4/9/16: Earnings per Share

You know the meme: corporate sector is healthy world over and the only reason there is no investment anywhere in sight on foot of the wonderfully robust earnings is that… err… political uncertainty around the U.S. elections. Because, of course, political uncertainty is everything…

Except when you look at EPS

H/T @zerohedge 

Now, what the above is showing?

1) EPS is down in the politically ‘uncertain’ U.S.

2) EPS is even more down in the politically less ‘uncertain’ Europe (though you can read on that subject here: http://trueeconomics.blogspot.com/2016/09/4916-some-points-on-russian-european.html

3) EPS has been falling off the cliff since the ‘political uncertainty’ (apparently) set in 4Q 2012 in the U.S. One guess is the markets expected, correctly, the epic battle between The Joker and the Corporate Godzilla back then. And in Europe, since mid 2013, apparently, markets had foresight of who knows what back then.

But never mind, there is no secular stagnation anywhere, because earnings are, apparently very very healthy… very robust… very encouraging… All of which means just one thing: the markets are not overpriced or overbought. Pass de Kool-Aid, lads!

4/9/16: Some Points on Russian & European Policy Uncertainty Trends

With some positive (albeit very weak still) changes in the Russian macroeconomic news in recent months, it is worth looking at the evolution of trends in Russian policy uncertainty, as measured by the http://www.policyuncertainty.com/ data.

Here is an updated (through August 2016) chart comparing news-based indices of policy uncertainty in Russia and the EU

Note, series above are rebased to the same starting point for the EU and Russia (to 1994 annual average) to make them compatible.

Things of note:

• Russian policy uncertainty continues to trend below that of the EU
• The above conclusion is also confirmed in raw data 3mo averages and 3mo exponential moving averages
• This is nothing new, as general policy uncertainty has been systemically lower in Russia than in Europe since the peak of the Russian Default crisis of the late 1990s, with exception for two episodes: brief period in 2006-2007 - the starting point of Russian-Georgian trade and migration pressures; and 2014-2015 period - marked by first economic slowdown in the early 2014, followed by the Russian-Ukrainian conflict and the Ruble crisis
• Generally, the EU continues to show growing trend divergence with Russia when it comes to policy uncertainty, despite the more moderation in the underlying series since the end of the latest spike caused by the Greek crisis earlier this year (IMF participation and Tranche 2 disbursement)

It is worth noting that, despite a rise in the U.S. uncertainty index due to the ongoing election cycle, the U.S. comparatives are similar to those of Russia, as opposed to the EU. 

3/9/16: Fintech, Banking and Dinosaurs with Wings

Here is an interesting study from McKinsey on fintech role in facilitating banking sector adjustments to technological evolution and changes in consumer demand for banking services:
http://www.mckinsey.com/business-functions/risk/our-insights/the-value-in-digitally-transforming-credit-risk-management?cid=other-eml-alt-mip-mck-oth-1608

The key here is that fintech is viewed by McKinsey as a core driver for changes in risk management. And the banks responses to fintech challenge are telling. Per McKinsey: “More recently, banks have begun to capture efficiency gains in the SME and commercial-banking segments by digitizing key steps of credit processes, such as the automation of credit decision engines.”

The potential for rewards from innovation  is substantial: “The automation of credit processes and the digitization of the key steps in the credit value chain can yield cost savings of up to 50 percent. The benefits of digitizing credit risk go well beyond even these improvements. Digitization can also protect bank revenue, potentially reducing leakage by 5 to 10 percent.”

McKinsey reference one example of improved efficiencies: “…by putting in place real-time credit decision making in the front line, banks reduce the risk of losing creditworthy clients to competitors as a result of slow approval processes.”

Blockchain technology offers several pathways to delivering significant gains for banks in the area of risk management:

• It is real-time transactions tracking mechanism which can be integrated into live systems of data analytics to reduce lags and costs in risk management;
• It is also the most secure form of data transmission to-date;
• It offers greater ability to automate individual loans portfolios on the basis of each client (irrespective of the client size); and 
• It provides potentially seamless integration of various sub-segments of lending portfolios, including loans originated in unsecured peer-to-peer lending venues and loans originated by the banks.

