Obscure Berkshire Hathaway Filing Reveals What Warren Buffett Thinks About Kraft Heinz

Berkshire’s investment in Kraft Heinz has not gone smoothly. After taking Heinz private together with private equity investor 3G in 2013, Berkshire invested additional funds in the takeover of Kraft Foods in 2015, and was ready to finance a quickly-withdrawn $143 billion offer for Unilever.

Things have gone south for KHC recently. The company’s stock price and valuation have shrunk considerably over the last two years versus that of its peers. We can see KHC’s EV/NTM EBITDA multiple (in thick red below) starting at the top of its peer group two years ago, but recently declining to very the bottom compared with other US food companies (interactive chart link). We do expect the multiple to move up somewhat over the next few days as analysts adjust their estimates after the call.

 

KHC also had to delay its financial reporting this year due to certain actions by former employeesthat led to small restatements, and more notably, the company took a $15 billion write-down of brand values and cut its dividend in February of this year. In the most recent call on August 8, 2019, the company, now with a new CEO from AB In-Bev, did not provide guidance, took additional impairments, and warned of future impairments.

The travails at KHC are a topic of increasing concern to Berkshire Hathaway investors. The number of mentions of Kraft on the most recent shareholder meeting more than tripled versus last year to 27, up from just 8 in 2018.

Buffett himself did admit that Berkshire overpaid for Kraft but still thinks KHC is a “wonderful business.”

 

While getting color from the transcripts is nice, we were also interested in what is actually in the Berkshire filings, so we redlined the second quarter 10-Q filed with the SEC on August 5, 2019 against the Q1 version of the document. We saw a lot of new language around KHC.

What we’re seeing is a lower fair value of the investment (easily observable, since KHC is publicly traded). The carrying value was reduced because of the KHC financials’ restatement. We also see some puts and takes around the KHC YTD filings.

Most interesting to us was the inserted paragraph at the bottom; Berkshire reviewed KHC for impairments, and as of June 30, 2019, decided against it.

 

But Why?

The answer comes in a more obscure SEC filing, called CORRESP for Correspondence. CORRESP and UPLOAD are two forms of formal communication between the Commission and the filers. When a letter is directed from the SEC, it appears in filings as UPLOAD, and when the filer responds to the regulator, it is a CORRESP. 

Berkshire filed two UPLOAD and two CORRESP forms on July 24, 2019, though the exchange between the company and the regulator had taken place in May and June. 

The SEC was interested in how Berkshire was accounting for Kraft Heinz in the Q1 2019 10-Q filing. 

 

And here is how Berkshire responded in great detail to the SEC: KHC’s stock price decline and the length of this decline was not substantial enough. Also, the operating results, while currently poor, will be better (divestitures, brand power, reduced but not eliminated dividend)

 

On the following page, we see more information related to how Berkshire thinks about KHC: there are no plans to sell the stock at any time, and the KHC restatements are immaterial. 

 

Given Berkshire’s long involvement with Heinz, and then with Kraft Heinz, including Board of Directors representation, and its continuous investments in the space, we view these disclosures as material: Warren Buffett still thinks KHC is a long-term holding position that will recover.

New call-to-action

What We Learned From Redlining Beyond Meat’s (Nasdaq: BYND) Secondary Offering Documents

Meat alternative marketer and recent IPO Beyond Meat (Nasdaq: BYND) reported quarterly earnings a few days ago, and, concurrently, the company surprised the market with a secondary offering well ahead of the indicated 180-day IPO period (a rare occurrence in the last 10 years). The stock’s performance since its IPO has been stunning: the IPO priced at $25 on May 01, 2019, and the stock went up over 8 times leading up to the earnings announcement.

The secondary offering priced at $160, well below the closing price of $222.13, the last price before the Q2 results and secondary announcement on July 29, 2019.

The offering was mostly pre-IPO investors and insiders selling (3,000,000 shares), along with the company selling 250,000 shares itself to fund its operations:

The secondary came just as BYND’s market capitalization surpassed that of a number of consumer staples companies in the S&P 500:

(interactive chart link)

We were curious to read the secondary offering document using redlining, to see what has changed since the IPO documents. Keep in mind that there will be changes solely due to the fact that BYND is now publicly traded, versus the pre-IPO language. We redlined the secondary S-1/A filed on July 31, 2019, against the final IPO S-1/A from April 29, 2019.

