the changing nature of equity markets and the need for
TRANSCRIPT
Active Management
The Changing Nature of Equity Markets and the Need for More Active Management
There are two sweet spots of active equity management.
Understand. Act.
2
3
Active Management
Content
Imprint
4 Higher correlations and lower volatility – challenges to active management
6 How to react?
6 How to increase the level of risk diligently
6 Measuring activity in a portfolio with active share
7 Higher active share translates into higher returns
7 Concentrated stock pickers and diversified stock pickers
8 How concentrated should concentrated stock pickers be?
9 Cremers & Petajisto’s notion of a stock picker vs. stock picker in factor-risk models
10 How to increase the return per unit of risk diligently
Allianz Global Investors Europe GmbHBockenheimer Landstr. 42 – 4460323 Frankfurt am MainGlobal Capital Markets & Thematic ResearchHans-Jörg Naumer (hjn)Stefan Scheurer (st)Dora Janikovszky
Data origin – if not otherwise noted: Thomson Reuters Datastream
Active Management
4
The Changing Nature of Equity Markets 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 increased the return per risk by expanding the invest-ment universe, the strategy set and the imple-mentation set.
Higher correlations and lower volatility – challenges to active management
Over the past 30 years, global active equity managers have generated substantial value for clients, according to Mercer’s GIMD data-base. However, more recently, the pace of outperformance has slowed significantly, and at the end of 2013, the median global active equity 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 have generated substantial value for clients, according to Mercer’s GIMD database. However, more recently, the pace of out-performance has slowed significantly and there is a need for more active management.
5
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
• Thediversifiedstockpickersuccessfullydelivering 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 taken their 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 InvestorsData 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 be aware that due to the exchange rate fluctuations the performance shown may be higher or lower if converted into the investor’s local currency.
Risk LeverIncrease Risk Taking
Return per Risk LeverExpand Investment Universe
Two ways to react to lower returns per risk: Increase risk, or increase return per risk.
Return per Risk LeverExpand Strategy Set
Return per Risk LeverExpand Implementation Set
3 41 2
High
High
Liquidity Profile
Activ
e Sh
are
/ Tra
ckin
g Er
ror
Tracking Error
130 / 30
InformationGain
Long-onlyConstraint
Activ
e Re
turn
Low
Low
All-CapMulti-Sector
Local
Geographic
Asse
t Cla
ss /
Sect
or
Global
Single-Cap/Sector
CountryAllocation
SectorAllocation
InvestmentStyles
Short TermTradingStrategiesTradingCosts
FundamentalCompany Research
MacroEconomicExposures
MarketTiming
Figure 2: Capability Levers for New Active Management
Source: CaseyQuik (2013); Allianz Global Investors
Active Management
6
How to increase the level of risk diligently
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 to gauge 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 share measure 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 benchmarks, and, in particular, the rise of indexing. This homogenisation of investor practice has led to a loss of diversity in stock behaviour and hence to an increase in cor-relations.
As a result, although the relatively high level of correlations may not only be a cyclical phenomenon, correlations can be secularly higher due to the rise of institutionalisation.
How to react?
If past levels of portfolio activity and portfolio risk deliver only compressed alphas instead of the ample alphas of the past, we see two principal ways to deal with this challenge:
• Increase the level of risk• Increase the return per unit of risk
The following chapters will provide a detailed discussion 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
Rela
tive
Perfo
rman
ce
Quintiles of tracking errorQuintiles of active shareLow 2 3 4 high
Rela
tive
Perfo
rman
ce
3.0
2.0
1.0
0.0
Figure 3: Active Share matters.
Source: Cremers & Petajisto [2013], Data from 1/1990 – 12/2009, Allianz Global InvestorsPast performance is not a reliable indicator of future results.
Relative Performance of US Equity Funds by Active Share
Relative Performance of US Equity Funds by Tracking Error
7
Empirically, there is evidence that investors who are seeking to raise the active profile of their portfolios should – as a first step – look at increasing active share rather than at increasing tracking error.
But then – as a second step – high-alpha products also need to increase tracking error further, while high information-ratio products should go for moderate-tracking-error, high-active-share solutions.
Cremers & Petajisto’s [2009] work showed that – based on data of US equity mutual funds from 1980 to 2003 as well as their benchmark indices1 – empirically higher active share means higher returns. However, the same does not hold true for the tracking error per se.