Note the impact matrix above.

Blockchain solutions, such as for example AID:Tech platform for payments facilitation, can offer tangible benefits across all three pillars of digital credit risk management process for a bank:

• Meeting customer demand for real-time decisions? Check. Self-service demand? Check. Integration with third parties’ platforms? Check. Dynamic risk-adjusted pricing and limits? Check
• Reduced cost of risk mitigation? Yes, especially in line with real-time analytics engines and monitoring efficiency
• Reduced operational costs? The entire reason for blockchain is lower transactions costs

What the above matrix is missing is the bullet point of radical innovation, such as, for example, offering not just better solutions, but cardinally new solutions. Example of this: predictive or forecast-based financing (see my earlier post on this http://trueeconomics.blogspot.com/2016/09/2916-forecast-based-financing-and.html).

A recent McKinsey report (http://www.mckinsey.com/industries/financial-services/our-insights/blockchain-in-insurance-opportunity-or-threat) attempted to map the same path for insurance industry, but utterly failed in respect of seeing the insurance model evolution forward beyond traditional insurance structuring (again, for example, FBF is not even mentioned in the report, nor does the report devote any attention to the blockchain capacity to facilitate predictive analytics-based insurance models). Tellingly, the same points are again missed in this month’s McKinsey report on digital innovation in insurance sector: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-digital-strategy-a-reality-in-insurance.

This might be due to the fact that McKinsey database is skewed to just 350 larger (by now legacy) blockchain platforms with little anchoring to current and future innovators in the space. In a world where technology evolves with the speed of blockchain disruption, one can’t be faulted for falling behind the curve by simply referencing already established offers.

Which brings us to the point of what really should we expect from fintech innovation taken beyond d simply tinkering on the margins of big legacy providers?

As those of you who follow my work know, I recently wrote about fintech disruption in the banking sector for the International Banker (see http://trueeconomics.blogspot.com/2016/06/13616-twin-tech-challenge-to.html). The role of fintech in providing back-office solutions in banking services is something that is undoubtedly worth exploring. However, it is also a dimension of innovation where banks are well-positioned to accept and absorb change. The real challenge lies within the areas of core financial services competition presented (for now only marginally) by the fintech. Once, however, the marginal innovation gains speed and breadth, traditional banking models will be severely stretched and the opening for fintech challengers in the sector will expand dramatically. The reason for this is simple: you can’t successfully transform a centuries-old business model to accommodate revolutionary change. You might bolt onto it few blows and whistles of new processes and new solutions. But that is hardly a herald of innovation.

At some point in evolution, dinosaurs with wings die out, and birds fly.

3/9/16: Innovation policies scorecards: Euro Area and BRIC

An interesting, albeit rather arbitrary (in terms of methodology) assessment matrix for innovation environment rankings across a range of countries, via EU Commission.

Here are the BRIC economies:

All clustered in the “Above Average Harmful Policies” (negative institutional factors) and “Below Average / Average Beneficial Policies” (positive institutional factors). Surprisingly, however, India sports the worst innovation policies environment, followed by China (where “Beneficial Policies” are, of course, skewed by state supports for key sectors). Russia comes in third (where the beneficial policies are most likely skewed to the upside by so-called strategic sectors, also with heavy state involvement). You might laugh, because with Brazil being fourth 'least detrimental' environment for innovation, the EU rankings are clearly at odds with actual innovation outcomes (https://www.globalinnovationindex.org/userfiles/file/reportpdf/GII-2015-v5.pdf) where

• China = rank 29
• Russia = rank 48
• Brazil = rank 70
• India = rank 81

Looking at the contrasting case of key advanced economies with strong supports, one wonders how much of Ireland’s policy environment is due to multinationals’ accommodation and just how on earth can such an ‘innovation-centric’ economy be so ‘average’ in terms of its innovation policies despite hundreds of millions pumped into supporting indigenous innovation. 

Then again, look at Finland with its stellar innovation policies culture and… err… economy in total coma

Makes you think… 

2/9/16: Does bank competition reduce cost of credit?

