A year in Review

Francisca Dias

Introduction

From Apple and Microsoft large amounts of cash, to Whole Foods and Amazon partnership, not to mention Bitcoin recent price, this is a year in review.

In [ ]:
from plotly.graph_objs import *
import plotly.offline as py
py.init_notebook_mode(connected=True)

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fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')

Source: World Bank

The year is not over yet, so the data is from 2016.

It should come as no surprise that poor countries tend to have a higher unemployment rate.

The relationship between unemployment and losses in prodution is mentioned in Economics by Okun's law:

"Output depends on the amount of labor used in the production process, so there is a positive relationship between output and employment. Total employment equals the labor force minus the unemployed, so there is a negative relationship between output and unemployment (conditional on the labor force)."

In [8]:
from plotly.graph_objs import *
import plotly.offline as py
py.init_notebook_mode(connected=True)

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  "y": ["Johnson & J", "Procter", "PepsiCo", "Pfizer", "Amazon", "Merck", "Intel", "Coca-Cola", "Gilead Sc.", "Qualcomm", "Amgen", "GE", "Oracle", "Cisco", "Alphabet", "Microsoft", "Apple"], 
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data = Data([trace1])
layout = {
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  "hovermode": "closest", 
  "title": "17 US Companies with the Biggest Piles of Cash", 
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fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='cash')

American companies are sitting in great amounts of cash, but a huge part of that cash is not held in the US.

In November, Bank of America estimated the cash amount being held overseas.

They state taht American companies are sitting on $1,01 trillion cash — with 80 per cent of those reserves held overseas.

These 17 companies together have over $800 billion cash overseas.

In [9]:
py.init_notebook_mode(connected=True)

trace1 = {
  "x": ["Q1 2014", "Q2 2014", "Q3 2014", "Q4 2014", "Q1 2015", "Q2 2015", "Q3 2015", "Q4 2015", "Q1 2016", "Q2 2016", "Q3 2016", "Q4 2016", "Q1 2017", "Q2 2017", "Q3 2017", "Q4 2017 (est)"], 
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data = Data([trace1])
layout = {
  "hovermode": "closest", 
  "title": "Whole Foods revenue growth (change from a year earlier)", 
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fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')

Source: Bloomberg

Amazon's $ 13.7 billion purchase of Whole Foods was set in August this year.

These companies wanted to do some changes for the benefit of its costumers and it looks like it is working.

  • Over time, Whole Foods will get cheaper in general;
  • Amazon Prime members will get special discounts;
  • You'll be able to buy Whole Foods' private label products on Amazon Prime;
  • Whole Foods stores will have Amazon lockers;
  • Whole Foods will grow its team;
  • The companies will integrate point-of-sale and merchandising systems meaning that Amazon's brands will be available at Whole Foods, and vice versa.

What will stay the same? John Mackey will stay on as CEO.

Whole Foods recently released its final financial report, which included about one month under new owner.

Whole Foods disclosed that sales in its fiscal fourth quarter ended Sept. 24 rose about 4.4 percent from a year earlier.

According to Bloomberg data, that is the fastest rate of sales growth in two years.

It's impossible to know for sure how much of that faster revenue growth happened in the last month of the quarter under Amazon's ownership. But analysts have figured the headlines about Amazon's takeover and attention about the company's selective price cuts on Whole Foods merchandise helped draw in more shoppers.

In [10]:
py.init_notebook_mode(connected=True)

