Forecasting P/S for Magna

Auto parts maker Magna (MGA) started the month of November trading around $53 and then, wham!, it lost $5 in one day on the announcement of the Trans Pacific Partnership (TPP). The TPP is
a proposed partnership agreement that would establish terms of trade between 12 Pacific Rim countries: Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, the U.S., and Vietnam. One of  the concerns for auto parts maker Magna is that under NAFTA, only auto parts containing 60% North American content could move duty free between Canada, Mexico, and the US. The TPP reduces the local content threshold to something in the range of 35% to 40%.




Here is a plot of Magna's quarterly sales. The Great Recession had a huge impact on slowing Magna's sales, but afterward, sales climbed steadily until late 2014.

One interesting question is how does Magna currently compare on a standard valuation measure like price to sales (P/S). The P/S ratio, like P/E, P/B, and P/CF, is a measure of valuation. Lower values are preferred (generally less than 1 for P/S), but it is important to take into account industry effects. Also, like other valuation measures, a low P/S could indicate a value stock or it could indicate something else is wrong with the company.

My approach is to compare price to trailing 12 month sales with price to forward 12 month sales. If the forward P/S is less than the trailing P/S the company may be undervalued. For this comparison I will need forecasts of future sales.

I use a variety of different uni-variate models to forecast Magna's quarterly sales.  Forecasting approaches include  simple averages, Holt, Holt-Winters, ETS, ARIMA, and ANN. Data from the first quarter of 2001 to the fourth quarter of 2012 are used for training. Models are tested on out-of-sample forecasts over the period 2013:1 to 2015:3.

Here is table of forecast accuracy measures ranked on MASE.

	ME	RMSE	MAE	MPE	MAPE	MASE
Holt Winters training	10.3605	416.6794	272.9042	-0.1470	5.7363	0.3097
ETS training	101.3534	432.8289	308.9523	1.6208	6.4568	0.3506
STL training	112.1135	451.0617	310.0710	1.9976	6.5669	0.3519
ARIMA training	-0.1629	447.0967	321.3737	-0.3155	6.3713	0.3647
ANN2 training	0.9323	498.2682	385.9291	-0.9804	7.5023	0.4379
ANN training	-0.3573	501.1293	387.6661	-1.0337	7.5453	0.4399
Holt linear training	3.4478	516.7124	389.2279	-0.4852	7.8051	0.4417
Holt ES training	32.4932	522.0256	394.1387	0.2721	7.8724	0.4472
Holt dampled training	49.4785	519.0834	396.3995	0.3593	7.8883	0.4498
ES training	107.7185	527.2579	408.3396	1.5849	7.9818	0.4634
Naive training	110.0000	532.8356	417.0213	1.6185	8.1515	0.4732
Holt linear test	-151.2323	765.2259	576.4225	-2.2910	6.9925	0.6541
Holt damped test	297.9467	656.7112	583.8959	3.0695	6.7486	0.6626
Holt Winters test	24.8474	691.3100	601.4257	-0.1406	7.1852	0.6825
Holt ES test	-274.5440	859.5667	625.6039	-3.7699	7.6591	0.7099
Naive test	510.9091	739.0499	626.0000	5.6066	7.1001	0.7104
ES test	510.9714	739.0929	626.0396	5.6074	7.1005	0.7104
ARIMA test	-298.4399	823.3481	638.6404	-4.0160	7.7919	0.7247
STL test	578.2432	789.4180	648.2347	6.4402	7.3382	0.7356
ETS test	649.8151	815.6195	685.7139	7.2909	7.7528	0.7781
S. Naive test	864.0909	966.5054	864.0909	9.8515	9.8515	0.9805
S. Naive training	450.2500	1132.3782	881.2500	6.6540	17.4009	1.0000
ANN test	1073.7787	1149.9015	1073.7787	12.3361	12.3361	1.2185
ANN2 test	1105.4155	1178.8549	1105.4155	12.7111	12.7111	1.2544
Mean training	0.0000	1550.3499	1302.3281	-11.0193	29.8710	1.4778
Mean test	3184.2841	3228.7508	3184.2841	37.0207	37.0207	3.6134

Based on the MASE for the test measures, Holt linear trend ranks lowest. Notice, however, that Holt-Winters has the lowest absolute ME among the test measures. I will estimate the Holt-Winters approach on data from 2001:1 to 2015:3, and then forecast 6 quarters ahead.

