What can you do? Here’s what you can do. Calculate a Net Income margin in real time. This morning, Friday October 22, 2021 at 9:25 EDT, we retrieved all S&P500 firms that had reported this period. There are 103 firms in our sample as of that time.
Net Income for those firms was 129 Billion. Revenue was 790 Billion for a margin of 16.3%
Last year for those same firms, the number are 75.8 B Non Net Income and 698B in revenues for a margin of 10.9%.
Get the Excel Add in and have a go yourself!
Netflix reported its latest quarterly report last night after the market close. As the investing world read the news (and maybe watched Squid Games), here at Calcbench, we fired up our engines. At 4:03pm, we had the data in house!
A few minutes later, we had modeled this spreadsheet. (Please note that the formulas will only work with an active Calcbench subscription. That said, this data will auto-poulate for next quarter with a quick copy paste that you could do right now!)
Some takeaways. Here are the subscribers by region for the annual periods until 2020 and the subsequent quarters through Q3 2021.
Last three quarters of subscriber growth by region:
Revenues by region are below. In this chart, we combined annual and quarterly. We did something simple and scaled the quarterly by multiplying by 4 to make the charts look good, but feel free to do your own thing.
There was no Plan A. There was also no Plan B. Calcbench started out as a necessity enterprise.
After working as financial analysts for a number of years, co-founders Pranav Ghai and Alex Rapp, former college roommates, found themselves out of a job during the Great Recession. They wanted something meaningful to keep them occupied and employed. With a little time on their hands, Pranav and Alex developed Calcbench. The year was 2011.
Ten years later, Calcbench’s financial data platform is integrated into the workflow of companies and organizations that rely on corporate filings, from enforcement agencies, to auditing firms, to corporations and asset management shops. Calcbench is used by U.S-based and global companies for financial analysis, and by universities around the world for research and as a teaching aid. Today Calcbench clients can access data from two million filings, from 12,000+ entities, going back to 2009.
For how it all began to the company’s current success, read on. It’s a real-life journey of a startup that has endured and prevailed over a decade and the many people involved. With deep gratitude to all those who have helped us along the way, we share our story.
An Idea Over Coffee
In 2011, in a fortuitous New York City encounter, Calcbench co-founder and CEO Pranav Ghai ran into Campbell Pryde, now CEO and President of XBRL US, at a coffee shop.
Colleagues from their days at Morgan Stanley, Ghai and Pryde talked about XBRL. Campbell invited Pranav and his friends to try XBRL US data to see how they could commercialize it. A week after that impromptu coffee-shop meeting, Pranav and Alex Rapp, co-founder and CTO, developed a prototype that could read XBRL-tagged financial data from corporate filings. Encouraged by this early success and after a couple of iterations of the prototype, Alex and Pranav sat at a bar, drank a beer, and hatched a new company called Calcbench.
Pictured above: Alex and Pranav at their first board meeting
Developing the prototype whet the duo’s appetite to enter an XBRL contest — which they won, giving them $20,000 to get their business off the ground. In 2012 Calcbench was also a finalist in the Mass Challenge. That same year, Calcbench was invited to speak at the AICPA’s annual conference and given a seat at a Treasury department jam.
Pictured above: Calcbench’s first logo
Through these early events, Pranav and Alex met some of Calcbench’s first users, including Jack Ciesielski and Paul Chaney (who still use the platform today).
Yep, It’s Real
Though the promise of the data and interest from potential users kept them engaged, during their first couple of years in business, Pranav and Alex were unsure whether they were “all in.” They tried consulting for unrelated companies, but after a couple of engagements, the two co-founders decided to dedicate time to Calcbench in earnest. Fortunately, their wives were supportive and had health insurance.
Others chipped in to help them stay the course. Friends at the Polaris Ventures incubator gave them free space. Abby Fitchner, a start-up evangelist, gave them free cloud computing through Microsoft’s BizSpark program. Investors gave them the foundational money to keep them afloat and to grow. The year was 2013. Pranav and Alex hired their first Calcbench “employee,” Ariel Markelevich, an accounting professor whom they knew through XBRL US. That same year, Calcbench landed its first clients.
Once they received capital and had viable clients, it seemed like there was a clear and linear path to success. But “build it and they will come” wasn’t exactly how it worked.
Some corporate finance professionals, looking to get competitive data, found Calcbench through due diligence. Some university professors, looking to make data wrangling easier, stumbled upon Calcbench at XBRL conferences. Some individual investors looking for a cheaper financial data provider switched to Calcbench. Mostly, however, clients were small and wanted one-off information such as pension and tax data not readily available from some of the larger, cookie-cutter financial data providers.
Calcbench was not profitable for a few years and there were questions about the feasibility of the business. Can the business sustain? Is the data clean? Will XBRL become mainstream? Can others replicate Calcbench?
It wasn’t until 2014 when some of the early adopters found homes for the platform that the business really took off. One of those early adopters was Dave Zion. At the time, Dave was looking at corporate procedures and brought Calcbench into Credit Suisse. Another early adopter came from the Public Company Accounting Oversight Board. Our contact had a stack of 10-Ks on his desk and was looking for product warranty information. He thought Calcbench could help him save weeks searching for the information. He was right, but was only able to bring in Calcbench after an RFP win and a budget sequester ended. Other early adopters included Susan Yount, who brought Calcbench into Workiva. 2014 was the same year that Calcbench hired senior software engineer and financial analyst, Andrew Kittredge.
In 2015, Calcbench started to scale. Calcbench was notified that the SEC had an RFP for a financial data provider two weeks before the due date. After round-the-clock work to complete the RFP, Calcbench won the contract, a game changer for the company.
From then on, Pranav and Alex joined numerous XBRL and data quality committees. They became go-to experts. Calcbench data and insights could be found in the Wall Street Journal, Washington Post, Fortune, Marketwatch and top academic publications. Hundreds of blog posts were created, read by thousands of financial analysts.
New features and functionality arrived, such as data from the non-XBRL portions of corporate filings, tying numbers to text, normalizing footnote data, and of course, earnings press releases. This opened the door to new customers like the Financial Accounting Standards Board, some of the largest asset management firms in the world, as well as prestigious universities.
The Marathon Continues
A lot of spaghetti has been thrown against the wall over the years. Some ideas worked. Some did not. Regardless, there were a lot of sleepless nights, chaos, and uncertainty. Eventually it all became fun.
XBRL is messy; if it were clean, more people would build off the data. This past decade, Pranav and Alex have seen numerous companies try to replicate Calcbench’s success, but few have been able to do it. It’s been a long and winding road, but this adventure has been an exciting one for Calcbench, its co-founders, its team, and its clients. We are grateful to all of you who have taken this journey with us and for those who will join us over the next decade. Many thanks.
