27/521: Euromoney on China's Belt-and-Road Initiative

 

Euromoney recently covered update of risk analysis involving development of China-led Belt and Road initiative: https://www.euromoneycountryrisk.com/article/b1rs0cw1v2yv4d/ebri-q1-2021-results-chinas-belt-and-road-potential-outweighs-challenges. Includes my POV.

25/4/21: Impact Finance perspective of the systemic threats to blockchain applications

 

New paper (pre-print version): 

Gurdgiev, Constantin and Fleming, Adam, Informational efficiency and cybersecurity threats: A Social Impact Finance perspective of the systemic threats to blockchain applications (April 25, 2021). Forthcoming, Chapter 12 in Innovations in Social Finance: Transitioning Beyond Economic Value, eds. Thomas Walker, Jane McGaughey, Sherif Goubran, and Nadra Wagdy, Palgrave Macmillan, 2021, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3834032. 

Abstract: 

Crypto-assets and blockchain technologies hold the promise of providing more secure systems for managing public and private data, enhancing public trust in data collection, and increasing the efficiency of social impact finance transactions. However, to-date, blockchain technologies have struggled to deliver on these promises. Specifically, cybersecurity threats to blockchain technologies are accelerating and becoming more impactful over time, generating growing risk to the use of the blockchain technologies in social impact finance services provision. Our analysis data on cybersecurity breaches involving cryptocurrencies trading platforms from 2014 through 2019 shows that cryptocurrencies markets have, to-date, failed to develop informational efficiencies necessary to sustain these technologies’ deployment in impact finance. Faced with increasing cybersecurity threats permissionless blockchain systems appear to be more vulnerable to shocks, than they were in the past. Cyber breaches in the cryptocurrency markets create major risk contagion pathways, which are dramatically increasing volatility of both directly attacked currencies and other major cryptocurrencies; as well as present an increased risk of system-wide attacks that threaten not only the accounting and transactional accuracy and efficiency of the crypto-based fintech solutions, but also the data stored using public blockchain protocols. These findings lead us to conclude that, absent dramatic improvements in the regulation of cryptocurrencies and exchanges, public blockchains based on traded crypto-assets are not suitable for large scale deployment in social impact finance applications.

12/12/20: Hungary's risk improvement in peril

 My comment for Euromoney on changes in sovereign risk profile for Hungary: https://www.euromoneycountryrisk.com/article/b1pjjpq2xr2bfy/hungarys-risk-improvement-in-peril-as-it-bites-the-hand-that-feeds-it. 

31/10/20: Gold Coins Market is Still Hedging Residual Covid Risk

Sales of the U.S. Mint gold coins have moderated off their pandemic highs, but remain elevated by historical standards, especially controlling for higher gold prices:

Since hitting a pandemic-period high of 216,500 oz in March 2020 (the highest sales volume since April 2013), the demand has moderated through June, topped 145,000 in July and 149,000 oz in August, and has been around 91,500 through the four weeks of October. This puts October sales above the last three years' average.

Average gold weight per coin sold remains relatively elevated and is co-trending with price per oz, most likely indicating lasting FOMO effect (herding by investors). The correlation is weaker than during prior episodes of major crises and recessions, suggesting that the pandemic-period demand is probably less influenced by the herding effects than in prior crises.

Annualized data through October also confirms precautionary, but not 'flight to safety' type of demand:

As the pandemic re-accelerates, it will be interesting to see how seasonality (uplift in end-of-year sales) plays out against the pandemic-related hedging positioning of investors.

17/9/20: Exploding errors: COVID19 and VUCA world of economic growth forecasts

 

Just as I covered the latest changes in Eurozone growth indicators (https://trueeconomics.blogspot.com/2020/09/17920-eurocoin-leading-growth-indicator.html), it is worth noting the absolutely massive explosion in forecast errors triggered by the VUCA environment around COVID19 pandemic.

My past and current students know that I am a big fan of looking at risk analysis frameworks from the point of view of their incompleteness, as they exclude environments of deeper uncertainty, complexity and ambiguity in which we live in the real world. Well, here is a good illustration:

You can see an absolute explosion in the error term for growth forecasts vs actual outrun in the three quarters of 2020 so far. The errors are off-the-scale compared to what we witnessed in prior recessions/crises. 

