c95f5842-95ad-4186-ba97-be2e10dcc7ab_Elastic-Premium-Analysis.pdf
Elastic Premium Analysis
SECTION 1: Product Overview
The Elastic stack includes Elasticsearch, Logstash, Beats and Kibana.
Elastic has partnered with cloud infrastructure companies, such as Microsoft Azure, Amazon AWS, Google Cloud and Alibaba Cloud. The Elastic Stack works well with Kubernetes, which is the machine learning powerhouse for containerized applications.
Elasticsearch:
Elasticsearch is the core product and search engine that allows for storing, searching and analyzing data.
Google search is built for users to query hundreds of thousands of terabytes of HTML data (or about 20 petabytes per day). In contrast, Elasticsearch is built for developers who need to design more complex application searches. For instance, Elasticsearch can forecast data center storage capacity with queries or can search customer sentiment that is determined by natural language processing.
To further illustrate, here are some examples of how Elasticsearch is used today:
Business-to-consumer:
• Pairing a passenger with an Uber driver with search
• Recommending grocery items on Instacart
• Matching online dating profiles for Tinder dates
• Processing billions of log events for Sprint to monitor outages
• Processing billions of log events for Fitbit, which has a rate of 250,000 logs per second, to enhance data discovery and validate failures
Business-to-business:
• E-trade uses Elasticsearch to identify trading anomalies across 115 terabytes of data
• Adobe uses Elasticsearch to search across both textual and non-textual formats, such as images, videos, 3D templates. This helps assist computer vision, which trains computers with ML to have a high-level understanding of non-textual items. This is done at a rate of 600 queries per second and an ingestion rate of 25,000 per second.
• Blizzard uses Elasticsearch to make sure their games are running at peak performance
• Cox Communications and TV2 use Elasticsearch to analyze billions of content delivery logs
• John Deere uses Elasticsearch to handle 18 billion documents and 11 terabytes of data storage, running 20,000 events every second. This helps to support remote management, variable rate application and field and water management.
Products such as Elastic Cloud Enterprise allows companies to run the entire stack in the cloud with a SaaS offering. The Elastic Stack is also available on-premise, or a hybrid of both. Elastic Cloud Kubernetes extends the Elastic Stack for use on cloud native technologies and containerized architectures.
Logstash and Beats:
Logstash and Beats are ingestion tools that can be put on thousands of applications to query external systems. From the examples above, John Deere is ingesting data from thousands of external sensors in the field, for instance. This helps complete the stack for the optimal use of Elasticsearch.
Ingestion can become quite technical. For instance, eBay uses Beats to break down the silos from containerized machine learning platforms, such as Docker and Kubernetes. This helps to automate application deployment and keep up with fast-evolving application lifecycles. The Beat product also has an auto-discovery feature to ingest newly discovered workloads, to collect and enrich data, and to send to the internal monitoring system.
In eBay’s case, the company built a way to tag the metadata from Beats auto-discovery so that users could access the information with familiar labels. A real-life example of this might be analyzing data from website logs, call centers and competitor website scans with the end result being a system that can the difference between skis named SALOMON QST 92 17/18, Salomon QST 92 2017-18 and Salomon QST 92 Skis 2018 – and also measure rising popularity perhaps with social media.
Kibana:
Kibana is a free, open source tool that integrates tightly with Elasticsearch.
The visualization and exploration tools include interactive charts, mapping support, pre-built aggregations and filters, plus easily accessible dashboards. Elastic allows enterprises to pull more data in for visualization purposes with products such as Elastic Cloud Enterprise or Elastic Cloud on Kubernetes.
SECTION 2: Elastic Fundamentals
A major fundamental risk for Elastic is that revenue growth may not be rewarded in 2020 as we’ve seen plenty of evidence that investors’ appetites are turning more towards profitability, which Elastic is far from reaching.
The market was not kind to Elastic in 2019. Tech growth companies with negative earnings that did not have a perfect earnings report were penalized last year. Elastic was no exception.
Analysts are projecting EPS estimate of negative -$1.35 in fiscal year 2021 compared to negative -$1.22 in fiscal year 2020 ending in April. Revenue in fiscal year 2020 ending in April is estimated at $416 million and fiscal year 2021 at $567 million. The company has $307 million in cash and $42 million in debt.