Here is a plot of the forecasts.

Here are the forecasted values in table form.

         Qtr1     Qtr2     Qtr3     Qtr4
2015                            8385.564
2016 8185.603 8534.537 8029.078 8783.678
2017 8569.665 

Here is a comparison between the trailing P/S and the forward P/S.

     P/S ttm    P/S forward
[1,]  0.5509724   0.5480994

The forward P/S ratio is slightly less than the trailing P/S ratio indicating slight undervaluation.

It is important to compare company P/S ratios to industry averages. For this I use the auto parts P/S value of  0.69 from Damodaran, indicating that based on P/S, Magna is undervalued relative to the industry average.

As with any valuation exercise, it is important to compare these results for P/S with those of other valuation ratios like P/E, P/B, and P/CF.


The R code and data are posted below.

#########################################################
#  Economic forecasting and analysis
#  Fall 2015
#  Perry Sadorsky
#  Forecasting sales of Magna
#  with smoothing methods, ARIMA, and ANN
##########################################################


# load libraries
library(fpp)


# import data
as1_data <- read.csv("C:/econ 6210/6210f15/week 10/as1_data.csv")
View(as1_data)

df = as1_data


# define as time series
df = ts(df, start=2000, frequency=4)
df

# extract sales
 y = df[,"MGA"]
# y = df[,"SPLS"]
y

                      
# some graphs
par(font.axis = 2)
par(font.lab = 2)
plot(y, main = "MGA quarterly sales ($ millions)", xlab="", ylab="" , col ="blue", lwd=2)
tsdisplay(y)
par(mfrow = c(1,1))


# generate some returns
y.ret = diff(log(y)) * 100
tsdisplay(y.ret)
par(mfrow = c(1,1))                    


# training period
train <- window(y,start=c(2001, 1),end=c(2012, 4))
train

# test period
test <- window(y, start=2013)

# number of steps to forecast
h = length(test)

# out of sample forecast
y5 <- window(y,start=c(2001, 1)  )
h2 = 6

##########################################################
# forecast using simple methods
##########################################################


yfit1 <- meanf(train, h=h)
yfit2 <- naive(train, h=h)
yfit3 <- snaive(train, h=h)

plot(yfit1)
plot(yfit2)
plot(yfit3)

# make a nice plot showing the forecasts
plot(yfit1, plot.conf=FALSE,
main="Forecasts for quarterly TGT sales")
lines(yfit2$mean,col=2)
lines(yfit3$mean,col=3)
legend("topleft",lty=1,col=c(4,2,3),
legend=c("Mean method","Naive method","Seasonal naive method"))


# plot with forecasts and actual values
plot(yfit1, plot.conf=FALSE,
     main="Forecasts for quarterly TGT sales")
lines(yfit2$mean,col=2)
lines(yfit3$mean,col=3)
lines(y)
legend("topleft",lty=1,col=c(4,2,3),
       legend=c("Mean method","Naive method","Seasonal naive method"),bty="n")



##########################################################
# exponential smoothing approaches
##########################################################

# simple exponential moving averages
yfit4 <- ses(train, h = h)
summary(yfit4)
plot(yfit4)


# holt's linear trend method
yfit5 <- holt(train,  h=h)
summary(yfit5)
plot(yfit5)


# holt's exponential trend method
yfit6 <- holt(train, exponential=TRUE, h=h)
summary(yfit6)
plot(yfit6)


# holt's damped trend method
yfit7 <- holt(train, damped=TRUE, h=h)
summary(yfit7)
plot(yfit7)


# holt winter's  method
yfit8 <- hw(train, seasonal="multiplicative", h=h)
summary(yfit8)
plot(yfit8)


# ETS  method
y.ets <- ets(train, model="ZZZ")
summary(y.ets)
yfit9 <- forecast(y.ets, h=h)
summary(yfit9)
plot(yfit9)


# STL  method
y.stl <- stl(train, t.window=15, s.window="periodic", robust=TRUE)
summary(y.stl)
yfit10 <- forecast(y.stl, method="naive",h=h)
summary(yfit10)
plot(yfit10)


