Tag Archives: machine learning

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.

The Bionic Invisible Hand

Technology is omnipresent. The impacts of technology on markets and market structures are a topic of much debate recently. Some point to its influence to explain the lack of volatility in equity markets (ignoring this week’s wobble). Marko Kolanovic, a JPMorgan analyst, has been reported to have estimated that a mere 10% US equity market trading is now conducted by discretionary human traders.

The first wave of high frequency trading (HFT) brought about distortive practises by certain players such as front running and spoofing, as detailed in Michael Lewis’s bestselling exposé Flash Boys. Now HFT firms are struggling to wring profits from the incremental millisecond, as reported in this FT article, with 2017 revenues for HFT firms trading US stocks falling below $1 billion in 2017 from over $7 billion in 2009, according to the consultancy Tabb Group. According to Doug Duquette of Vertex Analytics “it has got to the point where the speed is so ubiquitous that there really isn’t much left to get”.

The focus now is on the impact of various rules-based automatic investment systems, ranging from exchange traded funds (ETFs) to computerised high-speed trading programs to new machine learning and artificial intelligence (AI) innovations. As Tom Watson said about HFT in 2011, these new technologies have the potential to give “Adam Smith’s invisible hand a bionic upgrade by making it better, stronger and faster like Steve Austin in the Six Million Dollar Man”.

As reported in another FT article, some experts estimate that computers are now generating around 50% to 70% of trading in equity markets, 60% of futures and more than 50% of treasuries. According to Morningstar, by year-end 2017 the total assets of actively managed funds stood at $11.4 trillion compared with $6.7 trillion for passive funds in the US.

Although the term “quant fund” covers a multitude of mutual and hedge fund strategies, assuming certain classifications are estimated to manage around $1 trillion in assets out of total assets under management (AUM) invested in mutual funds globally of over $40 trillion. It is believed that machine learning or AI only drives a small subset of quant funds’ trades although such systems are thought to be used as investment tools for developing strategies by an increasing number of investment professionals.

Before I delve into these issues further, I want to take a brief detour into the wonderful world of quantitative finance expert Paul Wilmott and his recent book, with David Orrell, called “The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets”. I am going to try to summarize the pertinent issues highlighted by the authors in the following sequence of my favourite quotes from the book:

“If anybody can flog an already sick horse to death, it is an economist.”

“Whenever a model becomes too popular, it influences the market and therefore tends to undermine the assumptions on which it was built.”

“Real price data tend to follow something closer to a power-law distribution and are characterized by extreme events and bursts of intense volatility…which are typical of complex systems that are operating at a state known as self-organized criticality…sometimes called the edge of chaos.”

“In quantitative finance, the weakest links are the models.”

“The only half decent, yet still toy, models in finance are the lognormal random walk models for those instruments whose level we don’t care about.”

“The more apparently realistic you make a model, the less useful it often becomes, and the complexity of the equations turns the model into a black box. The key then is to keep with simple models, but make sure that the model is capturing the key dynamics of the system, and only use it within its zone of validity.”

“The economy is not a machine, it is a living, organic system, and the numbers it produces have a complicated relationship with the underlying reality.”

“Calibration is a simple way of hiding model risk, you choose the parameters so that your model superficially appears to value everything correctly when really, it’s doing no such thing.”

“When their [quants] schemes, their quantitative seizing – cratered, the central banks stepped in to fill the hole with quantitative easing.”

“Bandwagons beget bubbles, and bubbles beget crashes.”

“Today, it is the risk that has been created by high speed algorithms, all based on similar models, all racing to be the first to do the same thing.”

“We have outsourced ethical judgments to the invisible hand, or increasingly to algorithms, with the result that our own ability to make ethical decisions in economic matters has atrophied.”

According to Morningstar’s annual fund flow report, flows into US mutual funds and ETFs reached a record $684.6 billion in 2017 due to massive inflows into passive funds. Among fund categories, the biggest winners were passive U.S. equity, international equity and taxable bond funds with each having inflows of more than $200 billion. “Indexing is no longer limited to U.S. equity and expanding into other asset classes” according to the Morningstar report.

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Paul Singer of Elliott hedge fund, known for its aggressive activism and distressed debt focus (famous for its Argentine debt battles), dramatically said “passive investing is in danger of devouring capitalism” and called it “a blob which is destructive to the growth-creating and consensus-building prospects of free market capitalism”.

