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.

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 post Guide to Using Alternative Data In Equity Research to Deliver Alpha: The 13 Stock Picks for H2 2019 appeared first on Sentieo.

We analyzed Thomson Reuters’ I/B/E/S dataset and looked at instances where analysts were unanimously bullish or bearish on a stock.  It turns out that analysts recommendations correlated strongly with share price performance.  However, there was one tiny caveat: the buys dramatically underperformed the sells in 2016.  The unanimous buys were up 4.5% while the unanimous sells were up 20.7% so a market neutral consensus portfolio lost ~16.2% last year.  It turns out that 2016 was a year where betting against the analyst herd paid off!

The chart above shows the distribution in share price performance between both cohorts. (The bullish group had outliers up +1618% and +1189% that are not shown.) While the winners in both groups performed roughly the same, the losers in the bullish group fell more than the bearish group.

One major factor behind the underperformance was the bullishness surrounding small-cap development-stage pharma stocks.  For healthcare stocks (which were mostly small-cap pharma names in our cohorts), the number of consensus buys outnumbered the sells by a factor of 13.7X versus a baseline rate of 2.24X.

Quick methodology notes

We looked at stocks where either (A) every analyst on a stock had a buy rating or (B) every analyst had a non-buy rating as of Dec 17, 2015.  A stock with 100% holds and 0% sells would fall in the bearish group since it had 0 buy ratings.  Our look at analyst ratings basically considers holds to be a polite way for an investment bank analyst to put a sell rating on a stock.

Please take our data with a grain of salt.  This was a quick analysis where we used share price performance instead of total return.  Performance ignores dividends, corporate actions, survivorship bias, and stock borrow/lending fees.  Where price data was unavailable or had errors, some stocks were omitted.

Bullish on biotech

The unanimous buy cohort was quite bullish towards the healthcare sector.  315 out of the 1031 unanimous buys were for healthcare stocks, while 23 out of the 459 unanimous sells were for healthcare stocks.  That’s 30.55% of the buy cohort compared to 5.01% of the sell cohort.  This was a very unusual imbalance.

Also note that the fixation over biotech has been growing over the past few years.  Since Dec 2012, healthcare stocks have grown from 20.77% of the buy cohort to 30.55% of the Dec 2015 cohort.  In that same timeframe, healthcare representation in the sell cohort fell from 5.80% to 5.01%.  The next most imbalanced sector was technology, which made up 12.42% of the Dec 2015 buy cohort and 7.84% of the sell cohort.  Technology was not nearly as imbalanced as healthcare.

Keep in mind that the “healthcare” stocks in our cohorts aren’t the Pfizers and Mercks of the world.  Due to small sample sizes, small cap stocks are significantly over-represented.  Small caps are more likely to have a unanimous consensus especially if there is only 1 analyst rating a stock.  (We could reduce the sample size problem by requiring a minimum of 5 ratings.  This would widen the performance gap for 2016.)  So, the “healthcare” stocks in the cohorts are actually mostly small-cap pharma stocks.

A historical perspective

2016 was an unusual year where the unanimous sells strongly outperformed the unanimous buys.   That hasn’t always been the case.  Suppose that the strategy is to hold stocks for 1 year.  Cohorts are chosen based on the unanimous buys/sells from the middle of December of the prior year.  Here are the historical spreads between the unanimous sells and buys:

2016: 15.5% (unanimous sells outperformed buys; contrarianism paid off)
2015: 2.8%
2014: -3.4% (following the herd paid off)
2013: -2.5%

Note that some portion of the spread has been driven by sector exposure to smaller biotech stocks.  We can use the equal-weighted biotech ETF XBI as a proxy for biotech.  XBI’s share price performance in the past 4 years has been:

2016: -15.7%
2015: 13.0%
2014: 43.2%
2013: 48.1%

So, biotech exposure partially explains the performance gap but does not explain everything.  Suppose that we removed all healthcare stocks from the Dec 2015 cohort used for the 2016 performance figures.  This would reduce the spread from 15.5% to 7.1%.

