Tag Archives: monetary policy

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).

click to enlarge

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!

Pimping the Peers (Part 2)

In the last post on this topic, I highlighted how new technologies, broadly under the fintech tag, had the potential to disrupt the banking sector, primarily by means of automating processes rather than any major reinventing of business models (although I did end that post with a bit of a rant about innovation and human behaviour). Blockchain is the hot topic that seems to be cropping up everywhere (I’ll leave that for another time). This post is about insurance and new technology, or in the jargon, insurtech.

The traditional business model in the insurance industry is not reacting well to a world of low or negative interest rates. For the life insurance sector, the duration mismatch between their liabilities and their assets is having a perverse impact as interest rates have fallen. Savings returns for aging populations have been sacrificed in Central Bank’s attempt to stimulate economic growth.

In addition, the traditional distribution channel for selling life insurer’s products, and the old adage is that these products are sold rather than bought, has relied too heavily on aging tied agents whose focus is on the wealthy client that can generate more fees than the middle class. The industry is generally at a loss on how to sell products in a low interest world to the mass market and to the new tech savvy generation. As a result, the industry and others are throwing money at a rash of new start-ups in insurance, as the exhibit on some of the current hyped firms focusing on life insurance below illustrates.

click to enlargelife-insurance-big-data

As the exhibit illustrates, the focus of these new start-ups is weighted towards technologies around product development, distribution, and underwriting. Some will likely succeed in trying to differentiate further the existing clientele of life insurers (e.g. real time health data). Many will be gobbled up or disappear. Differing attitudes between those aged under 34 and the older generation towards online distribution channels can be clearly seen in the survey results in the exhibit below.

click to enlargeattitudes-to-life-insurance-distribution-channels

With longevity and low interest rates the dominant challenges for life insurers today, automation of processes will assist in cutting expenses in the provision of products (mainly to the existing customer base) but will not likely meaningfully address the twin elephants in the room.  Citigroup reckons that in 20 of the largest OECD countries the unfunded government liability for pensions is around $78 trillion which compares to approximately $50 trillion in GDP for all OECD countries in 2015. I look forward to conversing with a robo-advisor in the near future on what products it recommends for that problem!

Insurance itself is hundreds of years old and although the wonderfully namely bottomry (the earliest form of marine hull insurance) or ancient burial societies are early examples, non-life insurance really took off with mass markets after the great fire of London in 1666.

The most hyped example of insurtech in the non-life sector is the impact of technologies on the motor business like drive-less cars and car telematics. This paper from Swiss Re shows that the impact over the next 20 years of such advances on motor premia could be dramatic.

Much of the focus from insurtech innovation is on reducing expenses, an item that the industry is not light on. The graph below shows examples of the level of acquisition and overhead expenses in the non-life sector across different jurisdictions.

click to enlargenonlife-expense-ratios

A recent report from Aon Benfield went further and looked at expenses across the value chain in the US P&C insurance sector, as below. Aon Benfield estimated overall expenses make up approximately half of gross risk premium, much of which represents juicy disruption targets for new technology in the insurtech world.

click to enlargeexpenses-across-the-value-chain

Insurance itself is based upon the law of large numbers and serves a socially useful function in reducing economic volatility by transferring risks from businesses and consumers. In 1906, Alfred Manes defined insurance as “an economic institution resting on the principle of mutuality, established for the purpose of supplying a fund, the need for which arises from a chance occurrence whose probability can be estimated”.

One of the issues identified with the current non-life insurance sector is the so-called protection gap. This is in effect where insurers’ risk management practises have got incredibly adapt at identifying and excluding those risks most likely to result in a claim. Although good for profits, it does bring the social usefulness of the transference of only the pristine risks into question (for everybody else). The graph below from Swiss Re illustrates the point by showing economic and insured losses from natural catastrophe events as a % of GDP.

click to enlargeinsurance-protection-gap-uninsured-vrs-insured-losses

It’s in the context of low investment returns and competitive underwriting markets (in themselves being driven by low risk premia across asset classes) that a new technology driven approach to the mutual insurance model is being used to attack expense and protection gap issues.

