Auf der Suche nach alternativem Ertrag - Multi-Premia-Strategien AIC/BAI_AIC... · 2018-05-30 ·...

16. Mai 2018

Auf der Suche nach alternativem Ertrag - Multi-Premia-Strategien BAI Konferenz

– For professional clients only / Not for retail investors –

CONFIDENTIAL Property of AXA IM and its affiliates – Any reproduction or disclosure of this document, in whole or in part, is prohibited

Contents

Multi Premia Strategy

1

1. Chorus Overview

2. What Differentiates Us

3. Multi Premia Strategy

4. Annex and Fund Information

Chorus Overview


* Source: AXA IM – December 2017 ** Source: AXA IM Chorus – April 2018

3

AXA Investment Managers Chorus (Chorus) was set up in 2016, and is a majority owned subsidiary of AXA Investment Managers, a leading asset manager with EUR 746bn* asset under management, itself part of the AXA Group.

What We Do

Our aim: invest in liquid assets to deliver sustainable returns, un-correlated with markets

• Research and Technology driven approach

• Trading equities, interest rates, currencies, futures and derivatives in 30+ countries through

a set of diversified investment strategies

• More than 30 front office professionals, based in Hong Kong and Paris

• Benefiting from AXA IM’s scaled structure and established controls and processes

through shared and support functions

• Flagship fund AWF Multi Premia launched on April 20th, 2017, with currently USD 1.39bn

AUM**


Front Office IT (13 Professionals)

Research and Modelling (12 Professionals)

Portfolio and Front Office Risk Management

General Management

Trading

Risk Management and Compliance

Operations

Other AXA IM Support Functions

How We Structure Our Firm

Employment of more than 30 front office professionals, dedicated to generate performance

Source: AXA IM Chorus – April 2018

4

Ch

oru

s A

XA

IM F

un

ctio

ns

• Data Engineering • Execution Infrastructure • Software Development

• Front Office Development

• Alpha Generation and Combination • Portfolio Construction and

Optimization

• Advanced Techniques (Machine Learning, Natural Language Processing)

• Portfolio Management • Portfolio Analytics • Cost Minimization

• Risk Management

• Corporate Governance • Service Provider Monitoring • AXA IM Delegated Function

Supervision • General Administration

• Trading • Best Execution • Trade Management

• Monitoring of Market, Liquidity, Counterparty and Operational Risk

• Setting of Internal Guidelines

• Second level of control via ad hoc Control Monitoring

• Sales and Marketing • Human Resources • Finance

• Legal • General IT • …

• Settlements, Collateral • Controls on Positions • Valuations and NAV

• Monitoring of Investment Guidelines

• Accounting


Our Core Beliefs

We aim to deliver value for our clients via a systematic, research and data driven approach. This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved.

5

Control portfolio risk:

Strict adherence to market neutrality

Minimize all investment costs:

Obvious and hidden

Produce predictive signals:

Beyond the classic academic premia

• Strict adherence to market neutrality (including removal of secondary / hidden risks)

• Diversification in terms of asset groups, geography, number of signals and positions

• Risk constraints

• Leverage control (no reliance on past correlations)

• Diversified set of in-house built signals, mostly based on behavioral finance

• Going beyond academic signals to (i) avoid crowding and (ii) improve performance sustainability

• Statistical significance of all signals rigorously evaluated

• New signals continuously researched

• Cost saved = riskless addition to performance

• Favorable terms obtained from counterparties

• Execution style designed to minimize market impact

• Focus on often overlooked hidden costs (funding and margining, corporate actions, dividend tax)


Asset Group and Premia Family Mix

✓(1) Expected to be in production by May 2018

6

Fundamentals Carry Momentum Imbalances Sentiment Investment

Universe

Equities ✓ ✓ ✓ ✓ ~2000 stocks covering

America, Asia and Europe

Interest Rates ✓ ✓ ✓ ✓ IRS in G7 currencies (up to 8 per currency)

Foreign Exchange

✓ ✓ ✓ ✓ 24 Emerging and

Developed Market currencies

Volatility ✓ Book of Listed Equity

Index Options

Fundamental

assets with cheap / strong

fundamental ratios could outperform

assets with expensive / weak

ratios

Carry

benefits from the tendency of

higher yielding assets to perform better than lower

yielding assets

Momentum

looking for assets that performed

well recently which could continue to

perform well in the near future or the

opposite

Imbalances

built to benefit from market imbalances,

such as investors’ structural

constraints, translating into

abnormal returns

Sentiment

signals inferring future asset price

behavior from positive or negative market sentiment,

coming from consumers,

professionals or other economic

factors

Ass

et G

rou

ps

Premia Families


7

We aim to deploy a systematic investment process, from data all the way to order generation and execution. Each step is critical and, if properly approached, is supposed to add value to performance

