5/4/21: The Coming Wave of Financial Repression

 

In a recent article for The Currency, I covered the topic of the forthcoming wave of financial repression, as Governments worldwide pursue non-conventional fiscal tightening in years to come: Make no mistake, financial repression is coming in the US: https://thecurrency.news/articles/36547/make-no-mistake-financial-repression-is-coming-in-the-us/. 

8/6/20: 30 years of Financial Markets Manipulation

Students in my course Applied Investment and Trading in TCD would be familiar with the market impact of the differential bid-ask spreads in intraday trading. For those who might have forgotten, and those who did not take my course, here is the reminder: early in the day (at and around market opening times), spreads are wide and depths of the market are thin (liquidity is low); late in the trading day (closer to market close), spreads are narrow and depths are thick (liquidity is higher). Hence, a trading order placed near market open times tends to have stronger impact by moving the securities prices more; in contrast, an equally-sized order placed near market close will have lower impact.

Now, you will also remember that, in general, investment returns arise from two sources: 

1. Round-trip trading gains that arise from buying a security at P(1) and selling it one period later at P(2), net of costs of buy and sell orders execution; and 
2. Mark-to-market capital gains that arise from changes in the market-quoted price for security between times P(1) and P(2+).

The long-running 'Strategy' used by some institutional investors is, therefore as follows: 

Here is the illustration of the 'Strategy' via Bruce Knuteson paper "Celebrating Three Decades of Worldwide Stock Market Manipulation", available here: https://arxiv.org/pdf/1912.01708.pdf.

• Step 1: Accumulate a large long portfolio of assets;
• Step 2: At the start of the day, buy some more assets dominating your portfolio at P(1) - generating larger impact of your buy orders, even if you are carrying a larger cost adverse to your trade;
• Step 3: At the end of the day, sell at P(2) - generating lower impact from your sell orders, again carrying the cost.

On a daily basis, you generate losses in trading account, as you are paying higher costs of buy and sell orders (due to buy-sell asymmetry and intraday bid-ask spreads differences), but you are also generating positive impact of buy trades, net of sell trades, so you are triggering positive mark-to-market gains on your original portfolio at the start of the day.

Knuteson shows that, over the last 30 years, overnight returns in the markets vastly outstrip intraday returns. 

Per author, "The obvious, mechanical explanation of the highly suspicious return patterns shown in Figures 2 and 3 is someone trading in a way that pushes prices up before or at market open, thus causing the blue curve, and then trading in a way that pushes prices down between market open (not including market open) and market close (including market close), thus causing the green curve. The consistency with which this is done points to the actions of a few quantitative trading firms rather than

the uncoordinated, manual trading of millions of people."

Sounds bad? It is. Again, per Knuteson: "The tens of trillions of dollars your use of the Strategy has created out of thin air have mostly gone to the already-wealthy: 

• Company executives and existing shareholders benefi tting directly from rising stock prices; 
• Owners of private companies and other assets, including real estate, whose values tend to rise and fall with the stock market; and 
• Those in the financial industry and elsewhere with opportunities to privatize the gains and socialize the losses."

These gains to capital over the last three decades have contributed directly and signi ficantly to the current level of wealth inequality in the United States and elsewhere. As a general matter, widespread mispricing leads to misallocation of capital and human effort, and widespread inequality negatively a effects our social structure and the perceived social contract."

16/4/20: Lessons in Corporate Finance: COVID19

My article summing up some corporate finance lessons from the COVID19 crisis is now available at The Currency: https://www.thecurrency.news/articles/14752/as-we-rebuild-the-economy-we-need-to-escape-the-perilous-spiral-of-corporate-debt.

8/1/20: Creative destruction and consumer credit

My new article for @TheCurrency_, titled "Creative destruction and consumer credit: A Fintech song for the Irish banks" is out. Link: https://www.thecurrency.news/articles/6150/creative-destruction-and-consumer-credit-fintech-song-for-the-irish-banks.

Key takeaways: Irish banks need to embrace the trend toward higher degree of automation in management of clients' services and accounts, opening up the sector to fintech solutions rather than waiting for them to eat the banks' lunch. Currently, no Irish bank is on-track to deploy meaningful fintech solutions. The impetus for change is more than the traditional competitive pressures from the technology curve. One of the key drivers for fintech solutions is also a threat to the banks' traditional model of business: reliance on short-term household credit as a driver of  profit margins.

