The China Economy Compass by Hideyuki Takahashi

The China compass • We present two quantitative tools to help forecast China’s

economy: a composite leading indicator (Nomura CLI) and a heat-map that tracks changes in 32 important headline and leading indicators in China. The two measures have been designed to complement each other in helping to navigate the direction of China’s economy.

• With a six-month lead, the Nomura CLI has a stronger correlation with industrial production (IP) growth than China’s

official PMI. The CLI’s turning points have an average two-month lead on IP growth turning points, while the official PMI’s turning points lag those of IP growth. The number of hot indicators identified on the heat-map also helps to forecast turning points in IP growth, which of all the core monthly data is the most correlated to quarterly GDP.

• The latest readings show that, after reaching a bottom in November 2011, the Nomura CLI has risen for four straight months to March 2012, while the number of hot indicators in our heat map has also increased over this period. This is a signal that economic growth will likely rebound beyond Q1 2012, consistent with our H2 2012 GDP growth forecasts.

I. Nomura’s China composite leading indicator 4

Comparing the Nomura CLI with other

leading indicators 5

Caveat and remedies 6

Nomura CLI and equity and commodity prices 7

Methodology and components 8

II. Nomura’s China heat-map 10

III. Heat-map indicators 13

GLOBAL ECONOMICS AND

STRATEGY

Apr i l 2012

AN

CH

OR

RE

PO

RT China economy

See Appendix A-1 for analyst certification, important disclosures and the status of non-US analysts.

April 23, 2012

Research analysts

Economists

Zhiwei Zhang

[email protected]

+852 2536 7433

Wendy Chen

[email protected] +86 21 6193 7237

Economists

Tomo Kinoshita

[email protected]

+852 2536 1858

Aman Mohunta

[email protected]

+91 22 6617 5595

Nomura | Asia Special Report 23 April 2012

2

Table of Contents

Foreword 3

Nomura‟s China composite leading indicator 4

Comparing the Nomura CLI with other leading indicators 5

Caveat and remedies 6

Nomura CLI and equity and commodity prices 7

Methodology and components 8

Nomura‟s China heat-map 10

Selecting data for the heat-map 12

Heat-map indicators 13

Fixed asset investment (FAI) 13

Real estate investment 14

New floor space started 15

Automobile sales 16

Freight carried 17

Exports 18

Imports 19

Imports (ex processing and assembly) 20

Import volume index of intermediate goods 21

Overseas orders indicator 22

M2 money supply 23

Quasi money 24

Shanghai Composite (SHCOMP) 25

Shanghai stock market turnover 26

Gap between PPI output and input price inflation 27

Yield spread 28

Economic Climate Index (ECI) - Leading index 29

OECD China Leading Index (CLI) 30

OECD total leading index 31

Official Purchasing Managers Index (PMI): 32

Official PMI, seasonally adjusted (sa) 33

Sales revenue growth of industrial enterprises 34

Electricity production 35

Automobile production 35

Steel production 36

Chemical fiber production 36

10 non-ferrous metals production 37

Cement production 37

Cloth production 38

Yarn production 38

Chemical fertilizer production 39

Metal cutting machinery production 39

Recent Asia Special Reports 40


3

Foreword

China‟s economy is now the world‟s second-largest, contributing over two-fifths of

global GDP growth last year – yet the availability, and many would argue, the quality,

of its economic statistics is lagging. It is difficult to get a clear read on the pulse of

China‟s rapidly transforming economy, let alone its outlook. Just last month, the official

PMI and an alternative private-sector measure parted ways.

To help fill this void, we are pleased to present Nomura‟s latest Anchor Report, The

China compass, in which we present two proprietary quantitative tools that we

developed to help navigate China‟s economy: Nomura‟s composite leading indicator

(Nomura CLI) and a heat-map that tracks changes in 32 important headline and

leading economic indicators in China.

Our two measures complement each other, and after considerable back-testing we are

confident that they will prove useful additions to your tool-kit in helping to forecast

turning points in China‟s economy.

We shall provide an update of The China compass each month.

This report builds on Nomura‟s continued effort to provide our clients with cutting-edge

research. In terms of our China coverage, it follows November 2011‟s Anchor Report

on China risks, in which we presented our China Stress Index (CSI), developed to

assess the risk of a hard economic landing.

As ever, we welcome your feedback as we continue our tradition of delivering unique

investment insight and ideas to our clients.

Hideyuki Takahashi

Head of Global Research

http://intranet.nomuranow.com/research/globalresearchportal/GetPub.aspx?pid=471517


4

Nomura‟s China composite leading indicator

Which indicators best forecast China‟s economy? This is one of the most frequently

asked questions as people are bombarded by anecdotes on the health of China‟s

economy, with a swathe of partial indicators from which to choose, ranging from

electricity consumption to port container traffic. Yet, these data are narrow and not

always reliable. China's economy has become so critically important for the global

economy, and yet, a composite leading economic index that is both timely and reliable,

to help objectively forecast China‟s economic outlook, has been lacking.

Our composite leading indicator (Nomura CLI) of China's economic cycle is designed

to help identify turning points in advance. We reviewed a broad range of 52 monthly

indicators for their leading properties, quality and timeliness before choosing the best

nine to formulate the Nomura CLI. These nine indicators are: quasi money; the OECD

leading indicator for developed and major emerging economies (OECD total); yield

spread between 3-year and 6-month government securities; Shanghai stock market

turnover; gap between producer‟s output prices and input prices; and the production of

steel, automobiles, chemical fiber and metal cutting machinery. More details of these

indicators and the methodology used in constructing the index are explained on page

8. We will update the Nomura CLI on a monthly basis, incorporating all information

available by the release date.

The Nomura CLI picked up in December 2011 and over the first three months of 2012,

after trending down since November 2010 (Figure 1). Historical data indicate the

turning points of our CLI leads those of industrial production growth by an average of

two months over the past 10 years. Based on the Nomura CLI, we conclude that

economic growth bottomed in Q1 and is now more likely to rebound than decline. The

reversal in the CLI supports our forecast of 8.4% GDP growth for 2012.

With respect to which factors caused the Nomura CLI to rise recently, of the nine

components, five have risen since November and four declined (Figure 2). Quasi

money jumped partly due to monetary policy loosening. Stock market turnover picked

up, which reflects improved sentiment due to policy easing, and may help

consumption through positive wealth and confidence effects. The gap between

producers‟ output and input prices rose slightly, improving corporate profit margins.

Steel output improved marginally. The OECD leading indicator for developed and

major emerging economies rose, which suggests that external demand is improving.

On the downside, production of autos, chemical fiber, and metal cutting machinery

continued to weaken, while the yield spread narrowed.

Fig. 1: Nomura CLI and industrial production growth

Source: CEIC and Nomura Global Economics.

Fig. 2: Changes of components of Nomura CLI from November 2011 to March 2012

Note: „Machine‟ refers to metal cutting machinery. Source: CEIC and Nomura Global Economics.