Investment Process

Adding Value at Each Step of the Investment Process

Signals Sub-Portfolio Construction

Weighting Between

Sub-Portfolios

Execution Data



Weighting Between

Sub-Portfolios

Execution Data

Investment Process

Data

8

• Scalable in house data management systems

• Automatized daily update: checking and mapping large amounts of data

• Data sets include market-related, fundamental and alternative sets


Investment Process

Signals

9


Weighting Between

Sub-Portfolios

Execution Data

• Computed daily

• Rigorous framework to research, analyze and select signals

• Adjusted for unwanted risk exposures (e.g. industry, size)

• Aggregated to provide an estimate of each asset’s future relative return


Investment Process

Sub-Portfolio Construction

10


Weighting Between

Sub-Portfolios

Execution Data

• Assets pooled in sub-portfolios (e.g. FX Developed Markets, Equity Long Short Asia)

• Compute target weights via optimization subject to risk constraints

• Optimal trading speed & order size


Investment Process

Weighting Between Sub-Portfolios

11


Weighting Between

Sub-Portfolios

Execution Data

• Stable weights

• Correlation agnostic (previously uncorrelated strategies might drawdown simultaneously)

• Leverage control


Investment Process

Execution

12


Weighting Between

Sub-Portfolios

Execution Data

• Competitive conditions negotiated ex-ante with our counterparties

• Execution style focused on minimizing bid-offer and market impact

• Dedicated resources from AXA IM’s execution team

What Differentiates Us


Risk Management at Every Step of the Investment Process

Risk management is embedded at every step of our investment process

14

Weighting Between Sub-Portfolios

We look at performance, correlations, skew, co-skew, but in the end “we do not blindly believe in past correlations” and take a

drawdown approach to cap the weights

Optimization

We apply further constraints (e.g. beta, sector, country concentration, etc.) in portfolio construction to obtain target

weights for each instrument

Signal Combination

We combine our signals to maximize diversification

Individual Signal

We aim to identify unwanted hidden risks and immunize our signals accordingly


The illustration hereto does not represent actual results and actual results may significantly differ from the theoretical portfolio being presented. No representation is made that any investment will be split as shown herein.

Risk Mitigation at Every Step of the Investment Process

Illustration on Size

With size control

Long Short

Large caps

Small caps

Long Short

This portfolio is beta-neutral but vulnerable to divergence between small and large caps

This portfolio is beta-neutral and size-neutral immune to divergence between small and large caps

No size control

Large caps

Small caps

15


Unified framework to analyze the predictive power of signals

Two simple illustrations amongst the (many) analytics we use

Source AXA IM Chorus – April 2018 – Non Audited Figures The results and characteristics displayed are illustrations and do not represent actual results, that may significantly differ from the theoretical returns being presented. No representation is made that any investment will achieve performance and characteristics similar to that shown herein. An investor’s actual return will be reduced by management fees and other expenses the investor may incur.

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-0,4%

-0,3%

-0,2%

-0,1%

0,0%

0,1%

0,2%

0,3%

-2 -1 0 1 2

Forw

ard

Ret

urn

s

Median Score

Score vs Forward Returns

-20%

-10%

0%

10%

20%

30%

1998 2000 2002 2004 2006 2008 2010 2012 2014

Spea

rman

Ran

k C

orr

elat

ion

Rolling Information Coefficient

The information coefficient is the correlation between score and forward returns. We look for information coefficients that are in average positive and that are relatively stable.

This analytics looks at the relationship between score and average forward returns. We look for increasing and monotonic relationships.


Research in Behavioral Finance Applied to Alpha Generation

17

Behavioral Finance Insights:

Develop new signals + boost classics

Why is research effort so crucial? Why can certain signals be

predictive?