"Irish banks are simply unprepared to face these challenges. Looking across the IT infrastructure landscape for the banking sector in Ireland, one encounters a series of large-scale IT systems failures across virtually all major banking institutions here. These failures are linked to the legacy of the banks’ operating systems."

"In terms of technological services innovation frontier, Irish banks are still trading in a world where basic on-line and mobile banking is barely functioning and requires a push against consumers’ will by the cost-cutting banks and supportive regulators. To expect Irish banking behemoths to outcompete international fintech solutions providers is equivalent to betting on a tortoise getting to the Olympic podium in a 10K race."

2/4/19: World Economic Conditions and Yield Signals

Global divergence...

... until global convergence?

More likely, the latter: https://www.reuters.com/article/us-ecb-policy-rates-idUSKCN1R81B3. 

3/10/18: Dumping Ice bags into Overheating Reactor: Bonds & Stocks Bubbles

Wading through the ever-excellent Yardeni Research notes of recent, I have stumbled on a handful of charts worth highlighting and a related blog post from my friends at the Global Macro Monitor that I want to share with you all.

Let's start with the stark warning regarding the U.S. Treasuries market from the Global Macro Monitor, accessible here: https://macromon.wordpress.com/2018/10/03/alea-iacta-est/.  To give you my sense from reading this, two quotes with my quick takes:

"Supply shortages, induced mainly by central bank quantitative easing have been a major factor driving asset markets, in our opinion.  Not all, but a big part." So forget the 'not all' and think about risks pairings in a complex financial system of today: equities and bonds are linked through demand for yield (gains) and demand for safety. If both are underpricing true risks (and bond markets are underpricing risks, as the quote implies), it takes one to scratch for the other to blow. Systems couplings get more fragile the tighter they become.

"The float of total U.S. equities has shrunk dramatically, in part, due to cheap financing to fund share buybacks.   The technical shortage of stocks have helped boost U.S. equity markets and killed off most bears and short sellers." In other words, as I have warned repeatedly for years now, U.S. equity markets are now dangerously concentrated (see this blog for posts involving concentration risks). This concentration is driven by three factors: M&As and shares buy-backs, plus declined IPOs activity. The former two are additional links to monetary policies and, thus to the bond markets (coupling is getting even tighter), the latter is structural decline in enterprise formation and acceleration rates (secular stagnation). This adds complexity to tight coupling of risk systems. Bad, very bad combination if you are running a nuclear power plant or a major dam, or any other system prone to catastrophic risk exposures.

How bad the things are?

Since 1Q 2009, total cumulative shares buy-backs for S&P500 amounted (through 2Q 2018) to USD 4.2769 trillion.

Now, those charts.

Chart 1, via Yardeni Research's "Stock Market Indicators: S&P 500 Buybacks & Dividends" book from October 3rd (https://www.yardeni.com/pub/buybackdiv.pdf)

What am I looking at here? The signals revealing flow of corporate earnings toward investment, or, the signs of the build up in the future economic capacity of the private sector. The red line in the lower panel puts this into proportional terms, the gap between the yellow line and the green line in the top panel puts it into absolute terms. And both are frightening. Corporate earnings are on a healthy trend and at healthy levels. But corporate investment is not and has not been since 1Q 2014. This chart under-reports the extent of corporate under-investment through two things not included in the red line: (1) M&As - high risk 'investment' strategies by corporates that, if adjusted for that risk, would have pushed the actual investment growth even lower than it is implied by the red line; and (2) Risk-adjustments to the organic investments by companies. In simple terms, there is no meaningful translation from higher earnings into new investment in the U.S. economy so far in 2018 and there has not been one since 2014. Put differently, U.S. economy has been starved of organic investment for a good part of the 'boom' years.

Chart 2, via the same note:

Spot something new in the charts? That's right: buybacks are accelerating in 1H 2018, with 2Q 2018 marking an absolute historical high at USD 1.0803 trillion (annualized rate) of buybacks. Guess what does this mean for the markets? Well, this:

And what causes the latest spike in buybacks? No, not growing earnings (which are appreciating, but moderately). The fiscal policy under the Tax Cuts and Jobs Act 2017, or Trump Tax Cuts.

