[WHITEPAPER] Outsmart The Competition: 7 Best Practices For Becoming A Competitive Intel Ninja

Globalization and the information age have fundamentally changed corporate competitive intelligence.

Not only do you have more information to process than ever before, but you also have to actively monitor developments outside of your field.

So how do you stay on top of everything that is happening with your clients, suppliers and competitors? How can you surface relevant, meaningful information faster? CI professionals should be leveraging existing IP inside your organization, answering ad-hoc questions in a timely and valid manner, and minimizing time spent on rote tasks to focus on more important projects.

We’re here to help! Becoming a CI ninja might seem daunting, which is why we put together this guide to mastering your craft.

Below is a sneak preview of our best practices ebook:

For the rest of our 7 best practices, download the full whitepaper here!

New eBook: How To Get The Most Out Of Conference Season 2019

Once earnings season winds down, equity analysts start thinking about investment bank conferences and upcoming meetings with management. While the largest concentration of conferences happens in July and September, conference season happens year-round, so we thought now would be a good time to share our guide.

As conference season begins, you’ve got a lot on your mind. You and your team are getting ready to catch a flight, prepping for multiple days of back-to-back meetings. You want to be as prepared as possible, to ensure that you get the most out of your time away.

But as former analysts, we’ve been in your shoes, so we put together a quick guide to help you out.

Get Mobile

When you’re on the road, your mobile device is your best friend. Don’t miss anything while you’re gone; use a mobile cloud software that allows you to review documents and financials while traveling — anywhere, anytime.

  • Reference financial data and prior conference transcripts on your tablet and phone during meetings with management, so your questions are sharp and you get the most out of your meetings.

 

  • With mobile access, you won’t fall behind if you’re away from an onsite data terminal. (And try not to forget your phone charger at home).

 

Listen Carefully and Read Management’s Body Language

Management presentations will typically seem optimistic, but look for clues and ask tough questions to get to key nuggets of information. Try to scope out and take notes on indicators such as:

  • Management speaker confidence
  • Openness about risks (generally thought of as a sign of genuine enthusiasm)
  • Covering one’s neck (i.e. subconsciously seeking to protect a vulnerable part of their body) is a sign of discomfort, as is adjusting a tie, loosening a collar, or rubbing the forehead.
  • Executives who are uncomfortable with what they are saying often use “distancing language,” changing pronouns from “I” and “we” to “the company.”

 

Some other things to look out for in management presentations:

  • Comments on liquidity: trends in cash flow (or lack there-of) on balance sheet
  • Use of credit lines (are they too dependent on these?)
  • Press activity (press releases, PR firm hiring, poaching talented executives, new product announcements, focus on research and development, or R&D)

 

Prep Your Questions 

 

  1. Prepare and store your questions for conference meetings within a system that you can access while you’re in the meeting room, ideally from your mobile device.
  2. As the meetings occur, keep organized and also take notes within your mobile app. Tag them by ticker, topic, or your own keywords for easy retrieval after the trip.

For the rest of our tips, please download the whole eBook here!

 

Investment Bank Conferences in 2019

We compiled a list of websites with the dates for investment bank conferences, below for your reference. There are a few large banks (Goldman Sachs, Morgan Stanley) and smaller firms that do not have lists, but this should help you make sure you don’t miss any upcoming conferences.

 

JP Morgan

 

Credit Suisse

 

Deutsche Bank

 

UBS

 

Barclays

 

Citi Equities

 

Jefferies

 

Baird

 

Raymond James

 

For the rest of our tips, please download the whole eBook here.

Word On The Street: The Most Popular Transcript Keywords Of 2018, By Sector (An Annual Report By Sentieo)

Every year, thousands of pages of earnings call transcripts are generated and analyzed by equity analysts for signals about how individual companies — and the market — will perform. Our 2018 Word On The Street report demonstrates how Sentieo’s Document Search and Transcript Sentiment tools make that data more accessible to researchers.

How It Works

We used Sentieo’s natural language processing technology to scrape all the earnings transcripts published in the last year. We then processed and cleaned the data to distinguish the keywords in the text. We highlighted the words in each transcript that occurred on the greatest weighted average basis. We also eliminated filler words (like “or”, “and”, etc.) and conducted analytics on the cleaned data, like part-of-speech tagging (picking out nouns and verbs for semantic analysis) and sentiment analysis (quantifying the tone of the text).

Natural language processing powers the Sentieo platform’s document search and transcript sentiment functionality, putting its users at the forefront of financial research technology.

