# data[].attributes.body
Q1
a) 7.116%
b)6.23%
Q2
mean=10.8
S= 17.5699
Activity 2
Q1
historical data can not show the future result
Q2
Arithmetic mean: short term
geometric mean: long term

# data[].attributes.headline
Group 4 Shixuan Song, Shuqi Liu Changhong Zhou

# data[].attributes.subject
Group 4 Shixuan Song, Shuqi Liu Changhong Zhou

# data[].attributes.body
Activity 1
Question 1
a. Arithmetic mean:  0.071166667
b. Geometiric mean: 6.23%
Question 2
a. Arithmetic mean: 11%
b. Standard deviation: 0.175698606
Question 3
Arithmetic mean: 3.67%
Standard deviation: 0.423606736
Question 4
Arithmetic mean: 25%
Standard deviation: 0.352950799
Question 5
Arithmetic mean for Lowes Realized Return: 25%
Arithmetic mean for Home Depot Realized Return: 4%
covariance: 0.104087733

Activity 2
Question 1
The data is a forecast, not the correcct number, which means it would differeent to the result.
Question 2
For arithmetic mean return, investors can know the average  return of the project in several years.
For geometric mean return ,investor would get when holding an investment over a period of several years.
Question 3
low correlation: Apple and AstraZeneca
high correlation: Apple and foxconn

# data[].attributes.headline
group3 Lecong Li, Yongjie Cao，Mengrong Liu and Pingcuozhuoma

# data[].attributes.subject
group3 Lecong Li, Yongjie Cao，Mengrong Liu and Pingcuozhuoma

# data[].attributes.body
question 1
a. arithmetic mean=(11.1%-5.2%+20.3%+26.7%-12.4%+2.2%)/6 =7.12=7.12%
b. geometric mean=(1+11.1%)*(1-5.2%)*(1+20.3%)*(1+26.7%)*(1-12.4%)*(1+2.2%)^1/6 -1=1.4372^1/6-1= 1.0623-1=0.0623=6.23%
question 2
average return = (22%+33%+1%-12%+10%)/5=10.8%
standard deviation=(22-10.8)^2+(33-10.8)^2 +(1-10.8)^2+(-12-10.8)^2+(10-10.8)^2/5-1 need to square root=17.57%

question 3. D 42% need to calculate standard deviation
question 4. D 35% need to calculate standard deviation
question 5 C 0.1  Use the covariance formula
activity 2
1.Past performance is not necessarily indicative of future results: Just because an asset performed well in the past does not guarantee that it will continue to perform well in the future. There may be changes in the market, the economy, or other factors that affect the asset's performance going forward.
2.An investor should use the arithmetic mean return when they want to calculate the average return over a given period of time. The geometric mean return is calculated by taking the nth root of the product of n returns, where n is the number of periods under consideration. It is most commonly used for investments such as mutual funds, which are held for longer periods of time and experience compounding of returns.

# data[].attributes.body
Activity1:
Q1:
a.（1.11*0.948*1.203*1.267*0.876*1.022）/6-1=7.27%
b. (1.11*0.948*1.203*1.267*0.876*1.022)^(1/6)-1=6.22%
Q2:
1.
(22 + 33 + 1 - 12 + 10) / 5=10.8%
2.
SQRT( ((22%-10.8%)^2 + (33%-10.8%)^2 + (1%-10.8%)^2 + (-12%-10.8%)^2 + (10%-10%)^2) / 4 )=17.6%

Activity2:
Q1:
Past performance does not guarantee future results: The historical returns and standard deviations may not accurately reflect the future performance of an investment.

2.Historical data may be limited, especially for newer investments
Q2:
If an investor is interested in analyzing the short-term performance of an investment or portfolio, the arithmetic mean return may be more appropriate. However, if the investor is interested in analyzing the long-term performance of an investment, the geometric mean return may be more appropriate.
Q3:
An example of two industries that might exhibit low correlation of returns are the technology industry and the healthcare industry. The technology industry is often characterized by high growth potential and volatility, while the healthcare industry is characterized by stability and lower growth potential. These differences in the characteristics of the two industries may result in low correlation of returns between them. For example, if the technology industry experiences a downturn due to changes in consumer preferences or technological disruptions, the healthcare industry may not be affected to the same extent.

An example of two industries that might exhibit high correlation of returns are the energy industry and the materials industry. These two industries are often closely linked due to the fact that they both involve the extraction and processing of natural resources. For example, if there is a shortage of raw materials, this may affect the profitability of companies in both industries. Similarly, if there is a change in commodity prices or supply chain disruptions, this may impact the returns of companies in both industries in a similar manner. As a result, the returns of these industries are likely to be highly correlated.