WEBVTT 00:00.270 --> 00:04.560 In the last video we created a price chart with the Levin sector indexes. 00:04.740 --> 00:10.480 And from that chart we could say that the customer services index here in red. 00:10.500 --> 00:17.220 So the highest return however is pretty impossible to compare these indexes here based on risk return 00:17.220 --> 00:24.330 performance and therefore in a first step we are going to calculate the annualized risk and return based 00:24.330 --> 00:26.320 on daily simple returns. 00:26.580 --> 00:29.220 And then we create an appropriate plot. 00:29.220 --> 00:36.540 And finally we also calculate the sharp ratio and we compare Mary's portfolio with the other indexes 00:36.810 --> 00:40.850 in terms of Sharpe ratio and we we're still important here. 00:40.860 --> 00:49.210 The indexes data frame and with the percentage change method we can simply calculate a simple daily 00:49.210 --> 00:52.070 returns and we save for those returns. 00:52.090 --> 00:54.040 And the variable returns 00:57.020 --> 01:03.710 and from the video lectures we also copy to the user defined function annualized the risk and return 01:04.020 --> 01:07.490 we have we simply have to pass a returns data frame. 01:07.640 --> 01:13.760 So let's define the function here and let's pass our returns that frame and be safe. 01:13.760 --> 01:16.970 The resulting summary data frame and the variable summary 01:19.400 --> 01:26.480 so here we have the annualized the risk and return for the eleven industries for the most recent four 01:26.480 --> 01:27.860 year period. 01:28.160 --> 01:32.630 And still the performance of our health care index is pretty impressive. 01:32.630 --> 01:38.100 So on average we had a annual return of 14 percent. 01:38.270 --> 01:45.620 And finally we can also plot our 11 industries as scatter plot where we f on the x axis the total risk 01:45.620 --> 01:49.520 here and on the y axis the annualized return. 01:50.000 --> 01:59.480 So we you see the plot method and we select a scatter plot and we pass uh the risk column to the experimenter 01:59.510 --> 02:02.980 and the return column to the right parameter. 02:02.990 --> 02:11.630 And we also annotate the points for the thoughts that we create with the respect the label here. 02:11.960 --> 02:18.910 So capital goods or energy finance and let's simply run here the cell. 02:19.100 --> 02:22.610 So that's the total risk return plot. 02:22.700 --> 02:25.220 And here we have actually our health care index. 02:25.220 --> 02:31.160 And it's pretty good actually because it is desire to have low risk and high return. 02:31.490 --> 02:40.340 So it's a desire to be here on the upper left corner and the health care index is pretty much here. 02:40.340 --> 02:47.000 So compared to other industries like basic industries transportation finance or energy. 02:47.000 --> 02:54.470 And to be on the safe side and to be even more exact we have to calculate the shop ratio and the father 02:54.470 --> 03:02.360 shop ratio we need the risk free return of the risk free asset and for the period from 214 to till 80 03:02.360 --> 03:09.390 in the coupon for a four year U.S. government bond was approximately one point three percent. 03:09.530 --> 03:19.550 So we say here a list with the risk free return and the risk free risk and the RAB risk free and with 03:19.670 --> 03:26.870 this we can finally calculate the sharp ratio for our eleven indexes and we are creating the new column 03:26.870 --> 03:35.120 sharp and we can actually calculate the sharp ratio by taking the returns off our indexes minus the 03:35.120 --> 03:41.930 risk free return and then divide by the risk of the respective indexes. 03:41.930 --> 03:48.600 So with this we created the new column sharp and we can also sort our rows by the shop raised through 03:48.600 --> 03:52.150 all by the column sharp in a descending order. 03:52.180 --> 04:00.700 So starting from the industry or the index with the highest chop ratio so let's have a look here and 04:00.700 --> 04:07.540 obviously so healthcare is on the second position with the second highest chop ratio here 0 point 8 04:07.530 --> 04:08.380 7 8. 04:08.470 --> 04:12.910 And that's actually only one index or one sector with a higher shop ratio. 04:12.910 --> 04:16.300 And this is the consumer services sector. 04:16.870 --> 04:21.390 And here we have actually a sharp ratio of one point to 7 1. 04:21.430 --> 04:30.060 And finally here at the bottom we have the energy sector with a sharp ratio of only 0 point 1 5 8 so 04:30.060 --> 04:36.630 the health care sector and the marriage portfolio performed uh pretty good on a standalone basis in 04:36.810 --> 04:39.040 the recent past. 04:39.060 --> 04:41.250 So in the last four years. 04:41.310 --> 04:47.100 But the question is whether Mary could have improved the sharp ratio of her portfolio by adding other 04:47.100 --> 04:48.270 sectors. 04:48.330 --> 04:50.870 So that's the question for us step 6. 04:50.880 --> 04:52.650 And I hope to see also there by.