Overview of Big-Tech Companies

Over the last several years, we have witnessed the continual growth of firms all over the United States (and globally). In particular, the growth of big multinational firms have been of particular interest. We are talking about firms such as Facebook, Apple, Microsoft, Alphabet (who run Google), and last but most definitely not least, Amazon. For groups like Apple and Microsoft, growth has come in the form of selling new products such as the apple watch or microsoft phones and software items. For groups like Amazon, their delivery service has seen revenue rise astronomically. Additionally, one way these firms have been able to gain revenue is through advertisement. However, there are costs associated with advertising your products and those costs seep into revenue. Additionally, we also seek to look at the debt-asset ratio of three of these five companies (Apple, Amazon, and Google) as a way to assess their performance. There is a plethora of different ways to analyze the performance of a company. However, with limited time and for simplicity, we would only analyze the growth through these three methods. Given these methods of analysis, which of these 5 companies show the most promise in terms of growth for the future and what can explain the growth trends that we see?

To measure how revenue for the five firms, Amazon, Apple, Alphabet, Facebook, and Microsoft has changed, we set up a line graph. From our initial analysis, the first glaring observation that we can make is that compared to the other firms, Apple has seen a significantly higher level of revenue compared to all the other firms, bringing in between $220 billion and $280 billion in revenue between 2018 and 2020. This would make sense as those were the years that Apple were producing new products such as phones and apple watches that you did not see much in the other groups. Given that the sources of revenue from a group such as Apple comes from a variety of areas and sectors, it would make sense that they would see a significant amount of revenue compared to everyone else. However, despite that, they are also the only group of the 5 that has seen a decrease in revenue as well. Between 2019 and 2020, although there were quite a few products being produced by Apple, the volume of the products may not have been as high and compared to other years, they may not have been as big a hit which may explain their decrease in revenue.

bigtech_revenue <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/bigtech_revenue.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Company = col_character(),
##   rev_2018 = col_double(),
##   employee_2018 = col_double(),
##   rev_2019 = col_double(),
##   employee_2019 = col_double(),
##   rev_2020 = col_double(),
##   employee_2020 = col_double()
## )
revenue <- bigtech_revenue %>%  #tidying dataset for graphing
  pivot_longer(cols = c("rev_2018","rev_2019","rev_2020"), names_to = "Year",values_to = "Revenue_in_billion")%>%
  mutate(Year = str_replace_all(Year, "rev_",""))   

#Creating a graph that capture the revenue over past 3 years
p <- ggplot(data = revenue, aes(x = Year, y = Revenue_in_billion), na.rm = TRUE) +
  geom_point(aes(color = Company))+
  scale_color_discrete(breaks = c("Apple","Amazon","Alphabet","Microsoft","Facebook"))+
  geom_line(aes(group = Company,color = Company)) +
  labs(title = "Revenue of Biggest Tech Companies 2018-2020",
         x = "Year",
         y = "Revenue (in billions of USD)")

ggplotly(p)

What you will also notice is that there is not as much information on Amazon’s revenue post 2018. This was one of the limitations in our data as their revenue is not readily scrappable to use for our data analysis. Similarly, Facebook does not show revenue information in 2018 but shows that information for 2019 and 2020 and we see that there has been some growth in revenue which may be attributed to there being more facebook users from 2019 to 2020. However, comparatively, Facebook has seen the least amount of revenue of the 5 groups examined here.

Microsoft and Alphabet (who is the parent company of Google) have seen the most consistent level of growth over the three years. Alphabet oversees the activity of Google, YouTube, iCloud, etc. A majority of Alphabet’s revenues come from Google Searches and YouTube ads. We would suspect that the volume of Google searches would stay consistent as do the volume of YouTube ads because in a given year, there are always things to search for and advertise for and even if there was something occurring in a given year that would exponentially increase Google searches or something that exponentially increases Youtube ads, it would cancel itself out eventually. Similarly for Microsoft, the use of the services is ever increasing as more people switch from Mac to Microsoft Office. We were surprised that revenue growth was not exponential but it still makes sense that there would be a constant increase in revenue for Microsoft.

