WEBVTT 00:01.090 --> 00:07.180 In this video we are going to compare the performance of our six stocks in terms of risk and return. 00:07.180 --> 00:14.080 So in this video we are working also with the NUM pay packets and first of all we want to plot our six 00:14.080 --> 00:22.840 stocks where we have here a starting value or a basic value of 100 for each stock and we can see that 00:22.840 --> 00:31.570 actually Boeing showed the best performance in terms of return for the period 2010 to 2019. 00:31.570 --> 00:36.070 And actually IBM showed here the worst performance. 00:36.070 --> 00:41.440 However when we are comparing financial performance you must also take into account the risk. 00:41.440 --> 00:49.520 So if you take a higher risk then you should also be rewarded with a higher return and vice versa so 00:49.520 --> 00:55.790 simply saying that the Boeing stock showed you the best performance is not really accurate because you 00:55.790 --> 01:02.050 have to take into account risk and return and therefore on this video we are going to compare the historical 01:02.090 --> 01:09.240 risk and the return for these six stocks and finance typical metrics for risk and return to actually 01:09.250 --> 01:10.630 the mean return. 01:10.790 --> 01:17.780 And the standard deviation of returns and this is exactly what we are going to calculate here. 01:17.780 --> 01:21.790 So first of all let's have a look here at our close data frame. 01:21.800 --> 01:29.450 So here we have the closing prices the daily closing prices for all six stocks and in the last video 01:29.450 --> 01:33.170 we learned how to calculate returns for one single stock here. 01:33.170 --> 01:35.540 So for example for the Apple stock. 01:35.780 --> 01:38.880 But we can also apply the percentage change method. 01:38.960 --> 01:43.040 Also on the entire data frame and on all columns. 01:43.040 --> 01:52.450 So let's do this here and we can actually see the daily returns for all six stocks and of course here 01:52.450 --> 01:57.800 at our very first timestamp we do not have a return and therefore we shot. 01:57.850 --> 02:04.660 Also you will see at the drop and a method to drop all arrows that we have missing values. 02:04.660 --> 02:07.030 So here we are dropping the very first row. 02:07.570 --> 02:13.870 And once they're happy here with our data frame where we have the daily returns we can also save the 02:13.870 --> 02:18.170 data frame and we do this and the grab a return or red. 02:18.670 --> 02:24.220 So let's do this and let's have a look here. 02:24.250 --> 02:27.420 So here we have a data frame this daily returns. 02:27.580 --> 02:33.790 And as I said before we are mainly interested in the summary statistics the mean and standard deviation 02:33.790 --> 02:35.470 of these returns. 02:35.470 --> 02:41.020 And first of all we can call here the described method on our returns data frame 02:44.920 --> 02:51.970 so we get four or six stocks that mean return the standard deviation the minimum return the maximum 02:51.970 --> 02:59.640 return and so on and actually if you want to create now a summary data frame we have a few on the left 02:59.640 --> 03:07.320 hand side as labels our stocks and as columns the mean and standard deviation and therefore we have 03:07.320 --> 03:12.590 to transpose here the data frame we can do this with the transpose attribute. 03:12.600 --> 03:17.960 T capital T. 03:18.090 --> 03:25.410 So now we have transposed here our summary statistics and now we want to only select the columns mean 03:25.440 --> 03:29.940 and standard deviation and we can do this with the local operator. 03:29.970 --> 03:34.890 So we want to have all the rows they want to have all stocks and we want to have the columns mean and 03:34.890 --> 03:44.710 standard deviation so this is our data frame with the summary statistics mean return and standard deviation 03:44.710 --> 03:51.280 of returns and we can also save you the data frame and the variable summary. 03:51.370 --> 03:53.150 So here we are. 03:53.980 --> 04:00.270 And here we can see now that we have for the mean are the average daily returns and we ask the standard 04:00.280 --> 04:04.960 deviation of daily returns and these are quite small numbers. 04:04.960 --> 04:10.110 And actually it's more intuitive to have these summary statistics annualized. 