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PMI sometimes gave right signals but is not consistent OECD China leading indicator is released with a two-month lag We launch our Nomura China leading indicator in this report

The Nomura CLI has risen since December 2011…

… as five of nine components have been positive since December

Investors need reliable and timely leading indicators for China


5

Comparing the Nomura CLI with other leading indicators

China‟s official Purchasing Managers‟ Index (PMI) arguably gets the most market

attention as a leading indicator for China. While it is timely and has worked well, it is

volatile and has also at times given the wrong signal. For example, in 2007-08, GDP

and industrial output data indicated that economic momentum peaked in June 2007

and trended down in 2008, but the PMI rose in H1 2008 and reached its highest level

in April 2008 (59.2) before plummeting sharply to 38.8 in November 2008 (Figure 3).

Also, in 2009-10, industrial output growth peaked in November 2009 but the PMI

continued to rise until January 2010.

The OECD‟s China CLI has worked well in forecasting business cycles. It has

successfully identified, in advance, all the turning points in China‟s industrial output

growth since 1979. However, a big drawback is that it is released with a considerable

two-month lag (the latest reading released on 10 April reflects data up to February

only), and therefore does not get as much market attention as the official PMI (more

discussion on pages 30 and 33).

The Nomura CLI, when back-tested, works well in forecasting industrial output growth

over the past 10 years. Compared to the official PMI, the Nomura CLI has had a

stronger correlation with industrial output growth since 2005. With a one-month lead,

the correlation coefficient between the Nomura CLI and industrial output growth is 0.70,

while the correlation coefficient between the official PMI and industrial output growth is

0.55. This difference is persistent over longer forecast horizons as well. With a six-

month lead, the correlation coefficient between the Nomura CLI and IP growth is 0.69,

while it is 0.56 between the PMI and IP growth.

In terms of indentifying turning points, the Nomura CLI also performs well (Figure 4).

There have been six distinct turning points (three peaks and three troughs) in China‟s

industrial output growth over the past 10 years. Our CLI correctly signals all six turning

points with an average two-month lead, compared to a one-month lead of the OECD

China CLI. In fact, since 2005 turning points in the official PMI actually lag turning

points in industrial output by an average of 3.7 months.

The Nomura CLI performs better than the PMI in identifying turning points in the

business cycle because the two indicators are designed for different purposes. The

PMI is designed to measure the change of economic momentum on a month-to-month

basis. For instance, the PMI new order index shows how many firms received more

orders this month compared to the last. The answer can be volatile and highly

seasonal. The Nomura CLI, on the other hand, is designed to forecast turning points in

business cycles and all components are selected based on their forecasting ability.

Fig. 3: PMI and industrial production growth

Source: OECD, CEIC and Nomura Global Economics.

Fig. 4: Peaks and troughs of Nomura CLI, OECD CLI, official PMI and IP growth


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IP growth OECD CLI Official PMI Nomura CLI

Trough Nov-01 Nov-01 Sep-01

Peak Feb-04 Jan-04 Jan-04

Trough Feb-05 Mar-05 Jul-05 Dec-04

Peak Jun-07 Sep-07 Sep-07 May-07

Trough Nov-08 Nov-08 Nov-08 Nov-08

Peak Nov-09 Dec-09 Dec-09 Sep-09

Trough Sep-11 Nov-11 Nov-11

Turning points of the Nomura CLI precede those of China‟s economy by an average of two months

The official PMI does not consistently provide correct signals

The OECD China leading indicator is released with a two-month lag

Nomura CLI forecasts China‟s economy well


6

Caveat and remedies

Leading indicators based on statistical tools often face an end-point problem: the

values (particularly for those months that are close to the end of the sample) are

subject to revision when new data becomes available. For instance, the Nomura CLI

did not clearly identify November 2008 as a trough until data for February 2009 was

made available, because the value of the CLI for December 2008 was revised when

new data became available (Figure 5). The OECD China leading indicator is also

subject to this problem. Its release in March shows the leading indicator trended down,

while its release on 10 April shows the leading indicator was revised and a bottom in

September 2011 was identified.

This issue is problematic, but unfortunately also unavoidable. It is a common problem

in econometric analysis. An intuitive way to think about it is that underlying data are

volatile; hence, we need to have several months of observations to confirm whether

the change in the index is the beginning of a genuine upturn or downturn. To be

specific and somewhat technical, we need to apply statistical filters to underlying data,

and the filtered results are not always highly robust at the end of the sample.

How much does this problem affect the Nomura CLI‟s forecasts in practice? One way

to address this question is to calculate for each turning point in the Nomura CLI how

many months after the turning point is first observed until we are reasonably confident

that it is genuine. To do this we conducted a back-testing exercise, and conclude that

a turning point can be confirmed, on average, 2.3 months after it is first seen. Of the

six turning points, in the best case the turning point was detected immediately after the

data for the next month was made available (September 2001); in the worst case, it

took four months to confirm the December 2004 trough.

Despite the end-point problem, the Nomura CLI is useful for economic forecasting in

real time. In March 2012, for example, the official PMI and the HSBC PMI moved in

opposite directions and there was a high level of uncertainty in the market in whether

growth bottomed in Q1. Around that time (mid-March), the latest data point for the

Nomura CLI was February, which had identified a bottom in November 2011. The

Nomura CLI turning point prediction was a robust signal given that it had risen for

three straight months (more than the 2.3 month average) after the turning point, and

therefore useful for investors to draw an inference on the likelihood of an economic

rebound ahead. On 10 April, the OECD China leading indicator also identified a

bottom in Q4 2011, which is consistent with the signal from our CLI.

Fig. 5: Revision of the Nomura CLI in 2009


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One problem of the Nomura CLI is that forecasts are subject to revisions when new data arrive

Turning points are confirmed usually two months after they take place

Nonetheless, the Nomura CLI is still useful for economic forecasting in real time


7

Nomura CLI and equity and commodity prices

The Nomura CLI has been strongly correlated with the Shanghai stock market index

since 2005 (Figure 6) and has led it in some periods. In 2007, the Nomura CLI peaked

in May while the Shanghai index peaked in October. In 2008, both the Nomura CLI

and the Shanghai index bottomed in November. In 2009, the Shanghai index peaked

in August while the Nomura CLI peaked in September. In the most recent cycle, the

Nomura CLI bottomed in December 2011 while the Shanghai index bottomed on 5

January 2012.

The Nomura CLI has also led copper futures prices in Shanghai on several occasions.

The leading index peaked in May 2007, while copper futures were range-bound for

most of 2007 and dropped sharply in 2008. The leading index rebounded in December

2008, while copper futures bottomed in January 2009. The turning points of the

leading index in 2009 and 2010 also preceded turning points in copper prices.

We think the high contemporaneous correlation between our leading indicator and

asset prices is primarily due to the fact that asset prices are forward-looking and

incorporate important events, such as policy changes, in a timely manner. In late 2008,

China implemented its RMB4trn fiscal stimulus program which pushed up both the

Shanghai index and economic activity. Hence the rebound of these two indicators was

synchronized. In August 2009, monetary policy was adjusted from a super-loose

stance to one that was more neutral. The equity market responded quickly, while

economic activity started to weaken with a slight lag. In December 2011, monetary

policy was loosened and the market rebounded in response. Again, economic activity

responded with a lag to policy loosening as the transmission of policy to economic

activity took time.

