China Update – The Beatings Will Continue Until Morale Improves

It’s essentially illegal to short a stock in China today.

This developed in response to the more than 40  percent drop in market value  of most stocks– as in the chart for Shenzhen Index B shown below.

shenzhen

B shares (Chinese: B股, officially Domestically Listed Foreign Investment Shares) on the Shanghai and Shenzhen stock exchanges refers to those that are traded in foreign currencies. Shares that are traded on the two mainland Chinese stock exchanges in Renminbi, the currency in mainland China, are called A shares.

China Seen Delaying Shenzhen Stock Link Until 2016 Amid Turmoil

Chinese authorities have gone to extreme lengths to prop up the stock market as both the Shanghai Composite Index and the Shenzhen Composite Index lost more than 40 percent from their June highs. They’ve banned major shareholders from selling stakes, armed a state-run margin trader with billions of dollars to buy equities, allowed hundreds of companies to halt their shares and placed curbs on bearish bets in the futures market.

One casualty has been the World’s Biggest Stock-Index Futures Market

Volumes in the country’s CSI 300 Index and CSI 500 Index futures sank to record lows on Wednesday after falling 99 percent from their June highs. Ranked by the World Federation of Exchanges as the most active market for index futures as recently as July, liquidity in China has dried up as authorities raised margin requirements, tightened position limits and started a police probe into bearish wagers.

China’s Response to Stock Plunge Rattles Traders

A business journalist has been detained and shown apologizing on national television for writing an article that could have hurt the market.

Caijin

Apparently, the beatings will continue until morale improves.

The spectacle of Wang Xiaolu, reporter at a Chinese business publication, enacting Monday what authorities called a confession of spreading “panic and disorder” in the stock market through his reporting should scare investors.

The announcement Sunday by China’s Ministry of Public Security that 197 people had been punished for spreading rumors about stocks and other issues should scare investors.

That the head of hedge fund manager Man Group Plc’s China unit has been taken into custody, as reported by Bloomberg on Monday, should scare global investors.

This is not to say that China isn’t capable of browbeating and arm-twisting and public money spending its way to a higher stock market. China asked brokerages to increase their support of a market rescue fund by 100 billion yuan last week, according to reports, a move perhaps tied to a desire for a rally, or at least stability, ahead of Thursday’s parade commemorating victory over Japan…….

They do know that a huge country is scared so badly of something that they will not, cannot, allow things to be said that cause stocks to fall.

Reflections

These strong-arm tactics go beyond anything seen in the West, in terms of damage control. Transparency,  an key feature in financial analysis, has been thrown out the window. With Goons going around bullying people to hold stocks that have lost 40 percent of their value, pressing journalists to keep quiet about problems in the market, it is a strange world in Chinese finance and stocks. People “disappear” – no record of arrest being announced.

There is no question but that Chinese macroeconomic statistics must also be viewed in a more skeptical light.

This makes assessment of the Chinese slowdown more difficult.

One Month Forecast of SPY ETF Price Level – Extreme Value Prediction Algorithm (EVPA)

Here is a chart showing backtests and a prediction for the midpoints of the monthly trading ranges of the SPY exchange traded fund.

MonthSPY

The orange line traces out the sequence of actual monthly midpoints of the price range – the average of the high and low price for the month.

The blue line charts the backtests going back to 2013 and a forecast for September 2015 – which is the right-most endpoint of the blue line. The predicted September midpoint is $190.43.

The predictions come from the EVPA, an algorithm backtested to 1994 for the SPY. Since 1994, the forecast accuracy, measured by the MAPE or mean absolute percent error, is 2.4 percent. This significantly improves on a No Change model, one of the alternative forecast benchmarks for this series, and the OOS R2 of the forecast of the midpoint of the monthly trading range is a solid 0.33.

Just from eyeballing the graph above, it seems like there are systematic errors to the downside in the forecasts.

However, the reverse appears to happen, when the SPY prices are falling over a longer period of time.

2008SPYMonth

I would suggest, therefore, that the prediction of about $190 for September is probably going to be higher than the actual number.

Now – disclaimer. This discussion is provided for scientific and entertainment purposes only. We assume no responsibility for any trading actions taken, based on this information. The stock market, particularly now, is volatile. There are lots of balls in the air – China, potential Fed actions on interest rates, more companies missing profit expectations, and so forth – so literally anything may happen.

The EVPA is purely probabilistic. It is right more often than wrong. Its metrics are better than those of alternative forecast models in the cases I have studied. But, withal, it still is a gamble to act on its predictions.

But the performance of the EVPA is remarkable, and I believe further improvements are within reach.

Economic Impact Modeling

I had a chance, recently, to watch computer simulations and interact with a regional economic impact model called REMI. This is a multi-equation model of some vintage (dating back the 1980’s) that has continued to evolve. It’s probably currently the leader in the field and has seen recent application to assessing proposals for increasing the minimum wage – in California, Vermont, San Francisco – and to evaluating  a carbon tax for the Citizen’s Climate Initiative  (see the video presentation at the end of this post).

One way to interact with REMI is to click on blocks in a computer screen based on the following schematic

REMIblocs

I watched Brian Lewandowski do this at Colorado University’s Leeds School of Business.

Brian set parameters for increases in labor productivity for professional services and changes in investment in primary and secondary educational by clicking on boxes or blocks. Brian, Richard Wobbekind (pictured below), and I discussed results, and how REMI is helpful in exploring “what-if’s” and might have applications to  optimizing tax policies at the state level..

RichWobbekind

Wobbekind is himself a leader in preparing and presenting State-level forecasts for Colorado, and is active in the International Institute of Forecasters (IIF) which sponsors the International Journal of Forecasting and Foresight – as well as being an Associate Dean of CU’s Leeds School of Business.

Key Point About Multi-Equation, Multivariate Economic Models

From the standpoint of forecasting, the best way I can understand where REMI should be placed in the tool-kit is to remember the distinction between conditional and unconditional forecasts.

REMI model documentation indicates that,

The REMI model consists of thousands of simultaneous equations with a structure that is relatively straightforward. The exact number of equations used varies depending on the extent of industry, demographic, demand, and other detail in the specific model being used. The overall structure of the model can be summarized in five major blocks: (1) Output, (2) Labor and Capital Demand, (3) Population and Labor Supply, (4) Wages, Prices, and Costs, and (5) Market Shares

So you might have equations such as,

X1t = a0 + a1Z1t +..+ akZkt

X2t = b0 + b1Z1t +..+ brZrt

In order to predict unconditionally what (X1t,X2t) will be at some specific future time T*, it is necessary to correctly derive the parameters (a0,a1,..,ak,b0,b1,,…,br).

And it also is necessary, for an unconditional forecast, to predict the future values of all the exogenous variables on the right-hand side of the equation – that is all the Z variables that are not in fact X variables.

This usually means that unconditional forecasts from multivariate forecast models have wide and rapidly diverging confidence intervals.

Thus, if you try to forecast future employment in, say, California with such models, they may underperform simpler, single equation models – such as those based on exponential smoothing, for example.

This does not invalidate general systems models such as REMI.

Assuming the flows and linkages of sectors and blocks are realistic and correctly modeled, such models can help think through the consequences of policy decisions, new legislation, and infrastructure investments.

This is essentially to say that these models may present good conditional forecasts – basically “what-if’s” without being the best forecasting tool available.

Here is a video presentation based on the Citizen’s Climate Initiative application of REMI to assessing a carbon tax – an interesting proposal.