In the wake of the Global Financial Crisis, there has been quite a debate about the virtues and the peril of competitive pressures in the banking sector. In a paper, published few years back in the Comparative Economic Studies (Vol. 56, Issue 2, pp. 295-312, 2014 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2329815), myself, Charles Larkin and Brian Lucey have touched upon some of the aspects of this debate.

There are, broadly-speaking two schools of thought on this subject:

1. The market power hypothesis - implying a negative relationship between bank competition and the cost of credit (as greater competition reduces the market power of banks and induces more competitive pricing of loans). This argument is advanced by those who believe that harmful levels of competition can lead to banks mispricing risk while competing with each other. 
2. The information hypothesis postulates a positive link between credit cost and competition, as the banks may be facing an incentive to invest in soft information. 

Now, a recent paper from the Bank of Finland, titled “Does bank competition reduce cost of credit? Cross-country evidence from Europe” (authored by Zuzana Fungáčová, Anastasiya Shamshur and Laurent Weill, BOFIT Discussion Papers 6/2016, 30.3.2016) looks at the subject in depth.

Per authors, “despite the extensive debate on the effects of bank competition, only a handful of single-country studies deal with the impact of bank competition on the cost of credit. We contribute to the literature by investigating the impact of bank competition on the cost of credit in a cross-country setting.” The authors take a panel of companies across 20 European countries “covering the period 2001–2011” and study “a broad set of measures of bank competition, including two structural measures (Herfindahl-Hirschman index and CR5), and two non-structural indicators (Lerner index and H-statistic).”

The findings are interesting:

• “bank competition increases the cost of credit and …the positive influence of bank competition is stronger for smaller companies”
• These results confirm “the information hypothesis, whereby a lack of competition incentivizes banks to invest in soft information and conversely increased competition raises the cost of credit.” 
• “The positive impact of bank competition is influenced by two additional characteristics. It is lower during periods of crisis, and the institutional and economic framework influences the relation between competition and the cost of credit.”
• Overall, however, the “positive impact of bank competition is …influenced by the institutional and economic framework, as well as by the crisis.”

The authors ‘take-away lesson” for policymakers is that “pro-competitive policies in the banking industry can have detrimental effects, … [and] banking competition can have a detrimental influence on financial stability and bank efficiency.”

I disagree. Judging by the above, higher costs of credit overall, and higher costs of credit for smaller firms, may be exactly what is needed to induce greater efficiency and reduce harmful distortions from over-lending. As long as these higher costs reflect actual risk levels.

2/9/16: Forecast-based Financing and Blockchain Solutions

As the readers of this blog know, AID:Tech (https://aid.technology/) is a new venture I am involved with that uses blockchain platform for provision of key payments facilitation services for people in need of emergency and continued assistance (refugees, international aid recipients, disaster relief aid and general social supports payments). As a part of the market analysis and strategy, we have encountered an interesting, rapidly evolving services segment relating to disaster relief: the concept of Forecast-based Financing (FBF) worth highlighting here.

Under FBF, aid providers release humanitarian aid-related funding ahead of the adverse event taking place, based on forecast information that aids in predicting the severity, timing and impact distribution of the disaster (natural or man-made). This approach to aid delivery aims to:

• Reduce key risks (e.g. assuring that delivery is timed in line with the adverse shock, focused on key geographic and demographic audiences, uses pre-disaster - and thus more efficient - supply chain networks, etc), 
• Enhance preparedness and response (by increasing quality of aid targeting and allowing to concentrate resources in the areas where they are needed most and ahead of the actual disaster impact), and 
• Make disaster risk management overall more effective by assuring that aid resources are present at the time of the disaster and after the disaster impact, thus reducing losses and delays in delivery of aid that may arise as the result of the disaster (e.g. destruction of roads and disruptions in power supplies, etc).

In general, FBF framework is open to several questions and objections, all requiring addressing.

How does FBF work? 

A humanitarian aid agency and stakeholders (e.g. meteorological services and communities at risk) jointly create a contingency plan, outlining key actions to be taken ahead of the probabilistically likely disaster or shock. They also set out specific metrics that define the trigger for aid pre-delivery, based on a model risk forecast reaching a specific threshold of probability. Linked to severity of forecast shock, specific budgets are set aside for activation. Once the risk probability threshold is breached, aid is delivered to the location of possible disaster, using pre-disaster supply chain management structures before these get disrupted by the event.