In 2016, JP Morgan’s Nikolaos Panagirtzoglou stated that “the shift towards passive funds has the potential to concentrate investments to a few large products” and “this concentration potentially increases systemic risk making markets more susceptible to the flows of a few large passive products”. He further stated that “this shift exacerbates the market uptrend creating more protracted periods of low volatility and momentum” and that “when markets eventually reverse, the correction becomes deeper and volatility rises as money flows away from passive funds back towards active managers who tend to outperform in periods of weak market performance”.

The International Organization of Securities Commissions (IOSCO), proving that regulators are always late to the party (hopefully not too late), is to broaden its analysis on the ETF sector in 2018, beyond a previous review on liquidity management, to consider whether serious market distortions might occur due to the growth of ETFs, as per this FT article. Paul Andrews, a veteran US regulator and secretary general of IOSCO, called ETFs “financial engineering at its finest”, stated that “ETFs are [now] a critical piece of market infrastructure” and that “we are on autopilot in many respects with market capitalisation-weighted ETFs”.

Artemis Capital Management, in this report highlighted in my previous post, believe that “passive investing is now just a momentum play on liquidity” and that “large capital flows into stocks occur for no reason other than the fact that they are highly liquid members of an index”. Artemis believes that “active managers serve as a volatility buffer” and that if such a buffer is withdrawn then “there is no incremental seller to control overvaluation on the way up and no incremental buyer to stop a crash on the way down”.

Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader.

Machine learning uses statistical techniques to infer relationships between data. The artificial intelligence “agent” does not have an algorithm to tell it which relationships it should find but infers, or learns if you like, from the data using statistical analysis to revise its hypotheses. In supervised learning, the machine is presented with examples of input data together with the desired output. The AI agent works out a relationship between the two and uses this relationship to make predictions given further input data. Supervised learning techniques, such as Bayesian regression, are useful where firms have a flow of input data and would like to make predictions.

Unsupervised learning, in contrast, does without learning examples. The AI agent instead tries to find relationships between input data by itself. Unsupervised learning can be used for classification problems determining which data points are similar to each other. As an example of unsupervised learning, cluster analysis is a statistical technique whereby data or objects are classified into groups (clusters) that are similar to one another but different from data or objects in other clusters.

Firms like Bloomberg use cluster analysis in their liquidity assessment tool which aims to cluster bonds with sufficiently similar behaviour so their historical data can be shared and used to make general predictions for all bonds in that cluster. Naz Quadri of Bloomberg, with the wonderful title of head of quant engineering and research, said that “some applications of clustering were more useful than others” and that their analysis suggests “clustering is most useful, and results are more stable, when it is used with a structural market impact model”. Market impact models are widely used to minimise the effect of a firm’s own trading on market prices and are an example of machine learning in practise.

In November 2017, the Financial Stability Board (FSB) released a report called “Artificial Intelligence and Machine Learning in Financial Services”. In the report the FSB highlighted some of the current and potential use cases of AI and machine learning, as follows:

  • Financial institutions and vendors are using AI and machine learning methods to assess credit quality, to price and market insurance contracts, and to automate client interaction.
  • Institutions are optimising scarce capital with AI and machine learning techniques, as well as back-testing models and analysing the market impact of trading large positions.
  • Hedge funds, broker-dealers, and other firms are using AI and machine learning to find signals for higher (and uncorrelated) returns and optimise trading execution.
  • Both public and private sector institutions may use these technologies for regulatory compliance, surveillance, data quality assessment, and fraud detection.

The FSB report states that “applications of AI and machine learning could result in new and unexpected forms of interconnectedness” and that “the lack of interpretability or ‘auditability’ of AI and machine learning methods has the potential to contribute to macro-level risk”. Worryingly they say that “many of the models that result from the use of AI or machine learning techniques are difficult or impossible to interpret” and that “many AI and machine learning developed models are being ‘trained’ in a period of low volatility”. As such “the models may not suggest optimal actions in a significant economic downturn or in a financial crisis, or the models may not suggest appropriate management of long-term risks” and “should there be widespread use of opaque models, it would likely result in unintended consequences”.

With increased use of machine learning and AI, we are seeing the potential rise of self-driving investment vehicles. Using self-driving cars as a metaphor, Artemis Capital highlights that “the fatal flaw is that your driving algorithm has never seen a mountain road” and that “as machines trade with against each other, self-reflexivity is amplified”. Others point out that machine learning in trading may involve machine learning algorithms learning the behaviour of other machine learning algorithms, in a regressive loop, all drawing on the same data and the same methodology. 13D Research opined that “when algorithms coexist in complex systems with subjectivity and unpredictability of human behaviour, unforeseen and destabilising downsides result”.