##########################################################
# arima method
##########################################################

y.arima <- auto.arima(train)
yfit11 <- forecast(y.arima, h=h)
plot(yfit11)


##########################################################
# ANN
##########################################################

fit.ann <- nnetar(train)
yfit12 = forecast(fit.ann,h=h)
plot(yfit12)


fit.ann2 <- nnetar(train, repeats= 100)
yfit13 = forecast(fit.ann2,h=h)
plot(yfit13)



##########################################################
# accuracy measures
##########################################################


a1 = accuracy(yfit1, test)
a2 = accuracy(yfit2, test)
a3 = accuracy(yfit3, test)
a4 = accuracy(yfit4, test)
a5 = accuracy(yfit5, test)
a6 = accuracy(yfit6, test)
a7 = accuracy(yfit7, test)
a8 = accuracy(yfit8, test)
a9 = accuracy(yfit9, test)
a10 = accuracy(yfit10, test)
a11 = accuracy(yfit11, test)
a12 = accuracy(yfit12, test)
a13 = accuracy(yfit13, test)


#Combining forecast summary statistics into a table with row names
a.table<-rbind(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13)

row.names(a.table)<-c('Mean training','Mean test', 'Naive training', 'Naive test', 'S. Naive training', 'S. Naive test' ,
                      'ES training','ES test', 'Holt linear training', 'Holt linear test', 'Holt ES training', 'Holt ES test' ,
                      'Holt dampled training','Holt damped test', 'Holt Winters training', 'Holt Winters test', 'ETS training', 'ETS test' ,     
                  'STL training','STL test', 'ARIMA training','ARIMA test', 'ANN training', 'ANN test','ANN2 training', 'ANN2 test' )

# order the table according to MASE
a.table<-as.data.frame(a.table)
a.table<-a.table[order(a.table$MASE),]
a.table

# write table to csv file
# write.csv(a.table, "C:/econ 6210/6210f15/week 10/atable.csv")


## forecast 6 periods into the future

plot(hw(y5, seasonal="multiplicative", h=h2))
par(mfrow = c(1,1))

y_forc =hw(y5, seasonal="multiplicative", h=h2)$mean
# y_forc = holt(y5, h=h2)$mean


# forecasted sales for 2016
# sales_f = y_forc[3] + y_forc[4] + y_forc[5] + y_forc[6]

sales_f = 0
for (i in 1:4){
sales_f = sales_f + y_forc[i]
  }
sales_f





# calculate price to forward sales
# data on November 21, 2015
price  = 44.94    # current stock prices
shares = 404.12   # millions of shares outstanding


ptos_f = price/(sales_f/shares)
ptos_f




# calculate price to trailing sales
last = tail(y,4)
sales_t = 0
for (i in 1:4){
  sales_t = sales_t + last[i]
}
sales_t


ptos_t = price/( sales_t /shares)
ptos_t

ps = cbind(ptos_t, ptos_f)
colnames(ps) = cbind("P/S ttm   ", "P/S forward")
ps

# compare with industry average
# http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/psdata.html
# auto parts 0.69


Data

datacqtr MGA 1 2000Q1 2808 2 2000Q2 2610 3 2000Q3 2354 4 2000Q4 2741 5 2001Q1 2863 6 2001Q2 2817 7 2001Q3 2517 8 2001Q4 2829 9 2002Q1 3121 10 2002Q2 2896 11 2002Q3 2962 12 2002Q4 3443 13 2003Q1 3496 14 2003Q2 3660 15 2003Q3 3566 16 2003Q4 4623 17 2004Q1 5103 18 2004Q2 5113 19 2004Q3 4784 20 2004Q4 5653 21 2005Q1 5718 22 2005Q2 5858 23 2005Q3 5381 24 2005Q4 5854 25 2006Q1 6019 26 2006Q2 6369 27 2006Q3 5424 28 2006Q4 6368 29 2007Q1 6423 30 2007Q2 6731 31 2007Q3 6077 32 2007Q4 6836 33 2008Q1 6622 34 2008Q2 6713 35 2008Q3 5533 36 2008Q4 4836 37 2009Q1 3574 38 2009Q2 3705 39 2009Q3 4669 40 2009Q4 5419 41 2010Q1 5512 42 2010Q2 6050 43 2010Q3 5942 44 2010Q4 6598 45 2011Q1 7189 46 2011Q2 7338 47 2011Q3 6970 48 2011Q4 7251 49 2012Q1 7666 50 2012Q2 7727 51 2012Q3 7411 52 2012Q4 8033 53 2013Q1 8361 54 2013Q2 8962 55 2013Q3 8338 56 2013Q4 9174 57 2014Q1 8455 58 2014Q2 8911 59 2014Q3 8820 60 2014Q4 9396 61 2015Q1 7772 62 2015Q2 8133 63 2015Q3 7661

The Shale Oil Effect?

