WEBVTT 00:01.180 --> 00:06.390 In this video we are going to create a price weighted stock index containing our six stocks. 00:06.520 --> 00:14.020 So the stocks Amazon Boeing Disney IBM and Microsoft are the so-called constituents of our customized 00:14.050 --> 00:14.980 index. 00:15.190 --> 00:20.560 And actually the weights of the constituents and the index are determined by the stock prices. 00:21.100 --> 00:23.170 So first of all let's have a look at our stocks. 00:23.170 --> 00:24.810 We are still important. 00:24.850 --> 00:25.720 The stocks. 00:25.740 --> 00:26.400 Yes we're. 00:27.250 --> 00:32.270 So these are the first five four hours here and we also still import it. 00:32.320 --> 00:42.130 The normalized prices starting from 100 at the thirty first of December 2000 and 13 and actually creating 00:42.130 --> 00:47.800 a price weighted index is nothing else than having for all constituents an equal amount of stocks in 00:47.800 --> 00:48.950 the index. 00:48.970 --> 00:54.340 So the easiest example is to have exactly one stock per constituent in the index. 00:54.580 --> 01:01.180 And consequently the total price of our index would be at each point in time simply the sum of the constituent 01:01.180 --> 01:02.410 prices. 01:02.410 --> 01:04.480 So let's have a look at an example here. 01:05.050 --> 01:11.170 So this is our first time stamp and here are the stock prices for our six stocks. 01:11.170 --> 01:18.080 And if you put that one stock per constituent into one portfolio on index then the value of the portfolio 01:18.080 --> 01:23.040 of the index is simply the sum of the stock prices here. 01:23.090 --> 01:27.900 And action on a first step you want to calculate the sum for all rows here. 01:27.970 --> 01:35.970 So far the timestamps and we can do the same with the sum method and passing one to the access parameter. 01:36.070 --> 01:40.690 So we are calculating the sum for all rows or along the columns. 01:40.690 --> 01:49.720 So let's say this here and for example here for the very first time stamp the sum of all constituent 01:49.720 --> 01:52.400 prices is eight hundred seventy seven. 01:52.430 --> 01:58.750 Then at the next day I hadn't read 74 and so on and we are actually almost finished. 01:58.750 --> 02:01.690 So this is our price weighted index. 02:01.870 --> 02:04.700 And typically we would normalize the index. 02:04.720 --> 02:10.010 So we want to have at the very first time stamp the value of 100. 02:10.210 --> 02:16.140 So we can simply select here the very first price at index Position ZERO. 02:16.150 --> 02:23.170 So here we have the eight hundred seventy seven and now actually for our time stamps you can normalize 02:23.170 --> 02:29.920 them the price of our index by dividing all values by the initial value and we can do this by having 02:29.920 --> 02:36.630 here the sum for all rows and then we divide the sum for all rows by the very first element. 02:36.640 --> 02:41.850 So let's have a look here. 02:41.860 --> 02:45.770 So now our index is normalized to a value of 1. 02:45.880 --> 02:54.340 And as it's more common to have a base value of 100 we actually multiply all values here with 100 and 02:54.340 --> 02:57.430 we can do this with the Mayan method. 02:57.430 --> 03:03.260 So let's sort this here and this is now actually our price weighted index. 03:03.280 --> 03:07.710 So at the very first time stamp we have a base value of 100. 03:08.230 --> 03:14.290 And then our index price changes depending on the performance of the constituent stocks. 03:14.830 --> 03:18.260 So let's have a look at the very last time stamp. 03:18.280 --> 03:26.740 So here at the end of two 18 we are already at a value of two hundred forty six and that's actually 03:26.740 --> 03:28.050 a quite good multiplier. 03:28.100 --> 03:28.360 Yes. 03:28.380 --> 03:32.270 So we have a multiplier of two point four six. 03:32.600 --> 03:35.590 And this only within five years. 03:35.590 --> 03:37.570 So this is pretty good. 03:37.670 --> 03:43.740 Now actually we can add our price weighted index to our norm data frame here on the right hand side. 03:43.840 --> 03:49.070 So we create an additional column called price weighted index. 03:49.360 --> 03:52.730 And we use exactly the code that we derived here. 03:52.780 --> 03:56.380 So let's run this year and let's have a look. 03:56.380 --> 04:02.030 So here we have our six constituents and our price weighted index. 04:02.070 --> 04:08.140 Now we can also plot all six stocks in our price weighted index by using the plot method here on our 04:08.140 --> 04:09.650 norm data frames for that. 04:09.660 --> 04:15.340 So this and the price weighted index graph is actually here. 04:15.340 --> 04:19.400 The dark blue graph so it's pretty hard to see here. 04:19.570 --> 04:26.620 And as we can see here from the above from the uh non normalized stock prices by far the highest stock 04:26.620 --> 04:29.560 price we have here for the Amazon stock. 