9/4/18: Some evidence on Chinese tech & IP practices

Not being a fan of the current U.S. Presidential Administration (easy enough to confess to that, being a libertarian), and not being a fan of trade wars (even easier to confess to that, being a libertarian), I must note that the U.S. does indeed have a serious and legitimate problem with Chinese long-term industrial and economic development strategies.

And the U.S. is not alone in that, for Europe - a major engine of innovation, and to a lesser extent, Japan and South Korea, as well as pretty much every other nation injecting new technologies into the modern global economy - also have the same China problem. That problem is: Chinese State policy-linked practices of predatory technology transfers from the Western companies to Chinese markets and industries.

How do we know? Well, besides the Chinese own strategic approach to demanding technology transfers by global multinationals and other innovating firms alike as a ticket to accessing the Chinese markets, we also have empirical studies that attempt to capture data on the West-to-China technology leakages.

Here is one. "International Joint Ventures and Internal vs. External Technology Transfer: Evidence from China" authored by Kun Jiang, Wolfgang Keller, Larry D. Qiu, and William Ridley and released as the  NBER Working Paper No. 24455 (March 2018: http://www.nber.org/papers/w24455) used "administrative data on all international joint ventures in China from 1998 to 2007—roughly a quarter of all international joint ventures in the world".

The authors found that:

1) "... Chinese firms chosen to be partners of foreign investors tend to be larger, more productive, and more likely subsidized than other Chinese firms". In other words, your technology partner in China is more likely to be a State-connected firm.

2) "... there is substantial technology transfer both to the joint venture and to the Chinese joint venture partner". In other words, technology transfers leak within joint ventures - your partner in China is your first channel for losing intellectual property control.

3) "... with technology spillovers typically outweighing negative competition effects, joint ventures generate on net positive externalities to other Chinese firms in the same industry. Joint venture externalities are large, perhaps twice the size of wholly-owned FDI spillovers, and it is R&D-intensive firms, including the joint ventures themselves, that benefit most from these externalities". In other words, your technology feeds Chinese partners, although it benefits your joint venture too.

4) "... external effects from joint ventures are highest in R&D-intensive industries, and the largest externalities tend to arise in industries with a large concentration of joint ventures with a U.S. partner". In other words, if you are bringing into an joint venture an R&D intensive technology, your impact on diffusing your own intellectual property to broader Chinese markets will be greater.

To sum all of this up: over the period 1998-2007, China-based international joint ventures involving R&D intensive, technology-rich foreign partners acted as effective channels for diffusion of new, predominantly Western, but also Japanese and Korean, technologies into the Chinese markets. Which would be fine, if it were not driven by the direct dictate from Beijing.

8/4/18: U.S. economy: entrepreneurship is not 'the new thing' outside Academia

These days, every business school on every university campus is sporting a burgeoning post-graduate program in Entrepreneurship. And this trend is driven by the perceived - and often hyped up by the media and by business futurists - rise in entrepreneurship in the modern society. Apparently, allegedly, an increasing proportion of today's business students want to start their own businesses (despite the fact the vast majority have never worked in a start-up and have no expertise to run one). The new dynamism of the economy, the college-to-start up model of business education, the 'can do' attitude (or aspirations) are all part and parcel of the mythological creature that is the New Economy.

In reality, of course, brutally put, there is no evidence of rising demand for entrepreneurship. And, worse, in fact, there has been a dramatic decline in entrepreneurial rates in the U.S. economy:

Source: http://rooseveltinstitute.org/wp-content/uploads/2018/03/Powerless.pdf.

Now, consider the two data series above, together: firm entry (new firms creation) and firm exist rates. As the blue line trended down, rapidly, without a pause, the green line remained relatively flat. Which means that the ratio of entries to exits has fallen over the time, pretty dramatically. In the 1970s and 1980s, firms entries had, on average been more frequent and less likely to be associated with higher firm exits. In the 1990s,  both relationship deteriorated. In the 2000s, both literally went down the drain.