Part of the correlation between the Nomura CLI and the Shanghai index is due to the

utilisation of stock market turnover as a component. However, we do not believe this is

the key reason, because: 1) it is only has an 11% weighting in the Nomura CLI; and 2)

it does not explain why the Nomura CLI led the collapse of the Shanghai index in

October 2007.

Fig. 6: Nomura CLI and the Shanghai A-share index


Fig. 7: Nomura CLI and copper futures prices


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The Nomura CLI is well correlated with the Shanghai stock index

Stock prices are forward-looking and reflect policy changes that drive both the CLI and stock index

The Nomura CLI has also led copper futures prices


8

Methodology and components

We chose China‟s industrial production growth as our reference series (i.e., the data

we attempt to forecast). Industrial production was chosen instead of GDP for three

reasons. First, industrial production is a monthly indicator that helps provide a more

timely measure of the economy, while GDP is only available quarterly. Second,

industrial production growth is a good proxy for GDP growth as the two are highly

correlated (Figure 8). Third, industrial production growth has worked well in the past as

a coincident indicator of business cycles. For example, it dropped sharply after the US

financial crisis escalated in September 2008 and rebounded quickly in early 2009 after

China implemented its fiscal stimulus program.

The Nomura CLI is constructed as a composite of nine leading indicators, each given

an equal weighting. These were selected from a large pool of monthly variables, many

of which are discussed in the “Heat-map indicators” section of this report. The nine

indicators are:

1. Quasi money. Quasi money refers to assets that can be readily converted

into cash. In China, it is mainly made up of demand deposits. It is a measure

of liquidity, with stronger leading properties than others such as M2 (Figure 9).

This indicator is influenced by both monetary policy as well as firms‟ and

households‟ decision to allocate assets.

2. OECD total leading indicator. This is the OECD‟s aggregate leading

indicator for all OECD countries and six emerging markets combined. It is a

measure of external demand, which leads China‟s export growth well (Figure

10).

3. Steel production. Steel is a highly cyclical sector and is highly dependent

upon housing construction (about 30% of demand goes directly into the

housing sector). It is closely watched by investors as a barometer of

economic momentum.

4. Automobile production. The automobile sector has quickly become an

important sector of China‟s economy.

5. The differential between the producers’ ex-factory price index and the

input price index. This measure indicates how profit margins are affected by

costs (input prices) and market demand (ex-factory prices). It influences both

profitability (Figure 11) and investment decisions, and hence leads IP growth.

6. Shanghai stock market turnover. This is a measure of market sentiment,

which sometimes works well in reflecting changes in policy. We also found

that stock market turnover is a better leading indicator of IP growth than stock

indexes.

7. Chemical fiber production. Chemical fiber is extensively used as an input

for industrial production and is highly sensitive to both demand- and supply-

side factors such as oil prices.

Fig. 8: Quarterly growth of industrial production and GDP


Fig. 9: Growth of quasi money, M2, and industrial production


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We choose to forecast IP growth because it is available monthly and tracks GDP well

We have selected nine indicators as components of the Nomura CLI


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8. Production of metal cutting machinery. This is an upstream indicator of

metal production.

9. The differential between 3-year government bond yields and 6-month

government bill yields. This is a proxy for monetary policy and market

liquidity.

More details of these variables, such as historical data and correlations with IP growth,

are provided in the “Nomura‟s China heat-map” and “Heat-map indicators” sections.

We used four criteria in choosing our nine component indicators.

1. Data must be released on a monthly basis and must have been

available for more than five years so that a proper statistical analysis is

feasible. Indeed, for eight of the nine variables, there is more than 12 years of

data available so we can test how the composite indicator has performed

over the past decade.

2. We favour data without measurement problems. For instance, the

housing sector is very important, but many housing data have measurement

issues that make statistical analysis difficult, so we chose steel production

instead. Steel production depends heavily on housing yet is superior to

housing indicators in forecasting industrial production growth. We also try to

avoid data with strong price effects that are hard to control.

3. From a statistical perspective, all the inputs must be good leading

indicators of industrial production growth. To check this, we ran simple

correlation tests with different lead months over a large pool of indicators and

filtered out all but those with the highest correlations.

4. The variables must complement each other. A horse race among

indicators helps to rank them in terms of individual predictive power, but a

combination of the best individual indicators does not give the best composite

indicator, so we tested different combinations to determine the mix of

variables that gave the best collective forecast.

We seasonally adjust these variables, de-trend them using the Hodrick-Prescott filter

and then combine them in an equal weighting to form the Nomura CLI. We could have

assigned weightings subjectively to better fit with industrial production growth over the

past 10 years, but the difference is not very substantial and it will not necessarily

improve future forecast accuracy.

Fig. 10: OECD leading indicator and China’s export growth

Source: OECD, CEIC and Nomura Global Economics.

Fig. 11: Differential between producers’ ex-factory price index and input price index, and corporate profit growth

Note: Corporate profit was reported on a quarterly basis from 2007 to 2011, hence the gap in this period for some months in this chart. Source: CEIC and Nomura Global Economics.

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We select our indicators based on availability and their capacity to forecast IP growth…

… and give each an equal weighting in the Nomura CLI


10

Nomura‟s China heat-map

Our heat-map gauges economic momentum across a wide range of the best macro

and micro indicators for China. We thoroughly examined 52 high-frequency indicators

that we think are valuable in forecasting China's economy, and filtered out all but 32

based on their ability to forecast industrial production growth. The final product is a

map that covers, in our view, all the useful leading indicators of China's economy,

including the widely watched PMI and OECD leading indicators, as well as lesser-

known variables such as the production of metal cutting machinery and chemical fibers.

The heat-map is designed to supplement the Nomura CLI in economic analysis –

consider it a kind of robustness check. The Nomura CLI comprises only nine indicators

and is compiled following a statistical procedure based on in-sample forecasts. The

heat-map, on the other hand, includes 32 indicators, covering investment, consumption,

foreign trade, monetary policy, financial market indicators and select micro-level

indicators. The purpose of the Nomura CLI is to forecast economic turning points, while

the purpose of the heat-map is to offer readers a more comprehensive view on the

positive and negative drivers of the economy, supported by a wide range of data.

The heat-map provides a useful signal for turning points in economic cycles – the

number of “hot” indicators, defined as data where the year-on-year growth rate has

increased from the previous month, is historically volatile, but a significant change in

this number around the time of a potential economic turning point can help to validate

whether a turning point has actually been reached (Figure 12). For example:

In December 2008, the number of hot indicators rose to 13 from 8 in

November on a 3-month-moving-average (3mma) basis. Note that industrial

production growth was still in its downtrend in December, the PMI was at 42 –

well below the 50 threshold. In January 2009, when these data were available,

there were serious concerns over China‟s economic outlook. The Shanghai A

share index rose from 2,089 at end-January 2009 when all December data

were released to 3,582 by July 2009.