There’s another method for controlling for differences in sector exposure.  We can adjust the performance figures by grouping the stocks into baskets based on sector.  Then, we can change the weights on the baskets in the unanimous sell cohort so that sector exposure matches the buys.  The historical spreads become:

2016: 7.6%
2015: -2.8%
2014: -3.9%
2013: -1.3%

Removing distortions caused by sector exposure shows that the spread has roughly averaged out to zero over the the past four years (with perhaps a slight edge to the buys).  However, the unanimous buys have been on the wrong side of predicting where sectors have been headed.  Over the past four years, the sells have outperformed the buys by about 1.6% annualized if sector-weighting differences are included.  Due to limitations with the methodology, reliable conclusions cannot be drawn about whether or not analyst recommendations can help predict stock prices.  As well, the numbers here likely do not generalize to all investment analysts polled by I/B/E/S.  Prestigious bulge-bracket investment banks, star analysts, and independent research providers (that do not seek investment banking business) are likely severely underrepresented in the cohorts.

Will biotech giveth or taketh away?

What we can safely conclude is that 2017 looks like yet another year where biotech will be a major player.  30.87% of unanimous buys (from Dec 15, 2015) were for healthcare stocks- slightly higher than the previous year where that number stood at 30.55%.  There seems to be a segment of the analyst community that is unusually exuberant about biotech stocks.  It will be interesting to see whether or not their optimism becomes reality.

Some interesting links

Barber, Lehavy, McNichols, and Trueman wrote a paper “Reassessing the returns to analysts’ stock recommendations” that examines the value of analyst recommendations from 1986-2001.  Their conclusion?

Even with their poor performance in 2000-2001, for the longer (16-year) 1986-2001 period, the most highly recommended stocks still generated significantly greater average annual market-adjusted returns than did those least favored (2.44 percent as compared with -9.94 percent).  These relative returns reflect favorably on the long-term value of analyst recommendations, as long as the 2000-01 results are simply an aberration that is unlikely to be repeated.

The post Wall Street Consensus Trades Fell Apart in 2016: 16.2% Underperformance appeared first on Sentieo.

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The O&G industry reports tons of data in both volume and detail—from drilling rig and pressure pumping data to well production info. Looking for and analyzing all of this information for your investment ideas is a very necessary but time consuming process. Designed by buysiders for buysiders, Sentieo is the best tool on the market for leveraging technology to rapidly compress your research cycle and give you more time to generate true alpha insights.

In this post, I’m going to show you a glimpse into the world of oil & gas research using Sentieo—so that you can spend more time analyzing your the findings and try to come up with answers to questions such as:

Which E&P companies might be at risk of defaulting on their loan obligations?

Has an E&P operator you are following announced those new well results yet?

What would this company specific data would look if I plotted it against other metrics?

What are some ways I can use Sentieo to research industry trends?

What are companies are saying about break-even oil prices and well-economics?

How many drilled but uncompleted wells are in a company’s backlog?

Let’s get started.

Digging deep through SEC filings: Missed interest payments

Let’s assume you know nothing about the E&P space but want to take a look at companies that have missed interest payments. You can try running a simple search for ‘interest payment’ or ‘missed interest payments’, but this is likely to return too few or too many results. After doing a bit of reading I noticed that in most instances where a company mentions a missed interest payment, there will be some combination of the following words in the paragraph:

• grace period
• elect(ed)
• interest payment

I ran a search asking Sentieo to return all results where these words were within 30 words of each other. These were the results:

I sifted through a couple of the documents and took a few highlights, labeling these highlights under the custom label Grace Period. These highlights were automatically tagged and stored in my Sentieo Notebook for easy recall. In this view, I am looking at three separate documents where I applied the Grace Period highlight.  You can use these names as a possible starting point on finding companies that might be running into liquidity issues or are at risk of default.