The Most Popular Transcript Keywords Of 2018, By Sector

Our 2018 report spans all sectors – from consumer discretionary to utilities. Here’s a sample page of our report on the Consumer Staples sector. For the full, free report, please download it here.

We want to hear your thoughts on this report, or any of our other whitepapers! Your feedback is welcome at hello@sentieo.com. For a free trial of Sentieo or to learn more, get in touch here.

Reading The Tea Leaves From Federal Reserve Statements

When economists talk about inflation, they may describe themselves as “hawkish,” – in favor of policies which combat inflation – or “dovish,” that is, less concerned about inflation pressures in the economy.

This nomenclature has migrated into the often jingoistic discussions around US Central Bank interest rate policy. In this world, “hawkish” refers to the Federal Reserve’s inclination to raise the overnight borrowing rate, and “dovish” conversely reflects a tendency to lower rates or leave rates unchanged.

The Federal Reserve sets monetary policy via the Federal Funds Rate, which is the rate of interbank lending of excess reserves. This interbank lending rate passes into the economy through Fed member banks who pass the higher input cost of money on to their customers in turn.

In effect, the Federal Reserve controls the price of money in the US economy. Therefore, figuring out which way the Fed is leaning in terms of “hawkishness” or “dovishness” is of great interest to money market participants.

Natural Language Processing

One of our core competencies here at Sentieo is Natural Language Processing. What NLP allows us to do is build predictive models from various sets of document data. We might be working with SEC documents for a specific company to extract company-specific key performance indicators, we might be chaining financial tables together over time for spreadsheet models, we might train models to extract guidance statements from company press releases, or classify research reports by type. In all cases, we’re using machine learning and deep learning for predictive analytics on a “corpus” of documents.

As an exercise, we took a similar approach to Federal Reserve Meeting Minutes and then applied what we learned from this modeling to Fed Statements.

The Data

The Federal Open Market Committee (FOMC) meets on a regular basis several times a year to discuss the state of the US economy and decide on where to set the level of short term interest rates. These Meeting Minutes and Statements are published at https://www.federalreserve.gov/monetarypolicy/fomccalendars.htm

As background, the Statements come out coincidentally with the Fed’s market action, usually around 2:15 on the afternoon of the last day of the meeting. The Minutes are then published a couple of weeks later.

Market participants have very little time (because the market is open when the Fed releases its Statement) and very little data to work with from an empirical perspective (because the Statements are a lot shorter and less descriptive than the Minutes) when the Statements come out.

For these reasons, parsing Fed Statements has become an industry unto itself.

Methodology

We scraped the documents in two sets: the Meeting Minutes and Meeting Statements. We used the Meeting Minutes to train a machine learning classification model for Meetings where the Fed raised rates and where they lowered rates. We used the classifiers from that model to then create a “FedSpeak” lexicon which we applied to the Meeting Statements in an effort to measure the relative “hawkishness” of the Statements dataset over time. We used this mixed approach (machine learning and lexicon) to facilitate sense-making over the multi-year interregnum period when the Fed left rates at zero.

We trained our model on the Meeting Minutes because these are longer files with more data about the FOMC deliberations. We assembled the Minutes into a dataframe arranged by date, and further split the data into sentences and then filtered the dataframe to remove non-meeting-related text (description of open market operations, list of attendees, etc.).

Prior to modeling, we took a look at the data by simple word frequency. For visualization purposes, we classified each meeting as a “hike” or a “cut” meeting, and then arranged the most common words in each type of meeting.

After performing this tokenizing step (with tokenizing meaning splitting the text into individual words) and then additionally creating a sparse matrix for use in our machine learning model, we had 3,412 observations and 3,347 features in the Fed Minutes matrix ready for processing.

We then joined our “hike” or “cut” classification variable to the Fed Minutes to act as the response variable for prediction.

In effect, we sought to determine the “hike” probability based on two classes of Fed Minutes: those where the Fed raised rates and those where the Fed lowered rates.

(Why not use Meeting Minutes from the long interregnum period where rates stayed at zero through the period after 2008? We really tried. We used 1 month T Bill rates, Libor, and 3 month T Bill rates in a multivariate logistic regression model and found that since rates rarely moved when the Minutes came out we were then training a model to predict nothing from nothing, and found that our overall probability of successfully predicting rate hike language became very small as a result. We will probably revisit this issue in future posts, and it’s a goal of ours to include the interregnum against some form of significant, exogenous classifier, perhaps the Ted spread, etc.)

Given our final matrix of input data, we then trained our classification model using the R package “glmnet” to fit a logistic regression model with LASSO regularization.