The global economy is currently in a period of slow economic growth. At the same time, global oil production has continued to increase while global oil demand remains stagnate. The result of these two market forces is an oil supply glut leading to rapidly falling oil prices.

Here is a plot of WTI oil prices. Between 2011 and 2014, oil prices held fairly steady around $100 per barrel. Then in the latter half of 2014, oil prices plunged.



Lets take a look at the relationship between global oil supply and oil prices (measured using WTI).



Up until 75,0000 barrels per day, the relationship between oil prices and World oil production looks like a typical supply curve: upward sloping. After 75,0000 barrels per day, however, the upward sloping relationship breaks down.
 
Now look at how World oil production has evolved across time. Notice that 75,000 barrels per day occurred in 2011 and after 2011, oil supply increased considerably.
 
What is so special about 75,000 barrels per day and the year 2011?  For one thing, US shale oil production started to increase dramatically in 2011.

 

It is hard to make a case that US shale oil production is the only source of excess supply in the global oil market, but the timing does look interesting. US shale oil production has helped the US to dramatically lower oil imports. If foreign oil producers do not cut their oil production, then lower US oil imports means more oil on global markets for which there is no immediate demand. When oil demand is unchanging, small changes in oil supply can have large effects on oil prices. 



R code and data are posted below.

 #########################################################
#  Economic forecasting and analysis
#  Perry Sadorsky
#  Oil supply
#  November 2015
##########################################################


rm(list=ls())
# load libraries
library(fpp)

# import data
global_oil_text <- read.csv("C:/oil/Nov 2015/global_oil_text.csv")
View(global_oil_text)


df = global_oil_text

# tell R that data set is a time series
df = ts(df, start=c(1986,1), frequency=12)
df


# scatter plot

par(font.axis = 2)
par(font.lab = 2)
plot(df[,"price"] ~ df[,"supply"],  xlab="World oil production (thousands bbl per day)", ylab="Oil prices (WTI $/bbl)")
abline(v=75000)


plot( df[,"supply"],  main="World oil production (thousands bbl per day)", ylab="", xlab="")
abline(h=75000,col="blue")
abline(v=2011,col="blue")


plot( df[,"price"],  main="Oil prices (WTI $/bbl)", ylab="", xlab="")
abline(h=100,col="blue")
abline(v=2011,col="blue")