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trace2 = {
  "x": ["12-11-2016", "12-18-2016", "12-25-2016", "1-1-2017", "1-8-2017", "1-15-2017", "1-22-2017", "1-29-2017", "2-5-2017", "2-12-2017", "2-19-2017", "2-26-2017", "3-5-2017", "3-12-2017", "3-19-2017", "3-26-2017", "4-2-2017", "4-9-2017", "4-16-2017", "4-23-2017", "4-30-2017", "5-7-2017", "5-14-2017", "5-21-2017", "5-28-2017", "6-4-2017", "6-11-2017", "6-18-2017", "6-25-2017", "7-2-2017", "7-9-2017", "7-16-2017", "7-23-2017", "7-30-2017", "8-6-2017", "8-13-2017", "8-20-2017", "8-27-2017", "9-3-2017", "9-10-2017", "9-17-2017", "9-24-2017", "10-1-2017", "10-8-2017", "10-15-2017", "10-22-2017", "10-9-2017", "11-5-2017", "11-12-2017", "11-19-2017", "11-26-2017", "12-3-2017"], 
  "y": ["761.8", "784.4", "858.6", "816.1", "738.7", "805.1", "884.2", "910.0", "936.4", "975.9", "1,044.1", "1,130.9", "971.0", "935.1", "891.0", "950.1", "1,081.0", "1,163.5", "1,190.4", "1,326.9", "1,403.3", "1,575.1", "1,720.0", "1,810.0", "1,955.5", "2,376.4", "2,050.0", "2,421.0", "2,208.1", "2,302.0", "1,975.7", "1,835.8", "2,404.6", "2,595.0", "3,156.0", "3,836.1", "3,600.0", "4,175.7", "3,900.1", "2,981.0", "3,476.3", "3,620.0", "4,134.9", "4,417.6", "5,114.3", "5,367.9", "5,662.9", "6,166.0", "5,426.0", "7,652.0", "8,769.2", "10,299.0"], 
  "line": {
    "color": "rgb(127, 127, 127)", 
    "width": 3.5
  }, 
  "name": "Bitcoin Low Price", 
  "opacity": 1, 
  "type": "scatter", 
  "uid": "5716cb", 
  "xsrc": "FranciscaDias:46:067009", 
  "ysrc": "FranciscaDias:46:8c17b8"
}
data = Data([trace1, trace2])
layout = {
  "title": "One year series on weekly Bitcoin Price", 
  "xaxis": {
    "autorange": True, 
    "range": [0, 51],
    "showgrid": False,
    "type": "category"
  }, 
  "yaxis": {
    "autorange": True, 
    "range": [115.905555556, 12571.7944444],
    "showgrid": False,
    "type": "linear"
  }
}
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')

Bitcoin price in dollars has multiplied ten-fold this year. It is now being traded at US 10.000 whereas one year ago its price was not even US 1.000.

On Wall Street, the sentiment is "unsustainability".

Many believe that we are in a bubble, but Google says otherwise. Searches as "Bitcoin loan" or "Bitcoin credit" are on the rise.

In [11]:
py.init_notebook_mode(connected=True)

trace1 = {
  "x": ["China Literature", "Wuxi Biologics", "Razer Inc", "ZhongAn", "Zhongyuan Bank", "Guangzhou Rural Commercial Bank"], 
  "y": ["86.2 %", "37.1 %", "24 %", "9.2 %", "1.6 %", "0.2 %"], 
  "hoverinfo": "y+text+name", 
  "marker": {"color": "rgb(122, 125, 131)"}, 
  "name": "y", 
  "opacity": 0.6, 
  "text": ["China Literature", "Wuxi Biologics", "Razer Inc", "ZhongAn", "Zhongyuan Bank", "Guangzhou Rural Commercial Bank"], 
  "textsrc": "FranciscaDias:50:ee6817", 
  "type": "bar", 
  "uid": "7c92cb", 
  "xsrc": "FranciscaDias:50:ee6817", 
  "ysrc": "FranciscaDias:50:51dccc"
}
data = Data([trace1])
layout = {
  "title": "First-day performance of largest IPO in Hong-Kong", 
  "xaxis": {
    "autorange": True, 
    "range": [-0.5, 5.5], 
    "title": "", 
    "type": "category"
  }, 
  "yaxis": {
    "autorange": True, 
    "range": [0, 90.7368421053], 
    "title": "", 
    "type": "linear"
  }
}

fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')

Source: Bloomberg

China Literature’s shares rose as high as HK 110 in early trade, compared to its offer price of HK 55 per share, giving it a market value of nearly $ 13 billion. The shares closed at HK102.4, a gain of 86 per cent.

China Literature Limited operates an online literature platform.

The Company sells e-books and also manages an online publishing house. Sort of Amazon Kindle.

The business model is interesting: it is similar to the game business where someone can read a few chapters for free and then you pay if you want to continue read. They have plans to expand, not only on the literature area, but also other parts of entertainment.