Importantly, the variable selection that LASSO regularization performs allowed us to determine which words were most important for prediction.

We then used the sort order of importance to build our FedSpeak Lexicon. Words with positive coefficients predicting “hike” were termed “hawkish.” Words with negative coefficients were termed “dovish.”

Results

As an example, we show the top 10 hawkish and dovish words in the model.

Model Performance

As a way of visualizing how our predictive model performed, we’ve included a chart showing prediction results: the % probability of “hike” for each of the Fed Minutes we used in our classification model.

The forest green box plots are Fed Minutes where the Fed raised rates. The blue box plots are Minutes where the Fed lowered rates. As expected, in cases where the Fed raised rates, our model predicted the same, and in cases where the Fed lowered rates, the probability of “hike” is very low.

The black dots are outliers. The size of the boxes gives a sense of their variance. The bottom of the box is the lower quartile of the data, the top of the box is the upper quartile, and the line inside the box is the median of the dataset.

Results Applied to Fed Statements

Lastly, we turned away from the Fed Minutes, and loaded our dataframe of the history of the Fed’s Statements from 2008 onwards.

We applied the coefficient-driven FedSpeak Lexicon to each Statement to get a relative sense of each Statement’s “hawkishness.”

Candidly, we like the Lexicon approach to Statements because it is fast, simple, easy to explain, and easy to visualize. We could have directly applied the trained Minutes model on the Statements and we do intend to explore this option more fully in future work.

Having said this, the Lexicon approach shows the Fed has clearly transitioned from “dovish” in the 2008 period to “hawkish” in 2018, with a downtick in “hawkishness” in the most recent meeting.

We believe that most market participants would agree the Fed took a less aggressive position vis a vis rates into year end 2018.

Additionally, a review of the Federal Reserve’s “dot plot” of forward rate expectations from the December meeting vs the June meeting can be reviewed on page 3 of each of the following documents:

December: https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20181219.pdf

June: https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20180613.pdf

The “dot plot” or forward rate curve has declined significantly since June of last year.

For reference, we’ve added a LOESS regression line as a sort of smoothed rolling average to offer a sense of trend over time.

For more information and a free trial of our research platform software, please sign up here.

Here Are 11 Stocks You Should Own in 2019 (Q1 Edition): How We’re Using Alternative Data to Predict This Year’s Winners

After a tumultuous end to 2018, many Sentieo clients and friends have asked us for a shopping list of attractive stocks for 2019. As the only financial data platform that combines traditional financial data with alternative data, Sentieo is uniquely positioned to identify stocks that had positive Q4 topline performances which should translate into stock upside in 2019.

We’ll cover the 3 picks below in this blog post, but download the full report to see all 11 of Sentieo’s Stock Picks.

  1. LULU
  2. NTDOY
  3. TREE

Our Methodology: Why Does Alternative Data Predict Future Results?

Alternative datasets are a powerful new tool in an investor’s toolkit. Focusing on digital ‘breadcrumbs’ left by consumers as they browse the web, search for products and websites and discuss products on social media, we are given a bird’s eye view of weekly demand trends for various consumer and tech businesses.

Sentieo curates a number of these alternative datasets and marries them with traditional financial data such as revenues and company KPIs, analyzing both history and forward analyst consensus estimates. Using Sentieo’s Mosaic tool, we are able to visualize and regress these datasets to generate signals on companies that show:

a) High correlations between revenue/KPI and alternative data, and

b) Large accelerations in alternative data trends versus expectations, which we use as a proxy for end user demand

Last year, the team accurately predicted the Netflix, Snapchat, Twitter, Skechers, and GrubHub beats using Sentieo’s Mosaic Index.

For our 2019 Picks, we use Sentieo’s Mosaic Index as the key initial screen and then pair it with our team’s 60 years of qualitative buyside stock picking experience. On the qualitative side, we don’t hew to a single investment style or approach, but we focus on revenue growth as the most important long-term driver of returns.

Our ideal businesses have great revenue growth because they:

a) Are in growth industries supported by long-term secular megatrends, and

b) Have market leadership positions in these industries

This approach naturally yields a growth and momentum bias to our portfolio, which we are comfortable with but seek to partially offset by focusing on businesses that are cheap relative to their growth rates. Our picks sport a median forward P/E of 33x and a median PEG ratio of 1.2. We are also heavily focused on earnings momentum for these businesses to prove out their market leadership positions and justify their valuations. We achieve this through a combination of classic earnings revisions models and our proprietary Sentieo Mosaic Index.

