Tag Archives: Amazon

Value Matters

I recently saw an interview with Damian Lewis, the actor who plays hedge fund billionaire Bobby “Axe” Axelrod in the TV show Billions, where he commented on the differences in reaction to the character in the US and the UK. Lewis said that in the US, the character is treated like an inspirational hero, whereas in the UK he’s seen as a villain. We all like to see a big shot hedgie fall flat on their face so us mere mortals can feel less stupid.

The case of David Einhorn is not so clear cut. A somewhat geekie character, the recent run of bad results of his hedge fund, Greenlight Capital, is raising some interesting questions amongst the talking heads of the merits of value stocks over the run away success of growth stocks in recent years. Einhorn’s recent results can be seen in a historical context, based upon published figures, in the graph below.

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Einhorn recently commented that “the reality is that the market is cyclical and given the extreme anomaly, reversion to the mean should happen sooner rather than later” whilst adding that “we just can’t say when“. The under-performance of value stocks is also highlighted by Alliance Bernstein in this article, as per the graph below.

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As an aside, Alliance Bernstein also have another interesting article which shows the percentage of debt to capital of S&P500 firms, as below.

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Einhorn not only invests in value stocks, like BrightHouse Financial (BHF) and General Motors (GM), but he also shorts highly valued so-called growth stocks like Tesla (TSLA), Amazon (AMZN) and Netflix (NFLX), his bubble basket. In fact, Einhorn’s bubble basket has been one of the reasons behind his recent poor performance. He queries AMZN on the basis that just because they “can disrupt somebody else’s profit stream, it doesn’t mean that AMZN earns that profit stream“. He trashes TSLA and its ability to deliver safe mass produced electric cars and points to the growing competition from “old media” firms for NFLX.

A quick look at the 2019 projected forward PE ratios, based off today’s valuations against average analysts estimates for 2018 and 2019 EPS numbers from Yahoo Finance of some of today’s most hyped growth stocks plus their Chinese counterparts plus some more “normal” firms like T and VZ as a counter weight, provides considerable justification to Einhorn’s arguments.

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[As an another aside, I am keeping an eye on Chinese valuations, hit by trade war concerns, for opportunities in case Trump’s trade war turns out to be another “huge” deal where he folds like the penny hustler he is.]

And the graph above shows only the firms with positive earnings to have a PE ratio in 2019 (eh, hello TSLA)!! In fact, the graph makes Einhorn’s rationale seem downright sensible to me.

Now, that’s not something you could say about Axe!

Heterogeneous Future

It seems like wherever you look these days there is references to the transformational power of artificial intelligence (AI), including cognitive or machine learning (ML), on businesses and our future. A previous post on AI and insurance referred to some of the changes ongoing in financial services in relation to core business processes and costs. This industry article highlights how machine learning (specifically multi-objective genetic algorithms) can be used in portfolio optimization by (re)insurers. To further my understanding on the topic, I recently bought a copy of a new book called “Advances in Financial Machine Learning” by Marcos Lopez de Prado, although I suspect I will be out of my depth on the technical elements of the book. Other posts on this blog (such as this one) on the telecom sector refer to the impact intelligent networks are having on telecom business models. One example is the efficiencies Centurylink (CTL) have shown in their capital expenditure allocation processes from using AI and this got me thinking about the competitive impact such technology will have on costs across numerous traditional industries.

AI is a complex topic and in its broadest context it covers computer systems that can sense their environment, think, and in some cases learn, and take applicable actions according to their objectives. To illustrate the complexity of the topic, neural networks are a subset of machine learning techniques. Essentially, they are AI systems based on simulating connected “neural units” loosely modelling the way that neurons interact in the brain. Neural networks need large data sets to be “trained” and the number of layers of simulated interconnected neurons, often numbering in their millions, determine how “deep” the learning can be. Before I embarrass myself in demonstrating how little I know about the technicalities of this topic, it’s safe to say AI as referred to in this post encompasses the broadest definition, unless a referenced report or article specifically narrows the definition to a subset of the broader definition and is highlighted as such.