In July 2009, the number of hot indicators on a 3-month moving average

(3mma) basis dropped to 14.7 from 16 in June. This was six months before IP

growth peaked in January 2010, four months before the PMI peaked in

November 2009, and two months before Nomura CLI peaked in September

2009. When the number of hot indicators declined, the Shanghai A share

index ended its seven-month rally and fell by 22% in August 2009.

The number of hot indicators can help to cross-check the turning points identified by

the Nomura CLI (Figure 13). It is particularly useful because it is much less subject to

revision when new data are released. For example, the turning point of the Nomura CLI

in November 2008 could not be confirmed until March 2009, when data for February

2009 was made available, while the number of hot indicators picked up in December

which helped to confirm that momentum had indeed picked up.

The latest reading of the heat-map shows tentative signs of an economic rebound, with

the number of hot indicators rising from 11 in November to 14 in March 2012, and from

12 to 15 on a 3mma basis.

Fig. 12: IP growth and number of hot indictors


Fig. 13: Nomura CLI and number of hot indicators

Source: Nomura Global Economics.

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Fig. 14: China heat-map

Notes: Indicators with an * are indexed, while the rest are y-o-y growth numbers. Source: CEIC and Nomura Global Economics.

y-o-y growth, unless otherwise mentionedLeading

MonthNov-11 Dec-11 Jan-12 Feb-12 Mar-12

Industrial production 12.40 12.80 11.40 11.40 11.90

Investment

FAI (ytd) 1 27.89 25.07 21.47 21.47 21.29

Real Estate Investment 1 20.15 12.33 27.79 27.79 19.58

New floor space started 1 9.09 -18.85 5.06 5.06 -4.15

Consumption

Automobile sales 4 -2.42 1.38 -6.47 -6.47 0.55

Freight Carried 0 14.53 24.99 11.77 11.77

Foreign trade

Export 0 13.80 13.35 6.88 6.88 8.89

Import 0 22.02 11.82 7.70 7.70 5.26

Imports (exclud. Processing and assembly) 0 27.40 14.36 -10.58 -10.58 7.76

Import volume index: Intermediate Goods 3 10.70 6.40 -21.10 31.60

Overseas Order* 0 48.73 48.73 46.93 46.93 46.93

Monetary and financial indicators

M2 5 16.21 17.32 16.63 17.80 18.13

Quasi money 6 20.66 22.32 24.10 25.15 25.56

Shanghai Stock Exchange: Composite 1 -17.26 -21.68 -17.85 -16.40 -22.72

Turnover: Value: Shanghai SE: Stock 4 -64.44 -61.22 -55.24 -18.10 -45.11

Gap between output and input price inflation* 6 -2.34 -1.77 -1.27 -0.96 -0.43

Yield spread* 6 59.00 59.00 27.00 15.00 22.00

Survey and third party indicators

ECI leading index* 1 100.25 100.22 100.83 101.27

OECD CLI* 2 99.56 99.88 100.40 101.00

OECD Total* 0 100.05 100.18 100.35 100.53

PMI* 0 49.00 50.30 50.50 51.00 53.10

PMI s.a.* 4 49.99 50.19 50.75 52.19 51.61

Sales Revenue: ytd 0 28.16 27.23 13.40 13.40

Selected micro level indicators

Production: Electricity 0 7.52 9.80 6.12 6.12 7.29

Production: Autombile 3 1.44 -5.13 -4.43 -4.43 0.82

Production: Steel products 1 -0.58 1.24 -1.37 -1.37 3.64

Production: Chemical fibre 1 5.31 -0.40 17.67 17.67 12.55

Production: Ten non-ferrous metals 1 5.44 8.45 9.50 9.50 1.49

Production: Cement 3 6.50 3.19 5.67 5.67 8.95

Production: Cloth 1 -3.60 -8.26 9.50 9.50 6.92

Production: Yarn 2 11.57 8.11 12.78 12.78 20.55

Production: Chemical fertilizers 5 -11.56 -13.74 15.05 15.05 15.81

Production: Metal cutting machine 0 -5.63 -8.45 11.00 11.00 2.39

No of indicator with accelerated growth 11 13 19 11 14


12

Selecting data for the heat-map

In creating the heat-map, we started by scanning all available high frequency

indicators (monthly, weekly and daily) and select 52 indicators that we think may be

useful for either representing an important part of the economy (fixed asset investment

is one example), or forecasting economic growth (PMI is included for this purpose).

This set of variables includes eight indicators for investment, seven for consumption,

six for foreign trade, six financial and monetary indicators, eight survey and third party

indicators, 16 selected micro level indicators and one price data point on steel. We

preferred variables with at least 10 years of historical data in order to properly perform

a statistical test of their forecasting ability, but allowed exceptions, such as PMI data

which has only been available since 2005.

From this sample, we carried out a statistical procedure to select a group of 32

variables that were highly correlated with IP growth or enjoyed a long lead time. For

macro and financial indicators we dropped those with a correlation of less than 0.2

(the two exceptions are freight carried, and gap between output and input price index).

For micro level indicators that are subcomponents of overall industrial production (for

instance steel production), we excluded those leading indicators with a correlation of

less than 0.35. After this procedure, 32 indicators remained in our pool.

Fig. 15: Correlation table

Notes: Indicators with an * are indexed, while the rest are y-o-y growth numbers. The correlations in this sheet are calculated with data from Jan 2000, adjusting for Chinese New Year effects by averaging growth for Jan-Feb.

Source: CEIC, WIND and Nomura Global Economics.

Correlation

Leading

month Correlation

Leading

month

Investment Survey and third party indicators

FAI (ytd) 0.46 1 ECI leading index* 0.59 1

Real estate investment growth 0.42 1 OECD CLI* 0.70 2

New floor space started 0.23 1 OECD Total* 0.47 0

Consumption PMI* 0.61 0

Automobile sales 0.37 4 PMI s.a.* 0.68 4

Freight carried 0.20 0 Sales revenue (ytd) 0.49 0

Foreign trade Selected micro level indicators

Exports 0.43 0 Production: Electricity 0.72 0

Imports 0.42 0 Production: Autombile 0.42 3

Imports excluding processing trade 0.29 0 Production: Steel products 0.50 1

Import volume index of intermediate goods 0.50 3 Production: Chemical fibre 0.37 1

Reference seies: Overseas order* 0.61 0 Production: Ten non-ferrous metals 0.47 1

Monetary and financial indicators Production: Cement 0.35 3

M2 money supply 0.26 5 Production: Cloth 0.46 1

Quasi money 0.23 6 Production: Yarn 0.41 2

Shanghai Composite Stock Exchange Index 0.34 1 Production: Chemical fertilizers 0.35 5

Shanghai stock turnover 0.23 4 Production: Metal cutting machine 0.40 0

Gap between output and input price inflation* 0.12 6

Yield spread* 0.29 6


13

Heat-map indicators

This section provides background information and comments on each indicator in our

heat-map. We apply a three-star scale to illustrate the importance of each, with three

stars being the most important. The importance of each indicator is judged mostly by

its relative capacity to forecast industrial production growth.

