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Active Management
The Changing Nature of
quity Mar ets an t eee or More Activeanagement
There are two sweet spots of activeequ ty management.
Understand. Act.
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Active Management
Content
Imprint
4 Hig er corre ations an ower vo ati ity– c a enges to active management
6 How to react?
6 How to increase t e eve of risi igent y
6 Measuring activity in a portfo io witactive s are
7 Hig er active s are trans ates intoig er returns
7 Concentrate stoc pic ers aniversifie stoc pic ers
8 How concentrate s ou concentratestoc pic ers e?
9 Cremers & Petajisto’s notion of a stocpic er vs. stoc pic er in factor-rismo e s
10 How to increase t e return per unitof ris i igent y
Allianz Global Investors
Europe GmbH
Bockenheimer Landstr. 42 – 44
60323 Frankfurt am Main
Global Capital Markets & Thematic Research
Hans-Jörg Naumer (hjn)
tefan Scheurer (st)
Dora Janikovszky
Data origin – if not otherwise noted:
Thomson Reuters Datastream
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Active Management
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The Changing Nature of EquityMarkets and the Need for More
Active Management
Understand. Act.
Low levels of volatility and high levels of
correlation translate today’s active portfolio
positions into lower tracking error risk, resul-
ting in lower expected alphas than in the past.
There are two principal ways to deal with this:
• Increase level of risk diligently• Increase the return per unit of risk
following the agenda outlined by the
fundamental law of active management
We at Allianz Global Investors have reacted
to the challenges posed by low volatility and
have increased the level of risk as measured
by the active share. We have also increasedthe return per risk by expanding the invest-
ment universe, the strategy set and the imple-
mentation set.
Higher correlations and lower volatility – challenges to activemanagement
Over the past 30 years, global active equity
anagers have generated substantial value
for clients, according to Mercer’s GIMD data-
ase. However, more recently, the pace of
outperformance has slowed significantly, and
at the end of 2013, the median global activeequity manager was trailing the benchmark
on a three- and five-year basis.
Andreas Utermann,Global CIO Allianz
Global Investors
Over the past 30 years, global active equity managers havegenerated substantial value for clients, according to Mercer’sGIMD database. However, more recently, the pace of out-performance has slowed significantly and there is a need formore active management.
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Furthermore, we have positioned our equity
portfolios to sit in the two sweet spots of
active equity management as highlighted by
Cremers & Petajisto [2010]:
• The concentrated stock picker successfully
delivering high alpha
• The diversified stock picker successfully
delivering stable alpha and high informa-tion ratio
Low volatility as the Global Financial Crisis
eases is one explanation for the headwinds
active managers are facing. Low volatility
has pushed down tracking errors and, conse-
quently, active returns.
In addition, high correlation and the resulting
low dispersion of equity returns have takentheir toll on active managers as low dispersion
means that there is less to gain from picking
the right stocks.
500
Performance MSCI World in USD (indexed) Relative performance
vs. MSCI World in USD
400
300
200
Dec-93
relative performance, median manager (rhs)
Dec-96 Dec-99 Dec-02 Dec-05 Dec-08 Dec-11
100
40 %
30 %
20 %
10 %
–10 %
0 %
0
MSCI World (lhs)
1.6 %p. a. relative
Figure 1: Active Equity Managers Have Generated Substantial Value for Clients over the
Long Run (Mercer Database)
Relative performance of global equity managers according to Mercer’s GIMD database
Source: Mercer, Allianz Global Investors
Data as of December 2013 Past performance is not a reliable indicator of future results. If the currency in which the past
performance is displayed differs from the currency of the country in which the investor resides then the investor should
e aware t at ue to t e exc ange rate uctuat ons t e per ormance s own may e g er or ower converte nto t e
investor’s local currency.
Risk Lever
Increase Risk Taking
Return per Risk Lever
Expand Investment Universe
Two ways to react to lower returns per risk: Increase risk, or increase return per risk.