This highlights the fact that during periods of elevated deeper uncertainty, any and all forecasting models run into the technical problem of risk (probabilities and impact assessments) not being representative of the true underlying environment with which we are forced to work.  

25/7/20: Markets and COVID19: Unwinding Restrictions

My column for Manning Financial on the direction of the markets in the period of COVID19 restrictions unwinding: https://cfc.ie/2020/07/10/where-to-mr-market/.

13/6/20: Gold Coins Sales are Up, and the Markets are Screaming Something New

Some interesting movements in demand for gold coins in recent months, worth watching:

Price is up, and volume of sales is quite volatile. Still, sales are hitting highs.

• Following weaker y/y January and February, March 2020 sales rose almost x10 y/y in volume and average coin sold size rose from 0.5 oz in March 2019 to 0.674 oz in March 2020. 
• April 2020 sales were x3.25 times sales in April 2019 by volume, with average coin size rising to 1.0 oz. May saw a major fall-off in demand m/m but still posted sales x2.26 time those of May 2019, with average coin sold size down to 0.538 oz per coin. 
• Through June 13, June monthly sales are already x2.75 times higher than sales for the entire month of June 2019, with average coin size sold so far this June running at 0.985 oz per coin, against 0.625 oz per coin in June 2019. 

It seems investors still showing little signs of moderating safe haven demand for gold, despite robust performance in the financial markets until this week profit-taking blowout. 

Gold can be seen as both a hedge and safe haven against a range of key financial and political risks, but it can also be viewed as a long-run wealth storage tool, and, given its liquid nature, as a precautionary savings instrument for tail risks. If the current demand remains robust in the face of rising gold prices, we are likely witnessing a risk-return relationship/expectations shift amongst the savers and investors, away from considering trend-driven investment strategy of thee recent years and in favour of investing against a major concern for significant tail risks driving the markets in months and years ahead.

30/4/20: No, Healthcare Systems are Not Lean Startups, Mr. Musk

A tweet from @elonmusk yesterday has prompted a brief response from myself:

https://twitter.com/GTCost/status/1255681426445365248?s=20

For two reasons, as follows, it is worth elaborating on my argument a little more:

1. I have seen similar sentiment toward authorities' over-providing healthcare system capacity in other countries as well, including, for example in Ireland, where the public has raised some concerns with the State contracting private hospitals for surplus capacity; and
2. Quite a few people have engaged with my response to Musk.

So here are some more thoughts on the subject:

'Lean startups' is an idea that goes hand-in-hand with the notion that a startup needs some organic growth runway. In other words, it needs to ‘nail’ parts of its business model first, before ‘scaling’ the model up. ‘Nailing’ bit is done using highly scarce resources pre-extensive funding (which is a ‘scaling’ phase). It makes perfect sense for a start up, imo, for a startup.

But in the ‘nailing’ stage, when financial resources are scarce, the startup enterprise has another resource is relies upon to execute on a ‘lean’ strategy: time. Why? Because a ‘lean’ startup is a smaller undertaking than a scaling startup. As a result, failure at that stage carries lower costs. In other words, you can be ‘lean’ because you are allowed to fail, because if you do fail in that stage of development, you can re-group and re-launch. You can afford to be reactive to news flows and changes in your environment, which means you do not need to over-provide resources in being predictive or pro-active. Your startup can survive on lean funding.