Fixed asset investment (FAI)

Importance:

Compiled by: National Bureau of Statistics.

Frequency: Monthly.

Release date: Usually on day 9 after month-end.

Availability: February 1992.

Compilation methodology: Fixed asset investment (FAI) growth is the cumulative

growth of social fixed asset investment on a year-on-year basis. The NBS made

revisions to its FAI statistics in January 2011, including: 1) new statistical criteria for

FAI was increased from RMB500k to RMB5m; 2) monthly FAI investment expanded to

include investment of rural entities and organizations, leading to a change in name

such that monthly FAI became known as “fixed asset investment (ex rural households)”

from “urban FAI”.

Nomura comments: FAI growth (ytd) is the best indicator of investment, which is an

important part of the economy.

Pros:

Strong FAI growth, especially investment in the manufacturing sector,

foreshadows strong growth in industrial production.

A lead of one month helps to predict the dynamics of China‟s industrial

production.

Cons:

The correlation between FAI and industrial production is not particularly

strong.

FAI is reported only in nominal terms and hence is subject to price distortion.

Fig. 16: IP growth and fixed asset investment growth


Fig. 17: Nomura CLI and fixed asset investment growth


0

12

24

36

48

60

0

5

10

15

20

25

Apr-00 Apr-03 Apr-06 Apr-09 Apr-12

% y-o-y, ytd% y-o-y Industrial production

FAI (1m lead), rhs

0

12

24

36

48

60

95

97

99

101

103

105

Mar-00 Mar-03 Mar-06 Mar-09 Mar-12

% y-o-y, ytdIndexNCLI FAI, rhs


14

Real estate investment

Importance:


Frequency: Monthly.


Availability: December 1995.

Compilation methodology: Real estate investment covers all investment on structure

construction (including residential, office and commercial buildings) and land

development.

Nomura comments: Real estate investment growth is a leading indicator of China‟s

industrial production growth, with a lead of one month.

Pros:

A lead of one month helps to predict the dynamics of China‟s industrial

production.

It has a proven track record in predicting the turning points of industrial

production (e.g., in November 2008 and November 2009; Figure 18).

Cons:

It is very volatile and is reported only in nominal terms.

Fig. 18: IP growth and real estate investment growth


Fig. 19: Nomura CLI and real estate investment growth


0

10

20

30

40

50

0

5

10

15

20

25


% y-o-y% y-o-y Industrial production

Real estate investment (1m lead), rhs

0

10

20

30

40

50

95

97

99

101

103

105


% y-o-yIndex NCLI

Real estate investment, rhs


15

New floor space started

Importance:


Frequency: Monthly.


Availability: December 1995.

Compilation methodology: New floor space started includes residential, office and

commercial buildings.

Nomura comments: Growth of new floor space started shows some leading

relationship with China‟s industrial production.

Pros:

The property market is a very important part of China‟s economy and directly

accounts for about 12% of GDP.

Housing new starts is a good leading indicator of future housing investment.

Cons:

Growth of floor space newly started is much more volatile than industrial

production growth.

The data occasionally makes a large move which is difficult to interpret.

Fig. 20: IP growth and floor space newly started


Fig. 21: Nomura CLI and floor space newly started


-20

12

44

76

108

140

0

5

10

15

20

25


% y-o-y% y-o-y

Industrial production

Floor space newly started (1m lead), rhs

-20

12

44

76

108

140

95

97

99

101

103

105


% y-o-yIndex NCLI

Floor space newly started, rhs


16

Automobile sales

Importance:

Compiled by: China Association of Automobile Manufacturers.

Frequency: Monthly.

Release date: Usually day 10-20 after month-end.

Availability: January 2000.

Compilation methodology: Automobile sales is the sum of both passenger and

commercial autos sold in the month.

Nomura comments: Growth of automobile sales has a good coincident relationship

with China‟s industrial production.

Pros:

It has a proven track record, consistent with the turning points of industrial


The importance of this indicator should increase as China becomes a more

consumption-driven economy.

Cons:

It is volatile and has sometimes given false signals.

Fig. 22: IP growth and automobile sales growth


Fig. 23: Nomura CLI and automobile sales growth


-20

4

28

52

76

100

0

5

10

15

20

25



Automobile sales, rhs

-20

4

28

52

76

100

95

97

99

101

103

105


% y-o-yIndex NCLI

Automobile sales, rhs


17

Freight carried

Importance:


Frequency: Monthly.

Release date: During day 25-28 after month-end.

Availability: August 1998.

Compilation methodology: Freight carried is the sum of all freight carried by rail,

ship, highway, air and pipe transport during the month.

Nomura comments: Growth of freight carried has a good coincident relationship with

China‟s industrial production.

Pros:

It has a proven track record, moving in line with turning points of industrial

production in November 2008 and November 2009 (Figure 24).

Freight carried is a good indicator to cross-check macro activity indicators.

Cons:

The release of freight carried data is not timely, usually much later than the

industrial production series.

It is volatile and sometimes gives a false signal.

Fig. 24: IP growth and freight carried growth


Fig. 25: Nomura CLI and freight carried growth


-20

-6

8

22

36

50

0

5

10

15

20

25



Freight carried, rhs

-20

-6

8

22

36

50

95

97

99

101

103

105


% y-o-yIndexNCLI Freight carried, rhs


18

Exports

Importance:

Compiled by: China‟s General Administration of Customs.

Frequency: Monthly.

Release date: Day 10 after month-end.


Compilation methodology: Exports are the sum of goods exported during the month.

Nomura comments: Export growth shares the same cycle as that of China‟s

industrial production.

Pros:

Exports are important for China‟s economy, although export dependency has

fallen in recent years. There is a close relationship between exports and


Cons:

As goods are produced and calculated into industrial production before being

exported, export growth somewhat lags industrial production.

Fig. 26: IP growth and export growth


Fig. 27: Nomura CLI and export growth


-40

-20

0

20

40

60

0

5

10

15

20

25


% y-o-y% y-o-y

Industrial production Exports, rhs

-40

-20

0

20

40

60

95

97

99

101

103

105


%IndexNCLI Exports, rhs


19

Imports

Importance:


Frequency: Monthly.



Compilation methodology: Imports are the sum of all goods imported during the

month.

Nomura comments: Import growth shows some coincident relationship with China‟s


Pros:

It has moved in line with the turning points for industrial production (e.g., in

November 2008 and November 2009; Figure 28).

Cons:

Import growth sometimes lags behind industrial production series.

Fig. 28: IP growth and import growth


Fig. 29: Nomura CLI and import growth


-40

-20

0

20

40

60

0

5

10

15

20

25


% y-o-y% y-o-yIndustrial production Imports, rhs

-40

-20

0

20

40

60

95

97

99

101

103

105


%IndexNCLI Imports, rhs


20

Imports (ex processing and assembly)

Importance:

Compiled by: Nomura.