We believe each stock on our list is poised to generate great absolute and relative returns in 2019. Since most of our team’s background is in the consumer and technology sectors and our Sentieo Mosaic Index is focused on these sectors, we have mainly focused our picks on these two sectors. This list is also primarily U.S. focused. However, we will be releasing broader lists as Sentieo evolves throughout 2019.

More About Sentieo’s Mosaic Tool

The alternative datasets offered in the Sentieo platform can provide an edge in analyzing consumer and tech businesses, as they often have a high correlation with revenue growth and are available ahead of traditional financial metrics for the period.

In the graphs below, we are presenting quarterly YoY growth using alternative datasets. In all cases, we have compared the data against quarterly revenue growth or a related KPI such as same store sales. The Sentieo Mosaic Index (alternative data composite) is used to assess divergences in alternative data trends versus Street estimates. (Below each chart is a link to the interactive version of the graph if you want to dig deeper.)

As consumer behavior shifts more and more towards digital, indicators like these datasets have become more predictive of tech and consumer company results.

To see all 11 of our top stock picks for 2019, please download our free report here.

Our 2019 Picks

Lululemon (LULU)

Megatrends: Activewear/Casualwear, Fitness, Technology in Clothing

Lululemon essentially created the category of yogawear, and has leveraged this position to become a leading player in the growing casualwear revolution. As the world sheds suits and ties for more comfortable clothing, LULU is a prime beneficiary. As more folks understand the health risks of the modern sedentary lifestyle, they are seeking ways to be more active. LULU is also a leader in technology forward clothing, deploying its years of R&D and millions of pairs of yoga pants shipped to push the envelope on new synthetic fabrics that provide moisture wicking, odor mitigation and better fit in its products.

Despite these powerful megatrends, LULU struggled from 2012-2017, with revenue doubling but EBITDA growing only 33% over the same period. After 15 years of rapid growth, it entered 2012 in a position where it was over-earning and could not keep growing sales at the same margins without serious delivery, quality and brand issues. After spending five years addressing these issues, LULU returned to margin accretive accelerating growth in 2018 and the stock responded, up nearly 100% at peak. Despite a stellar Q3 print, the stock is now down 25% from peak through a combination of disappointing guidance and a brutal tape for momentum and growth stocks.

We believe that management was very conservative with their Q4 guidance, especially given the market volatility. However, we believe that the business momentum remains intact. Overall, U.S. holiday retail sales were very healthy this year, with Mastercard Advisors reporting 5% total growth. Sentieo’s Mosaic Index data suggests this upside was magnified in LULU’s results. Management stated that same store sales were running above guidance when they reported in December and we believe comps accelerated throughout the holiday season. LULU will attend the ICR conference in mid-January and we expect a strong holiday report ahead of the conference.

In the chart below, we compare LULU’s same store sales growth year-over-year (black line) against the Sentieo Mosaic Index’s prediction for same store sales growth (blue line). This prediction, which is created from a regression against alternative datasets, should be used as a directional indicator. Since we are dealing with (1) a small number of quarterly points in our regressions and (2) underlying datasets that can be volatile, we recommend that investors focus less on the magnitude of each point and more on the directional changes. For example, the LULU Sentieo Mosaic Index moved in the same direction as same store sales in 7 of the last 8 quarters. Consensus same store sales estimates (dotted portion of the black line) are calling for a deceleration in the Jan quarter. The Sentieo Mosaic Index is calling for a major positive inflection, however, indicating a potential positive surprise.

Interactive chart

LULU has many levers to continue fueling its current sales momentum. The business has typically been female-driven; however, the men’s business is just hitting its stride and is currently growing 100% year over year. The female/male mix is 78/22 today but there is no reason to think this can’t get to a 65/35 split in the coming years, especially as it continues taking share from male-focused peers like NKE and UA.

LULU has the potential to double its store base in North America, which comprises 88% of sales today. Equally compelling, over the next decade it should be able to replicate its North American playbook in Europe and Asia, providing massive open-ended growth. In 10 years, it’s not unreasonable to think that its geographic mix will be 50/50 North America/Rest of World (ROW), compared to 35/65 for NKE today.

For this open-ended growth, LULU is a very cheap stock, at only 27x 2019 consensus numbers that we think are much too low. On an absolute basis LULU’s $18B market cap is modest compared to NKE at $118B and ADDY at $43B.

