Download Emerging markets` alpha beta soup

The Invesco White Paper Series — Invesco Fixed Income
June 10, 2016
Emerging markets’
alpha beta soup
Executive summary
Arnab Das
Head of Emerging Markets
Macro Research
Invesco Fixed Income
Rashique Rahman
Head of Emerging Markets
Invesco Fixed Income
How much opportunity is there to generate alpha in emerging markets (EM) fixed income? In this
study we attempt to break down the drivers of returns across different EM segments into systematic
and idiosyncratic factors to determine what proportion of returns is “beta” driven (systematic factors)
and what portion represents opportunity for “alpha” generation (idiosyncratic factors). We find that
constituent returns across EM asset classes tend to be driven primarily by common — that is,
systematic — as opposed to idiosyncratic factors, although this varies across time and asset classes.
These results tie directly into our analytical framework and our investment process. Assessing the
direction and level of systematic risk, or beta, is crucial, in our view, in calibrating risk appropriately
in portfolios, given that these macro (high-level) risks will tend to be the key determinants of portfolio
performance. However, we believe having the right bottom-up, idiosyncratic view can substantially
enhance returns in a portfolio that is appropriately oriented to common global macro ‘beta’ factors.
Key takeaways
•We utilize two methods — principal component analysis (PCA), and regression analysis — to gauge
the extent of ‘beta-ness’ of EM asset classes.
•Contrary to what may be presumed, we find that EM corporate credit exhibits the highest level
of systematic influence on returns over time. On average, nearly 70% of the variation in EM
corporate credit constituent market returns is driven by common — not idiosyncratic, name-specific
factors.
•In contrast, EM equities and EM duration (local government bonds) are the least systematically
driven; but even in these two asset classes, we find that about half of returns are explained by
common global macro factors.
•Another important result is that the influence of systematic factors, regardless of analytical
method, varies considerably over time. Therefore, idiosyncratic factors do have major influence
at times on market outcomes — though not on average.
•What explains these results? Broadly speaking, we believe the more globally integrated the EM
asset class, the greater the role of systematic factors in influencing returns; the less integrated,
the greater the potential for idiosyncratic factors to influence returns.
•Our results suggest that constructing a market view on EM asset classes requires, first and
foremost, a thorough understanding of top-down macro factors, followed by EM country macro
analysis, complemented by bottom-up single name credit analysis. And, there will be times of
transition, when these factors change places.
Jay Raol
Senior Macro Analyst
Invesco Fixed Income
This document is intended only for Professional Clients and Financial Advisers in Continental Europe, for Professional Clients
in Dubai, Guernsey, Ireland, the Isle of Man, Jersey and the UK; in Hong Kong for Professional Investors, in Japan for Qualified
Institutional Investors; in Switzerland for Qualified Investors; in Taiwan for certain specific Qualified Institutions/Sophisticated
Investors only; in Singapore for Institutional/Accredited Investors, in New Zealand for persons who are not members of the
public (as defined in the Securities Act), and in Australia, and the USA for Institutional Investors. In Canada, the document is
intended only for accredited investors as defined under National Instrument 45-106. It is not intended for and should not be
distributed to, or relied upon, by the public.
Two methodologies
We utilize two methods — principal component analysis (PCA), and regression analysis — to gauge
the extent of ‘beta-ness’ of EM asset classes. The ‘beta-ness’ of a market is the extent to which
common factors explain the variation in returns within an asset class — in other words, the sensitivity
to these common, global factors. It is the systematic component of returns. Principal component
analysis (PCA) calculates the percentage of co-variation in returns for each asset class to ascertain
the percentage of returns which are derived from common factors. The drawback of this approach
is that the exact factors influencing returns are not known, nor are any changes in the drivers over
time. However, PCA does reveal to what extent returns are influenced by systematic — as opposed
to idiosyncratic — factors.
We compare the above PCA to a regression-based approach, which determines the extent of return
variation explained by a predetermined set of market factors. We derive the R-squared, or
explanatory power, of a regression using a set of independent variables widely used to represent
each market factor against each individual asset class return. The drawback of this approach is that
not all systematic components of returns may be represented; it depends on which metrics are
included. That is, this approach can significantly understate the influence of common factors. This
flaw becomes more serious if the systematic components of return vary over time — a problem from
which PCA does not suffer.
Using PCA, Figure 1 shows the extent to which common factors drive returns across EM asset
classes. They range from EM corporate credit on the high side at 68% — to EM local duration
and EM equity on the low side at 51%.
Figure 1: Principal components analysis — systematic component of returns
(period average, Q1/04 – Q1/16)
%
80
Sovereign credit
Local currency
Local duration
70
60
50
64
Corporate credit
Equity
68
60
51
51
40
30
20
10
Source: Invesco, data from 9 January 2004 to 11 March 2016. Sovereign credit is represented by constituents of the
JPMorgan EMBI Global Diversified Index. Local currency is represented by spot currencies, data from Bloomberg L.P. Local
duration is represented by constituents of the JPMorgan GBI-EM Broad USD Hedged Index. Corporate credit is represented
by constituents of the JPMorgan CEMBI Broad Diversified Index. Equity is represented by the MSCI EM USD Hedged Index.