In [12]:
py.init_notebook_mode(connected=True)

trace1 = {
  "x": ["Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct"], 
  "y": ["8.3","8.2", "8.1", "8.0", "7.9", "7.8", "7.7", "7.6", "7.6", "7.5", "7.5", "7.4"], 
  "line": {
    "color": "rgb(204, 0, 102)", 
    "width": 3.5
  }, 
  "name": "EU (28 Countries)", 
  "opacity": 1, 
  "type": "scatter", 
  "uid": "2beef7", 
  "xsrc": "FranciscaDias:46:067009", 
  "ysrc": "FranciscaDias:46:39d529"
}
trace2 = {
  "x": ["Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct"], 
  "y": ["10.5","10.1", "10.1", "9.9", "9.7", "9.5", "9.2", "9.1", "8.9", "8.8", "8.5", "8.5"], 
  "line": {
    "color": "rgb(127, 127, 127)", 
    "width": 3.5
  }, 
  "name": "Portugal", 
  "opacity": 1, 
  "type": "scatter", 
  "uid": "5716cb", 
  "xsrc": "FranciscaDias:46:067009", 
  "ysrc": "FranciscaDias:46:8c17b8"
}


trace3 = {
  "x": ["Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct"], 
  "y": ["16.7","18.7", "18.5", "18.4", "18.2", "18", "17.6", "17.3", "16.9", "16.8", "16.7", "16.7"], 
  "line": {
    "color": "rgb(23, 190, 207)", 
    "width": 3.5
  }, 
  "name": "Spain", 
  "opacity": 1, 
  "type": "scatter", 
  "uid": "5716cb", 
  "xsrc": "FranciscaDias:46:067009", 
  "ysrc": "FranciscaDias:46:8c17b8"
}

data = Data([trace1, trace2, trace3])
layout = {
  "title": "Unemployment Rate, as % of Labour Force", 
  "xaxis": {
    "autorange": True, 
    "range": [0, 51],
    "showgrid": False,
    "type": "category"
  }, 
  "yaxis": {
    "autorange": True, 
    "range": [115.905555556, 12571.7944444],
    "showgrid": False,
    "type": "linear"
  }
}
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')

Source: Eurostat

Although unemployment has dropped overall in Europe, countries like Spain and Portugal, the jobless rate remains around or above 8 per cent percent in those southern states.

In [13]:
py.init_notebook_mode(connected=True)

trace1 = {
  "z": ["34", "1001", "258", "508", "322", "215", "476", "156", "526", "117", "2287", "1101", "192", "174", "2595", "589", "654", "135", "485", "98", "616", "65", "73", "714", "2161", "193", "906", "2458", "233", "896", "346", "153", "372", "437", "129", "430", "362", "256", "390"], 
  "autocolorscale": False, 
  "colorbar": {
    "outlinecolor": "rgb(247, 240, 240)", 
    "outlinewidth": 0.25, 
    "tickfont": {"color": "rgb(123, 80, 80)"}, 
    "ticklen": 1, 
    "ticks": "outside", 
    "tickwidth": -1
  }, 
  "colorscale": [
    [0, "rgb(190, 207, 182)"], [0.35, "rgb(125, 178, 143)"], [0.5, "rgb(40, 144, 126)"], [0.6, "rgb(16, 125, 121)"], [0.7, "rgb(24, 97, 108)"], [1, "rgb(28, 71, 93)"]], 
  "locationmode": "ISO-3", 
  "locations": ["AND", "GRC", "SRB", "DEU", "CHE", "SVK", "BEL", "ROU", "HUN", "MDA", "DNK", "IRL", "FIN", "UKR", "PRT", "SVN", "GBR", "LUX", "ESP", "BLR", "MCO", "RUS", "CYP", "NOR", "FRA", "MNE", "CZE", "ISL", "LVA", "ITA", "BIH", "BGR", "SWE", "EST", "LIE", "AUT", "LTU", "POL", "HRV"], 
  "locationssrc": "FranciscaDias:56:64161e", 
  "name": "Airbnb Listings", 
  "reversescale": False, 
  "showscale": True, 
  "type": "choropleth", 
  "uid": "36f307", 
  "zauto": False, 
  "zmax": 2595, 
  "zmin": 34, 
  "zsrc": "FranciscaDias:56:20a5c0"
}
data = Data([trace1])
layout = {
  "autosize": True, 
  "geo": {"scope": "europe"}, 
  "hovermode": "closest", 
  "title": "Airbnb Listings in Europe, per 100.000 inhabitants"
}
      
fig = Figure(data=data, layout=layout)
py.iplot(fig, filename='')       

Source:

  • Airdna.com for the number of Listings for each country;
  • World Bank for Total Population.

On the top ranking for the highest number airbnb listings per 100.000 inhabitants is Portugal, France, Denmark and Iceland, all with more than 2.000 airbnb listings.

Portugal leads this rank with 2.595 listings per 100.000 inhabitants.