Peak iPhone

This will be a very interesting week on the stock market, not least the US mid-terms and the ongoing US/China trade saga, which will likely determine the short-term direction of the market. Apple (AAPL) reported last week and another stellar report was hoped for to calm technology weakness. Instead of a stellar report the market got weak Q1 guidance and the news that AAPL would drop detailed product reporting for their FY2019. Given that there is a massive industry dedicated to examining iPhone trends, the lack of specific numbers being disclosed has caused consternation amongst commentators.

It has been about a year since I last posted on AAPL (here) when it traded around $170. Of course, it has since traded up to a high of $230 before falling back to just above $200 currently. There is no doubt that the smartphone market is saturated with IDC estimating global smartphone shipments falling in Q3 by 6% to 355 million unit. In this environment, it makes sense to me for AAPL to focus on higher value smartphones and to extracting increased fees from services on their installed base. Extrapolating on the iPhone installed base analysis from my last post, I estimate that the iPhone installed base will peak around 650 units based upon iPhone unit sales fall to 200 million and 190 million in FY2019 and FY2020 respectively from 218/217 million in FY2018/2017. The active installed base, excluding non-core users, peaks around 570 million. My projections are shown below.

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I have also assumed that the ASP for FY2019 and FY2020 increases to $819 and $847 respectively from $759 in FY2018. I further assumed that service revenue increases as a percentage of total revenue to 18% for FY2020 from 14% in FY2018. I suspect this may be too light given AAPL’s decision to move its reporting focus away from products to services. Although AAPL’s net cash pile is slowly dwindling (approx. $120 billion at end September from $170 billion at the end of December 2017), I think a more focused move by AAPL into the home and content to take on Netflix and Amazon will be a feature of the next few years (bring on the NFLX rumours, again!). My resulting quarterly revenue estimates into FY2020 are shown below.

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As you can see, these estimates do show overall revenue moderating with revenue for FY2019 and FY2020 at $270 billion and $273 billion respectively from $266 billion in FY2018. My diluted EPS estimates, assuming the same trend of share buy-backs, for FY2019 and FY2020 are $13.30 and $14.80, representing EPS growth of 12% and 11% respectively. These EPS estimates are consistent with current consensus. At a share price of $200, the forward PE would be 15 and 13.5 for FY2019 and FY2020 respectively.

My usual forward PE excluding cash graph, at an AAPL stock price of $200, is below. If AAPL were to return to its historical average multiple since 2009 of 9, then AAPL’s stock could fall back to $160 or below if the market gets really spooked about peak iPhone.

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The question therefore is how the market is going to react to AAPL’s attempt to move the focus from its hardware results and more towards its service business from its massive and loyal installed base. Changing the market’s obsession from iPhone sales will be no easy task. AAPL is an emotive stock, not only because of its products but for its incredible historical value creation. It is the one stock that I have always regretted selling any of. I do not think now is the time to sell AAPL but I will wait for the stock price to settle, particularly in the current volatility, to consider buying more. A fall towards $170 would be too tempting to ignore for this wonderful firm. Mr Buffet and the firm’s own buy-back programme make such a fall unlikely in my view but one can only hope!

Peak Earnings?

With the S&P500 down 9% off its high this month after last week, the question everybody is asking is whether this is a buying opportunity or the beginning of a new phase in the market. I have no idea. Nobody really does. I suspect this week will be bumpy but will rally off Fridays’ lows as there is some cheap names who have been hit hard. I have been modestly dipping my toe in on some names but am waiting before making any big moves. I hope to post on a few of the stocks regularly mentioned in this blog in the coming weeks.

The underlying concerns about the global economy and trade, the impact of US rate increases and quantitative tightening, Italy, to name but a few, have been and continue to be real issues to consider. The fact that the market has turned on a penny and is now all worried about the issues it shrugged off a few weeks ago is, well just how markets are!