 

Here are our notes:

1) Stunning distribution and sales growth, along with successful partnerships and major increases in media impressions:

 

2) Expanding distribution in Europe and growing product lines:

 

 

3) Since the company is now publicly traded, there is a new warning around stock price volatility and potential losses:

 

4) Certain US states have introduced legislature regarding what products can be called “meat.” We saw this reflected in the added “state regulators” to this risk factor:

 

5) The rapid distribution growth noted above has resulted in some shifts in the major distribution partners:

 

6) There are no written contracts with the US co-manufacturers (note the EU deal mentioned above):

 

7) Big drop in local unemployment in the area around their Columbia, MO, facility warranted an update in this risk factor:

 

8) Entirely new risk factors: the growth will not last forever, and there might be serious fluctuations in the results:

 

9) Negative development for BYND in a lawsuit brought against them by a former co-manufacturer:

 

 

10) A relatively new development in IPO filings is the disclosure of use of Professional Employer Organizations for a number of HR/payroll tasks:

 

11) Surprisingly, the number of pending patent applications have dropped:

 

12) Added language around compliance and internal controls:

 

 

13) As we saw in the price action after the secondary offering was announced, the share price can fall. There is added language around secondary offerings’ effects:

 

14) Since the stock is now publicly traded, there is a whole new paragraph on the effect of research analyst coverage:

 

15) We can see the company balance sheet pro-forma of the offering. Note the increase in Cash and Cash equivalents, along with the increase in Additional paid-in capital:

 

16) New language on revenue seasonality (“summer grilling season”):

 

17) The IPO also lead to a simplification of the capital structure of the business (also note the Warrant crossed off in the table above):

 

18) The company has a small balance on its revolving line of credit:

 

19) New obligations: a 5-year office lease and minimum purchase commitments:

 

20) There is a lot of detail on Sales and Marketing activity (of course, all numbers are up: sales team, promotional events, samples, followers):

 

21) Notably, no changes in the serious celebrity endorser line-up:

 

22) New executive hire and one anticipated Board of Directors departure:

 

23) There is quite a bit of detail around the lock-up agreement (which was waived for this offering)

 

24) The underwriters have the standard “greenshoe” option to sell additional shares: 

Get in touch with Sentieo to try your own redlining!

 

New call-to-action

Doing Better Analysis with Non-Financial Data Sets in Sentieo Plotter

Sentieo’s main data visualization tool, Plotter, contains a veritable treasure trove of data series. While some financial and valuation data sets are commonly available, Sentieo Plotter stands out with its numerous non-financial data sets that are available within Sentieo, or created by you. (For example, you may have pulled Document Search statistics or data generated from Table Explorer, our ML-driven SEC filings table chaining tool).

Sentieo users can also upload their own data sets. Your individual data series and full charts can be saved for future use. Your charts can then also be added to longer notes or a formatted thesis. They can also be shared with your team or publicly to non-Sentieo viewers.

Below, we’ll go over a few of the non-financial data sets that can help you do better analysis.

 

FRED Macro

We recently integrated around 1,000 of the most popular data series from the data site maintained by the Federal Reserve Bank of St. Louis. 

An analyst needs to understand the macro currents that affect the companies and sectors under her coverage. For example, below we plotted the Civilian Unemployment Rate from FRED vs. a simple average rolling NTM P/E of four staffing companies. We can immediately see that the sector valuation is very closely tied to the unemployment rate. 

Interactive chart link

 

Online Activity Data and the Sentieo Index

Sentieo Plotter pulls Twitter mentions, Alexa website traffic, Google Search trends, and Instagram mentions. Through our Mosaic module, we also run a multivariate regression on these data sets against Wall Street estimates for revenue growth and KPIs. This Sentieo Index is also available inside Plotter, ready to be displayed alongside other data sets. 