According to IDC (here), “interest and awareness of AI is at a fever pitch” and global spending on AI systems is projected to grow from approximately $20 billion this year to $50 billion in 2021. David Schubmehl of IDC stated that “by 2019, 40% of digital transformation initiatives will use AI services and by 2021, 75% of enterprise applications will use AI”. By the end of this year, retail will be the largest spender on AI, followed by banking, discrete manufacturing, and healthcare. Retail AI use cases include automated customer service agents, expert shopping advisors and product recommendations, and merchandising for omni channel operations. Banking AI use cases include automated threat intelligence and prevention systems, fraud analysis and investigation, and program advisors and recommendation systems. Discrete manufacturing AI use cases including automated preventative maintenance, quality management investigation and recommendation systems. Improved diagnosis and treatment systems are a key focus in healthcare.

In this April 2018 report, McKinsey highlights numerous use cases concluding that ”AI can most often be adopted and create value where other analytics methods and techniques are also creating value”. McKinsey emphasis that “abundant volumes of rich data from images, audio, and video, and large-scale text are the essential starting point and lifeblood of creating value with AI”. McKinsey’s AI focus in the report is particularly in relation to deep learning techniques such as feed forward neural networks, recurrent neural networks, and convolutional neural networks.

Examples highlighted by McKinsey include a European trucking company who reduced fuel costs by 15 percent by using AI to optimize routing of delivery traffic, an airline who uses AI to predict congestion and weather-related problems to avoid costly cancellations, and a travel company who increase ancillary revenue by 10-15% using a recommender system algorithm trained on product and customer data to offer additional services. Other specific areas highlighted by McKinsey are captured in the following paragraph:

“AI’s ability to conduct preventive maintenance and field force scheduling, as well as optimizing production and assembly processes, means that it also has considerable application possibilities and value potential across sectors including advanced electronics and semiconductors, automotive and assembly, chemicals, basic materials, transportation and logistics, oil and gas, pharmaceuticals and medical products, aerospace and defense, agriculture, and consumer packaged goods. In advanced electronics and semiconductors, for example, harnessing data to adjust production and supply-chain operations can minimize spending on utilities and raw materials, cutting overall production costs by 5 to 10 percent in our use cases.”

McKinsey calculated the value potential of AI from neural networks across numerous sectors, as per the graph below, amounting to $3.5 to $5.8 trillion. Value potential is defined as both in the form of increased profits for companies and lower prices or higher quality products and services captured by customers, based off the 2016 global economy. They did not estimate the value potential of creating entirely new product or service categories, such as autonomous driving.

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McKinsey identified several challenges and limitations with applying AI techniques, as follows:

  • Making an effective use of neural networks requires labelled training data sets and therefore data quality is a key issue. Ironically, machine learning often requires large amounts of manual effort in “teaching” machines to learn. The experience of Microsoft with their chatter bot Tay in 2016 illustrates the shortcoming of learning from bad data!
  • Obtaining data sets that are sufficiently large and comprehensive to be used for comprehensive training is also an issue. According to the authors of the book “Deep Learning”, a supervised deep-learning algorithm will generally achieve acceptable performance with around 5,000 labelled examples per category and will match or exceed human level performance when trained with a data set containing at least 10 million labelled examples.
  • Explaining the results from large and complex models in terms of existing practices and regulatory frameworks is another issue. Product certifications in health care, automotive, chemicals, aerospace industries and regulations in the financial services sector can be an obstacle if processes and outcomes are not clearly explainable and auditable. Some nascent approaches to increasing model transparency, including local-interpretable-model-agnostic explanations (LIME), may help resolve this explanation challenge.
  • AI models continue to have difficulties in carrying their experiences from one set of circumstances to another, applying a generalisation to learning. That means companies must commit resources to train new models for similar use cases. Transfer learning, in which an AI model is trained to accomplish a certain task and then quickly applies that learning to a similar but distinct activity, is one area of focus in response to this issue.
  • Finally, one area that has been the subject of focus is the risk of bias in data and algorithms. As bias is part of the human condition, it is engrained in our behaviour and historical data. This article in the New Scientist highlights five examples.

