4/1/17: BRIC Services PMIs: 4Q & FY 2016

I posted my analysis of BRIC quarterly Manufacturing PMIs here: http://trueeconomics.blogspot.com/2017/01/4117-bric-manufacturing-pmi-4q-2016-and.html.

Now, let’s look at Services sector. Table below summaries latest data

Brazil Services PMI for 4Q 2016 came in at 44.5, unchanged on 3Q 2016 and marking rapid rate of contraction in the country’s Services economy. This is 9th consecutive quarter of sub-50 readings, and 12th consecutive quarter of PMI readings statistically at or below 50.0 mark. Services recession continues to be worse than Manufacturing recession for the seventh quarter in a row.

Russian Services PMI ended 2016 with a bang. 4Q 2016 reading averaged 54.6, up on 3Q reading of 53.8. FY 2016 average is solid 52.9, which is a big contrast to 48.5 FY average for 2015. This is the strongest rate of quarterly average growth since 1Q 2013. Overall, dynamics in the Services sector support the view that Russian Services economy has now moved solidly out of the recession and into broad expansion. To translate this into overall economic outlook for growth, however, we need at least one (preferably two) quarters of above 52 readings in Manufacturing.

Chinese Services PMI also gained strength in 4Q 2016, ending the last quarter at an average of 53.0, up on 3Q 2016 reading of 51.9. FY 2016 average reading for the sector is robust 52.2 which is marginally better than 52.0 average for the the FY 2015.

India Services posted a surprising rapid contraction, falling for 4Q 2016 to 49.3 from 52.9 average for 3Q 2016. This marks the first sub-50 reading since 2Q 2015 and is hard to interpret as anything but a volatility induced by monetary reforms and a couple of other policy blunders. Still, 2016 FY average for the sector is at 51.8 which is virtually unchanged compared to 51.7 average for FY 2015.

Looking at the trends:

1) Russian rate of Services sector growth is now on par with pre-crisis period (2013 and earlier). China is taking second place in terms of Services growth momentum, albeit its expansion is both weaker than Russian, and sustained by superficial means (monetary and fiscal stimuli - not present in Russia).

2) India is on a sharp volatility down, which needs to be confirmed if we are to talk about general weaknesses in the economy.

3) Brazil remains the sickest of all BRICS, confirming the same positioning in country Manufacturing.

4) Again, tracing out longer term trends, Russian general slowdown set on around 2Q 2013 in Services has now been broken to the upside. While Chinese Services continue to trend along shallow growth line, and India’s trend (highly volatile) is suggesting some weaknesses in growth. Brazil’s Services weaknesses (turned decline in 4Q 2014) that started around 4Q 2012 - 1Q 2013 is still pronounced.

4/1/17: BRIC Manufacturing PMI: 4Q 2016 and FY 2016

Manufacturing PMIs for BRIC economies are out for December, so let’s update my quarterly series. As readers of this blog know, I primarily switched away from covering monthly PMIs because there is little one can add to the Markit own analysis. Instead, I have been focusing on covering quarterly results.

Table below summarises key levels of average quarterly PMIs for Manufacturing:

Brazil’s continued recession, over the course of 2016 remained deeper, judging by Manufacturing PMIs than both 2014 and 2015. 4Q 2016 Manufacturing PMI reading came in at 45.9, which signals no change in the rate of contraction on 3Q 2016 (45.9) and a slight improvement on 4Q 2015 (44.5). All in, Brazil’s Manufacturing remained at below 50.0 reading for 11th quarter in a row, and controlling for statistical significance, the country Manufacturing sector have not seen any expansion since 1Q 2013. In these terms, the country is in a far worse shape than any other BRIC economy. FY 2016 PMI average for Brazil’s Manufacturing is at 45.1, which is worse than 2015 average (46.5) and 2014 average (49.6). Even in the dire days of 2009, Brazil’s Manufacturing PMI managed to average 48.2. In other words, Brazil’s state of Manufacturing currently is worse than at any time on record.

Russian Manufacturing PMI for 4Q 2016 came in at 53.2, marking second consecutive quarter of above 50 readings, and the first quarter of statistically significant expansion. This is a welcome sign, confirming economic recovery, albeit still fragile one. To call a full recovery we need to see at least one-two more quarters of above 52.0 readings. Nonetheless, 2016 FY average is at 50.6, which is way better than 2015 FY average (48.7) and 2014 average (49.6). In fact, 4Q 2016 reading is the highest in 23 quarters (we have to go back to 1Q 2011 to get a higher level) and the seventh highest since 1Q 2006.

Chinese Manufacturing PMI averaged 51.3 over 4Q 2016, up on 50.2 average in 3Q 2016. As in the case of the Russian Manufacturing, Chinese PMIs posted second consecutive quarter of expansionary readings (adjusting for statistical significance both 3Q and 4Q were not significantly above 50 line). However, unlike Russian Manufacturing PMI, Chinese Manufacturing PMI remained below 50.0 mark for FY 2016 (at 49.8) and this marked the third year in a row that the average FY PMI was below expansion line (2015 FY average was 48.7 and 2014 FY average was 49.7).