           supply  price
Jan 1986 55650.36  22.93
Feb 1986 55660.37  15.46
Mar 1986 55162.50  12.61
Apr 1986 55266.68  12.84
May 1986 56447.75  15.38
Jun 1986 57220.10  13.43
Jul 1986 58359.09  11.59
Aug 1986 59013.24  15.10
Sep 1986 54978.18  14.87
Oct 1986 55334.80  14.90
Nov 1986 56264.43  15.22
Dec 1986 56434.73  16.11
Jan 1987 55634.93  18.65
Feb 1987 54938.33  17.75
Mar 1987 54196.69  18.30
Apr 1987 54870.27  18.68
May 1987 55674.96  19.44
Jun 1987 55375.71  20.07
Jul 1987 57939.92  21.34
Aug 1987 58737.60  20.31
Sep 1987 58130.08  19.53
Oct 1987 58325.25  19.86
Nov 1987 57862.85  18.85
Dec 1987 57936.91  17.28
Jan 1988 57137.66  17.13
Feb 1988 57217.71  16.80
Mar 1988 57578.92  16.20
Apr 1988 57890.10  17.86
May 1988 57606.93  17.42
Jun 1988 57271.74  16.53
Jul 1988 57695.99  15.50
Aug 1988 58852.67  15.52
Sep 1988 59206.88  14.54
Oct 1988 60890.87  13.77
Nov 1988 61350.14  14.14
Dec 1988 61597.29  16.38
Jan 1989 58706.81  18.02
Feb 1989 58227.58  17.94
Mar 1989 58629.78  19.48
Apr 1989 59059.99  21.07
May 1989 58980.95  20.12
Jun 1989 59017.96  20.05
Jul 1989 59536.22  19.78
Aug 1989 60428.66  18.58
Sep 1989 60511.70  19.59
Oct 1989 61081.98  20.10
Nov 1989 61842.35  19.86
Dec 1989 61485.18  21.10
Jan 1990 60921.39  22.86
Feb 1990 61178.88  22.11
Mar 1990 62082.42  20.39
Apr 1990 61805.93  18.43
May 1990 61238.41  18.20
Jun 1990 60409.37  16.70
Jul 1990 60513.80  18.45
Aug 1990 56965.63  27.31
Sep 1990 59513.98  33.51
Oct 1990 59854.23  36.04
Nov 1990 60672.39  32.33
Dec 1990 60883.96  27.28
Jan 1991 60637.25  25.23
Feb 1991 60326.57  20.48
Mar 1991 60581.69  19.90
Apr 1991 59183.33  20.83
May 1991 59006.36  21.23
Jun 1991 59198.90  20.19
Jul 1991 60191.61  21.40
Aug 1991 59495.34  21.69
Sep 1991 60534.06  21.89
Oct 1991 60489.45  23.23
Nov 1991 60735.22  22.46
Dec 1991 61143.30  19.50
Jan 1992 61259.80  18.79
Feb 1992 60422.66  19.01
Mar 1992 59773.36  18.92
Apr 1992 60133.34  20.23
May 1992 59005.23  20.98
Jun 1992 59247.74  22.39
Jul 1992 59712.62  21.78
Aug 1992 59699.38  21.34
Sep 1992 59960.92  21.88
Oct 1992 60758.67  21.69
Nov 1992 60469.68  20.34
Dec 1992 60790.38  19.41
Jan 1993 60623.27  19.03
Feb 1993 60978.60  20.09
Mar 1993 60263.60  20.32
Apr 1993 59560.25  20.25
May 1993 59746.64  19.95
Jun 1993 59462.45  19.09
Jul 1993 60068.67  17.89
Aug 1993 59890.97  18.01
Sep 1993 59902.29  17.50
Oct 1993 60366.06  18.15
Nov 1993 60429.91  16.61
Dec 1993 60817.46  14.52
Jan 1994 61137.55  15.03
Feb 1994 60959.68  14.78
Mar 1994 60866.98  14.68
Apr 1994 60409.54  16.42
May 1994 60851.50  17.89
Jun 1994 61161.43  19.06
Jul 1994 60767.31  19.66
Aug 1994 60661.91  18.38
Sep 1994 61270.47  17.45
Oct 1994 61768.27  17.72
Nov 1994 61943.60  18.07
Dec 1994 62265.65  17.16
Jan 1995 61841.11  18.04
Feb 1995 62343.97  18.57
Mar 1995 61605.44  18.54
Apr 1995 62402.88  19.90
May 1995 62413.88  19.74
Jun 1995 61555.18  18.45
Jul 1995 62507.51  17.33
Aug 1995 62641.35  18.02
Sep 1995 63036.63  18.23
Oct 1995 62673.54  17.43