In 2016, Accenture estimated that US GDP could be $8.3 trillion higher in 2035 because of AI, doubling growth rates largely due to AI induced productivity gains. More recently in February this year, PwC published a report on an extensive macro-economic impact of AI and projected a baseline scenario that global GDP will be 14% higher due to AI, with the US and China benefiting the most. Using a Spatial Computable General Equilibrium Model (SCGE) of the global economy, PwC quantifies the total economic impact (as measured by GDP) of AI on the global economy via both productivity gains and consumption-side product enhancements over the period 2017-2030. The impact on the seven regions modelled by 2030 can be seen below.

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PwC estimates that the economic impact of AI will be driven by productivity gains from businesses automating processes as well as augmenting their existing labour force with AI technologies (assisted, autonomous and augmented intelligence) and by increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.

In terms of sectors, PwC estimate the services industry that encompasses health, education, public services and recreation stands to gain the most, with retail and wholesale trade as well as accommodation and food services also expected to see a large boost. Transport and logistics as well as financial and professional services will also see significant but smaller GDP gains by 2030 because of AI although they estimate that the financial service sector gains relatively quickly in the short term. Unsurprisingly, PwC finds that capital intensive industries have the greatest productivity gains from AI uptake and specifically highlight the Technology, Media and Telecommunications (TMT) sector as having substantial marginal productivity gains from uptaking replacement and augmenting AI. The sectoral gains estimated by PwC by 2030 are shown below.

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A key element of these new processes is the computing capabilities needed to process so much data that underlies AI. This recent article in the FT highlighted how the postulated demise of Moore’s law after its 50-year run is impacting the micro-chip sector. Mike Mayberry of Intel commented that “the future is more heterogeneous” when referring to the need for the chip industry to optimise chip design for specific tasks. DARPA, the US defence department’s research arm, has allocated $1.5 billion in research grants on the chips of the future, such as chip architectures that combine both power and flexibility using reprogrammable “software-defined hardware”. This increase in focus from the US is a direct counter against China’s plans to develop its intellectual and technical abilities in semiconductors over the coming years beyond simple manufacturing.

One of the current leaders in specialised chip design is Nvidia (NVDA) who developed software lead chips for video cards in the gaming sector through their graphics processing unit (GPU). The GPU accelerates applications running on standard central processing units (CPU) by offloading some of the compute-intensive and time-consuming portions of the code whilst the rest of the application still runs on the CPU. The chips developed by NVDA for gamers have proven ideal in handling the huge volumes of data needed to train deep learning systems that are used in AI. The exhibit below from NVDA illustrates how they assert that new processes such as GPU can overcome the slowdown in capability from the density limitation of Moore’s Law.

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NVDA, whose stock is up over 400% in the past 24 months, has been a darling of the stock market in recent years and reported strong financial figures for their quarter to end April, as shown below. Their quarterly figures to the end of July are eagerly expected next month. NVDA has been range bound in recent months, with the trade war often cited as a concern with their products sold approximately 20%, 20%, and 30% into supply chains in China, other Asia Pacific countries, and Taiwan respectively

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Although seen as the current leader, NVDA is not alone in this space. AMD recently reported strong Q1 2018 results, with revenues up 40%, and has a range of specialised chip designs to compete in the datacentre, auto, and machine learning sectors. AMD’s improved results also reduce risk on their balance sheet with leverage decreasing from 4.6X to 3.4X and projected to decline further. AMD’s stock is up approximately 70% year to date. AMD’s 7-nanonmeter product launch planned for later this year also compares favourably against Intel’s delayed release date to 2019 for its 10-nanometer chips.