Higher active share translates into higher returns
Cremers & Petajisto’s [2009] observation – higher active share means higher return – is well supported by a number of other studies.
High active share is indicative of a high degree of stock picking activity. As Cremers &Petajistos[2009]analysisshowed,activestock pickers exhibit the highest active share and are the most likely to outperform their benchmarks. This can be supported by a body
of empirical evidence demonstrating that portfolio managers have stock picking skills.
• Wermers [2000], for instance, used a holdings-based performance analysis of US mutual funds and showed that active managers indeed have stock picking skills – the stock holdings of mutual funds outperformed the market by 1.3 %.2
• Similarly, Coggin, Fabozzi and Rahman [1993] showed that managers of US pension funds do earn a positive stock-selection alpha.3
• Both Cohen, Polk and Silli [2010]4 and Jiang, Verbeek and Wang [2013]5 found that a portfolio of the most prominent high-conviction ideas from mutual funds outperform the market by 1 % to 4 % percent per quarter.
• Chen, Jegadeesh and Wermers [2000]6 found that funds, on aggregate, tend to buy stocks that subsequently outperform the stocks they sell by 2 % per year, adjusted for the characteristics of these stocks.
Concentrated stock pickers and diversified stock pickers
Cremers & Petajisto [2009] also analysed the interaction of active share and tracking error, concluding that there are two sweet spots for active equity managers.
Active Share Quintile
Tracking Error Quintile
Low 2 3 4 High
High 1.9 % 1.7 % 3.3 % 3.0 % 3.6 %
4 2.2 % 2.0 % 1.6 % 0.6 % 1.1 %
3 2.0 % 1.4 % 0.9 % 1.1 % 0.0 %
2 0.6 % 0.8 % 0.5 % 0.3 % – 1.3 %
Low 0.1 % 0.4 % 0.4 % 0.2 % – 0.8 %
Diversified Stock Picker Concentrated Stock Picker
Figure 4: Two Sweet Spots for Active ManagementRelative Performance of US Equity Funds as a Function of Tracking Error and Active Share
Source: Cremers & Petajisto [2009], Data from 1990–2003, Allianz Global InvestorsPast performance is not a reliable indicator of future results.
1Cremers&Petajistosanaly-sis used daily data on mutual fund returns and monthly returns on benchmark indices from 1980 to 2003. Portfolio composition of mutual funds is based on the CDA/Spec-trum mutual fund holding database. 19 indices are used as benchmarks for the funds: S&P500, S&P500/Barra Growth, S&P500/Barra Value, S&P MidCap 400, S&P Small-Cap 600. Russell 1000, Russell 2000, Russell 3000, Russel Midcap indices and the value and growth components of each, as well as the Wilshire 5000 and the Wilshire 4500.
2 Wermers used quarterly portfolio holdings for all US equity mutual funds existing between January 1975 and December 1994 from the CDA Investment Technologies database and merged this data with an additional data set from CSRP which includes monthly data on net returns, annual data on portfolio turnover, expense ratios for all US mutual funds existing between January 1962 and December 1997.
3 Their study used monthly returns for the period of Janu-ary 1983 through December 1990 for a random sample of 71 U.S equity pension fund managers.
4 The analysis used sample data of US equity funds from January 1991 to December 2005 from Thomson Reuters, stock return data comes from CRSP and covers assets traded on the NYSE, AMEX, NASDAQ.
5 Based on combined data for the period 1984-2008 from CRSP US Mutual Fund Data-base and the CDA/Spectrum Mutual Fund Holdings Data-base from Thomson Financial. Analysis only includes active mutual funds that invest primarily in U.S equity funds. Monthly return data for stocks traded on the NADAQ, AMEX, NYSE are from CRSP.
6 Mutual fund holdings data is based on quarterly stockhold-ings data for all existing US mutual funds between 1975 and 1995 from the CDA database. These funds hold and trade stocks listed on the NYSE, AMEX, NASDAQ.
Active Management
8
Cremers&PetajistosanalysisofUSmutualfund 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]9 all found that fund managers were hardly able to demonstrate market-timing capability10.
