Information is alpha- as long as you know what to do with it…

In a world that often seems to be drowning in data masquerading as information, how is “alpha” or an “edge” to be found?

There seem to be two main alternative routes: one either has to have better information than one’s competitors; or, with the same information, a superior ability to identify and exploit patterns in it to identify both risks and opportunities.

It is obvious that there is an escalating “arms race” in acquiring “better” information, with the term “alternative data” now widely used in business and financial circles. For example, the use of data from commercial satellites is becoming increasingly common for hedge funds and others as a means to acquire “non-public” data. Yet, paradoxically, this simply leads towards a scenario in which all those who can afford it, have it. The edge is increasingly blunted. This then leads to the search for the next “alternative” source, but begs the question of whether “the next big thing” has any real value.

So, what about superior pattern recognition? Human beings are, after all, programmed by evolution to look for patterns in what their senses perceive as a means to avoid the lion lurking in the underbrush. What began as a mechanism necessary for survival has become a dominant trait, with the ability to recognize patterns, for example, visually/spatially considered an essential component of intelligence.

In the world of credit and risk analysis, the ability to understand and forecast what may happen in respect of a particular obligor or scenario is essential. To a large extent, this involves the ability to discern patterns that one knows from experience and acquired knowledge are likely to lead to a particular outcome, good or bad- for example, over-leverage, or insufficient liquidity. However, it also involves being able to distinguish between patterns that are meaningful (a signal) and those which are merely distracting noise, as well as to recognize that there may be a new pattern or paradigm, because one can be lulled into a false sense of comfort by failing to question what one perceives or “knows”.

Naturally, the growth of AI has led to something of a frenzy in terms of interrogating data for patterns that no-one else has yet discovered. Within certain parameters, specialized AI (for that is all that exists at present), backed by ever-rising processing and computing power has the potential ability to see things quicker, or differently from human beings, no matter how experienced or skilled. One only has to look at the fact that AI systems can now overwhelm even the best human players of chess or Go (to mention only two examples) to understand that.

However, the world is a complex, non-linear place, which means that, for now at least, even if the existential risk from AI to the role of (re)insurance underwriters in high-volume, commoditized product lines looms ever nearer, in the more complex areas in which being able to understand causation, correlation, constraints and the nuances of game theory and human behaviour are critical, the pattern-recognition abilities of human operators should prosper for much longer.

While we are eternally paranoid at Awbury about mistaking noise for a signal, or to our thesis being simply wrong, we believe that there is hope for us yet, given our relentless focus on complex, non-standard risks!

The Awbury Team


The chaos beneath the surface…

“Civilization is hideously fragile… there’s not much between us and the horrors underneath, just about a coat of varnish”- CP Snow

Reading Seth Klarman’s (of Baupost fame and fortune) year-end letter set us thinking about how fragile seemingly stable environments can be.

Those of us who are fortunate to live in what are considered well-ordered and reasonably well-governed societies, tend rather smugly to believe that “‘twill ever be thus”. This is a good example of recency bias and the availability heuristic in action: because it is so, and has been for a long time (in our terms); because our experience has always been the same, we find it hard to bring ourselves to believe that our world will not simply continue as before. In market terms, for example, there has been an almost 36-year bull market in US bonds. Careers have passed without the experience of a real bear market. Knowledge has been lost. What happens when a true reversal starts?

As the CP Snow quotation warns, there is often a fine line between order and chaos- systems or trends are stable, until they are not. Disruptive forces can evolve remarkably quickly, such that seemingly invincible and secure companies, long-standing markets or even governments find themselves at risk of degradation, dissolution or irrelevance. Who would have thought that December 2018 would bring the worst final month for major equity indices since 1931- the depths of the Great Depression?

In this context, the quality, agility and effectiveness of analysis and decision-making become paramount.

Unfortunately, as Klarman pointed out in his letter, there are signs that US markets in particular are leveraged not just in monetary terms, but also in structure, algorithmic bias and investor psychology, such that the historic tendency to “herd” becomes potentially even more exaggerated in scope. For example, if trading algorithms are designed by human beings (as they still are) and those human beings share the same experiences and biases, the speed of algorithms once put into use can overwhelm markets given the fact that the majority of US stock trading (and probably increasingly that in many other markets) is actually initiated and conducted by algorithms.

