Home builders Stocks as of 8/30/2018

작년 이맘때보다 모게지 금리가 80 포인트 베이스 상승했습니다.

https://fred.stlouisfed.org/series/MORTGAGE30US#0

일단 홈빌더 주식들이 하락한것은 시장에서는 주택시장이 하락할것으로 예상하고 있습니다. 홈빌더의 주식은 10% 에서 20% 가량하락해습니다. 이것은 주택시장의 쿨다운을 시장은 예상하고 있습니다.

http://etfdb.com/type/sector/materials/homebuilders/

DHL LEN NVR PHM TOL KBH

2007-12-31 -47.732473 -63.991166 -13.6026381 -67.133566 -36.195926 -54.9031180

2008-12-31 -44.854122 -49.840215 -12.9293893 5.015468 6.829517 -34.3600804

2009-12-31 56.123686 49.681235 55.7720596 -8.508703 -12.225873 2.1461327

2010-12-31 11.163752 48.294569 -2.7704692 -24.799986 1.010123 0.4691123

2011-12-30 7.139115 5.734973 -0.7264652 -16.090445 7.473689 -48.8717548

2012-12-31 59.500091 97.916694 34.1107872 187.797248 58.325180 138.3307382

2013-12-31 13.739675 2.749150 11.5228272 13.140396 14.444767 16.3234351

2014-12-31 14.310820 13.741847 24.2999526 6.568453 -7.378364 -8.9230000

2015-12-31 27.860780 9.509387 28.8294055 -15.556872 -2.830473 -24.9599428

2016-12-30 -13.667535 -11.906478 1.5824711 5.140536 -6.906909 29.1857416

2017-12-29 89.055639 50.278024 110.1989198 83.432056 55.852288 103.0458577

2018-08-30 -12.080127 -17.859522 -23.6188113 -15.556325 -23.971507 -22.2883068

Note: For 2018, the figures are as of 08/30/2018

D.R Horton (DHI)

https://www.macrotrends.net/stocks/charts/DHI/dr-horton/stock-price-history

https://finance.yahoo.com/quote/DHI?p=DHI&.tsrc=fin-srch

Lennar (LEN)

https://www.macrotrends.net/stocks/charts/LEN/lennar/stock-price-history

https://finance.yahoo.com/quote/LEN?p=LEN&.tsrc=fin-srch

NVR (NVR)

https://www.macrotrends.net/stocks/charts/NVR/nvr/stock-price-history

https://finance.yahoo.com/quote/NVR?p=NVR&.tsrc=fin-srch

Pulte Group (PHM)

https://www.macrotrends.net/stocks/charts/PHM/pultegroup/stock-price-history

https://finance.yahoo.com/quote/PHM?p=PHM&.tsrc=fin-srch

TOLL Brother (TOL)

https://www.macrotrends.net/stocks/charts/TOL/toll-brothers/stock-price-history

https://finance.yahoo.com/quote/TOL?p=TOL&.tsrc=fin-srch

KB Homes (KBH)

https://www.macrotrends.net/stocks/charts/KBH/kb-home/stock-price-history

https://finance.yahoo.com/quote/KBH?p=KBH&.tsrc=fin-srch

Three Countries: Korea vs Japan vs China

The following codes would generate the charts in python.

import wbdata
import pandas
import matplotlib.pyplot as plt

#set up the countries I want
countries = [“CN”,”KR”,”JP”]

#set up the indicator I want (just build up the dict if you want more than one)
indicators = {‘NY.GNP.PCAP.CD’:’GNI per Capita’}

#grab indicators above for countires above and load into data frame
df = wbdata.get_dataframe(indicators, country=countries, convert_date=False)

#df is “pivoted”, pandas’ unstack fucntion helps reshape it into something plottable
dfu = df.unstack(level=0)

# a simple matplotlib plot with legend, labels and a title
dfu.plot();
plt.legend(loc=’best’);
plt.title(“GNI Per Capita ($USD, Atlas Method)”);
plt.xlabel(‘Date’); plt.ylabel(‘GNI Per Capita ($USD, Atlas Method’);

 

#set up the countries I want
countries = [“CN”,”KR”,”JP”]

#set up the indicator I want (just build up the dict if you want more than one)
indicators = {‘NY.GDP.MKTP.CD’:’GDP in current US dollars’}

#grab indicators above for countires above and load into data frame
df = wbdata.get_dataframe(indicators, country=countries, convert_date=False)

#df is “pivoted”, pandas’ unstack fucntion helps reshape it into something plottable
dfu = df.unstack(level=0)

# a simple matplotlib plot with legend, labels and a title
dfu.plot();
plt.legend(loc=’best’);
plt.title(“GDP in current US dollars ($USD)”);
plt.xlabel(‘Date’); plt.ylabel(‘GDP in current US dollars’);

 

import matplotlib.pyplot as plt

pct=dfu[:].pct_change(periods=1)*100
pct.plot()
plt.legend(loc=’best’);
plt.title(“Annual Changes in Current GDP ($USD)”);
plt.xlabel(‘Date’); plt.ylabel(‘Annual Changes’);

Source: https://data.worldbank.org/

 