Return per Risk Lever
Expand Strategy Set
Return per Risk Lever
Expand Implementation Set
3 41 2
High
High
Liquidity Profile
A c t i v e S h a r e / T r a c k i n g E r r o r
Tracking Error
130 / 30
InformationGain
Long-onlyConstraint
A c t i v e R e t u r n
Low
Low
All-CapMulti-Sector
Local
Geographic
A s s e t C l a s s / S e c t o r
Global
Single-Cap/Sector
Country Allocation
Sector Allocation
InvestmentStyles
Short TermTradingStrategies
TradingCosts
FundamentalCompany Research
MacroEconomicExposures
MarketTiming
Figure 2: Capability Levers for New Active Management
Source: CaseyQu 2013 ; A anz G o a Investors
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Active Management
6
How to increase the level of riskdiligently
The tracking error of a portfolio is the tradi-
tional measure used to gauge the level of
portfolio activity. However, there are some
weaknesses with this concept. The track-ing error of a portfolio depends not only on
the active decisions made by the portfolio
manager, but also on the overall level of mar-
ket volatility and the average correlation of
returns. As a result, the same level of active
decisions in a portfolio can translate into very
different tracking errors depending on overall
market volatility and correlations.
Measuring activity in a portfolio
with active share
Active share is an alternative measure togauge how active a portfolio manager really
is. It directly measures the degree of stock
picking activity in a portfolio as it is calculated
as the sum of all positive active single stock
weightings. This measure makes it possible to
quantify the level of stock picking activity in
a portfolio without any inference from mar-
ket volatility and stock correlations. The flip
side of this approach is that the active sharemeasure cannot reveal how diversified or how
undiversified these stock picks are.
To make things more complicated, correla-
tions after 2003 are significantly higher than
correlations before 2003. A possible explana-
tion for today’s greater correlations may be
found in the increase of institutionalisation
of the asset management business – i. e., the
use of commonly accepted sector definitions,common risk models, common cap-weighted
enchmarks, and, in particular, the rise of
indexing. This homogenisation of investor
ractice has led to a loss of diversity in stock
ehaviour and hence to an increase in cor-
elations.
As a result, although the relatively high level
of correlations may not only be a cyclical
henomenon, correlations can be secularly
igher due to the rise of institutionalisation.
How to react?
f past levels of portfolio activity and portfolio
isk deliver only compressed alphas instead
of the ample alphas of the past, we see two
rincipal ways to deal with this challenge:
• Increase the level of risk
• Increase the return per unit of risk
The following chapters will provide a detaileddiscussion of these two levers for enhancing
the proceeds from active management and
derive applicable practical implications for
day-to-day portfolio management.
Low 2 3 4 high
3.0
2.0
1.0
0.0
R e l a t i v e P e r f o r m a n c e
Quintiles of tracking errorQuintiles of active share
Low 2 3 4 high
R e l a t i v e P e r f o r m a n c e
3.0
2.0
1.0
0.0
Figure 3: Active Share matters.
ource: Cremers & Petajisto [2013], Data from 1/1990 – 12/2009, Allianz Global Investors
Past per ormance s not a re a e n cator o uture resuts.
elative Performance of US Equity Funds
y Active Share
Relative Performance of US Equity Funds
by Tracking Error
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Active Management
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Cremers & Petajistos analysis of US mutual
fund returns from the period of 1990 – 2003
also highlights a weak spot for active manag-ers, which is characterised by low active share,but high tracking error. This is a typical setting
for market timing strategies as low or high
beta portfolios can be constructed with rela-tively low active share, but will usually have a
high tracking error. The poor performance of
low-active-share, high-tracking-error portfo-
lios therefore reflects the poor empirical track
record of market-timing strategies.
Henriksson [1984]7 Coggin, Fabozzi and
Rahman [1993]; Daniel, et al. [1997]8 and
Blake, Lehmann and Timmermann [1999] all
found that fund managers were hardly able to
demonstrate market-timing capability10
How concentrated shouldconcentrated stock pickers be?
What is the optimal level of portfolio concen-
tration?
Generally, the higher the level of portfolio
concentration is, the higher the expected
return will be – for a skilful manager. Of
course, if portfolio concentration is pushedtoo far, portfolio volatility will spike at some
stage. However, a number of papers have
irst, there are the concentrated stock pick-ers. Their portfolios are characterised by avery high level of active share that reflects
their high level of stock picking activity.
Their portfolios are also characterised by a
igher tracking error that reflects their more
concentrated approach to stock selectionecause high-active-share, high-tracking-
error portfolios are concentrated stock picking
ortfolios that target high alphas – either in a
enchmark-relative core equity setting or in a
enchmark-agnostic unconstrained setting.
And there is also a second sweet spot, the
diversified stock picking approach. While theevel of stock picking activity as measured by
active share is quite similar for diversified as
ell as for concentrated stock pickers – activeshare is only moderately lower for diversified
stock pickers – both approaches differ in the
degree of diversification of single stock picks.
Diversified stock pickers take a much morediversified approach to stock selection and
uild portfolios with a high level of active
share, but a relatively lower tracking error.