It is said that there is nothing magical about quant strategies. Quantitative investing is an approach for implementing investment strategies in an automated (or semi-automated) way. The key seems to be data, its quality and its uniqueness. A hypothesis is developed and tested and tested again against various themes to identify anomalies or inefficiencies. Jim Simons of Renaissance Technologies (called RenTec), one of the oldest and most successful quant funds, said that the “efficient market theory is correct in that there are no gross inefficiencies” but “we look at anomalies that may be small in size and brief in time. We make our forecast. Then, shortly thereafter, we re-evaluate the situation and revise our forecast and our portfolio. We do this all-day long. We’re always in and out and out and in. So we’re dependent on activity to make money“. Simons emphasised that RenTec “don’t start with models” but “we start with data” and “we don’t have any preconceived notions”. They “look for things that can be replicated thousands of times”.

The recently departed co-CEO Robert Mercer of RenTec [yes the Mercer who backs Breitbart which adds a scary political Big Brother surveillance angle to this story] has said “RenTec gets a trillion bytes of data a day, from newspapers, AP wire, all the trades, quotes, weather reports, energy reports, government reports, all with the goal of trying to figure out what’s going to be the price of something or other at every point in the future… The information we have today is a garbled version of what the price is going to be next week. People don’t really grasp how noisy the market is. It’s very hard to find information, but it is there, and in some cases it’s been there for a long long time. It’s very close to science’s needle in a haystack problem

Kumesh Aroomoogan of Accern recently said that “quant hedge funds are buying as much data as they can”. The so-called “alternative data” market was worth about $200 million in the US in 2017 and is expected to double in four years, according to research and consulting firm Tabb Group. The explosion of data that has and is becoming available in this technological revolution should keep the quants busy, for a while.

However, what’s scaring me is that these incredibly clever people will inevitably end up farming through the same data sets, coming to broadly similar conclusions, and the machines who have learned each other’s secrets will all start heading for the exits at the same time, in real time, in a mother of all quant flash crashes. That sounds too much like science fiction to ever happen though, right?

A frazzled Goldilocks?

Whatever measure you look at, equities in the US are overvalued, arguably in bubble territory. Investors poured record amounts into equity funds in recent weeks as the market melt-up takes hold. One of the intriguing features of the bull market over the past 18 months has been the extraordinary low volatility. Hamish Preston of S&P Dow Jones Indices estimated that the average observed 1-month volatility in the S&P 500 in 2017 is “lower than in any other year since 1970”. To illustrate the point, the graph below shows the monthly change in the S&P500 over recent years.

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The lack of any action below 0% since November 2016 and any pullback greater than 2% since January 2016 is striking. “Don’t confuse lack of volatility with stability, ever” is a quote from Nassim Nicolas Taleb that’s seems particularly apt today.

Andrew Lapthorne of SocGen highlighted that low risk markets tend to have a big knock on effect with a “positive feedback mechanism embedded in many risk models”. In other words, the less risk is observed in the market and used as the basis for model inputs, the more risk the quant models allow investors to take! [The impact of quant models and shadow risks from passive investing and machine learning are areas I hope to explore further in a future post.]

One risk that has the potential to spoil the party in 2018 is the planned phased normalisation of monetary policy around the world after the great experimentations of recent years. The market is currently assuming that Central Banks will guarantee that Goldilocks will remain unfrazzled as they deftly steer the ship back to normality. A global “Goldilocks put” if I could plagiarize “the Greenspan put”! Or a steady move away from the existing policy that no greater an economic brain than Donald Trump summarized as being: “they’re keeping the rates down so that everything else doesn’t go down”.

The problem for Central Banks is that if inflation stays muted in the short-term and monetary policy remains loose than the asset bubbles will reach unsustainable levels and require pricking. Or alternatively, any attempt at monetary policy normalization may dramatically show how Central Banks have become the primary providers of liquidity in capital markets and that even modest tightening could result in dangerously imbalances within the now structurally dependent system.