The Calcbench Team
Another day, another nifty time-saving feature we are happy to offer Calcbench subscribers. This time around it’s an ability to export earnings release data directly to Excel, almost immediately after said earnings release hits the wires.
Here’s how it works. Start at our always-popular Recent Filings page. This is where Calcbench presents the latest filings from public firms, which we capture and prep typically within a few minutes of those firms filing their disclosures to the Securities and Exchange Commission. See Figure 1, below.
Notice that column on the right-hand side that says “Export to Excel” in numerous places. Behind the scenes, Calcbench has indexed the data in those filings so that they can be, you guessed it, exported directly to Excel.
When you click on that option, an Excel file will download onto your computer that you can open, read, and start using. For example, we downloaded the Citigroup ($C) third-quarter earnings release filed this morning, and immediately saw this, Figure 2.
The spreadsheet shows the financial results Citi presented in its earnings release: 3Q 2021, plus the prior quarter, plus the year-ago quarter. You can even see quarter-over-quarter and year-over-year change!
Figure 2 is just one example of what you’d see from Citigroup; there’s a lot more in the Excel spreadsheet that we haven’t shown. Our Export to Excel feature can capture just about any table with tagged data that a company includes in its earnings release, from a summary of financial performance, to full income statement or balance sheet, and even reconciliation statements.
The flip side is that if a firm doesn’t file lots of information in its earnings release, the Excel spreadsheet will be smaller. For example, we also have this Q3 summary from Morgan Stanley ($MS), also filed today, in Figure 3, below.
Morgan Stanley only offered 3Q 2021 and 3Q 2020 data in its release, so that’s all we can pull. If you want 2Q 2021 or other periods, you gotta wait for Morgan Stanley to file its full 10-Q report in a few weeks.
We should also add that our new exporting capability works for some earnings releases, but not all. Some firms will structure their releases in quirky ways that leave the data beyond indexing. Tesla ($TSLA), for example, is a party-pooper that files its earnings release as a JPEG image. Humans can read that, but algorithms can’t — so you won’t be able to do this nifty stuff with Tesla. (If you look back to Figure 1, you can see a few other firms where Export to Excel isn’t available either. Most of the S&P 500 will be, but it’s not universal.)
You can, however, still read filings from Tesla or any other errant firm in our usual Interactive Disclosures database, and export the data to Excel from there. It’s one extra step, but you do end up in the same place. For more information on earnings press releases, check out our how-to video.
The Calcbench database of Segments, Rollforwards and Breakouts is quite the nifty tool, able to help analysts find all sorts of specific, segment-level disclosures and insights.
Our case in point for today: top-selling pharmaceutical drugs.
People might not know this, but blockbuster drugs (those grossing $1 billion or more in annual sales) do typically qualify as a separate operating segment for the pharmaceutical firms selling those drugs. Which means you can look up sales by operating segment in the pharmaceutical sector, and find revenue figures for the individual drugs that you might see advertised on television or discuss with your doctor.
So Calcbench did that the other day. Table 1, below, shows the 20 biggest blockbuster drugs based on 2020 sales.
So we can see that even though AbbVie ($ABBV) had the single biggest blockbuster with its Humira drug for Crohn’s disease and related illnesses, Bristol Myers Squibb ($BMY) was actually the biggest performer overall because it has three blockbusters on the list.
Even better, once you know the revenues from specific blockbuster drugs, one can then calculate the percentage of total revenue those blockbusters provide to their respective pharmaceutical manufacturers. Figure 2, below, shows those percentages for various firms in Big Pharma.
Analysts can then use that information in various other ways. For example, if you know when a blockbuster is going to lose patent protection, you can model when the pharma firm might see a significant drop in revenue as generic competitors enter the market, and how that drop might affect the firm’s overall revenues. You could also ask the CFO or CEO on the next earnings call how they plan to replenish the revenue pipeline with other drug candidates still in development.
Bonus for Calcbench subscribers: We even have an Excel template you can use with all the pharma formulas already baked into the spreadsheet, so you can see how revenue from blockbuster drugs has changed over the last few years. (The template tracks many more specific drugs than the Top 20 we showed in Table 1.)
You do need to be a Calcbench subscriber for the template to work. Then, if you want to go down the rabbit hole, well…
Calcbench now has standardized face financial data from earnings press releases minutes after they are published. This will be useful for quantitative asset managers who want to include fundamental signals in their model.
Calcbench is extracting the metrics from the income statement, balance sheet, and statement of cash flows. This includes market moving revenue and earnings per share numbers. Quality is improved by Calcbench’s ten years of experience parsing the XBRL 10-K/Qs.
We listen for the news wires and earnings announcements filed as 8-Ks on the SEC’s Edgar sight. The Calcbench extraction process is entirely automated so you have numbers minutes after they are published.
For back-testing purposes we have about 10 years of history with time-stamps of when the data was available. Coverage is almost all US public companies.
A sample for the 30 companies in the DOW is @ https://www.dropbox.com/s/vazfcnlbhhqyh2n/blog_data.csv?dl=0. An example of a script to get the data is @ https://github.com/calcbench/notebooks/tree/master/filing_listener. For a larger sample and to discuss integrating the data into your process email
The other day we were reading the latest from Calcbench columnist Jason Voss, where he was constructing his own statement of cash flows from General Electric’s ($GE) other financial statements.
We saw that Voss’s column included this line: “Note that GE’s numbers are occasionally a tiny bit off, because the firm rounds the numbers it reports.”
Well that’s intriguing, we thought. The discrepancies due to rounding never add up to anything material, but precision still matters in financial analysis. If nothing else, an analyst might notice those discrepancies in whatever model you’re using, and start poking through Excel formulas to identify where you might have made a mistake — when there was no mistake at all; the firm was just rounding.
For example, GE itself says this in its disclosure of accounting policies:
We have reclassified certain prior-year amounts to conform to the current-year’s presentation. Unless otherwise noted, tables are presented in U.S. dollars in millions. Certain columns and rows may not add due to the use of rounded numbers.
So how many other firms out there use rounding to the point that sometimes numbers don’t add up nice and neatly?
To find the answer, a person only needs to enter a clever search string in our Interactive Disclosures database. Just select the firms you want to study, go to the text search field, and enter "rounded add"~10. (Yes, the “rounded add” should be in quotes that go right into the search box.)
That will search for the words “rounded” and “add” within 10 words of each other. See Figure 1, below.
We searched that string for the S&P 500’s 2020 reports, and nearly two dozen companies that mentioned their columns may not always add up due to rounding — businesses ranging from IBM ($IBM) to Baker Hughes ($BKR) to Becton Dickinson ($BDX), and others.