Let's circle back: monetary policy madness of the past has been holding court in bond markets and stock markets, pushing mispricing of risks to absolutely astronomical highs. We have just added to that already risky equation fiscal policy push for more mispricing of risks in equity markets.

This is like dumping picnic-sized bags of ice into the cooling system to run the reactor hotter. And no one seems to care that the bags of ice are running low in the delivery truck... You can light a smoke and watch ice melt. Or you can run for the parking lot to drive away. As an investor, you always have a right choice to make. Until you no longer have any choices left.

28/9/18: 10th Anniversary of the Irish Banking Crisis

Yahoo Finance's Edmund Heaphy on 10th Anniversary of the Irish Bank Guarantee: https://uk.finance.yahoo.com/news/destructive-goal-decade-irish-bank-guarantee-090447485.html with extensive quotes from myself.

21/6/17: BusinessInsider: The Most Important Charts in the World

My contribution to the BusinessInsider "The most important charts in the world from the brightest minds on Wall Street" is out. See my chart here: http://www.businessinsider.com/most-important-charts-markets-finance-chartbook-2017-6?op=0#/#constantin-gurdgiev-52 and the full deck of charts here: http://www.businessinsider.com/most-important-charts-markets-finance-chartbook-2017-6.

16/6/17: Replicating Scientific Research: Ugly Truth

Continuing with the theme on 'What I've been reading lately?', here is a smashing paper on 'accuracy' of empirical economic studies.

The paper, authored by Hou, Kewei and Xue, Chen and Zhang, Lu, and titled "Replicating Anomalies" (most recent version is from June 12, 2017, but it is also available in an earlier version via NBER) effectively blows a whistle on what is going on in empirical research in economics and finance. Per authors, the vast literature that detects financial markets anomalies (or deviations away from the efficient markets hypothesis / economic rationality) "is infested with widespread p-hacking".

What's p-hacking? Well, it's a shady practice whereby researchers manipulate (by selective inclusion or exclusion) sample criteria (which data points to exclude from estimation) and test procedures (including model specifications and selective reporting of favourable test results), until insignificant results become significant. In other words, under p-hacking, researchers attempt to superficially maximise model and explanatory variables significance, or, put differently, they attempt to achieve results that confirm their intuition or biases.

What's anomalies? Anomalies are departures in the markets (e.g. in share prices) from the predictions generated by the models consistent with rational expectations and the efficient markets hypothesis. In other words, anomalies occur when markets efficiency fails.

There are scores of anomalies detected in the academic literature, prompting many researchers to advocate abandonment (in all its forms, weak and strong) of the idea that markets are efficient.

Hou, Xue and Zhang take these anomalies to the test. The compile "a large data library with 447 anomalies". The authors then control for a key problem with data across many studies: microcaps. Microcaps - or small capitalization firms - are numerous in the markets (accounting for roughly 60% of all stocks), but represent only 3% of total market capitalization. This is true for key markets, such as NYSE, Amex and NASDAQ. Yet, as authors note, evidence shows that microcaps "not only have the highest equal-weighted returns, but also the largest cross-sectional standard deviations in returns and anomaly variables among microcaps, small stocks, and big stocks." In other words, these are higher risk, higher return class of securities. Yet, despite this, "many studies overweight microcaps with equal-weighted returns, and often together with NYSE-Amex-NASDAQ breakpoints, in portfolio sorts." Worse, many (hundreds) of studies use 1970s regression technique that actually assigns more weight to microcaps. In simple terms, microcaps are the most common outlier and despite this they are given either same weight in analysis as non-outliers or their weight is actually elevated relative to normal assets, despite the fact that microcaps have little meaning in driving the actual markets (their weight in the total market is just about 3% in total).

So the study corrects for these problems and finds that, once microcaps are accounted for, the grand total of 286 anomalies (64% of all anomalies studied), and under more strict statistical signifcance test 380 (of 85% of all anomalies) "including 95 out of 102 liquidity variables (93%) are insignificant at the 5% level." In other words, the original studies claims that these anomalies were significant enough to warrant rejection of markets efficiency were not true when one recognizes one basic and simple problem with the data. Worse, per authors, "even for the 161 significant anomalies, their magnitudes are often much lower than originally reported. Among the 161, the q-factor model leaves 115 alphas insignificant (150 with t < 3)."

