Tag Archives: artificial intelligence

Flying High

As the S&P 500 grapples around the 2,800 mark, it has achieved an impressive 12% year to date gain. A pause or a pull-back whilst macro events like Brexit and the US-China trade talks are resolved are a possibility given the near 17 forward PE. I thought it would be worthwhile looking at some of the high flyers in the market to search for value.

I selected a group of 12 stocks that have increased by 25% on average since the beginning of the year. The list is dominated by business software firms that are squarely in the SaaS, cloud and AI hype. Firms like ServiceNow (NOW), Workday (WDAY), Tableau Software (DATA), Splunk (SPLK), Adobe (ADBE), Salesforce (CRM), Palo Alto Networks (PANW) and the smaller Altair Engineering (ALTR). Others included in my sample are Square (SQ), Paypal (PYPL), VMWare (VMW) and my old friend Nvidia (NVDA).

Using data from Yahoo Finance, I compared each of the firm’s valuation, based upon today’s close, using their 2019 projected PE against their PEGs, using projected EPS growth for the next 3 years. The results are below.

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These are not cheap stocks (a PEG at or below 1 is considered undervalued). As per this FT article, the CEO of ServiceNow John Donahoe summed up the market’s love of some of these stocks by saying “investors value, first and foremost, growth”. By any measure, “value” in that quote is an understatement. I have never been good at playing hyped stocks, I just can’t get my head around these valuations. I do think it indicates that the market has got ahead of itself in its love of growth. I am going to focus on the two most “reasonably” valued stocks on a PEG basis in the graph above – Nvidia and Altair – by running my own numbers (I always distrust consensus figures).

I have posted on my journey with Nvidia previously, most recently here in November after their first revenue warning. Amazingly, even after a second big revenue warning in January from ongoing inventory and crypto-mining headwinds, the stock recovered from the 130’s into the 150’s before again trading into the 160’s in recent weeks following the Mellanox merger announcement. NVDA purchased Mellanox, an admired data centre equipment maker, at 25 times 2018 earnings (which seems reasonable given Mellanox is growing revenues at 25%).

NVDA’s recent quarterly results were not only worrying for its near 50% sequential decline in gaming but also for the 14% sequential decline in its data centre business, its second largest segment which was growing strongly. Despite management’s assertion that the gaming segment’s quarterly run rate is $1.4 billion (Q4 was below $1 billion), I am struggling to match analyst revenue estimates for FY2020 and FY2021. The most optimistic figures that I can get to (pre-Mellanox), assuming the crypto-mining boom is removed from the trend, is $10.3 billion and $12.8 billion for FY2020 and FY2021, 8% and 4% less than the consensus (pre-Mellanox), as below.

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Based upon management’s guidance on expenses (it is impressive that nearly 9,500 of their 13,300 employees are engaged in R&D), on the Mellanox deal closing in calendar year Q3 2019, and on 15 million shares repurchased each year, my estimates for EPS for FY2020 and FY2021 are $5.00 and $7.77 respectively (this FY2020 EPS figure is below analyst estimates which exclude any Mellanox contribution). At today’s share price that’s a PE of 33 and 21 for their FY2020 and FY2021. That may look reasonable enough, given the valuations above, for a combined business that will likely grow at 20%+ in the years thereafter. However, NVDA is a firm that has just missed its quarterly numbers by over 30% and it should be treated with a degree of “show me the money”. I think the consensus figures for FY2020 on NVDA are too optimistic so I shall watch NVDA’s progress with interest from the sidelines.

Altair Engineering (ALTR) is not the usual hyped firm. ALTR provide an integrated suite of multi-disciplinary computer aided engineering software that optimizes design performance across various disciplines which recently purchased an AI firm called Datawatch. ALTR is led by the impressive James Scapa and have built a highly specialised platform with significant growth potential. The revenue projections for the firm, including Datawatch and another acquisition SimSolid, with 2018 and prior on an ASC 605 basis and 2019 on an ASC 606 basis are below. The reason for the relatively flat Q/Q is the conversion of the Datawatch business to a SaaS basis and integration into the Altair platforms.

