Category Archives: Equity Market

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 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 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.

Keep on moving, 2018

As I re-read my eve of 2017 post, its clear that the trepidation coming into 2017, primarily caused by Brexit and Trump’s election, proved unfounded in the short term. In economic terms, stability proved to be the byword in 2017 in terms of inflation, monetary policy and economic growth, resulting in what the Financial Times are calling a “goldilocks year” for markets in 2017 with the S&P500 gaining an impressive 18%.

Politically, the madness that is British politics resulted in the June election result and the year ended in a classic European fudge of an agreement on the terms of the Brexit divorce, where everybody seemingly got what they wanted. My anxiety over the possibility of a European populist curveball in 2017 proved unfounded with Emmanuel Macron’s election. Indeed, Germany’s election result has proven a brake on any dramatic federalist push by Macron (again the goldilocks metaphor springs to mind).

My prediction that “volatility is likely to be ever present” in US markets as the “realities of governing and the limitations of Trump’s brusque approach becomes apparent” also proved to be misguided – the volatility part not the part about Trump’s brusque approach! According to the fact checkers, Trump made nearly 2,000 false or misleading claims in his first year, that’s an average of over 5 a day! Trump has claimed credit for the amazing performance of the 2017 equity market no less than 85 times (something that may well come back to bite him in the years ahead). The graph below does show the amazing smooth performance of the S&P500 in 2017 compared to historical analysts’ predictions at the beginning of the year (see this recent post on my views relating to the current valuation of the S&P500).

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As for the equity market in 2018, I can’t but help think that volatility will make a come-back in a big way. Looking at the near unanimous positive commentators’ predictions for the US equity market, I am struck by a passage from Andrew Lo’s excellent book “Adaptive Markets” (which I am currently reading) which states that “it seems risk-averse investors process the risk of monetary loss with the same circuit they contemplate viscerally disgusting things, while risk-seeking investors process their potential winnings with the same reward circuits used by drugs like cocaine”. Lo further opines that “if financial gain is associated with risky activities, a potentially devastating loop of positive feedback can emerge in the brain from a period of lucky investments”.

In a recent example of feeding the loop of positive feedback, Credit Suisse stated that “historically, strong returns tend to be followed by strong returns in the subsequent year”. Let’s party on! With a recent survey of retail investors in the US showing that over 50% are bullish and believe now is a good time to get into equities, it looks like now is a time where positive feedback should be restrained rather than being espoused, as Trump’s mistimed plutocratic policies are currently doing. Add in a new FED chair, Jay Powell, and the rotation of many in the FOMC in 2018 which could result in any restriction on the punch bowl getting a pass in the short term. Continuing the goldilocks theme feeding the loop, many commentators are currently predicting that the 10-year treasury yield wouldn’t even breach 3% in 2018! But hey, what do I know? This party will likely just keep on moving through 2018 before it comes to a messy end in 2019 or even 2020.

As my post proved last year, trying to predict the next 12 months is a mugs game. So eh, proving my mug credentials, here goes…

  • I am not even going to try to make any predictions about Trump (I’m not that big of a mug). If the Democrats can get their act together in 2018 and capitalize on Trump’s disapproval ratings with sensible policies and candidates, I think they should win back the House in the November mid-terms. But also gaining control of the Senate may be too big an ask, given the number of Trump strong-holds they’ll have to defend.
  • Will a Brexit deal, both the final divorce terms and an outline on trade terms, get the same fudge treatment by October in 2018? Or could it all fall apart with a Conservative implosion and another possible election in the UK? My guess is on the fudge, kicking the can down the transition road seems the best way out for all. I also don’t see a Prime Minster Corbyn, or a Prime Minister Johnson for that matter. In fact, I suspect this time next year Theresa May will still be the UK leader!
  • China will keep on growing (according to official figures anyway), both in economics terms and in global influence, and despite the IMF’s recent warning about a high probability of financial distress, will continue to massage their economy through choppy waters.
  • Despite a likely messy result in the Italian elections in March with the usual subsequent drawn out coalition drama, a return of Silvio Berlusconi on a bandwagon of populist right-wing policies to power is even too pythonesque for today’s reality (image both Trump and Berlusconi on the world stage!).
  • North Korea is the one that scares me the most, so I hope that the consensus that neither side will go there holds. The increasingly hawkish noises from the US security advisors is a worry.
  • Finally, as always, the winner of the World Cup in June will be ……. the bookies! Boom boom.