What I do know is that this bull market has all been about earnings and margin growth, nothing else matters. So, I took the latest operating EPS and sales estimates for 2019 from S&P, extrapolated them into 2020, assuming a modest slowing of the EPS growth. These operating margin figures and assumed sales figures form the basis of Scenario 1. Stressed operating margin and sales formed the basis for Scenarios 2 and 3, with Scenario 2 falling back to the 2013-17 average operating margin of 9.5% and Scenario 3 falling more severely to the 2008-18 average of 8.75%. The graph below shows the operating margin assumptions in a historical context for each scenario.

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Assuming a price for the S&P500 as per Friday’s close of 2,659, the EPS figures with respective trailing and future 12-month PEs are as per the graphs below.

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So, if the current operating estimates for 2019 stand up and continue into 2020 as per Scenario 1, then I would say the current dip is a buying opportunity with a forward 2019 and 2020 PE of 15 and 14 respectively. If, however, you feel that we have reached peak earnings and a modest enough EPS retrenchment over 2019 and 2020 is likely as per Scenario 2, then the current S&P500 level looks vulnerable to further downside as the implied forward PEs of 17.5 and 18.7 for 2019 and 2020 look rich in a downward trending EPS environment. If, as per Scenario 3, the EPS retrenchment is more severe, then we are in for a very bumpy ride with another 15% to 25% downside possible.

To state the obvious, the current market focus is all about the earnings outlook for 2019 and 2020. The mid-terms over the next few weeks will be another factor to consider. It will be interesting to see if the market focus moves away from the economic prospects over the next few years and more towards 2018 bonuses and end of year window dressing as this quarter progresses!

Crises Chronicles

Ray Dalio is on a mission to share insights gained over 40 years at the helm of the vastly successful Brightwater Associates. After the financial crisis, Dalio published an excellent article on the workings of the economy. In 2017, he expanded on his previously published philosophy in a book called Principles (as per this summary). More recently he published a free downloadable book called A Template for Understanding Big Debt Crises, available here.

I have a copy of Principles but I must admit I have yet to finish reading it (on my to do list!). I have read the first section of the latest Big Debt Crises book and would highly recommend it. The second section of the book has detailed case studies (the US 2007-11, the US 1928-37, and Germany 1918-24) and the third section is a compendium of 48 other case studies.

Two graphs below from the Big Debt Crises book are worth reproducing, reflecting key factors behind our economy today.

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Dalio is an astute student of history. He recently commented that “the biggest mistake that most people make is to judge what will be good by what has been good lately”.

Amen to that.

More ILS illuminations

A continuation of the theme in this post.

The pictures and stories that have emerged from the impact of the tsunami from the Sulawesi earthquake in Indonesia are heart-breaking. With nearly 2,000 officially declared dead, it is estimated that another 5,000 are missing with hundreds of thousands more severely impacted. This event will be used as an vivid example of the impact of soil liquefaction whereby water pressure generated by the earthquake causes soil to behave like a liquid with massive destructive impacts. The effect on so many people of this natural disaster in this part of the world contrasts sharply with the impact on developed countries of natural disasters. It again highlights the wealth divide within our world and how technologies in the western world could benefit so many people around the world if only money and wealth were not such a determinant of who survives and who dies from nature’s wrath.

The death toll from Hurricane Florence on the US, in contrast, is around 40 people. The possibility of another US hurricane making landfall this week, currently called Tropical Storm Michael, is unfolding. The economic losses of Hurricane Florence are currently estimated between $25 billion and $30 billion, primarily from flood damage. Insured losses will be low in comparison, with some estimates around $3-5 billion (one estimate is as high as $10 billion). The insured losses are likely to be incurred by the National Flood Insurance Program (NFIP), private flood insurers (surplus line players including some Lloyds’ Syndicates), crop and auto insurers, with a modest level of losses ceded to the traditional reinsurance and insurance-linked securities (ILS) markets.

