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FINA 6112 Factor Investing
Creation date：2024-04-22 16:31:49
Belonging category：金融Finance
Factor Investing

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FINA 6112 FC & FD


Updated Questions for “Smart Beta Exchange-Traded Funds and Factor Investing” (red
words are added):

1. What are Fama and French's findings from their six-factor (or five-factor +
momentum) model? There are two schools of thought explaining these empirical findings.
What are the main differences between these two schools? How may these affect investors’
equity investment decisions?

2. Using the data in Exhibit 1, calculate the annualized average returns for the six groups
of quintile portfolios (beta, size, B/M, profitability, investment, momentum) for 2005 through
2014 and plot the results for each factor. Do the findings of Fama and French still hold true
for this period? Explain and discuss using the arguments of the efficient market school and
the arguments of the behavioral finance school. Is/are there other possible argument(s) or
reason(s) for the differences between these recent 10 years and older years?

3. What is meant by factor or smart beta investing? What is the rationale behind it?
Characterize smart beta ETFs.

4. This set of questions asks you to compare portfolios created from the MSCI factor
indexes and standard market capitalization-weighted iShares ETFs, along with the MSCI
USA Diversified Multiple-Factor Index and the MSCI US large-mid-cap index (the MSCI
USA Index).
a. Using the data in Exhibit 3, in one chart, plot the minimum-variance frontiers
constructed from:
• The four MSCI individual factor indexes
• The iShares large-mid-cap Russell 1000 ETF and iShares small-cap Russell 2000
ETF.
In the diagram, include the data points for the MSCI USA Diversified Multiple-Factor
Index (which is a proxy for iShares' potential smart beta multifactor ETF) and the
MSCI USA Index.
b. What do you conclude about the value added from an investment perspective of the
combination of the individual factor indexes (which are proxies for iShares' smart beta
individual factor ETFs) over traditional value-weighted investing? How does the
multifactor index compare to traditional investing? How does it compare to the
combination of the factor indexes?

5. This set of questions asks you to compare minimum-variance portfolios created from
iShares' bond ETF (AGG) combined with various MSCI indexes.
a. Using the data in Exhibit 4, in one chart, plot the minimum-variance frontiers
constructed from the iShares bond ETF (AGG) with, respectively:
• The four MSCI individual factor indexes together
• The MSCI USA Diversified Multiple-Factor Index
• The MSCI USA Index
In the diagram, include the data points for the MSCI USA Diversified Multiple-Factor
Index and the MSCI USA Index.
b. Beyond Question 4, does your answer to 5a provide any additional insights into the
value added of the four MSCI factor indexes (which are proxies for iShares' smart beta
individual factor ETFs) over traditional value-weighted investing? What about the MSCI
multifactor index (which is a proxy for iShares' potential smart beta multifactor ETF)?

6. Should iShares introduce the new US multifactor large-mid-cap ETF? Why or why not?
What should be considered?
Case category
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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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