Forecast errors: are these really costly?

Probabilistic forecasts are never 100% accurate, which means that in some instances, aid will be delivered to the communities where the adverse event (a shock) might end up not materialising, despite probability models generating high likelihood of such an event. In a way, this is the risk of aid agencies providing disaster relief “in vain” or “wasting” scarce resources. It is worth noting that probabilistic errors of “wastage” can be significantly over-estimated, as some disasters can be relatively well forecast in advance (http://www.nat-hazards-earth-syst-sci.net/15/895/2015/nhess-15-895-2015.pdf). Quality of forecasting will, of course, co-determine losses in the system.

To achieve system-wide efficiency and secure gains from implementing an FBF programme, one has to be able to counter-balance the benefits of early response,including those arising from more efficiency in accessing supply chains pre-disaster and reducing the cost of disaster, against the likelihood of a loss due to probabilistic basis for the action. This can be done via two channels:

1. Assuring that during planning, the cost of acting pre-emptively, including the cost of probabilistic ‘waste’, is factored into planning for which forms of aid should be pre-delivered and on what scale; and
2. Assuring that aid supply chain and forecasting models are optimised to delver highest efficiencies possible.

Over time, development of FBF will also require changes in supply chain management to mitigate losses due to “wastage”. For example, putting more emphasis on local (or proximate) sources for supply of critical aid can reduce “wastage” by lowering cost of deliveries and by closely anchoring pre-disaster deliveries to existent markets for goods and services (so at least some pre-delivered aid can be returned into local markets in the case if probabilistically likely disaster does not materialise).

In other words, aid agencies and potentially impacted communities need to have access to timely and accurate information on which resources are needed in responding to a specific disaster, on what scale and, crucially, which resources are already available in the supply chain and in the local or proximate markets. The key element to this is ability to track in real time supply chains of goods and services accessible at differential cost to specific communities in cases of specific disaster events. The agreed (in advance) standard operating procedures (SOPs) that are set between the aid providers and the recipient communities must be both realistic (reflective of measures necessary in the case of specific disaster) and effective (reflective of the balance of cost-benefit).

Put differently, the process of FBF is the process of, first and foremost, planning and data relating to supply chain management.


Are there any tangible experiences with FBF?

One early example of FBF implementation is the case of the Red Cross Red Crescent Movement that has field-tested an FBF programme Uganda and Togo. This project bridged financial and technical support from the German government and Red Cross, and used technical support from the Climate Centre.

Another case is of FoodSECuRE initiative by the World Food Programme that is currently in planning stages. In this programme, private sector partners (aviation services providers, insurance companies etc) are engaged in FBF planning for alleviation of potential flooding due to El Niño effects in Peru (http://www.climatecentre.org/downloads/files/FbF%20Brochure4.pdf and http://www.climatecentre.org/programmes-engagement/forecast-based-financing). Both of these experiences show also the importance of setting aside sufficient response funds for FBF delivery.

Further afield, FBF pilots are being run or planned by the WFP and other organisations in Bangladesh, the Dominican Republic, Haiti, Mozambique, Nepal and the Philippines.

Note: the above cases were provided by the UNFDP research.


Overall, FBF is becoming one of the cornerstones of the global disaster aid delivery programmes and was endorsed by UN OCHA and the IFRC. FBF was also included in the International Federation’s special report ahead of the World Humanitarian Summit in Istanbul. The report included a pledge to facilitate a doubling of FbF within the Movement by 2018.

However, despite the aid agencies enthusiasm, the key problem relating to FBF remains largely unaddressed: currently, with some 20 percent of disaster aid being lost due to insufficient supply chain management, fraud and theft, delivering properly structured FBF requires exponentially greater exposure to data collection and analysis, as well as to strengthening of real-time supply chain visibility systems.

As AID:Tech example shows, these objectives can be supported via private and semi-private blockchain solutions.

