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FINC 3330 Quant Project Seungho Baek Due: Sunday, Aug. 16, 2020, 11:59 P.M. Consolidate all excel spreadsheets from part1 in a single Excel file. Please make a cover page (name and ID) in Sheet1 in...

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FINC 3330 Quant Project
Seungho Baek
Due: Sunday, Aug. 16, 2020, 11:59 P.M.
Consolidate all excel spreadsheets from part1 in a single Excel file. Please make a cover page (name and
ID) in Sheet1 in your EXCEL file. To submit this project electronically, use Blackboard. DO NOT send me
it via email. Please submit your project in a single EXCEL file. ONLY an EXCEL FILE will be accepted.
You have write up with your own words. Do not copy from others’ work. In the last page, you have to show
all your references for your project in the last sheet in your Excel.
Part 1 Analysis of Economic Indicators
Instruction: You have been retained by the SHB investment group to provide a fundamental analysis
eport to the president of the company, Seungho Baek. You are asked to examine the value of economic
indicators as below.
1. TED spread: The TED spread is an indicator of perceived credit risk in the general economy. An
increase in the TED spread is a sign that lenders believe the risk of default on inte
ank loans (also
known as counterparty risk) is increasing.
2. Credit spread: A credit spread is the difference in yield between a U.S. T- bond and a debt security
with the same maturity but of lesser quality. Widening credit spreads indicate growing concern about
the ability of corporate (and other private) bo
owers to service their debt while na
owing credit
spreads indicate improving private creditworthiness.
3. Term spread: Term spreads, also known as interest rate spreads, represent the difference between the
long-term interest rates and short-term interest rates on debt instruments such as bonds Widening
term spreads indicate boom economy in the future whereas na
owing credit spreads indicate economic
ecession in the future.
4. Term Structure of Yields: The term structure of interest rates is the relationship between interest rates
or bond yields and different terms or maturities. When graphed, the term structure of interest rates
is known as a yield curve, and it plays a central role in an economy. 1
5. Cumulative returns of S&P market index: A cumulative return is the aggregate amount an investment
has gained or lost over time, independent of the period of time involved. Upward trend of cumulative
eturns generally indicates bull stock market while downward trend indicates bear market.
In order to generate these economic indicators, you need to use two data resources to collect economic
variables such as Federal Reserve Economic Data (FRED) 2 and Yahoo! Finance. The interest rates necessary
for computing spreads is located in FRED. You are able to obtain the historical stock index prices from Yahoo!
Finance. To locate the historical s&p 500 index price, please follow the below steps.
• Enter ˆGSPC in the search bar for Quote “Lookup”.
1Note that the term structure reflects expectations of market participants about future changes in interest rates and thei
assessment of monetary policy conditions. The yield curve represents the changes in interests rates associated with a particula
security based on length of time until maturity. Unlike other metrics, the yield curve is not produced by a single entity o
government. Instead, it is set by measuring the feel of the market at the time, often refe
ing to investor knowledge to help
create the baseline. The direction of the yield curve is considered a solid indicator regarding the cu
ent direction of an economy.
2https:
fred.stlouisfed.org
1
• Click Historical Data tab.
• Set “Time Period” Jan XXXXXXXXXXDec. 2019.
• Set “Frequency Monthly”.
• Click Apply and click Download.
You must write a report including your findings, all relevant information and computations, and provide
evidence. For this project, you must download monthly historical data from Jan. 2001 to Dec. 2019.
Problem 1. Investigate important economic risk events since the year of 2000 and summarize them.
Problem 2. TED spread is defined as the difference between 3 month LIBOR (loan) rate - 3 month T-bill
ate.3 Compute monthly TED spreads from Jan. 2000 to Dec. 2019 and plot the TED spreads
over time.
Problem 3. Let us define a credit spread as the difference between US coporate bond yields and 10 yea
Treasury rate. For this exercise, use Bank of America Me
ill Lynch US Corporate AAA
Effective Yield as a corporate bond rate and 10-Year Treasury Constant Maturity Rate as
Treasury rate.4 Compute monthly credit spreads from Jan. 2000 to Dec. 2019 and plot the
credit spreads.
Problem 4. In practice, a term spread is defined as 10 year Treasury rate minus 1 year Treasury rate.5
Calculate monthly term spreads from Jan. 2000 to Dec. 2019 and plot the term spreads.