Turning to government (and no matter what one’s political affiliations may be), there are also worrying signs of a deterioration in the quality and rationality of policy- and decision-making in many jurisdictions. Of course, politicians acting irrationally and for partisan purposes is not exactly a new phenomenon, and by the standards of history, political discourse is actually quite restrained in most true democracies. However, in a complex world, where new media enable the dissemination of thoughts almost instantaneously, the risk of a statement or assertion causing disruption rises inexorably. By the time anyone actually stops to think it is too late. The Latin tag “festina lente” (literally “hurry slowly”- more haste less speed) is worth bearing in mind in this context.

And what of the world of (re)insurance? In a still consolidating industry, as market power becomes ever more concentrated within the traditional business models (and perhaps more volatile in the realm of alternative capital), there is a risk (always present in the industry) of doing what everyone else is doing, because “the market” cannot be wrong, when clearly it can. As we have written before, unbridled enthusiasm for certain types of risk (e.g., cyber) can lead to a deterioration in the quality of thought being applied to understanding, defining and managing the risks entailed.

We are in no sense saying that the “end is nigh”. However, we do think that, for example, (re)insurers should constantly re-assess and test the robustness and continuing fitness for purpose of their decision-making systems and processes to minimize the probability of tipping over into the abyss of fundamental error or misguided belief.

The Awbury Team


Certainty is a delusion…

To be absolutely certain about something, one must know everything or nothing about it”- Henry Kissinger

As the wry old joke goes: “The only certainties in life are death and taxes”, yet human beings like to extend the aura of certainty to areas in which it has no credible place, with sometimes unfortunate consequences.

Even in “hard” science what are sometimes stated or perceived to be “certain” facts (axioms), turn out to be simply untrue, incomplete, or more nuanced and complex- whether the nature of the cosmos and Earth’s place within it, atomic theory, or Newtonian mechanics. Karl Popper stated that for something to be scientific it must be able to be proven false. If things are falsifiable (able to possibly be proven false) then they can be used in scientific studies and inquiry. In other, words, even “certainties” need constantly to be tested.

This fact was brought to mind recently in reading David Quammen’s excellent book “The Tangled Tree: A Radical New History of Life”, which tells the story, amongst other things, of how as recently as 1977 it was axiomatic that the “tree of life” (popularized by Darwin’s work) had only two main branches from its trunk- for bacteria and for eukaryotes (essentially everything else) and that species only changed vertically through mutation and natural selection over time. That was until a molecular biologist called Carl Woese (who was not even looking for the result), realized that, in fact, there are three branches- the two mentioned previously, and what are now called archaea. Not only that, but genes can be swapped between species- via horizontal gene transfer. So much for the certainties of what you were taught in high school biology. In fact, the “tree” is now considered to look more like a tangled web, so perhaps it should be the Thicket of Life?

When it comes to the world of credit risk analysis and management, it would be wonderful if one could be certain of anything and everything! One could then predict the true risk of loss and one’s pricing would be perfect. Dwight Eisenhower (during World War II) pointed out: “Plans are useless, planning is essential.” Of course we build models and make forecasts. These are an essential component of any financial business. However, any experienced analyst knows that actual outcomes can easily be very different from what was assumed because of some unforeseen or unexpected factor (which is one reason why we are also habitually paranoid). The real world places constraints on possible outcomes, but the number and weighting of the variables that go into assessing, say, the risk of default is inherently probabilistic. The real skill lies in understanding the range and scope of potential outcomes over time, and how to manage and mitigate actual outcomes if they deviate significantly from the range of reasonable expectations- planning, not plans, constantly updated.

History also teaches that seemingly minor variations in decision-making or apparently unconnected events can interact and cascade in ways such that what seemed “certain” and inevitable, turns out very differently. At Dunkirk, in 1940, it seemed certain that the trapped British and French forces would be overrun by the Nazi German Wehrmacht. Yet that did not happen, with the outcome that everyone knows.