Not to forget about India: Indian Manufacturing PMI averaged 52.1 in 4Q 2016, down slightly on 52.2 average through Q3 2016, but up on 50.0 reading in 4Q 2015. FY 2016 average reading is 51.7, which is marginally better than 51.5 average for FY 2015, but worse than 52.1 average for the FY 2014. India now had 13 consecutive quarters of above 50 readings for Manufacturing PMI (controlling for statistical significance, just two consecutive quarters).

Key takeaways:

1) As the chart below clearly shows, Chinese Manufacturing PMIs have been bouncing within statistical zero growth range since the start of H2 2011. Russian Manufacturing PMIs exhibited broadly the same dynamics since the start of 2Q 2013. Brazil’s PMIs have been in a disaster zone from around the same time as Russia’s started signalling stagnation. In fact, with exception of 4Q 2012 and 1Q 2013, BRIC Manufacturing PMIs were in the doldrums since 3Q 2011 on. Which, sort of, exposes the lie of the Russian recession being caused by geopolitical risks and sanctions. It was not. The recession was long coming and its causes are coincident across China, Brazil and Russia, with India being an exception to the BRIC grouping throughout the entire period covered by data.

2) Also per chart above, BRIC Manufacturing is now on a recovery trend that is still requiring confirmation over the next 2 quarters. This trend is in line with Global PMI index trend for the sector.

3) Russia is now the strongest performing BRIC economy in Manufacturing terms, followed by India, and with a significant gap - China. Brazil, meanwhile, continuing to act as a drag on both BRIC and global Manufacturing growth.


As an aside: I am glad that my 3Q 2016 analysis for @businessinsider @AkinOyedele Most Important Charts feature is being confirmed by 4Q data as well.

4/1/17: In 2016, U.S. IPOs Fell off the Cliff. VCs Barely Hanging on...

The golden VC model of finance is getting hammered by the lack of IPOs. Let’s take it from the top. majority of VCs fund companies on the basis of a visible exit (at least strategic visibility), which in the vast majority of cases implies either a sale (M&A by a bigger fish) or an IPO. Rarely do they explicitly factor into company valuations a possibility of a buy-out (for if they did, their models of funding would involve debt, rather than equity) or even less frequently, a possibility of earning a return through organic growth (for if they did, their models will set RRR closer to 5-10 percent pa over a longer time horizon, not quintuple that over a short run). So VC ‘industry’ by and large depends on IPOs. And these IPOs are now exceedingly rare on the ground and their valuations are exceedingly shallower.

Here’s data from FactSet:

Per FactSet:

1) “The number of companies going public on United States exchanges amounted to 33 in the fourth quarter, which represented a 6.5% uptick from the year-ago quarter (31 IPOs), but a 5.7% decline from Q3 (35 IPOs).” Aha, you say, a silver lining! Not quite so. “Despite the increase, this number was still well-below the average fourth quarter IPO count going back to 2000 (47 IPOs).“ And worse: “On an annual basis, there were 106 companies that went public on U.S. exchanges in 2016, which was a 35.4% downtick from 2015 (164 IPOs). The number of initial public offerings in 2016 marked the lowest annual count since 2009, when the number was 64.” 2009?! Wasn’t that the year when the world was crumbling to bits around us? Yes. And 2016? wasn’t this the year when Obamanomics celebrated miracles of labour markets recovery and stock markets indices heading for all time highs? Yes. So something is rotten somewhere, right?

2) Yes, things are rotten. “Gross proceeds (including over-allotment) amounted to $7.1 billion in the fourth quarter, which was a 7.3% decrease year-over-year. On an annual basis, gross proceeds in 2016 represented the smallest total since 2003.” 2003? Was that not the year after the dot.com crash when the investors were still shying away from tech and general start ups? Yes. Which means something is really rotten.

3) Scratch deeper: “During 2016, there were only 13 VC-backed initial public offerings in the Technology Services and Electronic Technology sectors. This marked the lowest annual number since 2009 (4 IPOs).” Of the above 13, only one was in electronic technology and 12 were in technology services. Overall, technology services IPOs count in 2016 was the third lowest on record (since 2007). Technology Services IPOs total proceeds in 2016 were USD2.77 billion, down from USD6.6 billion in 2015 and the lowest reading since 2010

4) And for some more rotten tomatoes: “In Q4, the average first day performance of initial public offerings was 6.7%. This marked a decline from the average first day pop of 8.8% in Q4 2015 and a significant drop from the 18.8% in Q3… On an annual basis, the average first day performance of IPOs in 2016 was 11.7%, which represented the smallest price pop since 2011 (9.8%).”


5) Like it or not, VCs are now being forced to wait longer for IPO exits:

So things are looking pretty barren for traditional VCs. Which might be a matter of a cyclical swing or a structural trend. Either way, the glamor of Series A-Z unicorns is not exactly shining on the proverbial hill.

