Chanos Scenario

Chanos 2010 Hard Landing Scenario

This site shows the China Hard Landing Scenario that we developed as part of the TST China System in 2009-10 after we became familar with Chanos’ Hard Landing Scenario.  The model below compares the risk/reward of the 4 BRICs, EU, Japan US under the Soft and Hard Landing Scenarios as of 2010.  The view extracts the China analysis.  China goes from 2nd under the Soft Landing Scenario to last under the Hard Landing Scenario. The benefit is that users can (1) models all of the factors (QL+QT) that impact the risk/reward of the 7 countries, (2) set the outer bounds of what they believe may happen, and then (3) update the scenarios for the outer bounds and any scenario between as of any point in time, and (4) compare the results for the 3 scenarios on one page, and (5) play “what if” at high speed until satisfied that their reasoninga and results are correct.  These features make the models more comprehensive, dynamic and easier-to-understand that numbers-only models.

ChinaScenarios2

The Hard Landing Catalyst – The Empty/Ghost Cities

China’s is famous for its ghost cities. Below are photos of the most famous one, Ordos in Inner Mongolia.   As of 2010, Ordos has enough buildings for tens of thousands of people but the buildings were mostly empty years after the buildings were completed.

Empty high rises in Ordos

OrdosNew

The Largest Shopping Mall in the World: Empty

The New South China Mall — the biggest shopping mall in the world — spans five million square feet of shopping area, making it the largest in the world in terms of leasable space — more than twice the size of Mall of America, the biggest shopping center in the United States.  It was empty as of 2010.

SouthChinaMall
SouthChinaMall2

The 4 Cornestones for Comparing the 7 Largest Economies

We used the 4 cornerstones of a healthy society developed by David Darst in “The Art of Asset Allocation: Principles and Investment Strategies for Any Market” as the foundation for the system: political, social, economic and financial.  We then added a set of flexible metrics: catalysts, risks and x factors.

ChinaCornerstonesThe Multi-Tier Model Framework

The table below illustrates Tiers 1 and 2 of the framework and the 2 sets of driver categories for the Multi-Tier Model.

Fixed and Dynamic Drivers Structure

Taking the Empty Cities Catalyst to its Logical Conclusion: The Hard Landing

The Hard Landing Scenario assumes the “Empty Cities – – –” will be the catalyst for a chain reaction leading to a Hard Landing.  TST China enables users to take that assumption to its logical conclusion in terms of its impact on the political, social, economic and financial drivers of China.

 Fixed Drivers

The Fixed Drivers categories are permanent, but the drivers within those categories are dynamic. They are modeled in sub-models whose results link up to the 1PAGER that synthesizes them into the two Fixed Drivers categories on the synthesis page: (1) Political & Social and (2) Economic and Financial.

Govt Stability

For example, in the government stability category, if the empty cities, the South China Mall, and the vast supply of unoccupied property in China remained underutilized, this would lead to an “X Negative” impact on the Communist Party.  The authoritarian rule of the Communist Party is based upon an agreement with the Chinese people that it will provide economic prosperity in exchange for full control of the government.  The failure to do so could well lead to the overthrow of the Communist government by the people.

Geopolitics

The geopolitical issues included:

  • US/China relations
  • Natural resource access
  • South China Sea
  • Japan
  • Middle East

The judgment was that China’s geopolitical situation at the time had far less impact on the risk/reward than the risk to government stability.  Geopolitics was thus low-weighted and had minimal impact on the results.

True Asset Values & Liabilities

In the True Asset Values & Liabilities category, the issue was whether the money that went into the real estate, infrastructure and state-owned enterprises generated true economic value.  The GDP growth at the time was 9%, according to the Chinese authorities.  However, the GDP growth percentage assumed that the assets were worth the funds invested to build them. This was particularly important because such a huge percentage of the money was borrowed and the debt had to be repaid.

Dynamic Drivers

The Dynamic Drivers categories are designed for flexibility, speed and visualization.  The model structure limits the Tier 1 categories to 5 columns so they can fit on one page. The (1) Political & Social category and (2) Economic and Financial category fill the first two columns and the Dynamic Drivers the next 3 columns.

Catalysts & X Factors

Catalysts and X Factors are defined generally.  Catalysts are events, such as news, new policies and alliances, that trigger a change in expectations about China. X-Factors are events, qualities and traits of China not covered in the other categories and are critical to analyzing China.

Catalyst

The Catalyst was the “Empty Cities – – -“ previously described.  The “Empty Cities – – -“ catalyst contrasted to the 400-600 million people moving into the middle class.  If that many people moved into the middle class, then the empty cities could fill up.  If the number was significantly less, such as 200 million, then the cities would remain underoccupied and the buildings would never be worth as much as they cost, and real GDP growth would be far less than 9%.

Risks

The 3 sub-risks selected were Middle Class, Policy and Currency.  To fit the 3 sub-risks in one small column width, a letter abbreviation was used for each sub-risk: M for Middle Class risk; P for Policy Risk; and C for Currency Risk.  The judgment was then made as to whether each country had above or below average risk for each sub-risk.  If above average risk, the judgment was reflected by inputting the letter; if below average risk, then blank.  There were 8 choices including: All for all 3 sub-risks; M for Middle Class risk only; P&C for Policy and Currency risk;and None for none of the sub-risks.  They scale from Best (None) to Worst (All) based upon the number of risks and their level of seriousness.

ChinaHardLandingRisks

X Factors

The X Factor was that the 9% GDP growth was counterfeit. The risk was that the Chinese economy was  a bubble similar to the sub-prime mortgage and housing bubble in the U.S., but on a much larger scale.  Just like the U.S. housing bubble, the debt used to finance the asset base had to eventually be repaid.  If the assets were worth only a fraction of the debt, then the problem would ripple through the Chinese economy and financial system.

Synthesis

The analysis was then synthesized as a logical whole below.  The flexibility and dynamism of the model was illustrated by the 0% weight for Economic and Financial metric.  This was based on the assumption that the Economic and Financial numbers published by the Chinese government were counterfeit and meaningless in terms of their impact of the overall results.  Rather than go through the process of updating a set of meaningless numbers, the 0% weight removed them from the overall analysis and the cell for Economic and Financial left blank.  As you can see, it was then easy to update the model by simply updating the small set of assumptions for political and social, catalysts, risks and x factors for China and the other 6 economies.

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