Frequency: Monthly.



Compilation methodology: Imports (ex processing and assembly) are the sum of

goods imported excluding those imported for the purposes of processing and

assembly during the month. In other words, it is mainly composed of ordinary imports

for the purpose of domestic consumption.

Nomura comments: Imports (ex processing and assembly) growth has a coincident


Pros:

A large portion of imports (about 40% in 2011) are for re-export purposes in

China. Import growth (ex processing and assembly) is a better measure of

domestic demand growth than total import growth.

Cons:

It sometimes lags the industrial production series.

Fig. 30: IP growth and import (ex processing and assembly) growth


Fig. 31: Nomura CLI and imports (ex processing and assembly) growth


-40

-20

0

20

40

60

80

0

5

10

15

20

25



Imports (excluding assembly & processing), rhs

-40

-20

0

20

40

60

80

95

97

99

101

103

105


% y-o-yIndex NCLI

Imports (excluding assembly & processing), rhs


21

Import volume index of intermediate goods

Importance:


Frequency: Monthly.

Release date: During day 25-28 after month-end.


Compilation methodology: The import volume index of intermediate goods is based

on the import volume of goods that are still in the process of production at the time of

import (the classification of intermediate goods, final goods according to System of

National Accounts – SNA).

Nomura comments: The import volume index of intermediate goods has a leading


Pros:

It moves quite closely with the turning points of industrial production (e.g., in

November 2008 and November 2009; Figure 32).

It captures changes in volume and therefore is free of price distortions.

Cons:

It does not always have a strong leading effect. In 2009, its rebound lagged

the recovery in industrial production.

Fig. 32: IP growth and import volume index of intermediate goods


Fig. 33: Nomura CLI and import volume index of intermediate goods


-20

-6

8

22

36

50

0

5

10

15

20

25

Jun-06 Jun-07 Jun-08 Jun-09 Jun-10 Jun-11 Jun-12


Import volume index: intermediate goods (3m lead), rhs

-20

-6

8

22

36

50

95

97

99

101

103

105


% y-o-yIndex NCLI

Import volume index: intermediate goods, rhs


22

Overseas orders indicator

Importance:

Compiled by: The People‟s Bank of China (PBC).

Frequency: Quarterly.

Release date: Day 15-20 of the last month of the quarter.

Availability: June 1992.

Compilation methodology: The overseas orders indicator is a diffusion index based

on a survey of 5000 enterprises, with 50 as the breakeven level. A reading above 50

indicates overseas orders are improving on a quarter-on-quarter basis, while a reading

below 50 suggests a contraction.

Nomura comments: The overseas orders indicator shows some coincident


Pros:

It has moved in line with turning points of industrial production (e.g., in June

2007 and November 2008; Figure 34).

Cons:

It is released quarterly rather than monthly.

It does not always lead industrial production. In 2009, its rebound lagged the

recovery of industrial production.

Fig. 34: IP growth and overseas orders indicator


Fig. 35: Nomura CLI and overseas orders indicator


40

44

48

52

56

60

0

5

10

15

20

25



Diffusion index: overseas order level, rhs

40

44

48

52

56

60

95

97

99

101

103

105


%Index NCLI

Diffusion index: overseas order level, rhs


23

M2 money supply

Importance:

Compiled by: The People‟s Bank of China (PBC).

Frequency: Monthly.

Release date: Day 11-15 after month-end.

Availability: March 1997.

Compilation methodology: M2 money supply is a broad money supply indicator,

including currency in circulation, demand deposits, time deposits, savings deposits

and other deposits.

Nomura comments: M2 money supply growth is a good leading indicator of China‟s


Pros:

It enjoys a lead time of five months.


production (e.g., in December 2003, November 2008 and November 2009;

Figure 36).

It is a good measure of broad liquidity in the market and sensitive to policy

changes.

Cons:

The quasi money indicator has a better leading effect than M2 growth.

Fig. 36: IP growth and M2 money supply growth


Fig. 37: Nomura CLI and M2 money supply growth


10

14

18

22

26

30

0

5

10

15

20

25

Aug-00 Aug-03 Aug-06 Aug-09 Aug-12

% y-o-y% y-o-yIndustrial production M2 (5m lead), rhs

10

14

18

22

26

30

95

97

99

101

103

105


% y-o-yIndexNCLI M2, rhs


24

Quasi money

Importance:

Compiled by: The People's Bank of China (PBC).

Frequency: Monthly.

Release date: Day 11-15 after month-end.


Compilation methodology: Quasi money is the difference between the M1 and M2

measurements of money supply. It includes savings deposits, time deposits and other

deposits.

Nomura comments: Quasi money is a good leading indicator of China‟s industrial

production and performed better than M2 in our testing.

Pros:

It enjoys a lead time of six months.



Figure 38).

It is sensitive to changes in monetary policy and hence is a good leading

indicator of business cycles.

Cons:

It is influenced by inflation while other inputs to our composite leading

indicator are real variables, without a price effect.

Fig. 38: IP growth and quasi money growth


Fig. 39: Nomura CLI and quasi money growth


10

14

18

22

26

30

0

5

10

15

20

25

Sep-00 Sep-03 Sep-06 Sep-09 Sep-12


Quasi money (6m lead), rhs

10

14

18

22

26

30

95

97

99

101

103

105


% y-o-yIndex

NCLI Quasi money, rhs


25

Shanghai Composite (SHCOMP)

Importance:

Compiled by: Shanghai Stock Exchange.

Frequency: Daily.

Release date: Market close on every business day.

Availability: 19 December 1992.

Compilation methodology: SHCOMP is a weighted stock index covering all listed

stocks on the Shanghai Stock Exchange.

Nomura comments: The year-on-year rate of change of the SHCOMP is a good

leading indicator of China‟s industrial production.

Pros:

It enjoys a lead time of one month.

It has a proven track record in predicting turning points of industrial


Figure 40).

It is sensitive to policy changes.

Cons:

It is volatile and has sometimes given a false signal.

Fig. 40: IP growth and SHCOMP


Fig. 41: Nomura CLI and SHCOMP


-80

-16

48

112

176

240

0

5

10

15

20

25



SHCOMP (1m lead), rhs

-80

-16

48

112

176

240

95

97

99

101

103

105


% y-o-yIndex

NCLI SHCOMP, rhs


26

Shanghai stock market turnover

Importance:

Compiled by: Shanghai Stock Exchange.

Frequency: Daily.

Release date: Market close every business day.

Availability: 5 October 1998.

Compilation methodology: Shanghai stock market turnover refers to the value of

turnover on the Shanghai Stock Exchange during the day.

Nomura comments: The year-on-year rate of change in stock turnover is a good

leading indicator of China‟s industrial production.

Pros:

It enjoys a lead time of four months.



A sudden change in turnover sometimes reflects policy change.

It is available on a daily basis, hence is a timely indicator.

Cons:

Turnover is volatile and it sometimes gives false signals.