A happy and health New Year to all.

Happy Returns

A recently published paper, called “The Rate of Return on Everything, 1870–2015”, looks extremely interesting. The authors – Òscar Jordà, Katharina Knoll, Dmitry Kuvshinov, Moritz Schularick, and Alan M. Taylor – have collected a unique dataset of total returns for equity, housing, bonds, and treasury bills covering 16 advanced economies from 1870 to 2015.

The paper calculates real returns across asset classes on a global GDP weighted basis, as per this graph.

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The paper contains some fascinating conclusions, such as housing and equities having similar returns but with housing being considerably less volatile, as per this graph.

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Another fascinating graph is on the risk premium between risky and safe assets, as below.

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Given the time of year, I haven’t had an opportunity to consider the paper in detail but will hopefully get a chance over the Christmas break (and now back to wrapping presents!!).

A very happy Christmas to all who spend any time here. Have a great time and I hope Santa is kind!

Broken Record

Whilst the equity market marches on regardless, hitting highs again today, writing about the never-ending debates over equity valuations makes one feel like a broken record at times. At its current value, I estimate the S&P500 has returned an annualised rate of nearly 11%, excluding dividends, since its low in March 2009. As of the end of September 2017, First Trust estimated the total return from the S&P500 at 18% since March 2009, as per the graph below.

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Goldman Sachs recently published an analysis on a portfolio of 60% in the S&P 500 and 40% in 10-year U.S. Treasuries, as per the graph below, and commented that “we are nearing the longest bull market for balanced equity/bond portfolios in over a century, boosted by a Goldilocks backdrop of strong growth without inflation”. They further stated that “it has seldom been the case that all assets are expensive at the same time—historical examples include the Roaring ‘20s and Golden ‘50s. While in the near term, growth might stay strong and valuations could pick up further, they should become a speed limit for returns”.

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My most recent post on the topic of US equity valuations in May looked at the bull and bear arguments on low interest rates and heighten profit margins by Jeremy Grantham and John Hussman. In that post I further highlighted some of the other factors which are part of the valuation debate such as the elevated corporate leverage levels, reduced capital expenditures, and increased financial risk taking as outlined in the April IMF Global Financial Stability report. I also highlighted, in my view, another influential factor related to aging populations, namely the higher level of risk assets in public pensions as the number of retired members increases.

In other posts, such as this one on the cyclically adjusted PE (CAPE or PE10), I have highlighted the debates around the use of historically applicable earnings data in the use of valuation metrics. Adjustments around changes in accounting methodology (such as FAS 142/144 on intangible write downs), relevant time periods to reflect structural changes in the economy, changes in dividend pay-out ratios, the increased contribution of foreign earnings in US firms, and the reduced contribution of labour costs (due to low real wage inflation) are just some examples of items to consider.

The FT’s John Authers provided an update in June on the debate between Robert Shiller and Jeremy Siegel over CAPE from a CFA conference earlier this year. Jeremy Siegel articulated his critique of the Shiller CAPE in this piece last year. In an article by Robert Shiller in September article, called “The coming bear market?”, he concluded that “the US stock market today looks a lot like it did at the peaks before most of the country’s 13 previous bear markets”.

The contribution of technology firms to the bull market, particularly the so-called FANG or FAANG stocks, has also been a much-debated issue of late. The graph below shows the historical sector breakdown of the S&P500 since 1995.