• New data sources (e.g. based on natural language processing)

• Generation of proprietary ideas

• Understanding of economic rationale reduces the risk of overfitting

• Our view: investors hold biased beliefs and are subject to institutional frictions

• This leads to pricing anomalies

• Examples:

• Under-reaction to small information and over-reaction to salient news

• Some mutual funds cannot short and are benchmarked

• Classic academic premia might become crowded: potential decay and underperformance during deleveraging episodes

• Proprietary / new signals likely to be traded by smaller set of investors: higher potential performance and lower crowding risk


Beyond the “Classic” Academic Premia

Alpha generated beyond academic signals

* OLS refers to Ordinary Least Squares, it is a methodology to estimate the coefficients of a linear regression model. In this case, Returns are regressed on classic academic factors to obtain the beta, alpha and residual. ** We assume here a global equity portfolio composed of 25% Asian stocks, 25% European stocks and 50% North American stocks. The illustration hereto is purely theoretical and does not represent actual results, that may significantly differ from the theoretical returns being presented. No representation is made that any investment will achieve performance and characteristics similar to that shown herein. An investor’s actual return will be reduced by management fees and other expenses the investor may incur. Source: AXA IM Chorus – April 2018

18

-10%

0%

10%

20%

30%

40%

50%

60%

70%

2003 2005 2007 2009 2011 2013 2015

Cumulative Returns of Academic Premia vs Proprietary Alpha

Proprietary Alpha Dynamic Academic Premia Static Academic Alpha

We decompose our returns in two components using OLS regressions*: Rt = ( 𝛽𝑖 𝐹𝑎𝑐𝑡𝑜𝑟𝑖,𝑡𝑖 ) + (α + εt)

Academic Premia Proprietary Alpha

Analysis for illustrative global equity portfolio**:

- Using this methodology, we can breakdown between performance coming from academic premia versus proprietary alpha

- Static Academic premia is obtained through a full sample regression, dynamic via a rolling regression

- Academic Premia only explains a fraction of performance

- Proprietary Alpha is large and statistically significant


We assume here a global equity portfolio composed of 25% Asian stocks, 25% European stocks and 50% North American stocks. The illustration hereto is purely theoretical and does not represent actual results, that may significantly differ from the theoretical returns being presented. No representation is made that any investment will achieve performance and characteristics similar to that shown herein. An investor’s actual return will be reduced by management fees and other expenses the investor may incur. *Regression computed from 1st July 2003 to 31st March 2017, simulated gross returns in USD. Because AXA IM Chorus constantly upgrades its models, the results can be subject to modification without notification. Simulated performances are not reliable indicators of future performances. OLS refers to Ordinary Least Squares, it is a methodology to estimate the coefficients of a linear regression model. A T-test is a test used in statistics to measure the statistical significance of a coefficient. Source: AXA IM Chorus – April 2018

19

In the example below, performance increases and a significant alpha is generated versus the academic version

Beyond the “Classic” Academic Premia

Illustration: Momentum

Coefficients Standard

Error t-stat

Alpha 0.000126 0.000015 8.35

Academic Momentum

0.352836 0.003393 104.0

Daily OLS Regression of Family Momentum Returns on Academic Momentum Returns*

-20%

0%

20%

40%

60%

80%

2003 2005 2007 2009 2011 2013 2015 2017

Cu

mu

lati

ve R

etu

rns

Comparison of Momentum Returns

Family Momentum Academic Momentum

Sharpe: 1.40

Sharpe: 0.35


• 1$ cost saved is 1$ in additional performance. Cost saving is sure performance whereas possible future returns are uncertain and their measurement can be biased.

• We look to minimize every cost, obvious or hidden.

Disciplined Optimization of All Investment Costs

Obvious and easy to measure

Obvious but less easy to measure

Hidden

• Opportunity cost of collateral posted

• Difference in interest rate benchmarks between longs and shorts

• Voluntary corporate actions

• …

• Market Impact

• Stock lending fees on hard to borrow stocks

• Dividend payout ratio difference between longs and shorts

• …

• Commissions

• Bid-Offer Spread

• Other fees (e.g. stamp duty, sales tax, exchange fees)

• Funding spread on long position

• Stock lending fees for easy to borrow stocks

• …

20


A Sharpe Ratio is a measure of risk-adjusted returns- it is calculated here as Annualized Returns / Standard Deviation of Returns Figures are provided for illustrative purposes only. Source: AXA IM Chorus

21

• Our simulations include a precise modeling of turnover costs (commissions, market impact etc.)