Nov 1995 62892.32  17.99
Dec 1995 63286.53  19.03
Jan 1996 63236.80  18.86
Feb 1996 63633.93  19.09
Mar 1996 63486.32  21.33
Apr 1996 63338.64  23.50
May 1996 63338.60  21.17
Jun 1996 63660.57  20.42
Jul 1996 63737.67  21.30
Aug 1996 63386.74  21.90
Sep 1996 63858.81  23.97
Oct 1996 64222.40  24.88
Nov 1996 64670.32  23.71
Dec 1996 65244.27  25.23
Jan 1997 65182.45  25.13
Feb 1997 65541.03  22.18
Mar 1997 65528.60  20.97
Apr 1997 66047.78  19.70
May 1997 65398.23  20.82
Jun 1997 64625.69  19.26
Jul 1997 65070.24  19.66
Aug 1997 65950.21  19.95
Sep 1997 66312.32  19.80
Oct 1997 66826.90  21.33
Nov 1997 66680.22  20.19
Dec 1997 66496.43  18.33
Jan 1998 67706.50  16.72
Feb 1998 68081.35  16.06
Mar 1998 67965.25  15.12
Apr 1998 67828.25  15.35
May 1998 67293.60  14.91
Jun 1998 67014.12  13.72
Jul 1998 66877.51  14.17
Aug 1998 65903.82  13.47
Sep 1998 65984.28  15.03
Oct 1998 66098.41  14.46
Nov 1998 66946.70  13.00
Dec 1998 66771.57  11.35
Jan 1999 66986.56  12.52
Feb 1999 67311.78  12.01
Mar 1999 66988.91  14.68
Apr 1999 65545.34  17.31
May 1999 65351.49  17.72
Jun 1999 64307.10  17.92
Jul 1999 65818.11  20.10
Aug 1999 65704.19  21.28
Sep 1999 65747.39  23.80
Oct 1999 66253.79  22.69
Nov 1999 66236.07  25.00
Dec 1999 65422.50  26.10
Jan 2000 66449.89  27.26
Feb 2000 67065.69  29.37
Mar 2000 67098.94  29.84
Apr 2000 67757.84  25.72
May 2000 68276.19  28.79
Jun 2000 68074.38  31.82
Jul 2000 68695.12  29.70
Aug 2000 69526.13  31.26
Sep 2000 69542.82  33.88
Oct 2000 69980.34  33.11
Nov 2000 70536.25  34.42
Dec 2000 69280.21  28.44
Jan 2001 69197.45  29.59
Feb 2001 68718.72  29.61
Mar 2001 69380.85  27.25
Apr 2001 68451.23  27.49
May 2001 67760.76  28.63
Jun 2001 66206.98  27.60
Jul 2001 68146.13  26.43
Aug 2001 68319.83  27.37
Sep 2001 67852.78  26.20
Oct 2001 67750.04  22.17
Nov 2001 68100.41  19.64
Dec 2001 67692.72  19.39
Jan 2002 66957.16  19.72
Feb 2002 67028.49  20.72
Mar 2002 66824.85  24.53
Apr 2002 66288.45  26.18
May 2002 66859.48  27.04
Jun 2002 66705.97  25.52
Jul 2002 67186.75  26.97
Aug 2002 66914.73  28.39
Sep 2002 67465.25  29.66
Oct 2002 68876.21  28.84
Nov 2002 69006.41  26.35
Dec 2002 67352.85  29.46
Jan 2003 67750.17  32.95
Feb 2003 69368.13  35.83
Mar 2003 69883.66  33.51
Apr 2003 68809.99  28.17
May 2003 68772.78  28.11
Jun 2003 67977.18  30.66
Jul 2003 68603.80  30.76
Aug 2003 69058.90  31.57
Sep 2003 69660.75  28.31
Oct 2003 70623.51  30.34
Nov 2003 70860.66  31.11
Dec 2003 72127.32  32.13
Jan 2004 71908.24  34.31
Feb 2004 71883.68  34.69
Mar 2004 71812.53  36.74
Apr 2004 71780.99  36.75
May 2004 71415.96  40.28
Jun 2004 72973.42  38.03
Jul 2004 73514.84  40.78
Aug 2004 72484.39  44.90
Sep 2004 73112.26  45.94
Oct 2004 73726.30  53.28
Nov 2004 73419.80  48.47
Dec 2004 73093.08  43.15
Jan 2005 73339.66  46.84
Feb 2005 73647.20  48.15
Mar 2005 73950.20  54.19
Apr 2005 74253.15  52.98
May 2005 74361.09  49.83
Jun 2005 73971.47  56.35
Jul 2005 73868.22  59.00
Aug 2005 73843.76  64.99
