The Sentieo Dashboard: A Customizable Home For Your Research

Modern investment processes require the consumption of vast amounts of information from disparate datasets, including news, documents, financials, and your own data. Since these datasets are often living in different places, you might be incurring an accumulation of switching costs that is losing you hours of productivity per week.

Sentieo’s new Dashboard aggregates relevant information across datasets so that you don’t miss key information or lose efficiency from switching contexts to find information. This new interface is comprised of widgets that you can customize to fit your workflow. Whether you are using Sentieo for documents, note-taking, financials, or alternative data, you will want to utilize your Dashboard.

Prices, Financials, Thesis and Alternative Data

The Price Monitor helps you prioritize your portfolio down to your most important companies or tickers. In addition to providing prices and financials, the Price Monitor integrates your alternative data and custom thesis data for a truly cutting-edge process.

For the first time, you can figure out which of your watchlist companies have upside to your own price target, which are trading cheap relative to historical multiples, and which show sentiment at all time highs!

Documents, Saved Searches, Notes, and News

With so much information disseminated across documents, broker research, news, and tweets, it’s really easy to miss pieces of information that could impact your decision making. The Dashboard integrates all of these content sources so that you’re getting the best insights.

A New Way to Work

Gain productivity by accessing the data (from multiple sources) that is most relevant to your entire portfolio. In addition to surfacing the new insights you find from accessing a broader range of data, you will also get back hours of time savings. Try out Sentieo today.

Watch our Webinar all about Dashboard!

We’ve Added 650+ News and Industry Sources To Sentieo’s Document Search

Sentieo’s industry-leading Document Search welcomes a major new addition: over 650 sources from a leading news provider, inclusive of mainstream publications and industry journals going back 2+ years. This new feature will be available to all current clients as a 30-day trial: please contact your account executive or customer success manager for details, or just contact general support.

We’ve expanded Sentieo’s existing searchable document database that already includes global corporate filings, transcripts, presentations, broker research, and any internally generated documents or notes that you’ve uploaded, such as private deal documents and thesis write-ups.  

These news sources are now an integral part of the Document Search function, with the same full set of advanced functionalities that our clients praise. The integration also means that you get a fuller, global news source coverage for the companies you follow, all in one convenient location.

Full AI-Driven Search: These news sources are (1) fully indexed and searchable with our full suite of synonyms, proximity, Boolean, and other specifications, as well as our improved Relevance scoring, and (2) they go back two years. Users also have granular control over which sources they want included, just as they already do with different filing types or with broker research.

 

Document Ticker Tagging: The articles are auto-tagged with the tickers you are researching, and they work with your existing Watchlists, saving you hours that you may have previously spent organizing.

 

Integration With Sentieo’s Notebook: just like with any other corporate document, the news and industry journals’ text is highlightable, already tagged with the primary ticker, and ready for your additional tagging/annotation, and sharing. Your highlight will then be fully searchable by ticker and tags, just like a highlight from a filing or a transcript.

 

Saved Search Alerts: Sentieo’s saved search alerts already solve a major pain point for knowledge workers by automating and pushing relevant information as it becomes available. Alerts from the newly added sources work just like all other saved search alerts on the platform.

Here are the industries that our partnership covers:

  • Consumer Discretionary
  • Consumer Staples
  • Energy and Utilities
  • Financial & Real Estate
  • Health Care
  • Industrials
  • Macroeconomic
  • Materials
  • Technology & Telecom

Plus:

  • Regulatory Sources
  • Press Releases

 

You can download the full source list hereAgain, please contact your account executive or customer success manager for details, or just contact general support. 

We’ll be having a webinar on April 10th (sign up here) that will cover everything you need to know about searching through these additional sources in Sentieo.

And don’t miss our Table Explorer webinar this Friday!

 

Introducing Table Explorer: Enhanced Table Extraction and Analysis Across Time

At Sentieo, we are always striving to give fundamental managers the tools to make better decisions faster. Table Explorer (TableX), the next iteration of our suite of table analysis tools, was developed with this vision in mind.  

Model-building is an essential part of the investment process of fundamental managers. Models afford them the ability to develop a deep and differentiated understanding of the machinery of a business, via the interplay between revenues, expenses, and capital accounts. Models also allow for a parameterized flex of assumptions for establishing a differentiated view on a company’s earnings prospects and — by extension — the value of the business overall.