The stock has a 52-week high of $104 in July and hit a new 52-week low of $61 a month ago on December 5th. The most recent earnings report revealed a slowdown in annual billings from 53% in the year-ago quarter to 45% in the most recent quarter. The company beat on all other estimates, including revenue, EPS and net expansion rate – which is phenomenal at 130. Despite this, the billing slowdown was enough to cause a 20% drawdown on stock price following the earnings report, from $78 to $61.
The recent drawdown has resulted in Elastic trading at one of its lowest valuations yet as a public company. The stock is currently trading at 12 Forward EV/Revenue with an enterprise value of $5.03 billion. When considering revenue growth, this is an attractive valuation as Elastic is reporting 59% YoY revenue growth. Notably, revenue has decelerated from 79% YoY in early 2018 — yet has remained stable at 59% for the past three quarters.
So, the question that remains, will Elastic have an earnings surprise, which is the purpose of this report. There is an important catalyst to consider, which I’ve outlined below. I believe it’s highly probable that Elastic’s fundamentals improve in the coming year due to its entry into endpoint security.
Market Size & Competitors
Determining the market size for Elastic is challenging for a few reasons. The first is that the company does not neatly fit into a specific category. In fact, the company is absent from Gartner’s magic quadrant for Insight Engines, although Elastic is mentioned as the brains behind two products on the quadrant: Intrafind and Lucidworks. Here is what Gartner says about Elastic:
“Of the vendors most often shortlisted by the reference customers we surveyed, Elastic (Elasticsearch) appears in the top five, and Apache (Solr) in the top 10. Neither Elasticsearch nor Solr are considered insight engines — extensive development is required for them to meet the market definition. However, they do provide highly effective search engines for those seeking only search capability or wishing to undertake development. Consequently, they form a foundational layer in the stacks of a number of commercial insight engines, including two in this Magic Quadrant: Lucidworks and IntraFind.” Of the vendors most often shortlisted by the reference customers we surveyed, Elastic (Elasticsearch) appears in the top five, and Apache (Solr) in the top 10. Neither Elasticsearch nor Solr are considered insight engines — extensive development is required for them to meet the market definition. However, they do provide highly effective search engines for those seeking only search capability or wishing to undertake development. Consequently, they form a foundational layer in the stacks of a number of commercial insight engines, including two in this Magic Quadrant: Lucidworks and IntraFind.”
According to Jefferies’ analyst John DiFucci, Elastic’s addressable market is around $40 billion and could double to $71 billion by 2022. He cites the technology is only limited by the “creativity of the customers.” I agree that the limitations for Elastic’s growth will eventually lift as we are in the early stages of machine learning (ML), computer vision and natural language processing (NLP).
ML and NLP are two markets that will grow rapidly over the next decade, with Elastic a recipient. NLP will grow from $3 billion in 2017 to $43 billion in 2025 (source: Statista). Research on Global Markets estimates ML will reach $19.4 billion by 2023, at a CAGR of 48.3%. Big data analytics is quite large at $168 billion in 2018 and forecast to grow at a CAGR of 13.2% to $274 billion by 2022. Elastic sits somewhere between these three markets.
The companies listed on the Insight Engines magic quadrant are not direct competitors. Rather, I would consider Splunk the biggest competitor as their customer value proposition of providing log analytics most closely overlaps. Elastic Stack and Splunk frequently compete for customers. According to developers who work with the products, Splunk is more mature while Elastic is more flexible due to its roots in open source. Other competitors include Sumo Logic on the enterprise side and Greylog on the open source side. The Elasticsearch open source tools are also available through Amazon without the need for enterprises to upgrade through Elastic (although the more optimal experience is with Elastic Stack).
SECTION 3: Endpoint Security
The log management and analytics dashboard offered by both Splunk and Elastic has evolved into an important secondary offering for Security Information and Events Management (SIEM). In other words, because these companies monitor so many endpoints for data ingestion, the platforms are also useful for security monitoring.
By using Search Processing Language (SPL), log management systems are able to perform high-order security analysis and assessments regarding the collective state of these systems from a single interface. For instance, Elastic has been used for threat protection by organizations such as the University of Indiana to monitor hundreds of thousands of devices across students, faculty, and staff.
In October of 2019, Elastic closed the acquisition for Endgame, a leader in endpoint security for $234 million. This followed Elastic launching a Security Information and Events Management (SIEM) product in June. Endgame has enough credibility to be used by the U.S. Navy and the U.S. Air Force.
To raise the stakes, Elastic will be the first to charge for endpoint security by the amount of the data stored rather than by machine. This will allow companies to scale endpoint security more efficiently and will help Elastic compete with Splunk.
