5/2/19: The Myth of the Euro: Economic Convergence

The last eight years of Euro's 20 years in existence have been a disaster for the thesis of economic convergence - the idea that the common currency is a necessary condition for delivering economic growth to the 'peripheral' euro area economies in the need of 'convergence' with the more advanced economies levels of economic development.

The chart below plots annual rates of GDP growth for the original Eurozone 12 economies, broken into two groups: the more advanced EA8 economies and the so-called Club Med or the 'peripheral' economies.

It is clear from the chart that in  growth terms, using annual rates or the averages over each decade, the Euro creation did not sustain significant enough convergence of the 'peripheral' economies of Greece, Italy, Portugal and Spain with the EA8 more advanced economies of the original euro 12 states. Worse, since the Global Financial Crisis onset, we are witnessing a massive divergence in economic activity.

To highlight the compounding effects of these annual growth rates dynamics, consider an index of real GDP levels set at 100 for 1990 levels for both the EA8 and the 'peripheral' states:

Not only the divergence is dramatic, but the euro area 'peripheral' economies have not fully recovered from the 2008-2013 crisis, with their total real GDP sitting still 3.2 percentage points below the pre-crisis peak (attained in 2007), marking 2018 as the eleventh year of the crisis for these economies.  With Italy now in a technical recession - posting two consecutive quarters of negative growth in 3Q and 4Q 2018 based on preliminary data, and that recession accelerating (from -0.1% contraction in 3Q to -0.2% drop in 4Q) we are unlikely to see any fabled 'Euro-induced convergence' between the lower income states of the so-called Euro 'periphery' and the Euro area 8 states.

17/1/19: Why limits to AI are VUCA-rich and human-centric

Why ethics, and proper understanding of VUCA environments (environments characterized by volatility/risk, uncertainty, complexity and ambiguity) will matter more in the future than they matter even today? Because AI will require human control, and that control won't happen along programming skills axis, but will trace ethical and VUCA environments considerations.

Here's a neat intro: https://qz.com/1211313/artificial-intelligences-paper-clip-maximizer-metaphor-can-explain-humanitys-imminent-doom/. The examples are neat, but now consider one of them, touched in passim in the article: translation and interpretation. Near-perfect (native-level) language capabilities for AI are not only 'visible on the horizon', but are approaching us with a break-neck speed. Hardware - bio-tech link that can be embedded into our hearing and speech systems - is 'visible on the horizon'. With that, routine translation-requiring exchanges, such as basic meetings and discussions that do not involve complex, ambiguous and highly costly terms, are likely to be automated or outsourced to the AI. But there will remain the 'black swan' interactions - exchanges that involve huge costs of getting the meaning of the exchange exactly right, and also trace VUCA-type environment of the exchange (ambiguity and complexity are natural domains of semiotics). Here, human oversight over AI and even human displacement of AI will be required. And this oversight will not be based on technical / terminological skills of translators or interpreters, but on their ability to manage ambiguity and complexity. That, and ethics...

Another example is even closer to our times: AI-managed trading in financial assets.  In normal markets, when there is a clear, stable and historically anchored trend for asset prices, AI can't be beat in terms of efficiency of trades placements and execution. By removing / controlling for our human behavioral biases, AI can effectively avoid big risk spillovers across traders and investors sharing the same information in the markets (although, AI can also amplify some costly biases, such as herding). However, this advantage becomes turns a loss, when markets are trading in a VUCA environment. When ambiguity about investors sentiment and/or direction, or complexity of counterparties underlying a transaction, or uncertainty about price trends enters the decision-making equation, algorithmic trading platforms have three sets of problems they must confront simultaneously:

1. How do we detect the need for, structure, price and execute a potential shift in investment strategy (for example, from optimizing yield to maximizing portfolio resilience)? 
2. How do we use AI to identify the points for switching from consensus strategy to contrarian strategy, especially if algos are subject to herding risks?
3. How do we migrate across unstable information sets (as information fades in and out of relevance or stability of core statistics is undermined)?

For a professional trader/investor, these are 'natural' spaces for decision making. They are also VUCA-rich environments. And they are environments in which errors carry significant costs. They can also be coincident with ethical considerations, especially for mandated investment undertakings, such as ESG funds. Like in the case of translation/interpretation, nuance can be more important than the core algorithm, and this is especially true when ambiguity and complexity rule.