While modeling is critical to the process, the investment world is increasingly driven by algorithm-enabled trading and the large data science departments of multi-billion-dollar hedge funds. Therefore, managers are obligated to pay close attention to the areas where modeling and data visualization can happen faster, and more accurately.

At Sentieo, we believe in an augmented investing approach that uses machine learning and data science to add value to a human-driven investing process.

We’ve rebuilt our table suite from the ground up using a machine learning driven approach validated by trained financial analysts. The new TableX enables Excel-style visualization directly from company filings in a dual-pane window, showing both the Excel data and the source document side by side, enabling an immediate audit of every number.

We’ve added the ability to quickly plot discrete line items in the same window, enabling simple thesis exploration either as a complement or antecedent to further model work. We’ve also added inline calculations to impute fourth-quarter numbers from 10K data, common size analysis, and YoY or QoQ percentage changes. And we show a table of contents of all tables in a document to speed up navigation through company filings.

table explorer table extraction

Then, we integrated TableX with our Plotter app, to further facilitate regression analysis of fundamental metrics like revenue with other metrics like exogenous macroeconomic data or website traffic.

We also integrated TableX with our Notebook app to facilitate thesis sharing among a team or when reporting to a portfolio manager. These workflow tools help money managers develop ideas faster and stay more organized with their ideas.

Lastly, we facilitate export to Excel so that the audited financial data can be used as a backing database for your own models.  We include links back to the document in the export, and in future releases, we will enable “one-click” refreshing of base data sheets in models.

The end goal of Table Explorer is to give fundamental money managers the tools to observe-orient-decide-act faster, in order to win in a high velocity investing landscape. Get a free trial of Sentieo here, or tell us what you think of the new TableX!

4-Minute Videos on How to Use Table Explorer:

Navigating Table Explorer:

Common Size Analysis in Table Explorer:

Get a free trial of Sentieo here, or tell us what you think of the new TableX!

Introducing Sentieo Relevance Search

Sentieo’s overarching mission is to augment human decision-making through the application of the latest technologies. We are proud to demonstrate this with our most recent improvement to the platform:

Sentieo Continues to Lead In Search Tech with Relevance Search

Clients already love our smart Document Search, which offers:

  • tens of millions of documents, searchable across many dimensions
  • 1000+ synonyms/acronyms
  • extended Boolean operators and proximity
  • in-section/in-table/in-footnote searches
  • saved search alerts
  • extensive document tagging including sell-side report classifications
  • search statistics for quantifying trends
  • seamless integration into notes, and much more.

The newly released Relevance scoring algorithm, custom-built by our global team of data scientists, will surface documents most likely to be relevant to your search based on multiple factors derived from the statistical properties of the documents and the queries. For example, some of the factors considered are density within the document (inclusive of synonym matches), clustering, and recency. Relevance scoring is based on true machine learning and will only improve as Sentieo DocSearch “learns” over time. It gives you, the user, more time to focus on analysis where you add value, rather than on tedious searches.

But we didn’t stop there. Another new sorting option is Sorting by Hits — because we know that sometimes you need the most recent documents at the top, sometimes you need the document with the most hits (mentions), and sometimes you need the one that is the most relevant. You can now toggle between these sorting options in one click, which allows you to compare the results before you dive in. You can also sort by document size and by ticker for your multi/all ticker searches. You are in control of the format that fits your needs.

Users will find relevance searches suitable for broad initial overviews of topics: your search might pick up sector primer reports from the sell side, or a very topical deck from a corporate. Users more familiar with a topic might still prefer the reverse-chronological order of documents for the latest on the topic. And, for people getting specifically into details, we have the specialized searches, such as in-transcript, said-by-the-CEO, or in-table searches that let you find the numbers around your topic within seconds.

relevance

And there is still a lot more to come. The 2019 Document Search product pipeline is better than ever, and you will see additional applications of AI/ML coming soon!

 

[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!?

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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.

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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.

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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.

Check out the following video interview below, which features Sentieo’s Senior Data Scientist, Jed Gore, discussing these findings.

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.

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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.

stock picks 2019

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.”

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

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

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

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

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

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

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