WEBVTT 00:00.880 --> 00:06.640 In the last videos we created the price weighted equal weighted and very weighted indexes based on price 00:06.640 --> 00:13.660 returns only because we used the closing prices of our stocks that do not include dividends which are 00:13.660 --> 00:17.920 actually an important part of total performance or total return. 00:17.980 --> 00:24.370 And in this video we will create the price weighted Total Return Index are and in other words the price 00:24.370 --> 00:30.780 weighted Performance Index and it's actually more complicated than one might think in a Total Return 00:30.780 --> 00:31.300 Index. 00:31.300 --> 00:38.860 The returns of our index constituents should be based on the adjusted close price data but the weighting 00:38.860 --> 00:45.250 of constituents must be based on the close prices and therefore it would be wrong to simply add the 00:45.250 --> 00:48.010 adjusted prices as we did before. 00:48.010 --> 00:56.900 With the price way that price return index so coding wise the procedure follows the same intuition as 00:56.900 --> 01:00.110 calculating the value weighted index. 01:00.110 --> 01:07.250 First of all we use the returns of constituents based on adjusted close prices and then we need to wait. 01:07.280 --> 01:13.970 This returns with a weight data frame that we derive from closing prices. 01:14.000 --> 01:18.800 So this is the the most general way to create an index that always works. 01:18.800 --> 01:24.260 But before we have seen with the price way that price return index and the equal weighted price return 01:24.260 --> 01:30.240 index that there are some shortcuts but not in this case here and. 01:30.320 --> 01:37.600 First of all we import pandas again and we import our index stocks CSP file. 01:38.150 --> 01:45.970 So here we can see the adjusted close prices and the close prices so let's first of all select only 01:45.970 --> 01:55.310 the adjusted close prices and uh we save this in a new data frame adjusted close and then we can decorate. 01:55.430 --> 01:59.720 The daily total returns by using the percentage change method. 01:59.720 --> 02:01.560 So this is nothing new. 02:02.870 --> 02:11.570 So these are the daily returns of our stocks and also we need to select uh the close prices to get the 02:11.570 --> 02:19.510 weightings and we save the close data frame and the variable close and then we can calculate the weights 02:19.510 --> 02:22.920 for our six stocks on each and every timestamp. 02:22.930 --> 02:29.380 And we can do this here with a combination of the diff method and the sum method. 02:29.380 --> 02:30.700 This is actually nothing new. 02:30.730 --> 02:35.100 We did this when we created the value weighted index. 02:35.470 --> 02:36.720 And let's have a look here. 02:39.550 --> 02:42.690 And then we did this with that evaluated index. 02:42.800 --> 02:50.560 Instead of using the close price data frame we use the data frame with the market capitalizations. 02:50.560 --> 02:54.420 So the intention here is nevertheless the same. 02:54.550 --> 02:56.470 And these are our weights. 02:56.920 --> 03:05.470 So no surprise that the Amazon stock has the highest weights as the closing prices are by far the highest 03:07.270 --> 03:14.650 and we've said in part that the normalized data frame with the normalized stock prices for our six stocks 03:15.070 --> 03:22.420 and with the the indexes or the value weighted index the price weighted index and the equal weighted 03:22.420 --> 03:23.090 index. 03:23.130 --> 03:26.060 And these are all price return index. 03:26.260 --> 03:32.260 And we create a new column price weighted index the performance and first of all we assign assigned 03:32.260 --> 03:37.250 to each and every timestamp the value of one hundred and several here. 03:37.270 --> 03:43.300 So here on the very right hand side we create our new price weighted Performance Index and having now 03:43.300 --> 03:50.770 the total return data frame with the daily total returns and the weights data frame then we can actually 03:50.770 --> 03:54.190 calculate the cumulative investment multiples. 03:54.550 --> 03:56.880 So this is nothing new. 03:57.010 --> 04:02.730 And we derive to this code and did this before when we created the evaluated index. 04:02.770 --> 04:09.220 So let's simply run the sell here and we actually overwrite all values and the price weighted performance 04:09.250 --> 04:13.070 index starting from the second element here. 04:13.300 --> 04:15.850 And let's again have a look at our norm data frame. 04:15.850 --> 04:20.170 So here we have our price weighted performance index. 04:20.170 --> 04:23.730 And now we can also create these summary data frame. 04:23.830 --> 04:26.300 So also this is nothing new. 04:26.530 --> 04:35.170 And we actually annualize risk and return are mean and standard deviation so here we have now the annualized 04:35.200 --> 04:38.980 mean and the annualized standard deviation of returns. 04:38.980 --> 04:42.070 And here we can see the price where that price return index. 04:42.070 --> 04:50.500 And here we have an annualized return of 20 percent and an annualized risk of 21 percent and comparing 04:50.500 --> 04:53.570 this to the price weighted performance index. 04:53.740 --> 04:57.070 We have a annualized return of 22 percent. 04:57.070 --> 05:03.670 So we have 2 percent more return and this is simply the dividend payments and the risk is actually here 05:03.670 --> 05:04.180 the same. 05:04.180 --> 05:06.280 So we have here 21 percent. 05:06.570 --> 05:13.520 And finally we can also compare price return and the total return by creating here a line plot. 05:13.570 --> 05:20.370 So we are plotting here actually the price weighted index price return and the price weighted index 05:20.420 --> 05:26.470 performance return and here we have in blue the price return. 05:26.610 --> 05:34.560 And in RI in the performance return and both start actually at the beginning of 214 at the base value 05:34.560 --> 05:35.630 of 100. 05:35.640 --> 05:43.140 But then as the time goes on the constituents pay dividends and we actually reinvest our dividends into 05:43.140 --> 05:49.580 our index and therefore the difference between the performance index and the price return Index increases 05:49.640 --> 05:50.350 here. 05:50.460 --> 05:55.050 And it's no surprise that the green graph is above the blue graph. 05:55.050 --> 05:58.420 And as I said before this difference is simply that dividends. 05:58.440 --> 06:01.440 So that's the difference between price return and total return. 06:01.500 --> 06:07.530 And it always depends on the specific case whether we want to work with price returns or total returns 06:08.340 --> 06:13.410 and for the time being we are now finished and I hope to see you also in the next video by.