For example, here is what one-month moving average page views, search trends, and Twitter mentions of the recent IPO Fiverr look like. Plotter lets you customize your data set display options, including moving averages (for smoothing) or YoY change metrics. 

Interactive chart link

 

Document Search Statistics

Part of your own IP is your ability to see trends: but how can you quantify trends with non-financial data sets? One way is to generate your own document search statistics to see if documents with key term mentions are going up or down. For example, below we searched all restaurant transcripts for mentions of “delivery.” We can see a clear increase in document mentions starting in 2017.  

Interactive chart link

 

Company-Specific KPIs

In some cases, investors and industry analysts give more weight on KPIs and KPI trends. Sentieo Plotter has company-specific KPIs that can help users “paint a picture” of the underlying business trends. 

In this example, we have pulled KPIs (comparable store sales) for five casual dining stocks, and have done an unweighted average for our “custom index” to see what is going on in this segment of the industry. 

Interactive chart link

 

Custom KPIs

Not all KPIs that an analyst might find relevant are available through legacy standardized data providers, but are disclosed in company filings. Sentieo’s Plotter is integrated with Sentieo’s machine learning-based Table Explorer tool, which identifies and chains past tables in SEC filings. 

For example, here we are pulling Royal Caribbean Cruises’ APCD (Available Passenger Cruise Days) in a few clicks into Plotter.

Interactive chart link

 

Linguistic Sentiment

Sentieo uses NLP (Natural Language Processing) for analyzing quarterly transcripts (management and analyst sentiment + spread between the two, along with keyword surfacing for both groups). These sentiment metrics can also be pulled into Plotter. For example, here we are looking at Tesla’s management sentiment reaching a high point when reported sales growth reached its highest point (summer of 2018, when the company started filling its backlog). 

Interactive chart link 

 

Your own data sets

Sentieo Plotter enhances your workflow, efficiency and visualization capabilities with two other very useful functions: uploading your own data in CSV format and giving you the ability to save generated data sets for quick recall in other Plotters so that you can re-use your IP. 

What we have seen is that some Sentieo clients upload their own proprietary data to combine with and visualize against what Plotter already has. Other clients like using publicly available but obscure data sets, such as very niche US Census data or BLS statistics. 

Sentieo can do a lot more than just data visualization. Please get in touch to request a demo

 

New call-to-action

Disrupting Transcripts: Spike In “Deflections” on Tesla’s Latest Call

In our final earnings week blog post discussing our new, machine learning-based transcript Smart Summary™, we’re taking a look at the electric car maker, Tesla. The company reported earnings and held its regular quarterly call after market hours on Wednesday, July 24, 2019. The stock dropped immediately upon the results release, and continued dropping during the call and into the next day.

While every quarter at Tesla has many puts and takes (like international shipments, tax credits, specific model volumes), what really stood out for us when we applied the Smart Summary™ was the really serious increase of “deflection” instances quarter over quarter. Our tweet on the topic got almost 10,000 views in a few hours:

 

So what is “deflection?”

Our new transcript Smart Summary™ tool has two underlying mechanisms. One is based on a specially trained machine learning system in which we, mostly former buysider product managers, trained the system to parse and classify sentences into a large training data set across industries and companies. This tool automated what we used to do during earnings season, and will only get better as our users submit direct feedback. Transcript Classifications are not mutually exclusive; a sentence might often be a Guidance and a KPI sentence at the same time. 

The second mechanism powering Smart Summary™ is natural language processing. We have had overall sentiment reports for management and analyst teams, and keyword surfacing applied to transcripts in the product for a long time. What we’re introducing with the Smart Summary™ is the scoring of specific sentences (positive/neutral/negative) across all Classifications (so a sentence might be “positive Guidance” and “positive KPI” at the same time). The other NLP application is what we call “deflection.” We dug through the academic literature and identified a specific lexicon that has shown to lead to future negative corporate events. 

Looking at the Deflection summary view in the Tesla call, we see quite a bit of the usual enthusiasm and grand visions from CEO Elon Musk.

