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In this paper we present ideas on how to derive signals from
national sector PMI data and use these in active equity investment
strategies for both Japan and the US.
Following on from our previous papers, our analysis is again
based on an investment approach that a) derives sector signals from
the perspectives of momentum and relative performance and b) uses a
naïve strategy where funds are invested into those categories with
the strongest signals.
Results are once again positive, with signals from the PMI data
generating excess returns compared to a naïve benchmark strategy in
a coherent and consistent fashion.
The paper proceeds as follows.
Sections 1 and 2 provide the background to the PMI datasets and
the methodology that we employ. Readers already familiar with our
earlier work are welcome to skip these two sections, though may
find them useful as a refresher.
Section 3 looks at the modelling performance of both US and
Japanese national sector PMIs. Section 4 summarises.
1. Background
Available for the world's largest developed and emerging
economies, Purchasing Managers' Index™ (PMI™) surveys for the
manufacturing, services and construction sectors are widely viewed
by economists, policymakers, and financial market participants as
key benchmark indicators of economic conditions.
The data are derived from questionnaires sent to senior company
decision-makers, with respondents asked to state whether variables
such as output, orders, prices, and inventories have improved,
deteriorated, or stayed the same compared with the previous
month.
From the aggregate results of these questionnaires, so-called
diffusion indexes are then calculated for each variable (the
percentage of positive responses plus half the percentage of
neutral responses). These indexes vary between 0 and 100 with
levels of 50.0 signalling no change on the previous month. Readings
above 50.0 signal an improvement or increase on the previous month.
Readings below 50.0 signal a deterioration or decrease on the
previous month. The greater the divergence from 50.0 the greater
the rate of change signalled.
Given the extensive global coverage of our national PMIs - over
28,000 companies are polled each month across more than 40
countries - IHS Markit has in recent years created broad sector PMI
data for four regions: Global, Europe, Asia, and the US.
However, further interrogation of the datasets has recently led
to the development of new sector PMI indices for constituent
European, Asian, and emerging market (EM) nations - as well as more
granular US sectors.
Derived from IHS Markit's national PMI survey panel data, these
sectors are structured according to legacy Markit Sector
classifications, a 5-tier structure based on stock market sectors
and covering 3 out of 5 tiers (Tiers 2-4).
The datasets can create insight into sector profitability and
offer tools for investment strategy and asset allocation by
providing monthly indicators of business trends across variables
such as output, order books, prices, inventories, and employment
for eight major industry groups including:
Basic Materials
Consumer Goods
Consumer Services
Financials
Healthcare
Industrials
Technology
Telecommunication Services
A wide range of detailed sectors and subsectors of those groups
is also available.
2. Methodological Approach
Rather than taking a typical approach that uses pure diffusion
index data to compare sector performance, an alternative - and in
some respects more intuitive - method based on a simple PMI
z-scoring system can be employed.
Assume that we decide to use a single PMI index to score the
relative performance of a sector, and that performance is defined
as the one-month change in the index. This focus on the one-month
change is important as it allows us to measure relative sector
performance from the perspective of momentum.
Indeed, consider an individual sector i for a time-period, t,
with this change in the index value compared against the average
change across all sectors i = (1, 2, … N) during time-period,
t.
Dividing this difference by the standard deviation of all sector
changes will subsequently provide a z-score for sector i as shown
in the equation below:
Repeating this process for N sectors provides the basis for
comparison: those sectors with the higher z-scores will have the
strongest momentum and as such could be viewed as having a 'buy'
signal.
However, rather than using a single index to gauge sector
momentum, a range of PMI sub-index data and various transformations
to these sub-indices may be preferable.
For instance, a modeller may wish to take a view of both
short-term data trends - such as changes in order books, current
pricing power etc - but also look at slightly longer-term
influences on future financial performance such as capacity
constraints.
This approach allows the modeller to then build up a much more
rounded, holistic view of sector health and relative momentum
compared to just using a single index.
3. Case Studies: US and Japan
Having successfully employed these methods with EU data, we
chose to further test our theories with national PMI data for two
of the world's largest economies: Japan and the United States.
We again sought intuitive derived barometers of company
financial health from the PMI datasets, armed with the belief that
these would have the greatest potential for creating positive
investment signals. These include:
Three-monthly change in output prices (pricing power)
Difference between three-monthly changes in output prices and
input costs (margins)
Three-monthly change in new orders (demand growth)
Difference between latest backlogs of work index and 12-month
average (capacity constraints).