• Higher weight given to cost saving (cost is certain) relative to back-tested performance (alpha is uncertain and its measurement possibly over-estimated)

Disciplined Optimization of All Investment Costs

Illustration: Optimizing Turnover

Proper modeling of trading cost structure leads to significant gains expected performance

0

0,4

0,8

1,2

1,6

2

2,4

2,8

3,2Sharpe

Trading Speed

Gross Sharpe Turnover Cost Discounted Gross Sharpe

Fast Slow

Optimal Point

0,8

0,9

1,0

1,1Net Discounted Sharpe

Trading Speed

Fast Slow

Multi Premia Strategy


*Target Volatility and Expected Sharpe ratio can change over time. It is presented here only for illustration purposes and is not mentioned in the prospectus.

23

The Multi Premia Strategy seeks to provide capital growth with an expected low correlation to traditional asset classes. To do so, it offers investors access to a diversified set of strategies across a broad range of liquid assets (i.e. equities, interest rates, currencies) and on all global markets.

Objectives

Achieve a long-term net Sharpe

ratio of 1*

Target annualized volatility averaging 8-10%* over a full

market cycle

Target low correlation to

traditional asset classes

Trade in liquid instruments to provide daily

liquidity such as:

Transparency

• Strategies are designed to be market neutral

• Global Single Name Equities

• Global Futures, Interest Rate Swaps and Treasuries

• Global Currency Forwards

• Book of Options

• Clearly Defined Investment Process

• Detailed Reporting


What is a Premia?

24

Explanations

Behavioral:

the over- and / or under-reaction of certain investors lead to abnormal returns (returns beyond market returns) in certain signals

Structural Constraints:

aiming to compensate for holding assets that other investors are unable or unwilling to purchase due to institutional, regulatory, leverage or other constraints

Risk:

aiming to compensate for taking risks such as systematic risk, tail risk, illiquidity… They are often known as “Risk Premia”

Characteristics

Persistent:

generate consistent returns over long periods of time (over multiple business cycles)

Pervasive:

holds on multiple geographies and asset classes

Economic Meaning:

strong economic rationale and well supported by research (either academic or in house)

Data Driven and Systematic

Liquid and Tradable:

implementable in practice, liquid and scalable

Uncorrelated:

little correlated with the traditional asset classes and with each other


Simulated Performance

Backtest computed from 31st December 2002 to 31st March 2017. The backtest is presented as of March 2017. Because AXA IM Chorus constantly upgrades its models, the backtest results can be subject to modification without notification. For Gross of Fees Undiscounted performance, original performance less an estimation of transaction costs was taken. For Gross of Fees Discounted performance, the Undiscounted performance was given a discretionary haircut to be conservative. For Net of Fees performance, management and performance fees were deducted. Simulated performances are not reliable indicators of future performances. As a result, AXA IM Chorus expects a meaningful decrease in effective risk adjusted returns. Note that the model can fail to reach the investment objective of the strategy. These simulations are for illustrative purposes only, on the basis of the following hypotheses: daily reallocation across portfolios and investment universe composed of equities, rates, FX using listed as well as OTC derivative instruments that may include leverage. Simulated results have many inherent limitations. In particular, simulations are performed with a limited number of variables and are based on historical data or assumptions such as trade execution and market impact cost that may be different of the actual data and could evolve in the future. Additionally, simulated returns are often prepared with the benefit of hindsight, meaning that models used in these simulations may have been developed explicitly with the benefit of data from the time period covered by these simulations. Should any information used in the above simulations prove to be inaccurate, the simulated results themselves may be inaccurate. There can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. These simulated return should not be relied upon and no representation is being made that any fund or strategy will or is likely to achieve profits or losses similar to those shown herein.

25

Simulated Performance (in log Scale)

Gross of Fees, Undiscounted

Discounted Net of Fees

Annualized Returns

19.38% 9.25%

Volatility 9.38% 8.24%

Sharpe Ratio 2.07 1.12

Max Drawdown -11.13% -10.69%

This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved.