17/1/19: Gonzo: Deplatforming the Mensheviks

My contribution to Max Keiser and Stacy Herbert’s new documentary series ‘Gonzo’ https://www.youtube.com/watch?v=lyTWT7jpCyg starting at about 14:50.

17/1/19: 2019 Outlook

My post on economic outlook for 2019 is now available from the Focus Economics: https://www.focus-economics.com/blog/constantin-gurdgiev-thoughts-on-the-global-economy-for-2019

17/1/19: U.S. Imports Demand and Final Household Consumption

A great post from the Federal Reserve Bank of San Francisco blog (https://www.frbsf.org/economic-research/publications/economic-letter/2019/january/how-much-do-we-spend-on-imports/) showing estimates for total imports content of the U.S. household consumption, with a break down of imports content across domestic value additive activities and foreign activities.

Key results: “Our estimates show that nearly half the amount spent on goods and services made abroad stays in the United States, paying for the local component of the retail price of these goods. At the same time, imports of intermediate inputs make up about 5% of the cost of production of U.S. goods and services. Overall, about 11% of U.S. consumer spending can be traced to imported goods. This ratio has remained nearly unchanged in the past 15 years”.

Note: Top bars in both panels are computed directly from PCE and headline trade data. Bottom bars in both panels reflect authors’ adjustments to account for imported content of U.S. goods and U.S. content of imported goods.

The above shows that imports play far lesser role in the U.S. households' consumption than popular media and public opinion tend to believe. This, in part, explains why Trump tariffs war with China has had a very limited adverse impact on domestic demand in the U.S.

17/1/19: Eurocoin December 2018 Reading Indicates a Structural Problem in the Euro Area Economy

December 2018 reading for Eurocoin, a lead growth indicator for euro area posted a second consecutive monthly decline, falling from 0.47 in November to 0.42 in December. December reading now puts Eurocoin at its lowest levels since October 2016.

Charts below show dynamics of Eurocoin, set against actual and forecast growth rates in the euro area GDP and  inflation:

Per last chart above, the pick up in inflation, measured by the ECB’s target rate of HICP, from 1.4% at the end of 3Q 2017 to 1.7% in 3Q 2018 has been associated with decreasing growth momentum (Eurocoin falling from 0.67 q/q to 0.48, and growth falling from the recorded 0.7% q/q in 3Q 2017 to 0.2% q/q in 3Q 2018).

With this significant downward pressure on growth happening even before any material monetary tightening by the ECB, Which suggests that euro area growth problem is structural, rather than policy-induced. While QE did boost growth from the crisis period-lows, it failed to provide a sustainable momentum for significantly expanding potential growth. Thus, even a gradual slowdown in monetary easing has been associated with a combination of subdued, but accelerating inflation and falling growth.

12/1/19: Global Liquidity Conditions

Things are getting ugly in the global liquidity environment.

1) The U.S. Treasuries demand from foreign buyers is drifting down - a trend that has been on-going since mid-2016. As of mid-4Q 2018, the combined foreign institutional holdings of U.S. Treasuries was at its lowest levels since the start of 2015.

2) The U.S. Dollar strength is now at its highest levels since early 2002.

Meanwhile, liquidity is falling:

3) Global liquidity supply is turning down, having trended relatively flat since the start of 2015

This is not a good set of signs, especially as this data is not reflecting, yet, the ECB tightening.

11/1/19: Herding: the steady state of the uncertain markets

Markets are herds. Care to believe in behavioral economics or not, safety is in liquidity and in benchmarking. Both mean that once large investors start rotating out of one asset class and into another, the herd follows, because what everyone is buying is liquid, and when everyone is buying, they are setting benchmark expected returns. If you, as a manager, perform in line with the market, you are safe at the times of uncertainty and ambiguity. In other words, it is better to bet on losing or underperforming alongside the crowd of others, than to bet on a more volatile expected returns, even though these might offer a higher upside.

How does this work? Here:

Everyone loves Corporate debt, until everyone runs out of it and into Government debt. Everyone hates Government debt, until everyone hates corporate debt. It's ugly. But it is real. Herding is what drives markets, even though everyone is keen on paying analysts top dollar not to herd.

11/1/19: A Behavioral Experiment: Irish License Plates and Household Demand for Cars

While a relatively well known and understood fact in Ireland, this is an interesting snapshot of data for our students in Behavioral Finance and Economics course at MIIS.


