WEBVTT 00:00.670 --> 00:06.190 Welcome to part fourth of our project and we have analyzed and educated Mary on the past performance 00:06.190 --> 00:14.230 of our portfolio and your task is now to compare her portfolio with a similar design portfolio indexes 00:14.230 --> 00:16.030 of other sectors. 00:16.030 --> 00:22.540 So to say the five largest company of each sector and your colleagues already prepared appropriate indexes 00:22.540 --> 00:27.830 for the other sectors so you can find these in the sector indexes. 00:27.880 --> 00:34.630 Yes we file and with this we should analyze and compare the performance of Mary's large cap health care 00:34.630 --> 00:44.380 portfolio with other sectors for the most recent four year period from 2015 until 2000 and 18 and then 00:44.380 --> 00:50.370 we shall provide Mary with an index of normalized price chart where she can play around with. 00:50.410 --> 00:51.940 So let's start coding here 00:58.890 --> 01:03.420 and we still imported our large health care index. 01:03.420 --> 01:05.910 So that's our index. 01:06.090 --> 01:12.950 And then we imported the indexes for the other sectors from the UCSB fired sector indexes and we save 01:12.950 --> 01:15.060 for the data frame and the rebel indexes. 01:15.060 --> 01:22.140 So let's have a look here so here we have the other indexes for example for these sectors. 01:22.140 --> 01:27.380 Basic industries energy finance technology or transportation. 01:27.540 --> 01:36.330 And we have actually normalized the price data starting from 100 from the very last day of 2014 and 01:36.800 --> 01:39.320 the end of 2000 and 18. 01:39.660 --> 01:47.190 And in our next step we want to copy our health care index to our data frame indexes and we can create 01:47.310 --> 01:48.770 a new column health care. 01:49.260 --> 01:52.970 And we simply pass here our health care index. 01:52.980 --> 02:01.470 So our panacea is here and let's have a look and actually pan us automatically alliances based on the 02:01.470 --> 02:02.950 daytime index. 02:02.970 --> 02:11.350 So here at the very last day of 2018 we have our health care index ending at eight hundred eighty eight. 02:11.430 --> 02:15.060 And it's also for look at the very first rows here 02:20.060 --> 02:25.650 so that's the first rows beginning with the thirty first of December 2014. 02:25.740 --> 02:30.180 And um all indexes are starting with a base value of 100. 02:30.290 --> 02:37.280 But here we have them on the right hand side our health care index starting at a value of five hundred 02:37.460 --> 02:38.940 thirty one. 02:39.030 --> 02:45.140 Originally our health care index started at the very last day of the year nineteen hundred ninety six 02:45.440 --> 02:52.550 and therefore our first task is here to again normalize our health care index and we can do this by 02:52.550 --> 02:58.780 dividing all values here by the very first value five hundred thirty one. 02:58.970 --> 03:04.940 So we are actually overriding here the health care column in our index this data frame. 03:04.940 --> 03:13.510 And as I said we divide each and every element by the first element and then we multiply with 100 so 03:13.540 --> 03:20.950 let's have a look again here and now also our health care index starts at a base value of 100 at the 03:20.980 --> 03:24.250 thirty first of December 2014. 03:24.250 --> 03:26.500 And it's also quality at the Inform method 03:29.860 --> 03:36.770 so we have total 11 sectors and we have a total of 1000 and six timestamps or rows. 03:37.000 --> 03:43.510 And our final task here and step for is to create an interactive price chart and we can do this with 03:43.580 --> 03:45.010 the plot Lee and cufflinks. 03:45.580 --> 03:53.440 So we need to import your cufflinks as the C F and uh we can create an index of price chart with the 03:53.520 --> 03:54.730 I plot method 03:59.430 --> 04:04.350 so that's our index the price chart and then blue we have our healthcare index. 04:04.350 --> 04:11.630 So it might make sense to select here so close up stayed on Hava so that's here. 04:11.640 --> 04:19.560 Healthcare and actually at a very first glance the performance of the health care index is good. 04:19.600 --> 04:24.070 Uh compared to the other indexes but it's actually not exceptional. 04:24.090 --> 04:30.740 So we have here for example and read the consumer service sector are here in Orange. 04:30.780 --> 04:37.380 The technology sector and actually from our pure price chart it is very hard to determine whether a 04:37.380 --> 04:42.960 stock or a sector showed a good performance in terms of risk and return. 04:43.410 --> 04:50.850 And therefore we have to do here a more detailed analysis and we will do this and the next steps step 04:50.860 --> 04:51.280 5. 04:51.300 --> 04:52.740 So to see there by.