Fig. 42: IP growth and growth of Shanghai stock turnover


Fig. 43: Nomura CLI and growth of Shanghai stock turnover


-100

120

340

560

780

1,000

0

5

10

15

20

25

Jul-00 Jul-03 Jul-06 Jul-09 Jul-12


Shanghai Stock Turnover (4m lead), rhs

-100

120

340

560

780

1,000

95

97

99

101

103

105


% y-o-yIndexNCLI Shanghai Stock Turnover, rhs


27

Gap between PPI output and input price inflation

Importance:


Frequency: Monthly.



Compilation methodology: The gap between PPI output and input prices is the

difference between the producer price index and purchasing price index.

Nomura comments: The gap between PPI output and input prices is a good leading

indicator of China‟s industrial production.

Pros:



Cons:

Its performance as a leading indicator is not stable, missing some industrial

production growth turning points in the early 2000s.

Fig. 44: IP growth and the gap between output and input price index


Fig. 45: Nomura CLI and the gap between output and input price index


-6

-4

-2

0

2

4

0

5

10

15

20

25


%% y-o-y Industrial production

Gap between output and input price inflation (6m lead), rhs

-6

-4

-2

0

2

4

95

97

99

101

103

105


% Index NCLI

Gap between output and input price inflation, rhs


28

Yield spread

Importance:


Frequency: Monthly.

Release date: Month-end.


Compilation methodology: Yield spread is the difference between the 3yr and 6m

China sovereign bond yields.

Nomura comments: The yield spread is a good leading indicator of China‟s industrial

production.

Pros:

It enjoys a lead time of six months.



It partly reflects monetary policy‟s effect on market liquidity.

Cons:

It is quite volatile and therefore sometimes gives a false signal.

Its record as a leading indicator is less tested than others‟ as it has only been

available since 2003.

Fig. 46: IP growth and yield spread


Fig. 47: Nomura CLI and yield spread


0

32

64

96

128

160

0

5

10

15

20

25


bp% y-o-yIndustrial production Yield spread (6m lead), rhs

0

32

64

96

128

160

95

97

99

101

103

105


bpIndexNCLI Yield spread, rhs


29

Economic Climate Index (ECI) - Leading index

Importance:

Compiled by: China Economic Monitor and Analysis Center, under the NBS.

Frequency: Monthly.



Compilation methodology: The ECI leading index is composed of eight sub-

components: industrial production/sales ratio, the HSBC mainland freeload index, new

investment projects, interest rate differential, logistics index, consumer expectation

index, M2 money supply growth and real estate development leading index.

Nomura comments: The ECI leading index is a good leading indicator of China‟s

industrial production activities.

Pros:

It has a proven track record, successfully predicting the turning points of

industrial production (e.g., in November 2008 and November 2009 (Figure

48).

It enjoys a lead time of one month.

Cons:

The methodology of this index is not highly transparent. Some of its

components (such as the logistics index) are not publicly available.

Fig. 48: IP growth and ECI leading index


Fig. 49: Nomura CLI and ECI leading index


96

98

100

102

104

106

0

5

10

15

20

25


Index% y-o-y


ECI leading index (1m lead), rhs

96

98

100

102

104

106

95

97

99

101

103

105


IndexIndexNCLI ECI leading index, rhs


30

OECD China Leading Indicator (CLI)

Importance:

Compiled by: Organisation for Economic Cooperation and Development.

Frequency: Monthly.

Release date: Day 10-20 on the second month after month-end (or approximately 40-

50 days after month-end).


Compilation methodology: The OECD CLI is a composite leading index composed

of seven components: production of chemical fertilizer (volume); M2 money supply

(RMB); production of manufactured crude steel (volume); 5000 industrial enterprise

diffusion index – overseas orders level (%); production of buildings (m2); production of

motor vehicles (volume); and the turnover value on the Shanghai Stock Exchange.

Nomura comments: The OECD leading indicator is a good leading indicator of

China‟s industrial production activity.

Pros:

It has a proven track record that has successfully predicted the turning points

of industrial production (e.g., in November 2008 and November 2009; Figure

50).

It enjoys a lead time of two month.

Cons:

The release is not very timely, lagging by two months.

Fig. 50: IP growth and OECD CLI


Fig. 51: Nomura CLI and OECD CLI


92

94

96

98

100

102

104

0

5

10

15

20

25

May-00 May-03 May-06 May-09 May-12

Index% y-o-y Industrial production

OECD CLI (2m lead), rhs

92

94

96

98

100

102

104

95

97

99

101

103

105


IndexIndexNCLI OECD CLI, rhs


31

OECD total leading index

Importance:

Compiled by: Organisation for Economic Cooperation and Development.

Frequency: Monthly.

Release date: Day 10-20 of the second month after month-end (or approximately 40-

50 days after month-end).


Compilation methodology: The OECD total leading index is a composite leading

index covering all OECD countries and six emerging markets.

Nomura comments: The OECD total leading index shows some leading relationship

with China‟s export growth, as it is a measure of external demand.

Pros:

The index provides a reliable measure of external demand for China‟s

exports.

Cons:

The release of the OECD total leading index is not particularly timely, lagging

by about two months.

The index does not cover many emerging markets that have become

increasingly important as China‟s trading partners.

Fig. 52: IP growth and OECD Total leading index


Fig. 53: Nomura CLI and OECD Total leading index


95

96

97

98

99

100

101

102

0

5

10

15

20

25


Index% y-o-y

Industrial production OECD-total, rhs

95

97

99

101

103


IndexNCLI OECD-total


32

Official Purchasing Managers Index (PMI):

Importance:

Compiled by: National Bureau of Statistics, China Federation of Logistics and

Purchasing.

Frequency: Monthly.

Release date: First day after month-end.


Compilation methodology: The official manufacturing PMI is a diffusion index based

on a survey of purchasing managers in the manufacturing sector. A reading above 50

indicates an expansion of manufacturing activity, while a reading below 50 suggests a

contraction. The index is composed of five subcomponents: production (weight: 25%),

new orders (30%), employment (20%), supplier delivery time (15%) and inventory

(10%).

Nomura comments: The official PMI is arguably one of the best monthly leading

indicators for China‟s economy.

Pros:

It has a proven track record and successfully predicted turning points of

industrial production in November 2008 and November 2009 (Figure 54).

It is available before other leading indicators, as each month‟s PMI is

released on the first day after month-end.

Cons:

The official PMI has a strong seasonality (Figure 54). A seasonally adjusted

PMI is more meaningful for economic analysis (page 35).

Its track record is constrained by its short sample (starting from 2005).

Fig. 54: IP growth and Official PMI


Fig. 55: Nomura CLI and Official PMI


40

44

48

52

56

60

0

5

10

15

20

25


Index% y-o-y

Industrial production Official PMI, rhs

40

44

48

52

56

60

95

97

99

101

103

105


IndexIndexNCLI Official PMI, rhs


33

Official PMI, seasonally adjusted (sa)

Importance:


Frequency: Monthly.

Release date: Day after month-end.