So as the rate of new businesses additions went down, the rate of old businesses exiting did not change that much. So much for dynamism and for 'entrepreneurial spirits' of the young. The start ups mythology is strong. But the reality of the U.S. economy is that of concentration, market power, monopolization and decline of entrepreneurship. Funny thing, how Silicon Valley propaganda works, right?

How do we know the bit about monopolization? Why, look at profit share of output:

Still want to build up that 'entrepreneurship program' in the University? Because students want to learn about starting their own businesses? Should you really think twice?

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.

8/4/18: Talent vs Luck: Differentiating Success from Failure

In their paper, "Talent vs Luck: the role of randomness in success and failure", A. Pluchino. A. E. Biondo, A. Rapisarda (25 Feb 2018: https://arxiv.org/pdf/1802.07068.pdf) tackle the mythology of the "dominant meritocratic paradigm of highly competitive Western cultures... rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking".

The authors note that, although "sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant material success, ...it is rather common to underestimate the importance of external forces in individual successful stories".

Some priors first: "intelligence or talent exhibit a Gaussian distribution among the population, whereas the distribution of wealth - considered a proxy of success - follows typically a power law (Pareto law). Such a discrepancy between a Normal distribution of inputs, suggests that some hidden ingredient is at work behind the scenes."

The authors show evidence that suggests that "such an [missing] ingredient is just randomness". Or, put differently, a chance.

The authors "show that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by mediocre but sensibly luckier individuals."

Two pictures are worth a 1000 words, each:

Figure 5 taken from the paper shows:

• In panel (a): Total number of lucky events and
• In panel (b): Total number of unlucky events 

Both are shown as "function of the capital/success of the agents"

Overall, "the plot shows the existence of a strong correlation between success and luck: the most successful individuals are also the luckiest ones, while the less successful are also the unluckiest ones."

Figure 7 shows:
In panel (a): Distribution of the final capital/success for a population with different random initial conditions, that follows a power law.
In panel (b): The final capital of the most successful individuals is "reported as function of their talent".

Overall, "people with a medium-high talent result to be, on average, more successful than people with low or medium-low talent, but very often the most successful individual is a moderately gifted agent and only rarely the most talented one.


Main conclusions on the paper are:

• "The model shows the importance, very frequently underestimated, of lucky events in determining the final level of individual success." 
• "Since rewards and resources are usually given to those that have already reached a high level of success, mistakenly considered as a measure of competence/talent, this result is even a more harmful disincentive, causing a lack of opportunities for the most talented ones."

The results are "a warning against the risks of what we call the ”naive meritocracy” which, underestimating the role of randomness among the determinants of success, often fail to give honors and rewards to the most competent people."

7/4/18: Markets Message Indicator: Ouuuuch... it hurts

An interesting chart from the VUCA family, courtesy of @Business:

'Markets Message Indicator', created by Jim Paulsen, chief investment strategist at Leuthold Weeden Capital Management, takes 5 different data ratios: stock market relative performance compared to the bond market, cyclical stocks performance relative to defensive stocks, corporate bond spreads, the copper-to-gold price ratio, and a U.S. dollar index. The idea is to capture broad stress build up across a range of markets and asset classes, or, in VUCA terms - tallying up stress on all financial roads that investors my use to escape pressure in one of the asset markets.

Bloomberg runs some analysis of these five components here: https://www.bloomberg.com/news/articles/2018-04-03/paulsen-says-proceed-with-caution-across-many-asset-classes. And it is a scary read through the charts. But...

... the real kicker comes from looking back at the chart above. The red oval puts emphasis on the most recent market correction, the downturn and increased volatility that shattered the myth of the Goldilocks Markets. And it barely makes a splash in drawing down the excess stress built across the 'Markets Message Indicator'.

Now, that is a scary thought.