In 2013, Ireland introduced a new set of car license plates that created a de facto natural experiment in behavioural economics. Prior to 2013, Irish license plates contained, as the first two digits, the year of car production (see lower two images). Since 2013, prompted by the ‘fear of the number ’13’’, the license plates contain three first digits designating the year and the half-year of the make.

Prior to 2013 change in licenses, Irish car buyers were heavily concentrated in the first two months of each year - a ‘vanity effect’ of license plates that provided additional utility to the earlier months’ car purchasers from having a vehicle with current year identifier for a longer period of time. Post-2013 changes, therefore can be expected to yield two effects:
1) The ‘vanity effect’ should be split between the first two months of 1H of the year, and the first two months of 2H of the year; and
2) Overall, ‘vanity effect’ across two segments of the year should be higher than the same for th period pre-2013 change.

As chart above illustrates, both of these factors are confirmed in the data. Irish buyers are now (post-2013) more concentrated in the January, February, July and August months than prior to 2013. In 2009-2012, average share of annual sales that fell onto these four months stood at 44.8 percent. This rose to 55.75 percent for the period starting in 2014. This difference is statistically significant at 5% percent level.

The share of annual sales that fell onto January-February remained statistically unchanged, nominally rising from 31.77 percent for 2009-2012 average to 32.56 percent since 2014. This difference is not statistically significant at even 10%. However, share of sales falling into July-August period rose from 13.04 percent in 2009-2012 to 23.19 percent since the start of 2014 This increase is statistically significantly greater than zero at 1 percent level.

Similar, qualitatively and statistically, results can be gained from looking at 2002-2008 average. Moving out to pre-2002 average, the only difference is that increases in concentration of sales in January-February period become statistically significant.

In simple terms, what is interesting about the Irish data is the fact that license plate format - in particular identification of year of the car make - strongly induces a ‘vanity effect’ in purchaser behaviour, and that this effect is sensitive to the granularity of the signal contained in the license plate format. What would be interesting at this point is to look at seasonal variation of pricing data, including that for used vehicles, controlling for hedonic characteristics of cars being sold and accounting for variable promotions and discounts applied by brokers.

11/1/19: Euromoney on U.S. and Global Credit Risk: 2018 in review

My comment on key trends in the U.S. credit risk changes in 2018 and a glimpse into 2019 'crystal ball' for Euromoney and ECR: https://www.euromoney.com/article/b1cmrkm17q1cm9/ecr-survey-results-2018-us-decouples-from-improving-g10-trend-angola-egypt-lead-africa-recovery.

11/1/19: Capital Gains Tax: Human Capital vs Other Forms of Capital

This is exactly the source of policy-induced wealth inequality in the modern advanced economies: the disparity between labor income tax and capital gains tax that (1) incentivises accumulation of capital gains generating assets; (2) increases wealth inequality arising from non-meritocratic transfers (spousal and inheritance); and (3) reduces gains from meritocratic investment in human capital.

Now, factor this into tax-adjusted returns on various forms of capital: Intangible Capital returns are taxed at a corporate tax level at below the Physical Capital returns tax rates, which fall lower than the Capital Gains tax rate. Meanwhile, returns to the [intangible] Human Capital are taxed at the rates of higher margin Income tax rates. Go figure why wealth inequality is rising (as entrepreneurship is shrinking).

10/1/19: QE or QT? Look at the markets for signals

With U.S. Fed entering the stage where the markets expectations for a pause in monetary tightening is running against the Fed statements on the matter, and the ambiguity of the Fed's forward guidance runs against the contradictory claims from the individual Fed policymakers, the real signals as to the Fed's actual decisions factors can be found in the historical data.

Here is the history of the monetary easing by the Fed, the ECB, the Bank of England and the BOJ since the start of the Global Financial Crisis in two charts:

Chart 1: looking at the timeline of various QE programs against the Fed's balancesheet and the St. Louis Fed Financial Stress Index:

There is a strong correlation between adverse changes in the financial stress index and the subsequent launches of new QE programs, globally.

Chart 2: looking at the timeline for QE programs and the evolution of S&P 500 index:

Once again, financial markets conditions strongly determine monetary authorities' responses.

Which brings us to the latest episode of increases in the financial stress, since the end of 3Q 2018 and the questions as to whether the Fed is nearing the point of inflection on its Quantitative Tightening  (QT) policy.