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For 2019 through 2021, my estimates for EPS are $0.62, $0.81 and $1.17 respectively (2019 and 2020 figures are over 10% higher than consensus). At the current share price of $38.32, that’s PE ratios of 63, 47, and 33. A rich valuation indeed. And therein lies the problem with high growth stocks. ALTR is a fantastic firm but its valuation is not. Another one for the watchlist.

A naughty or nice 2019?

They say if you keep making the same prediction, at some stage it will come true. Well, my 2018 post a year ago on the return of volatility eventually proved prescient (I made the same prediction for 2017!). Besides the equity markets (multiple posts with the latest one here), the non-company specific topics covered in this blog in 2018 ranged from the telecom sector (here), insurance (here, here, and here), climate change (here and here), to my own favourite posts on artificial intelligence (here, here and here).

The most popular post (by far thanks to a repost by InsuranceLinked)) this year was on the Lloyds’ of London market (here) and I again undertake to try to post more on insurance specific topics in 2019. My company specific posts in 2018 centered on CenturyLink (CTL), Apple (AAPL), PaddyPowerBetfair (PPB.L), and Nvidia (NVDA). Given that I am now on the side-lines on all these names, except CTL, until their operating results justify my estimate of fair value and the market direction is clearer, I hope to widen the range of firms I will post on in 2019, time permitting. Although this blog is primarily a means of trying to clarify my own thoughts on various topics by means of a public diary of sorts, it is gratifying to see that I got the highest number of views and visitors in 2018. I am most grateful to you, dear reader, for that.

In terms of predictions for the 2019 equity markets, the graph below shows the latest targets from market analysts. Given the volatility in Q4 2018, it is unsurprising that the range of estimates for 2019 is wider than previously. At the beginning of 2018, the consensus EPS estimate for the S&P500 was $146.00 with an average multiple just below 20. Current 2018 estimates of $157.00 resulted in a multiple of 16 for the year end S&P500 number. The drop from 20 to 16 illustrates the level of uncertainty in the current market

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For 2019, the consensus EPS estimate is (currently) $171.00 with an average 2019 year-end target of 2,900 implying a 17 multiple. Given that this EPS estimate of 9% growth includes sectors such as energy with an assumed healthy 10% EPS growth projection despite the oil price drop, it’s probable that this EPS estimate will come down during the upcoming earnings season as firms err on the conservative side for their 2019 projections.

The bears point to building pressures on top-line growth and on record profit margins. The golden boy of the moment, Michael Wilson of Morgan Stanley, calls the current 2019 EPS estimates “lofty”. The bulls point to the newly established (as of last Friday) Powell Put and the likely resolution of the US-China trade spat (because both sides need it). I am still dubious on a significant or timely relaxation of global quantitative tightening and don’t feel particularly inclined to bet money on the Orange One’s negotiating prowess with China. My guess is the Chinese will give enough for a fudge but not enough to satisfy Trump’s narcissistic need (and political need?) for a visible outright victory. The NAFTA negotiations and his stance on the Wall show outcomes bear little relation to the rhetoric of the man. These issues will be the story of 2019. Plus Brexit of course (or as I suspect the lack thereof).

Until we get further insight from the Q4 earnings calls, my current base assumption of 4% EPS growth to $164 with a multiple of 15 to 16 implies the S&P500 will be range bound around current levels of 2,400 – 2,600. Hopefully with less big moves up or down!

Historically, a non-recessionary bear market lasts on average 7 months according to Ed Clissold of Ned Davis Research (see their 2019 report here). According to Bank of America, since 1950 the S&P 500 has endured 11 retreats of 12% or more in prolonged bull markets with these corrections lasting 8 months on average. The exhibit below suggests that such corrections only take 5 months to recover peak to trough.

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To get a feel for the possible direction of the S&P500 over 2019, I looked at the historical path of the index over 300 trading days after a peak for 4 non-recessionary and 4 recessionary periods (remember recessions are usually declared after they have begun), as below.