Sep 2005 73400.82  65.59
Oct 2005 73477.49  62.26
Nov 2005 74002.10  58.32
Dec 2005 74262.01  59.41
Jan 2006 73615.30  65.49
Feb 2006 73618.16  61.63
Mar 2006 73471.33  62.69
Apr 2006 73481.70  69.44
May 2006 73055.31  70.84
Jun 2006 73046.19  70.95
Jul 2006 74057.77  74.41
Aug 2006 73707.14  73.04
Sep 2006 73397.34  63.80
Oct 2006 73742.62  58.89
Nov 2006 73315.94  59.08
Dec 2006 73177.34  61.96
Jan 2007 72880.72  54.51
Feb 2007 73149.19  59.28
Mar 2007 73105.49  60.44
Apr 2007 73334.45  63.98
May 2007 72856.48  63.46
Jun 2007 72399.25  67.49
Jul 2007 73010.59  74.12
Aug 2007 72404.48  72.36
Sep 2007 73153.84  79.92
Oct 2007 73910.88  85.80
Nov 2007 73618.15  94.77
Dec 2007 74124.33  91.69
Jan 2008 74181.22  92.97
Feb 2008 74362.90  95.39
Mar 2008 74548.65 105.45
Apr 2008 73992.41 112.58
May 2008 74319.47 125.40
Jun 2008 74328.01 133.88
Jul 2008 75085.07 133.37
Aug 2008 73955.00 116.67
Sep 2008 72980.02 104.11
Oct 2008 74068.02  76.61
Nov 2008 73884.87  57.31
Dec 2008 73070.30  41.12
Jan 2009 72019.68  41.71
Feb 2009 72494.34  39.09
Mar 2009 72329.59  47.94
Apr 2009 72624.92  49.65
May 2009 72227.01  59.03
Jun 2009 72396.61  69.64
Jul 2009 73358.35  64.15
Aug 2009 72795.45  71.05
Sep 2009 73263.57  69.41
Oct 2009 73747.24  75.72
Nov 2009 73824.71  77.99
Dec 2009 73625.77  74.47
Jan 2010 73632.93  78.33
Feb 2010 74063.96  76.39
Mar 2010 74408.76  81.20
Apr 2010 74333.61  84.29
May 2010 74401.81  73.74
Jun 2010 74467.73  75.34
Jul 2010 74846.09  76.32
Aug 2010 74749.35  76.60
Sep 2010 75035.95  75.24
Oct 2010 74940.15  81.89
Nov 2010 75411.37  84.25
Dec 2010 75345.10  89.15
Jan 2011 75931.03  89.17
Feb 2011 75134.36  88.58
Mar 2011 74107.92 102.86
Apr 2011 73999.31 109.53
May 2011 73290.18 100.90
Jun 2011 74086.23  96.26
Jul 2011 74457.62  97.30
Aug 2011 74918.48  86.33
Sep 2011 74270.84  85.52
Oct 2011 74781.26  86.32
Nov 2011 75749.90  97.16
Dec 2011 76164.56  98.56
Jan 2012 76309.87 100.27
Feb 2012 76605.41 102.20
Mar 2012 76249.59 106.16
Apr 2012 76579.83 103.32
May 2012 75844.27  94.66
Jun 2012 75741.28  82.30
Jul 2012 75935.54  87.90
Aug 2012 75939.75  94.13
Sep 2012 75433.90  94.51
Oct 2012 75967.65  89.49
Nov 2012 76485.91  86.53
Dec 2012 76548.01  87.86
Jan 2013 75841.70  94.76
Feb 2013 75619.31  95.31
Mar 2013 75829.48  92.94
Apr 2013 76265.67  92.02
May 2013 76224.17  94.51
Jun 2013 76206.31  95.77
Jul 2013 76685.55 104.67
Aug 2013 76427.00 106.57
Sep 2013 75902.27 106.29
Oct 2013 76280.91 100.54
Nov 2013 76563.42  93.86
Dec 2013 76906.65  97.63
Jan 2014 77250.37  94.62
Feb 2014 77787.54 100.82
Mar 2014 77182.17 100.80
Apr 2014 77208.42 102.07
May 2014 76966.98 102.18
Jun 2014 77309.30 105.79
Jul 2014 77549.51 103.59
Aug 2014 77731.23  96.54
Sep 2014 78557.70  93.21
Oct 2014 79219.63  84.40
Nov 2014 79111.22  75.79
Dec 2014 79920.56  59.29
Jan 2015 79331.65  47.22
Feb 2015 79289.10  50.58
Mar 2015 80063.86  47.82
Apr 2015 79886.39  54.45
May 2015 79277.44  59.27
Jun 2015 79998.37  59.82
Jul 2015 80415.85  50.90