An Apple Appetite

Recently I have been trying to dig deeper into Apple (AAPL) to get a handle on what the near term may mean for this amazing company and thereby get an insight into APPL’s valuation. I have struggled with AAPL’s valuation in previous posts (here and here) but after each of my musings the share price continued on its upward trajectory.

Irrespective of whether iPhone 8 and iPhone X unit sales disappoint (due to unit shortages or otherwise) over the coming months, it seems highly probable to me that Apple will be successful in segmenting their iPhone market further over the medium term and break through the $1000 per iPhone spend in a significant way. Their R&D spend of over $10 billion (including nearly $2 billion of share options) goes a long way to ensuring customers will pay for their innovations.

The reason why AAPL are following the current strategy is a hot topic of debate with analysts. Some see the new iPhone models feed into a super-cycle of updates and continued installed base growth, pointing to the approximate 40% of the current iPhone installed base older than 2 years. Other analysts believe that the smartphone market has plateaued (see graph from Mary Meeker below) and Apple is embarking upon a segmentation strategy to harvest their loyal customer base.

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The estimates for the iPhone installed base vary significantly across analysts from 550 to 750 million units and some, such as Deutsche Bank and BoA ML further, break the base down to core and secondary non-core users. Although most of the estimates are likely out of date as they were published prior to the iPhone 8 and iPhone X announcements, the graphic below illustrates the differing views.

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It is likely no surprise that I am in the plateau camp on future growth of the installed base. I have assumed an installed base of 640 million as at end September 2017 and 40% or approximately 250 million of these are potential iPhones upgraders with phones older than 2 years. I have further assumed that a proportion of the installed base, I selected 10%, are secondary non-core users with a very low propensity to upgrade. That leaves an approximate 190 million potential upgrades for the FY2018. Despite the lack of growth of the market, I assumed another 10 million sales from new purchasers giving a target iPhone unit sales of 200 million for FY2018. 200 million of annual unit iPhone sales is well below most analyst estimates which average around 240 -260 million for FY2018.

Of the 200 million iPhone unit sales for FY2018, I have further assumed 45 million are iPhone X and just over half are iPhone 8, with the remainder being iPhone 7 and older models. For Q42017, I am assuming only 9 million iPhone 8 sales with 35 million of iPhone 7 and older models (influenced by the amount of inventory clearance sales I have seen in retail stores). The graph below shows my installed base assumptions, with my estimates for sales of the iPhone 8, iPhone X and it successor models over FY 2018 and FY2019 (I am assuming 200 million units is the new normal for annual iPhone sales through to FY2020).

click to enlargeAAPL iPhone Installed Base 2014 to 2019

The resulting average selling price (ASP) for FY2018 is $785 with annual FY2018 revenues from iPhone of $157 billion. For FY2019, I have assumed a ASP of $860 with annual FY2019 iPhone revenues of $172 billion. The graph below shows my revenue assumptions over FY 2018 and FY2019 across all products.

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The EPS estimates coming out of my model, using the assumptions above (amongst others), for FY2018, FY2019 and FY2020 are $10.17, $11.45 and $11.81 respectively (I agree with the estimates of $9.00 for FY2017). That represents 13% EPS growth for 2018 and 2019, slowing to 3% in 2020. At the current share price of $160, the forward PE (excluding cash) would look as per the graph below.

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My analysis suggests that AAPL either deserves a higher multiple than the recent past to justify its current value or it will have to convince enough new iPhone users to buy its new products to take market share from its competitors and sell more than 200 million iPhone annually for the foreseeable future.

Given the potential headwinds for iPhone 8 and iPhone X over the short term, the current price may be difficult to defend near term as the market gets used to lower iPhone sales at higher prices (and hopefully margins too). Then again, going negative on AAPL hasn’t proven fruitful in the past and the analysts are currently hyping up AAPL’s prospects with price targets heading solidly towards $200.