Note: These graphs have been subsequently updated for the S&P500 close to the 18th January 2019. 

click to enlarges&p500 q42018 drop compared to 4 nonrecession drops in 1962 1987 1998 & 2015 updated

 

click to enlarges&p500 q42018 drop compared to 4 recession drops in 1957 1974 1990 & 2000 updated

 

I will leave it to you, dear reader, to decide which path represents the most likely one for 2019. It is interesting that the 1957 track most closely matches the moves to date  (Ed: as per the date of the post, obviously not after that date!) but history rarely exactly rhymes. I have no idea whether 2019 will be naughty or nice for equity investors. I can predict with 100% certainty that it will not be dull….

Given that Brightwater’s pure Alpha fund has reportingly returned an impressive 14.6% for 2018 net of fees, I will leave the last word to Ray Dalio, who has featured regularly in this blog in 2018, as per his recent article (which I highly recommend):

Typically at this phase of the short-term debt cycle (which is where we are now), the prices of the hottest stocks and other equity-like assets that do well when growth is strong (e.g., private equity and real estate) decline and corporate credit spreads and credit risks start to rise. Typically, that happens in the areas that have had the biggest debt growth, especially if that happens in the largely unregulated shadow banking system (i.e., the non-bank lending system). In the last cycle, it was in the mortgage debt market. In this cycle, it has been in corporate and government debt markets.

When the cracks start to appear, both those problems that one can anticipate and those that one can’t start to appear, so it is especially important to identify them quickly and stay one step ahead of them.

So, it appears to me that we are in the late stages of both the short-term and long-term debt cycles. In other words, a) we are in the late-cycle phase of the short-term debt cycle when profit and earnings growth are still strong and the tightening of credit is causing asset prices to decline, and b) we are in the late-cycle phase of the long-term debt cycle when asset prices and economies are sensitive to tightenings and when central banks don’t have much power to ease credit.

A very happy and healthy 2019 to all.

CTL: Pain before gain?

Before I unleash my musings on the latest Centurylink (CTL) results, building on this recent CTL post, I will touch on some industry trends and some CTL specific items that are relevant in my opinion. As regular readers will know, the increased use of artificial intelligence (AI) by businesses, particularly in business processes, is an area that fascinates me (as per this post). How such process improvements will change a capital- and labour-intensive sector such as telecom (as per this post) is one of the reasons I see such potential for CTL.

Whilst reading some recent articles on digital developments (such as this and this and this), I cannot but be struck by the expanded networking needs of this future. All this vast amount of new data will have to be crunched by machines, likely in data centres, and updated constantly by real time data from the field. Networks in this era (see this post on 5G) will need to be highly efficient, fluid and scalable, and have a deep reach. Very different from the fixed cost dumb pipe telecoms of old!

CTL have outlined their ambition to be such a network provider and are undertaking a digital transformation programme of their business to achieve that goal. CEO Jeff Storey has gone as far as saying that CTL “is not a telecom company, but that we are a technology company”. Time will tell on that one!

Today, industry trends from business telecom revenues (i.e. enterprises from SME to global giants plus wholesale business) are flat to declining, as highlighted in this post. Deciphering recent trends has not been made any easier by the introduction of the new revenue recognition accounting standard ASC606. Where possible, the updated graph below shows revenues under the new standard from Q1 2018.

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This data shows an estimated annual decline in overall annual revenues for 2018 of 1.5%, compared to 1.2% in 2017 and 2% for each of the preceding 2 years. Over the past 8 quarters, that’s about a 33-basis point sequential quarterly drop on average. Different firms are showing differing impacts from the accounting change on their business revenue. Comcast showed a 6.5% jump in Q1 2018 before returning to trend whilst AT&T showed a 4% drop in Q1 2018 before returning to more normal quarterly changes. Rather than trying to dismantle the impact of the accounting change, its easier to simply accept the change as its obvious the underlying trends remain, as the bottom graph above illustrates. Whilst accepting these 5 firms do not make up all the US, let alone the global, telecom market, some interesting statistics from this data are shown below.

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Although the accounting change has likely skewed figures in the short term, the exhibit above shows that AT&T is losing market share whilst the cable firms are growing their business revenues albeit from lower bases than the big players. Verizon and the new CTL have performed slightly below market trends (i.e. 50 basis point average quarterly sequential declines versus overall at 33 basis points).

Before I get onto CTL’s Q3 results, this article from Light Reading illustrates some of the changes underway at the firm to transform its business. The changes are centred around 4 themes – increasing network visibility, delivering business-owned automation, encouraging a lean mindset, and skills transformation.

