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FINS5513 portfolio weight
创建日期：2024-06-11 14:29:04
所属分类：金融Finance
标签： FINS5513
portfolio weight

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FINS5513 Data Exercise Instructions

Group C
1. Submission deadline: The assessment links will be accessible on Moodle from 10:00 AM,
18th January to 23:55 PM, 29th January, Sydney Time.
• You may attempt the assessment at any time during the provided window, but
please note that the assessment will close sharply at 11:55 PM on January 29th. For
instance, if you begin your attempt at 11:25 PM on January 29th, you will have
only 30 minutes to complete the task.
• Please plan your time accordingly and avoid waiting until the last minute. Please
be aware that any failure to submit due to technical issues near the deadline will not
be considered a valid reason for applying for special consideration.
• Late submission: Late submissions without prior approval for special consideration
will incur a 5% mark deduction for each day or part thereof (including weekends)
beyond the due date and time.
2. Submission format:
• Please submit BOTH questionnaire and Excel work. If you only submit one of
them, you will not be able to receive any marks for this assessment. Both of these
links can be found in the Moodle section "Assessment - iLab: Data Exercise"
• Questionnaire: To submit your solutions for each question, please use the "Data
exercise Questionnaire" link. For successful submissions:
i. Please report your solutions as decimals (not percentages) exactly
following the example provided in the questionnaire.
ii. Only ONE attempt is allowed. So please prepare your solutions ready
before you start the questionnaire to avoid any technical issues.
iii. Your attempt will be submitted if and only if you click "Submit
questionnaire". Please note that you cannot make any further changes and
resubmit the questionnaire after you click the submission button.
iv. All the questions should be attempted (i.e., no questions left blank).
• Excel: Please submit your Excel spreadsheet via the "Data exercise Excel
submission" link.
i. Your marks will be determined by your answers in the questionnaire rather
than the Excel file. However, the Excel file may be used/investigated by
grader if any clarification or checking of your solutions is required. The
Excel file does not require a specific format, as long as it is clear and easily
understandable. For clarity, you might consider creating separate sheets for
different questions and labeling them appropriately.
3. About enquiries: If you have questions regarding this assessment, please raise them on Moodle
discussion forum. However, please keep in mind that this assessment is one of the
individual assessments for this course (i.e., please treat it as a takehome exam). Therefore,
only clarification-type questions (e.g., the ambiguity of the question) will be answered.
4. The total mark of this assessment is 54. It accounts for 20% of the final mark for this course.
2
You are evaluating a portfolio of U.S. equities drawn from the S&P500. The five stocks in
your portfolio have the FactSet identifiers as follows:
Group A Group A FactSet stock identifier
STOCK1 EL-US
STOCK2 INTC-US
STOCK3 C-US
STOCK4 WFC-US
STOCK5 BA-US
S&P500 SP50
• Assume the annualised risk-free rate is 3% for this assessment
• The monthly return data can be found in the Excel “Data pool” on Moodle
NOTE:
• = 12 × ℎ
• = 12 × ℎ
• = ( ) = (12) ×
ℎ
• ℎ = −
3
Gift mark for assigned group number.
Q1: what is your assigned group number which can be found on the first page of your
instruction file? (2 marks)
For Q1 to Q46: using the monthly data from 2014 Jan to 2018 Dec for the portfolio construction.
Please calculate the annualised average return and annualised variance of your assigned stocks and
S&P 500 index over the sample period (i.e., 2014 Jan to 2018 Dec), and check your basic summary
statistics with the solutions provided in the excel “Data pool” before you start solving the
questions. Your calculation of the basic summary statistics will not be marked but the
purpose of this step is to make sure you do not make naive mistakes (e.g., copy and paste
errors) at the very beginning of this assessment.
1. Markowitz optimization
• For the group of stocks assigned to you, form the minimum variance frontier. What is
the minimum attainable annualised standard deviation of the portfolio:
Q2: at an annualised average return level of 0% (1 mark)
Q3: at an annualised average return level of 15% (1 mark)
Q4: at an annualised average return level of 30% (1 mark)
• Calculate the portfolio weight for Global Minimum Variance Portfolio (GMVP). What is
GMVP portfolio weight? What is the annualised expected return and annualised standard
deviation of GMVP?