Mutuals represent the original business model for many insurers (back to burial schemes and the great fire of 1666) and still represent approximately a third of the sector in the US and Europe today. Peer to peer insurers are what some are calling the new technology driven mutuals. In fact, most of the successful P2P models to date, firms like Guevara, Friendsurance, and Inspeer are really intermediaries who pool consumers together for group discounts or self-financing of high deductibles.

Lemonade, which launched in New York this week, is a peer to peer platform which issues its own insurance policies and seeks to address the protection gap issue by offering broader coverage. The firm has been heavily reinsured by some big names in insurance like Berkshire Hathaway and Lloyd’s. It offers a fee based model, whereby the policyholders pay claims through mutualisation (assumingly by pools determined by pre-defined criteria). Daniel Schreiber, CEO and co-founder of Lemonade says that the firm will be ”the only insurer that doesn’t make money by denying claims”. Dan Ariely, a big deal in the world of Behavioral Economics, has been named as Chief Behavioral Officer, presumably in an effort to assist in constructing pools of well behaved policyholders.

The graphic below tries to illustrate how the business model is evolving (or should that be repeating?). Technology offers policyholders the opportunity to join with others to pool risk, hitherto a process that was confined to associations amongst professional groups or groups bound by location. Whether technology offers the same opportunity to underwrite risks profitably (or at least not at a loss) but with a larger reach remains to be seen.

click to enlargeinsurance-business-models

It does occur to me that it may be successful in addressing areas of dislocation in the industry, such as shortfalls in coverage for flood insurance, where a common risk and mitigant can be identified and addressed in the terms of the respective pool taking the risks on.

For specialty re/insurers, we have already seen a bifurcation between the capital providers/risk takers and the risk portfolio managers in the ILS arena. Newer technology driven mutual based insurers also offer the industry a separation of the management of risk pools and the risk capital provided to underwrite them. I wish them well in their attempts at updating this most ancient of businesses and I repeat what I said in part 1 of this post – don’t let the sweet scent of shiny new technology distract you from the smell of the risk…..

Thoughts on ILS Pricing

Valuations in the specialty insurance and reinsurance sector have been given a bump up with all of the M&A activity and the on-going speculation about who will be next. The Artemis website reported this week that Deutsche Bank believe the market is not differentiating enough between firms and that even with a lower cost of capital some are over-valued, particularly when lower market prices and the relaxation in terms and conditions are taken into account. Although subject to hyperbole, industry veteran John Charman now running Endurance, stated in a recent interview that market conditions in reinsurance are the most “brutal” he has seen in his 44 year career.

One interesting development is the re-emergence of Richard Brindle with a new hybrid hedge fund type $2 billion firm, as per this Bloomberg article. Given the money Brindle made out of Lancashire, I am surprised that he is coming back with a business plan that looks more like a jump onto the convergence hedge fund reinsurer band wagon than anything more substantive given current market conditions. Maybe he has nothing to lose and is bored! It will be interesting to see how that one develops.

There have been noises coming out of the market that insurance linked securities (ILS) pricing has reached a floor. Given that the Florida wind exposure is ground zero for the ILS market, I had a look through some of the deals on the Artemis website, to see what pricing was like. The graph below does only have a small number of data points covering different deal structures so any conclusions have to be tempered. Nonetheless, it does suggest that rate reductions are at least slowing in 2015.

click to enlargeFlorida ILS Pricing

Any review of ILS pricing, particularly for US wind perils, should be seen in the context of a run of low storm recent activity in the US for category 3 or above. In their Q3-2014 call, Renaissance Re commented (as Eddie pointed out in the comments to this post) that the probability of a category 3 or above not making landfall in the past 9 years is statistically at a level below 1%. The graph below shows some wind and earthquake pricing by vintage (the quake deals tend to be the lower priced ones).

click to enlargeWind & Quake ILS Pricing by year

This graph does suggest that a floor has been reached but doesn’t exactly inspire any massive confidence that pricing in recent deals is any more adequate than that achieved in 2014.

