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

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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.

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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

 

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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!

 

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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

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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

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Guide to Using Alternative Data In Equity Research to Deliver Alpha: The 13 Stock Picks for H2 2019

Alternative data has become a mainstream source of data for investment managers, with up to 50% of funds now using it as a part of their research process. The question of how to get value from alternative data is no longer about access to the data sets; there are hundreds of vendors offering alternative datasets or services. Rather, it is a question of how to make that data useful to every equity analyst driving investment strategy.

Download the Picks Whitepaper now.

Large funds have made multi-million dollar investments in data science teams and big data infrastructures in an attempt to win an alternative data arms race that is predominantly driven on the hope of finding the needle in the haystack, big bet investment. However, with our 13 Alternative Data Stock Picks, we have proven that the most successful approach to alternative data is to directly embed it into the core research workflow for analysts, effectively democratizing alternative data.

This guide outlines how we used the Sentieo platform to pick our initial 11 stocks from the first half of the year, the performance of these stocks, and our Alternative Data Picks for the second half of 2019.

How Sentieo Makes the Alternative Data Stock Picks

For both the original Sentieo 11 and the new Sentieo 13 picks, we used the same methodology:

We started with the Sentieo Mosaic screen, where we looked for:

  •     A high correlation between the alternative data composite and revenue growth and/or KPI
  •     Large acceleration in the alternative data (our proxy for end-user demand) versus the consensus expectations

These correlations and significant changes in trends are what drive analyst usage of Alternative Data.

Usage of Alternative Data in Mosaic is easy to customize and is completely transparent: users can see basket weights, as well as data set performance for different time frames.

Our next step is to marry the broad screen results with our team’s 60+ years of collective fundamental, qualitative investing experience. Sentieo augments human decision-making: the charts and the stats will not give you the “why.” We do not adhere to specific investment style boxes, but we do look for revenue growth as the single largest driver of long-term results. No business ever shrank its way to greatness.

The ideal picks have strong revenue growth because they:

  •     Operate in high-growth industries, supported by long-term secular megatrends
  •     Are the leaders in their respective industries
  •     Tend to be underpriced relative to their growth rates

As a result, this set of long ideas has relatively high near-term P/E. We also looked at earnings momentum through a combination of the classic earnings upwards revision, plus our alternative data Mosaic index. Most alternative data sets do come from consumer-generated data, and most of our picks are in these two broad groups. As more and more consumer behaviors shift to digital, we expect to see the alternative data sets become more and more predictive.

Sentieo 13 H2 2019 Alternative Data Picks

Our latest set of picks is based on exactly the same methodology as before, but we have expanded our focus a little wider. Note: These are not stock recommendations, and we are sharing them to show the power of the Sentieo platform in bringing together the power of a complete financial research platform with both traditional and alternative datasets.

1) SNAP

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2) PLNT

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3) TWTR

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4) CROX


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To see Sentieo’s 9 other picks, download the full whitepaper here.

We’ll also be discussing all the picks and how we made them during our upcoming live webinar, featuring Sentieo’s CEO, Alap Shah. Register here.

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Disclaimer

The content of this report references opinions and is presented for product demonstration purposes only. It does not constitute, nor is it intended to be, investment advice or recommendations. Readers should assume that Sentieo staff members hold direct and/or derivative positions in all securities mentioned, and may transact in any and all of these securities, at any time, without notice. Seek a duly licensed investment professional for investment advice. Sentieo is not registered in any investment advisory capacity in any jurisdiction globally.

The 7 Habits of Highly Effective Analysts: The First 3 Habits

Why Make Effectiveness A Habit?

At Sentieo, we care about helping equity analysts become more successful. It is core to our mission as a company. While we have broadened our purview to finance and general knowledge professionals, we still have buy-side in our DNA. Our CEO and co-founder, as well as many of our executives and product owners, were analysts in their previous lives.

The mountain of data that analysts have to process to find nuggets of unique truths is growing more massive each year, leading to information overload. In the face of these challenges, we still believe there is a role for human analysts, augmented with better technology and process to help them fight off those challenges. Thus, we’ve updated our most popular whitepaper, The 7 Habits of Highly Effective Analysts. Read the preview below, or download the whole thing!

Looking Beyond The Echo Chamber For Ideas

The world of finance tends to be an enormous echo chamber where even ostensibly intelligent participants can get caught up in manias and panics of groupthink: how much of your communication is with fellow buy-siders versus outsiders? We actually found it more instructive to steal ideas from outside of finance.  

All three of the above have similar constraints on resources and relevance to the ultimate research output. Of course, we don’t think there is any better authority on general personal effectiveness than the original Franklin Covey book (The 7 Habits of Highly Effective People), which also obviously served as an inspiration for this paper.

 

Three Individual Equity Analyst Habits:

 

1) Be Proactive: Optimize Your Coverage Universe and Expand Your Circle of Influence 

You only have so many hours in a day. Valentine (book referenced above) and most major hedge funds recommend covering up to 40-50 stocks at most. If you can’t find a good idea — long or short — in those, you are doing something wrong. If you are trading hundreds of stocks a year, you are going too broad to have any edge over quant-driven processes (and probably paying too much in commissions). The only way humans will have a sustainable and permanent advantage in investing is by doing deep fundamental research. Fortunately, we at Sentieo are continuously working on building technology to augment, not replace, the human. The universe of thousands of stocks can be hard to narrow down. Luckily, Sentieo’s platform has a built-in powerful screener with thousands of categories just for that purpose. 

Use Sentieo’s Screener to filter down to the companies you care about.