Advertising Costs

top_website <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/top_website.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Rank = col_double(),
##   Domain = col_character(),
##   Org_traffic = col_character(),
##   Traffic_cost = col_character(),
##   Company = col_character()
## )
bigtech_web <- top_website%>%  #tidy dataset for graphing
  filter(!str_detect(Company, "about."))%>%
  mutate(visits_in_billions = ifelse(str_detect(Org_traffic, "M"),
                                     (parse_number(Org_traffic))/1000,  #convert from million to billion of USD
                                     parse_number(Org_traffic)),
         cost_in_million = ifelse(str_detect(Traffic_cost, "M"),
                                     parse_number(Traffic_cost),
                                     (parse_number(Traffic_cost))*1000),
         Company = str_to_title(Company))

p1<- ggplot(bigtech_web, aes(x = reorder(Company,desc(cost_in_million)))) +
  geom_col(aes(y = cost_in_million, fill = Company), show.legend = FALSE) +
  geom_point(aes(y = visits_in_billions)) +
  labs(title = "Advertising Cost vs Visit per Month Count",
       x="Company",
       y = "Advertising Cost (in Millions of USD)")+
  theme_bw()
ggplotly(p1)

One of the important drivers of revenue is advertising. With it, there are associated costs that a company must accrue. Comparing advertising costs accrued (one part of their total costs) and revenue gained is another way to see how a company is doing and whether they show promise for the future. How well a company advertises their products and services can generally determine how well company will do with revenue. In order to measure advertising costs, we created a bar chart representing advertising cost with dots indicating the number of visits in 2020. What to note about this graph is that the visits are measured in billions of people although the y-axis shows the value in units of millions of USD. For example, for Facebook, there are about 5 billion visits per month (even though the units are in $) while Facebook accrued advertising costs of a little over $80 million. Amazon comes in as a close second, accruing around $70 million in advertising costs while seeing around the same amount of visitors on their website. The other three show a significantly lower amount in advertising costs but either a higher or similar volume of visits monthly. You will also notice how Alphabet is now represented by Google in this graph. This would make sense as for Apple and Microsoft, there is not as much a need to rely on advertising costs as their products are much more well known. Although advertising is important, they would not need to rely on it as much to gain revenue. Of the five, Google receives the most visits monthly. While this is not shocking, I would expect that given the nature of Google, there would be a higher volume of visits monthly compared to the other groups.

Average Revenue and Cost Comparison

bigtech_revenue %>%
  select(Company, rev_2020) %>%
  mutate(Company = str_replace_all(Company,"Alphabet","Google"))%>%
  rename(rev_in_billion = rev_2020)%>%
  full_join(bigtech_web)%>%  #join 2 dataset to get advertising cost and revenue
  select(-Rank, -Domain, -Org_traffic, -Traffic_cost)%>%
  pivot_longer(cols = c(rev_in_billion, cost_in_million), names_to = "Type", values_to = "USD_Amount")%>%
  ggplot(aes(fill=Type, y=USD_Amount, x=reorder(Company,USD_Amount))) + 
  geom_bar(position="dodge", stat="identity")+
  labs(title = "Advertising Cost and Revenue Effects",
       x = "Company",
       y ="")+
  scale_fill_discrete(name = "", labels = c("Advertising Cost (in Millions)","Revenue (in Billions)")) +
  theme_bw()
## Joining, by = "Company"
## Warning: Removed 1 rows containing missing values (geom_bar).