The reason for the low level of insured loss is the low take-up rate of flood policies (flood is excluded from standard homeowner policies), estimated around 15% of insurance policies in the impacted region, with a higher propensity on the commercial side. Florence again highlights the protection gap issue (i.e. percentage difference between insured and economic loss) whereby insurance is failing in its fundamental economic purpose of spreading the economic impact of unforeseen natural events. Indeed, the contrast with the Sulawesi earthquake shows insurance failings on a global inequality level. If insurance and the sector is not performing its economic purpose, then it simply is a rent taker and a drag on economic development.

After that last sentiment, it may therefore seem strange for me to spend the rest of this blog highlighting a potential underestimating of risk premia for improbable events when a string of events has been artfully dodged by the sector (hey, I am guilty of many inconsistencies)!

As outlined in this recent post, the insurance sector is grappling with the effect of new capital dampening pricing after the 2017 losses, directly flattening the insurance cycle. It can be argued that this new source of low-cost capital is having a positive impact on insurance availability and could be the answer to protection gap issues, such as those outlined above. And that may be true, although under-priced risk premia have a way of coming home to roost with serious longer-term effects.

The objective of most business models in the financial services sector is to maximise the risk adjusted returns from a selected portfolio, whether that be stocks or bonds for asset managers, credit risks for banks or insurance risks for insurers. Many of these firms have many thousands of potential risks to select from and so the skill or alpha that each claim derives from their ability to select risks and to build a robust portfolio. If for example, a manager wants to build a portfolio of 20 risks from a possible 100 risks, the combinations are 536 trillion (with 18 zeros as per the British definition)! And that doesn’t consider the sizing of each of the 20 positions in the portfolio. It’s no wonder that the financial sector is embracing artificial intelligence (AI) as a tool to assist firms in optimizing portfolios and potential risk weighted returns (here and here are interesting recent articles from the asset management and reinsurance sectors). I have little doubt that AI and machine learning will be a core technique in any portfolio optimisation process of the future.

I decided to look at the mechanics behind the ILS fund sector again (previous posts on the topic include this post and this old post). I constructed an “average” portfolio that broadly reflects current market conditions. It’s important to stress that there is a whole variety of portfolios that can be constructed from the relatively small number of available ILS assets out there. Some are pure natural catastrophe only, some are focused at the high excess level only, the vintage and risk profile of the assets of many will reflect the length of time they have been in business, many consist of an increasing number of private negotiated deals. As a result, the risk-return profiles of many ILS portfolios will dramatically differ from the “average”. This exercise is simply to highlight the impact of the change of several variables on an assumed, albeit imperfect, sample portfolio. The profile of my “average” sample portfolio is shown below, by exposure, expected loss and pricing.

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The weighted average expected loss of the portfolio is 2.5% versus the aggregate coupon of 5%. It’s important to highlight that the expected loss of a portfolio of low probability events can be misleading and is often misunderstood. Its not the loss expected but simply the average over all simulations. The likelihood of there being any losses is low, by definition, and in the clear majority of cases losses are small.

To illustrate the point, using my assumed loss exceedance curves for each exposure, with no correlation between the exposures except for the multi-peril coverage within each region, I looked at the distribution of losses over net premium, as below. Net premium is the aggregate coupon received less a management fee. The management fee is on assets under management and is assumed to be 1.5% for the sample portfolio, resulting in a net premium of 3.5% in the base scenario. I also looked at the impact of price increases and decreases averaging approximate +/-20% across the portfolio, resulting in net premium of 4.5% and 2.5% respectively. I guesstimate that the +20% scenario is roughly where an “average” ILS portfolio was 5 years ago.

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I have no doubt that the experts in the field would quibble with my model assumptions as they are crude. However, experience has thought me that over-modelling can lead to false sense of security and an over optimistic benefit for diversification (which is my concern about the ILS sector in general). My distributions are based upon 250,000 simulations. Others will point out that I haven’t considered the return on invested collateral assets. I would counter this with my belief that investors should only consider insurance risk premium when considering ILS investments as the return on collateral assets is a return they could make without taking any insurance risk.