Problem 5. Examine time series plots from problem 2, 3, and 4 and suggest your findings. Suggest you
findings on how these indicators responded against systemic shocks that you answered in
problem 1.
Problem 6. Examine yield curve shapes using daily yield curve rates provided by U.S. department of
treasury6.
a) Report treasure yield rates (1 month, 3 month, 6 month, 1 year, 2 year, 3 year, 5 year, 7
year, 10 year, 20 year, 30 year) on Jan. 02, 2014, Jan. 02, 2015, Jan. 04, 2016, Jan. 03,
2017, Jan. 02, 2018, Jan. 02, 2019, and Jan. 02, 2020.
) Compute each term spreads for seven term structures.
c) Plot seven yield curves that show the relation between yields and maturities.
d) Provide your findings thoroughly from b) and c).
Problem 7. Compute monthly S&P 500 index returns. To calculate monthly returns using S&500 index
price, you need to use formula as Rt =
Pt−Pt−1
Pt−1
where t represents time (In our case, t refers to
month; Pt represents the S&P index price at time t. For example, if you would like to compute
a return on Feb., 2000, you may be able to specify an equation as RFeb,2000 =
PFeb,2000−PJan,2000
PJan,2000
Problem 8. Using monthly returns that you obtained from the previous problem, compute monthly cu-
mulative returns. To compute monthly cumulative returns, use the formula as CRt = Rt +∑T
j=1Rt−j where CRt represents a cumulative return at time t.
Problem 9. Plot monthly cumulative returns and examine how cumulative return series are affected by
economic crisis. Also indicate bear or bull stages in the US stock market since the year of
2000.
3TED spread =3 month loan rate - 3 month T-bill rate
4BofA Me
ill Lynch US Corporate AAA Effective Yield - 10-Year Treasury Constant Maturity Rate
510-Year Treasury Constant Maturity Rate - 1-Year Treasury Constant Maturity Rate
6https:
www.treasury.gov
esource-cente
data-chart-cente
interest-rates/pages/textview.aspx?data=yield
2
Part 2 Portfolio Return and Risk
Instruction: You are a portfolio investment analyst at Goldman Sachs. Your investment unit manages two
equity portfolios - one portfolio, named P1, consists of two stock assets (Apple (AAPL) and Microsoft(Ticker:
MSFT)) and the other portfolio, named P2, consists of five stocks (Disney(Ticker:DIS), Boeing(Ticker:BA),
Amazon(Ticker:AMZN), Tesla(Ticker:TSLA), Netflix (Ticker:NFLX)). Now you are asked to compute two
portfolio returns and risk measures. To do this, first download monthly stock prices from Dec.2009 to Dec.
2019 from Yahoo! Finance and compute monthly stock returns from Jan.2010 to Dec XXXXXXXXXX
Problem 1. Compute the respective average, standard deviation, and covariance of monthly stock returns.8
Problem 2. Make two covariance matrices using two portfolio components. Note that you have to make a
completed form of a matrix as below.9
ΣP1 =
(
σ2AAPL = σAAPL,AAPL σAAPL,MSFT = σMSFT,AAPL
σMSFT,AAPL = σAAPL,MSFT σ
2
MSFT = σMSFT,MSFT
)
(1)
ΣP2 =

σ2DIS σDIS,BA σDIS,AMZN σDIS,TLSA σDIS,NFLX
σBA,DIS σ
2
BA σBA,AMZN σBA,TLSA σBA,NFLX
σAMZN,DIS σAMZN,BA σ
2
AMZN σAMZN,TLSA σAMZN,NFLX
σ2TLSA,DIS σTLSA,BA σTLSA,AMZN σ
2
TLSA σTLSA,,NFLX
σ2NFLX,DIS σNFLX,BA σNFLX,AMZN σNFLX,TLSA σ
2
NFLX
 (2)
Problem 3. Using the obtained statistics from problem 1, calculate an equal weighted portfolio return and
portfolio variance for the first portfolio using the below equations.
E(RP1) = wAAPLr̄AAPL + wMSFT r̄MSFT (3)
σ2P1 = w
2
AAPLσ
2
AAPL + w
2
MSFTσ
2
MSFT + 2wAAPLwMSFTσAAPL,MSFT (4)
Problem 4. Using the obtained statistics from problem 1, calculate an equal weighted portfolio return and
portfolio variance for the second portfolio using the below equations.