From looking through the statistics on the Artemis website, I thought that a comparison to corporate bond spreads would be interesting. In general (and again generalities temper the validity of conclusions), ILS public catastrophe bonds are rated around BB so I compared the historical spreads of BB corporate against the average ILS spreads, as per the graph below.

click to enlargeILS Spreads vrs BB Corporate Spread

The graph shows that the spreads are moving in the same direction in the current environment. Of course, it’s important to remember that the price of risk is cheap across many asset classes as a direct result of the current monetary policy across the developed world of stimulating economic activity through encouraging risk taking.

Comparing spreads in themselves has its limitation as the underlying exposure in the deals is also changing. Artemis uses a metric for ILS that divides the spread by the expected loss, referred to herein as the ILS multiple. The expected loss in ILS deals is based upon the catastrophe modeller’s catalogue of hurricane and earthquake events which are closely aligned to the historical data of known events. To get a similar statistic to the ILS multiple for corporate bonds, I divided the BB spreads by the 20 year average of historical default rates from 1995 to 2014 for BB corporate risks. The historical multiples are in the graph below.

click to enlargeILS vrs BB Corporate Multiples

Accepting that any conclusions from the graph above needs to consider the assumptions made and their limitations, the trends in multiples suggests that investors risk appetite in the ILS space is now more aggressive than that in the corporate bond space. Now that’s a frightening thought.

Cheap risk premia never ends well and no fancy new hybrid business model can get around that reality.

Follow-up: Lane Financial LLC has a sector report out with some interesting statistics. One comment that catch my eye is that they estimate a well spread portfolio by a property catastrophic reinsurer who holds capital at a 1-in-100 and a 1-in-250 level would only achieve a ROE of 8% and 6.8% respectively at todays ILS prices compared to a ROE of 18% and 13.3% in 2012. They question “the sustainability of the independent catastrophe reinsurer” in this pricing environment and offer it as an explanation “why we have begun to see mergers and acquisitions, not between two pure catastrophe reinsurers but with cat writers partnering with multi-lines writers“.

Debt in a greying age

The book “Capital in the Twenty-First Century” by Thomas Piketty is unquestioningly one of the books of 2014. This blogger is currently reading Piketty’s book after finding it in his Santa sock this Christmas. There were not many other books from 2014 that caught my attention. One book that I am very much looking forward to in 2015 is the new Steven Drobny book “The New House of Money” with detailed interviews of money managers. On his website, Drobny has already released the first two chapters – one on Kyle Bass and the other with Jim Chanos.

The views of Kyle Bass, in particular, on Japan got me thinking again about demographics and the ability to withstand large debt loads. The views of Bass are also articulated in a piece on the investor perspectives website. For example, Bass states that currently in Japan debt repayments take up 25% of government tax revenues but that a 100bps rise in interest rates in Japan would mean that 100% of tax revenues would go to repayments. That leaves very little room for error!

This week, Buttonwood also has an article on the restrictions that large debt loads places on the effectiveness of monetary policy.

In terms of age profiles, the graphic below shows the profiles of the 9 largest economies (according to the World Bank).

click to enlargeTop 9 Demographics

Japan and Germany clearly stand out as countries with an aging population, currently with 26% and 21% of their populations over 65 respectively. I also compared the age profiles against the public and private debt figures (excluding that from banking and other financial service firms) from the “Deleveraging. What Deleveraging?report (see previous post on that report), as per the graph below.

click to enlargeMajor Country Public & Private Debt ex financial versus Age Profile

The graph does show that there is a clear relationship between debt loads and age profiles, particularly the percentages for the over 55s. However, the outliers of Germany and Russia on the low debt side and indeed Japan on the high debt side show that there are many other factors at play, not least the historic cultural characteristics of each country. The burden that high debt loads places on future generations is clearly an important policy issue for the global economy, one that will become ever more important if interest rates are to return to anyway close to normality.

As a follow-on to this post, I have been looking through the UN population projections and the following graphs represent population profiles I found interesting. The first is the world population historical figures and projections by age profile.

click to enlargeWorld Population Projections & Age Profile

The next graph is the world population split by continent (with Japan and China split out of Asia).

click to enlargeWorld Population Projections by Continent

And finally, a graph of the world population aged over 60 and aged under 15 split by continent.

click to enlargeWorld Population Over 60s & Under 15s by Continent