My analysis shows that currently investors should only make a loss on this “average” portfolio once every 4 years (i.e. 25% of the time). Back 5 years ago, I estimate that probability at approximately 17% or roughly once every 6 years. If pricing deteriorates further, to the point where net premium is equal to the aggregate expected loss on the portfolio, that probability increases to 36% or roughly once every 3 years

The statistics on the tail show that in the base scenario of a net premium of 3.5% the 1 in 500-year aggregate loss on the portfolio is 430% of net premium compared to 340% for a net premium of 4.5% and 600% for a net premium of 2.5%. At an extreme level of a 1 in 10,000-year aggregate loss to the portfolio is 600% of net premium compared to 480% for a net premium of 4.5% and 800% for a net premium of 2.5%.

If I further assume a pure property catastrophe reinsurer (of which there are none left) had to hold capital sufficient to cover a 1 in 10,000-year loss to compete with a fully collaterised ILS player, then the 600% of net premium equates to collateral of 21%. Using reverse engineering, it could therefore be said that ILS capital providers must have diversification benefits (assuming they do collaterise at 100% rather than use leverage or hedge with other ILS providers or reinsurers) of approximately 80% on their capital to be able to compete with pure property catastrophe reinsurers. That is a significant level of diversification ILS capital providers are assuming for this “non-correlating asset class”. By the way, a more likely level of capital for a pure property catastrophe reinsurer would be 1 in 500 which means the ILS investor is likely assuming diversification benefits of more that 85%. Assuming a mega-catastrophic event or string of large events only requires marginal capital of 15% or less with other economic-driven assets may be seen to be optimistic in the future in my view (although I hope the scenario will never be illustrated in real life!).

Finally, given the pressure management fees are under in the ILS sector (as per this post), I thought it would be interesting to look at the base scenario of an aggregate coupon of 5% with different management fee levels, as below. As you would expect, the portfolio risk profile improves as the level of management fees decrease.

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Given the ongoing pressure on insurance risk premia, it is likely that pressure on fees and other expenses will intensify and the use of machines and IA in portfolio construction will increase. The commodification of insurance risks looks set to expand and increase, all driven by an over-optimistic view of diversification within the insurance class and between other asset classes. But then again, that may just lead to the more wide-spread availability of insurance in catastrophe exposed regions. Maybe one day, even in places like Sulawesi.

The New Normal (Again)

I expect that next week’s reinsurance jamboree in Monte Carlo will be full of talk of innovative and technology streaming-lining business models (as per this post on AI and insurance). This recent article from the FT is just one example of claims that technology like blockchain can reduce costs by 30%. The article highlights questions about whether insurers are prepared to give up ownership of data, arguably their competitive advantage, if the technology is really to be scaled up in the sector.

As a reminder of the reinsurance sector’s cost issues, as per this post on Lloyds’, the graph below illustrates the trend across Lloyds’, the Aon Benfield Aggregate portfolio, and Munich’s P&C reinsurance business.

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Until the sector gets serious about cutting costs, such as overpaid executives on luxury islands or expensive cities and antiquated business practises such as holding get togethers in places like Monte Carlo, I suspect expenses will remain an issue. In their July review, Willis stated that a “number of traditional carriers are well advanced in their plans to reduce their costs, including difficult decisions around headcount” and that “in addition to cost savings, the more proactively managed carriers are applying far greater rigor in examining the profitability of every line of business they are accepting”. Willis highlighted the potential difficulties for the vastly inefficient MGA business that many have been so actively pursuing. As an example of the type of guff executives will trot out next week, Swiss Re CEO, Christian Mumenthaler, said “we remain convinced that technology will fundamentally change the re/insurance value chain”, likely speaking from some flash office block in one of the most expensive cities in the world!

On market conditions, there was positive developments on reinsurance pricing at the January renewals after the 2017 losses with underlying insurance rates improving, as illustrated by the Marsh composite commercial rate index (example from US below).