This is pretty damning for those of us who believe, based on empirical results published over the years, that markets are bounded-efficient, and it is outright savaging for those who claim that markets are perfectly inefficient. But, this tendency of researchers to silverplate statistics is hardly new.

Hou, Xue and Zhang provide a nice summary of research on p-hacking and non-replicability of statistical results across a range of fields. It is worth reading, because it dents significantly ones confidence in the quality of peer review and the quality of scientific research.

As the authors note, "in economics, Leamer (1983) exposes the fragility of empirical results to small specification changes, and proposes to “take the con out of econometrics” by reporting extensive sensitivity analysis to show how key results vary with perturbations in regression specification and in functional form." The latter call was never implemented in the research community.

"In an influential study, Dewald, Thursby, and Anderson (1986) attempt to replicate empirical results published at Journal of Money, Credit, and Banking [a top-tier journal], and find that inadvertent errors are so commonplace that the original results often cannot be reproduced."

"McCullough and Vinod (2003) report that nonlinear maximization routines from different software packages often produce very different estimates, and many articles published at American Economic Review [highest rated journal in economics] fail to test their solutions across different software packages."

"Chang and Li (2015) report a success rate of less than 50% from replicating 67 published papers from 13 economics journals, and Camerer et al. (2016) show a success rate of 61% from replicating 18 studies in experimental economics."

"Collecting more than 50,000 tests published in American Economic Review, Journal of Political Economy, and Quarterly Journal of Economics, [three top rated journals in economics] Brodeur, L´e, Sangnier, and Zylberberg (2016) document a troubling two-humped pattern of test statistics. The pattern features a first hump with high p-values, a sizeable under-representation of p-values just above 5%, and a second hump with p-values slightly below 5%. The evidence indicates p-hacking that authors search for specifications that deliver just-significant results and ignore those that give just-insignificant results to make their work more publishable."

If you think this phenomena is encountered only in economics and finance, think again. Here are some findings from other ' hard science' disciplines where, you know, lab coats do not lie.

"...replication failures have been widely documented across scientific disciplines in the past decade. Fanelli (2010) reports that “positive” results increase down the hierarchy of sciences, with hard sciences such as space science and physics at the top and soft sciences such as psychology, economics, and business at the bottom. In oncology, Prinz, Schlange, and Asadullah (2011) report that scientists at Bayer fail to reproduce two thirds of 67 published studies. Begley and Ellis (2012) report that scientists at Amgen attempt to replicate 53 landmark studies in cancer research, but reproduce the original results in only six. Freedman, Cockburn, and Simcoe (2015) estimate the economic costs of irreproducible preclinical studies amount to about 28 billion dollars in the U.S. alone. In psychology, Open Science Collaboration (2015), which consists of about 270 researchers, conducts replications of 100 studies published in top three academic journals, and reports a success rate of only 36%."

Let's get down to real farce: everyone in sciences knows the above: "Baker (2016) reports that 80% of the respondents in a survey of 1,576 scientists conducted by Nature believe that there exists a reproducibility crisis in the published scientific literature. The surveyed scientists cover diverse fields such as chemistry, biology, physics and engineering, medicine, earth sciences, and others. More than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than 50% have failed to reproduce their own experiments. Selective reporting, pressure to publish, and poor use of statistics are three leading causes."

Yeah, you get the idea: you need years of research, testing, re-testing and, more often then not, you get the results are not significant or weakly significant. Which means that after years of research you end up with unpublishable paper (no journal would welcome a paper without significant results, even though absence of evidence is as important in science as evidence of presence), no tenure, no job, no pension, no prospect of a career. So what do you do then? Ah, well... p-hack the shit out of data until the editor is happy and the referees are satisfied.

Which, for you, the reader, should mean the following: when we say that 'scientific research established fact A' based on reputable journals publishing high quality peer reviewed papers on the subject, know that around half of the findings claimed in these papers, on average, most likely cannot be replicated or verified. And then remember, it takes one or two scientists to turn the world around from believing (based on scientific consensus at the time) that the Earth is flat and is the centre of the Universe, to believing in the world as we know it to be today.