Let’s look at this next graph. Here we get a side-by-side comparison between the revenue and the advertising costs for the year 2020. You will notice that once again, Apple’s revenue is much higher compared to the other firms. What you will also notice is that they have the second lowest advertising costs of the five firms in 2020 (the year of COVID-19). Google also follows a similar pattern but to a lesser extent while Microsoft has a lower revenue amount for 2020 and also lowest advertising costs. Amazon does not have a value for revenue in 2020 (because of the restrictions from the scrapped website), but their advertising costs close in on $70 million. The reason being that Amazon was not listed as one of the big tech firms of 2020 as its business is more catered towards retail. The only outlier here is Facebook. They did not gain much in revenue but accrued a lot in advertising expenses. Why could that be? One possible explanation for Facebook’s rather low revenue for 2020 could be that most users who once had Facebook began to move away from it to use other social media domains such as Instagram, Snapchat, and most notably, Tiktok. The appeal for Facebook has gradually deteriorated as more younger users begin to shift away from it. Additionally, most of Facebook’s revenue nowadays comes from advertising and as mentioned earlier, with less people becoming interested in using Facebook as their primary social domain, advertising becomes less productive and thus, revenue cannot be generated at the same rate as it once was.

A clearer look at their revenue over 10 years

bigtech_revenue2 <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/bigtech_revenue2.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Time = col_date(format = ""),
##   Amazon = col_double(),
##   Apple = col_double(),
##   Microsoft = col_double(),
##   Google = col_double(),
##   Facebook = col_double()
## )
rev1 <- bigtech_revenue2%>%
  pivot_longer(cols = c(2:6), names_to = "Company", values_to = "Revenue")%>%
  mutate(Revenue = Revenue/1000)

ggplot(rev1)+
  geom_line(aes(x = Time, y = Revenue, color = Company))+
  labs(title = "Revenue 2005-2020",
       y = "Revenue (in billions of USD)")+
  theme_bw()
## Warning: Removed 20 row(s) containing missing values (geom_path).

Fortunately, we found a website that publishes quarter revenue from these companies since 2005 to 2020. The revenue growth of these big techs is displayed with a line graph represented in billions of USD. Throughout the period, Amazon has been the one with the fastest revenue growth, from the 3rd or 4th highest in 2005 to the highest revenue in 2020. With the same pattern, Apple and Google move to the top 3 firms with highest revenue. Although Microsoft and Facebook does not have those significant moves, they maintain the growth over the year consistently. Moreover, what we find interesting about these revenue is the seasonal growth, in which from the final quarter of previous year to the next quarter of new year, it shows a significant drop, and then increases again. This makes sense intuitively as people tends to buy and purchase at the end of year more than the beginning of a new year, hence revenue from these companies also indicates this fluctuation.

Debt

bigtech_debt <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/bigtech_debt.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Time = col_date(format = ""),
##   Amazon = col_double(),
##   Apple = col_double(),
##   Microsoft = col_double(),
##   Google = col_double(),
##   Facebook = col_double()
## )
debt1 <- bigtech_debt %>%   #pivot a data frame so it would have the right format for joining with other datasets
  pivot_longer(cols = c(2:6), names_to = "Company", values_to = "Debt")%>%
  mutate(Debt = Debt/1000)
try1 <- debt1 %>%
  left_join(rev1, by = c("Time", "Company"))%>%  #join to combine debt and revenue quarterly
  ggplot(aes(x = Time))+
  geom_col(aes(y = Debt), fill = "#4FC0A3")+
  geom_line(aes(y = Revenue), color = "salmon")+
  facet_wrap( ~ Company, nrow = 3)+
  theme_bw()+
  labs(title = "Debt and Revenue 2005-2020 (in billions of USD)",
       y = "")

ggplotly(try1)
## Warning: Removed 122 rows containing missing values (position_stack).