 

Nintendo 7974.JP or NTDOY (US Pink Sheet ADR)

Megatrends: Online Gaming, AR/VR

Nintendo creates video game hardware and software. NTDOY’s newest gaming console, The Switch, appears to be a growing success that the market has not yet discounted. The Switch was released in March 2017 and was sold out for holiday 2017. Inventory finally reached in stock positions this summer and sales have slightly disappointed in the last couple quarters, which are seasonally less important. These small sales disappointments together with the Q4 market pullback has taken NTDOY from a spring peak of $55 to $35 today. Nintendo’s most popular game franchise is Super Smash Bros, and the Switch version was just released in early December.

Our Sentieo Mosaic Index data suggests that this release, timed perfectly for the holiday season, appears to be driving a huge uptick in Switch sales, inline with Street estimates. Furthermore, using Japan as a leading indicator for the rest of the word suggests significant additional demand upside in the rest of the world in 2019. While the graph below doesn’t suggest a large Q4 beat such as most other graphs in this piece, we believe in-line numbers are enough for a significant stock move, as the buyside expectations are below consensus numbers.

Interactive chart

While the Switch is the key driver of near term earnings upside, there is much more to be excited about. Nintendo is a leading Japanese company and tends to be conservative in its speed and approach compared to western peers. This means that it has only just released micropayments for its online game worlds over the past year, whereas western peers like ATVI, EA and TTWO have done so nearly a decade ago, with this business generating ~50% of bookings, according to analyst estimates. Furthermore, NTDOY has a stable of classic characters with deep audience affinity such as Mario, Luigi, Yoshi, Donkey Kong, and Pokémon* that are finally being leveraged for movie and TV content. In this sense NTDOY reminds us a lot of Marvel Studios right before it was bought for a song by DIS, leading to the ubiquitous Marvel Superhero content today. Finally, NTDOY is in a privileged position for the coming Augmented Reality / Virtual Reality (AR/VR) wave. NTDOY owns ~20% of Niantic Studios that touched off the Pokémon Go craze in 2016 that doubled NTDOY’s stock price. A deeper slate of AR/VR content releases from both NTDOY and Niantic are expected over the next 18 months, and The Switch also has meaningful AR capabilities that NTDOY has teased with its recent Switch Labo release.

NTDOY offers all this upside at very reasonable price. At 14x Calendar 2019 EPS, it trades at a 25% multiple discount to peers like ATVI and EA despite better revenue growth. It also sports an eye watering 3.7% dividend yield and at a $17B EV it is also relatively small compared to peers and the valuation it will be able to grow into over the next few years.

*NTODY owns 33-50% of Pokémon but appears to exert significant majority-like control

 

LendingTree (TREE)

Megatrends: Online Leadgen, Comparison Shopping

LendingTree is a lead generation business that matches consumers looking for financial products with financial providers. TREE built its business on connecting homebuyers with mortgage lenders, and has used this market share and know how to steadily expand and acquire into new financial service verticals, including student loans, credit cards, auto loans, small business loans and personal loans.

As the market leader for financial leadgen, TREE has been a huge beneficiary of customers switching to the online channel to comparison shop for financial products. This has propelled the stock from $4 a decade ago to $250 today. TREE’s average P/E multiple has been 50x over the past three years, as annual revenue growth has averaged 55% over the past three years. However, TREE’s stock suffered in 2018 as mortgage rates began to climb and the mortgage origination market slowed substantially for the first time in this cycle. With its high multiple, the stock suffered disproportionately, falling from a high of $400 in spring to a low of $200 in the fall despite only one quarterly miss and annual sales and estimates that have continued to rise.

We believe TREE is well positioned to rebound in 2019. As the fed has turned dovish over the past month and global growth and inflation prints have come in below expectations, treasury yields have retraced much of their 2018 surge. Mortgage rates are now coming down accordingly, with rates flat YoY for the spring selling season for homes.

In addition to improvements in the mortgage market, TREE has successfully diversified its business model. In Q3 mortgage revenues comprised only 28% of total revenues, down from 89% in 2013. In addition, TREE announced the acquisition of QuoteWizard in November, giving it a beachhead into the high growth insurance lead-gen market and dropping its mortgage concentration to 22% of total revenues.

Our Sentieo Mosaic Index data is also highly supportive of a rebound. The strong Q4 trend inflection after a year of declines gives us great confidence in our thesis.

Interactive Chart

At only 32x forward EPS, TREE is trading close to its cheapest level in years. The street expects 30% top and bottom line growth for 2019 which we believe is highly conservative, especially since 60% of this growth is inorganic from the QuoteWizard acquisition. As the power of TREE’s diversified model begins showing through in 2019 we believe the multiple can expand substantially and the stock can move past previous highs. Finally, with 27% of TREE’s float short the stock, the stock’s move is likely to be amplified on any good news.