Difference between latest output charges index and 12-month
average (historical pricing power)
To stress, given the range of data that are available, there are
many permutations with regard to combinations of transformations
and sub-indices that could be deployed.
Nonetheless, with a hypothesis that high z-scores should
encourage higher returns for a particular sector, we separately
downloaded individual company equity data from Refinitiv for both
Japan and the US.
We then equally weighted market return proxies that broadly
matched up with eleven 'tier 3' national PMI sectors for the US and
six 'tier 2' PMI sectors for Japan.
Analysis covered the period since 2011 for the United States and
since 2009 for Japan (these starting dates simply reflect
differences in data availability for the respective nations).
We again assume that investors would initiate any changes to
their portfolios within a day of receiving PMI data from IHS
Markit. Based on historical release dates - that are readily
available to users of the data - we generated a time series of
returns for each sub-sector for holding periods of one-, three- and
six- months respectively.
To test our hypothesis that higher z-scores are associated with
'buy' signals, a simple approach to investment strategy was
considered: for any positive signal for PMI sector(s) - defined as
z-scores > 1 - then our portfolio would be fully tilted towards
those sector(s).
Performance of our strategy is measured against a baseline model
that simply weights a portfolio across all sectors where we seek to
a) generate excess returns and b) consistently succeed on a 'per
event' basis.
That is, out of the times (or events) that we observed 'buy'
signals for specific sectors, how often in percentage times did
this strategy outperform the passive, equal-weighted approach?
a) United States
When analysing the potential of different strategies in creating
investment signals across US equities markets, we tried several
weighting combinations and found that charges and margins data had
strong short-term influences in generating positive excess
returns.
Indeed, the following mix margins (60%), charges (20%) and input
prices (20%) worked particularly well. When compared against the
benchmark of an equally weighted combination across all tested
sectors, our active investment strategy was able to generate excess
returns over the one-month, three-month and six-month holding
periods (see table 1).
Moreover, this active strategy garnered excess returns when
compared to an equal weighting combination in well over half of the
monthly 'events' that were observed during our ten-year analysis
period1.
b) Japan
Using the active strategy across a sample of Japanese equities
since January 2009, in contrast to the US we found that both new
orders and backlogs data were especially strong indicators for
generating excess returns.
Using a weighted combination of the new orders (75%) and
backlogs (25%) measures described in the methodology section, our
strategy generated superior returns for one-month, three-month and
six-month holding periods.
Moreover, in each month that PMI data provided a signal for
active investment, the active strategy outperformed the equal
weighted strategy well over 50% of the time, with the results for a
three-month holding period being particularly impressive2.
4. Summary
Revisiting previous work on European PMI data, we have extended
our ideas on how best to utilise national sector PMI data in active
investment strategies to two major global economies, namely Japan
and the United States.
Our analysis is again rooted in the belief that PMI sub-index
data can be transformed and combined to provide holistic overviews
of sector performance. These 'momentum' signals can then be used to
generate excess returns in a consistent manner when compared to a
simple passive strategy.
We would again stress that the degree of success will be
sensitive to the weighting and variable combinations employed and,
of course, the period of analysis that is being tested. It is
interesting to note, for instance, that the success of US
strategies was linked closely to developments in PMI price
variables, but for Japan, order book and backlogs data were
relatively more important.
Overall, we hope that the results provide a suitable baseline
for investors to further test and consider the potential of the
national sector PMI data in their strategies and research.
IHS Markit Sector PMI indices are currently available for the
constituent European, Asian, and emerging market (EM) nations, plus
the US, and trials of the data are available for interested
users.
Further information and details can be provided by emailing
economics@ihsmarkit.com
1 Out of the 120 months that covered our US analysis, there were
75 months or 'events' where the active investment strategy was
enacted.
2 For Japanese data, of over 140 months of data analysis, there
were just under 60 occurrences when the active investment strategy
was used.
Sian Jones, Economist, Economic Indices, IHS Markit
Purchasing Managers' Index™ (PMI™) data are compiled by IHS Markit for more than 40 economies worldwide. The monthly data are derived from surveys of senior executives at private sector companies, and are available only via subscription. The PMI dataset features a headline number, which indicates the overall health of an economy, and sub-indices, which provide insights into other key economic drivers such as GDP, inflation, exports, capacity utilization, employment and inventories. The PMI data are used by financial and corporate professionals to better understand where economies and markets are headed, and to uncover opportunities.