Let’s take a look at how much total debt was accumulated from these five firms over the years. To get a better picture of how debt has grown over the years, we created a faceted bar chart for each of the companies starting from 2005. We decided that a bar chart would better compare the debt than a line graph. When taking into account each company’s respective revenue, it seems that firms with higher revenue would accrue more debt. Right off the bat, we see that, over time, Apple has accrued the most debt and have gradually seen an increase in debt. As of September 2020, Apple has accumulated nearly $100 billion in debt. In recent years however, Apple has seen its debt growth come to a halt and begin to slowly decrease. The other group with a notable accumulation of debt is Microsoft. Between the years of 2015 and 2017, Microsoft saw a very sharp increase in its debt but then that was followed by a few years of a steady decline in its debt. Before 2010, Microsoft had a very minimal amount of debt and as a result, that could not be captured by the graph. Amazon had a significantly lower amount of debt compared to Microsoft and Apple but showed a rather gradual increase in its debt accumulation over the years. Additionally, there was data for Amazon’s debt dating back to 2005 unlike the other groups. Comparatively, Facebook and Google showed very little debt accumulation. This would intuitively make sense as both these companies’ products would be online. There would not be much in terms of production that would increase the amount of debt for both these groups.

Asset and Cash

bigtech_AC2 <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/bigtech_AC2.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Time = col_double(),
##   Company = col_character(),
##   Asset = col_double(),
##   Cash = col_double()
## )
bigtech_AC2<- bigtech_AC2 %>%
  pivot_longer(cols = c(3,4), names_to = "Type", values_to = "Amount")%>%
  mutate(Amount = Amount/1000)
  
company.labs <- c("Amazon", "Apple", "Google")
names(company.labs) <- c("Amz", "Appl","Googl")

p3<- ggplot(bigtech_AC2, aes(fill = Type, x = Time, y = Amount))+
  geom_bar(position = "stack", stat = "identity") +
  facet_wrap(~Company, nrow = 3, labeller = labeller(Company = company.labs)) +
  scale_y_continuous(labels = scales::dollar) + 
  labs(title = "Asset vs Cash 2005-2020 (in billions of USD)",
       x = "Year",
       y = "")+
  theme_bw()
ggplotly(p3)

Our next graph was concerning the aggregate of the cash and assets of each of three of the five groups, Amazon, Apple, and Google, thereby giving us the value of that company. These are segmented bar charts that just compare the aggregated cash and asset values between 2005 and 2020 for Apple, Amazon, and Google. The second set of graphs is a segmented bar chart that compares the proportion of assets to cash for Amazon, Google, and Apple over the years. We decided to run both graphs because the first one would give us an idea of how much in cash and assets each company made over the years while the second graph would show us how much of that value was cash and how much of it was revenue. Starting with Amazon, the value of the company showed exponential growth between 2005 and 2020. From the second graph, it seems as though the increase in the value of the company could be attributed to its value coming more from assets than from cash over the years as we see a gradual increase in the amount of cash that goes into the valuation of the company from 2005 to 2010. Apple also shows a similar trend; an exponential growth in its value from 2005 to 2020 while showing a decrease in the proportion of cash that goes into the company’s value. We see this same trend with Google as well. From this analysis, we ask ourselves two questions, firstly, why is there an exponential growth in value? And why is that showing a correlation with a decrease in the proportion of cash? One thing that may explain the sudden exponential growth of the three companies in terms of value can be the inflation rate. The numbers put out there were the nominal values and so possibly when adjusting for inflation, the growth may be different. Secondly, assets would be better to hold in other items rather than cash since companies would bring extra cash to invest in other activities and keep a certain amount for security. As the years go on, holding on to assets in many different areas to determine the value of the company would probably be more beneficial than holding on to cash. They say that a common-sense strategy may be to allocate no less than 5% of your portfolio to cash, and many prudent professionals may prefer to keep between 10% and 20% on hand at a minimum. The facet_wrap graph gives us a side-by-side comparison between these three groups in terms of valuation. While Google has shown steady more or less linear growth in value, Amazon has seen a steady exponential growth while Apple shows us inconsistent growth.

p4 <- ggplot(bigtech_AC2, aes(fill = Type, x = Time, y = Amount))+
  geom_bar(position = "fill", stat = "identity") +
  facet_wrap( .~Company, nrow = 3, labeller = labeller(Company = company.labs)) +
  scale_y_continuous(labels = scales::percent) + 
  labs(title = "Asset vs Cash 2005-2020",
       x = "Year",
       y = "")+
  theme_bw()
ggplotly(p4)