To see all 11 of our top stock picks for 2019, please download our free report here.

Using Sentieo To Quantify the Upcoming Lease Accounting Changes

A major change in lease accounting reporting for public companies is coming up in 2019 (for fiscal years starting after December 15, 2018, to be precise). In essence, leases will be recorded on the balance sheet, resulting in an increase in both assets and liabilities. The change enhances the comparability of balance sheets between companies in the same industries that choose to lease vs. own. For the accounting enthusiasts, we recommend PwC’s 316-page pdf Guide to Lease Accounting and EY’s 397-page guide.

What can analysts using Sentieo do to be better prepared for 2019? We recommend the following DocSearch query for an efficient update on company estimates of the impact within your coverage universe: ASU 2016-02 BEFORE250 (million OR billion)

This query will search for the new standard mentions before a numerical disclosure (up to 250 words). Below, we’ve highlighted a few of the results from our own searches.

Walmart Stores (WMT) “estimates total assets and liabilities will increase approximately $14.5 billion to $16.5 billion upon adoption, before considering deferred taxes.”

View larger.

Ross Stores (ROSS) is indicating around $4 billion.

View larger.

Dollar Tree (DLTR) is looking at a range of $5.5-$6.5 billion.

View larger.

Besides retail, we are seeing more substantive changes coming in transportation.

Union Pacific (UNP) is looking at around $2 billion impact.

View larger.

Hawaiian Airlines (HA) will be adding around $500 million from its aircraft and engine leases.

View larger.

XPO Logistics (XPO) has around $2 billion in operating leases.

View larger.

 

To find out more about how the Sentieo platform can make your research process better, get in touch with us here.

Balance Sheet Seasonality Visualization

One of the most fundamental questions in financial analysis is understanding business seasonality. Seasonality is not always a yes-or-no question, but rather a continuum: some business are distinctly seasonal, and some less so. We all “know” that most tax preparation happens at a certain time of the year, while retail stocks vary quite a bit. But while revenue/Income Statement seasonality is “obvious,” in this post we will focus on Balance Sheet seasonality. We will use Ulta Beauty Inc. (ULTA) as an example.

As is the case with many retailers, ULTA skews towards Q4 (FYE is January). We can see how revenue and EBIT spike in Q4, but how does this affect Balance Sheet items? (chart viewer)

Let’s start with Cash and Equivalents on the Balance Sheet. The YoY balance will vary (for example, ULTA has used cash for share buybacks) but we can see that cash is at a low point in Q3 and spikes in Q4: the holiday season sales in Q4 build-up cash, while the inventory build-up into Q3 for the holiday selling season depletes cash. Unlike a B2B business, revenues translate to cash fairly quickly: consumers pay for their purchases in cash/card so there are no material Accounts Receivable. (chart viewer)

Speaking of Inventory on the Balance Sheet, we see the seasonality there, too. Inventory YoY increases as the company has more stores, but comparing Q3 to Q4, we can see the build-up and draw-down over Q3 and Q4. (chart viewer)

Since Inventory levels are related to sales, we can see the seasonality when we compare Inventory to Sales using the Days Sales Outstanding metric. We see the distinctive build-up into Q3 and then drop-down in Q4. Adding a 4-quarter moving average shows us that the company has gotten more efficient recently as managing inventory. (chart viewer)

Since ULTA is building up inventory into the holiday shopping season, we would expect that we can see a seasonable increase in Accounts Payable, as vendors ship product into Inventory but are not paid fully right away. The spikes then drop by Q4 end, as bills get paid. (chart viewer)

Similar to the Days Sales Outstanding in relation to Inventory, we can see the seasonality in Days Payables Outstanding in relation to Ulta’s payables. (chart viewer)

Our final highlight is Property, Plant and Equipment (PP&E) on the Balance Sheet: it also exhibits seasonality. In Ulta’s case, this line item grows through capital expenditures, mostly for new stores. We can see that how the balance of the account flatlines in sync with the selling season: the company opens very few stores during the main selling season, and then ramps back up into the new year. (chart viewer)

If you are interested in how the Sentieo platform can make your research process more efficient, please get in touch.