These portfolios – being lower tracking-error
ortfolios – target stable alpha, not neces-
sarily the highest possible alpha, whereasconcentrated stock pickers explicitly targetthe highest alpha.
Figure 5: Number of Stocks Required to Achieve a 90 % Reduction of the Idiosyncratic Risk
ource: A exeev, V. & Tapon, F. 2013
80
Number of stocks
60
70
50
40
30
1975
average number of stocks to reach the risk reduction on average
1980 1985 1990 1995 2000 2005 2010
20
0
10
average number of stocks to reach the risk reduction with 90 % certainty
7 Tests examined the
performance of 116 open-
end mutual funds using
mont y ata rom Fe ru-
ary 1968 to June 1980. The
return data was obtained
from Standard & Poors.
8 Data use conssts oquartery equty o ngs o
all equity mutual funds that
existed between Decem-
ber 1974 and December
1994 provided by CDA
Investment Tec no og es.
9 Analysis is based on
monthly observations of
360 U.K. pension funds
rom 1986 to 1994 pro-
vided by the WM company.
10 Please note that there
is no guarantee that themp ementat on o any
nvestment strategy w
produce positive results.
During different market
conditions, different strate-
gies will perform better.
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9
shown that noticeable diversification benefits
can already be achieved by owning as few as
20 to 30 stocks. But looking at the average vol-
atility of concentrated portfolios disguises the
fact that the realised volatility of an individual
20-to-30-stock portfolio can be much higher.
More stocks are therefore needed to reliably
reduce the portfolio volatility. The chart below
demonstrates that forthe US that although it
takes about 20 to 30 stocks to reduce volatility
on average, it actually takes 40 to 60 stocks toreduce volatility reliably.
Therefore, the optimum level of portfolio con-
centration for concentrated stock pickers will
be in a range of 20 to 60 names, depending
on how important it is to reliably reduce the
diversifiable risk. There is no point for concen-
trated stock pickers to go beyond 60 stocks.11
Cremers & Petajisto’s notion of
a stock picker vs. stock picker infactor-risk models
Cremers & Petajisto’s work measures stockpicking activity in quite a different way than afactor-based risk model would. Stock pickers
as defined by Cremers & Petajisto are charac-
terised by a high level of active share, whereas
stock pickers in a risk model are character-
ised by a high level of idiosyncratic risk. As a
result, stock pickers as defined by Cremers &
Petajisto simply take a high number of bets intheir preferred stocks, irrespective of region,
sector or investment-style constraints.
tock picking in a factor-risk model is pretty
much the opposite of just picking preferred
stocks irrespective of region, sector or invest-
ment-style constraints.
It means picking the stocks one likes while at
the same time making sure to broadly match
the major factor risks of the benchmark, like
regions, sectors or investment styles – oth-
erwise the resulting portfolio would load up
too much factor risk to be classified as a stockpicking portfolio any longer. Stock picking in
the sense of a factor-risk model is a rather
narrow term that allows for stock picking only
in a rather constrained peer-to-peer compari-
son and does not introduce strong biases with
respect to the risk factors of the risk model.
As such, many investors that consider them-
selves stock pickers are not stock pickers in
the narrow sense of factor risks models, but
are stock pickers as defined by Cremers &Petajisto. The latter definition of a stock picker
clearly matches much better what investors
intuitively classify as a stock picker. In addi-
tion, this definition has a strong empirical
backing as a successful investment approach.
e therefore believe that this is the more
appropriate concept of stock picking.
11 Based on daily return
data from 1975 to 2011 on
common stocks listed on
the NYSE-AMEX, the NAS-
DAQ, t e Lon on, To yo,
Toronto and Australian
stock exchanges.
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Active Management
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1. Quality of return predictionsFirst and most obviously, the quality of returnpredictions is crucial. The world never standsstill, so equity managers need to constantly
challenge their stock picking process and
improve their stock picking skills both in
terms of directional accuracy and stabil-ity over time. Accuracy may be increasedby deepening the research into well-known
companies or by taking up coverage of less-
well-researched small-cap names.
Allianz Global Investors has significantly
increased its research coverage in recent years from about 1,000 to some 2,000 stocks
in order to improve the quality of return
predictions. It has been well documented in
academic research that there are higher stockselection opportunities within the small-cap
segment, especially when stocks are under-
researched with low analyst coverage.
At the same time, we have increased the
focus on picking high-conviction ideas by
introducing a more focused vote distribution
system, which we call “80 – 20”.