3/1/17: Dead or Dying... A Requiem for the Traditional Banking Model

Earlier today, I briefly posted on Twitter my thoughts about the evolving nature of traditional banking. Here, let me elaborate on these.

The truth about the traditional banking model: it is dead. Ok, to be temporally current, it is dying. Six reasons why.

1) Banks can’t price risk in lending - we know as much since the revelations of 2007-2008. If they cannot do so, banks-based funding model for investment is a metronome ticking off a crisis-to-boom cyclicality. That policymakers (and thus regulators) cannot comprehend this is not the proposition we should care to worry about. Instead, the real concern should be why are equity and direct lending - the other forms of funding - not taking over. The answer is complex. Informational asymmetries abound, making it virtually impossible to develop retail (broad) markets for both (excluding listed equity). Tax preferences for debt is another part of the fallacious equation. Habits / status quo biases in allocating funds is the third. Inertia in the markets, with legacy lenders being at scale, while challengers being below the scale. Protectionism (regulatory and policy) favours banks over other forms of lending and finance. And more. But these factors are only insurmountable today. As they are being eroded, direct financing will gain at the expense of banks.

The side question is why the banks are no longer able to price risks in lending, having been relatively decent about doing so in previous centuries? The answer is complex. Firstly, banks are legacy institutions that have knowledge, models, memory and intellectual infrastructure that traces back to the industrial age. Time moved on, but banks did not move on as rapidly. Hence, today's firms are distinct from Coasean transaction cost minimisers. Instead, today's firms are much more complex entities, dealing with radically faster pace of innovation and disruption, with higher markets volatility and, crucially, trading in the environment that is more about uncertainty than risk (Knightian world). Here, risk pricing and risk management are not as closely aligned with risk modeling as in the age of industrial enterprises. Guess what: if firms are existing in a different world from the one inhabited by the banks, so are people working for these firms (aka banks' retail customers). Secondly, banks' own funding and operations models have become extremely complex (see on this below), which means that even simple loan transaction, such as a mortgage, is now interwoven into a web of risky contracts, e.g. securitisation, and involves multiple risky counterparties. Thirdly, demographic changes have meant changes in risk regulation environment (increased emphasis on consumer protection, bankruptcy reforms, data security, transparency, etc) all of which compound the uncertainty mentioned above. And so on...

2) Banks can’t provide security for depositors - we know, courtesy of pari passu clauses that treat depositors equivalently with risk investors. The deposits guarantee schemes are fig leaf decorations. For two reasons. One: they are exogenous to banks, and as such should not be used to give banks a market advantage. Of course, they are being used as such. Two: they are only as good as the sovereign guarantors’ willingness / ability to cover them. Does anyone, looking at the advancement of the cashless society in which the state is about to renew on its own promissory fiat at least across anonymity and extreme risk hedging functions of cash, really thinks the guarantees are irrevocable? That they cannot be diluted? If the answer is no, then that’s the beginning of an end for the traditional deposits-gathering, but bonds-funded banking hybrids.

More fundamentally, consider corporate governance structure of a traditional bank. Board and executives preside (more often, executives preside over the board due to information asymmetries and agency problems). Shareholders are given asymmetric voting rights (activist institutional shareholders are treated above ordinary retail shareholders). Bondholders have direct access to C-suite and even Board members that no other player gets. And the funders of the bank, the depositors? Why, they have no say in the bank. Not even a pro forma one. This asymmetry of power is not accidental. It is an outrun of the centuries of corporate evolution, driven by pursuit of higher returns on equity. But, roots aside, it certainly means that depositors are not the key client of the bank's executive. If they were, they would be put to the top of the corporate governance pyramid.

Still think that the bank is here to protect your deposits?

3) Banks can’t provide efficient platforms for transactions - we know, courtesy of #FinTech solutions. Banks charge excessive fees for simple transactions, such as currency exchanges, cross border payments, debt cards, some forms of regular utility payments, etc. They charge to issue you access to your money and to renew access when it deteriorates or is lost. They charge for all the things that many FinTech platforms do not charge for. And they provide highly restricted (i.e. costly) platform migration options (switching banks, for example). Some FinTech platforms now offer seamless, low cost migration options, e.g. aggregators and some new tech-enabled banks, e.g. KNAB. Anecdotal evidence to bear: two of my banks on two sides of the Atlantic can’t compete on fees and time-to-execute lags with a small firm doing my forex conversions that is literally 10 times cheaper than the lower cost bank and 5 days faster in delivering the service.

If you want an analogy: banking sector today is what music industry was just at the moment of iTunes launch.