Full link to the paper: Charles A. Dice Center Working Paper No. 2017-10; Fisher College of Business Working Paper No. 2017-03-010. Available at SSRN: https://ssrn.com/abstract=2961979.

7/6/17: Markets, Investors Exuberance and Fundamentals

Latest data from FactSet on S&P500 core metrics is an interesting read. Here are a couple of charts that caught my attention:

Look first at the last 6 months worth of EPS data through estimated 2Q 2017 (based on 99% of companies reporting). The trend continues: EPS is declining, while prices are rising. On a longer time scale, EPS have been virtually flat in 2014-2016, but are forecast to rise nicely in 2017 and 2018. Whatever the forecast might be for 2018, 2017 increase would do little to generate a meaningful reversion in EPS to price trend

However, the good news is, expectations on rising EPS are driven by rising sales for 2017, and to a lesser extent in 2018. This would be (if materialised) an improvement on the 2014-2016 core drivers, including shares repurchases (chart below).

Next, consider P/E ratios:

As the chart above indicates, P/E ratios are expected to continue rising in the next 12 months. In other words, the markets are going to get more expensive, relative to underlying earnings. Worse, on a 5-year average basis, all sectors, excluding Financials, are at above x14. Hardly a comfort zone for 'go long' investors. The overvalued nature of the market is clearly confirmed by both forward and trailing P/E ratios over the last 10 years:

Forward expectations are now literally a run-away train, relative to the past 10 years record (chart above), while trailing (lagged) P/Es are dangerously close to crisis-triggering levels of exuberance (chart below).

In summary, thus, latest data (through end-of-May) shows continued buildup of risks in the equity markets. At what point the dam will crack is not something I can attempt to answer, but the lake of investors' expectations is now breaching the top, and the spillways aren't doing the trick on abating them.

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.

13/6/16: Twin Tech Challenge to Traditional Banks

My article for the International Banker looking at the fintech and cybercrime disruption threats to traditional banking models is out.

The long-term fallout from the 2008 global financial crisis created several deep fractures in traditional-banking models. Most of the sectoral attention today has focused on weak operating profits and balance-sheet performance, especially the risks arising from the negative-rates environment and the collapse in yields on traditional assets, such as highly rated sovereign and corporate debt. Second-tier concerns in boardrooms and amidst C-level executives relate to the continuously evolving regulatory and supervisory pressures and rising associated costs. Finally, the anemic dynamics of the global economic recovery are also seen as a key risk to traditional banks’ profitability.

However, from the longer-term perspective, the real risks to the universal banks’ well-established business model come from an entirely distinct direction: the digital-disruption channels that simultaneously put pressure on big banks’ core earnings lines and create ample opportunities for undermining the banking sector’s key unique selling proposition—that is, security of customer funds, data and transactions, and by corollary, enhancing customer loyalty. These channels are FinTech innovations—including rising data intensity of products on offer and technological threats, such as rising risks to cybersecurity. This two-pronged challenge is not unique to the banking sector, but its disruptive potential is a challenge that today’s traditional banking institutions are neither equipped to address nor fully enabled to grasp.

Read more here: Gurdgiev, Constantin, Is the Rise of Financial Digital Disruptors Knocking Traditional Banks Off the Track? (June 13, 2016). International Banker, June 2016. Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2795113.

30/5/15: The New Financial Regulation: Part 10: Legal v Operational Logistics

My post for @learnsignal blog on EU financial regulation covering operational logistics is now available: http://blog.learnsignal.com/?p=188.

21/5/15: Global M&A and Economic Fundamentals

Here are some select slides from my presentation at this week's Alltech's Rebelation conference in Lexington, KY.

15/5/15: New Financial Regulation: Part 8: An Overconfidence Bias Awaits

My latest blog post on European Union innovations in financial regulation, continuing coverage of the European Banking Union is now available here: http://blog.learnsignal.com/?p=181

8/5/15: BIS on Build Up of Financial Imbalances

There is a scary, fully frightening presentation out there. Titled "The international monetary and financial system: Its Achilles heel and what to do about it" and authored by Claudio Borio of the Bank for International Settlements, it was delivered at the Institute for New Economic Thinking (INET) “2015 Annual Conference: Liberté, Égalité, Fragilité” Paris, on 8-11 April 2015.