Python -import pandas as pd

import pandas as pd
import numpy as np
%matplotlib inline

pd.core.common.is_list_like=pd.api.types.is_list_like

import pandas_datareader as web
import datetime
start=datetime.datetime(2000,1,1)
end=datetime.datetime(2018,8,30)
gdp=web.DataReader(‘A191RL1Q225SBEA’, ‘fred’, start, end)
inflation = web.DataReader([‘CPIAUCSL’, ‘CPILFESL’], ‘fred’, start, end)

In [148]:

df =inflation[“CPIAUCSL”].pct_change(periods=12)*100

df.plot.line()

Out[148]:

In [134]:

df.plot.hist(alpha=0.5)

Out[134]:

In [136]:

df.plot.box()

Out[136]:

In [140]:

df=df.cumsum()
df.plot()

Out[140]:

In [64]:

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.plotly as py

Pct_Annual=inflation[“CPIAUCSL”].pct_change(periods=12)*100

y=Pct_Annual
N=len(y)
x=range(N)
width=1/1.5
plt.bar(x,y, width, color=”blue”)

Out[64]:

In [150]:

gdp.plot()
df.plot(secondary_y=True, style=’g’)

Out[150]:

In [159]:

plt.figure()
df.plot()

Out[159]:

In [57]:

import pandas as pd
import numpy as np
%matplotlib inline

pd.core.common.is_list_like=pd.api.types.is_list_like

import pandas_datareader as web
import datetime
start=datetime.datetime(2000,1,1)
end=datetime.datetime(2018,8,30)
gdp=web.DataReader(‘A191RL1Q225SBEA’, ‘fred’, start, end)
inflation = web.DataReader([‘CPIAUCSL’, ‘CPILFESL’], ‘fred’, start, end)

gdp.head()
gdp.tail()
inflation.plot()
gdp.plot()

Out[57]:

Analyzing major IT stocks

Analyzing major IT stocks

현재 총시가 총액의 탑5위를 차지하는 기업들이 전부 기술주들입니다: Apple, Amazon, Google, Microsoft and Facebook.

애플의 판매 액수의 25% 정도가 중국에서 벌어들이고 있습니다. 만약 무역전쟁이 장기화되고 중국정부가 애플기업을 보복한다면 중국에서 벌어들이는 매출이 줄어들 것입니다.

현재 시가총액으로 2위인 아마존의 성장세가 무섭습니다. 지난 올해만 56% 상승했습니다. 같은 기간동안 애플은 24% 성장했습니다. 아마존의 성장세가 이렇게 지속된다면 애플을 제칠 것으로 보입니다. 또한 마이크로 소프트의 성장세도 무섭습니다. 지난 올해만 27% 성장했습니다. 만약 이 성장속도를 유지한다면 애플을 제칠 수도 있습니다. 넥플리스는 아직시가 총액은 높지않지만 FAANG 기업들중에 제일 많이 주가가 상승했습니다. 여기 FAANG 주식의 P/E ratios 를 보면 애플의 P/E ratio 가 가장 낮습니다. 그리고 구글 입니다. 페북의 주식은 올해 여러가지 이유로 몇번 등락을 경험하면서 겨우 0.7% 밖에 오르지못했습니다. 이기술주의 오른 이유는 일단 중국에 노출이 적게되어 있고 무엇보다 중국과의 지적재산권 문제가 해결되면 이기업들의 수익이 오를것으로 보입니다. 또한 중국이 미국의 요구를 들어주면 이기업들이 중국에 진출할것으로 보입니다.

Top largest five companies in market caps are: Apple, Amazon, Google, Microsoft and Facebook.

About 25% of Apple’s sales come from China. If the trade war is prolonged and the Chinese government retaliates against Apple companies, sales in China will decrease. The growth of Amazon, which is the second largest market capitalization, is scary. Last year it rose 56%. Over the same period, Apple grew 24%. If Amazon continues to grow at this rate,, it will surpass Apple’s market cap. Microsoft’s growth is also frightening. It has been growing 27% this year. If Microsoft continues to grow at this growth rate, Microsoft can also beat Apple. Netflix’s market cap is not the highest price yet, but the share price has risen the most among FAANG companies.

The reason for this increase for IT major companies is that these companies’ exposure to China is very low. Also, if the intellectual property rights issue with China is resolved, the profits of the companies will increase substantially. Also, if China accepts the demands of the United States, it is likely that these industries will enter China.

The P / E ratios of FAANG: Apple’s P / E ratio is the lowest. And it is Google. The stocks of Facebook grew only 0.7% while experiencing fluctuations several times this year for various reasons.

P/E ratios of FAANG stocks –

Apple (18), Goog (33), MSFT(28), FB(26), AMZN(170), NFLX(147)

FB AMZN GOOGLE MSFT NFLX AAPL
12/31/2013 105.3% 59.0% 58.4% 44.3% 297.6% 25.8%
12/31/2014 42.8% -22.2% -5.4% 27.6% -7.2% 50.9%
12/31/2015 34.1% 117.8% 46.6% 22.7% 134.4% -3.0%
12/30/2016 9.9% 10.9% 1.9% 15.1% 8.2% 12.5%
12/29/2017 53.4% 56.0% 32.9% 40.7% 55.1% 48.5%
8/3/2018 0.7% 55.9% 17.5% 27.4% 78.7% 23.9%

Chart – Cumulative Returns of FAANG stocks since 1/1/2018 to 8/3/2018

Sources:

https://www.macrotrends.net/stocks/stock-screener

https://www.investopedia.com/terms/f/faang-stocks.asp

https://www.macrotrends.net/stocks/stock-screener