29/3/18: Credit downgrades and the sunny horizons of peak growth

The global economy is picking up steam. The U.S. economy is roaring to strength. 2018 is going to be another 'peak year'. Tax cuts are driving equity valuations up. Corporate balance sheets are getting healthier by a day... and so on.

The positivity of recent headline has been contrasted by the realities of the gargantuan bubble in corporate debt. A bubble that is not going to get any healthier any time soon. In fact, based on the latest data (through 4Q 2017) from the S&P Global Market Intelligence, H1 2017 trend toward relatively balanced (or rather relatively moderately negative skew) credit ratings has turned decisively negative in 2H 2017. Worse, 4Q 2017 dynamics were markedly worse than 3Q 2017 dynamics:

Which brings up the following question: if things are getting downgraded that fast, what's likely to happen with the Fed policy 'normalization' impact on the corporate credit markets? Answers on tears-proof napkins, please.

29/3/18: Matthew Rojansky on U.S.-Russia relations

It is rather rare that an occasion comes up on which I comment on political issues directly (absent the prism of economics or finance). A rarer, yet, are the occasions when such comments involve a positive assessment of the power-broker or 'power elite' analysts contributions on the topic of the U.S.-Russia relations.

This is an occasion to do both. Here is an interview that is a must-watch: https://www.youtube.com/watch?time_continue=7&v=oiOrU5_JWao&utm_content=buffer011ee&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer.

In it, Matthew Rojansky, Director of the Wilson Center’s Kenan Institute, discusses US-Russia relations in the Trump-Putin era and makes several pivotal points, some of which I have raised before, but I have not heard being raised by an analyst who is, like Rojansky, is wired into Washington elite. At just over 5 minutes, is is a MUST-watch.

25/3/18: Average Tariffs: 2000-2016

So how do the world's largest 50 economies (by size) score when it comes to the average trade tariffs they have in place? Who is the free trade champion? And who is not?

Here is the data on top 50 largest global economies (I have aggregated EU members of the top 50) into one group, as they share common tariffs against the rest of the world:

Source: data from the World Bank

One thing is clear: tariffs did come down quite substantially between 2000 and 2016. Average world-wide tariff in 2000 stood at just over 8.69%, which fell to just under 4.29% by 2016.

Another interesting fact is that the U.S. average tariff of 1.61% is matched by the EU's 1.6%, with both higher than Australia's 1.17%, Canada's 0.85%, Japan's 1.35%, and Norway's 1.02%. So, the free trade champions of the U.S. and EU are, sort of, poorer than average for the advanced economies, when it comes to trading free of tariffs protection.

Third point worth noting relates to the BRICS: these the largest emerging economies, jointly accounting for 32.0% of the global GDP (PPP-adjusted). Brazil's average tariff in 2016 stood at 8.01%, down from 12.69% in 2000. Russia's average tariff in 2016 stood at 3.43% and we do not have that figure for 2000, while India's was at 6.32% (down from 23.28% in 2000), China's fell from 14.67% in 2000 to 3.54% in 2016, while South Africa's average tariff declined from 4.5% in 2000 to 4.19% in 2016. So, amongst the BRICS, today, Brazil imposes the highest tariffs (86.8% higher than the global average), followed by India (47.4% above the global average), S. Africa (2.3% below the global average),  China (17.4% below the global average), and Russia (20% below the global average). In other words, based on average tariffs, Russia is the most open to trade economy in the BRICS group, followed by China.

Of course, tariffs are not the only barriers to trade, and in fact, non-tariff protectionism measures have been more important in the era of the WTO agreements. However, the data on tariffs is somewhat illustrative.

Here is the same data, covering 2010 and 2016 periods, arranged by the order of magnitude for 2016 tariffs:

Source: data from the World Bank

24/3/18: Secular Stagnations Visit Morgan Stanley

Morgan Stanley jumps onto the secular stagnations thesis band wagon: http://www.morganstanley.com/ideas/ruchir-sharma-trends-2018 and adds an obvious cross-driver to the equation: monetary policy heading for the end of the Great Liquidity Wash.