What can an analyst do with this information? First, you can put aside any fears that you’re crazy: the numbers really don’t add up; it’s not a flaw in your Excel formulas.
Second, we also offered this example to show how a Calcbench user can perform more complex text searches in a company’s footnotes. Whenever you want to search for two keywords in proximity to each other, just use that structure we offered earlier: the two words in quotes, followed by the ~ symbol and then whatever number you want to use. (We recommend 10 since that’s enough to capture complex sentence structure, but not so large that you start to get useless results.)
Just another way Calcbench tries to be helpful to our users. Go forth and analyze!
Good news for all you Excel enthusiasts out there, which is pretty much everyone in the analyst world: Calcbench has updated our Excel Add-In to embed numerous standard templates right into the software.
You can run templates for income tax analysis, DuPont ratios, impairments, and more. Here’s how it all works.
First, install the Calcbench Excel Add-In. Our most sturdy and versatile version is for Windows; we also have limited versions for Office365 and GoogleSheets, if you’re running those systems.
Now the good stuff. Once you install the Add-In, you’ll notice a feature in the menu bar called “Templates.” See Figure 1, below.
Click on that Templates button, and a pull-down menu of various templates will appear. See Figure 2, below.
From there, select the template that you want to use. It will automatically populate in Excel, and then you just need to start entering ticker symbols in the appropriate field. The template will automatically pull data from Calcbench and drop it straight into your model.
Figure 3, below, is an example. It is the valuation template, using data from Johnson & Johnson ($JNJ).
That’s all there is to it! Enjoy, and if you have other ideas for functionality we should offer Calcbench subscribers, drop us a line at email@example.com any time.
By Jason Apollo Voss
Back when you were learning accounting, perhaps your professor, textbook author, or somebody else explained that rather than relying on the cash flow statement reported by a company, you can create your own cash flow statement from information on the firm’s income statement, balance sheet, and footnotes.
If you’ve never done this exercise, give it a try. You’ll be surprised by the insights that you can glean from doing it. Those insights gained include:
For those not in the know, let’s walk through how to create your own cash flow statement with disclosures in a company’s income statement, balance sheet, and footnote disclosures. (We’ll use the indirect method, since most companies don’t use the direct method and our comparisons wouldn’t be harmonious. Plus, most companies don’t report the values necessary to compile a direct-method cash flow statement.)
Here are the steps for creating your own cash flow statement. I’ve also added in parentheses where you can usually find the necessary information.
Generally, you can follow the same flow of accounts that the company uses in reporting its own cash flow statement, starting with its calculation of operating cash flow, continuing on to cash flows from investing activities, and concluding with cash flows from financing.
Following steps 2 through 4 should allow you to arrive at operating cash flow.
For cash flows from investing activities, you follow the same protocols described above using the balance sheet’s non-current assets section, and look at the changes in values of those accounts. Be careful to look at non-depreciated values for property, plant and equipment, as well as non-amortized amounts for goodwill.
For cash flows from financing activities, follow the same protocols, but most of the changes are going to be in the non-current liabilities and shareholders’ equity accounts.
March through all of that, and you should arrive at an independent cash flow estimate.
Let's use the disclosures in General Electric’s 10-K filing as an example. Here are the reported amounts from GE, along with my estimates for these same accounts. (The sources of the figures are shown in parentheses.)
As you can see from the above estimates, there is a difference of $569 million, or 15.8 percent, in GE’s reported cash flow from operations ($3,596 million) compared to my estimate ($4,165). (Also note that GE’s numbers are occasionally a tiny bit off, because the firm rounds the numbers it reports.)
While this may seem like a wide disparity, I must give GE credit. Why? Because my usual experience in doing this exercise is that it’s almost impossible to derive the values reported by companies in their cash flow statements. For example, years ago I did an analysis for Luis Vuitton. Items like depreciation were more than 100 percent off, despite a careful reading of the company’s 20-F and its many disclosures.
To my thinking, one of the great unaddressed issues in finance and investing is that deriving an accurate estimate of cash flow by this method is so difficult. If the accounting and disclosures are sound, we should be able to do this with ease.
But for GE, most of the numbers are exactly the same when comparing the amounts GE reports on its cash flow statement to those estimated by me, and gathered from the income statement, balance sheet, and footnotes.
In fact, of the above accounts shown, 15 of the 18 accounts are within 10 percent of one another. Trust me: this is not a bad result. That said, some of the accounts are wildly off — and this is where things get very interesting for a research analyst.
As an example, GE’s “(Gains) losses on equity securities (Notes 2, 19)” is 3.7 percent different. But if we examine the company’s footnote disclosure, there is nothing reported that accounts for the difference.
Next, take a look at the company’s “Cash recovered (paid) during the year for income taxes (Note 15)” which is off by a less respectable 10.5 percent. Yet, the amount I estimated comes directly from GE’s footnote.
What accounts for the difference? We don’t know. Nowhere in the financial statements are we given enough information to assess this for ourselves. In both cases, it appears that GE is underreporting its cash flows from operations.
You may think this is not so interesting. But within a company’s reporting, we would assume that the net working capital accounts — that is, the ones closest to being cash — should be the easiest for us to estimate, and the hardest to have discrepancies.
Wrong. In GE’s case these accounts are where everything starts to break down. For example, inventories are among the easiest of accounts for us to estimate: this year’s inventories minus last year’s inventories. But for GE, even when accounting for the inventory nuances reported in its Footnote 6, the estimated cash inflow should be $1,325 million. Instead it's a paltry $1,105 million.
This is mysterious, and suggests a class of questions derived from this exercise that we should direct at investor relations pros:
Each of these are key questions that this exercise makes possible.
Continuing on with GE, the company’s accounts payable and equipment project accruals should also be easy to estimate from balance sheet entries — but they’re off by more than $300 million. Sadly, GE does not provide a reconciliation or explanation for what accounts for the differences. Ouch.
Most mysterious of all of GE’s operating cash flow accounts is its ill-defined, ill-described “All other operating activities.” Of all of the accounts where my estimate is off by more than 10 percent, this one concerns me the most. That single account, even accepting GE’s reported figure of $2,040 million, is a whopping 56.7 percent of its total reported operating cash flow of $3,596 million.
What’s going on in there? We have no way of knowing. The figure I estimated (of $2,257 million) took me nearly an hour to engineer by combining different accounts with one another. Still, my estimate is very different — and I have a masters’ degree in accounting!
In all likelihood, nothing nefarious is happening. Then again, investing is a lot like watching an automobile race: It's not worth watching to see people drive in circles for hours, but we pay close attention for the occasional fiery crash.