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A recent article from GMO called “FAANG SCHMAANG: Don’t Blame the Over-valuation of the S&P Solely on Information Technology” tried to quantify the impact that the shift in sector composition upon valuations and concluded that “today’s higher S&P 500 weight in the relatively expensive Information Technology sector is cause for some of its expensiveness, but it does not explain away the bulk of its high absolute and relative valuation level. No matter how you cut it, the S&P 500 (and most other markets for that matter) is expensive”. The graph below shows that they estimate the over-valuation of the S&P500, as at the end of September 2017, using their PE10 measure is only reduced from 46% to 39% if re-balanced to take account of today’s sector weightings.

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In his recent article this month, John Hussman (who meekly referred to “his incorrectly tagged reputation as a permabear”!!) stated that “there’s no need to take a hard-negative outlook here, but don’t allow impatience, fear of missing out, or the illusion of permanently rising stock prices to entice you into entrusting your financial future to the single most overvalued market extreme in history”.

As discussed in my May post, Hussman reiterated his counter-argument to Jeremy Grantham’s argument that structurally low interest rates, in the recent past and in the medium term, can justify a “this time it’s different” case. Hussman again states that “the extreme level of valuations cannot, in fact, be “justified” on the basis of depressed interest rates” and that “lower interest rates only justify higher valuations if the stream of future cash flows is held constant” and that “one of the reasons why reliable valuation measures have retained such a high correlation with subsequent market returns across history, regardless of the level of interest rates, is that the impact of interest rates and growth rates on “terminal” valuations systematically offset each other”.

Hussman also again counters the argument that higher profit margins are the new normal, stating that “it’s important to recognize just how dependent elevated profit margins are on maintaining permanently depressed wages and salaries, as a share of GDP” and that “simply put, elevated corporate profit margins are the precise mirror-image of depressed labour compensation” which he contends is unlikely to last in a low unemployment environment.

Hussman presents a profit margin adjusted CAPE as of the 3rd of November, reproduced below, which he contends shows that “market valuations are now more extreme than at any point in history, including the 1929 and 2000 market highs”.

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However, I think that his profit margin analysis is harsh. If you adjust historical earnings upwards for newer higher margin levels, of course the historical earning multiples will be lower. I got to thinking about what current valuations would look like against the past if higher historical profit margins, and therefore earnings, had resulted in higher multiples. Using data from Shiller’s website, the graph below does present a striking representation of the relationship between corporate profits (accepting the weaknesses in using profits as a percentage of US GDP) and interest rates.

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Purely as a thought experiment, I played with Shiller’s data, updating the reported earnings for estimates through 2018 (with a small discount to reflect over-zealous estimates as per recent trends of earnings revisions), recent consensus end 2018 S&P500 targets, and consensus inflation and the 10-year US interest rates through 2018. Basically, I tried to represent the base case from current commentators of slowly increasing inflation and interest rates over the short term, with 2018 reported EPS growth of 8% and the S&P500 growing to 2,900 by year end 2018. I then calculated the valuation metrics PE10, the regular PE (using trailing twelve month reported earnings called PE ttm), and the future PE (using forward twelve month reported earnings called PE ftm) to the end of 2018. I further adjusted the earnings multiples, for 2007 and prior, by applying an (principally upward) adjustment equal to a ratio of the pre-2007 actual  corporate profits percentage to GDP divided by a newly assumed normalised percentage of 8.5% (lower than the past 10-year average around 9% to factor in some upward wage pressures over the medium term). The resulting historical multiples and averages are shown below.

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Based upon this analysis, whilst accepting its deeply flawed assumptions, if 2018 follows the base case currently expected (i.e. no external shocks, no big inflation or interest rates moves, steady if not spectacular earnings growth), the S&P500 currently looks over-valued by 50% to 20% using historical norms. If this time it is different and higher profit margins and lower interest rates are the new normal, then the S&P500 looks roughly fairly-valued and current targets for 2018 around 2,900 look achievable. Mind you, it’s a huge leap in mind-set to assume that the long-term average PE is justifiably in the mid-20s.

I continue to be concerned about increasing corporate leverage levels, as highlighted in my May post from the IMF Global Financial Stability report in April, and the unforeseen consequences of rising interest rate after such a long period of abnormally low rates.

In the interim, to paraphrase an ex-President, it’s all about the earnings stupid!