On network viability, CTL is layering federation tools on top of its existing systems. Federated architecture (FA) is a pattern in enterprise architecture that allows interoperability and information sharing between semi-autonomous de-centrally organized lines of business, information technology systems and applications. The initial phase of this federation was with customer and sales systems such as those used for quoting, order entry, order status, inventory management and ticketing. The goal is to move towards a common sales ecosystem and standard portals that automate customer’s journeys from order to activation and beyond. A common narrative of CTL’s transformation is to give customers the tools to manage their networking capabilities like they do using the cloud. This is more of a network as a service or network on demand that CTL say is the future for telecom providers. This interview with the newly appointed CTO of CTL gives further insight into what the firm is doing in this on demand area, including changes underway to meet the increased SD-WAN demand and the upcoming deluge of data in the 5G era.

Business owned automation is allowing different business units to own their own automation projects, whilst been supported by centralised centres of excellence in areas such as robotic process automation (RPA), digital collaboration, mobility and analytics. Training is provided by the centralised units. Empowering the business units encourages a key cultural change in adopting a lean mindset across the firm. Ensuring that people in the firm are retrained and motivated is a core part of CTL’s plans as change only comes from within and as the firm continues to downsize (they have already reduced headcount by 12%) its important that staff morale and skills transformation is a focus as the business changes.

So, moving on to CTL’s Q3 results. The market has not reacted well to the Q on Q drop of 3.6% in revenues, with weakness seen across all business segments, and the stock is trading down around $19 as a result. The trends highlighted above have been exasperated by CTL dropping or renegotiating lower margin business such as contracts involving customer premises equipment (so called CPE). Of the $80 million quarterly revenue drop (under ASC606) in Q3, $30 million was attributed to the culling of low margin business. The remaining $50 million drop is about twice the average drop in recent times, thereby raising analyst concerns about an increase in trend revenue declines.

However, there are two points to note here. Firstly, using revenue figures before the application of ASC606, the net drop was more in line at $37 million (i.e. $67-$30) and comparable with the Q2 non-ASC606 drop of $40 million. Secondly, and more importantly, the trend is lumpy and given CTL’s transformation focus, it makes total sense to me for CTL to cull low margin non-network centric revenues. Management were explicit in stating their intention “to focus on the network-centric things” and that this business is “distracting our organization and it’s not giving us anything, so we’ll stop it”. To me, that demonstrates confidence in the direction of the business. As Storey emphasised, when referring to culling low margin business, “we manage this business for free cash flow, free cash flow per share, these are good things to be doing”.

Analysts concern that cutting expenses longer term cannot be a sustainable business plan without revenue growth at some point is certainly valid (and is one of the key risks with CTL). Indeed, I estimate that there is about $900 million and $500 million of quarterly legacy business and consumer revenues respectively (about 15% and 10% of total quarterly revenues) that could fall off at an accelerated pace as CTL refocuses the business over the medium term. CTL’s return to top line growth could be several years off yet. More on this later.

Another area of concern from analysts was the fact that CTL will spend approx. $500 million less on capex in 2018 compared to original projections (with levels projected to return to a more normal 16% of revenues for 2019 and beyond). This could be interrupted as a desire not to invest in the business to inflate free cash-flow, never a good sign for any company. However, again management explained this as a desire to refocus capital spending away from items like copper upgrades and towards strategic areas. They cited the approval to bring on-net another 7,000 to 8,000 buildings and the use of strategic targeting of capex (using AI) across consumer and business geographies to maximise returns in urban areas where 5G infrastructure will be needed in the future. Again, a more disciplined approach to capex makes total sense to me and demonstrates the discipline this management team is imposing on the business.

What seems to have been missed in the reaction to Q3 results is the extraordinary progress they have made on margin improvements. The EBITDA margin again grew to 39.3% with the projected operational synergies of $850 million now targeted to be achieved by year end. Management are keen to move the focus from integration to digital transformation from 2019. Achieving the targeted operational synergies so soon, particularly when we know that network expense synergies do not come through until 2 to 3 years after a merger, is an amazing achievement. It also highlights that their projected cost synergies of $850 million were way way under-baked. As I highlighted in this recent CTL post, I suspected this under-baking was to protect against the risk of any further acceleration in the underling margin erosion at the old CTL business as legacy business declined.