How concentrated should concentrated 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 pushed too far, portfolio volatility will spike at some stage. However, a number of papers have
First, there are the concentrated stock pick-ers. Their portfolios are characterised by a very high level of active share that reflects their high level of stock picking activity. Their portfolios are also characterised by a higher tracking error that reflects their more concentrated approach to stock selection because high-active-share, high-tracking-error portfolios are concentrated stock picking portfolios that target high alphas – either in a benchmark-relative core equity setting or in a benchmark-agnostic unconstrained setting.
And there is also a second sweet spot, the diversified stock picking approach. While the level of stock picking activity as measured by active share is quite similar for diversified as well as for concentrated stock pickers – active share 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 more diversified approach to stock selection and build portfolios with a high level of active share, but a relatively lower tracking error. These portfolios – being lower tracking-error portfolios – target stable alpha, not neces-sarily the highest possible alpha, whereas concentrated stock pickers explicitly target the highest alpha.
Figure 5: Number of Stocks Required to Achieve a 90 % Reduction of the Idiosyncratic Risk
Source: Alexeev, 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 monthly data from Febru-ary 1968 to June 1980. The return data was obtained fromStandard&Poors.
8 Data used consists of quarterly equity holdings of all equity mutual funds that existed between Decem-ber 1974 and December 1994 provided by CDA Investment Technologies.
9 Analysis is based on monthly observations of 360 U.K. pension funds from 1986 to 1994 pro-vided by the WM company.
10 Please note that there is no guarantee that the implementation of any investment strategy will produce positive results. During different market conditions, different strate-gies will perform better.
9
shownthatnoticeablediversificationbenefitscan 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 to reduce 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 in factor-risk models
Cremers & Petajisto’s work measures stock picking activity in quite a different way than a factor-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 in their preferred stocks, irrespective of region, sector or investment-style constraints.
Stock 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 stock picking 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. We 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, the London, Tokyo, Toronto and Australian stock exchanges.
Active Management
10
1. Quality of return predictionsFirst and most obviously, the quality of return predictions is crucial. The world never stands still, 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 increased by 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 stock selection 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 per unit of risk diligently
The last section on the risk lever argued that investors can diligently increase the level of risk taking in their portfolios to counter the alpha erosion that low levels of market volatility and high levels of individual stock correlation have resulted in.
This section on the return-per-risk lever outlines what investors can do to increase the return per unit of risk – or the information ratio – in their portfolios.
The fundamental law of active management provides a quantitative assessment of the information ratio that can be expected from any investment process. The fundamental law highlights 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
Let us take a closer look at these three drivers.
Understand
IR ≈ IC ∙ √BR ∙ TCInformationRatio≈QualityofReturnPredictions∙ Breadth of Strategies ∙ Quality of Implementation
where
IC: 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”
Source: Clarke R. & Thorley S. (2002)
Figure 6: Fundamental Law of Active Management
11
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 of higher-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 emphasis on 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 heightened awareness of absolute risk consequently leads to a more intensive analysis of the intrinsic quality of a business.
We 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: Allianz Global Investors, Smaller Caps are defined as stocks with market cap < 3bn EUR and above 250 mn EURDate as of December 2013
Figure 7: Exploring the Full Market Capitalization Range of Global EquitiesAllianz Global Investors has Expanded Research Coverage
Active Management
12
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 can make 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 source of 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
But improving the quality of return predic-tions does not mean dealing only with the level of IC. Improving the stability of ICs is as important as enhancing the level of ICs. As the research by Ding [2010] shows for broadly diversified portfolios, reducing the volatility of ICs by 50 % has the same effect on the infor-mation ratio of a portfolio as doubling the level of ICs.12 Increasing the stability over time is very much interlinked with increasing the breadth of strategies that return predictions are based on.
2. Breadth of strategiesSecondly, a larger breadth in implementa-tion is rewarded. In the context of the funda-mental law of active management, breadth refers to the number of independent bets in a 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 investment strategies It is beneficial to the risk-adjusted perfor-mance of an investment product if the manager 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
We at Allianz Global Investors firmly believe in the superiority of multi-strategy approaches over single-strategy approaches. This is why we explore a range of investment strategies in our portfolios.
