “Short- and long-horizon behav-
ioral factors,” The Review of Financial Studies, 33, 1673–1736.
Continued . . . /
Page 6 of 7
IB93F0
Topic III
Determinants of capital structure.
Background: How do firms choose their capital structure? This is a fundamental
question in financial economics and, indeed, the question is at the heart of the “cap-
ital structure puzzle” put forward by Myers (1984). Much of the research since
the seminal work of Modigliani and Miller (1958) has focused on testing the im-
plications of two traditional views of capital structure: the static trade-off model,
in which firms form a leverage target that optimally balances various costs (e.g.,
financial distress costs, stockholder-bondholder agency conflicts) and benefits (e.g.,
tax savings, mitigated manager-shareholder agency costs) of debt, and the pecking
order of Myers and Majluf (1984), in which firms follow a financing hierarchy de-
signed to minimize adverse selection costs of security issuance.
Research objective: Explain the heterogeneity in observed capital structures across
firms by identifying determinants and associating them with firm leverage. In par-
ticular, discuss/illustrate the importance of firm fixed effects.
Case category
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EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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