We see the recently appointed CFO discussing the challenges in the quarter-to-quarter business:

It was most interesting to us that the remarks by the outgoing CTO (a surprise departure) were also picking up as deflection:

The final statement on the call by the CEO was also marked as deflection: will Tesla beat the industry economics?

To find out how Sentieo can help with your process, please get in touch

New call-to-action

Disrupting Transcripts: Short Video Example on UPS

Last week, just in time for earnings season, we released Sentieo’s machine learning-based transcript Smart Summary™ feature. In short, we automated what we used to do as buyside analysts: reading through transcripts and classifying information.

This week, we’ve looked at the newest transcripts from Goldman Sachs and Haliburton in detail. Yesterday, we shared an in-depth video discussing Whirlpool: from redlining the 8-K, to looking at the stock price action post-call, to its Smart Summary™. 

Today’s video is shorter, as we only focus on the Smart Summary™ aspect and we discuss UPS. We were able to extract the important information in about 2 minutes.

The Smart Summary™ is one of two major ML-based product releases we had this month: take a look at our synonym suggestion tool as well. Your searches can be a lot more productive with this dynamic addition to our existing large synonym/acronym dictionary.

If you are ready to test out our platform, please get in touch.

New call-to-action

Disrupting Transcripts: A Smart Summary™ Video Tutorial

This week we’re showcasing our new, machine learning-based transcript Smart Summary™, an entirely new way of reading — and more importantly, summarizing — transcripts

Yesterday, we looked at Halliburton’s transcript Smart Summary™In today’s post, we’re sharing a video that covers Whirlpool (NYSE:WHR), a “classic” industrials company. We review the earnings 8-K, discuss the positive after-market reaction to the guidance increase, and then we dive into the transcript to see why the stock actually declined after the call. 

To try using Smart Summary™ in your own research workflow, sign up for a custom demo.

Disrupting Transcripts: How We Used Sentieo’s Transcript Smart Summary™ to Review the Halliburton (HAL) Call

Oilfield services provider Halliburton (NYSE:HAL) is the first installment in this week’s Smart Summary™ showcase. We are applying our newly released machine learning-based tool to improve our own reading while saving time. Below, we will highlight how we picked out the critical pieces of information on the call in just a handful of minutes. 

If you haven’t read this feature’s announcement post, here’s a recap. Smart Summary™ is a one-click application of machine learning that “slices” transcripts in several ways. One of these slices is a broad Classification, like “Guidance” or “KPIs. (See 1 below).

The second slice (2) is based off NLP-powered sentiment (green and red labels). The third application is based on surfaced Key Terms: products, brands, and geographies (3). The fourth and most innovative application is the removal of the chronological flow all together; sentences are sorted in a table view mode, and can be ranked by “signal strength.”(4) 

(1) (2) This is what the clickable Classifications look like for HAL’s transcript:

(3) Top NLP-surfaced Key Terms are clickable as well.

(4) Clicking the Table Icon in the upper right will open the sortable sentence Table View. (Click on image to zoom in).

With this in mind, here is what we are seeing as key pieces of information in HAL’s transcript.

Free Cash Flow has been and will continue to be positive. This is really good news given the FCF challenges in the energy industry:

Directly related to FCF, HAL is cutting back on capex and is focusing on margins:

Increased activity in several regions led revenue growth in the quarter, while the overall outlook is optimistic:

Switching from the full transcript view to the summary view for the Guidance classification, we see the summary at the end of the call: guarded optimism and focus on margins and capex. 

We complete the final review of the transcript using Table View: the non-chronological way to extract information from transcripts. We can see how key sentences on the topics highlighted above are picked up by NLP. Readers can sort sentences along several categories. 

In terms of Guidance “loading,” we see Free Cash Flow ranked highly multiple times:

Ranked purely on sentiment, we can see that the overall business did well in the quarter across several geographies:

Ranking for Products and Markets, we see new product discussions as well as the pricing signals.

Investors can also easily pick up the negatives in the results and in the outlook: large legacy frac fleet, soft pricing seen by competitors, some segments might see revenue declines. 

Reading non-chronologically does not mean reading out of context. Simply click on the Context link to see the phrase within context. 