As you scale startup, you accumulate resources (investment and retained earnings) forward. In other words, you are securing your organization by over-providing capacity. Why? Because failure is more expensive for a scaling startup than for a 'lean' early stage startup. The notion of retained and untilized cash is no longer the idea of waste, but, rather a prudential cushion. Tesla, Mr. Musk's company, carries cash reserves and lines of credit that it is NOT using at the moment in time precisely because not doing so risks smaller shocks to the company immediately escalating into existential shocks. And a failure of Tesla has larger impact than a failure of small 'lean' startup. In other words, Mr. Musk does not run a 'lean startup' for a good reason. Now, in a public health emergency with rapid rates of evolution and high degree of forecast uncertainty, you cannot be reactive. You must allocate resources to be pro-active, or anticipatory. In doing so, you do not have a choice, but to over-supply resources. You cannot be ‘lean’, because the potential (and highly probable) impact of any resource under-provision is a public health threat spinning out of control into a public health emergency and a systemic shock. ‘Lean’ startup methods work, when you are dealing with risk and uncertainty in a de-coupled systems with a limited degree of complexity involved and the range of shocks impact limited by the size of the organization/system being shocked. Public health emergence are the exact opposite of such a environment: we are dealing with severe uncertainty (as opposed to risk) with hugely substantial impacts of these shocks (think thousands of lives here, vs few million dollars in investment in an early stage start up failure). We are also dealing with severe extent of complexity. High speed of evolution of threats and shocks, uncertain and potentially ambiguous pathways for shocks propagation, and highly complex shock contagion pathways that go beyond the already hard-to-model disease contagion pathways. So a proper response to a pandemic, like the one we are witnessing today, is to use an extremely precautionary principle in providing resources and imposing controls. This means: (1) over-providing resources before they become needed (which, by definition, means having excess capacity ex-post shock realization); (2) over-imposing controls to create breaks on shock contagion (which, by definition, means doing too-much-tightening in social and economic environment), (3) doing (1) and (2) earlier in the threat evolution process rather than later (which means overpaying severely for spare capacity and controls, including - by design - at the time when these costs may appear irrational). And (4), relying on the worst-case-scenario parameterization of adverse impact in your probabilistic and forecasting analysis and planning. This basis for a public health threat means that responses to public health threat are the exact opposite to a ‘lean’ start up environment. In fact they are not comparable to the ‘scaling up’ start up environment either. A system that has a huge surplus capacity left in it, not utilized, in a case of a start up is equivalent to waste. Such system’s leadership should be penalized. A system that has a huge surplus capacity left un-utilized, in a case of a pandemic is equivalent to the best possible practice in prudential management of the public health threat. Such system’s leadership should be applauded.

And even more so in the case of COVID pandemic. Mr. Musk implies something being wrong with California secured hospital beds capacity running at more than double the rate of COVID patients arrivals. That's the great news, folks. COVID pandemic carries infection detection rates that double the population of infected individuals every 3-30 days, depending on the stage of contagion evolution. Earlier on, doubling times are closer to 3 days, later on, they are closer to 30 days. But, utilization of hospital beds follows an even more complex dynamic, because in addition to the arrival rates of new patients, you also need to account for the duration of hospital stay for patients arriving at different times in the pandemic. Let's be generous to sceptics, like Mr. Musk, and assume that duration-of-stay adjusted arrivals of new patients into the hospitals has a doubling time of the mid-point of 3-30 days or, close to two weeks. If California Government did NOT secure massively excessive capacity for COVID patients in advance of their arrival, the system would not have been able to add new capacity amidst the pandemic on time to match the doubling of new cases arrivals. This would have meant that some patients would be able to access beds only later in the disease progression period, arriving to hospital beds later in time, with more severe impact from the disease and in the need of longer stays and more aggressive interventions. The result would have been even faster doubling rate in the demand for hospital beds with a lag of few days. You can see how the system shortages would escalate out of control.

Running tight supply chains in a pandemic is the exact opposite to what has to be done. Running supply capacity at more than double the rate of realized demand is exactly what needs to be done. We do not cut corners on basic safety equipment. Boeing did, with 737-Max, and we know where they should be because of this. We most certainly should not treat public health pandemic as the basis for cutting surplus safety capacity in the system.

21/1/20: Investor Fear and Uncertainty in Cryptocurrencies

Our paper on behavioral biases in cryptocurrencies trading is now published by the Journal of Behavioral and Experimental Finance volume 25, 2020:

We cover investor sentiment effects on pricing processes of 10 largest (by market capitalization) crypto-currencies, showing direct but non-linear impact of herding and anchoring biases in investor behavior. We also show that these biases are themselves anchored to the specific trends/direction of price movements. Our results provide direct links between investors' sentiment toward:

1. Overall risky assets investment markets,
2. Cryptocurrencies investment markets, and
3. Macroeconomic conditions,

and market price dynamics for crypto-assets. We also show direct evidence that both markets uncertainty and investor fear sentiment drive price processes for crypto-assets.