So, when someone states that they are certain about something (a 100% probability!), the immediate response should be to ask how and why. At Awbury, we believe (and can demonstrate through outcomes) that our approach to risk analysis, management and mitigation is effective in relative and absolute terms; but we would never be so foolish or arrogant as to state that we are absolutely certain of the outcome on any of our transactions. That would be doing ourselves and our partners a singular dis-service. After all, our business model is founded upon assuming real risks.

The Awbury Team


It is the Devil’s excrement…

Readers may recall that this sentiment was uttered in the mid-1970s by Juan Alfonso, formerly Minister of Mines and Hydrocarbons of Venezuela, and often called “the Father of OPEC”. In full, it reads: “I call petroleum the devil’s excrement. It brings trouble… Look at this locura [madness]- waste corruption, consumption, our public services falling apart. And debt, we shall have debt for years.”

This was an allusion to the natural resources “curse”, most particularly that caused by the discovery of large oil and gas reserves. A politer term is “Dutch Disease”- namely, the negative impact on an economy of anything that gives rise to a sharp inflow of foreign currency. The currency inflows lead to currency appreciation, making the country’s other products less price competitive on the export market, as well as potentially to the economic and social distortions, endemic mismanagement and corruption to which Alfonso referred. An expectation of future export revenues from oil leads to the avoidance of putting in place sustainable government revenues based upon taxation.

It is a rare country that manages to overcome the “curse”. Canada, Norway and the Netherlands are examples of those that have, albeit with different approaches; while the sheer scale of the US’s economy has muted any material impact over time.

However, the catalogue of the “cursed” is a long one- including Russia, Saudi Arabia, most of the other Gulf states, Iran, Iraq and, of course, Alfonso’s own Venezuela, which is perhaps the most egregious and painful modern example.

In the case of the last, one can argue that it is the archetypal petro-state gone wrong, with oil revenues in the “good times” being used to influence and co-opt potential opposition, as well as to “bribe” the population through a latter-day version of “bread and circuses”. Unfortunately, when the subsequent “bad times” coincided with the demise of a charismatic leader (Chavez) and his replacement by a thuggish plodder (Maduro) desperate to cling to power (and supported by those who had benefitted from the corrupt largesse during the good times- the military, in particular), the result has been one of the most rapid collapses of a still-functioning state into “failed” status in recent history. And entirely self-inflicted.

The sheer scale of the regime’s stupidity beggars belief. No rational government would gut, coerce and starve of investment its primary source of revenues. Yet that is exactly what first Chavez and then Maduro have done to PDVSA, the national oil company (NOC). It used to be axiomatic that, even if the Venezuelan government could barely be trusted to do anything right or rational, it would not jeopardize PDVSA’s ability to produce and deliver oil from Venezuela’s abundant reserves. Nevertheless, that is exactly what has happened, with an end-game of regime change perhaps now in sight.

For underwriters of complex credit risks, such as the Awbury Team, events in Venezuela provide a salutary reminder that one has to judge the outcome of risks that depend on the decisions of others by reference to their track record, incentives and constraints. Assuming that people (and governments, like any other entity, consist of people) will actually act rationally and in their long term interests can prove quite misguided. One should always be willing to factor in the probability that actors and agents will not be rational (by the standards of the person making the assessment).

The Awbury Team


Only the lawyers (and accountants) get rich…

The New York Federal Reserve’s excellent Liberty Street blog recently published a series of 5 posts aimed at assessing the scale of value destruction, both direct and indirect, following the bankruptcy filing of Lehman Brothers on September 15, 2008, which now seems so long ago as to belong to another era.

What many may not realize is that the Chapter 11 proceedings for Lehman Brothers Holdings Inc. (LBHI) and a number of its US subsidiaries are still continuing, a period of time which is apparently some 8 times that of the average Chapter 11 proceeding of 14 months. What may also not be appreciated is that Lehman’s US broker-dealer subsidiary, Lehman Brothers Inc. (LBI), was resolved and liquidated under a separate process under the Securities Investor Protection Act (SIPA), which took some 4 ½ years to be essentially completed in March 2013.