4) Banks can’t escape maturity mismatch and other systemic risks - we know, courtesy of banks' reliance on interbank lending and securitisation. The core model of deposits being transformed into loans is hard enough to manage from the maturity mismatch perspective. But when one augments it with leveraged interbank funding and securitisation, we end up with 2007-2008 crisis. This is not an accident, but a logical corollary of the banking business model that requires increasing degrees of leverage to achieve higher returns on equity. Risks inherent in lending out of deposits are compounded by risks relating to lending out of borrowed funds, and both are correlated with risks arising from securitising payments on loans. The system is inherently unstable because second order effects (shutdown of securitised paper markets) on core business funding dominate the risk of an outright bank run by the punters. Worse, competitive re-positioning of the financial institutions is now running into the dense swamp of new risks, e.g. cybercrime and ICT-related systems risks (see more on this here: http://trueeconomics.blogspot.com/2017/01/2116-financial-digital-disruptors-and.html). No amount of macro- or micro-prudential risk management can address these effects. Most certainly not from the crowd of regulators and supervisors who are themselves lagging behind the already laggardly traditional banking curve.

As an aside, consider current demographic trends. As older generations draw down their deposits, younger generation is not accumulating the same amounts of cash as their predecessors were. The deposits base is shrinking, just at the time as transactions volumes are rising, just as weak income growth induces greater attention to transactions fees. Worse, as more and more younger workers find themselves in the contingent workforce or in entrepreneurship or part time work, their incomes become more volatile. This means they hold greater proportion of their overall shrinking savings in precuationary accounts (mental accounting applies). These savings are not termed deposits, but on-demand deposits, enhancing maturity mismatch risks.

5) Banks can’t provide advice to their clients worth paying for - we know this, thanks to the glut of alternative advice providers, and passive and active management venues. And thanks to the fact that banks have been aggressively ‘repairing margins’ by cutting back on customer services, which apparently does not damage their performance. Has anyone ever heard of cutting a value-adding line of business without adversely impacting value-added or margin? Nope, me neither. So banks doing away with advice-focused branches is just that - a self-acknowledgement that their advice is not worth paying for.

Worse, think of what has been happening in asset management sector. Fee-based advice is down. Fee-based investment funds (e.g. hedge funds) are shrinking violets. But all of these players bundle fees with performance-based metrics. And here we have a bunch of useless advice providers (banks) who supposed to charge fees for providing no performance-linked anchors?

6) Banks can’t keep up with the pace of innovation. How do we know that? Banks are already attempting to converge to FinTech platforms (automatisation of front and back office services, online banking, e-payments, etc,). Except they neither have technical capabilities to do so, nor integration room to achieve it without destroying own legacy systems and business, nor can their investors-required ROE sustain such a conversion. Beyond this, banking sector has one of the lowest employee mobility rates this side of civil service. Can you get innovation-driven talent into an institution where corporate culture is based on being a 'lifer'? Using Nassim Taleb's term, bankers are the 'IBM men' of today. Innovation-driven companies have none of these. For a good reason, not worth discussing here.


So WHAT function can banks carry out? Other than use private money to sustain superficial demand for overpriced Government debt and fuel bubbles in assets?

It is a rhetorical question. Banks, of course, are not going to disappear overnight. Like the combustion engine is not going to. But banks’ Tesla moment is already upon us. Today, banks, like the car companies pursuing Tesla, are throwing scarce resources at replicating FinTech. Most of the time they fail, put their tails between their legs and go shopping for FinTech start ups. Next, they will fail to integrate the start ups they bought into. After that, we will see banks consolidation moment, as the bigger ones start squeezing the smaller ones in pursuing shrinking market for their fees-laden services. And they will be running into other financial sector players, with deeper pockets and more sustainable (in the medium term) business models moving into their space - insurance companies and pension funds will start offering utility banking services to vertically integrate their customers. Along this path, banks' equity capital will be shrinking, which means their non-equity capital (costly CoCos and PE etc) will have to rise. Which means their ROEs will shrink some more.

Banking, as we know it, is dying. Banks, as we know them, will either vanish or mutate. If you are investing in banking stocks, make sure you are positioned for an efficient exit, make certain the bank you are investing in has the firepower to survive that mutation, and be confident in your valuation of that bank post-mutation. Otherwise, enjoy mindless gambling.

3/1/17: Euro growth greets 2017 with a bit of a bang

December marked another month of rising economic activity indicator for the euro area. Eurocoin, a leading growth indicator published by Banca d’Italia and CEPR notched up to 0.59 from 0.45 in November, implying annualised growth rate of 2.38 percent - the strongest growth signal in 67 months. It is worth remembering that in 2Q and 3Q 2016, real GDP growth slumped from 0.5% q/q recorded in 4Q 2015 - 1Q 2016 to 0.3% in Q2-Q3 2016. Latest 4Q 2016 reading for Eurocoin implies growth rate of around 0.47 percent, slightly below 1Q 2016 levels, but above the 0.31% average for the current expansionary cycle (from 2Q 2013 on).

Charts below illustrate these dynamics

Cyclical trends in growth rates currently imply ECB policy rate mispricing of around 2.0-2.5 percentage points (see chart below).

Meanwhile, inflationary dynamics, based on 12mo MA, suggest current monetary policy environment providing only a weak support to the upside.

