WEBVTT 00:01.170 --> 00:04.100 Welcome to the final step seven of our project. 00:04.280 --> 00:10.310 Even if we were able to explain Marilee portfolio diversification effect and to convince her to hold 00:10.340 --> 00:13.040 a more diversified stock portfolio. 00:13.040 --> 00:19.670 We were not able to convince her to hold a widely diversified portfolio but at least she's willing to 00:19.670 --> 00:22.760 add some more sectors to her portfolio. 00:22.760 --> 00:28.250 And that's pretty typical when dealing with private investors who have their own mind and who are not 00:28.250 --> 00:29.390 easy to handle. 00:29.540 --> 00:36.320 In these cases that might not be the best idea to force them into portfolios that are maybe optimal 00:36.320 --> 00:40.980 from an adviser view but that are simply not accepted by the client. 00:41.090 --> 00:45.570 And it always depends on the client's worth compared to the client's lifestyle. 00:45.620 --> 00:51.470 So if the client's portfolio is large compared to the costs of living then more individual declined 00:51.470 --> 00:57.060 requests can be accepted and less needs to be moderated by the adviser. 00:57.200 --> 00:59.370 And that's the case here with Maria. 00:59.540 --> 01:03.970 So she has a pretty large portfolio compared to her living costs. 01:04.010 --> 01:09.230 And finally we agreed to with Maria to add sectors that showed a positive. 01:09.230 --> 01:16.290 I find the most recent five year period and again that's no guarantee that the positive IFR will persist. 01:16.290 --> 01:23.780 Also in the future and also Maria expects some market turbulence in the near future and therefore it's 01:23.780 --> 01:30.770 appropriate to select more defensive sectors containing low market risk and these sectors have a beta 01:30.770 --> 01:32.910 of less than 1. 01:32.930 --> 01:39.590 So as a summary we as advisors can live with the outcome Sierra of the first advisory round even if 01:39.680 --> 01:45.740 there's no guarantee that positive alpha will persist but at least that's pretty likely that the low 01:45.740 --> 01:51.490 beta stocks and sectors remain low paid stocks and sectors in the future. 01:51.500 --> 01:56.220 And again there's only one guarantee or one free lunch in the investment market. 01:56.330 --> 01:58.790 It's the diversification effect. 01:58.990 --> 02:05.660 So in Step 7 here we have to identify sectors with positive alpha and a beta effect below 1. 02:06.230 --> 02:12.470 And as a market portfolio we should use the S&P 500 Total Return Index. 02:12.470 --> 02:16.930 And with this we have to calculate the following metrics annualized risk and return. 02:16.940 --> 02:23.270 Then the sharp ratio than the annualized total risk and variance units are the systematic risk the and 02:23.270 --> 02:31.700 systematic risk the beta the cap m return and the alpha in nexus and also for the market portfolio. 02:31.700 --> 02:40.250 And we have still imparted our returns data frame with the daily returns of our eleven indexes here. 02:40.610 --> 02:45.640 And also we have still important the normalized prices of our indexes. 02:45.680 --> 02:56.060 So here we are and then we import the S&P 500 Total Return Index price data from the UCSC file S&P 500 02:56.060 --> 03:03.260 Total Return and we are only interested in the close column so let's import unsafe the data and the 03:03.260 --> 03:04.940 variable S&P 500 03:08.320 --> 03:15.540 and actually here for our 11 indexes we have price and return data for the most recent five year period. 03:16.300 --> 03:23.830 And therefore we can re index the S&P 500 data frame by the index or the timestamps that we have in 03:23.830 --> 03:25.680 our index this data frame. 03:25.690 --> 03:34.330 So we re index the S&P 500 by the timestamps for the day time index of the index data frame and VOA. 03:34.330 --> 03:34.630 Right. 03:34.630 --> 03:41.010 The S&P 500 data frame and then we can also calculate that daily returns. 03:41.020 --> 03:42.790 So let's have a look here. 03:42.850 --> 03:51.090 So that's the daily returns of the S&P 500 Total Return Index from 250 into to 18. 03:51.160 --> 03:56.340 And finally we can also add another column to our returns data frame. 03:56.530 --> 04:06.430 The column S&P 500 with the returns of the S&P 500 Total Return Index and let's have a look here. 04:06.480 --> 04:15.300 So here on the very right hand side we have also the daily returns of the market portfolio the S&P 500 04:16.500 --> 04:23.940 and then the next slide we calculate annualized risk and return for the eleven indexes and the S&P 500 04:23.940 --> 04:34.650 index so here we are and then our next step we create the new column with the sharp ratio and we also 04:34.650 --> 04:42.210 calculate the total risk and variance units by actually squaring the risk in standard deviation units. 04:42.220 --> 04:43.410 So this is here. 04:43.410 --> 04:44.250 Nothing new 04:47.620 --> 04:53.620 and then the next step we create the covariance matrix with annualized core variances 04:56.150 --> 04:59.210 so let's have a look at our covariance matrix here 05:04.820 --> 05:12.330 and on the very right hand side we have the core variances of the indexes with the market portfolio. 05:12.470 --> 05:16.820 And this is actually the market risk or systematic risk. 05:16.910 --> 05:23.810 So here in the next step we can create the new column systematic risk and variance units by taking here 05:23.840 --> 05:27.560 the very last column here of our covariance matrix 05:30.940 --> 05:36.940 and then we can also calculate the end systematic risk for our eleven indexes and for the market portfolio 05:37.480 --> 05:45.460 by subtracting the systematic risk from the total risk and let's have a look here at our summary data 05:45.460 --> 05:46.600 frame. 05:46.600 --> 05:53.140 So here we have annualized the risk return the shop ratio or total risk systematic risk and and systematic 05:53.140 --> 06:01.670 risk and then we can also calculate debate affect which is the systematic risk normalized by the systematic 06:01.670 --> 06:04.870 risk or the variance of the market portfolio 06:08.540 --> 06:16.220 then we can calculate the cap m return which is the return of the risk free asset plus the return of 06:16.250 --> 06:25.500 the market portfolio minus the risk free asset multiply it with uh the beta factor and then finally 06:25.500 --> 06:32.880 we can calculate the alpha which is actually the actual return of our indexes the minus the cap m return 06:34.760 --> 06:42.600 so let's have a final look here on our summary data frame with a beta cap m return and Alpha and now 06:42.630 --> 06:51.580 we want to filter all sectors that meet the two conditions so the alpha must be positive and the beta 06:51.580 --> 06:53.400 must be below 1. 06:53.470 --> 07:01.090 So both conditions must be met and therefore we combine both conditions with then sign and uh finally 07:01.090 --> 07:05.160 we feel or we our summary data frame with the local operator. 07:05.200 --> 07:06.800 So let's have a look here. 07:07.270 --> 07:12.130 And here we have the four industries that are short form Mary's portfolio. 07:12.130 --> 07:19.800 Still we have the healthcare sector and also we have public utilities consumer durables and basic industries. 07:20.350 --> 07:24.670 So all sectors have a beat off below 1 and a positive. 07:24.700 --> 07:30.730 I find the most recent five year period we have finished with the final project. 07:30.730 --> 07:35.770 I hope you enjoyed it and I'm looking forward to see you also in part 4 by.