9/1/20: Herding and Anchoring in Cryptocurrency Markets

Our new paper, with Daniel O'Loughlin, titled "Herding and Anchoring in Cryptocurrency Markets: Investor Reaction to Fear and Uncertainty" has been accepted to the Journal of Behavioral and Experimental Finance, forthcoming February 2020.

The working paper version is available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3517006.

Abstract:
Cryptocurrencies have emerged as an innovative alternative investment asset class, traded in data-rich markets by globally distributed investors. Although significant attention has been devoted to their pricing properties, to-date, academic literature on behavioral drivers remains less developed. We explore the question of how price dynamics of cryptocurrencies are influenced by the interaction between behavioral factors behind investor decisions and publicly accessible data flows. We use sentiment analysis to model the effects of public sentiment toward investment markets in general, and cryptocurrencies in particular on crypto-assets’ valuations. Our results show that investor sentiment can predict the price direction of cryptocurrencies, indicating direct impact of herding and anchoring biases. We also discuss a new direction for analyzing behavioral drivers of the crypto assets based on the use of natural language AI to extract better quality data on investor sentiment.

7/1/20: Euromoney on 2020 Risk Outlook for the Eurozone

Euromoney on Eurozone risks into 2020, with some comments from me: https://www.euromoney.com/article/b1js9nd289vtm9/country-risk-2020-vision-brings-the-eurozones-risks-into-focus. 

16/10/19: Euromoney Risk Survey Q3 2019 Results

Euromoney analysis of Q3 2019 results for country risk surveys and risk outlook forward, with lots of comments from myself and others: https://www.euromoney.com/article/b1hjf7xr90tdkj/ecr-survey-results-q3-2019-us-china-canada-mexico-punished-by-tariffs.

2/7/19: Inverted Yield Curve

Inverting U.S. yield curve is one of the best early indicators of recessions. Or at least it used to be... before all the monetary policy shenanigans of the last 11 years. Regardless, the latest U.S. Treasury yields dynamics are quite disquieting:

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. 

28/12/18: BTCD is neither a hedge nor a safe haven for stocks

A quick - and dirty - run through the argument that Bitcoin serves as a hedge or a safe haven for stocks. This argument has been popular in cryptocurrencies analytical circles of recent, and is extensively covered in the research literature, when it comes to 2014-2017 dynamics, but not so much for 2018 or even more recent period dynamics.

First, simple definitions:

1. A financial instrument X is a hedge for a financial instrument Y, if - on average, over time - significant declines in the value of Y are associated with lower declines (weak hedge) or increases (strong hedge) in the value of X.
2. A financial instrument X is a safe haven for a financial instrument Y, if at the times of significant short-term drop in the value of Y, instrument X posts increases (strong safe haven) or shallower decreases (weak safe haven) in its own value.

So here are two charts for Safe Haven argument:

The first chart shows that over the last 12 months, there were 3 episodes when - over time, on average, based on daily prices, stocks acted as a strong hedge for BTCUSD. There are zero periods when BTCUSD acted as a hedge for stocks. The second chart shows that within the last month, based on 30 minutes intervals data (higher frequency data, not exactly suitable for hedge testing), BTCUSD did manage to act as a hedge for stocks in two periods. However, taken across both periods, overall, BTCUSD only acted as a weak hedge.

The key to the above is,  however, the time frame and the data frequency. A hedge is a longer-term, averages-defined relationship. Not an actively traded strategy. And this means that the first chart is more reflective of true hedging relationship than the later one. Still, even if we severely stretch the definition of a hedge, we are still left with two instances when the BTCUSD acts as a hedge for DJIA against two instances when DJIA acts as a hedge for BTCUSD.

People commonly confuse both hedging and safe haven as being defined by the negative symmetric correlation between assets X and Y, but in reality, both concepts are defined by the directional correlation: when X is falling, correlation myst be negative with Y, and when Y is falling, correlation must be negative with X. The downside episodes are what matters, not any volatility.