Splunk was ranked number one in SIEM by market share in 2018. The SEIM market was worth $5.3 billion in 2018 and is expected to grow at a 19.7% annual rate to reach $12.9 billion by 2023. Elastic should be able to compete very closely with Splunk due to Elastic Stack having similar capabilities of ingesting data from endpoints. Even claiming a small portion of this market should help boost Elastic’s $500 million in annual revenue.
We believe Elastic’s market entry into endpoint security along with competitive pricing will be an important catalyst for the company in the near future.
SECTION 4: Basic Technical Analysis and Closing the Gap
By Knox Ridley
Elastic (ESTC) is in a clear downtrend. After testing the all-time lows at $60.10, Elastic is bouncing back towards an important resistance price region at $67.50, highlighted with a red dotted line in the chart. If Elastic can close above this price, we will likely see a closing of the gap around $77.50, which is highlighted in yellow. This move would be about a 14% move from current prices (and about 24% from its recent bottom).
Furthermore, it’s worth noting that Elastic has taken back its 10-day Exponential Moving Average (EMA) and closed just below the 21-day EMA. The volume of this bounce is on very light volume, which is descending as the price is ascending. This means that the market is not buying this beyond just a bear market bounce, and should be factored into any buying plan.
Internals (MACD, RSI, MFI)
Looking at the internal strength of Elastic (ESTC), the MACD, Relative Strength Index (RSI), and Money Flow Index (MFI) are all telling an interesting story. First off, they are all in well-defined downtrends, which is highlighted by the dashed red lines.
The RSI and MACD are also bouncing against support while trending down, and doing so at lower levels, which looks to be coiling for a move up. We will want to see these indicators break this downtrend in unison with the price of ESTC breaking out as well to indicate a possible change in trend.
The MFI, which is basically the RSI with volume factored in, is showing a positive divergence. In other words, as the price is making lower lows, the MFI is making higher lows. This is always a great indication of fading selling, and usually indicates a shift in sentiment.
Closing above the start of this gap with high volume, and internals that are breaking out would be a key pivot that would further identify that Elastic is in a new uptrend, and not just a bear market correction.
Price Symmetry
Symmetry is a very powerful force in technical analysis. When a move in a certain direction reaches the length of prior moves, we usually see this resistance zone terminate the short-term, corrective move, and signal the continuation of the bigger trend. On the other hand, if the price can break through, it’s a strong indication that the larger trend has possibly changed.
The above chart shows the length of the last correction in the current downtrend. The price increased by about 24% before continuing the larger trend to new lows. After bottoming recently, a 24% move up from the low would coincide with closing the gap just discussed. There is a lot of overhead resistance above Elastic, and until we see ESTC break above this region with heavy volume, we can only assume that this move is a correction in a larger downtrend trend.
Elliott Wave Count
Elastic’s price structure, since its IPO, has been a series of overlapping structures. An overlapping structure, where the price retreats most of the previous move, indicates the uncertainty in the market.
The above count shows Elastic is completing a corrective, 3-wave move up, which is highlighted by the red (A), (B), (C), and then began a new, corrective 3-wave move back down. In both 3-wave trends, the C wave unfolded in a 5-wave move, which is highlighted in blue and also common in 3-wave corrections. My best count has us in the final 4th wave of the C wave, which would suggest newer lows ahead.
The alternative count has us as completing the 5th wave down, which would suggest we are in the very early stages of a renewed uptrend. I have a problem with this count due to Elliott Wave rules; however, if Elastic can close the above gap on heavy volume and close above the 100% extension, I will take that as evidence that this alternative count is in play.
How to Trade
Elastic is a high conviction idea, that may take some time to play out. The market obviously hasn’t seen the value in Elastic over the past month, hence the current trend. We do not think this will last, but how early we are is the real question. Depending on your investment style, we have 3 suggestions:
Buy now and Forget: If you want to buy your position today and hold it, a good stop would be just under $53. Below here and we are entering uncharted territory with all-time lows.
Layering in: If you prefer to layer in, the same stop above would hold. Then, if Elastic hits lower, you can layer in more in the green target region on the chart, between $58.50-$53.50. if ESTC decides to continue higher from current prices, if it closes above $74.50 on heavy volume, with the internals breaking the current downtrend, then you can put the remainder of your position in with a 25% trailing stop.
We plan to trade with the layering in approach.
Wait and See: If you prefer to wait and see, I’d target the green box in the chart between $58.50-$53.50