E(RP2) = wDIS r̄DIS + wBAr̄BA + wAMZN r̄AMZN (5)
+ wTLSAr̄TLSA + wNFLX r̄NFLX
σ2P2 = w
2
DISσ
2
DIS + w
2
BAσ
2
BA + w
2
AMZNσ
2
AMZN + w
2
TLSAσ
2
TLSA + w
2
NFLXσ
2
NFLX (6)
+ 2wDISwBAσDIS,BA + 2wDISwAMZNσDIS,AMZN + 2wDISwTLSAσDIS,TLSA
+ 2wDISwNFLXσDIS,NFLX + 2wBAwAMZNσBA,AMZN + 2wBAwTLSAσBA,TLSA
+ 2wBAwNFLXσBA,NFLX + 2wAMZNwTLSAσAMZN,TLSA + 2wAMZNwNFLXσAMZN,NFLX
+ 2wTLSAwNFLXσTLSA,NFLX
7Rt =
Pt−Pt−1
Pt−1
where Rt represents a stock return at time t, Pt is a stock price at time t.
8Use STDEV.P in Excel, not STDEV.S. Note that the formula for standard deviation based a sample is given by σ2Sample =∑N
i=1
(X−X̄)2
N−1 , while the formula for standard deviation based on a population is written as σ
2
Population =
∑N
i=1
(X−X̄)2
N
.
9Use Data Analysis Toolpak to compute a covariance matrix.
3
Problem 5. Using a matrix multiplication (i.e. MMULT in Excel.), compute two portfolio returns and
portfolio variances.10
E(RP ) = w · rT (7)
σ2P = w · Σ · wT (8)
Problem 6. With the first portfolio, create a table that shows the benefit of diversification using Data
Table in Excel. (Note that the table shows portfolio returns and portfolio standard deviation
with respect to scenarios of weights on AAPL.)
Problem 7. Using the table obtained from problem 6, Plot expected returns against portfolio risk (standard
deviations) displaying efficient portfolios.
Problem 8. Compute 99%-VaR and ES for the first and second portfolio components and explain these
values.
V aR(99%) = E(RP ) − Z99%σP = E(RP ) − Φ−1(99%)σP (9)
ES(99%) = E(RP ) − σP
φ(Φ−1(1 − 0.99)
1 − 0.99
(10)
where φ(·) is the normal density function, Φ−1(·) is the inverse cumulative normal density
function.
Note for Excel:
• To compute Z99% (i.e.Φ−1(0.99) ), use NORM.S.INV (0.99)
• To compute φ(Φ
−1(1−0.99)
1−0.99 , use NORM.S.DIST(NORM.S.INV (0.01),FALSE)/0.01
10Let w be a weight matrix that has N elements, w = (w1, w2, ...., wn), and let r be a return matrix with N elements,
= (r1, r2, ...., rn). Let Σ be a N by N covariance matrix, Σ =

σ1,1 σ1,2
Answered Same Day Aug 14, 2021

Solution

Sweety answered on Aug 17 2021
141 Votes
Cover page
                    NAME
                    STUDENT ID
SOLUTION TO PART 2
    PART 2
    1)    SOLUTION TO PROBLEM 1
        EXPECTED RETURN & STANDARD DEVIATION
            PORTFOLIO 1        PORTFOLIO 2
            Apple     Microsoft    DISNEY    BOEING    AMAZON    TESLA    NETFLIX
        Avaerage Return(X1)    2.19%    1.56%    1.55%    1.75%    2.53%    3.67%    4.52%
        Standard Deviation P    7.41    6.11    5.88    7.05    10.95    15.62    17.17
        COVARIANCE MATRIX
            PORTFOLIO 1        PORTFOLIO 2
            Apple     Microsoft    DISNEY    BOEING    AMAZON    TESLA    NETFLIX
        PORTFOLIO 1
        Apple    - 0    18.77    - 0    - 0    - 0    - 0    - 0
        MSFT    18.77    - 0    - 0    - 0    - 0    - 0    - 0
        PORTFOLIO 2
        DISNEY    - 0    - 0    - 0    17.01    13.66    7.54    16.36
        BOEING    - 0    - 0    17.01    - 0    20.19    0.29    24.68
        AMAZON    - 0    - 0    13.66    20.19    - 0    3.49    47.31
        TESLA    - 0    - 0    7.54    0.29    3.49    - 0    46.93
        NETFLIX    - 0    - 0    16.36    24.68    47.31    46.93    - 0
    3)    SOLUTION TO PROBLEM 3
            Weight
(A)    Expected Return
(B)    Standard Deviation
( C )    A*B
        Apple     0.5    2.19%    7.41    1.09%
        Microsoft    0.5    1.56%    6.11    0.78%
                        1.88%
                    
        Expected Return of portfolio    1.88%
        Standard Deviation    6.75