Compilation methodology: We seasonally adjust the official PMI ourselves, which

allows us to strip out the effect of repetitive events on the official PMI. The seasonally

adjusted PMI is still a diffusion index, so a reading above 50 also indicates an

expansion of manufacturing activity while a sub-50 reading suggests a contraction.

Nomura comments: The official PMI (sa) is also one of the best monthly leading

indicators for China‟s economy.

Pros:



It is available before other leading indicators, as each month‟s PMI is

released on the first day of the following month.

With regular seasonal influences on the PMI stripped out, the official PMI (sa)

can better grasp potential economic dynamics.

Cons:

Its track record is constrained by its short sample (starting from 2005).

Fig. 56: IP growth and PMI (sa)

Source: CEIC, WIND and Nomura Global Economics Estimates.

Fig. 57: Nomura CLI and PMI (sa)

Source: CEIC, WIND and Nomura Global Economics Estimates.

40

44

48

52

56

60

0

5

10

15

20

25


Index% y-o-y

Industrial production PMI (sa), rhs

40

44

48

52

56

60

95

97

99

101

103

105


IndexIndexNCLI PMI (sa), rhs


34

Sales revenue growth of industrial enterprises

Importance:


Frequency: Irregular release (often at three-month interval) over the period 2007-10.

Monthly releases resumed in January 2011.



Compilation methodology: Sales revenue growth of industrial enterprises is the

cumulative revenue growth during the period. Industrial enterprises refer to those

above a designated size, with the new statistical criteria for main business revenue

increasing from RMB5m to RMB20m since January 2011.

Nomura comments: Sales revenue growth of industrial enterprises is a good

indicator of China‟s economic activity.

Pros:

The data covers the whole industrial production sector.

Cons:

The release of sales revenue of industrial enterprises lags behind the

industrial production series.

It does not always lead the industrial production. In 2009 its rebound lagged

the recovery of industrial production.

Fig. 58: IP growth and sales revenue of industrial enterprises


Fig. 59: Nomura CLI and sales revenue of industrial enterprises


-10

0

10

20

30

40

0

5

10

15

20

25


% y-o-y, ytd% y-o-y Industrial production

Sales revenue of industrial enterprises, rhs

-10

0

10

20

30

40

95

97

99

101

103

105


% y-o-y, ytdIndexNCLI

Sales revenue of industrial enterprises, rhs


35

Electricity production

Importance:


Frequency: Monthly.



Nomura comments: It is a good indicator to cross-check with macro activity indicators.

Automobile production

Importance:

Compiled by: China Association of Automobile Manufacturers.

Frequency: Monthly.

Release date: Usually during day 10-15 after month-end.


Nomura comments: It is a good indicator to cross-check with macro activity

indicators.

Fig. 60: IP growth and electricity production growth


Fig. 61: IP growth and automobile production growth


-10

-2

6

14

22

30

0

5

10

15

20

25



Electricity production, rhs

-20

8

36

64

92

120

0

5

10

15

20

25

Jun-00 Jun-03 Jun-06 Jun-09 Jun-12


Automobile (3m lead), rhs


36

Steel production

Importance:


Frequency: Monthly.




indicators.

Chemical fiber production

Importance:


Frequency: Monthly.




indicators.

Fig. 62: IP growth and steel production growth


Fig. 63: IP growth and chemical fiber production growth


-20

-6

8

22

36

50

0

5

10

15

20

25



Steel production (1m lead), rhs

-10

2

14

26

38

50

0

5

10

15

20

25



Chemical fiber production (1m lead), rhs


37

10 non-ferrous metals production

Importance:


Frequency: Monthly.




indicators.

Cement production

Importance:


Frequency: Monthly.




indicators.

Fig. 64: IP growth and 10 non-ferrous metals production growth


Fig. 65: IP growth and cement production growth


-20

-8

4

16

28

40

0

5

10

15

20

25



10 non-ferrous metals production (1m lead), rhs

0

6

12

18

24

30

0

5

10

15

20

25

Jun-00 Jun-03 Jun-06 Jun-09 Jun-12


Cement production (3m lead), rhs


38

Cloth production

Importance:


Frequency: Monthly.



Nomura comments: It is a good indicator to cross check with macro activity indicators.

Yarn production

Importance:


Frequency: Monthly.




Fig. 66: IP growth and cloth production growth


Fig. 67: IP growth and yarn production growth


-10

0

10

20

30

40

0

5

10

15

20

25



Cloth production (1m lead), rhs

-10

0

10

20

30

40

0

5

10

15

20

25

May-00 May-03 May-06 May-09 May-12


Yarn production (2m lead), rhs


39

Chemical fertilizer production

Importance:


Frequency: Monthly.



Compilation methodology: The NBS releases about 72 major industrial production

data every month.

Nomura comments: It is part of the OECD China Leading Indicator.

Pros:

It has been available since 1990 and has a long track record as a good

leading indicator.

Cons:

It is volatile and has strong seasonality.

The importance of the agriculture sector is declining in China‟s economy.

Metal cutting machinery production

Importance:


Frequency: Monthly.




Fig. 68: IP growth and chemical fertilizers production growth


Fig. 69: IP growth and metal cutting machine production growth


-10

0

10

20

30

40

0

5

10

15

20

25

Aug-00 Aug-03 Aug-06 Aug-09 Aug-12

% y-o-y% y-o-y


Chemical fertilizer (5m lead), rhs

-30

-12

6

24

42

60

0

5

10

15

20

25



Metal cutting machine, rhs


40

Recent Asia Special Reports

Date Report Title

16-Apr-12 Korea: Uncomfortable trade-off

11-Apr-12 India: Four cyclical tailwinds to watch

27-Mar-12 Capital account liberalisation in China

9-Mar-12 India budget preview: Fiscal cheer

1-Mar-12 Asia: What if oil prices keep rising?

23-Feb-12 Philippines – Fiscal space to maneuver

16-Jan-12 Decoding India‟s stubbornly high inflation

20-Dec-11 Implications from North Korea

18-Nov-11 A cold winter in China

3-Nov-11 Thailand: Dealing with another disaster

31-Oct-11 China Risks

19-Oct-11 Korea: Falling, converging bond yields

21-Sep-11 China: The case for structurally higher inflation

8-Aug-11 Global market turbulence: Implications for Asia

7-Jun-11 Indonesia: Building momentum

10-Mar-11 Vietnam: Prioritizing macro stability

3-Mar-11 South Korea‟s demographic sweet spot

14-Jan-11 India's 2011 outlook: Rising symptoms of a supply-constrained economy

1-Nov-10 The case for capital controls in Asia

11-Sep-10 The coming surge in food prices

6-Aug-10 Another step towards becoming an offshore RMB centre

28-May-10 The heat is on

26-May-10 Brinkmanship returns to the Korean peninsula

9-Nov-09 China: Not just an investment boom

19-Oct-09 What Japan‟s 1980s experience means for China

8-Sep-09 South Korea: Household debt: Myths and reality

23-Jul-09 China & Hong Kong: RMB trade settlement: New opportunities, new risks

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41

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