allianz_changing nature of equity markets - active management

8/9/2019 Allianz_changing Nature of Equity Markets - Active Management

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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.


Expand Strategy Set


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

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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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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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Active Management

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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

Versus Statistical Relevance, Accounting and

Finance”, Vol. 44, No. 2, pp 203–222. Available

at SSRN: http://ssrn.com/abstract=544290.

Wermers, R. (2000), “Mutual Fund Perfor-

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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Investing involves risk. The value of an investment and the income from it will fluctuate and investors may not get back the

principal invested. Past performance is not indicative of future performance. This is a marketing communication. It is for

informational purposes only. This document does not constitute investment advice or a recommendation to buy, sell or hold

any security and shall not be deemed an offer to sell or a solicitation of an offer to buy any security.

The views and opinions expressed herein, which are subject to change without notice, are those of the issuer or its affiliated

companies at the time of publication. Certain data used are derived from various sources believed to be reliable, but theaccuracy or completeness of the data is not guaranteed and no liability is assumed for any direct or consequential losses

arising from their use. The duplication, publication, extraction or transmission of the contents, irrespective of the form, is not

permitted.

This material has not been reviewed by any regulatory authorities. In mainland China, it is used only as supporting material to

the offshore investment products offered by commercial banks under the Qualified Domestic Institutional Investors scheme

pursuant to applicable rules and regulations.

This document is being distributed by the following Allianz Global Investors companies: Allianz Global Investors US LLC,

an investment adviser registered with the US Securities and Exchange Commission (SEC); Allianz Global Investors Europe

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