Intel has historically rolled out a new generation of computer chips every two years, enabling chips that were consistently more powerful than their predecessors even as the cost of that computing power fell. But as Intel has run up against the limits of physics, they have reverted to making upgrades to its aging 14nm processor node, which they say performs 70% better than when initially released four years ago. Despite advances by NVDA and AMD in data centres, Intel chips still dominate. In relation to the AI market, Intel is focused on an approach called field-programmable gate array (FPGA) which is an integrated circuit designed to be configured by a customer or a designer after manufacturing. This approach of domain-specific architectures is seen as an important trend in the sector for the future.

Another interesting development is Google (GOOG) recently reported move to commercially sell, through its cloud-computing service, its own big-data chip design that it has been using internally for some time. Known as a tensor processing unit (TPU), the chip was specifically developed by GOOG for neural network machine learning and is an AI accelerator application-specific integrated circuit (ASIC). For example, in Google photos an individual TPU can process over 100 million photos a day. What GOOG will do with this technology will be an interesting development to watch.

Given the need for access to large labelled data sets and significant computing infrastructure, the large internet firms like Google, Facebook (FB), Microsoft (MSFT), Amazon (AMZN) and Chinese firms like Baidu (BIDU) and Tencent (TCEHY) are natural leaders in using and commercialising AI. Other firms highlighted by analysts as riding the AI wave include Xilinx (XLNX), a developer of high-performance FPGAs, and Yext (YEXT), who specialise in managing digital information relevant to specific brands, and Twilio (TWLO), a specialist invoice and text communication analysis. YEXT and TWLO are loss making. All of these stocks, possibly excluding the Chinese ones, are trading at lofty valuations. If the current wobbles on the stock market do lead to a significant fall in technology valuations, the stocks on my watchlist will be NVDA, BIDU and GOOG. I’d ignore the one trick ponys, particularly the loss making ones! Specifically, Google is one I have been trying to get in for years at a sensible value and I will watch NVDA’s results next month with keen interest as they have consistently broken estimates in recent quarters. Now, if only the market would fall from its current heights to allow for a sensible entry point…….maybe enabled by algorithmic trading or a massive trend move by the passives!

Artificial Insurance

The digital transformation of existing business models is a theme of our age. Robotic process automation (RPA) is one of the many acronyms to have found its way into the terminology of businesses today. I highlighted the potential for telecoms to digitalise their business models in this post. Klaus Schwab of the World Economic Forum in his book “Fourth Industrial Revolution” refers to the current era as one whereby “new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human”.

The financial services business is one that is regularly touted as been rife for transformation with fintech being the much-hyped buzz word. I last posted here and here on fintech and insurtech, the use of technology innovations designed to squeeze out savings and efficiency from existing insurance business models.

Artificial intelligence (AI) is used as an umbrella term for everything from process automation, to robotics and to machine learning. As referred to in this post on equity markets, the Financial Stability Board (FSB) released a report called “Artificial Intelligence and Machine Learning in Financial Services” in November 2017. In relation to insurance, the FSB report highlights that “some insurance companies are actively using machine learning to improve the pricing or marketing of insurance products by incorporating real-time, highly granular data, such as online shopping behaviour or telemetrics (sensors in connected devices, such as car odometers)”. Other areas highlighted include machine learning techniques in claims processing and the preventative benefits of remote sensors connected through the internet of things. Consultants are falling over themselves to get on the bandwagon as reports from the likes of Deloitte, EY, PwC, Capgemini, and Accenture illustrate.

One of the better recent reports on the topic is this one from the reinsurer SCOR. CEO Denis Kessler states that “information is becoming a commodity, and AI will enable us to process all of it” and that “AI and data will take us into a world of ex-ante predictability and ex-post monitoring, which will change the way risks are observed, carried, realized and settled”. Kessler believes that AI will impact the insurance sector in 3 ways:

  • Reducing information asymmetry and bringing comprehensive and dynamic observability in the insurance transaction,
  • Improving efficiencies and insurance product innovation, and
  • Creating new “intrinsic“ AI risks.