Tracking well results 

Energy investors that have an interest in the Anadarko Basin will probably be closely watching Newfield Exploration’s ($NFX) STACK infill spacing pilot which, if successful, could add to their inventory and provide NAV upside. The first of two planned pilots, the Chlouber, is currently underway with a second pilot scheduled to be spud once the appropriate location is found. Both pilots should be online by the end of 2016.

You can easily keep tabs on the results of this pilot by setting up a Sentieo keyword alert.  Sentieo will send you an e-mail and/or in-app notification when there are new results for your search so that you don’t have to keep running the same search time and time again.

Let’s explore this by setting up a Sentieo alert for mentions of the Chlouber pilot:

Step 1) Pick your search term. You are allowed more than one word, though usually one word will do.

Step 2) Add any filters you would like to apply to your search such as a company specific ticker, sector filter, etc. In this case, I am including NFX as my ticker and Chlouber as my search query.

Step 3) Click on the floppy disk icon to the right of the query search bar

Step 4) Select your alert preferences.  Click Desktop and/or E-mail and then hit Save.

The next time Chlouber is mentioned in any press release, presentation, 8-k, 10-K, 10-Q, etc, I will immediately be notified by Sentieo.  This can be especially useful during earnings season when there are multiple press releases and earnings calls at the same time.

Creating charts using data extracted from filings

Let’s say you are looking at a 10-K or 10-Q and come across some interesting data inside of a table you would like to further explore. Thanks to our table extraction technology, you can quickly send any data from a table directly to our data visualization tool (Plotter) and layer in additional data-sets (such as your own .csv file) or traditional financial metrics.

Given the state of the current commodity price environment, the health and quality of a company’s balance sheet has become more of an important factor when looking at E&P companies, maybe even more so than production growth and asset quality (this was more of a factor in 2015).

Pick a handful of the top-performing E&P’s in 2015 and you’ll likely notice that a lot of the top performers had low net debt/EBITDA multiples vs their under-performing peers.

In this example below, we have a couple of different data sets being displayed all at once.

I used the Time Series function to crawl though all of Diamondback’s previous filings and create a composite table of their historical average price of oil per barrel (dashed red line) versus WTI crude oil prices (dashed blue line). I then added Price/Cashflow (pink line) and EV/EBITDA multiples (green line). Finally, I threw in stock price (yellow dotted line) and created my own hybrid series, Debt/Shareholder Equity (solid blue line), by dividing Debt over Shareholder Equity.

In this one chart, we can visualize the following:

Average selling price of oil trending down with the price of WTI

Leverage trending down over time

Stock price, Price/Cash Flow, and EV/EBITDA shooting up

The best part about this is that you can save any chart as a template which will allow you recreate this graph for other companies by simply entering the ticker. The sky is the limit on the number and different types of data sets you overlay with Plotter.

Researching industry trends

Document search is another great way to find out what companies and analysts are talking about in the E&P space. By performing an open ended search (not specifying a ticker), you can find out what all companies in the space are saying about your particular search term. I’ve included some relevant searches an E&P analyst might be interested in running below:

Theme: Companies continuing to reveal improving capital efficiency and the ability to do more with less (production beats, lower than expected operating costs, improving well performance, declining q/q capital spending).
Keywords: Further efficiency gains, cost savings, more with less, what inning, operational gains

Theme: Companies delivering strong performance on costs which helps contribute to EPS beats and help drive stronger capital efficiency. There are many factors contributing to the substantial improvement in LOE costs (reduced water handling costs, lower fuel and electricity costs, better uptime well performance, improved base production decline management, and reduced workover activity)
Keywords: LOE, reduced costs, cost savings

Theme: Future E&P revenues are a function of the selling price of the various hydrocarbon streams based on market pricing (before taking the effect of hedging into account). Everyone from analysts to banks to E&P’s have different assumptions of where commodity prices will be in the future, so it is important to understand what prices management is assuming when they talk about any forward outlook.
Keywords: Price deck 2017, strip pricing