How to increase the return perunit of risk diligently
The last section on the risk lever argued thatinvestors can diligently increase the level
of risk taking in their portfolios to counter
the alpha erosion that low levels of marketvolatility and high levels of individual stock
correlation have resulted in.
This section on the return-per-risk leveroutlines what investors can do to increase the
eturn per unit of risk – or the information
atio – in their portfolios.
The fundamental law of active managementrovides a quantitative assessment of the
information ratio that can be expected fromany investment process. The fundamental law
ighlights that there are three levers
to increase the return per unit of risk:
• The quality of return predictions
• The breadth of strategies
• The quality of implementation
et us take a closer look at these three drivers.
Understand
IR ≈ IC √BR TCInformation Ratio ≈ Quality of Return Predictions Breadth of Strategies Quality of Implementation
where
: Information Coefficient, measures the quality of the return predictions
BR: Breadth, the number of independent predictions
TC: Transfer Coefficient, measures how accurate forecast are translated into portfolio weights
Information Coefficient” Breadth” “Transfer Coefficient”
ource: Clarke R. & Thorley S. (2002)
Figure 6: Fundamental Law of Active Management
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We introduced this in 2011 in order to better
align analyst effort with the requirements of
long-only investors. The new approach has
narrowed down the number of buy recom-
mendations to 20 % in order to focus research
activities much more on stocks with the most
potential upside and performance expecta-
tions. These stocks require deeper coverage,and the analyst will have an extensive under-
standing and closer following of these names
along with greater interest in their success.
At the same time, the number of neutral votes
has been restricted to 15 % to ensure a truly
active mindset in research. A stock can only
remain at a neutral vote for a limited time
period while the analyst determines the next
move up or down. This mirrors the move to
concentrate portfolios to a smaller number ofhigher-conviction names.
The introduction of a quality vote in 2010 was
a landmark step in aligning Allianz Global
Investors’s company research with the need
for more concentrated, unconstrained portfo-
lios. The natural result of increasing share in
the portfolios is to reduce the dependence on
the benchmark, ultimately leading to a more
unconstrained approach. This results in a dif-
ferent view of risk and a heightened emphasison quality.
Portfolio risk has traditionally been deter-
mined by relative volatility and potential
deviation from a benchmark. As portfolios
become more concentrated and more dif-
ferentiated from the benchmark, this exercise
becomes less useful. We look instead at
permanent loss of capital from operational,
financial or valuation risk. This heightenedawareness of absolute risk consequently leads
to a more intensive analysis of the intrinsic
quality of a business.
e assign a quality vote to each company
in our research universe. The quality vote is
broken down into three sub categories:
• Competitive Positioning Vote, which
analyses the traditional Porter Five Forces,
including barriers to entry, substitution,power with suppliers, regulation, new
entrants etc.
• Governance and Management Quality Vote
• Sustainability Vote, which focuses on the
intrinsic appeal of a business and tends to
be longer term as it is not affected by valu-
ation considerations
2,500
2,000
1,500
1,000
500
2002
Smaller Caps under Coverage (rhs)
2003 2005 2007 2009 2011 2013
0
1,000
800
600
400
200
0
2004 2012201020082006
Stocks under Coverage (lhs)
Source: A anz G o a Investors, Sma er Caps are e ne as stoc s w t mar et cap < 3 n EUR an a ove 250 mn EUR
Date as of December 2013
Figure 7: Exploring the Full Market Capitalization Range of Global Equities
Allianz Global Investors has Expanded Research Coverage
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Active Management
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on just one factor, like high quality or deep
value. An investment style grid can help to
improve diversity of single stock positions as
it makes sure that stock picks are distributed
along important risk dimensions and are not
clustered alongside one risk dimension only.
3. Quality of implementationFinally, the quality of implementation canmake a huge difference. The transfer coef-
ficient TC measures the quality of imple-mentation as the correlation between the
return prediction and the active weights in
a portfolio. A high quality implementation
would therefore be an implementation where
active portfolio weights closely follow return
predictions.
Typical constraints in portfolio construction,like constraints on country or sector devia-tions from the benchmark or on the allocation
of large caps vs. small caps, are often a sourceof implementation shortfall.