What Equity Analysts Need To Do About “606”

One of the most substantial recent changes in public company accounting is ASC 606: Revenues from Contracts with Customers. The changes affect a wide range of public filers, from SaaS companies to real estate managers, to fast food franchisors. This post will focus on what analysts can do to efficiently see what has changed in their coverage universe. For the accounting enthusiasts who want to know more about the changes themselves, we recommend KPMG’s 1,100-page implementation guide.

As the implementations have been rolling in the filers’ SEC filings, we find that two Doc Searches can help analysts find the adjustments provided by the companies.

For income statement changes, we use IN:TABLE search for revenue within 200 words of “606.” In Sentieo, analysts can search specific company documents, docs from several companies (tickers entered manually or in a saved watchlist), or general search (which can be modified by factors such as market cap, headquarters location, specific filing forms, sector, and many others).

For balance sheet changes, we use IN:TABLE search for equity, within 200 words of 606.

Using these searches enabled us to find what we need, in a fraction of the time that older search methods require. Here are some of our findings.

Intuit (INTU), parent of QuickBooks and TurboTax, went from a loss to almost break-even in its third quarter under the new standard.

SaaS company AutoDesk (ADSK): we see that both revenues and the closely watched ARR numbers are affected.

Dunkin’ Brands (DNKN), franchisor of Dunkin and Baskin Robbins, analyzed the applicability of 606 to its revenue streams (franchise fees, advertising fund fees, products, rents), and Sentieo provides a detailed breakdown.

Our balance sheet search mentioned above picked up some notable changes, too.

Commercial real estate broker and manager Jones Lang LaSalle (JLL) saw several changes in its balance sheet, notably the creation of several new line items related to the adoption of 606.

SVB Financial (SIVB), parent of Silicon Valley Bank, also reported major changes in its accounts receivable and its deferred revenues on its balance sheet.

Finally, telecom Sprint Corporation (S) saw balance sheet adjustments in a few categories, including the addition of $1.2 billion of customer contract acquisitions on the Assets side, and a $1.3 billion swing from negative accumulated earnings to positive.

If you’d like to learn more about how using Sentieo can help your process, get in touch.

Basic Forecasting of Revenues for Unit-Based Businesses (Using Chipotle as Example)

There are several approaches used in forecasting financial metrics for publicly traded businesses. In this post, we will focus on revenue forecasting for businesses that provide “unit-based” metrics.

What are unit-based metrics? Broadly speaking, these are the fundamental revenue-generating blocks of the businesses. We see them reported in different forms. For example, in retail and restaurants, the units are the actual physical locations. We also see unit-level metrics in subscription-based businesses from movie streaming giant Netflix (and the widely followed “subs” number) to home alarm company ADT to SAAS companies (where reported unit-based metrics include customer acquisitions costs, CAC, and churn.)

We will look at one example to illustrate our approach.

Let’s take restaurant chain Chipotle. Deriving a revenue forecast for the full year next year is relatively simple: we need to know the number of restaurants that the company will have “mid-year” (since we assume no seasonality in openings), the current average revenue per store (a number that is either provided or can be derived), and a certain level of “comparable sales” growth, generally in-line with current trends. Usually management will provide their annual guidance when they report Q4 but, until then, we can have a reasonable estimate.

In the last 8-K with the Q3 results that Chipotle filed, we find the following information:

Chipotle started the year with 2,408 units and expects that it will end the year “at the lower end” of the 2018 guidance range, or, about 130 extra units. We also see that about half were opened by Q2, indicating no seasonality. Our conservative estimate for 2018 year-end units is 2,538.

For 2019 then, since we have the guidance already, we can take the midpoint of the 140-155 guide, or 148 units. This will leave us with a year-end 2019 forecast of 2,538+148=2,686 units.

However, remember that this is the year-end number: not all of these restaurants will be generating full-year revenues, so for our annual 2019 revenue forecast, we will take half the growth, or 74 units. Our “mid-year” forecast for units is now 2,538+74=2,612 units.

We also need to know how much revenue each unit is generating, also known as AUV. Chipotle does provide this number: we can see that for the last quarter, the LTM number is $1.98 mm.

However, the AUV number is not static: Chipotle, and many other restaurants and retailers, provide a “comparable store sales” growth number every quarter. While there is no set industry standard, this number is usually derived from units that have been open for at least a year. We can see that last quarter the number was +4.4%, but also that the number has moved up and down quite a bit historically as the company recovered from its food safety issues, as we can see in Sentieo’s Plotter of this particular KPI.

We can set a range of 2% to 5% for 2019, and now we have all the assumptions that we need.