I found one article in the SCOR report by Nicolas Miailhe of the Future Society at the Harvard Kennedy School particularly interesting. Whilst talking about the overall AI market, Miailhe states that “the general consensus remains that the market is on the brink of a revolution, which will be characterized by an asymmetric global oligopoly” and the “market is qualified as oligopolistic because of the association between the scale effects and network effects which drive concentration”.  When referring to an oligopoly, Miailhe highlights two global blocks – GAFA (Google/Apple/Facebook/Amazon) and BATX (Baidu/Alibaba/Tencent/Xiaomi). In the insurance context, Miailhe states that “more often than not, this will mean that the insured must relinquish control, and at times, the ownership of data” and that “the delivery of these new services will intrude heavily on privacy”.

At a more mundane level, Miailhe highlights the difficulty for stakeholders such as auditors and regulators to understand the business models of the future which “delegate the risk-profiling process to computer systems that run software based on “black box” algorithms”. Miailhe also cautions that bias can infiltrate algorithms as “algorithms are written by people, and machine-learning algorithms adjust what they do according to people’s behaviour”.

In a statement that seems particularly relevant today in terms of the current issue around Facebook and data privacy, Miailhe warns that “the issues of auditability, certification and tension between transparency and competitive dynamics are becoming apparent and will play a key role in facilitating or hindering the dissemination of AI systems”.

Now, that’s not something you’ll hear from the usual cheer leaders.

Restrict the Renters?

It is no surprise that the populist revolt against globalisation in many developed countries is causing concern amongst the so called elite. The philosophy of the Economist magazine is based upon its founder’s opposition to the protectionist Corn Laws in 1843. It is therefore predictable that they would mount a strong argument for the benefits of free trade in their latest addition, citing multiple research sources. The Economist concludes that “a three pronged agenda of demand management, active labour-market policies and boosting competition would go a long way to tackling the problems that are unfairly laid at the door of globalisation”.

One of the studies referenced in the Economist articles which catch my eye is that by Jason Furman of the Council of Economic Advisors in the US. The graph below from Furman’s report shows the growth in return on invested capital (excluding goodwill)  of US publically quoted firms and the stunning divergence of those in the top 75th and 90th percentiles.

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These top firms, primarily in the technology sector, have increased their return on invested capital (ROIC) from 3 times the median in the 1990s to 8 times today, dramatically demonstrating their ability to generate economic rent in the digitized world we now live in.

Furman’s report includes the following paragraph:

“Traditionally, price fixing and collusion could be detected in the communications between businesses. The task of detecting undesirable price behaviour becomes more difficult with the use of increasingly complex algorithms for setting prices. This type of algorithmic price setting can lead to undesirable price behaviour, sometimes even unintentionally. The use of advanced machine learning algorithms to set prices and adapt product functionality would further increase opacity. Competition policy in the digital age brings with it new challenges for policymakers.”

IT firms have the highest operating margins of any sector in the S&P500, as can be seen below.

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And the increasing size of these technology firms have contributed materially to the increase in the overall operating margin of the S&P500, as can also be seen below. These expanding margins are a big factor in the rise of the equity market since 2009.

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It is somewhat ironic that one of the actions which may be needed to show the benefits of free trade and globalisation to citizens in the developed world is coherent policies to restrict the power of economic rent generating technology giants so prevalent in our world today…

Consistency with ambition, the case for TWTC

Valuations remain high (S&P PE at 19.5 and CAPE over 25) despite recent volatility and I have posted on my views previously. A recent post on Level3 (LVLT) in December referred to increases in telecom valuation multiples. Since then LVLT reported a very good end to the year and has rocketed to around $38, or an approx 9.4 EV to 2014 guided EBITDA multiple (and 8.7 to my 2015 estimated EBITDA). An analyst report, whilst upgrading the stock, commented “with a focus that has shifted from a slow deleveraging exercise via acquisitions to now focusing on integration and execution of assets the company possesses, we believe we are on the cusp of a sustained outperformance”. Although I generally ignore anything analysts say, I too am bullish on LVLT over the longer term based upon the virtuous circle of improving operating results and decreasing debt. However I think valuation may have gotten ahead of itself with LVLT up 70% in 6 months. I have taken some profits to buy some downside protection. There is likely to be some bumps on the road in 2014 both for companies like LVLT and from an overall market viewpoint. Structural changes in the rapidly changing telecom market like net neutrality or the proposed Comcast/Time Warner Cable (TWC) merger may also have an impact.