2
The regressions yield much less explanatory power for common factors than the PCA, as shown in
Figure 2. Notably, EM currency displays the least ‘beta-ness’ of all asset classes by this methodology.
In all likelihood, the larger influence of idiosyncratic factors implied by the regressions reflects the
choice of independent variables.
Figure 2: Regression analysis — systematic component of returns
(period average, Q1/04 – Q1/16)
%
50
Sovereign credit
40
41
Local currency
Local duration
Corporate credit
Equity
46
33
30
23
20
15
10
Source: Invesco, data from 9 January 2004 to 11 March 2016. Sovereign credit is represented by constituents of the
JPMorgan EMBI Global Diversified Index. Local currency is represented by spot currencies, data from Bloomberg L.P. Local
duration is represented by constituents of the JPMorgan GBI-EM Broad USD Hedged Index. Corporate credit is represented by
constituents of the JPMorgan CEMBI Broad Diversified Index. Equity is represented by the MSCI EM USD Hedged Index.
Adding more variables, however, would not necessarily increase explanatory power significantly and
consistently. After all, systematic risks in the global markets change rapidly. And EM economies and
financial systems are constantly evolving and integrating into global markets. Some of these changes
would increase exposure to common factors; others would reduce such exposure; yet others would
change the key factors, systematic or idiosyncratic.
Indeed, another important result is that the influence of systematic factors, regardless of method,
varies considerably over time. As Figure 3 demonstrates, in the case of EM equities, for example,
using PCA, we can see that the influence of common factors has been as low as 30% and as high
as 70% over the last decade. Therefore, idiosyncratic factors do have major influence at times on
market outcomes — though not on average.
Figure 3: Principal components analysis — systematic component of return over time
• Corporate credit • Sovereign credit • Local duration • Local currency • Equity
%
100
1/04
1/06
1/08
1/10
1/12
1/14
1/16
80
60
40
20
Source: Invesco, data from 9 January 2004 to 11 March 2016. Sovereign credit is represented by constituents of the
JPMorgan EMBI Global Diversified Index. Local currency is represented by spot currencies, data from Bloomberg L.P. Local
duration is represented by constituents of the JPMorgan GBI-EM Broad USD Hedged Index. Corporate credit is represented
by constituents of the JPMorgan CEMBI Broad Diversified Index. Equity is represented by the MSCI EM USD Hedged Index.
3
A question of global financial integration
What explains the differences in systematic influence across EM asset classes? Broadly, across the two
methods, our results tell a similar story — the more globally integrated the EM asset class, the greater
the role of systematic factors in influencing returns; the less integrated, the greater the potential for
idiosyncratic factors to influence returns. Indeed, the results are consistent with the trend of increased
exposure to the EM of advanced, or developed, markets (DM). Figure 4 demonstrates the increased
exposure of DMs to the EM. We find that the role of global macro factors is consistently the greatest in
credit sectors, followed by currencies — and the least in EM local government bonds and equities. This
finding is consistent with the fact that EM credit, being a largely US dollar-based, homogeneous asset
class, is not only highly integrated but also the most integrated of EM asset classes. This commonality
applies to both EM corporate and sovereign credit, which share similar “beta-ness.”
Figure 4: DM financial exposure to EM has risen over time, led by bonds
Bond allocation
USD
2005
Trillion
1.2
2006
2007
2008
2009
2010
2011
2012
2013
2014
1.0
0.8
0.6
0.4
0.2
Exposure to EM
• 1997 • 2005 • 2014
% of
GDP
10
Portfolio
Banks
8
6
4
2
Source: International Monetary Fund Global Stability Report, April 2016.
Currencies lie in the middle, reflecting their role in many kinds of transactions, including cross-border
portfolio and direct investment flows, income and dividend flows as well as economic transactions in
goods and services trade, including tourism. Thus, all types of economic and financial actors —
governments, banks, foreign and domestic firms and individuals - use currency. In most economies,
the bulk of such activity is in wholesale flows — outright foreign exchange trading, investment and
international trade. Such use exposes currencies to global beta factors; but transactions by
individuals for remittances or tourism create some insulation against global factors, being driven,
for example, by seasonality and geography.
4
EM local bonds and equities are the least integrated, given their varied jurisdictions and the
heterogeneous means of investing in these markets. Furthermore, resident investors and market
participants, including national central banks, domestic corporate or investment banks and
institutional investors tend to loom larger than non-residents in domestic debt and equity markets.