2/9/16: Investment in Italy: Banks, Capital and Firms Structures

In my course on the enterprise and financial risk last semester, we talked about the peculiar (or idiosyncratic) nature of Italian firms across a number of matters:

• Relationship banking;
• Firm governance: family ownership, equity distribution and aspects of firm strategy and operations;
• Firm capital structure (leverage risks in particular);
• Firm dividend policy choices, etc.

Now, let’s add to that literature something new. A recent paper from the Banca d’Italia, titled “Investment and investment financing in Italy: some evidence at the macro level” by Claire Giordano, Marco Marinucci and Andrea Silvestrini (Banca d’Italia Occasional paper Number 307 – February 2016) looks at the evolution of the Global Financial Crisis and the Great Recession across the Italian economy in terms of credit fundamentals.

As noted by the authors, “following the outbreak of the global financial crisis, the euro area experienced a large fall in gross fixed capital formation, both in 2008-09 and during the sovereign debt crisis. This drop was dramatic in the countries more exposed to tensions in government bond markets. In Italy, in particular, total real investment has suffered a loss of around 30 per cent since 2007, the pre-crisis peak, reverting to its lowest levels since the mid-1990s. Weak investment also remained a key drag on GDP growth in 2014, although more recent quarterly data on capital accumulation point to a slight increase over the first three quarters of 2015 relative to the corresponding period in 2014.”

In a typical European fashion, investment in Italy must equal debt. In part, as I usually cover in my courses on risk and corporate financial strategies, this is tied to the reluctance of the family-owned firms to release equity. And in part, it is a part of a broader European debt disease. Independent of the reasons, per authors, the worthy corner to check for key investment-crises interlinkages is the credit supply.

“The depressed growth of investment is in contrast with the substantially muted aggregate financing costs, which stem from the low interest rate environment resulting from the strongly expansionary stance of monetary policy in the euro area. In this context, one scenario is that investment demand will remain too low to absorb financial savings, inducing a persistent state of an excess supply of funds in capital markets.” In other words, Italians are discovering secular stagnation: interest rates are too low because investment is too low (and may be, also the other way around).

With that in mind, the authors proceed to show that “medium-term gross fixed capital formation trends in Italy may be summarised along the following lines”:

Pre–2007 capital expansion “was broad-based, both across institutional sectors and asset categories, although less marked for households”, and Post-2007 “exceptional downturn …affected all sectors and components, yet to a different extent. In particular, focusing on the most recent period, the decline in general government and non-financial corporations’ expenditure, cumulatively undertaking about two thirds of total investment in Italy, was sizeable (approximately 25 per cent), yet slightly more contained than the concurrent drop in household investment spending.”

Overall, “the total-economy investment rate in Italy currently stands at its lowest levels since data became available in the mid-1990s; current government and non-financial corporation investment rates are comparable only to those recorded in 1995; the household rate is even lower. From the asset side, in recent years construction investment, in both residential dwellings and non-residential buildings, which represents half of total expenditure, was the hardest-hit item, excluding transport equipment expenditure (small and volatile), whereas ICT investment and the accumulation of intangible assets weathered the recent double-dip recession better.”

But the age of lower interest rates is having another impact on Italian credit markets. This impact is aggressive rolling over of maturing loans amidst deteriorating credit quality of borrowers. As the result, two things can be documented in Italian credit market experience since 2008 bust:

Firstly, overall, the trend toward longer debt maturity is present in household, corporate and government debt sectors (charts below). Secondly, this is doing nothing to repair credit quality on banks balance sheets.

Which is rocking, for now, as low interest rates are keeping debt burden down and allowing leverage to rise. But the problem is that with longer maturity of debt, we are looking at higher long term susceptibility to debt servicing costs. Last time that happened, Italy became on of the PIIGS. Next time it will happen… oh, ok… we shall just wait and hope Mario Draghi says true to his Italian roots long enough for the miracle to happen.

2/9/16: Interest Rates, Financial Cycles and the Real Economy

Claudio Borio and his team at the Bank for International Settlements have just published another interesting working paper, titled “Monetary policy, the financial cycle and ultra-low interest rates” (BIS Working Papers No 569 by Mikael Juselius, Claudio Borio, Piti Disyatat and Mathias Drehmann Monetary and Economic Department July 2016).


