China, manufactured defaults and investment psychology

The week on, March 2–8, 2019

7 days montage 080319

Accounting shake-up set to hit China shadow banking

Banks brace for extra provisions under IFRS 9 for loans masquerading as investment products

Isda proposes fix for ‘manufactured defaults’

Failure-to-pay must be linked to a deterioration in creditworthiness to trigger CDS payouts

BlackRock’s psych team (yes) hunts for bias in trades

Portfolio managers asked to keep ‘trade diaries’ of the thinking that led up to poor investments


COMMENTARY: Shedding light on China

The problem in 2008 wasn’t just exposure to bad debt; it was opacity. No major bank knew exactly how much subprime risk it had taken on, nor how exposed it was to other subprime-heavy institutions. The result was fear and uncertainty, and a rapid and catastrophic shutdown of the interbank lending market.

And no-one saw the crisis coming – though observers, including, had been warning for years of looming losses in US subprime, the full scale was not expected. Instead, many anticipated a crisis that has not so far happened: driven by the collapse of the Chinese debt market.

Economic historian Adam Tooze, in his award-winning study of the crisis – Crashed – points out that the focus on China was the result of confusing flows of trade with flows of bank financing. The pipeline of manufactured goods from China to the US proved not to be as important a transmission mechanism as the even larger, but invisible, network of interbank financing between the US and Europe.

And China, despite some dramatic stock market crashes, has managed to avoid severe economic crisis both before and since 2008. This year, however, may be different. Chinese premier Li Keqiang warned the Chinese parliament this week that the country faced a “graver and more complicated environment as well as risks and challenges … that are greater in number and size”. “We must be fully prepared for a tough struggle,” he added.

Exactly how tough is a matter of debate. Chinese macroeconomic indicators are not reliable: one of the missions of the Chinese Earth observation satellite fleet is to provide Beijing with a more accurate picture of what is actually happening in provincial economies than they get from the province governments. Li himself famously remarked a few years ago that he used bank lending, rail traffic and electricity demand – the so-called Li Keqiang index – to gauge the true state of the Chinese economy, rather than relying on official growth figures.

Asset managers attempting to ‘nowcast’ China’s GDP have an even tougher time, using data that is either unreliable, inconsistent or missing to come up with results that are still, they hope, better than the official information; monthly figures for January would normally be published around the time of the Chinese New Year holiday, and so the government simply doesn’t bother releasing many of them.

China’s aggressive capital-driven economic policy may pull the country through a tough 2019, but at the cost of racking up still more dubious and opaque debt. More light is due to be shed by an impending accounting shake-up: the move to International Financial Reporting Standard 9 will reveal much of the country’s shadow banking for the first time.

But while exposing bank credit to businesses not previously included in lending figures, the move may not reveal the full extent of the bad debt Chinese banks hold – officially 2% of loans are non-performing; unofficially it could be more than 20%, some analysts estimate. Nor will it shed much light on the network of cross-guarantees of corporate loans supporting the credit of many struggling borrowers, that may ensure that the crash, if it comes, will be truly systemic.



Citing increased “underlying vulnerabilities” in the global economy, the Bank of England assumes a peak-to-trough reduction in world GDP for its stress scenario of 2.6%, up from 2.4% in the 2018 scenario and the highest since the tests started in 2014. US GDP is assumed to fall peak-to-trough by 3.7%, China GDP 1.2% and Euro area GDP 4% compared to 3.5%, 1.2%, and 3.6% in the 2018 scenario.



“The world is a complicated place and forecasting stock returns is a difficult problem. You can’t just throw one of these [machine learning] algorithms at a problem and come out with something that works” – David Jessop, UBS

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