25/3/18: Quantum computing and cyber security: a perfectly VUCA mix?

One interesting topic worth discussing in the context of VUCA and systemic resilience is quantum computing. The promise of quantum computing offers a prospect of altering completely the existent encryption methods effectiveness. 

Here is one view:  https://www.sciencedirect.com/science/article/pii/S1361372317300519 suggesting that quantum computing is not a threat to current cryptographic systems, although the core argument here is that it is not a threat in its current state.

Less (scientifically) robust arguments to the opposite side are, for example, in https://www.wired.com/story/quantum-computing-is-coming-for-your-data/, and http://approachinfinity.ca/RecentPublications/Quantum-computing-overview.pdf 

Geopolitically, this puts quantum computing into an important security race position: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190646 and https://www.zerohedge.com/news/2018-03-25/everybody-was-quantum-fighting-those-computers-were-fast-lightning 

There is a lot of technical stuff involved, but an interesting topic from geopolitical risks perspective for sure, and involves long term strategic positioning by the usual adversaries, the U.S. and China. 

Beyond this issue, there are also implications of quantum computing in broader cybersecurity context, including enforcement and  monitoring: https://www.wired.com/story/quantum-computing-is-the-next-big-security-risk/, https://www.techrepublic.com/article/how-quantum-computing-could-create-unbreakable-encryption-and-save-the-future-of-cybersecurity/, and https://futurism.com/future-cybersecurity-quantum-encryption/.

24/3/18: Dysfunctional Labour Markets? Ireland’s Activity Rates 2007-2016

Having posted previously on the continued problem of low labour force participation rates in Ireland, here is another piece of supporting evidence that the recovery in unemployment figures has been masking some pretty disturbing underlying trends. The following chart shows labour force Activity Rates reported by Eurostat:

Note: per Eurostat: "According to the definitions of the International Labour Organisation (ILO) the activity rate is the percentage of economically active population aged 15-64 on the total population of the same age group."

Ireland’s showing is pretty poor across the board. At the end of 2016, Irish labour force activity rate stood at 69.3%, or 16th lowest in the EU. For Nordic countries, members of the EU, the rate stood at 71.2, while for Norway, Switzerland and Iceland, the average rate was 78.2.

Over time, compared to 2007-2008 average, Irish activity rate was still down 1.6 percentage points in 2016. In the Euro area, the movement was up 2 percentage points. Of all EU countries, only two: Cyprus and Finland, posted decreases in 2016 activity rates compared to 2007-2008 average.

For an economy with no pressing ageing concerns, Ireland has a labour market that appears to be dysfunctionally out of touch with realities of the modern economy. In part, this reflects a positive fact: Ireland sports high rates of younger adults in-education, helped by our healthy demographics. However, given the structure of Irish migration (especially net immigration of the younger skilled workers into Ireland) and given sky-high rates of disability claims in Ireland, the low activity rate also reflects low level of labour force participation. In this context, younger demographic make up of the country stands in stark contradiction to this factor.

According to Census 2016, "There was a total of 643,131 people with a disability in April 2016 accounting for 13.5 per cent of the population; this represented an increase of 47,796 persons on the 2011 figure of 595,335 when it accounted for 13.0 per cent of the population." (Source: http://www.cso.ie/en/media/csoie/newsevents/documents/census2016summaryresultspart2/Census_2016_Summary_Results_%E2%80%93_Part_2.pdf) However, "Of the total 643,131 persons with a disability 130,067 were at work, accounting for 6.5 per cent of the workforce. Among those aged 25-34, almost half (47.8%) were at work whereas by age 55 to 64 only 25 per cent of those with a disability were at work." Another potential driver of low economic activity rate in Ireland is the structure of long term care within the healthcare (or rather effective non-existent structure of such care), pushing large number of the Irish people of working age into provision of care for the long-term ill relatives.