In the paper, the authors ask whether “the prevailing unusually and persistently low real interest rates reflect a decline in the natural rate of interest as commonly thought?”

The authors “argue that this is only part of the story. The critical role of financial factors in influencing medium-term economic fluctuations must also be taken into account.” In other words, the authors attempt to control for purely financial factors driving interest rates first, and then consider predominantly real economic variables-determined rates (natural rates).

You might think that the currently low rates are facilitating the real economy, right? If so, then actual observed (already low) rates today should be coincident with even lower ‘natural’ rates (if real economy drags down the financial economy). Alas, as the authors find: accounting for the different sources of pressure on the interest rates (financial vs natural), in the case of the United States, “yields estimates of the natural rate that are higher and, at least since 2000, decline by less.”

Oops… so persistently low interest rates today are below natural rates and reflect the needs of the financial intermediation sector.

Notice the difference between the observed rates (yellow) and the ‘natural rates’ (red). Or as the lads from BIS put it: “As a result, policy rates have been persistently and systematically below this measure.”

But never mind. With time, things should get rebalanced, as the authors also find that “monetary policy, through the financial cycle, has a long-lasting impact on output and, by implication, on real interest rates. Therefore, a narrative that attributes the decline in real rates primarily to an exogenous fall in the natural rate is incomplete. The influence of monetary and financial factors should not be ignored. Exploiting these results, an illustrative counterfactual experiment suggests that a monetary policy rule that takes financial developments systematically into account during both good and bad times could help dampen the financial cycle, leading to higher output even in the long run.”

Yah, yah… lots of talk. What’s the meaning? Ok, the authors take two drivers of financial sector impact on the real economy: leverage and credit.

Leverage gap is defined as basically a credit to assets ratio for the economy - or how much credit does economy create per each unit of assets. Meanwhile debt service gap is the ratio of debt service payment, or more precisely, “the ratio of interest payments plus amortisations to income”.

To understand the dynamics of the monetary (interest rates) policy impact, the authors do a couple of experiments. The main one is worth discussing. The authors start with a leverage gap of -10%, so there is an excess of assets over credit in the economy and hence there is room to borrow, driving leverage gap up. Note: as the authors point out, the -10% leverage gap assumption is consistent with historical reality: in the late 80s and mid-2000s, “at their trough”, leverage gaps were -11% in 1987 and -20% in 2006 respectively.

So, as I noted above, “a negative leverage gap initially induces a credit boom that then turns into a bust… Initially, the negative leverage gap is followed by rapid credit growth, which in turn feeds into a positive, albeit small, increase in private sector expenditure. But as credit outgrows output, the credit-to-GDP ratio and with it the debt service gap start to rise, putting an increasing drag on output and asset prices. A severe and drawn-out recession follows.”

The dynamics match the Great Recession: “…at the start of 2005, the real-time estimate of the leverage gap was significantly negative while the debt-service gap was positive. Given this starting point, the adjustment dynamics of the system would have predicted much of the subsequent output decline during the Great Recession. This suggests that the recession was not a “black swan” caused by an exogenous shock but, rather, the outcome of the endogenous dynamics of the system – a reflection of the interaction between the financial factors and the
real economy.”

And here is the actual run of annual estimates of the two gaps:

Remember, the cyclicality? Negative leverage gap —> credit boom —> positive leverage gap and positive debt service gap —> bust.

Good thing we are not going to repeat THAT cycle this time around, right?.. Not with all the low interest rates not being lower than ‘natural’ rate… right?

2/9/16: Remember Banks Stress Tests: Tripple Farce and Still No Joy for Ireland

Couple of older, but still relevant notes have stacked up on my virtual desktop over the last few weeks. Catching up with these, here is a post on the banking sector 'bill of un-health' produced this summer by the EBA.


European banks street tests conducted by EBA last month combined the usual old farce with the novel new farce. Just to make sure the punters were not too scared of the European economy’s champions.