Debt and Asset Comparison

DA_comparison <- read_csv("~/Mscs 264b S21/Project/BaNguyen-Mutenda Final Project/DA_comparison.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Time = col_double(),
##   Company = col_character(),
##   Debt = col_double(),
##   Asset = col_double()
## )
DA_comparison <- DA_comparison%>%
  mutate(Debt_asset_ratio = Debt/Asset,
         Debt = Debt/1000,
         Asset = Asset/1000)
p5 <- ggplot(data = DA_comparison, aes(x = Time))+
  geom_col(aes(y = Asset, fill = Company)) +
  geom_line(aes(y = Debt)) +
  facet_wrap(~Company)+
  scale_y_continuous(labels = scales::dollar) + 
  labs(title = "Debt and Asset 2005-2020 (in billions of USD)",
       x = "Year",
       y = "") + 
  theme_bw()
ggplotly(p5)

We decided to also do a side by side comparison of Debt and Asset between Apple, Amazon, and Google using both a bar chart (to represent assets) and a line graph (to represent debt). We added a line graph to represent debt because we wanted to be able to analyze debt from a different angle as well. From here, we can see that, from these three firms, Apple saw the most increase in the amount of debt it accumulated between 2010 and 2020, surpassing the $100 billion mark. Meanwhile, while both Amazon and Google saw some growth in debt, it was not as substantial as that of Apple’s.

Debt to Asset ratio

p6<- ggplot(DA_comparison, aes(x = Time, y = Debt_asset_ratio)) +
  geom_point(aes(color = Company))+
  geom_line(aes(color = Company)) +
  labs(title = "Debt-Asset Ratio Over Time",
       x = "Year",
       y = "Debt-to-Asset Ratio")
ggplotly(p6)

One thing that this graph may not account for however is the debt ratio, which is essentially the ratio of the amount in total debts over total assets. The debt value alone may not be a sufficient enough indicator of whether a company is doing well. So to account for that, we also made a chart showing the Debt/Asset Ratio. Companies have their unique capital structure, but spread in a wide range less than 0.4. This means that the companies can operate well using their own asset allocation instead of using debt. Amazon’s debt peaked in 2005, but was paid down and fully paid in 2009. Since then, the company has maintained its debt to asset ratio at around 0.1 and 0.2. Google seems not to be a debt-fellow as the company has never raised debt more than 10% of its asset. Generally, most investors would look to invest in a company that has a debt ratio between 0.3 and 0.6. However, Apple, Amazon, and Google are all very well-established companies so the debt-asset ratio may not have as much of a bearing on whether or not investors will invest in them. The importance of this ratio is that it measures a company’s ability to be able to pay off its liabilities. From our analysis, Apple initially struggled with its Debt/Asset ratio, having a rather low value but eventually managing to get it to 0.3. Google has had a very low ratio, failing to get it over even 0.1. Finally, Amazon started off with a very good debt/asset ratio but it sharply decreased between 2005 and 2010 before steadily, but in a staggered manner, rising up to 0.2 before having it fall back down to 0.1.

Final Thoughts:

There are a lot of factors that go into determining whether or not a company is performing well that scale well beyond just revenue, assets, debt, etc. However, these indicators that we showed above are the first steps in determining the performance of a company. Knowing a company’s revenue, asset, cash, and debt will lay the groundwork for performing further financial analysis. From our basic financial analysis, Apple seems to retain its title as the king of the crop, outperforming all the other groups in every way imaginable. However, while that is the case, Google and Amazon show the most promise for future growth. This makes sense as, given the pandemic, many people have turned to retail groups such as Amazon in order to get what they need while stores were closed and shopping went virtual. Additionally, Google’s services were also used more as people would look to set up virtual meetings as opposed to meet each other in person (through Google Meets or other virtual programs) given the state of the pandemic. Consequently, Alphabet, Google’s parent company would also reap the benefits of Google’s growth. Microsoft, similar to Apple, also shows promise but its growth is still inhibited by the fact that Apple is still growing its revenue. Given the trajectory of the past year and the implications it sets for the coming years, it will be interesting to observe these growth patterns again and see what has changed or not changed and which companies of these five show promise or if there are other companies that we did not have a chance to analyze that will take center stage.