Theme: Looking at the unhedged outspend for 2017 on strip pricing can give an estimate of the sustainability of the investment program in a lower for longer environment. Operators that are far outspending unhedged cash flow could be more inclined to revisit capital programs and investment levels. Companies with substantial hedge books to lean on are in a position of strength (relative), allowing for a more aggressive approach to operating and planning in this current lower environment.
Keywords: Hedge book

Theme: Duration of the current lower oil price environment continues to be the million-dollar question, not how low oil prices will fall or whether they’ll recover to a level required to grow supply again.
Keywords: non-OPEC supply growth, OPEC production, demand growth, supply growth, rebalance market

Theme: Many believe the longer this lower for longer oil price environment lasts, the more pressure there will be for companies to consolidate.
Keywords: M&A, consolidate, bid ask spread, acquisition, divestiture, non-core

Theme: Big gas oversupply situation
Keywords: Marcellus curtailment, Utica curtailment, natural gas storage, winter weather, Utica potential

Theme: Natural gas and NGL beats being a positive data point to many analysts, MAYBE suggesting that gas to oil ratios (GORs) are moving higher which could be a positive sign for the oil macro.
Keywords: GOR, gas oil ratio, gas beat, NGL beat

Break-even costs well costs and economics

The cost of producing a barrel of oil varies greatly across the country, creating a bifurcation between economic and uneconomic basins. Besides production and well performance, key inputs that impact a producer’s expected rate of return and analysis on whether or not drilling the well makes sense include factors such as drilling cost, completion costs, transportation cost, royalty fees, etc.

In this example, Continental Resources ($CLR) uses well break-even data provided by Evercore ISI. CLR’s top tier plays break even and generate an after-tax IRR at oil prices as low as $35.

Anadarko Petroleum ($APC), an independent explorer, recently commented that the current level of break-even oil prices is around recent oil prices (~$45) for a U.S. onshore project.:

You can also use document search to help collect relevant economic input data for your models such as production information, well costs, and other relevant items. In this example, I ran a search for well economics or well costs:

What is a company is saying about their drilled but uncompleted well (DUC) backlog?

Let’s take a look at Continental Resources ($CLR) and see what they have said about their drilled but uncompleted backlog. A very simplified explanation of how oil and gas is pumped out of the ground can be explained in two steps:

1)Drill a hole into the Earth with a drilling rig

2)Blast a bunch of water and sand into said hole (fracking) to create pore space through which hydrocarbons can flow through at a faster rate.

The steep fall of oil prices has caused many cash-strapped operators to post-pone step 2 described above because they are not incentivized to spend lots of money on completing these wells and bring online new production into a low commodity price environment. As a result, we have a buildup of these uncompleted wells known as DUC (drilled but uncompleted well). A rebound in oil prices could mean the possibility that a large number of DUCs are all brought online at once, causing a potential uptick in supply that could create a ceiling on oil prices.

Below, I have run a search for the words drilled but uncompleted or DUC. You can exclude the ticker if you want to see what all players in the industry are saying about these DUCs, but in this case I have gone ahead and limited the search to only CLR. These first 5 documents contained information which I thought was relevant, so I went ahead and highlighted excerpts from each document and tagged these passages with a DUCS label.

All of these highlights are then sent to your Sentieo Notebook, where they are automatically tagged and labeled appropriately. Here, I have applied a filter so that I can look at all of my CLR notes that reference DUCs

Hopefully some of these tips help you speed up your existing day-to-day research process, or have given you some ideas on new ways to use Sentieo for your E&P research. If you would like to speak with any of our Product Managers about anything you’ve read in this post, please send an e-mail to success@sentieo.com

To see how Sentieo can help with your earnings prep, simply go to Sentieo.com and sign up for a free trial. If you would like to continually receive content related to topics of interest in the markets, don’t forget to subscribe to the Sentieo Blog so that we can notify you of new posts by e-mail. 