CTL’s discipline in extracting costs, as seen by actions such as the (painful) 12% headcount reduction, is central to my confidence in CTL’s management achieving their strategic aims. I do not believe that a further $250 million and $200 million of cost synergies in 2019 and 2020 respectfully through further synergies, network grooming efforts and the digital transformation initiative is unreasonable. That would bring overall cost synergies to $1.3 billion, a level consistent to what LVLT achieved in the TWTC merger.

So, given the likelihood of an increased purposeful erosion in low margin legacy business over the next several years combined with a higher level of cost extraction, I have recalculated my base and pessimistic scenarios from my previous post.

My base scenario, as per the graph below, shows annual revenues effectively flatlining over the next 3 years (2019 to 2021) around $23.3 to $23.6 billion before returning to modest top-line growth thereafter (i.e. between 1% and 1.5% annual growth) with an EBITDA margin of 42% achieved by the end of 2021 and maintained thereafter. This revenue profile mirrors that of previous LVLT mergers, albeit a longer period of flatlining revenues due to the amount of old legacy CTL to burn off. Capex is assumed at 16% of revenue from 2019 onwards. My projections also include further interest rate increases in 2019 and 2020 (as a reminder every 25-basis point change in interest rate results in an 8.5 basis point change in CTL’s blended rate). The current dividend rate is maintained throughout with FCF coverage ratio reducing from the low 70’s in 2019 to around 60% by the end of 2021. My DCF valuation for CTL under these base projections is $23 per share. That’s about 20% above its current level around $19 plus a 11% dividend yield.

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My pessimistic scenario, as per the graph below, assumes that the hoped-for revival of CTL into an on-demand service provider in the 5G age does not result in revenue growth after the legacy business has eroded for whatever reason (other technological advances over the need for a deep fiber network optic been the most likely). Annual revenue continues to decline to below $22 billion by 2021 and does not get above that level again until 2025. Although this scenario would be extreme, its not unknown in the telecom industry for future jumps in data traffic to result in falling revenues (eh, remember the telecom winter!). EBITDA margin levels get to 41% by the end of 2021 and slowly rise to 41.5% thereafter on further cost cutting. Capex and interest rate assumptions are as per the base scenario.

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In the pessimistic scenario the dividend level of $2.16 per share must be cut by 50% from 2020 to reflect the new reality and to deleverage the balance sheet. Although the share price would likely suffer greatly in such a scenario, my DCF valuation is $14 per share, 26% below the current $19 share price, not forgetting the reduced dividend yield after the 50% cut.

As per my previous post on CTL, I see little point in contemplating an optimistic scenario until such time as revenue trends are clearer. A buy-out at a juicy premium is the most likely upside case.

Consideration should be given in any projections over the medium term on the impact and timing of the next recession which is certain to happen over the 2019 to 2025 period. Jeff Storey has argued in the past that recession is good for firms like CTL as enterprises look to save money through switching from legacy services to more efficient on demand services. Although there is an element of truth to this argument, the next recession will likely put further pressures on CTL’s top-line (alternatively, an outbreak of inflation may help pricing pressures!!). Higher interest rates and lower multiples are a risk to the valuation of firms like CTL and the uncertainty over the future macro-economic environment make CTL a risky investment. Notwithstanding the inevitability of a recession at some time, I do feel that the revenue projections above are already conservative given the explosion in network demand that is likely over the next decade, although increased signs of recession in late 2019 or 2020 would temper my risk appetite on CTL.

To me, one of the biggest risks to CTL is the CEO’s health. Given Sunit Patel has left for T-Mobile (who I hope may be a potential buyer of CTL after they get the Sprint deal embedded and/or abandoned) and the new CFO will take some time to get accepted in the role, any potential for CTL not to have Jeff Storey at the helm over the next 2 years would be very damaging. Identifying and publicly promoting a successor to Jeff Storey is something the Board should be actively considering in their contingency planning.

For now, though, I am reasonably comfortable with the risk reward profile on CTL here, absent any significant slow down in the US economy.

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.