Now, to safe haven:

Again, it appears that stocks offer a safe haven against BTCUSD (6 occasions in the last 12 months) more often than BTCUSD offers a safe haven against stocks (2 occasions).  Worse, the cost of holding BTCUSD long as a safe haven for stocks is staggeringly high: some 60-65 percentage points over 12 months, not counting the cost of trading.

In simple terms, BTCUSD is worse than useless as either a hedge or a safe haven against the adverse movements in stocks.

29/10/18: Corporate Credit and the Debt Powder Keg

As it says on the tin: despite growth in earnings, the numbers of U.S. companies that are struggling with interest payments on the gargantuan mountain of corporate debt they carry remains high. The chart does not show those companies with EBIT/interest cover ratio below 1 that are at risk (e.g. with the ratio closer to 0.9) for the short term impact of rising interest rates. That said, the overall percent of firms classified as risky is at the third highest since the peak of the GFC. And that is some doing, given a decade of extremely low cost of debt financing.

Talking of a powder keg getting primed and fused…

25/4/18: Tesla: Lessons in Severe and Paired Risks and Uncertainties

Tesla, the darling of environmentally-sensible professors around the academia and financially ignorant herd-following investors around the U.S. urban-suburban enclaves of Tech Roundabouts, Silicon Valleys and Alleys, and Social Media Cul-de-Sacs, has been a master of cash raisings, cash burnings, and target settings. To see this, read this cold-blooded analysis of Tesla's financials: https://www.forbes.com/sites/jimcollins/2018/04/25/a-brief-history-of-tesla-19-billion-raised-and-9-billion-of-negative-cash-flow/2/#3364211daf3d.

Tesla, however, isn't that great at building quality cars in sustainable and risk-resilient ways. To see that, consider this:

1. Tesla can't procure new parts that would be consistent with quality controls norms used in traditional automotive industry: https://www.thecarconnection.com/news/1116291_tesla-turns-to-local-machine-shops-to-fix-parts-before-theyre-installed-on-new-cars.
2. Tesla's SCM systems are so bad, it is storing faulty components at its factory. As if lean SCM strategies have some how bypassed the 21st century Silicon Valley: http://www.thedrive.com/news/20114/defective-tesla-parts-are-stacked-outside-of-california-machine-shop-report-shows.
3. It's luxury vehicles line is littered with recalls relating to major faults: https://www.wired.com/story/tesla-model-s-steering-bolt-recall/. Which makes one pause and think: if Tesla can't secure quality design and execution at premium price points, what will you get for $45,000 Model 3?
4. Tesla burns through billions of cash year on year, and yet it cannot deliver on volume & quality mix for its 'make-or-break' Model 3: http://www.thetruthaboutcars.com/2018/04/hitting-ramp-tesla-built-nearly-21-percent-first-quarter-model-3s-last-week/.
5. Tesla's push toward automation is an experiment within an experiment, and, as such, it is a nesting of one tail risk uncertainty within another tail risk uncertainty. We don't have many examples of such, but here is one: https://arstechnica.com/cars/2018/04/experts-say-tesla-has-repeated-car-industry-mistakes-from-the-1980s/ and it did not end too well. The reason why? Because uncertainty is hard to deal with on its own. When two sources of uncertainty correlate positively in terms of their adverse impact, likelihood, velocity of evolution and proximity, you have a powerful conventional explosive wrapped around a tightly packed enriched uranium core. The end result can be fugly.
6. Build quality is poor: https://cleantechnica.com/2018/02/03/munro-compares-tesla-model-3-build-quality-kia-90s/.  So poor, Tesla is running "reworking" and "remanufacturing" poor quality cars facilities, including a set-aside factory next to its main production facilities, which takes in faulty vehicles rolled off the main production lines: https://www.bloomberg.com/view/articles/2018-03-22/elon-musk-is-a-modern-henry-ford-that-s-bad.
7. Meanwhile, and this is really a black eye for Tesla-promoting arm-chair tenured environmentalists, there is a pesky issue with Tesla's predatory workforce practices, ranging from allegations of discrimination https://www.sfgate.com/business/article/Tesla-Racial-Bias-Suit-Tests-the-Rights-of-12827883.php, to problems with unfair pay practices https://www.technologyreview.com/the-download/610186/tesla-says-it-has-a-plan-to-improve-working-conditions/, and unions busting: http://inthesetimes.com/working/entry/21065/tesla-workers-elon-musk-factory-fremont-united-auto-workers.  To be ahead of the curve here, consider Tesla an Uber-light governance minefield. The State of California, for one, is looking into some of that already: https://gizmodo.com/california-is-investigating-tesla-following-a-damning-r-1825368102.
8. Adding insult to the injury outlined in (7) above, Tesla seems to be institutionally unable to cope with change. In 2017, Musk attempted to address working conditions issues by providing new targets for fixing these: https://techcrunch.com/2017/02/24/elon-musk-addresses-working-condition-claims-in-tesla-staff-wide-email/. The attempt was largely an exercise in ignoring the problems, stating they don't exist, and then promising to fix them. A year later, problems are still there and no fixes have been delivered: https://www.buzzfeed.com/carolineodonovan/tesla-fremont-factory-injuries?utm_term=.qa8EzdgEw#.dto7Dnp7A. Then again, if Tesla can't deliver on core production targets, why would anyone expect it to act differently on non-core governance issues?