The post Oil & Gas Research the Smart Way with Sentieo $NFX $FANG $CLR $APC appeared first on Sentieo.

For full transparency, we wanted to go through in detail our math on the subscription meal-kit industry, where all players are unlisted. This is where Mosaic starts to come in handy to estimate real numbers.

Guesstimating Blue Apron as the anchor

The latest delivery number for Blue Apron is 8 million meals/month (more than double the June 2015 rate of 3 million meals/month) That Fortune article equates it to a $960m run rate on an ASP of $10, but we are skeptical as customers never pay the full list price with the myriad referral bonuses and other promotions common in the business. We reckon the real number is closer to $9, which puts Blue Apron on a still-impressive $720m revenue run rate.

Hellofresh as the other end of the vector

Hellofresh’s 2015 pre-IPO numbers were $290m globally, of which 60% is US revenues. That’s $150m for calendar 2015, over which the company quadrupled, so they exited with closer to $375m run rate in US revenues.

Using Mosaic and two known points to triangulate

We don’t know much about Plated and the other smaller players in the subscription kit space, but fortunately, they all run similar business models in the most transparent traffic market in the world. We used Mosaic to pull together three independent reads on Plated’s traffic and got extremely close results:

Using the market share data and known revenue numbers, we can put an estimate on Plated of about $135m in annual run rate:

This sounds in the ballpark considering it was pinned at $100m from this Inc article in June 2015. The closeness of fit of a linear curve shows that the revenues are strongly tied to traffic acquisition, but also that there is no clear barrier to entry or topline benefit to scale:

Getting a Handle on Industry Growth

So we now know that the industry is doing roughly $1.4bn/yr today but that number is meaningless in isolation because the rate of growth presents a constantly moving target, something traditional investors in retail and staples aren’t used to. We need get a handle on growth as well. Wouldn’t it be nice to have a platform where you could easily pull up that data from multiple sources?

Plated’s March peak in traffic is a one-off bump thanks to their deal with Mark Cuban on Shark Tank, but we reckon that the industry has roughly doubled year-on-year, which means that there was at least $700m in incremental revenues over 2015, backend weighted. What is a reasonable estimate for forward projections?

Forecasting the future is more art than science. If the industry saw 100% growth in 2015, is it fair to say it will grow 100% again in 2016? On the one hand, you are starting off a higher base and are going from early adopters to followers. On the other hand, you have better funding and cash flow and scale in everything from customer base to marketing campaigns to logistics. It is hard to take the over/under. We think a fair conservative estimate is $800m in incremental revenues, which is a slight acceleration year on year in dollar terms but actually a sizable deceleration percentage-wise, to +60% forward growth. We think the risk to this number is higher rather than lower as more funding and entrants like Amazon come into the space.

Calculating the Comp Sensitivity for WFM

1% of WFM’s 2015 revenues of $15bn is $150m, and given that WFM is not closing stores in any meaningful scale, this is a directly applicable number to calculate impact on comparable store sales. Every $150m taken away from WFM is 1% less in comps.

However, WFM does not solely bear the brunt of the disruption. A number of WFM’s peers, from Kroger to Sprouts Farmers Market, have all called out or commented on the potential impact on subscription meal-kit services. While WFM is a major player, it would be unfair to attribute the full amount of the disruption to them. Since WFM is approximately 25% of grocery industry revenues, we think somewhere in that ballpark would be appropriate, though pricing and demographic characteristics make WFM more susceptible to disruption than the general industry.

Taking all of the above into consideration, we are able to stress test a simple model of WFM comp sensitivity to the subscription meal-kit services, which is how we finally arrive at our Comp Sensitivity table used in the main WFM blog post.

The post $PLATED – Using Mosaic to ballpark unlisted company financials appeared first on Sentieo.