If the return predictions themselves are
unconstrained and not country / sector / size-
neutral, any constraints on these exposures
will hinder portfolio weights to follow return
predictions, which can produce an implemen-
tation shortfall. The research by Clark et al
[2002] confirms this.14
ut improving the quality of return predic-
tions does not mean dealing only with the
evel of IC. Improving the stability of ICs is as
important as enhancing the level of ICs. As the
esearch by Ding [2010] shows for broadly
diversified portfolios, reducing the volatility of
Cs by 50 % has the same effect on the infor-ation ratio of a portfolio as doubling the
evel of ICs.12 Increasing the stability over time
is very much interlinked with increasing the
readth of strategies that return predictions
are based on.
2. Breadth of strategiesecondly, a larger breadth in implementa-
tion is rewarded. In the context of the funda-
ental law of active management, breadth
efers to the number of independent bets ina portfolio per year. It is not just the number
of active positions in a portfolio, but also the
independence of bets that is crucial.
The breadth can be increased in a number
of ways that are interrelated, such as:
• Increasing the breadth of investment
strategies
• Increasing the diversity of single stock
positions
Increasing the breadth of investmentstrategiest is beneficial to the risk-adjusted perfor-
ance of an investment product if the
anager adds additional sources of alpha to
the process, even if those sources are small
in comparison to the major source of alpha
of the fund. Multi-strategy funds beat single-
strategy funds, as academic studies like Huij
and Derwall [2009] show.13
e at Allianz Global Investors firmly believe in
the superiority of multi-strategy approaches
over single-strategy approaches. This is why
e explore a range of investment strategies in
our portfolios.
Increasing the diversity of single-stockpositionsncreasing the breadth of a portfolio means
increasing the number of independent bets,
ot just increasing the number of bets. It istherefore important to make sure that single
stock positions are diverse and do not load up
Country Allocation
Sector Allocation
InvestmentStyles
Short-termTradingStrategies
TradingCosts
FundamentalCompany Research
MacroEconomicExposures
MarketTiming
ource: A anz G o a Investors,
asey Quirk, The Complete Firm 2013: Competing for the
1st Century Investor, February 2013
Figure 8: Allianz Global Investors Explores
A Range of Investment Strategies
Ding derived a gen-
era ze vers o n o t e
fundamental law of active
management. For his
analysis he used data from
the Russel 1000, 2000
an 3000 un verses rom
Decem er 1978 t August
2008.
Return data for the study
is obtained from the Morn-
ngstar ata ase, w c
covers monthly returns for
all global equity funds that
existed between January
1995 and December 2007.
14 Clark et al employed the
Barra portfolio optimizing
software and an S&P500
benchmark to perform
t e r ana y s s.
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A c t i v e W e i g h t s
transfer coefficient 0.98
stocks of the investment universesorted by forecasted return LowHigh
3 %
2 %
1 %
0 %
–1 %
–2 %
–3 %
A c t i v e W e i g h t s
4 %
3 %
2 %
1 %
0 %
–1 %
–2 %
transfer coefficient 0.31
stocks of the investment universesorted by forecasted return LowHigh
Higher transfer coefficients through long/short and unconstrained portfolio construction
Source: Car e R. & T orey S. 2002
Figure 9: The Case for Unconstrained Portfolios Constraints Can Hinder Active Weights in
Following Forecasts
Ideally, active weigths should closely follow forecasted return …
… but constraints hinder active weights in following forecasts
long/short,
unconstrained
long-only,
unconstrained
long-only,
market cap neutral
long-only,
multiple constraints
1
0.5
0
Source: C ar e R. & T orey S. 2002
Figure 10: The Case for Unconstrained Portfolios Constraints Can Hinder Active Weights
in Following Forecasts
Transfer coefficients for different implemenations of a forecast
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References
Alexeev, V. and Tapon, F. (2013) “Equity
ortfolio diversification: How many stocks
are enough? Evidence from five developed
arkets”
lake, Lehmann and Timmermann (1999),“Asset allocation dynamics and pension
fund performance, Journal of Business”, 72,
29–461
Casey Quirk, 2013, “Life After Benchmarks:
etooling Active Asset Management”
Clarke R., de Silva, H. and Thorley, S. (2002),
“Portfolio constraints and the fundamental
aw of active management”, Financial Ana-
ysts Journal, 58 (5), pp 48–66
Cohen, R. B. and Polk, C. and Silli, B., (March
5, 2010), “Best Ideas”. Available at SSRN:
ttp://ssrn.com/abstract=1364827 or
ttp://dx.doi.org/10.2139/ssrn.1364827
Coggin, T. D., Fabozzi, F. J. and Rahman, S.