Many analysts (and the number is surprising large) have been warning for some time about the impact of QE flows tightening in 2018. These warnings have been totally ignored by the market, as the lack of volatility illustrates. For example, in June 2017, Citi’s Matt King projected future Central Bank liquidity flows and warned that a “significant unbalancing is coming“. In November 2017, Deutsche Bank’s Alan Ruskin commented that “2018 will see the world’s most important Central Bank balance sheets shift from a 12 month expansion of more than $2 trillion, to a broadly flat position by the end of 2018, assuming the Fed and ECB act according to expectations”. The projections Deutsche Bank produced are below.

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Andrew Norelli of JP Morgan Asset Management in a piece called “Stock, Flow or Impulse?” stated that “It’s still central bank balance sheets, and specifically the flow of global quantitative easing (QE) that is maintaining the buoyancy in financial asset prices”. JP Morgan’s projections of the top 4 developed countries are below.

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Lance Roberts of RealInvestmentAdvice.com produced an interesting graph specifically relating to the Fed’s balance sheet, as below. Caution should be taken with any upward trending metric when compared to the S&P500 in recent years!

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Of course, we have been at pre-taper junctions many times before and every previous jitter has been met with soothing words from Central Banks and more liquidity creation. This time though it feels different. It has to be different. Or Central Bankers risk been viewed as emperors without cloths.

The views of commentators differ widely on this topic. Most of the business media talking heads are wildly positive (as they always are) on the Goldilocks status quo. John Mauldin of MauldinEconomics.com believes the number one risk factor in the US is Fed overreach and too much tightening. Bank of America Merrill Lynch chief investment strategist Michael Hartnett, fears a 1987/1994/1998-style flash crash within the next three months caused by a withdrawal of central bank support as interest rates rise.

Christopher Cole of Artemis Capital Management, in a wonderful report called “Volatility and the Alchemy of Risk”, pulls no punches about the impact of global central banks having pumped $15 trillion in cheap money stimulus into capital markets since 2009. Cole comments that “amid this mania for investment, the stock market has begun self-cannibalizing” and draws upon the image of the ouroboros, an ancient Greek symbol of a snake eating its own tail. Cole estimates that 40% of EPS growth and 30% of US equity gains since 2009 have been as a direct result of the financial engineering use of stock buy backs. Higher interest rates, according to Cole, will be needed to combat the higher inflation that will result from this liquidity bonanza and will cut off the supply for the annual $800 billion of share buybacks. Cole also points to the impact on the high yield corporate debt market and the overall impact on corporate defaults.

Another interesting report, from a specific investment strategy perspective, is Fasanara Capital’s Francesco Filia and the cheerfully entitled “Fragile Markets On The Edge of Chaos”. As economies transition from peak QE to quantitative tightening, Filia “expect markets to face their first real crash test in 10 years” and that “only then will we know what is real and what is not in today’s markets, only then will we be able to assess how sustainable is the global synchronized GDP growth spurred by global synchronized monetary printing”. I like the graphic below from the report.

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I found the reaction to the Trump’s administration misstep on dollar strength interesting this week. Aditya Bhave and Ethan Harris, economists at Bank of America, said of the episode that “the Fed will see the weak dollar as a sign of easy financial conditions and a green light to keep tightening monetary policy”. ECB President Mario Draghi was not happy about the weak dollar statement as that would complicate Europe’s quantitative tightening plans. It was also interesting to hear Benoit Coeure, a hawkish member ECB executive board, saying this week that “it’s happening at different paces across the region, but we are moving to the point where we see wages going up”.

I think many of the Central Banks in developed countries are running out of wriggle room and the markets have yet to fully digest that reality. I fear that Goldilocks is about to get frazzled.

Telecoms’ troubles

The telecom industry is in a funk. S&P recently said that their “global 2017 base-case forecast is for flat revenues” and other analysts are predicting little growth in traditional telecom’s top line over the coming years across most developed markets. This recent post shows that wireless revenue by the largest US firms has basically flatlined with growth of only 1% from 2015 to 2016. Cord cutting in favour of wireless has long been a feature of incumbent wireline firms but now wireless carrier’s lunch is increasingly being eaten by disruptive new players such as Facebook’s messenger, Apple’s FaceTime, Googles’ Hangouts, Skype, Tencent’s QQ or WeChat, and WhatsApp. These competitors are called over the top (OTT) providers and they use IP networks to provide communications (e.g. voice & SMS), content (e.g. video) and cloud-based (e.g. compute and storage) offerings. The telecom industry is walking a fine line between enabling these competitors whilst protecting their traditional businesses.

The graph below from a recent TeleGeography report provides an illustration of what has happened in the international long-distance business.