04:30.130 --> 04:37.450 So the Amazon stock has actually acquired a higher weight in our new index and maybe we can also see 04:37.450 --> 04:40.070 this from a graph here. 04:40.090 --> 04:42.190 So now we are only plotting. 04:42.220 --> 04:46.180 The Amazon stock and our price weighted index. 04:47.080 --> 04:51.580 And we can see here that they pretty much uh move together here. 04:51.820 --> 04:55.720 So to say there is a lot of Amazon yeah our price weighted index. 04:56.020 --> 05:00.730 And in contrast to that we could also plot the price weighted index. 05:00.760 --> 05:04.330 And for example a lower weight stock. 05:04.330 --> 05:10.060 So for example here the Coca-Cola stock that has only a little weight and our price weighted index. 05:10.060 --> 05:18.810 So let's throw this here so we are plotting Coca-Cola and our price weighted index and we can see that 05:18.810 --> 05:24.820 the correlation or the relationship between those two is uh fairly low actually. 05:25.110 --> 05:30.920 And this is simply because there's only very little Coca-Cola in our index here. 05:31.380 --> 05:38.730 So our index is actually dominated by the Amazon stock and we can also calculate the weights of the 05:38.760 --> 05:40.860 constituents over time. 05:40.860 --> 05:43.770 So let's again have a look at our stocks data frame. 05:43.770 --> 05:51.120 The first five rows and we can see here at the very first timestamp that the Amazon price is much higher 05:51.120 --> 05:53.080 than the Coca-Cola price. 05:53.160 --> 05:59.860 So we would expect a much higher rate for the Amazon stock than for the Coca-Cola stock and our customized 05:59.860 --> 06:01.590 to price index. 06:01.590 --> 06:07.530 And we can actually calculate the weights of the constituents on each timestamp by actually dividing 06:07.530 --> 06:07.700 here. 06:07.710 --> 06:11.030 Each value by the sum of each row. 06:11.130 --> 06:19.590 So we take uh the sum here for each row and then we divide all values in a row by the respective sum. 06:19.590 --> 06:25.610 So we use see the tough method and we pass the rows to the axis perimeter. 06:25.620 --> 06:30.690 So let's have a look and we can see at the very first timestamp. 06:30.750 --> 06:38.490 So the weight of the Amazon stock is 45 percent and the weight of the Coca-Cola stock is only four point 06:38.490 --> 06:39.860 seven percent. 06:40.110 --> 06:43.580 And we can also have a look at the very end here. 06:43.590 --> 06:50.040 So here we are at the end of 2018 and we can see here that the weight of the Amazon stock in our index 06:50.490 --> 06:53.280 even increased to 68 percent. 06:54.360 --> 07:01.110 And the weight of the Coca-Cola stock actually decreased to only two point two percent. 07:01.200 --> 07:06.870 So this is kind of a weight data frame and we can also save this and the variable weights the price 07:06.870 --> 07:14.730 weighted index and then we can also visualize the evolution of our weights over time by using the plot 07:14.730 --> 07:15.250 method. 07:15.270 --> 07:21.080 So let's have a look and we can see in blue the Amazon weight. 07:21.540 --> 07:32.820 So starting at a weight of 45 percent in 2014 we end up at a rate of almost 70 percent in the year 2000 07:32.850 --> 07:34.540 and 18. 07:34.590 --> 07:39.130 And actually the weight of all other stocks is uh yeah fairly low here. 07:39.210 --> 07:46.270 So our price weighted index is heavily influenced by the Amazon stock and uh the Amazon stock performance. 07:46.560 --> 07:51.930 And this is actually one of the pitfalls of a price weighted index because stocks with the highest prices 07:51.930 --> 07:58.560 have the highest rates and therefore the index has kind of a concentrated position in these stocks and 07:58.560 --> 08:05.310 is actually poorly diversified and also a high stock price does not mean that the company is large in 08:05.310 --> 08:07.800 a market capitalization sense. 08:07.800 --> 08:15.630 So for example if there were only 100 stocks of the Amazon stock then Amazon would be a pretty small 08:15.630 --> 08:23.010 company but with a huge position in our index and actually on the other hand side calculating and constructing 08:23.010 --> 08:25.090 a price weighted indexes. 08:25.140 --> 08:26.040 Pretty simple. 08:26.370 --> 08:31.700 So we actually only need a two or three lines of code and very little data. 08:31.730 --> 08:37.050 So we only need it actually the price data over time for our stocks. 08:37.050 --> 08:44.140 So as a summary there are pros and cons for a price weighted index and in the next video we will create. 08:44.160 --> 08:46.080 The equally weighted index. 08:46.080 --> 08:47.700 So hope to see you there by.