References

“Alphabet Announces Fourth Quarter and Fiscal Year 2019 Results.” Alphabet, Inc. 3 Feb. 2020. Abc.xyz Alphabet, https://abc.xyz/investor/static/pdf/2019Q4_alphabet_earnings_release.pdf. Press Release, PDF Download.

“AMAZON.COM ANNOUNCES FINANCIAL RESULTS AND CEO TRANSITION.” Amazon.com, Inc. 2 Feb. 2021. S2.q4cdn. https://s2.q4cdn.com/299287126/files/doc_financials/2020/q4/Amazon-Q4-2020-Earnings-Release.pdf. Press Release, PDF Download.

Benzinga. (n.d.). A Look Into Apple’s Debt. Yahoo! Finance. https://finance.yahoo.com/news/look-apples-debt-130426954.html.

Dybek, M. (2020, October 31). Apple Inc. (NASDAQ:AAPL): Analysis of Solvency Ratios. Stock Analysis on Net. https://www.stock-analysis-on.net/NASDAQ/Company/Apple-Inc/Ratios/Long-term-Debt-and-Solvency.

“Facebook Reports Fourth Quarter and Full Year 2020 Results.” Facebook, Inc. 27 Jan. 2021. PRNewswire Facebook, https://investor.fb.com/investor-news/press-release-details/2021/Facebook-Reports-Fourth-Quarter-and-Full-Year-2020-Results/default.aspx. Press Release, PDF Download.

Franek, K. (2020, August 9). Microsoft Annual Report: Financial Overview & Analysis 2020. KAMIL FRANEK | Business Analytics. https://www.kamilfranek.com/microsoft-annual-report-financial-overview-and-analysis/.

Websites for scrapping

Overview of revenue: https://www.rankranger.com/top-websites

Advertising costs: https://en.wikipedia.org/wiki/List_of_largest_technology_companies_by_revenue

Revenue:

https://www.macrotrends.net/stocks/charts/AMZN/amazon/revenue https://www.macrotrends.net/stocks/charts/AAPL/apple/revenue https://www.macrotrends.net/stocks/charts/MSFT/microsoft/revenue https://www.macrotrends.net/stocks/charts/FB/facebook/revenue https://www.macrotrends.net/stocks/charts/GOOG/alphabet/revenue

Debt:

https://www.macrotrends.net/stocks/charts/AMZN/amazon/long-term-debt https://www.macrotrends.net/stocks/charts/AAPL/apple/long-term-debt https://www.macrotrends.net/stocks/charts/MSFT/microsoft/long-term-debt https://www.macrotrends.net/stocks/charts/GOOG/alphabet/long-term-debt https://www.macrotrends.net/stocks/charts/FB/facebook/long-term-debt

Asset:

https://www.macrotrends.net/stocks/charts/AMZN/amazon/total-assets https://www.macrotrends.net/stocks/charts/AAPL/apple/total-assets https://www.macrotrends.net/stocks/charts/GOOG/alphabet/total-assets https://www.macrotrends.net/stocks/charts/MSFT/microsoft/total-assets https://www.macrotrends.net/stocks/charts/FB/facebook/total-assets

Cash:

https://www.macrotrends.net/stocks/charts/AMZN/amazon/cash-on-hand https://www.macrotrends.net/stocks/charts/AAPL/apple/cash-on-hand https://www.macrotrends.net/stocks/charts/GOOG/alphabet/cash-on-hand https://www.macrotrends.net/stocks/charts/MSFT/microsoft/cash-on-hand https://www.macrotrends.net/stocks/charts/FB/facebook/cash-on-hand