One way that investors can improve their results is to dive deeper into a company’s accounting. A time-tested method for doing this is to create an independently derived cash flow statement from a business’ disclosures in its income statement, balance sheet, and footnotes.
By comparing the reported figures with the estimated figures, many different insights may be gleaned about a prospective investment. Usually this is just a deeper understanding of the nuances of a company, but sometimes we can discover the ways that companies obscure their performance.
This is a monthly column written by Jason Apollo Voss — investment manager, financial analyst, and these days CEO of Deception and Truth Analysis, a financial analytics firm. You can find his previous columns on the Calcbench blog archives, usually running the first week of every month.
As many of you might already know, last week Calcbench hosted a webinar on the current state of goodwill assets, along with Valuation Research Corp. and the CFA Institute. We had a great time studying goodwill valuations, proposals to change how goodwill is reported on the balance sheet, and lots of other related issues.
Today we wanted to share some of the numbers we presented in that webinar, so everyone who missed it can also consider the trends in goodwill that are happening.
First, how has goodwill changed among the S&P 500 firms in recent years? Take a look at Figure 1, below.
As you can see, goodwill assets rose by more than $700 billion from 2017 through mid-2021— an increase of 24.5 percent. More firms are reporting goodwill, and the average goodwill per firm is also going up.
Second, how much net goodwill did the S&P 500 add annually over the last four years? Remember, you need to calculate that by first looking at gross goodwill added, then subtracting goodwill impairments, to arrive at the net change. See Figure 2, below.
What’s interesting here is that gross goodwill fell in 2020, while impairment charges spiked. That’s largely due to the pandemic — which pretty much froze M&A deals in the first half of the year, so less gross goodwill was added; and also forced companies to declare many more goodwill impairments than usual. So we’ll be curious to see how 2021 numbers look when firms start reporting them sometime next spring.
Despite the sheer size of those goodwill numbers, however, goodwill as a percentage of total assets and of book value held relatively steady in recent years. Figure 3, below, shows total goodwill among the S&P 500 as a percent of total assets.
That’s not much of a change, even as we went from booming economy before the pandemic to awful economy during it. Figure 4, below, shows goodwill as a percentage of book value.
OK, the percentage levels are higher, but they’re still as flat as a highway in North Dakota.
Always remember, however, that these percentage levels are for all S&P 500 companies combined. Individual firms can have much a much higher percentage of their value tied up in goodwill.
In fact, we found 88 S&P 500 firms that have negative book value if you eliminate the goodwill assets from their balance sheet. Which can sometimes happen without warning, and leave investors facing a bear of an unwanted earnings surprise.
We’d be remiss if we didn’t also include our favorite example of why keen understanding of goodwill is so important: Microsoft ($MSFT) and its purchase of LinkedIn in 2016. It’s an oldie, but still a goodie.
Microsoft announced the deal in June 2016, stating that it would acquire LinkedIn for $26.2 billion — $196 per share, a roughly 50 percent premium over where LinkedIn stock had been trading until then.
Only several months later in a 10-Q filing long after the gauzy headlines had faded, did Microsoft disclose the purchase price allocation for the deal. A whopping $16.8 billion was allocated to goodwill — 63.3 percent of the total purchase price. Another $7.9 billion was allocated to intangible assets. So more than 90 percent of the purchase price went to assets that don’t physically exist. See Figure 5, below.
Is that too much? Not enough? Totally crazy? It’s not the place of Calcbench to say. But we do have all the data to let you see the numbers and answer those questions yourself.
Another fascinating dispatch today from the crack Calcbench research department, this time looking at the kingpins in share repurchase programs.
Share repurchase programs have been all the rage on Wall Street for years as a means to boost earning per share. We found, however, that the total amount of dollars spent on share buybacks is increasingly top-heavy — to the point that in 2020, only 10 firms in the S&P 500 accounted for half of all money spent on share buybacks.
Moreover, that top-heaviness in share buybacks has been increasing over time. The 10 biggest spenders in any given year are accounting for more and more of all money the S&P 500 spends on buybacks. See Table 1, below.
The 10 specific firms might vary from one year to the next, although several big names — looking at you, Apple ($AAPL) — do recur plenty of times. We ranked the top 10 for 2020, and they’re in Table 2, below.
Spending on share repurchases did decline in 2020 amid the pandemic’s economic uncertainty, but now seems to be rebounding briskly for 2021. That’s been driven by healthy corporate profits so far this year, and the Fed allowing more banks to resume spending on share repurchase programs.
Who will crack the Top 10 list for 2021? We’ll let you know in a few more months!
We’re a bunch of cheapskates here at Calcbench, so when Hormel Foods filed its latest quarterly report the other day, we dove into it immediately. What did Hormel have to say about the cost of goods sold, and whether rising costs for supplies are pushing up its prices?
At first glance, that line item seems troubling for Hormel ($HRL). Cost of goods sold (technically reported as cost of products sold) was $2.44 billion in its quarter that ended July 25, up 24.6 percent from $1.96 billion in the year-ago period. That jump in costs was larger than the 20.2 percent increase in revenue that Hormel saw at the same time. So even though Hormel just reported its best revenue quarter ever, that increase in cost of goods sold kept gross profit essentially flat. See Figure 1, below.
But wait! There’s a more complex story here. In June 2021 Hormel closed a $3.4 billion acquisition of Planter’s, the snack nuts business, from Kraft Heinz Co. ($KHC). So Hormel’s latest quarterly numbers aren’t necessarily comparable to what the company reported one year ago.
To get a better sense of things, we opened our Interactive Disclosures database to look into the footnotes. There, in the Acquisitions & Divestitures section, Hormel offered some pro forma numbers of what its financial performance would have looked like if the Planters acquisition had happened in October 2019. See Figure 2, below.
Revenue would have been about $120 million higher, and net income would have been 22 percent higher. Here in the real world, however, higher Sales, General & Administrative costs, as well as higher interest expense, cut into operating profit and net income.
What you may want to take away from this disclosure is that when companies disclose Pro Forma numbers, those too, are available in the Calcbench database. Users can grab them and use them in models. Models like the one here which attempts to back out the Planters Revenue and Net Income in order to analyze the acquisition. Note that Hormel paid 3.4 billion dollars to acquire Planters from Kraft-Heinz! If our back of the envelope math is correct, it looks like it will take 88 quarters at current profitability (GAAP Net Income) to make back the purchase price.
Another notable piece of information from the Purchase Price Allocation disclosure that Hormel filed? The Goodwill paid for the Planters acquisition was $2.3 Billion, or 67% of the total acquisition price.