Based on Basel III criteria - CT1 ratio of 7% post shock - all but two banks (Italy’s Banca Monte dei Paschi di SAiena Spa and Austria’s Raiffeisen Zentralbank) have managed to escape the tests with CT1 ratios post-shock within the Basel III parameters. Or in other words, everyone passed, save for two who didn’t. Systemically, therefore, EBA can assure us all that euro area banking is just fine. Nothing to see, nothing to worry about.

However, the farce of the tests goes deep than this predicable and historically conditioned outcome. Because this time around, EBA no longer even bothered with determining who ‘failed’ and who did not. Like in Breznev’s USSR, in the EBUSSR, ‘friendship wins’ and ‘no one loses’.

There was another predictable trend in the EBA results. No matter how ‘flexible’ the models fort testing get, no matter how being the ‘stress assumptions’ get, Irish and Italian banks remain the sickest puppies in the entire ward of already not too healthy ones:

But, hey, despite much of the stock markets hullaballoo over recent months, the bidding of banks’ equity has not really done much in terms of beefing sufficiently their capital buffers. So here are some comparatives on 2014 stress tests against current ones.

Note: 2014 stress tests estimated impact of a shock out to 2016, while this year tests are estimating impact out to 2018.

So behold (via @FT):

Italy:

• 2016 state: Transitional CET1 ratio of 6.14 per cent v 8.42 per cent average - under performing the average by 228 bps
• 2018 state: Fully loaded CET1 ratio of 7.62 per cent v 9.2 per cent average - under performing the average by 158 bps
• Signals improvement, on the surface, but this is a cross comparative over tow somewhat different benchmarks

Ireland:

• 2016 state: Transitional CET1 ratio of 7.05 per cent v 8.42 per cent average, undershooting the average by 137 bps
• 2018 state: Fully loaded CET1 ratio of 5.21 per cent v 9.2 per cent average, underperforming by 399 bps
• Signalling, even if we are to totally disregard differential quality, this does not bode too well for Irish financial ‘giants’

FT did provide a handy chart showing changes in stress test shock-level CET1 ratios for Adverse Stress Scenarios in 2014 tests and 2016 tests (never mind the ‘actual’ levels as of 2015, as these are subject to market valuations etc).

What the above shows?

For a tiny banking system, Ireland’s one is sicker than any other. And this comes on foot of years of repairs, recapitalisations, arrears resolutions etc etc etc. Green Jerseying ain’t working, folks. All Spanish banks are performing better than the two Irish flagships. Majority of Italian banks (save for one) are better than the two Irish ‘giants’. All Portuguese banks are stronger than the Irish systemically-important institutions. And none have spent anything close to Ireland on ‘repairing’ their lenders.

Maybe, if we wait long enough, EBA will include a bunch of Greek and Cypriot banks next… to make ours look better…

2/9/16: Getting Back to the Blog

Folks, the joys of moving to my new job have been quite all-consuming over the month of August, and the joys of preparing for the move - over June and July - were testing as well. Now, with things more settled, it is time for me to come back to blogging, so stay tuned and read.

30/7/16: Europe's Bank Stability in Question

My comment for Euromoney on banking sector risks: http://www.euromoney.com/Article/3574602/Europes-bank-stability-is-in-question-say-country-risk-experts.html.

29/7/16: Tax Regime, Apple, Fraud?

We have finally arrived: a Nobel Prize winner, former Chief Economist and Senior Vice-President of the World Bank (1997-2000) on Bloomberg, calling Apple's use of the Irish Tax Regime 'a fraud': http://www.bloomberg.com/news/articles/2016-07-28/stiglitz-calls-apple-s-profit-reporting-in-ireland-a-fraud?utm_content=business&utm_campaign=socialflow-organic&utm_source=twitter&utm_medium=social&cmpid%253D=socialflow-twitter-business.

This gotta be doing marvels to our reputation as a place for doing business and for trading into Europe and the U.S.

The same as Facebook's newest troubles: http://www.irishtimes.com/business/technology/facebook-tax-bill-over-ireland-operation-could-cost-5-billion-1.2738677.

But do remember, officially, Ireland is not a tax haven, nor is there, officially, anything questionable going on anywhere here. Just 26.3 percent growth in GDP per annum, and booming corporate tax revenues that the Minister for Finance can't explain.