Here's the problem, summed up in a tight quote:

Now, personally, I admire Musk's entrepreneurial spirit and ability. But I do not own Tesla stock and do not intend to buy its cars. Because when on strips out all the hype surrounding this company, it's 'disruption' model borrows heavily from governance paradigms set up by another Silicon Valley 'disruption darling' - Uber, its financial model borrows heavily from the dot.com era pioneers, and its management model is more proximate to the 20th century Detroit than to the 21st century Germany.

If you hold Tesla stock, you need to decide whether all of the 8 points above can be addressed successfully, alongside the problems of production targets ramp up, new models launches and other core manufacturing bottlenecks, within an uncertain time frame that avoids triggering severe financial distress? If your answer is 'yes' I would love to hear from you how that can be possible for a company that never in its history delivered on a major target set on time. If your answer is 'no', you should consider timing your exit.

15/4/18: EuromoneyCountryRisk 1Q 2018 report

Euromoney Country Risk 1Q 2018 report (gated link) is out, quoting, amongst others, myself on geopolitical and macroeconomic headwinds to global economic growth:

Two interesting tables/charts:

My quote:

8/4/18: Tail Risk and Liquidity Risk: What about that Alpha?

An interesting data set that illustrates two key concepts relating to financial returns, covered extensively in my courses:

1. Liquidity risk factor - inducing added risk premium on lower liquidity assets; and
2. The importance of large scale corrections in long term data series (geometric vs arithmetic averaging for returns)

Indirectly, the above also indicates the ambiguous nature of returns alpha (also a subject of my class presentations, especially in the Applied Investment & Trading course in MSc Finance, TCD): micro- small- and to a lesser extent mid-cap stocks selections are often used to justify alpha-linked fees by investment advisers. Of course, in all, ranking in liquidity risks helps explain much of geometric returns rankings, while across all, geometric averaging discount over arithmetic averaging returns helps highlight the differentials in tail risks.

Sounds pretty much on the money.

15/1/18: Of Fraud and Whales: Bitcoin Price Manipulation

Recently, I wrote about the potential risks that concentration of Bitcoin in the hands of few holders ('whales') presents and the promising avenue for trading and investment fraud that this phenomena holds (see post here: http://trueeconomics.blogspot.com/2017/12/211217-of-taxes-and-whales-bitcoins-new.html).

Now, some serious evidence that these risks have played out in the past to superficially inflate the price of bitcoins: a popular version here https://techcrunch.com/2018/01/15/researchers-finds-that-one-person-likely-drove-bitcoin-from-150-to-1000/, and technical paper on which this is based here (ungated version) http://weis2017.econinfosec.org/wp-content/uploads/sites/3/2017/05/WEIS_2017_paper_21.pdf.

Key conclusion: "The suspicious trading activity of a single actor caused the massive spike in the USD-BTC exchange rate to rise from around $150 to over $1 000 in late 2013. The fall was even more dramatic and rapid, and it has taken more than three years for Bitcoin to match the rise prompted by fraudulent transactions."

Oops... so much for 'security' of Bitcoin...