If you’d like to hear more, head to our webinar on the 23rd. Register here.
Wow! Now, what did Hormel say about future pressures on cost of goods sold?
All we had to do was search for the word “inflation,” and things started coming into focus. Right at the start of the Management Discussion & Analysis, Hormel had this to say: “All four business segments absorbed higher input costs due to inflation on raw materials, freight, labor, and supplies.”
Further down, Hormel devoted an entire section to cost of goods sold. It started with this:
Cost of products sold for the third quarter and first nine months of fiscal 2021 increased due to inflationary pressures stemming from raw materials, packaging, freight, labor and many other inputs. The inclusion of the Planters snack nuts business during the third quarter also was a driver of higher costs.
All right, maybe we need to cut back on the peanuts during football season; that’s probably good for the blood pressure anyway. Then came this comparison of supply chain costs from last year to 2021:
Direct incremental supply chain costs related to the COVID-19 pandemic for the third quarter and first nine months of fiscal 2021 were approximately $2 million and $21 million, respectively. This compares to approximately $40 million and $60 million of higher operational costs related to the COVID-19 pandemic incurred in the third quarter and the first nine months of fiscal 2020.
The company expects to operate in a high cost environment for the remainder of the year.
Fair enough. But the question for financial analysts is which costs are increasing this year, and why. That is, we’re not surprised that supply costs rose sharply in 2020 — the world was suffering through enormous disruption, and scads of firms had to purchase more supplies for the health and safety of employees.
Were those higher costs in 2020 one-time expenditures for, say, plastic dividers on the shop floor? And if so, what does that mean for this year’s higher costs? Are these new costs new one-time expenditures, or transitory inflation, or something more permanent?
We at Calcbench don’t know; but we can help you find the data and disclosures companies are making, so you can ask better questions.
We kept reading through Hormel’s MD&A. At the discussion of gross profit, we found a quick table showing a year-over-year decline in the company’s gross profit for both the quarter and year to date. See Figure 3, below — and also pay close heed to the narrative disclosure underneath the numbers.
Hmmm. “Broad-based inflationary pressures” affecting all four Hormel business segments. A “lag in mitigating pricing actions,” which sounds like Hormel didn’t pass along higher costs to supermarket chains and consumers — yet.
Even more telling, Hormel expects gross profit to recover “as additional pricing actions go into effect,” so it sounds like those price hikes will be coming. We may be paying a bit more at the supermarket soon; the moths that live in our wallets will not be pleased.
Calcbench does make it easy for you to research the cost of goods sold at whatever firms you follow. Just visit our Multi-Company page and start typing “cost of…” in the standardized metrics field. You’ll quickly be able to search Cost of Goods Sold (COGS), the formal line item; as well as numerous metrics for guidance on cost of goods sold that companies might offer, including several non-GAAP metrics related to cost of goods sold.
But as our Hormel example demonstrates, once you find the numbers, read the footnotes anyway! Just use the Interactive Disclosures database to search relevant terms such as “inflation”, “supply chain,” or even “climate disaster” if you’re researching an agribusiness firm.
The data is in there somewhere. We’re here to help you pull it out.
As you might have already heard, later this month Calcbench will be hosting a webinar on the current state of goodwill on the corporate balance sheet — including a look at how goodwill assets figure into the value of M&A deals.
So in preparation for that webinar, we’ve been analyzing M&A deals overall. Some of the numbers we’ve found are so astonishing, we just had to share them here right away.
What have we found so far?
So right away, if you’re an investment banker or capital markets adviser looking to court the big fish, those 50 firms are the biggest. Our databases can empirically demonstrate who those firms are.
We also delved deeper into those 500 deals. Within that $2.943 trillion…
Now you see why we want to host a webinar on current issues in goodwill. Because it accounts for a lot of value in M&A deals.
To demonstrate that point, we leave you with Figure 1, below. This shows the percentage of total M&A value that was reported as goodwill, from 2010 through 2020.
That’s a lotta goodwill! We look forward to our webinar discussion on Sept. 23.
By Jason Apollo Voss
In last month’s column, I used Motorola as a case-study to explore the importance of examining the quality of long-term receivables to understand the reliability of a firm’s revenue disclosures. Given that our Motorola example was ancient history (the dot.com era of 1998-2000), this month let’s look at an example from just last year: General Motors.
Last month I offered three steps to follow when examining long-term financing receivables:
In General Motors’ case, we are going to evaluate its 2020 revenues by looking at its long-term financing receivables.
2020 was the Year of COVID-19. Globally, the virus shut down the economy. This meant massive job losses, and much less demand for transportation.
Those two forces taken together mean that the context for General Motors was less than ideal. If no one is driving anywhere, automobiles are not wearing out as fast. That reduces the demand for autos.
Also, if people’s incomes are lower because they don’t have jobs, they’re more likely to repair the automobiles they have before purchasing a new one. In some cases, they may even forego one good (their automobile) for a cheaper one: public transportation.
In other words, we can expect that GM’s 2020 context included:
Here are the past 10 years of GM’s disclosures around both “Long-term finance receivables” and “Net sales and revenue”:
Pay attention to the figures highlighted in yellow. In particular, look at the growth in “Total finance receivables” as a percentage of GM’s “Automotive net sales and revenues.”
In 2011, finance receivables were just 6.3 percent of total automotive net sales and revenues. By 2020, that figure ballooned to 55.2 percent. For example, in 2020 total finance receivables were reported as $59,970 million. When divided by $108,673 million of automotive net sales and revenue, that equals 55.2 percent.
As a research pro, you clearly would prefer that this kind of growth is “part of the plan.” If not, then this spells doom for GM, yes? Recall that in our Motorola example for the two year period of 1998 and 1999, the absolute growth of the company’s long-term finance receivables was larger than that of revenues. In other words, all new sales were financed sales, not cash sales.
In GM’s case, the third and fourth highlighted lines, above, show this story. As you can see, in every year for the last 10 (except 2016) finance receivables grew, on an absolute basis, faster than automotive net sales and revenues. The totals are that automotive net sales and revenues shrank by $40,193 million from 2011 through 2020; while finance receivables grew by $50,620 million. Wowza!
This is clearly not an ideal situation. Even if it were a part of GM’s strategy to grow its financing business, you would want the returns for financing to be high, since you are substituting a cash sale with a financed sale. Surely you know that the interest rate spread story 2011-2020 is not a good one. Spreads are low because interest rates are historically low.
Our data above suggest a high-wire act on the part of General Motors — an act that is only sustainable if GM can:
If creditworthiness tanks, GM likely does, too. Revenue sanctity is always a strategic issue for a company. No revenues = no business. Thus, GM doesn’t only have pressure to manage its loan-based customers well; it also has pressure on the automotive design team to create compelling products that attract high credit-quality customers. Marketing must also inform creditworthy customers of the GM story. And on and on.