Given my previous history of questioning AAPL’s valuation, maybe indecision is the best answer for the time being……

CAT Calls

Following on from a recent post on windstorms in the US, I have taken several loss preliminary estimates recently published by firms (and these are very early estimates and therefore subject to change) and overlaid them against the South-East US probable maximum loss (PML) curves and Atlantic hurricane scenarios previously presented, as below. The range of insured losses for Harvey, Irma and Maria (now referred to as HIM) are from $70 billion to $115 billion, averaging around $90 billion.

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The loss estimates by firm depend heavily upon the risk profile of each. As a generalisation, it could be said that the aggregate US wind losses are averaging around the 1 in 100 loss level.

Given there was over $20 billion of insured losses from H1 and factoring in developing losses such as the Mexico earthquake, the California wildfires and the current windstorm Ophelia hitting Ireland, annual insured losses for 2017 could easily reach $120 billion. The graph below shows the 2016 estimates from Swiss Re and my $120 billion 2017 guesstimate (it goes without saying that much could still happen for the remainder of the year).

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At a $120 billion level of insured loss for 2017, the 10 year average increase from around $55 billion to $65 billion. In a post in early 2016, I estimated that catastrophe pricing was about 25% too low based upon annual average losses of $40 billion per year. We will see whether the 2017 losses are enough to deplete the overcapitalisation in the market and return pricing towards their technical rate. I wouldn’t hold my breath on that as although there may be material aggregate losses in the private collateralised market and other pockets of the retrocession market, the appetite of yield seeking investors will likely remain unabated in the current interest rate environment.

Although the comparison between calendar year ratios and credit defaults is fraught with credibility issues (developed accident year ratios to developed default rates are arguably more comparable), I updated my previous underwriting cycle analysis (here in 2014 and here in 2013). Taking the calendar year net loss ratios of Munich Re and Lloyds of London excluding catastrophe and large losses (H1 results for 2017), I then applied a crude discount measure using historical risk-free rates plus 100 basis points to reflect the time value of money, and called the resulting metric the adjusted loss ratio (adjusted LR). I compared these adjusted LRs for Munich and Lloyds to S&P global bond credit default rates (by year of origin), as per the graph below.

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This shows that the years of relatively benign attritional claims together with the compounding impact of soft pricing over the past years may finally be coming to an end. Time will tell. All in all, it makes for a very interesting period for the market over the next 6 to 12 months.

In the interim, let’s hope for minimal human damage from the current California wildfires and windstorm Ophelia.

Remember deleveraging?

There is a lot of interesting stuff in the latest IMF Financial Stability Report. After much research on global debts levels (as per this post in 2014 and this one in 2015) over the past few years, the graph below on G20 gross debt levels from the IMF shows how little progress has been made.

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When looked at by advanced economy, the trend in gross debt from 2006 to 2016 looks startling, particularly for government debt.

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As the IMF state, “one lesson from the global financial crisis is that excessive debt that creates debt servicing problems can lead to financial strains” and “another lesson is that gross liabilities matter”.

The question does arise as to the economic impact of these debt levels if interest rates start to rise across advanced economies?

10 x Hopelessly Lax = ?

The economist Sir John Vickers, himself an ex Bank of England Chief Economist, recently had a pop at the current Bank of England’s governor and chair of the Financial Stability Board, Mark Carney.  He countered Carney’s assertion that “the largest banks are required to have as much as ten times more of the highest quality capital than before the crisis” with the quip that “ten times better than hopelessly lax is not a useful measure”. I particularly liked Vickers observation that equity capital is “a residual, the difference between two typically big numbers, of which the asset side is hard to measure given the nature of banking, and dependent on accounting rules”.

In a recent article in the FT, Martin Wolf joined in the Carney bashing by saying the ten times metric “is true only if one relies on the alchemy of risk-weighting” and that banking regulatory requirements have merely “gone from the insane to the merely ridiculous” since the crisis. Wolf acknowledges that “banks are in better shape, on many fronts, than they were a decade ago” but concludes that “their balance sheets are still not built to survive a big storm”.