    4)    SOLUTION TO PROBLEM 4
            Weight
(A)    Expected Return
(B)    Standard Deviation
( C )    A*B    Weight*Weight*standard deviation *standard deviation
        DISNEY    0.25    1.55%    5.88    0.39%    2.16
        BOEING    0.25    1.75%    7.05    0.44%    3.11
        AMAZON    0.25    2.53%    10.95    0.63%    7.49
        TESLA    0.25    3.67%    15.62    0.92%    15.25
        NETFLIX    0.25    4.52%    17.17    1.13%    18.43
                        0.82%    46.44
                    
         Table for calculating (2*Weight*Weight*Standard deviation*Standard Deviation)
            DISNEY    BOEING    AMAZON    TESLA    NETFLIX
        DISNEY    0.00    5.18    4.02    11.48    6.31
        BOEING    5.18    0.00    9.65    13.77    15.13
        AMAZON    4.02    9.65    0.00    21.38    23.50
        TESLA    11.48    13.77    21.38    0.00    33.52
        NETFLIX    6.31    15.13    23.50    33.52    0.00
        Expected Return of Portfolio    0.82%
        (Standard Deviation)2     190.38
        Standard deviation    13.8
    NOTE     The SOLTUION TO 5, 6 , 7 , 8, Can be explained from working note sheet
SOLUTION TO PART 3
    PART 2
    1)    SOLUTION TO PROBLEM 1
         Calculation of Weight of Minimum Variance portfolio using portfolio 1
        STOCK    Standard Deviation    Variance     cov(APPL,MSFT)
        Apple     7.41    54.91    18.77
        Microsoft    6.11    37.33
        Therefore optimum weight that minimizes the portfolio standard deviation are:
        Weight of Apple    0.34
        Weight of MSFT    0.66
    2)    SOLUTION TO PROBLEM 2
            Expectec return    STANDARD DEV
        Portfolio 1    1.88%    6.75
        Portfolio 2    0.82%    13.8
        Weight of Apple    0.28
        weight of microsoft    0.72
    3)    Computaion of VaR 99% IS
        MINIUM VARIANCE    119.93
        TANGENT PORTFOLIO    119.93
SOLUTION TO PART 1
    Jan-10    25.74
    Feb-10    27.21
    Mar-10    30.41
    Apr-10    32.09
    May-10    29.11
    Jun-10    27.44
    Jul-10    29.34
    Aug-10    28.34
    Sep-10    28.83
    Oct-10    31.47
    Nov-10    31.80
    Dec-10    32.67
    Jan-11    34.23
    Feb-11    38.52
    Mar-11    37.94
    Apr-11    37.95
    May-11    36.66
    Jun-11    34.38
    Jul-11    34.01
    Aug-11    29.99
    Sep-11    26.56
    Oct-11    30.71
    Nov-11    31.57
    Dec-11    33.02
    Jan-12    34.83
    Feb-12    37.60
    Mar-12    39.20
    Apr-12    38.60
    May-12    40.93
    Jun-12    43.42
    Jul-12    44.00
    Aug-12    44.29
    Sep-12    46.81
    Oct-12    43.98
    Nov-12    44.46
    Dec-12    44.58
    Jan-13    48.98
    Feb-13    49.63
    Mar-13    51.64
    Apr-13    57.13
    May-13    57.35
    Jun-13    57.41
    Jul-13    58.77
    Aug-13    55.30
    Sep-13    58.63
    Oct-13    62.35
    Nov-13    64.13
    Dec-13    69.45
    Jan-14    66.82
    Feb-14    74.37
    Mar-14    73.69
    Apr-14    73.02
    May-14    77.32
    Jun-14    78.91
    Jul-14    79.04
    Aug-14    82.72
    Sep-14    81.94
    Oct-14    84.10
    Nov-14    85.14
    Dec-14    86.68
    Jan-15    84.78
    Feb-15    97.00
    Mar-15    97.76
    Apr-15    101.33
    May-15    102.87
    Jun-15    106.38
    Jul-15    111.84
    Aug-15    95.51
    Sep-15    95.81
    Oct-15    106.62
    Nov-15    106.37
    Dec-15    98.51
    Jan-16    90.40
    Feb-16    90.12
    Mar-16    93.69
    Apr-16    97.42
    May-16    93.61
    Jun-16    92.29
    Jul-16    90.52
    Aug-16    89.77
    Sep-16    88.24
    Oct-16    88.08
    Nov-16    94.19
    Dec-16    99.04