In other words, GM can ill afford any mistakes.
Fortunately, the story here (based on GM’s disclosures) is encouraging. Specifically, the company reports the following credit statistics for customers in its 10-K filing for 2020:
Importantly, these figures show only the 2020 creditworthiness picture, even though it may look like the data go back to 2016. What the above data show are the vintages of loans made to both retail and dealership customers. In other words, there were $555 million of loans made to customers in 2016 where the credit rating is greater than 680 (that is, prime). While the data above are limited to 2020, they are instructive because of the perfect storm economic and operating environment that GM faced due to COVID-19 in 2020.
At a glance we can see that GM has done a good job of maintaining the creditworthiness of its customers. In fact, it looks as if the overwhelming majority of its retail and dealership customers are creditworthy. To make better sense of these figures, I calculated common-size statements for each of the years disclosed (that is “prior” through to 2020, and those customers/dealerships on “revolving” credit):
Here you can see that GM is doing a good job of staying up on the high wire. Most of its loans outstanding are in the hands of creditworthy customers. Whew!
[Let me interrupt this program, for a general financial statement analysis tip. Namely, get creative in your work! The above table does not exist in the GM 10(k). I took the liberty of creating my own “common-size” statement to help me tease out insights. And there are more such statements below.]
What about the percent of customers in each of the top creditworthy customers, and that cuts across retail and dealership customers? Here’s what that looks like:
Above, the most important column is the one furthest to the right. As you can see, over 80 percent of GM’s customers fall into the top two tiers of creditworthiness. We can also see across the vintages that GM has likely minted a higher-quality customer than it was previously. Either that, or the customers of previous vintages have been forced to hold on to their automobiles for longer. This story can also be extracted from the above chart. But take a look at the absolute figures shown in TABLE 2, above. GM seems to be doing OK.
Here is one final set of data to examine:
Here the denominator for every number in Table 5’s common-size figures is the $59,970 total finance receivables shown in 2020. Again, with the data presented in this way, you can see at a glance that GM has a credit portfolio that seems insulated from the wrath of COVID-19 in 2020. It also seems well poised to survive 2021, never mind that its finance receivables are growing much more rapidly than automotive net sales and revenues.
Finally, what about GM’s provision for loan losses relative to the size of its total finance receivables? In 2020 the company set aside $1,978 million against a total portfolio of $59,970, or 3.3 percent. When compared to the size of its < 620 FICO and Tier III & Tier IV customers, the figure is 18.3 percent ($1,978 million ($10,553 million FICO < 620 + ($253 million Tier III + $4 million Tier IV))).
Research pros can never take any figure in financial statements at face value. Even a supposedly simple figure like revenues can be better evaluated by considering a company’s operating and economic context, and dissecting its disclosures around receivables.
This is a monthly column written by Jason Apollo Voss — investment manager, financial analyst, and these days CEO of Deception and Truth Analysis, a financial analytics firm. You can find his previous columns on the Calcbench blog archives, usually running the first week of every month.
From time to time Calcbench likes to offer refreshers on basic financial reporting concepts and how to extract such data from our archives. Today let’s talk about book value.
Book value is one of the most basic, and important, concepts in financial analysis. You calculate it by subtracting a firm’s total liabilities from total assets. The difference is the firm’s book value, also commonly known as stockholder’s equity — because that difference is literally the equity that shareholders have in a firm, after netting out all those assets and liabilities.
When assets are greater than liabilities, book value is positive; that’s a good thing. When assets are less than liabilities, book value is negative. This is a bad thing because it means shareholders’ stake in the company has no value; the business owes more (in liabilities) than it possesses (in assets).
As we mentioned, you can calculate book value manually by subtracting liabilities from assets. Calcbench, however, also does this calculation for you automatically in our Multi-Company page. Just enter “stockholder’s equity” in the standardized metrics field on the left side of your screen, and we’ll return that number to you for whatever companies you’re researching.
Figure 1, below, shows the results for a few large companies we selected at random.
We included assets and liabilities along with stockholder’s equity only to demonstrate the math. You could just as well as search for stockholder’s equity alone and you’d get the same numbers you see above; or search for assets and liabilities and then work out the math yourself with a pencil. The results would be the same.
This also explains why we like to talk about goodwill and intangible assets so much — because an impairment to those assets will cut the size of total assets, and therefore lowers book value. In extreme cases, impairment could leave a firm with negative book value, which is disastrous.
Yes, we understand that other assets can be impaired too, such as an electric utility impairing the value of an expensive power plant it no longer uses. Any impairment can harm book value. But considering how goodwill keeps accumulating on the balance sheet, and how some firms have a majority of their total assets wrapped up in goodwill, that’s the impairment risk we watch most closely around here.
Anyway, if you want to find book value/stockholder equity, Calcbench provides it for any firm you’re researching with just a few keystrokes. That’s all there is to it.
For a while now, everyone has known that the Covid-19 pandemic scrambled business performance across the corporate landscape — with the general sense that larger firms have fared better, smaller firms worse.
Now we have some visual evidence of just how true that statement is.
As part of another project, the Calcbench research team was looking at the share of corporate revenues and profits that flowed to S&P 500 firms in recent years. We noticed, to no surprise, that the largest S&P 500 firms accounted for a disproportionate share of all S&P 500 revenue.
How disproportionate? Figure 1, below, shows what share of all S&P 500 revenue went to the largest 25 firms for the last 36 quarters.
That Top 25 share has stayed remarkably consistent year after year: from 40 percent in 2012, to a low of roughly 37 percent in the mid-2010s, to a high of 42 percent at the beginning of 2020 when the pandemic arrived.
That got us wondering. If revenue has been consistent among the 25 largest firms, what about the other 475? What churn have they experienced year after year?
So we ran another analysis. This one only looked at annual revenue and net income for the last nine years, but we grouped the S&P 500 into quintiles — the 100 largest, the next 100 largest, the third 100, and so forth.
Figure 2, below, shows the share of revenue for each quintile, 2012 through 2020.
Again, the consistency is remarkable. Each quintile fluctuated within a relatively narrow range despite a host of changing circumstances, including interest rate hikes in the mid-2010s to the pandemic in 2020.
Then we looked at net income. See Figure 3, below. You may want to sit down for this one.
OK, obviously the largest firms saw their share of net income soar during the pandemic, while the share going to the smallest 100 S&P 500 firms plummeted. Even more jarring, however, is that when you look closely at the bottom quintile of the S&P 500, they have not turned a collective profit in the last nine years.