The growth dynamics over the last 12 months are not exactly convincing. Even at currently above 2Q and 3Q forecast for 4Q 2016, FY 2016 growth is coming in at 1.58% annualised, against FY2015-2016 growth of 1.65%. Overall, this environment is unlikely to drive significant changes in ECB policy forward, as Frankfurt will continue to attempt supporting growth even if inflation ticks up to 0.4-0.5% q/q range for 12 months moving average basis.

2/1/16: Financial digital disruptors and cyber-security risks

My and Shaen Corbet's new paper titled Financial digital disruptors and cyber-security risks: paired and systemic (January 2, 2017), forthcoming in Journal of Terrorism & Cyber Insurance, Volume 1 Issue 2, 2017 is now available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2892842.

Abstract:
The scale and intensity of digital financial criminality has become more apparent and audacious over the past fifteen years. To counteract this escalating threat, financial technology (FinTech) and monetary and financial institutions (MFI) have attempted to upgrade their internal technological infrastructures to mitigate the risk of a catastrophic technological collapse. However, these attempts have been hampered through the financial stresses generated from the recent international banking crises. Significant contagion channels in the aftermath of cybercriminal events have also been recently uncovered, indicating that a single major event may generate sectoral and industry-wide volatility spillovers. As the skillset and variety of tactics used by cybercriminals develops further in an environment of stagnating and underfunded defensive technological structures, the probability of a devastating hacking event increases, along with the necessity for regulatory intervention. This paper explores and discusses the range of threats and consequences emanating from financial digital disruptors through cybercrime and potential avenues that may be utilised to counteract such risk.

2/1/16: Remember that America's Scariest Chart?

As promised in the previous post, here is a look at yet another wrinkle in the U.S. jobs creation saga. The following used to be referred to as the America's Scariest Chart some years back, until all analysts stopped tracking it. Well, all, save for myself - and for a good reason.

Since the election of Donald Trump, the U.S. media has been full of praise for President Obama's record on economic recovery, setting the stage for an argument that Trump Administration is about to inherit a very strong economy, the one that, in mainstream media's minds, Trump is likely to mess up.

So lets do a simple exercise. Take current level of employment (non-farm payrolls) and compare it to the pre-crisis average levels of employment. Represented as an index, this comparative can be performed for every recession since the end of WW2. Chart below illustrates the results:

As the chart above clearly shows:

1. Today's employment figures represent the worst recovery from a recession on record (for any terminal point of previous recoveries, current recovery is associated with lower employment levels).
2. Even stretching time of this recovery to present day - yielding the second longest period of a recovery since 1945, after the 1990 episode - current recovery is still the worst performing one.
3. Looking at the slope of the 2008 line, increases in employment relative to pre-crisis situation are weaker in the current post-crisis recovery than in every other recovery, except the 2001.

Now, this is not to put the blame for the weak recovery on the shoulders of President Obama. Presidential policies have little short term impact on unemployment and it takes cooperative Congress to structure and enact longer-term policies. But this does dispute the media-promoted view of the U.S. labour markets are being in rude health. President-elect is not about to inherit a spotless jobs market from his predecessor. America's Scariest Chart still confirms as much.

2/1/17: U.S. Unemployment Duration is Still Record-Busting

Throughout recent years, the recovery meme, played across the mainstream media in the U.S. has provided endless support to President Obama’s approval ratings. During POTUS 2016 election, the said meme was used by Hillary Clinton to challenge the ‘things aren’t so great in America’ views of Bernie Sanders and, subsequently, the echoes of the same from Donald Trump. Since the election, the recovery story has been billed as the ‘strong economy’ legacy that President Obama will be leaving for his predecessor to mess with - the basis for setting up the incoming Trump Administration for any potential fall, should economic fortunes of the recovery were to falter.

The central point of the U.S. recovery story - absent any appreciable growth in productivity, capital investment, and sectoral value added - was the only bright spot on the U.S. economic horizon: the labour markets. In fact, the U.S. headline unemployment figures have shown very strong gains, and jobs creation has been robust, with more recent data showing improvements (at long last) in households’ incomes. All of these indicators can and have been robustly challenged in terms of the extent to which they show true nature of improvements. However, they have been taken, predominantly, as read. Improvements are improvements, and gains are gains.

And as the readers of my blog and media articles would have known, the story is never complete, if one looks only at headline figures. Reality is always more complex.

So to show you this complexity at work, let’s look at one official indicator of the health of the labour markets in the U.S. - duration of unemployment. If the U.S. economy is really awash with jobs, and if the true unemployment rate is really sitting at 4.9 percent, the duration of unemployment should not only be declining on average, but it should be closer to ‘normal’ non-recessionary reading. Right?

Take a look at the following chart based on data from the St Louis Federal Reserve database, Fred:

Yes, duration of unemployment peaked in January 2011 at 40.7 weeks and since then fallen to 26.3 weeks (as of November 2016), but 26.3 weeks for average unemployment benefits duration is still above any previous recession since 1948 on.