(1993), “The Investment Performance of US
quity Pension Fund Managers: An Empirical
nvestigation”. The Journal of Finance 48,
p 1039–1055
Cremers, M. and Petajisto, A. (2009), “How
Active is Your Fund Manager? A New Meas-
ure That Predicts Performance”, AFA 2007
Chicago Meetings Paper; EFA 2007 Ljubljana
eetings Paper; Yale ICF Working Paper No.
06 – 14
aniel, K., Grinblatt, M., Titman, S., and
ermers, R. (1997), “Measuring Mutual
und Performance with Characteristic-Basedenchmarks”, Journal of Finance, Vol. 52,
ssue 3, pp. 1035 – 1058
Ding, Z. (2010), “The Fundamental Law of
Active Management: Time Series Dynamics
and Cross-Sectional Properties”
Henriksson, R. D., (1984), “Market Timing
and Mutual Fund Performance: An EmpiricalInvestigation”, The Journal of Business, Vol.
57, No. 1, Part 1, pp 73–96
Huij, J. and Derwall, J. (2009), “Global Equity
Fund Performance, Portfolio Concentration,
and the Fundamental Law of Active Manage-
ment”, Journal of Banking and Finance,
Vol. 35, 2011. Available at SSRN:
http://ssrn.com/abstract=1625834 or
http://dx.doi.org/10.2139/ssrn.1625834
Jegadeesh, N., Chen, H.-L. and Wermers, R.
(2000), “The Value of Active Mutual Fund
Management: An Examination of the Stock-
holdings and Trades of Fund Managers”. Jour-
nal of Financial and Quantitative Analysis 35,
pp 343–368
Jiang, H. and Verbeek, M. and Wang, Y.,
(August 2013), “Information Content When
Mutual Funds Deviate from Benchmarks”. AFA
2012 Chicago Meetings Paper. Available atSSRN: http://ssrn.com/abstract=1782692 or
http://dx.doi.org/10.2139/ssrn.1782692
Otten, R. and Bams, D. (2004), “How to Meas-
ure Mutual Fund Performance: Economic
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mance: An Empirical Decomposition intoStock-Picking Talent, Style, Transactions Costs,
and Expenses”. The Journal of Finance 55,
pp 1655–1703
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Do you know the other publications of Allianz GI Global Capital Markets &Thematic Research
Risk. Management. Reward.
→ Smart Risk with multi asset solutions
→ Smart Risk investing in times of financial repression
→ Strategic Asset Allocation
→ Managing Risk in a time of Deleveraging
→ Active Management
→ The New Zoology of Investment Risk Management
→ Constant Proportion Portfolio Insurance (CPPI)
→ Dynamic Risk Parity – a smart way to manage risks
→ Portfolio Health Check : Preparing for
„Financial Repression“
Financial Repression→ Shrinking mountains of debt
→ International monetary policy in the era of financial
repression: a paradigm shift
→ „Silent Deleveraging or debt haircut?“
– that is the question
→ Financial Repression – A silent way to reduce debt
→ Financial Repression – It is happening already
Bonds→ Duration Risk: Anatomy of modern bond bear markets
→ Emerging Market currencies are likely to appreciate in
the coming years
→ High Yield corporate bonds
→ US High-Yield Bond Market – Large, Liquid, Attractive
→ Credit Spread – Compensation for Default
→ Corporate Bonds
Active Management
→ The Changing Nature of Equity Markets and the Needfor a More Active Management.
→ Active Management: Can Capital Markets be efficient?
→ Harvesting risk premium in equity investing.
Strategy and Investment→ Equities – the “new safe option” for portfolios?
→ Is small beautiful?
→ Dividend Stocks – an attractive addition to a portfolio
Changing World→ Renewable Energies – Investing against the
climate change→ The green Kondratieff
→ Crises: The Creative Power of Destruction
→ Infrastructure – The Backbone of the Global Economy
Demography – Pension→ Discount rates low on the reporting dates
→ Financial Repression and Regulation: A Paradigm Shift
for Insurance Companies & Institutions for Occupational
Retirement Provision
→ IFRS Accounting of Pension Obligations
→ Demographic Turning Point (Part 1)
→ Pension Systems in a Demographic Transition (Part 2)
→ Demography as an Investment Opportunity (Part 3)
Behavioral Finance→ Reining in Lack of Investor Discipline:
The Ulysses Strategy
→ Overcoming Investor Paralysis: Invest more tomorrow
→ Outsmart yourself! – Investors are only human too
→ Two minds at work
All our publications, analysis and studies
can be found on the following webpage:
http://www.allianzglobalinvestors.com
@AllianzGI_VIEW
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