The Table View is also available for the NLP-surfaced Key Terms. In one glance we can see whether these terms are used more or less quarter-over-quarter, and by whom (analysts vs. management). We see how prominently “North America,” “capex,” and “Free Cash Flow” are featured. An added time-saving convenience is that these Key Terms are click-through: clicking on one will search all HAL transcripts for that term, so you can get immediate historical context in seconds. 

Here we have clicked on Capex, and we can see highlights in the most recent transcript: note how Sentieo’s synonym search picks up various forms of the term.

To learn more about how using the latest ML-based tools can help you and your team be more productive, please get in touch with us

 

New call-to-action

Introducing Sentieo’s New Document Search User Interface

Sentieo users are now able to try the beta release of our brand new Document Search user interface (UI). While it may seem familiar, the UI has been rebuilt using modern tech such as React. This new build enables significantly less memory consumption and greater responsiveness across the board. Plus, we’ve extended Sentieo’s sentiment and natural language processing capabilities to include a host of new AI-powered features detailed here.

AI-Powered Features

 

Smart Summary™

With Sentieo’s new, ML-driven transcript Smart Summary™, we enable analysts to pick up the important information across multiple classifications, such as guidance and KPIs.

To trigger Smart Summary™, users can simply click the “brain” icon on any transcript. To learn more about Smart Summary™, read this post.

 

 

Query Autocomplete

Our data science team has built an algorithm to identify key terms and topics for all companies within our database. Similar to a Google search, when a user starts typing inside of the query box, Sentieo will automatically populate suggested terms for you based on actual terms used by the company. This makes it much easier to find exactly the keywords you are looking for. Autocomplete intelligently populates based on which tickers and watchlists you have selected in the ticker box.

 

Suggested Synonyms

We are very excited to announce a giant leap forward for our Synonyms system. We have been using an NLP + machine learning-based algorithm for the last couple years to help us internally define and expand our deep synonym library. We are now very excited to open up that system to Sentieo users with our new Suggested Synonyms feature.

Using Suggested Synonyms, Sentieo is able to parse your query and automatically suggest possible synonyms to expand your search. While all of these synonyms may not be pertinent to your search, the function lets you quickly decide which you would like to add to your search. Just click the suggested synonym and it will automatically be added to your search query:

General Document Search Improvements

The Sources Selector

When we looked at usage analytics, we realized that the Document Type filter was the most used filter by a large margin. To make this filter more accessible and easier to use, we’ve made it a part of the main search interface. Additionally, we have renamed the filter to “Sources” to more accurately represent the functionality.

We have also added a quick “only” selector to the right of each source that lets users quickly focus on a single source. 

Additionally, click the > icon at the far right to load up the more granular filters:

All additional filtering is now available with the Filter Button:

 

Configure and Control Your Results Pane

We’ve added the ability for users to easily resize the results panel. Simply drag the divider or use the maximize/minimize button. As you expand, the application will automatically switch to a table view when enough space is present. 

While in the table view, you can also control what columns are visible. Currently, the list of options is limited but the Sentieo team will be adding to this over the coming months. 

For smaller screens, we’ve also added the option to toggle between split pane and results-only views.

 

Company Summary

If you run a search with a ticker but without a query, the Sentieo initial results page shows you all company documents, as well as a company summary and share price performance. We have taken some key metrics and links out of our EDT Summary and provided them within Document Search. Additionally, users can see a historical stock price graph in the company summary.

 

Improved Document Viewer

We’ve made serious upgrades to our Document Viewer as well. You will notice faster load times for documents across the board. Additionally, we have relocated the highlights and the index panel to the right of the document.

We have also relocated the Redlining function to the top right of the document viewer.

 

To try the new Document Search interface for yourself, please log in, or get in touch!

 

New call-to-action

Disrupting Transcripts: Applying Sentieo’s Smart Summary™ During Earnings Season

After last week’s release of Sentieo’s machine learning-based Smart Summary™ for transcriptsthis week we will be highlighting exactly how to use this powerful new feature to ensure that you don’t miss important information this earnings season, and that you also save time.  