Speaking of Time Warner, there is a telecom that was spun off from Time Warner in the late 1990s called TW Telecom (TWTC) that has a history over the past 10 years of outstanding execution. Over that time, TWTC has diversified itself away from its roots (top 10 customers make up 18% of revenues in 2013 compared to 23% 5 years ago and 40% 10 years ago) with a current focus on business Ethernet, data networking, IP VPN, Internet access, and network security services for enterprises. The graphic below illustrates how successful and consistent TWTC’s operating results has been. I would particularly highlight their results through the troubled 2007 to 2009 period. TWTC have had solid 35% EBITDA margins for the past 10 years with average capital expenditures of 25% as they build their last mile metro fiber network to their business customers on a success basis. Their execution is in no small measure down to one of the best (and most consistent) management teams in the business, led by long term CEO Larissa Herda.

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In addition to solid operating results, TWTC have always shown disciplined balance sheet management with net debt well below 2 times EBITDA in the past 5 years (except for 2013 at 2.3 times as per the changes below). As a result of the factors highlighted above, TWTC has always enjoyed a premium valuation multiple in the market as the graph (of enterprise value to twelve month trailing (TTM) and future twelve months (FTM) EBITDA) below shows.

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TWTC has long been talked of as an acquirer or a target for others but nothing of substance has materialised since their Xspedius acquisition back in 2006. The firm has increasingly undertaken shareholder friendly actions such as the $400 million spend on its own shares in 2013. TWTC has also bought back convertible debt and pushed out the maturities on its debt which has increased from YE2012 of $1.76 billion to just below $2 billion as at YE2013.

The reason for the increase in debt plus an additional one-off capital expenditure of $120 million in 2013 on capital leases (not included in graph above), with another one off $50 million due in 2014, is a strategic market expansion announced by TWTC in late 2013. The strategic market expansion is to extend its metro fiber footprint into 5 new high demand markets and accelerate the density of its metro-fiber footprint in 27 existing markets by 17%. Given TWTC’s history of execution, their plans for expansion and the (almost giddish) optimism of management during their Q4 conference call caught my attention. These are people who have not make such promises lightly in the past.

One of the factors behind their expansion is the success of new product innovation introduced in 2012, namely products called Enhanced Management and Dynamic Capacity. Such products allow enterprises to automate, manage and purchase network capacity on a flexible real time framework based upon their needs and offer flexibility in accessing connections to private, hybrid and public clouds. TWTC refer to their state of the art network as the Intelligent Network and are marketing their range of products on the basis of what they call their Constellation Platform which “will connect our customers nearly instantaneously through data centers directly to numerous applications in the cloud with increasing network automation”. All of these fancy products names and high minded assertions shouldn’t in themselves be taken as anything earth shattering in the rapidly changing IT and telecom market. What may be special is that TWTC has indicated increased interest in their offerings and that, through partnerships with cloud providers such as Amazon, they are getting interest from new enterprises with big data needs . TWTC state that their expansion is “a very targeted opportunity to rapidly increase our market density to drive additional revenue growth and greater cash flow” and that it “is all part of our broader vision of bringing better, faster and easier solutions to customers as we continue to innovate and create market differentiation”.

Given the history of execution by TWTC’s management, I would be positive on their ability to deliver on their promises. They have indicated that EBITDA margins will be under pressure in 2014 as they staff up for the new expansion. For 2015 & 2016, EBITDA expansion of 10% to 15% does not seem unreasonable to me based upon my calculations. Given a current EV/EBITDA on a TTM basis of over 11, TWTC is not cheap and, as stated in the beginning of this post, there are likely to be bumps in the road over 2014. Such bumps may provide an opportunity to back TWTC and its expansion at an attractive valuation.

I, for one, will be looking out for it.