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A recent McKinsey article predicts that in an aggressive scenario the share of messaging, fixed voice, and mobile voice revenue provided by OTT players could be within the ranges as per the graph below by 2018.

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Before the rapid rise of the OTT player, it was expected that telecoms could recover the loss of revenue from traditional services through increased data traffic over IP networks. Global IP traffic has exploded from 26 exabytes per annum in 2005 to 1.2 zettabytes in 2016 and is projected to grow, by the latest Cisco estimates here, at a CAGR of 24% to 2012. See this previous post on the ever-expanding metrics used for IP traffic (for reference, gigabyte/terabyte/petabyte/exabyte/zettabyte/yottabyte is a kilobyte to the power of 3, 4, 5, 6, 7 and 8 respectively).

According to the 2017 OTT Video Services Study conducted by Level 3 Communications, viewership of OTT video services, including Netflix, Hulu and Amazon Prime, will overtake traditional broadcast TV within the next five years, impacting cable firms and traditional telecom’s TV services alike. With OTT players eating telecom’s lunch, Ovum estimate a drop in spending on traditional communication services by a third over the next ten years.

Telecom and cable operators have long complained of unfair treatment given their investments in upgrading networks to handle the vast increase in data created by the very OTT players that are cannibalizing their revenue. For example, Netflix is estimated to consume as much as a third of total network bandwidth in the U.S. during peak times. Notwithstanding their growth, it’s important to see these OTT players as customers of the traditional telecoms as well as competitors and increasingly telecoms are coming to understand that they need to change and digitalise their business models to embrace new opportunities. The graphic below, not to scale, on changing usage trends illustrates the changing demands for telecoms as we enter the so called “digital lifestyle era”.

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The hype around the internet of things (IoT) is getting deafening. Just last week, IDC predicted that “by 2021, global IoT spending is expected to total nearly $1.4 trillion as organizations continue to invest in the hardware, software, services, and connectivity that enable the IoT”.

Bain & Co argue strongly in this article in February that telecoms, particularly those who have taken digital transformation seriously in their own operating models, are “uniquely qualified to facilitate the delivery of IoT solutions”. The reasons cited include their experience of delivering scale connectivity solutions, of managing extensive directories and the life cycles of millions of devices, and their strong position developing and managing analytics at the edge of the network across a range of industries and uses.

Upgrading network to 5G is seen as being necessary to enable the IoT age and the hype around 5G has increased along with the IoT hype and the growth in the smartphone ecosystem. But 5G is in a development stage and technological standards need to be finalised. S&P commented that “we don’t expect large scale commercial 5G rollout until 2020”.

So what can telecoms do in the interim about declining fundamentals? The answer is for telecoms to rationalise and digitalize their business. A recent McKinsey IT benchmarking study of 80 telecom companies worldwide found that top performers had removed redundant platforms, automated core processes, and consolidated overlapping capabilities. New technologies such as software-defined networks (SDN) and network-function virtualization (NFV) mean telecoms can radically reshape their operating models. Analytics can be used to determine smarter capital spending, machine learning can be used to increase efficiency and avoid overloads, back offices can be automated, and customer support can be digitalized. This McKinsey article claims that mobile operators could double their operating cashflow through digital transformation.

However, not all telecoms are made the same and some do not have a culture that readily embraces transformation. McKinsey say that “experience shows that telcoms have historically only found success in transversal products (for example, security, IoT, and cloud services for regional small and medium-size segments)” and that in other areas, “telcoms have developed great ideas but have failed to successfully execute them”.

Another article from Bain & Co argues that only “one out of eight providers could be considered capital effective, meaning that they have gained at least 1 percentage point of market share each year over the past five years without having spent significantly more than their fair share of capital to do so”. As can be seen below, the rest of the sector is either caught in an efficiency trap (e.g. spent less capital than competitors but not gaining market share) or are just wasteful wit their capex spend.

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So, although there are many challenges for this sector, there is also many opportunities. As with every enterprise in this digital age, it will be those firms who can execute at scale that will likely to be the big winners. Pure telecommunications companies could become extinct or so radically altered in focus and diversity of operations that telecoms as a term may be redundant. Content production could be mixed with delivery to make joint content communication giants. Or IT services such as security, cloud services, analytics, automation and machine learning could be combined with next generation intelligent networks. Who knows! One thing is for sure though, the successful firms will be the ones with management teams that can execute a clear strategy profitably in a fast changing competitive sector.