Yes, some firms in that quintile have turned a profit in some of the last years, but overall that quintile reported a net loss of $237.14 billion dollars. They never reported a positive net income number in any of the last nine years.
Why is that so? What ate away profits at those smaller large firms? We need to do further research on that question — but before we go, look at Table 1, below. It shows the exact share of revenue that each quintile took home for the 2012-2020 period.
The share of revenue for the fifth quintile has been going up year after year. Moreover, that quintile’s revenue has also more than doubled in absolute dollars, too: from $95.42 billion in 2012, to $225.14 billion in 2020 — and that 2020 number is only down marginally from $226.4 billion in 2019, before the pandemic.
So whatever is pressuring this fifth quintile is in the firms’ cost structure, not its revenue gains. Its revenue gains have been reliable.
What’s going on? We’re not yet sure — but Calcbench has the data, so we’ll keep digging.
Wonderful news for financial analysts and anyone else looking for a complete picture of how Calcbench can help you research special purpose acquisition companies: we have compiled all our previous SPAC posts into one white paper, now available for download on our Research page.
Close readers of this blog might already know that earlier this summer we launched our SPAC-tacular series to study the size of the SPAC market, the disclosures that SPACs make, and other odds and ends about these firms. Altogether, the series looked at:
The full white paper is 21 pages, replete with examples, excerpts, charts, and data. Download, give it a read, and then drop us a line at firstname.lastname@example.org to tell us what else about SPACs we should be researching!
In a post last month, we examined how Google ($GOOG) changed the estimated useful life of its computer servers. The company extended the estimated life of those servers from three years to four, and that change resulted in an increase to net income of $561 million for the quarter and $1.2 billion for the first six months of 2021.
Here is what Google said in its 10-Q report filed on July 28:
In January 2021, we completed an assessment of the useful lives of our servers and network equipment and adjusted the estimated useful life of our servers from three years to four years and the estimated useful life of certain network equipment from three years to five years. This change in accounting estimate was effective beginning in fiscal year 2021. Based on the carrying value of servers and certain network equipment as of December 31, 2020, and those acquired during the six months ended June 30, 2021, the effect of this change in estimate was a reduction in depreciation expense of $721 million and $1.6 billion and an increase in net income of $561 million and $1.2 billion, or $0.84 and $1.81 per basic and $0.83 and $1.78 per diluted share, for the three and six months ended June 30, 2021, respectively.
What’s interesting is that Google hasn’t been the only tech firm changing the estimated life of its equipment lately. Back on Oct. 27 of 2020, Microsoft ($MSFT) included in this in its 10-Q report:
In July 2020, we completed an assessment of the useful lives of our server and network equipment and determined we should increase the estimated useful life of server equipment from three years to four years and increase the estimated useful life of network equipment from two years to four years. This change in accounting estimate was effective beginning fiscal year 2021. Based on the carrying amount of server and network equipment included in property and equipment, net as of June 30, 2020, the effect of this change in estimate for the three months ended September 30, 2020, was an increase in operating income of $927 million and net income of $763 million, or $0.10 per both basic and diluted share.
Just like Google, Microsoft’s change in estimates significantly increased the useful lives of its servers — and also delivered a significant increase in Microsoft’s net income ($763 million, to be exact) for the quarter.
Given the similarity between these two disclosures, analysts following this sector might consider adjusting their earnings models and forecasts. Other tech firms such as IBM, Oracle, Amazon (don’t forget its booming Amazon Web Services division) or Apple may be next. Imitation is the sincerest form of flattery, after all.
Here is an Excel spreadsheet illustrating the effect on net income you may want to use. Stay tuned.
By Jason Apollo Voss, CFA
This month we begin a two-part series on the importance of examining long-term finance receivables when evaluating a firm’s revenues to see if there is a “there” there — that is, to assure yourself that, yes, a firm’s revenues are reliable.
Let me take you back to the heady days of the dot-com era, roughly from 1998 to 2000. Many newly minted web-based companies received exorbitant valuations, measured however you want to measure it. This was especially true when compared with older tech companies such as Xerox, IBM, or Motorola.
In 1999 I was invited to Motorola headquarters in Schaumburg, Ill., by my fellow research folks at the mutual fund where I served as a portfolio manager. On short notice, I was invited along for a second opinion about Motorola. This was a big deal because we were among its largest shareholders. We had to get the Motorola story right or lose big for our investors.
I conducted my usual financial statement analysis and discovered something disconcerting. Here, for convenience, are Motorola’s Consolidated Statements of Earnings and Consolidated Balance Sheets for 1995 through 1999:
Back before the days of Calcbench, I had to hand-enter this data to get all of the detail you see above. For example, look at all of that property, plant, and equipment granularity! I also calculated common-size statements over both revenues and assets.
Another thing I did to extract information was to look at the growth rates in different accounts. This tends to reveal the on-the-ground reality of a business much better than a management team’s narrative about its performance. Why? Because if some balance sheet accounts were growing way faster than revenues (as was the case with Motorola) this tells a research pro what is or isn’t hot in the real world operating context of the firm.
In Motorola’s case, accounts having to do with market-based investments, and a category of asset labeled “Long-term finance receivables,” were growing very fast relative to revenues. It made perfect sense that Motorola’s “Short-term investments,” “Fair value adjustment of certain cost-based investments,” and “Contributions to employees’ profit sharing funds” would be growing so fast, because the stock market was in full rocket ship mode at the time. So, these I could dismiss as not being interesting because this outcome was in alignment with expectations. Context is king!
That understanding of context, however, also led to my deep concern about Motorola’s growth in “Long-term finance receivables.” At that moment in history, Motorola was perceived as “your grandparent’s technology company.” Its products were seen as not up to par with those offered by the powerhouses like Cisco Systems, for example. Its customers included other old lion companies, and even (gasp!) the U.S. government. In other words, Motorola had an incentive to look current.
Let us return back to those “Long-term finance receivables” of Motorola’s. What the heck are those?
“Long-term finance receivables” are typically extensions of credit to customers, so the customers can buy the company’s products.
Automobile companies do this all the time. They frequently serve as both lender and seller (of the car) to their customers. In so doing, they capture value in interest payments that otherwise would be surrendered to financial institutions. Serving as lender to customers also allows the automakers to sell cars at a discount, because they know that they will more than recoup the price discount via interest payments. All well and good.
So, what troubled me about Motorola’s “Long-term finance receivables?” The following:
All these factors combined meant that one could argue that more than all of Motorola’s growth came from financing its customers. Clearly this wasn’t sustainable unless Motorola had access to some form of cheap financing of its own. Further, what if the creditworthiness of Motorola’s customers was not so good?