Per Borio, the Achilles heel of the global economy is the fact that international monetary and financial system (IMFS) "amplifies weakness of domestic monetary and financial regimes" via:

• "Excess (financial) elasticity”: inability to prevent the build-up of financial imbalances (FIs)
• FIs= unsustainable credit and asset price booms that overstretch balance sheets leading to serious financial crises and macroeconomic dislocations
• Failure to tame the procyclicality of the financial system
• Failure to tame the financial cycle (FC)

The manifestations of this are:

• Simultaneous build-up of FIs across countries, often financed across borders... watch out below - this is still happening... and
• Overly accommodative aggregate monetary conditions for global economy. Easing bias: expansionary in short term, contractionary longer-term. Now, what can possibly suggest that this might be the case today... other than all the massive QE programmes and unconventional 'lending' supports deployed everywhere with abandon...

So Borio's view (and I agree with him 100%) is that policymakers' "focus should be more on FIs than current account imbalances". Problem is, European policymakers and analysts have a strong penchant for ignoring the former and focusing exclusively on the latter.

Wonder why Borio is right? Because real imbalances (actual recessions) are much shallower than financial crises. And the latter are getting worse. Here's the US evidence:

Now, some think this is the proverbial Scary Chart because it shows how things got worse. But surely, the Real Scary Chart must reference the problem today and posit it into tomorrow, right? Well, hold on, for the imbalances responsible for the last blue line swing up in the chart above are not going away. In fact, the financial imbalance are getting stronger. Take a look at the following chart:

Note: Bank loans include cross-border and locally extended loans to non-banks outside the United States.

Get the point? Take 2008 crisis peak when USD swap lines were feeding all foreign banks operations in the U.S. and USD credit was around USD6 trillion. Since 'repairs' were completed across the European and other Western banking and financial systems, the pile of debt denominated in the USD has… increased. By mid-2014 it reached above USD9 trillion. That is 50% growth in under 6 years.

However, the above is USD stuff... the Really Really Scary Chart should up the ante on the one above and show the same happening broader, outside just the USD loans.

So behold the real Dracula popping his head from the darkness of the Monetary Stability graveyards:

Yep.  Now we have it: debt (already in an overhang) is rising, systemically, unhindered, as cost of debt falls. Like a drug addict faced with a flood of cheap crack on the market, the global economy continues to go back to the needle. Over and over and over again.

Anyone up for a reversal of the yields? Jump straight to the first chart… and hold onto your seats, for the next upswing in the blue line is already well underway. And this time it will be again different... to the upside...

7/5/15: Hedgies v Buffett Debate: It's Superficial on Both Ends

A heated, if perhaps somewhat esoteric debate has been launched by Dan Loeb of the Third Point hedge fund and Warren Buffett. The debate as to whether or not hedge funds are capable of outperforming the market and whether or not Warren Buffett is a hypocrite.

You can read on this here: http://www.zerohedge.com/news/2015-05-07/dan-loeb-slams-buffett-being-habitual-hypocrite

But what you won't read in the post above is that the debate is superficial at best. The problem is:

• Warren Buffett's investment style… setting aside his claims about it being Grahamian (aka fundamentals-driven)… is very much hedge fund-like. To see this read my post about what defines Buffett's exceptional returns here: http://trueeconomics.blogspot.ie/2014/10/28102014-buffetts-magic-cheap-leverage.html. Like a hedgie, he takes leverage. Like a hedgie (in very broad sense) he takes activist positions, often outside or beyond the secondary markets and in alternative asset classes, such as PE as well as across undefined time horizons; and like a hedgie, he has 'black box' management style; but unlike a hedgie, he has access to cheap, very cheap funding that is insensitive to time horizon of investments he takes. Finally, like a hedgie of the old, he manages risk well.
• And the concept of a hedge fund return is, shall we say… too complex to be useful for Buffett's bet/comparative. To see this, follow the thread of links from this, back, across four posts on the topic: http://trueeconomics.blogspot.ie/2015/03/hedge-funds-returns-part-4-to-higher.html