WEBVTT 00:00.960 --> 00:05.240 In this video I will give you another intermission on a stock Spader factor. 00:05.340 --> 00:07.420 And still we have important. 00:07.500 --> 00:14.180 The returns data frame and the summary data frame with the more realistic market portfolio with the 00:14.180 --> 00:17.460 weights corresponding to the market capitalization. 00:17.490 --> 00:20.350 So here they m cap portfolio. 00:20.760 --> 00:26.000 And here we can see on the right hand side that the beta effect pretty much differs. 00:26.010 --> 00:32.870 We have a beta effect of one point for two for Amazon and only a beta factor of four point three six 00:32.910 --> 00:34.100 for Coca Cola. 00:34.340 --> 00:38.820 And by definition we have a better effect of one for the market portfolio. 00:39.420 --> 00:44.640 And we actually learned before that the beta affect us that the slope of the regression line between 00:44.940 --> 00:51.840 the returns of the market cap portfolio and the returns of the stock and also for this example here 00:52.140 --> 00:58.410 we can draw the regression line between the market portfolio and the Amazon stock and we take you the 00:58.410 --> 01:05.400 daily returns that we have and our returns data frame and on the x axis we have the market portfolio 01:05.400 --> 01:08.920 returns and on the y axis the Emmerson returns. 01:09.030 --> 01:10.520 So let's have a look again here. 01:11.990 --> 01:19.010 So that's where the regression line and the slope of the regression line is actually the Peter factor 01:19.400 --> 01:26.600 and in our example here the slope should be a one point for two and we can also check this again with 01:26.600 --> 01:30.500 the pie and the poly fed function. 01:30.650 --> 01:33.950 So let's have a look here and this is the case here. 01:33.950 --> 01:36.980 So the slope is one point for two. 01:37.790 --> 01:42.860 So from a practical perspective what does a slope of a one point four mean. 01:43.010 --> 01:48.320 And let's assume that the return of the market portfolio increases by 1 percentage point. 01:48.800 --> 01:56.750 So for example from 2 percent at the 1 day to 3 percent at a day then with a bead effect of one point 01:56.750 --> 02:04.100 four on average the return of these stock increases by one point four percentage points and the same 02:04.100 --> 02:05.810 holds true for negative returns. 02:06.200 --> 02:13.250 So if the market portfolio return decreases by 1 percentage point then the stock return on average decreases 02:13.280 --> 02:16.480 by one point 4 percentage points. 02:16.580 --> 02:22.610 In a way this stock overreacts to the market and is therefore called a cyclical stock because it is 02:22.610 --> 02:24.950 heavily driven by market cycles. 02:25.430 --> 02:27.680 So that's the Amazon stock. 02:27.680 --> 02:36.530 And in contrast we have here the Coca-Cola stock with a beta of far below 1 so point 3 6. 02:36.710 --> 02:42.130 And let's also plotted the regression line for the Coca-Cola stock. 02:42.140 --> 02:45.410 So again we have the market portfolio on the x axis here 02:49.240 --> 02:56.230 and the slope here is the Coca-Cola Spader factor and the intuition is that if the market portfolio 02:56.410 --> 03:04.300 return increases by 1 percentage point then the return of the Coca-Cola stock only increases by 0 point 03:04.390 --> 03:06.880 3 6 percentage points. 03:06.880 --> 03:13.480 So in a way the Coca-Cola stock under reacts to the market and is therefore called a non cyclical stock 03:13.810 --> 03:18.990 or defensive stock because it's less influenced by the overall market. 03:19.060 --> 03:25.780 And the question is now why are some stocks and industries cyclical and others defensive. 03:25.810 --> 03:32.560 And the answer is that the cyclical stocks that typically sell luxury products are very expensive products 03:33.010 --> 03:40.510 that are not essential for life slow examples could be car manufacturers or producers of luxury watches 03:41.050 --> 03:47.380 and during economic growth or upswings these businesses capture most of the increases in spending as 03:47.380 --> 03:52.540 consumers are more willing to spend their additional earnings on luxury items. 03:52.660 --> 04:00.010 And in contrast during economic recessions consumers reduce their spendings as they tighten their budgets 04:00.520 --> 04:06.210 and they spend their money on essential goods like food gas electricity and so on. 04:06.220 --> 04:12.070 So Amazon is a cyclical stock as people tend to order more smartphones than upswings. 04:12.100 --> 04:17.620 And once the economy weakens that they stop buying things on Amazon they actually do not really need 04:17.620 --> 04:18.340 for life. 04:19.280 --> 04:26.480 So Coca-Cola is a non cyclical stock as the demand for Coke remains pretty sticky and independent of 04:26.540 --> 04:32.060 the state of the economy are in simple words that people drink Coke no matter if they are unemployed 04:32.090 --> 04:37.150 or successful and the coke is actually an essential everyday product. 04:38.260 --> 04:38.700 All right. 04:38.710 --> 04:45.790 Let's move on and let's have an additional graphical intuition on it so we download price data for Amazon 04:45.790 --> 04:54.340 and Coca-Cola from 2000 and 8 to 2000 and 18 and we only select the closing prices 05:00.080 --> 05:09.990 and finally we read sample the prices to monthly frequency and we calculate uh monthly returns and finally 05:10.220 --> 05:17.780 monthly rolling mean returns with a window of twelve months and we actually uh plotted to for Amazon 05:17.780 --> 05:20.210 and for Coca-Cola and let's have a look here. 05:22.740 --> 05:24.440 So here we have Amazon. 05:24.450 --> 05:26.030 And here we have Coca-Cola 05:29.720 --> 05:36.290 and it's pretty easy to see here that the rolling return for Amazon is more volatile and it actually 05:36.290 --> 05:39.030 overreacts market cycles. 05:39.260 --> 05:45.950 And here for Coca-Cola we can see that the rolling returns the less amplify it so actually at another 05:45.950 --> 05:48.350 reacts the market now. 05:48.430 --> 05:53.390 The final question is what are the implications of these findings for an investor. 05:53.390 --> 05:59.870 So let's assume an investor predicts our fears some market turbulence in the near future then it might 05:59.870 --> 06:06.140 be a good idea to decrease market risk and that means that the investor should decrease the average 06:06.140 --> 06:11.910 beta effect of his portfolio by investing in defensive stocks. 06:12.000 --> 06:12.540 All right. 06:12.540 --> 06:16.890 And with this we are finished here and I hope to see also in the next video by.