Once they have a sensibly defined investment universe, effective equity analysis are ready to go deep. Buy side analysts have a wealth of broker research resources at their fingertips, but often drown in the floods of daily push emails. Your inbox is valuable real estate; the most effective Equity Analysts access information on a “pull” basis—on demand, instead of having their time and attention demanded by outside parties.

Analysts should approach search with a specific intent in mind, and gather as many relevant facts as possible. Sentieo enables this by bringing users access to the full spectrum of Broker Research inside our Document Search platform.

Drill down by document type (or even document section) to only get the information you want. 

 

2) Begin With the End in Mind: Don’t Miss the Macro for the Micro, or the Structural for the Near-Term

All too often, analysts get worked up about basis point moves and minor catalysts when they should be focused on the tectonic shifts under their feet. Think beyond the movements of macro model inputs like commodity costs and currency fluctuations. The shifts from major global trends, ranging from “Software Eating the World” to regulatory and political shifts can serve as either incredible headwinds or tailwinds to investment theses that can far outweigh any modeling considerations for the next fiscal year.

Models are only as good as the assumptions going into them, and no amount of Excel wizardry will help if, for example, you try to buy the dip in global mining stocks ahead of a Chinese slowdown, or you seek value in consumer packaged goods while well-funded startups (instead of stale brands) are gaining ground in every category.

Although one should constrain their coverage universe (see Habit 1), no stock is an island. Our platform allows effective equity analysts to quickly get a read across adjacent sectors, to stay informed through plotting market data and document mentions. 

Sentieo’s Search Summary Analytics give you insight into the most keyword mentions by Company, Document Type, Sector, and Region

 

3) Put First Things First: Maintain A Catalyst Calendar and Do The Work Well Ahead

Stocks often run ahead of earnings. It is not an infrequent occurrence for a company to beat, but trade down. This cues puzzled commentary from the Street about the “inexplicable” reaction, while a 30% pre-earnings run-up was ignored. Funds need to be sized up at the start of these pre-event moves, not just the day or week beforehand. The proactive analyst with this end goal in mind should maintain a priority list that includes a catalyst calendar, and should ideally organize their work around the profit potential.

Key catalysts to track include earnings announcements, product launches, and transactions. Sentieo’s Calendar can feed in from a user’s watchlist, and it makes the forward calendar plain as day:

Track your tickers using an earnings calendar in Sentieo, on both desktop and mobile.

These are just 3 of the 7 Habits of Highly Effective Analysts. To read about all 7, download the full white paper. 

 

Missed Our “Ask Us Anything” Webinar? Here Are Your FAQs About Sentieo’s Dashboard, EDT, Screener, and Mosaic

Last month at Sentieo’s New York office, our Head of Research, Nick Mazing, hosted a two-hour “Ask Us Anything” live webinar. Nick answered questions from Sentieo’s current and prospective users, covering the many functionalities of the platform. In this post, we will share a few FAQs about Sentieo’s Dashboard, EDT, Screener, and Mosaic features.

Dashboard FAQs

How do I customize my Dashboard to fit my personal research workflow?

The Dashboard is the highly customizable screen that a Sentieo user can utilize to:

  • Monitor multiple financial and alternative data metrics for a list of stocks
  • See internal work being done on positions
  • See any documents coming in (such as new press releases or sell-side reports)
  • Get alerts on their saved searches

Extensive customization allows this screen to be just the way you need it to be, and it lets you save time with one quick look over everything that matters to you.

Mosaic FAQs

Mosaic is Sentieo’s alternative data module. Sentieo takes several data sets and does correlations against past performance to determine the optimal weights of these sets in the “basket.” We are completely transparent: you can see past performance (R-squared and hit rate), and you can see the basket weights.

Can you show us a couple examples on how to use Mosaic?

In this example, we looked at the individual brands mapped for Capri Holdings (formerly Michael Kors), Abercrombie and Fitch. We also looked at how investors could have avoided a 90%+ loss in Blue Apron shares by monitoring the Sentieo Index.

How did you come up with the Sentieo 11 stock picks in January?

Nick went over the popular eleven stock picks that we released in early January, which did really well. We look at the performance of the list, the alternative data that was used, along with the fundamental “overlay.”

Equity Data Terminal (EDT) FAQs

Sentieo’s EDT has everything you’d expect in terms of data for an individual equity, plus a lot more.

How do I try online “consensus” income statement models in EDT?

Nick showed how users can quickly enter their own assumptions about future growth, margins and multiples, and compare against the consensus. This function enables fast idea pre-qualification, saving you and your team time to focus on ideas that could “work” at a first pass.

We show a specific example, Royal Carribean, where we use our own estimates for sales growth, gross margin, and multiples to arrive at an internal price target above the current.

We also looked at the estimates/surprises and seasonality of returns for Royal Caribbean (RCL).

How do I use Plotter to visualize this?

Nick went to Sentieo’s data visualization tool, Plotter, to analyze further. He explored revenue and EBIT seasonality, compared it to the actual underlying cash flow, and then he calculated EBIT margin along with some descriptive statistics.

Revenue, earnings and other estimates are a popular feature in Sentieo’s Equity Data Terminal (EDT). We go over one example of the beats/misses function along with the classic “estimates revisions” page that shows changes in estimates by year and rolling NTM. Finally, we show how to export these to Sentieo Plotter, where users can overlay many other data sets.

Screener FAQs

Sentieo’s Screener is very powerful. There are thousands of possible options to screen on: not just by widely available financial metrics, but also by CAGRs and percentile ranks inside these metrics. Nick showed his “Large Cap Quality and Momentum” saved screen to illustrate.

Want to try using Sentieo yourself? Request a trial here.