These separate processes have had very different outcomes in terms of creditor recovery. In the case of LBI, customers received 100% of their claims- almost USD 190BN. In the case of LBHI and its other US subsidiaries outside the SIPA process, the outcome was much more complex, protracted and unsatisfactory.

When LBHI filed, its senior bonds implied a recovery of 30%, falling to 9% a month later. In early 2011, LBHI estate estimated recovery for its creditors at 16%. In a plan filed in June 2011, allowed claims by third-party creditors totaled USD 362BN, against which recovery, net of expenses, of USD 75BN was expected, or c.21%. The total for 16 distributions made to date is c. USD 94BN against estimated allowed claims of just over USD 300BN, implying a recovery rate of c. 31%. Of course, that is in nominal dollars. Discounted at UST yields, the recovery is c.26%. That brings home just how large the financial impact of the Lehman’s bankruptcy has been, without even taking into account the human and economic costs for its then 25,000 employees, many of whom were pitched into unemployment at a time when the financial system appeared to be in meltdown.

As the blog points out (even though there had been signs of “cracks” within the financial system in 2007), Lehman’s stock reached its all-time high in January 2008, then beginning a decline which accelerated mid-year and turned into a rout after its now-infamous “pre-announcement” on September 10 of disastrous Q3/08 results. Even as late as September 10, LBHI’s senior bonds were at USD 77 (“distressed” levels, but not “bankruptcy imminent”). Interestingly, with hindsight, the proverbial “canary in the coal mine” may well have been “free credit balances” (analogous to bank deposits) in LBI, Even though such balances were supposed to be segregated from those of LBI itself, they declined 60% between May and September 19 (the day on which LBI filed for bankruptcy). Of course, most of this would have been anecdotal and not that easily visible in the timeframe involved.

So, why should anyone care about any of this? Simply because it demonstrates that not only close and predictive monitoring of all counterparties is essential; but that one should also clearly understand the nature of one’s claim, in terms of both legal and structural ranking and subordination. Just accepting what the “market” believes is far from sufficient.

At Awbury, we aim to be rigorous in all aspects of our risk analysis, and that includes legal, regulatory and recovery risks. After all, we are fundamental credit analysts.

As an aside, the professional fees for LBI’s liquidation were USD 1.18BN; while those for LBHI’s continuing Chapter 11 process so far total USD 2.56BN, both according to calculations made by the Liberty Street economists.

The Awbury Team


Energy in Transition…

While the frenzy and hype over the “end of the Hydrocarbon Era” may have abated somewhat, only a fool would conclude that the issue has gone away.

There seems little doubt that we are moving through another period of transition from one primary energy base (hydrocarbons) to a more multi-faceted one- that of renewable energy sources.

Forecasting exactly when and how this transition will occur (and even the extent to which it must) is a mug’s game. Humans have a seemingly innate tendency to extrapolate from what happened in the past far into the future, while at the same time becoming over-enthused about a new technology. This is also coupled, particularly in the case of hydrocarbons (fossil fuels), with the “doom view”- either “oil is going to run out” as propagated by the once-popular Peak Oil scenario, “it’s soon going to be stranded in the ground” in the Peak Demand scenario.

In reality, there are so many factors involved in the current transition that the real skill will be in determining which will exert the most leverage, and the extent to which those will influence the speed and scale of change.

While past may not be prologue, energy transitions tend to take decades, not years. Research by Vaclav Smil shows that it took coal 55 years to go from 5% to 40% of global energy supply; while oil took 60 years for the same shift; and it has taken natural gas 55 years to go from 5% to 25%. If one contemplates the fact that renewables currently provide little more than 3% of overall global energy supplies, one can imagine that their transition to importance, let alone dominance, is unlikely to be measured in years.

Secondly, as the population of the world continues to expand, potentially reaching 10BN (from c. 7.5BN today) at its currently expected peak in 2050 (a forecast that will, no doubt, also be wrong!) absolute energy consumption is only likely to increase, even if some parts of the world (e.g., the EU) strive to reduce it. Existing technologies, based on hydrocarbons, seem more likely to supply that larger population with its energy needs in that timeframe, in the absence of some yet unforeseen technological breakthrough, or a draconian implementation of measures aimed at curbing the risk of irreversible climate change through curtailment of hydrocarbon use. While not impossible, both seem unlikely in the near to medium term.