    Jan-17    105.96
    Feb-17    105.42
    Mar-17    108.58
    Apr-17    110.70
    May-17    103.37
    Jun-17    101.75
    Jul-17    105.27
    Aug-17    97.63
    Sep-17    95.09
    Oct-17    94.36
    Nov-17    101.12
    Dec-17    103.72
    Jan-18    105.68
    Feb-18    100.32
    Mar-18    97.68
    Apr-18    97.57
    May-18    96.74
    Jun-18    101.93
    Jul-18    110.44
    Aug-18    109.82
    Sep-18    114.64
    Oct-18    112.57
    Nov-18    113.22
    Dec-18    107.49
    Jan-19    110.17
    Feb-19    111.48
    Mar-19    109.69
    Apr-19    135.32
    May-19    130.45
    Jun-19    137.95
    Jul-19    141.28
    Aug-19    136.44
    Sep-19    129.54
    Oct-19    129.15
    Nov-19    150.68
    Dec-19    143.77
40179    40210    40238    40269    40299    40330    40360    40391    40422    40452    40483    40513    40544    40575    40603    40634    40664    40695    40725    40756    40787    40817    40848    40878    40909    40940    40969    41000    41030    41061    41091    41122    41153    41183    41214    41244    41275    41306    41334    41365    41395    41426    41456    41487    41518    41548    41579    41609    41640    41671    41699    41730    41760    41791    41821    41852    41883    41913    41944    41974    42005    42036    42064    42095    42125    42156    42186    42217    42248    42278    42309    42339    42370    42401    42430    42461    42491    42522    42552    42583    42614    42644    42675    42705    42736    42767    42795    42826    42856    42887    42917    42948    42979    43009    43040    43070    43101    43132    43160    43191    43221    43252    43282    43313    43344    43374    43405    43435    43466    43497    43525    43556    43586    43617    43647    43678    43709    43739    43770    43800    25.738925999999999    27.210964000000001    30.407646    32.088740999999999    29.10981    27.437429000000002    29.344988000000001    28.343299999999999    28.831081000000001    31.470296999999999    31.801286999999999    32.672320999999997    34.227241999999997    38.515555999999997    37.943199    37.951999999999998    36.657589000000002    34.376938000000003    34.007111000000002    29.991772000000001    26.557594000000002    30.713825    31.567965000000001    33.020885    34.828868999999997    37.595486000000001    39.198146999999999    38.598274000000004    40.926158999999998    43.424168000000002    43.997188999999999    44.292659999999998    46.808562999999999    43.979281999999998    44.462772000000001    44.579163000000001    48.981926000000001    49.627380000000002    51.636471    57.127398999999997    57.345581000000003    57.409218000000003    58.772854000000002    55.300120999999997    58.627392    62.354686999999998    64.127410999999995    69.454696999999996    66.824157999999997    74.370750000000001    73.689719999999994    73.017899    77.315764999999999    78.907912999999994    79.036758000000006    82.718010000000007    81.935744999999997    84.098502999999994    85.138458    86.684593000000007    84.775940000000006    97.003951999999998    97.758887999999999    101.328491    102.86631800000001    106.38001300000001    111.841606    95.505775    95.805756000000002    106.623749    106.370651    98.505561999999998    90.400734    90.117690999999994    93.693352000000004    97.419944999999998    93.608436999999995    92.287612999999993    90.523369000000002    89.765015000000005    88.244536999999994    88.082977    94.193404999999998    99.039901999999998    105.960678    105.424408    108.584564    110.700897    103.365532    