The Smart Summary™ tool classifies a document’s language based on several categories such as Guidance, KPIs, and company-specific key terms (like product names) into a convenient, time-saving format. Further, it applies an NLP sentiment filter to highlight positive and negative language. 

Smart Summary™ is the latest machine learning and natural language processing-based tool released by Sentieo this year. We’ve also released a dynamic synonym suggestions feature for Document Search based on our own, internal synonym search tool. Additionally, we added company-specific autocomplete suggestions to our search. And earlier this year, we rolled out Table Explorer, a flexible and auditable tool that identifies, chains and visualizes tables in SEC filings. 

During the next few days, we will be selecting and publishing our highlights from current transcripts to show how effective Smart Summary™ can be in your research workflow. (Stay tuned).

For example, if we look at the transcript from the Goldman Sachs call last week (below), we can pick up essential corporate and macro information (color-coded as needed), under the Financials and KPIs classification. Scrolling down in the Smart Summary™, we see record assets under supervision: good news in green, versus slower Chinese GDP growth in red, a couple of paragraphs down. The full context is right there as well. 

In addition to viewing by Classification group, users can quickly scroll through all positive and negative highlights, regardless of Classification. 

In the Key Terms field, we catch up on Marcus, Goldman’s growing consumer banking franchise. We can see a mix of positive and neutral statements. 

For the next few days, the blog will be highlighting exactly how we’re applying this powerful new functionality. To try it yourself, please get in touch

New call-to-action

Disrupting Transcripts: Introducing Sentieo’s Smart Summary™

Just in time for earnings season, Sentieo is releasing Smart Summary™, a whole new way to read transcripts. 

We all know the problem that analysts and portfolio managers face four times per year: multiple companies reporting on the same day, which creates impossible scheduling conflicts, work overload, and forced prioritization, which then leads to costly missed information.

Analysts might dial into one or two consecutive high priority calls, and then go on to read another four to eight transcripts later in the day, depending on coverage. These transcripts are always read chronologically, starting with the boilerplate language on top, and ending with the analyst questions. 

Until now. 

With Sentieo’s new, ML-driven transcript Smart Summary™, we enable analysts to pick up the important information across multiple classifications, such as guidance and KPIs, while skipping over the “great quarter, guys!” parts. The segments based on these classifications can either be seen highlighted in the transcript itself or extracted, with context, in a special field. The text is also run through an NLP screen and color-coded in green or red, if needed, with enabled clickable user ratings that will continue to improve the accuracy of the learning system. In addition, Smart Summary offers keyword extractions, enabling readers to view text related to these surfaced key terms.

These features empower analysts to get the essential information in minutes, saving valuable time during the busiest periods of the year. The benefit of the ML-driven Smart Summaries does not end with faster information processing or expanded coverage; it also ensures that analysts are not missing important information on their last few transcripts at the end of the workday. 

Below we are looking at Facebook’s latest quarterly call transcript, and scrolling down to the Guidance classification text:

Below we are looking at the same Facebook transcript, but reviewing the Whatsapp key term text:

Another aspect of Smart Summary is “Table View,” which enables active sorting of sentences by the Classification score (ie. “which sentences have the most guidance language?”). This adds another layer of improved accuracy and productivity to the analyst workflow. 

The Table View is also available for surfaced keywords. Analysts can see useful statistics, like whether a keyword is new, or whether it got dropped. They can also see whether usage, by either management or analysts, is going up or down. This type of quantitative insight for keywords was previously so labor-intensive that it was rarely done, and only for one or two key terms at most.

 

How did we build Smart Summary™?

We spent countless hours training a machine learning model on a very large number of transcripts across all industries, effectively automating what an analyst would do. Once the model was up and running, we spent a lot of time on corrections and fine-tuning for this official release. 

Smart Summary™ is a blockbuster new addition to the Sentieo platform that serves our overarching mission: augmenting human decision-making through the latest technological tools. The release is a part of the latest ML/NLP update to our industry-leading Document Search, which now also includes the surfacing of synonym suggestions based on a machine learning tool trained on millions of corporate documents. (We will discuss this in an upcoming blog post).

 

To see how Sentieo’s complete workflow solution can work for you, please get in touch

New call-to-action