Now, as er return to normalcy. During 1990-1991 and 2001 recessions, recovery failed to completely reduce average duration of unemployment back to pre-recessionary norms. In simple terms, after the end of recession, in 1990-1991 and 2001 downturns, on average, unemployed people remained in unemployment longer than before recessions. These were the first two recession on record that resulted in this change in structural unemployment duration.

Now, consider 2008 recession. Chart below illustrates what happened to the ‘new normal’ duration of unemployment spells. Specifically, chart below plots the difference between average duration of unemployment during recession and recovery and the average duration of unemployment in 12 months prior to the onset of each recession. Returning to normal here would mean getting duration gap closer to zero.

Again, current (since 2008) recovery is clearly the worst for all post-recessionary episodes on record. Currently, duration of unemployment is 9.5 weeks, on average, longer than it was during the last 12 months of pre-2008 recession. Which is bad enough to be worse than the peak deviation for any recession in modern history.

What is happening here? The fabled U.S. jobs creation recovery is really a combination of several factors. One of these is genuine increases in jobs being created, which drives unemployment down. Another is demographic: U.S. labour force is expanding, and as it does, employment creation get swallowed by new entrants into labour force, while many existent unemployed are either exiting the labour force, or remaining on unemployment benefits longer. Of course, putting younger workers to work is a good thing. But squeezing older unemployed out of workforce is not.

There are serious problems with highly elevated (to-date) duration of U.S. unemployment that few politicians are willing to talk about. For one, longer duration of unemployment implies lower probability of transition into employment. Secondly, it also implies higher probability of future unemployment in future recessions. Thirdly, it implies more severe losses in skills, human capital, health, social well-being, etc. In other words, costs of unemployment rise faster for longer duration of unemployment.

Which makes you pause and think: is the legacy of the Obama administration on jobs is that impressive? Really? Well, stay tuned for more...

30/12/16: In IMF's Forecasts, Happiness is Always Around the Corner

Remember the promises of the imminent global growth recovery 'next year'? IMF, the leading light of exuberant growth expectations has been at this game for some years now. And every time, turning the calendar resets the fabled 'growth recovery' out another 12 months.

Well, here's a simple view of the extent to which the IMF has missed the boat called Realism and jumped onboard the boat called Hope

Table above posts cumulative 2010-2016 real GDP growth that was forecast by the IMF back in September 2011, against what the Fund now anticipates / estimates as of October 2016. The sea of red marks all the countries for which IMF's forecasts have been wildly on an optimistic side. Green marks the lonely four cases, including tax arbitrage-driven GDPs of Ireland and Luxembourg, where IMF forecasts turned out to be too conservative. German gap is minor in size - in fact, it is not even statistically different from zero. But Maltese one is a bit of an issue. Maltese economy has been growing fast in recent years, prompting the IMF to warn the Government this year that its banking sector is starting to get overexposed to construction sector, and its construction sector is becoming a bit of a bubble, and that all of this is too closely linked to Government spending and investment boom that cannot be sustained. Oh, and then there are inflows of labour from abroad to sustain all of this growth. Remember Ireland ca 2005-2006? Yep, Malta is a slightly milder version.

Notice the large negative gaps: Greece at -21 percentage points, Cyprus at -18 percentage points, Finland at -15 percentage points and so on... the bird-eye's view of the IMF's horrific errors is:

• Two 'programme' countries - where the IMF is one of the economic policy 'masters', so at the very least it should have known what was happening on the ground; and 
• IMF's sheer incomprehension of economic drivers for growth in the case of Finland, which, until the recession hit it, was the darling of IMF's 'competitiveness leaders board'.  

Median-average miss is between 4.33 and 4.97 percentage points in cumulative growth undershoot over 7 years, compared to IMF end-of-2011 projections.

So next time the Fund starts issuing 'happiness is just around the corner' updates, and anchoring them to the 'convincing' view of 'competitiveness' and 'structural drivers' stuff, take them with a grain of salt.

30/12/16: Corporate Debt Grows Faster than Cash Reserves

Based on the data from FactSet, U.S. corporate performance metrics remain weak.

On the positive side, corporate cash balances were up 7.6% to USD1.54 trillion in 3Q 2016 y/y, for S&P500 (ex-financials) companies. This includes short term investments, as well as cash reserves. Cash balances are now at their highest since the data records started in 2007.