Q5: GMVP portfolio weight in STOCK 1 (1 mark)
Q6: GMVP portfolio weight in STOCK 2 (1 mark)
Q7: GMVP portfolio weight in STOCK 3 (1 mark)
Q8: GMVP portfolio weight in STOCK 4 (1 mark)
Q9: GMVP portfolio weight in STOCK 5 (1 mark)
Q10: GMVP annualised average return (1 mark)
Q11: GMVP annualised standard deviation (1 mark)
• Calculate the portfolio weight for the Optimal Risky Portfolio (P*). What is the P* portfolio
weight? What is the annualised expected return and annualised standard deviation of P*?
Q12: P* portfolio weight in STOCK 1 (1 mark)
4
Q13: P* portfolio weight in STOCK 2 (1 mark)
Q14: P* portfolio weight in STOCK 3 (1 mark)
Q15: P* portfolio weight in STOCK 4 (1 mark)
Q16: P* portfolio weight in STOCK 5 (1 mark)
Q17: P* annualised expected return (1 mark)
Q18: P* annualised standard deviation (1 mark)
• Suppose your utility function is = () − . Form an optimal complete portfolio by
combining P* with the risk-free asset. What is the portfolio weight on each of individual asset
in this optimal complete portfolio? What is the max utility score that you can achieve?
Q19: complete portfolio weight in STOCK 1 (1 mark)
Q20: complete portfolio weight in STOCK 2 (1 mark)
Q21: complete portfolio weight in STOCK 3 (1 mark)
Q22: complete portfolio weight in STOCK 4 (1 mark)
Q23: complete portfolio weight in STOCK 5 (1 mark)
Q24: complete portfolio weight in Risk-free asset (1 mark)
Q25: Utility score is? (1 mark)
2. Short Selling Constraint
• Construct the GMVP and P* with the short selling constraint:
Q26: GMVP(with short selling constraint) annualised average return (1 mark)
Q27: GMVP(with short selling constraint) annualised standard deviation (1 mark)
Q28: P*(with short selling constraint) annualised average return (1 mark)
Q29: P*(with short selling constraint) annualised standard deviation (1 mark)
Q30: Compare the GMVP that you construct without and with short-selling constraints. Make
comments on their performance and explain why short selling constraints may affect the
optimization procedure (3 marks)
Q31: Compare the P* that you construct without and with short-selling constraints. Make
comments on their performance and explain why short selling constraints may affect the
optimization procedure (3 marks)
3. SIM Optimization
5
• Estimate the Single Index Model and , for each stock in your portfolio using the
regression equation:
= + +
where and are the excess return for individual stocks and S&P 500 index
Q32: What was the lowest beta out of your 5 stocks? (1 mark)
Q33: What was the highest beta out of your 5 stocks? (1 mark)
• Decompose the total variance using = + for all 5 stocks
Q34:What was the lowest 2 out of your 5 stocks? (1 mark)
Q35: What was the highest 2 out of your 5 stocks? (1 mark)
• Construct GMVP and P* under the SIM.
Q36: GMVP annualised average return (1 mark)
Q37: GMVP annualised standard deviation (1 mark)
Q38: P* annualised average return (1 mark)
Q39: P* annualised standard deviation (1 mark)
Q40: Briefly describe and comments on the key differences between the Markowitz and SIM
optimization procedure (hint: You should point out the additional assumption that SIM made
on the variance-covariance matrix construction, and provide your own opinion on whether the
assumption fits the data or not) (4 marks)
4. Treynor-Black Model
• Explore the potential mispricing opportunities of the 5 individual stocks under the
Treynor-Black Model. Form a new optimal risky portfolio (New P*) by combing the 5
individual stocks and S&P 500, what is your optimal portfolio weights on each stock and
S&P 500 in your new optimal risky portfolio:
Q41: STOCK 1 (1 mark)
Q42: STOCK 2 (1 mark)
Q43: STOCK 3 (1 mark)
Q44: STOCK 4 (1 mark)
Q45: STOCK 5 (1 mark)
Q46: S&P500 (1 mark)
6
5. Kind Reminder
Q46: Have you submitted your Excel via the related link as required for this assessment?
Notice that if you only submit the questionnaire but do not submit the Excel, you will not
receive any marks on this assessment.
案例分类
标签云
关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 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