101.74715399999999    105.271187    97.632110999999995    95.094832999999994    94.361626000000001    101.12447400000001    103.719643    105.682114    100.323616    97.678398000000001    97.571426000000002    96.735068999999996    101.928246    110.43765999999999    109.81568900000001    114.638885    112.570404    113.217415    107.49234    110.173546    111.477608    109.68946099999999    135.31626900000001    130.445786    137.954025    141.28334000000001    136.44253499999999    129.543869    129.146255    150.67726099999999    143.76864599999999    
SOLUTION TO PART 4
    PORFOLIO1
    MONTH    STOCK PRICE        RETURN (X)        (Given Return-Expected Return)
        (Given Return-Expected Return)*
(Given return -Actual Return)
        APPLE
(APPL)    MICROSOFT
(TICKER MSFT)    APPLE
(%)    MICROSOFT
(%)    APPLE    MICROSOFT    APPLE    MICROSOFT
    Dec-19    293.65    157.70
    1/1/20    309.51    170.23    5.40%    7.95%
    2/1/20    273.36    162.01    -11.68%    -4.83%    -13.78    -8.10    189.88    65.59
    3/1/20    254.29    157.71    -6.98%    -2.65%    -6.98    -2.65    48.67    7.04
    4/1/20    293.80    179.21    15.54%    13.63%    15.54    13.63    241.41    185.85
    5/1/20    317.94    183.25    8.22%    2.25%    8.22    2.25    67.51    5.08
                10.50%    16.35%            547.46    263.56
                2.10%    3.27%            11.7    7.26
    PORTFOLIO 2
    MONTH    STOCK PRICE                    RETURN (X)                    (Given Return-Expected Return)
                    (Given Return-Expected Return)*
(Given return -Actual Return)
        DISNEY    BOEING    AMAZON    TESLA    NETFLIX    DISNEY    BOEING    AMAZON    TESLA    NETFLIX    DISNEY    BOEING    AMAZON    TESLA    NETFLIX    DISNEY    BOEING    AMAZON    TESLA    NETFLIX
    Dec-19    143.77    325.76    1847.84    418.33    323.57
    1/1/20    138.31    318.27    2008.72    650.57    345.09    -3.80%    -2.30%    8.71%    55.52%    6.65%    -0.55    10.43    2.39    36.99    1.23    0.31    108.88    5.70    1368.03    1.50
    2/1/20    117.65    275.11    1883.75    667.99    369.03    -14.94%    -13.56%    -6.22%    2.68%    6.94%    -14.94    -13.56    -6.22    2.68    6.94    223.13    183.90    38.71    7.17    48.13
    3/1/20    96.60    149.14    1949.72    524.00    375.50    -17.89%    -45.79%    3.50%    -21.56%    1.75%    -17.89    -45.79    3.50    -21.56    1.75    320.13    2096.63    12.26    464.65    3.07
    4/1/20    108.15    141.02    2474.00    781.88    419.85    11.96%    -5.44%    26.89%    49.21%    11.81%    11.96    -5.44    26.89    49.21    11.81    142.96    29.64    723.07    2421.99    139.50
    5/1/20    117.30    145.85    2442.37    835.00    419.73    8.46%    3.43%    -1.28%    6.79%    -0.03%    8.46    3.43    -1.28    6.79    -0.03    71.58    11.73    1.63    46.16    0.00
                            -16.21%    -63.67%    31.60%    92.65%    27.12%                        758.10    2430.78    781.37    4308.00    192.20
                            -3.24%    -12.73%    6.32%    18.53%    5.42%                        12.31    22.05    12.5    29.35    6.2
WORKING NOTE PART 2 (P1)
    MONTH    STOCK PRICE        RETURN (X)        (Given Return-Expected Return)*
(Given return -Actual Return)        (Given Return-Expected Return)*
(Given return -Actual Return)        COV(APLLE,Microsoft)
        APPLE
(APPL)    MICROSOFT
(TICKER MSFT)    APPLE
(%)    MICRO
(%)    APPLE    MICRO    APPLE    MICRO
    Dec-09    30.10    30.48                ...
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