But, there’s been some bad news too:

1. Top 20 companies now account for 52.5% of the total S&P500 cash holdings, up on 50.8% in 3Q 2015.
2. Heaviest cash reserves are held by companies that favour off-shore holdings over repatriation of funds into the U.S., like Microsoft (USD136.9 billion, +37.8% y/y), Alphabet (USD83.1 billion, +14.1% y/y), Cisco (USD71 billion, +20.1% y/y), Oracle (USD68.4 billion, +22.3%) and Apple (USD67.2 billion, +61.4%). Per FactSet, “the Information Technology sector maintained the largest cash balance ($672.7 billion) at the end of the third quarter. The sector’s cash total made up 43.6% of the aggregate amount for the index, which was a jump from the 39.3% in Q3 2015”
3. Despite hefty cash reserves, net debt to EBITDA ratio has reached a new high (see green line in the first chart below), busting records for the sixth consecutive quarter - up 9.9% y/y. Again, per FactSet, “at the end of Q3, net debt to EBITDA for the S&P 500 (Ex-Financials) increased to 1.88.” So growth in debt has once again outpaced growth in cash. “At the end of the third quarter, aggregate debt for the S&P 500 (Ex-Financials) index reached its highest total in at least ten years, at $4.57 trillion. This marked a 7.8% increase from the debt amount in Q3 2015.” which is nothing new, as in the last 12 quarters, growth in debt exceeded growth in cash in all but one quarter (an outlier of 4Q 2013). 3Q 2016 cash to debt ratio for the S&P 500 (Ex-Financials) was 33.7%, on par with 3Q 2015 and 5.2% below the average ratio over the past 12 quarters.

Net debt issuance is also a problem: 3Q 2016 posted 10th highest quarter in net debt issuance in 10 years, despite a steep rise in debt costs.

While investment picked up (ex-energy sector), a large share of investment activity remains within the M&As. “The amount of cash spent on assets acquired from acquisitions amounted to $85.7 billion in Q3, which was the fifth largest quarterly total in the past ten years. Looking at mergers and acquisitions for the United States, M&A volume slowed in the third quarter (August - October) compared to the same period a year ago, but deal value rose. The number of transactions fell 7.3% year-over-year to 3078, while the aggregate deal value of these transactions increased 23% to $564.2 billion.”

The above, of course, suggests that quality of the deals being done (at least on valuations side) remains relatively weak: larger deals signal higher risks for acquirers. This is confirmed by data from Bloomberg, which shows that 2016 median Ebitda Multiple for M&A deals of > USD 1 Billion has declined to x12.7 in 2016 from an all-time high in 2015 of x14.3. Still, 2016 multiple is the 5th highest on record. In part, this reduction in risk took place at the very top of M&As distribution, as the number of so-called mega-deals (> USD 10 billion) has fallen to 35 in 2016, compared to 51 in 2015 (all time record). However, 2016 was still the sixth highest mega-deal year in 20 years.

Overall, based on Bloomberg data, 2015 was the fourth highest M&A deals year since 1996.

So in summary:

• While cash flow is improving, leading to some positive developments on R&D investment and general capex (ex-energy);
• Debt levels are rising and they are rising faster than cash reserves and earnings;
• Much of investment continues to flow through M&A pipeline, and the quality of this pipeline is improving only marginally.

Source: https://www.bloomberg.com/gadfly/articles/2016-12-30/trump-set-to-refill-m-a-punch-bowl-in-2017

29/12/16: Drowning in Debt

Recently, I posted about the return - with a vengeance - of one of the key drivers of the Global Financial Crisis and the Great Recession, the rapid rise of the debt bubble across the global economy. The original post is available here: http://trueeconomics.blogspot.com/2016/12/161216-root-of-2007-2010-crises-is-back.html

There is more evidence of the problem reaching beyond corporate finance side of the markets for debt. In fact, in the U.S. - the economy that led the de-risking and deleveraging efforts during the early stages of the recovery - household debt is now once again reaching danger levels.

Chart 1 below shows that, based on data from NY Federal Reserve through 3Q 2016, full year 2016 average household debt levels are likely to exceed 2005-2007 average by some 3 percent. In 3Q 2016, total average household debt was around USD98,312, a level comparable to USD98,906 in 2006.

And Chart 2 shows that overall, aggregate levels of household debt and per capita levels of household debt both are now in excess of 2005-2007 averages.

Finally, as Chart 3 below indicates, delinquencies rates are also rising, despite historically low interest rates and booming jobs markets. For Student Loans and Car Loans, 3Q 2016 delinquencies rates are 1 percentage points and 3.8 percentage points above the 2005-2007 average delinquency rates. For Mortgages, current delinquency rates are running pretty much at the 2005-2007 average. Only for Credit Cards do delinquency rates at the present trail behind the 2005-2007 average, by some 2 percentage points.

Now, consider the market expectations of 0.75-1 percentage increase in Fed rates in 2017 compared to 3Q 2016 (we are already 0.25 percentage points on the way with the most recent Fed decision). Based on the data from NY Fed, and assuming average 2015-2016 growth rates in credit forward, this will translate into extra household payments on debt servicing of around USD1,085-USD1,465 per annum depending on the passthrough rates from policy rate set by the Fed and the retail rates charged by the banks.

Given the state of the U.S. household finances, this will be some tough burden to shoulder.

So here you have it, folks:
1) Corporate debt bubble is at an all-time high
2) Government debt bubble is at an all-time high
3) Household debt bubble is at an all-time high.
Meanwhile, equity funding is slipping even for the usually credit-shy start ups.

And if you want another illustration, here is total global Government debt, based on IMF data:

We’ve learned no lessons from 2008.