Increasing the diversity of single-stock positions Increasing the breadth of a portfolio means increasing the number of independent bets, not just increasing the number of bets. It is therefore important to make sure that single stock positions are diverse and do not load up
CountryAllocation
SectorAllocation
InvestmentStyles
Short-termTradingStrategies
TradingCosts
FundamentalCompany Research
MacroEconomicExposures
MarketTiming
Source: Allianz Global Investors, Casey Quirk, The Complete Firm 2013: Competing for the 21st Century Investor, February 2013
Figure 8: Allianz Global Investors Explores A Range of Investment Strategies
12 Ding derived a gen-eralized version of the fundamental law of active management. For his analysis he used data from the Russel 1000, 2000 and 3000 universes from December 1978 till August 2008.
13 Return data for the study is obtained from the Morn-ingstar database, which 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 their analysis.
13
Activ
e W
eigh
ts
transfer coefficient 0.98
stocks of the investment universesorted by forecasted return LowHigh
3 %
2 %
1 %
0 %
–1 %
–2 %
–3 %
Activ
e W
eigh
ts
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: Clarke R. & Thorley S. (2002)
Figure 9: The Case for Unconstrained Portfolios Constraints Can Hinder Active Weights in Following ForecastsIdeally, 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: Clarke R. & Thorley 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
Active Management
14
References Alexeev, V. and Tapon, F. (2013) “Equity portfoliodiversification:Howmanystocksareenough?Evidencefromfivedevelopedmarkets”
Blake, Lehmann and Timmermann (1999), “Asset allocation dynamics and pension fund performance, Journal of Business”, 72, 429–461
Casey Quirk, 2013, “Life After Benchmarks: Retooling Active Asset Management”
Clarke R., de Silva, H. and Thorley, S. (2002), “Portfolio constraints and the fundamental law of active management”, Financial Ana-lysts Journal, 58 (5), pp 48–66
Cohen, R. B. and Polk, C. and Silli, B., (March 15, 2010), “Best Ideas”. Available at SSRN: http://ssrn.com/abstract=1364827 or http://dx.doi.org/10.2139/ssrn.1364827
Coggin, T. D., Fabozzi, F. J. and Rahman, S. (1993), “The Investment Performance of US Equity Pension Fund Managers: An Empirical Investigation”. The Journal of Finance 48, pp 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 Meetings Paper; Yale ICF Working Paper No. 06 – 14
Daniel, K., Grinblatt, M., Titman, S., and Wermers, R. (1997), “Measuring Mutual Fund Performance with Characteristic-Based Benchmarks”, Journal of Finance, Vol. 52, Issue 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 Empirical Investigation”, 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 at SSRN: 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 into Stock-Picking Talent, Style, Transactions Costs, and Expenses”. The Journal of Finance 55, pp 1655–1703
15
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 Need for 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
www.allianzglobalinvestors.comAllianz Global InvestorsEurope GmbHBockenheimer Landstr. 42 – 44 60323 Frankfurt am Main BR
7221
(00)
020
314
Mar
ch 2
014
Information herein is based on sources we believe to be accurate and reliable as at the date it was made. We reserve the right to revise any information herein at any time without notice.
No offer or solicitation to buy or sell securities, nor investment advice or recommendation is made herein. In making investment decisions, investors should not rely solely on this material but should seek independent professional advice.
Investment involves risks, in particular, risks associated with investment in emerging and less developed markets. Past performance is not indicative of future performance. Investors should read the offering documents for further details, including the risk factors, before investing.
This material has not been reviewed by the SFC in Hong Kong and the Monetary Authority of Singapore, and is pub-lished for information only, and where used in mainland China, only as supporting materials to the offshore investment products offered by commercial banks under the Qualified Domestic Institutional Investors scheme pursuant to appli-cable rules and regulations.
Issued by Allianz Global Investors Hong Kong Limited.
Allianz Global Investors Hong Kong Limited (27/F, ICBC Tower, 3 Garden Road, Central, Hong Kong) is the Hong Kong Representative and is regulated by the SFC (35/F, Cheung Kong Center, 2 Queen's Road Central, Hong Kong).
Allianz Global Investors Singapore Limited (12 Marina View, #13-02 Asia Square Tower 2, Singapore 018 961 (Co. Reg. No. 199907169Z)) is the Singapore representative and supervised by the Monetary Authority of Singapore.