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However, commentators have been getting ever more pessimistic as the year progresses, particularly after the mid-year renewals. Deutsche Bank recently called the reinsurance pricing outlook “very bleak”. A.M. Best stated that “the new normal for reinsurers appears to be one with returns that are less impressive and underwriting and fee income becoming a larger contributor to profits” and predicts, assuming a normal large loss level, an 8% ROE for 2018 for the sector. Willis, in their H1 report, puts the sectors ROE at 7.7% for H1 2018. S&P, in the latest report that is part of their Global Highlights series, also expects a ROE return for 2018 around 6% to 8% and estimates that “reinsurers are likely to barely cover their cost of capital in 2018 and 2019”.

S&P does question why “the market values the industry at a premium to book value today (on average at 1.24x at year-end 2017), and at near historical highs, given the challenges” and believes that potential capital returns, M&A and interest rate rises are all behind elevated valuations.  The recent Apollo PE deal for Aspen at 1.12 times book seems a large way off other recent multiples, as per this post, but Aspen has had performance issues. Still its interesting that no other insurer was tempted to have a go at Aspen with the obvious synergies that such a deal could have achieved. There is only a relatively small number of high quality players left for the M&A game and they will not be cheap!

As you are likely aware, I have been vocal on the impact the ILS sector has had in recent years (most recently here and here). With so-called alternative capital (at what size does it stop being alternative!) now at the $95 billion-mark according to Aon, A.M. Best makes the obvious point that “any hope for near-term improvement in the market is directly correlated to the current level of excess capacity in the overall market today, which is being compounded by the continued inflow of alternative capacity”. Insurers and reinsurers are not only increasing their usage of ILS in portfolio optimisation but are also heavily participating in the sector. The recent purchase by Markel of the industry leading and oldest ILS fund Nephila is an interesting development as Markel already had an ILS platform and is generally not prone to overpaying.

I did find this comment from Bob Swarup of Camdor in a recent Clear Path report on ILS particularly telling – “As an asset class matures it inevitably creates its own cycle and beta. At this point you expect fees to decline both as a function of the benefits of scale but also as it becomes more understood, less of it becomes alpha and more of it becomes beta” and “I do feel that the fees are most definitely too high right now and to a large extent this is because people are trying to treat this as an alternative asset class whereas it is large enough now to be part of the general mix”. Given the still relatively small size of the ILS sector, it’s difficult for ILS managers to demonstrate true alpha at scale (unless they are taking crazy leveraged bets!) and therefore pressure on current fees will become a feature.

A.M. Best articulated my views on ILS succinctly as follows: “The uncorrelated nature of the industry to traditional investments does appear to have value—so long as the overall risk-adjusted return remains appropriate”. The graph below from artemis.bm shows the latest differential between returns and expected cost across the portfolio they monitor.

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In terms of the returns from ILS funds, the graph below shows the underlying trend (with 2018 results assuming no abnormal catastrophic activity) of insurance only returns from indices calculated by Lane Financial (here) and Eurekahedge (here). Are recent 5 year average returns of between 500 and 250 basis points excess risk free enough to compensation for the risk of a relatively concentrated portfolio? Some think so. I don’t.

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Whether reinsurers and specialty insurers will be able to maintain superior (albeit just above CoC) recent returns over ILS, as illustrated in this post, through arbitrating lower return ILS capital or whether their bloated costs structures will catch them out will be a fascinating game to watch over the coming years. I found a section of a recent S&P report, part of their Global Highlights series, on cat exposures in the sector, amusing. It stated that in 2017 “the reinsurance industry recorded an aggregate loss that was assessed as likely to be incurred less than once in 20 years” whilst “this was the third time this had happened in less than 20 years“.

So, all in all, the story is depressingly familiar for the sector. The new normal, as so many commentators have recently called it, amounts to overcapacity, weak pricing power, bloated cost structures, and optimistic valuations. Let’s see if anybody has anything new or interesting to say in Monte Carlo next week.

As always, let’s hope there is minimal human damage from any hurricanes such as the developing Hurricane Florence or other catastrophic events in 2018.