Note, however, that the role of different categories of market participant varies over time and across
countries, with respect to the relative market shares of foreign and domestic market participants in
both portfolio flows and overall exposures.
Country-specific return drivers, of course, can represent both beta and alpha factors, and we will
follow up in forthcoming pieces with work on the relative weights of beta and alpha across EM
country returns. A country that is more highly geared to global growth or inflation factors, or risk
aversion/risk appetite drivers, will likely be driven by beta more than alpha relative to a more closed
economy. Thus, small or open EM economies whose business and financial cycles are commodity
dominated will tend to be much higher beta than more isolated EM economies, even those that are
similar in terms of income levels or financial development.
Finally, policy can make a major difference, overcoming structural or geographic features — but only
up to a point. A small, resource-rich or otherwise trade-heavy economy with a relatively small
population will tend to be dominated by global commodity and growth/inflation cycles; such features
are very difficult to change (for example, Ecuador) but policies that encourage savings and the
accumulation of precautionary official reserves of private foreign assets can help cushion cyclical
shifts and beta (for example, Singapore or Chile).
On the other hand, high reliance on cross-border portfolio capital flows and financial markets can
render even a relatively large, diversified economy with modest trade exposures heavily exposed to
global financial conditions and thereby, a high-beta country (for example, Brazil or Russia). A large
closed but high-deficit country would be high beta, but through external, fiscal/monetary and
structural adjustment can reduce exposure to global macro factors and risks (for example, India from
2013–14/15).
The overall framework of policies across countries can also make a major difference. Many EM
countries have moved from fixed exchange rates mainly against the dollar (which effectively limited
their monetary policy freedom) toward managed floats with inflation targeting (which means that
currencies now act as the main economic shock absorber, rather than interest rates, allowing central
banks much greater autonomy to set interest rates).
These changes in the typical EM macro policy regime imply shifts in the degree of systematic
influence across asset classes. When exchange rates were fixed, traded in corridors or in crawling
pegs, currencies would not adjust to external shocks — at least not initially, and usually not until
severe pressures had accumulated. Instead, the size of central bank balance sheets and domestic
interest rates would respond to global trade and capital flows. The financial and economic logic
underlying this result strongly suggests that this phenomenon will continue, maintaining the rank
order of “beta-ness” across asset classes, amid variability.
5
Gauging macro (high-level) risk — how we integrate the approach
The above results tie directly into our analytical framework and our investment process. We start
with the fact that top-down global macro trends tend to dominate bottom-up country-specific factors
in driving excess returns. Put another way, on average, we believe that having the right EM country
or credit (idiosyncratic or alpha) view will not save the performance of an EM investment portfolio
that is constructed with an inappropriate sensitivity to ‘common’ (systematic) factors. Assessing the
direction and level of systematic risk, or beta, to these risk factors, is crucial, in our view, in
calibrating risk appropriately in portfolios, given that these macro (high-level) risks will tend to be
the key determinants of portfolio performance.
We believe having the right bottom-up, idiosyncratic view can substantially enhance returns in a
portfolio that is appropriately oriented to common global macro ‘beta’ factors. We also believe the
uncertainty and variability in the weights of systematic and idiosyncratic factors in explaining returns
calls for complementing top-down with bottom-up analysis.
As such, as part and parcel of our process, we continuously assess the extent to which common
factors are driving asset returns. This allows us to calibrate the extent of ‘beta-ness’ we wish to
represent in our portfolios. This, as we have seen, varies across strategies, depending on whether
it is credit or local currency, for example.
Furthermore, we take active views on common factors that tend to influence market directionality.
We believe this provides us with greater scope to appropriately gauge the extent of macro risk
prevalent within our portfolios.
We then place EM country and single-name analysis within this beta framework. This approach
enables us to focus our analysis on specific cases where a bottom-up narrative can generate alpha,
even in the context of the beta view.
In forthcoming pieces, we will 1) seek to quantify the influence of alpha and beta in driving excess
returns at the country level; and 2) compare the “beta-ness” of EM against other asset classes as
well as consider the impact of sector-level beta factors in EM corporate credit returns.
Notes
1The independent variables used in the regression are the S&P 500 index, US 10-year yield, VIX Index, EUR/USD exchange
rate, Bloomberg Commodity Index, Barclays High Yield Spread Index, Citi Developed Market Growth Surprise Index of
G10 and Citi Developed Market Inflation Surprise Index of G10. The Citi Surprise Growth Index tracks the data releases
pertaining to real growth of the major economies against economist expectations. A large negative number represents
economic data that has come in below economist expectations. The Citi Surprise Inflation Index tracks the data releases
pertaining to inflation of the major economies against economist expectations. A large negative number represents
economic data that has come in below economist expectations.
2IMF Global Financial Stability Report, April 2014.
6
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This document is intended only for Professional Clients and Financial Advisers in Continental Europe, for Professional Clients in Dubai, Guernsey, Ireland, the Isle
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