Thirdly, sources such as hydrological and nuclear power simply do not have the capabilities to meet growing demand, even at the margin- the former because it is limited and localized; the latter because of lingering distrust and extremely long project lead times. Solar and wind are more scalable (as has already been seen), but require use of significant space, raising the issue of alternative land uses unless somehow located offshore.

Fourthly, people sometimes mistake the technology for the solution, rather than an incremental shift in application of an idea. The rise of electric vehicles as replacements for those powered by internal combustion may be inexorable, but some technology has to produce the energy that they will store in their batteries. Renewable sources are no more likely to be the source for that power than any other for now.

None of this is to argue that there will be no transition. Rather, it is to point out that fixating on any one path, technology or timeframe is inherently misguided. If ever a situation called for scenario planning and weighting of both probability and impact, this transition does.

At Awbury, our approach is always to make an analysis of all relevant factors affecting a risk, and then aim to ensure that our portfolio can survive any realistic scenario, however seemingly remote or extreme.

The Awbury Team


Time to re-assess CAT models?

As is now customary, Munich Re released its annual overview of CAT losses (

While the “headline” numbers of USD 160BN of losses and USD 80BN of insured losses were by no means as severe as the trauma of 2017 (USD 350BN and USD 140BN respectively), nevertheless, the insured losses were almost double the 30-year, inflation adjusted average of USD 41BN.

Of course, one can debate the validity of using simple “inflation adjustments” as a metric for what a “normalized” and average CAT year should produce; and there will be those who, quite reasonably, say that 2 years of results do not constitute a trend.

However, as the world’s population continues to increase and climate volatility seemingly rises, resulting in changing and increased patterns of severity, it would be a foolish risk manager who dismissed the outcomes of 2017 and 2018 as merely an aberration.

Unusually, the event that caused both the largest overall losses (USD 16.5BN) and largest insured losses (USD 12.5BN) was not a windstorm but a wildfire- the so-called Camp Fire in California. This followed a combination of drought, strong winds and difficulty of access to the affected area. Including other large wildfires, such overall wildfire losses in the state in 2018 were USD 24BN, of which USD 18BN (or 75%) was insured- the worst on record, for a second year.

Not surprisingly, Munich Re commented that the greater frequency of “unusual” events and possible links between them should cause insurers to question whether such events were already built into their CAT models. We would hazard that it is improbable that models in place at the start of 2018 included the possibility of 2 very severe wildfire seasons in a row; which begs the question of how quickly and rigorously such events can and will be incorporated and thus flow through to technical pricing models.

The other factor to notice is that, in the most severe events that occurred, the ratio of insured losses to overall losses showed an increasing trend- i.e., the scale of claims for the most severe events was proportionately higher than those seen historically overall in previous CAT years. One could argue that this was happenstance (i.e., California just happened to be the key epicenter in 2018). However, if climate change is leading to new patterns of risk, and these happen to become more concentrated (at least for now) in geographical areas with both a higher economic value and a higher insured loss percentage (because insurance penetration is much higher for that type of risk), changes to models will surely also have to include new assumptions on insured percentages. Otherwise, “negative surprises” will occur.

So, CAT modelers (and their clients and colleagues) will have to decide how to adjust and re-weight their PMLs and EMLs. Not to do so would expose underwriters to the increasing probability that their technical pricing models were no longer fit for purpose, which is hardly helpful in a market that is still struggling to achieve sustainable price increases to compensate for the CAT losses of the past 2 years.

Of course, Awbury does not write any form of property CAT risk. However, we are very much part of the overall (re)insurer ecosystem. As a result, it would be irresponsible of us to pretend that the CAT events of 2017 and 2018 have no relevance. At first order levels, they do not. However, our aim is always to look beyond the obvious to “further order” effects, including how behaviours and risk appetites may change. We would be remiss ourselves in not updating our own expectations and models.

The Awbury Team