Motorola’s disclosure in both its 1999 and 2000 proxy statements (DEF 14A) about its “Long-term finance receivables” gives us even more color. Below is the disclosure from 1999.
Here is the disclosure from Motorola’s 2000 proxy statement:
“Motorola Credit Corporation (MCC), the company's wholly owned finance subsidiary, is engaged principally in financing long-term commercial receivables arising out of equipment sales made by the company to customers throughout the United States and internationally.
“MCC’s interest revenue is included in the company’s consolidated net sales. Interest expense totaled $72 million in 1999, $37 million in 1998 and $13 million in 1997, and is included in manufacturing and other costs of sales. In addition, long-term finance receivables of $1.7 billion and $1.1 billion (net of allowance for losses on commercial receivables of $292 million and $167 million, respectively) at December 31, 1999 and 1998 are included in other assets.
Summary Financial Data of Motorola Credit Corporation”
From this information, I could calculate the implied interest rate being charged and compare that to market interest rates. For ’96 through ’99 the implied rates were: 4.11, 2.84, 2.78, and 3.57 percent, respectively. These rates were below market. (Calculation example for 1999: $72 million of “Interest expense” $2,015 million of “Total assets” = 3.57 percent.)
Recall from earlier that Motorola’s “Net sales” went down in 1998? Clearly it was easy to reject the thesis that the company may have been discounting prices, but making it up by collecting interest. Why? Because not only were sales down; the interest rate charged on those sales was below market.
My nerves would be calmed if Motorola were selling its products to creditworthy customers. After all, that would be exactly as the automobile industry has done for so long: find a way to be competitive on pricing, and also to make it up by financing.
So who were Motorola’s new customers according to its 2000 DEF 14A?
“Purchasers of the company’s infrastructure equipment continue to require suppliers to provide long-term financing in connection with equipment purchases. Financing may include all or a portion of the purchase price and working capital. The company also may assist customers in getting financing from banks and other sources. During 1999 the company significantly increased the amount of customer financing provided by its consolidated financing subsidiary. As a result of increased demand for customer financing, the company’s consolidated financing subsidiary borrowed more money in 1999. The company expects that the need to provide this type of financing or to arrange financing for its customers will continue and may increase in the future.”
Then a little later in the document we have:
“In the Networking and Computing Systems Group sales and orders increased as a result of the sales growth of key customers. In the Wireless Subscriber Systems Group sales increased and orders were significantly higher due to the rapid growth of the wireless telephone market, including sales growth within the company’s PCS segment, which is the group’s largest customer.”
And finally this frightening disclosure:
“Unbilled receivables which are included in accounts receivable but not yet billed to the customers were $737 million and $600 million at December 31, 1999 and 1998, respectively.”
In other words, relative to Motorola’s “net sales” or revenues, the company was extending credit to un-creditworthy customers. In exchange for near worthless IOUs from its customers, Motorola was sending product out the door.
Moreover, Motorola also had a gigantic proportion of its revenues that were “unbilled.” Umm, why? If product has been delivered, then why have customers not been billed? Whoa!
As I arrived in Schaumburg, guess which questions were top of mind for me?
Motorola’s investor relations pro was only too pleased to communicate to us that the new and biggest customers were mostly wireless telecom companies. In other words, Motorola was just as hip as the dot-com newbies. What he did not realize was my shareholder horror at recognizing that these new customers were not cash flow positive, and were heavily leveraged.
So what trouble did Motorola get into later in 2000? You guessed it — defaults on the part of many of its new, sexy customers.
This scandal was a preventable surprise because the whole story was there for the diligent research analyst who can bridge the quantitative disclosure with a qualitative understanding of context. The analyst who can recognize when there’s no “there” there.
To wrap up, let us review the important considerations when examining long-term financing receivables:
Knowing the answers to these questions allows a research pro to evaluate the substance of a company’s revenues both today and tomorrow.
Next month we use the techniques shared this month to examine General Motors. We will add some new wrinkles, such as an examination of the Allowance for Doubtful Accounts. And because we use recent history in this next example, our analyses will make use of Calcbench’s cool tools!
This is a monthly column written by Jason Apollo Voss — investment manager, financial analyst, and these days CEO of Deception and Truth Analysis, a financial analytics firm. You can find his previous columns on the Calcbench blog archives, usually running the first week of every month.
Google ($GOOG) dropped another monster quarterly report earlier this week, reporting $61.9 billion in revenue for Q2 2021 and $18.5 billion in net income. Net income was particularly impressive, in that it was more than double the $6.96 billion reported one year ago.
The vast majority of that amount came from Google’s services division, which includes online advertising, sales of apps and hardware, and subscription fees for various products. But tucked away in the footnotes, we noticed one additional gem: Google has extended the estimated useful life of its servers and other equipment, which added an extra $561 million to the bottom line in Q2.
Hey, every little bit helps, right?
The full disclosure is listed in Google’s Summary of Significant Accounting Policies, which you can find using Calcbench’s Interactive Disclosure viewer. There, under the headline “Change in Accounting Estimate,” the company says this:
In January 2021, we completed an assessment of the useful lives of our servers and network equipment and adjusted the estimated useful life of our servers from three years to four years and the estimated useful life of certain network equipment from three years to five years … The effect of this change in estimate was a reduction in depreciation expense of $721 million and $1.6 billion and an increase in net income of $561 million and $1.2 billion, or $0.84 and $1.81 per basic and $0.83 and $1.78 per diluted share, for the three and six months ended June 30, 2021, respectively.
Put more simply: Google decided its equipment can last longer than originally expected. That pushed down the company’s depreciation expense, and the savings fell directly into the net income line.
Of course, $561 million is only 3 percent of Google’s total net income for the quarter, which barely qualifies as material. We were more interested in the nature of the accounting change rather than its size — because fiddling with the estimated useful life of assets is something any company can do.
Our columnist Jason Voss, for example, recently wrote about the Property, Plant & Equipment line item and how an analyst might estimate the average life of fixed assets. As Voss noted in his column, “Many firms play games with depreciation & amortization to improve their short-term results, and such games depend on PP&E.”
To be clear, we are not suggesting that Google is playing with estimated asset lifespan here. Other firms, however, have been called out for such abuses. For example, in 2019 the Securities and Exchange Commission hammered Hertz on accounting fraud. One of the company’s schemes was to extend the estimated life of its rental fleet from 20 months (the industry norm) to 30 months, which kept depreciation costs low and therefore goosed net income up.
So Google’s morsel of disclosure is just another reminder that analysts should always be on the lookout for details that might tell more than you’d expect — and as always, Calcbench has the data!
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