They are 2,612 units selling $1.98 mm on average, times 2% to 5%. Our revenue range estimate for Chipotle then is 2,612 x $1.98 mm x 1.02 = $5,275 mm on the low end. Our high end estimate is $5,430 mm. We can see that this is not far from the current Street estimate for Chipotle’s 2019 revenues, as listed in Sentieo’s Equity Data Terminal.

What will make a real difference here is knowing whether the unit openings will be front-loaded, even, or back-loaded, as well as current comparable sales growth trends. We might not get additional clarity on that until the report. However, we can try to estimate the real-time comparable sales trend using Sentieo’s Mosaic alternative data composite index, which shows strong current KPI trend so we are comfortable being above the Street estimate.

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RGA Investment Advisors Saves Hours with Sentieo’s Document Search and Research Management Features


Challenge

The desire for a more powerful document search and the need for document tagging and annotation abilities were the primary reasons that the RGA team wanted to switch to Sentieo.

Prior to Sentieo, they were using Bloomberg. They have since removed a Bloomberg license, as Sentieo has rendered it redundant.

“Nothing else on the market came close to offering us a streamlined and collaborative platform for reviewing and annotating company documents and transcripts. We were intrigued by Sentieo’s impressive capability to pull as-reported data into Excel. A web-native, cloud platform was a big plus.” – Jason Gilbert, RGA Investment Advisors

 

Solution and Results

Jason’s partner Elliot found Sentieo through recommendations from like-minded investors discussing investments on Twitter. Sentieo’s powerful document search functionality was particularly appealing, along with the Sentieo Notebook highlighting and note-taking capabilities.

 

A Typical ‘Day In the Life’ with Sentieo

Jason wakes up around 5:30 am and starts the day reading through a vast array of morning digest emails and the WSJ via his iPad. Afterward, he logs into Sentieo to check his followed tickers and peruse any news that he may have missed. During earnings season, he’ll generally spend more time in Sentieo Document Search looking through earnings transcripts. If he sees that Elliot has already annotated a given transcript, he’ll generally access it through the Sentieo Notebook and use Elliot’s highlights as a guide.

For Elliot, no two days are alike, but he almost always starts by catching up on news in the morning:

“I have Sentieo email alerts setup for every stock in our portfolio and for our primary watchlist…That gives me something to look through every day before moving onto my primary research objectives.”

When screening for ideas or after being recently introduced to a stock, Elliot reads the most recent filings and 10-K, highlighting and annotating along the way. He runs document searches for key terms of interest and gathers some historical context. When building models, he uses Sentieo to extract tables directly from the 10-Q and 10-Ks. Like Jason’s, a good chunk of Elliot’s day is spent reading and marking up transcripts in Sentieo during earnings season and conference season. Jason and Elliot find themselves using Sentieo’s Document Search, Notebook, Watchlists, Alerts, and Equity Data Terminal the most frequently throughout their workdays.

 

Document Search and Excel Modeling

Elliot’s favorite feature is Document Search because it’s “so simple and easy to use and invaluable in numerous contexts.” Document Search and the Notebook are “without a doubt” Jason’s favorite features. They both run keyword searches multiple times a day.

 

Watchlists, Alerts, and Redlining

Elliot’s favorite feature is Document Search because it’s “so simple and easy to use and invaluable in numerous contexts.” Document Search and the Notebook are “without a doubt” Jason’s favorite features.

They both run keyword searches multiple times a day.

The team uses watchlists to track the companies tickers that interest them, opting into “crucial” email alerts for new documents and price news. They also heavily use Sentieo’s redlining comparison tool regularly during big filing seasons, and especially on 10-K’s.

 

The Notebook (Research Management System)

Jason and Elliot use the team note-sharing and collaboration features within the Notebook to share ideas and keep each other up-to-date. Elliot’s second favorite Sentieo feature is highlighting because “it’s fantastic to have all my markings stored in one place, neatly.” They both use document highlighting and annotation tools multiple times a day.

 

Equity Data Terminal

“It’s amazing how much cleaner this data is presented in Sentieo versus in Bloomberg. It was easy to overcome my inertia here.” -Jason Gilbert, RGA Investment Advisors

Jason uses the Sentieo Equity Data Terminal multiple times a day to check company summary pages, which display everything from the company description to valuation and price targets, historical charts, estimates, and much more.

“My biggest time savings come from having neat sorting and access to information in the Sentieo equity data terminal, and from easy recall of key things that I may have noted through the Sentieo Notebook.” – Elliot Turner, RGA Investment Advisors

 

Mobile

Jason uses the iPhone app often, mainly for staying updated on his followed tickers.

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