04:10.480 --> 04:17.020 So we want to have the annualized that mean return and we want to f the annualized the standard deviation 04:17.020 --> 04:24.490 of returns and we can do this by multiplying here the mean values by two hundred fifty two. 04:24.490 --> 04:29.590 So on average a calendar year has two hundred fifty two business days or trading days. 04:29.920 --> 04:33.700 So we multiply here the mean by 252. 04:34.060 --> 04:40.450 And we also want to annualize to the standard deviation and we can do this by multiplying the column 04:40.450 --> 04:45.490 standard deviation by the square root of 252. 04:45.580 --> 04:54.070 So let's run the cell and actually be about right at our column so let's re inspect our summary data 04:54.080 --> 04:55.190 frame. 04:55.550 --> 05:01.010 And here we have now the annualized mean return and the annualized standard deviation. 05:01.010 --> 05:09.680 So for example for Apple we have an annualized mean return in the period 2010 to 2019 of twenty two 05:09.680 --> 05:10.640 percent per year. 05:10.640 --> 05:12.860 So this is quite good. 05:12.860 --> 05:19.010 And we have an annualized standard deviation of 26 percent and actually to better compare here the stocks 05:19.400 --> 05:22.530 it might make sense to do this graphically. 05:23.030 --> 05:32.330 So we can create here a scatter plot where we have on the x axis the standard deviation and on the y 05:32.330 --> 05:34.280 axis the mean return. 05:34.880 --> 05:40.700 So we're actually creating here a scattered plot on our summary data frame and I expect to get to a 05:40.720 --> 05:45.490 six get us six dots for each stuck here actually. 05:45.500 --> 05:46.950 So let's run this out. 05:49.990 --> 05:56.110 So here we have on the x axis that the annualized the risk in terms of annualized the standard deviation 05:56.530 --> 06:00.870 and on the y axis we have actually the annualized the return. 06:00.910 --> 06:05.060 And here we have the six dots for our six stocks. 06:05.200 --> 06:12.790 And actually it might be more comfortable to also label here the dots and we can do this with the method 06:12.820 --> 06:21.160 purity that notate and therefore actually we are iterating here of our index and our summary data frame. 06:21.160 --> 06:28.000 So we take all six stocks here and for each stock we are creating a label with the ticker symbol and 06:28.000 --> 06:33.670 we place the label near the X Y coordinate at the top other point for the stock is located. 06:34.030 --> 06:41.680 So for each stock actually we place the label in our graph at the coordinate with the x value is where 06:41.680 --> 06:51.550 we have the standard deviation and divide where you mean so let's enable this here and let's have a 06:51.550 --> 06:51.820 look 06:54.680 --> 07:02.750 and as I said before a higher risk should be awarded with a higher return so we can see that the Coca-Cola 07:02.750 --> 07:07.010 stock has quite low risk and also quite low return. 07:07.370 --> 07:10.910 And we can see here that the Boeing stock and also the Apple stock. 07:11.010 --> 07:14.910 So quite high risk but also quite high return. 07:15.110 --> 07:21.590 So we can say that the Boeing stock and the Apple stock showed a better performance as the Coca-Cola 07:21.590 --> 07:23.250 stock in terms of return. 07:23.510 --> 07:30.620 But if you combine risk and return then we cannot really say that these two stocks showed a better performance. 07:30.620 --> 07:36.800 So they simply generated a higher return because then they also had a higher risk and actually also 07:36.830 --> 07:44.450 had the Disney stock and the Microsoft stock are actually in line with the risk return framework. 07:44.450 --> 07:52.130 So on a risk adjusted basis these five stocks showed actually more or less than the same risk adjusted 07:52.280 --> 07:54.110 return performance. 07:54.110 --> 07:59.600 However we can say that the IBM stock showed quite a weak performance. 07:59.600 --> 08:07.150 So it seems like the IBM stocks showed the weakest performance in terms of risk and return. 08:07.160 --> 08:11.030 However as I mentioned before here we have only the price returns. 08:11.030 --> 08:13.820 So we didn't take into account any dividends. 08:14.360 --> 08:21.140 So maybe we have a different conclusion if we also include your dividends but this is not a finance 08:21.140 --> 08:21.660 costs. 08:21.680 --> 08:28.130 And with this visualization here we are finished seventh this session and I hope to see you also in 08:28.130 --> 08:29.420 the next video by.