Create embedded formulae which generate statistical properties of the portfolio upon insertion of the weights. Observe the mean, the standard deviation, and the CV of the annual rate of return of the portfolio. Walmart Historical Annual Stock Price Data Year Average Stock Price Year Open Year High Year Low Year Close Annual % Change 2021 140.9952 146.5300 151.4500 127.5300 144.6900 0.37% 2020 129.6013 118.9400 152.7900 104.0500 144.1500 21.30% 2019 108.4054 93.3400 121.2800 92.8600 118.8400 27.58% 2018 92.3698 98.5900 109.5500 82.4000 93.1500 -5.67% 2017 78.9624 68.6600 99.6200 65.6600 98.7500 42.87% 2016 69.5471 61.4600 74.3000 60.8400 69.1200 12.76% 2015 72.4911 85.9000 90.4700 56.4200 61.3000 -28.62% 2014 77.3274 78.9100 87.5400 72.6600 85.8800 9.14% 2013 75.3205 69.2400 81.2100 68.3000 78.6900 15.33% 2012 67.2151 60.3300 77.1500 57.3600 68.2300 14.17% 2011 54.3535 54.5600 59.9900 48.4100 59.7600 10.81% 2010 53.0022 54.2300 55.9900 48.0000 53.9300 0.90% 2009 50.5517 57.1800 57.1800 46.4200 53.4500 -4.66% 2008 55.0489 46.9000 63.1700 45.7200 56.0600 17.95% 2007 46.9265 47.5500 51.2100 42.2700 47.5300 2.92% 2006 46.6282 46.2300 51.7500 43.0200 46.1800 -1.32% 2005 48.7378 53.3500 54.4900 42.4900 46.8000 -11.40% 2004 54.9097 52.3000 61.0500 51.3300 52.8200 -0.43% 2003 54.3115 51.6000 60.0800 46.7400 53.0500 5.03% 2002 55.3673 58.0500 63.7500 44.6000 50.5100 -12.23% 2001 51.9974 53.8800 58.4400 44.0000 57.5500 8.32%

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Create embedded formulae which generate statistical properties of the portfolio upon insertion of the weights. Observe the mean, the standard deviation, and the CV of the annual rate of return of the portfolio.

Walmart Historical Annual Stock Price Data
Year	Average Stock Price	Year Open	Year High	Year Low	Year Close	Annual % Change
2021	140.9952	146.5300	151.4500	127.5300	144.6900	0.37%
2020	129.6013	118.9400	152.7900	104.0500	144.1500	21.30%
2019	108.4054	93.3400	121.2800	92.8600	118.8400	27.58%
2018	92.3698	98.5900	109.5500	82.4000	93.1500	-5.67%
2017	78.9624	68.6600	99.6200	65.6600	98.7500	42.87%
2016	69.5471	61.4600	74.3000	60.8400	69.1200	12.76%
2015	72.4911	85.9000	90.4700	56.4200	61.3000	-28.62%
2014	77.3274	78.9100	87.5400	72.6600	85.8800	9.14%
2013	75.3205	69.2400	81.2100	68.3000	78.6900	15.33%
2012	67.2151	60.3300	77.1500	57.3600	68.2300	14.17%
2011	54.3535	54.5600	59.9900	48.4100	59.7600	10.81%
2010	53.0022	54.2300	55.9900	48.0000	53.9300	0.90%
2009	50.5517	57.1800	57.1800	46.4200	53.4500	-4.66%
2008	55.0489	46.9000	63.1700	45.7200	56.0600	17.95%
2007	46.9265	47.5500	51.2100	42.2700	47.5300	2.92%
2006	46.6282	46.2300	51.7500	43.0200	46.1800	-1.32%
2005	48.7378	53.3500	54.4900	42.4900	46.8000	-11.40%
2004	54.9097	52.3000	61.0500	51.3300	52.8200	-0.43%
2003	54.3115	51.6000	60.0800	46.7400	53.0500	5.03%
2002	55.3673	58.0500	63.7500	44.6000	50.5100	-12.23%
2001	51.9974	53.8800	58.4400	44.0000	57.5500	8.32%

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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