Sources for data:
https://www.nerdwallet.com/blog/average-credit-card-debt-household/
https://www.newyorkfed.org/microeconomics/data.html
http://www.imf.org/external/pubs/ft/weo/2016/02/weodata/index.aspx

19/12/16: Market Anomalies and Data Mining: Some Pretty Tough Love from Data

Investment anomalies (or in other words efficacy of exogenous factors in determining abnormal returns to investment) are a matter of puzzle for traditional investment analysis. In basic terms, we normally think about the investment as an undertaking that offers no ‘free lunch’ - if markets are liquid, deep and, once we control for risk factors, taxes and transaction costs, an average investor cannot expect to earn an above-market return. Put differently, there should be no ways to systematically (luck omitting) beat the market.

Anomalies represent the case where some factors do, in fact, generate such abnormal returns. There is a range of classic anomalies, most commonly known ones being Small Firms Outperform, January Effect, Low Book Value, Under-dogs or Discounted Assets or Dogs of the Dow, Reversals, Days of the Week, etc. In fact, there is an entire analytics industry built around markets that does one thing: mine for factors that can give investors a leg up on competition, or finding anomalies.

One recent paper have identified a list of some 314 factors that were found - in the literature - to generate abnormal returns. As noted by John Cochrane: “We thought 100% of the cross-sectional variation in expected returns came from the CAPM, now we think that’s about zero and a zoo of new factors describes the cross section.”

A recent paper published by NBER and authored by Juhani Linnainmaa and Michael Roberts (see link below) effectively tests this Cochrane’s proposition. To do this, the authors “examine cross-sectional anomalies in stock returns using hand-collected accounting data extending back to the start of the 20th century. Specifically, we investigate three potential explanations for these anomalies: unmodeled risk, mispricing, and data-snooping.” In other words, the authors look into three reasons as to why anomalies can exist:

1. Unmodeled risk reflects the view that some of risk premium paid out in the form of investment returns is not captured by traditional models of risk-return relations;
2. Mispricing reflects the view that markets’ participants routinely and over long run can misplace risk; and
3. Data-snooping view implies that anomalies generate returns in the historical data that do not replicate in forward-looking implementation because these anomalies basically arise from ad hoc empirical data mining.

The authors argue that “each of these explanations generate different testable implications across three eras encompassed by our data: (1) pre-sample data existing before the discovery of the anomaly, (2) in-sample data used to identify the anomaly, and (3) post-sample data accumulating after identification of the anomaly.”

In their first set of tests, the authors focus on profitability and investment factors, because prior literature shown that “these factors, in concert with the market and size factors, capture much of the cross-sectional variation in stock returns.”

Finding 1: the authors “find no statistically reliable premiums on the profitability and investment factors in the pre-1963 sample period… Between 1963 and 2014, these factors average” statistically and financially significant returns on average of “30 and 25 basis points per month, respectively.”

Finding 2: “The attenuations of the investment and profitability premiums in the pre-1963 data are representative of most of the other 33 anomalies that we examine. Just eight out of the 36 (investment, profitability, value, and 33 others) earn average returns that are positive and statistically significant at the 5% level in the pre-1963 period.

Finding 3: All of the measures of abnormal returns used in the study generate premiums that “decrease sharply and statistically significantly when we move out of the original study’s sample period by going either backward or forward in time.” In other words, anomalies tend to disappear or weaken every time the authors significantly broaden time horizon beyond that which corresponds to the time horizon used in the original study that uncovered such an anomaly.

As authors note, “these findings are consistent with data-snooping as the anomalies are clearly sensitive to the choice of sample period."

How? "...If the anomalies are a consequence of multidimensional risk that is not accurately accounted for by the empirical model (i.e., unmodeled risk), then we would have expected them to be similar across periods, absent structural breaks in the risks that matter to investors. Similarly, if the anomalies are a consequence of mispricing, then we would have expected them to be larger during the pre-discovery sample period when limits to arbitrage, such as transaction costs, were greater.”

But there is a note of caution due. “Our results do not suggest that all return anomalies are spurious. The average in-sample anomaly earns a CAPM alpha of 32 basis points per month (t-value = 10.87). The average alpha is 13 basis points (t-value = 4.42) per month for the pre-discovery sample and 14 basis points (t-value = 4.06) for the post-discovery sample. Although these estimates lie far below the in-sample numbers, they are highly statistically significant.”

The kicker is that “investors, however, face the uncertainty of not knowing which anomalies are real and which are spurious [or due to data mining], and so they need to treat them with caution. …because data-mining bias affects many facets of returns—averages, volatilities, and correlations—it is best to test asset pricing models out of sample," or absent such opportunity (perhaps due to tight data) - by selecting a model / factor that "is able to explain half of the in-sample alpha".




Full paper: Linnainmaa, Juhani T. and Roberts, Michael R., The History of the Cross Section of Stock Returns (December 2016). NBER Working Paper No. w22894. https://ssrn.com/abstract=2880332