Here is the OECD data (for 2016) on labour force participation rates:

Source: https://data.oecd.org/emp/labour-force-participation-rate.htm.

24/3/18: A Traders’ Nightmare: When all Risks Coincide

Really great analysis of recent volatility spike (early February correction) from the BIS Quarterly:

“The VIX is an index of one-month implied volatility constructed from S&P 500 option prices across a range of strike prices. …Because it is derived from option prices, theoretically the VIX is the sum of expected future volatility and the volatility risk premium. Model estimates indicate that the rise in the VIX on 5 February far exceeded the change in expectations about future volatility (Graph A1, centre panel). The magnitude of the risk premium (ie the model residual) suggests that the VIX spike was largely due to internal dynamics in equity options or VIX futures markets.”

“Indeed, the considerable expansion in the VIX futures market – market size (ie total open interest) rose from a daily average of about 180,000 contracts in 2011 to 590,000 in 2017 – means such dynamics are likely to have had a growing impact on the level of the VIX.”

And the dynamics were spectacular. Per BIS:

“Among the growing users of VIX futures are issuers of volatility exchange-traded products (ETPs). These products allow investors to trade volatility for hedging or speculative purposes. Issuers of leveraged volatility ETPs take long positions in VIX futures to magnify returns relative to the VIX – for example, a 2X VIX ETP with $200 million in assets would double the daily gains or losses for its investors by using leverage to build a $400 million notional position in VIX futures. Inverse volatility ETPs take short positions in VIX futures so as to allow investors to bet on lower volatility.” One that comes to mind immediately is XIV. 

And things went spectacularly South for these, once VIX started heading North.

“The assets of select leveraged and inverse volatility ETPs have expanded sharply over recent years, reaching about $15 billion at end-2017 (Graph A1, right-hand panel). …many market participants use these products to make long-term bets on volatility remaining low or becoming lower. Given the historical tendency of volatility increases to be rather sharp, such strategies can amount to “collecting pennies in front of a steamroller”.

“Even though the aggregate positions in these instruments are relatively small, systematic trading strategies of the issuers of leveraged and inverse volatility ETPs appear to have been a key factor behind the volatility spike that occurred on the afternoon of 5 February. Given the rise in the VIX earlier in the day, market participants could expect leveraged long volatility ETPs to rebalance their holdings by buying more VIX futures at the end of the day to maintain their target daily exposure (eg twice or three times their assets). They also knew that inverse volatility ETPs would have to buy VIX futures to cover the losses on their short position in VIX futures. So, both long and short volatility ETPs had to buy VIX futures. The rebalancing by both types of funds takes place right before 16:15, when they publish their daily net asset value. Hence, because the VIX had already been rising since the previous trading day, market participants knew that both types of ETP would be positioned on the same side of the VIX futures market right after New York equity market close.”

“The scene was set.” Or put differently, once information about leveraged funds having to go long at the end of the day became market information, arbitrage went to work like a sledgehammer over trading books. The impact risk, compounded by adverse price movements, went through the roof. The two key changes in trading environment were made even more egregious by the fact that intraday spreads are usually higher toward the day close, and risk of non-execution had become completely intolerable for the leveraged funds. Which means spreads ballooned. This was a classic trading nightmare:

“There were signs that other market participants began bidding up VIX futures prices at around 15:30 in anticipation of the end-of-day rebalancing by volatility ETPs (Graph A2, left-hand panel). Due to the mechanical nature of the rebalancing, a higher VIX futures price necessitated even greater VIX futures purchases by the ETPs, creating a feedback loop. Transaction data show a spike in trading volume to 115,862 VIX futures contracts, or roughly one quarter of the entire market, and at highly inflated prices, within one minute at 16:08. The value of one of the inverse volatility ETPs, XIV, fell 84% and the product was subsequently terminated.”