I looked through a few of the bigger banks’ reports (randomly selected) across Europe and the US to see what their current risk weighted assets (RWA) as a percentage of total assets and their tier 1 common equity (CET1) ratios looked like, as below. The wide range of RWAs to total assets, indicative of the differing business focus for each bank, contrasts against the relatively similar level of core “equity” buffers.

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Wolf and Vickers both argue that higher capital levels, such as those cited by Anat Admati and Martin Hellwig in The Bankers’ New Clothes, or more radical structural reform, such as that proposed by Mervyn King (see this post), should remain a goal for current policymakers like Carney.

The latest IMF Stability Report, published yesterday, has an interesting exhibit showing an adjusted capital ratio (which includes reserves against expected losses) for the global systemically important banks (GSIBs), as below.

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This exhibit confirms an increased capital resilience for the big banks. Hardly the multiple increases in safety that Mr Carney’s statements imply however.

Beautiful Models

It has been a while since I posted on dear old Solvency II (here). As highlighted in the previous post on potential losses, the insurance sector is perceived as having robust capital levels that mitigates against the current pricing and investment return headwinds. It is therefore interesting to look at some of detail emerging from the new Solvency II framework in Europe, including the mandatory disclosures in the new Solvency and Financial Condition Report (SFCR).

The June 2017 Financial Stability report from EIOPA, the European insurance regulatory, contains some interesting aggregate data from across the European insurance sector. The graph below shows solvency capital requirement (SCR) ratios, primarily driven by the standard formula, averaging consistently around 200% for non-life, life and composite insurers. The ratio is the regulatory capital requirement, as calculated by a mandated standard formula or a firm’s own internal model, divided by assets excess liabilities (as per Solvency II valuation rules). As the risk profile of each business model would suggest, the variability around the average SCR ratio is largest for the non-life insurers, followed by life insurers, with the least volatile being the composite insurers.

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For some reason, which I can’t completely comprehend, the EIOPA Financial Stability report highlights differences in the SCR breakdown (as per the standard formula, expressed as a % of net basic SCR) across countries, as per the graph below, assumingly due to the different profiles of each country’s insurance sector.

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A review across several SFCRs from the larger European insurers and reinsurers who use internal models to calculate their SCRs highlights the differences in their risk profiles. A health warning on any such comparison should be stressed given the different risk categories and modelling methodologies used by each firm (the varying treatment of asset credit risk or business/operational risk are good examples of the differing approaches). The graph below shows each main risk category as a percentage of the undiversified total SCR.

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By way of putting the internal model components in context, the graph below shows the SCR breakdown as a percentage of total assets (which obviously reflects insurance liabilities and the associated capital held against same). This comparison is also fraught with difficulty as an (re)insurers’ total assets is not necessarily a reliable measure of extreme insurance exposure in the same way as risk weighted assets is for banks (used as the denominator in bank capital ratios). For example, some life insurers can have low insurance related liabilities and associated assets (e.g. for mortality related business) compared to other insurance products (e.g. most non-life exposures).

Notwithstanding that caveat, the graph below shows a marked difference between firms depending upon whether they are a reinsurer or insurer, or whether they are a life, non-life or composite insurer (other items such as retail versus commercial business, local or cross-border, specialty versus homogeneous are also factors).

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Initial reactions by commentators on the insurance sector to the disclosures by European insurers through SFCRs have been mixed. Some have expressed disappointment at the level and consistency of detail being disclosed. Regulators will have their hands full in ensuring that sufficiently robust standards relating to such disclosures are met.

Regulators will also have to ensure a fair and consistent approach across all European jurisdictions is adopted in calculating SCRs, particularly for those calculated using internal models, whilst avoiding the pitfall of forcing everybody to use the same assumptions and methodology. Recent reports suggest that EIOPA is looking for a greater role in approving all internal models across Europe. Systemic model risk under the proposed Basel II banking regulatory rules published in 2004 is arguably one of the contributors to the financial crisis.

Only time will tell if Solvency II has avoided the mistakes of Basel II in the handling of such beautiful models.