100

1.000

10.000

2002 2007 2012 2017

Perf

orm

ance b

ased a

t 100

Gross of Fees, Undiscounted Gross of Fees, Discounted Discounted Net of Fees


Simulated Performance: Correlations

Low correlations between asset groups and with bonds and equities

Source: AXA IM Chorus Gross of fees, USD returns. Backtest computed from 31st December 2002 to 31st March 2017. The backtest is presented as of March 2017. Because AXA IM Chorus constantly upgrades its models, the backtest results can be subject to modification without notification. Simulated performances are not reliable indicators of future performances. As a result, AXA IM Chorus expects a meaningful decrease in effective risk adjusted returns. Note that the model can fail to reach the investment objective of the strategy. These simulations are for illustrative purposes only, on the basis of the following hypotheses: daily reallocation across portfolios and investment universe composed of equities, rates, FX using listed as well as OTC derivative instruments that may include leverage. Simulated results have many inherent limitations. In particular, simulations are performed with a limited number of variables and are based on historical data or assumptions such as trade execution and market impact cost that may be different of the actual data and could evolve in the future. Additionally, simulated returns are often prepared with the benefit of hindsight, meaning that models used in these simulations may have been developed explicitly with the benefit of data from the time period covered by these simulations. Should any information used in the above simulations prove to be inaccurate, the simulated results themselves may be inaccurate. There can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. These simulated return should not be relied upon and no representation is being made that any fund or strategy will or is likely to achieve profits or losses similar to those shown herein.

26

Barclays US

Agg MSCI World Equities Volatility Interest Rates

Foreign Exchange

Overall Simulated Portfolio

Barclays US Agg 1.00

MSCI World -0.23 1.00

Equities 0.00 -0.01 1.00

Volatility -0.10 0.27 0.05 1.00

Interest Rates 0.15 -0.06 0.00 0.02 1.00

Foreign Exchange 0.00 0.13 0.01 0.03 -0.03 1.00

Overall Simulated Portfolio

0.08 0.03 0.69 0.23 0.67 0.18 1.00


2002 2007 2012 2017

Sim

ula

ted

Ass

et C

lass

Per

form

ance

Equities

Volatility

Interest Rates

Foreign Exchange

Benefits From Diversification Across Asset Groups

Gross of fees, USD Returns. Backtest computed from 31st December 2002 to 31st March 2017. The backtest is presented as of March 2017. Because AXA IM Chorus constantly upgrades its models, the backtest results can be subject to modification without notification. Simulated performances are not reliable indicators of future performances. As a result, AXA IM Chorus expects a meaningful decrease in effective risk adjusted returns. Note that the model can fail to reach the investment objective of the strategy. These simulations are for illustrative purposes only, on the basis of the following hypotheses: daily reallocation across portfolios and investment universe composed of equities, rates, FX using listed as well as OTC derivative instruments that may include leverage. Simulated results have many inherent limitations. In particular, simulations are performed with a limited number of variables and are based on historical data or assumptions such as trade execution and market impact cost that may be different of the actual data and could evolve in the future. Additionally, simulated returns are often prepared with the benefit of hindsight, meaning that models used in these simulations may have been developed explicitly with the benefit of data from the time period covered by these simulations. Should any information used in the above simulations prove to be inaccurate, the simulated results themselves may be inaccurate. There can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. These simulated return should not be relied upon and no representation is being made that any fund or strategy will or is likely to achieve profits or losses similar to those shown herein.

27

Simulated Asset Class Performance (in log scale) This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved.


Decomposition of Long and Short Performances

Illustration on European Equity Portfolio

USD returns, gross of fees and transaction costs, and are computed as cumulative sums of daily returns Source AXA IM Chorus – October 2017 – Non Audited Figures Backtest computed from 31st December 2002 to 31st March 2017. The backtest is presented as of March 2017. Because AXA IM Chorus constantly upgrades its models, the backtest results can be subject to modification without notification.Simulated performances are not reliable indicators of future performances. As a result, AXA IM Chorus expects a meaningful decrease in effective risk adjusted returns. Note that the model can fail to reach the investment objective of the strategy. These simulations are for illustrative purposes only, on the basis of the following hypotheses: daily reallocation across portfolios and investment universe composed of equities, rates, FX using listed as well as OTC derivative instruments that may include leverage. Simulated results have many inherent limitations. In particular, simulations are performed with a limited number of variables and are based on historical data or assumptions such as trade execution and market impact cost that may be different of the actual data and could evolve in the future. Additionally, simulated returns are often prepared with the benefit of hindsight, meaning that models used in these simulations may have been developed explicitly with the benefit of data from the time period covered by these simulations. Should any information used in the above simulations prove to be inaccurate, the simulated results themselves may be inaccurate. There can be no assurance that the models will remain the same in the future or that an application of the current models in the future will produce similar results because the relevant market and economic conditions that prevailed during the hypothetical performance period will not necessarily recur. These simulated return should not be relied upon and no representation is being made that any fund or strategy will or is likely to achieve profits or losses similar to those shown herein.

28

-20%

0%

20%

40%

60%

80%

100%

120%

140%

160%

2002 2004 2006 2008 2010 2012 2014 2016

Per

form

ance

Long vs Short Performance for European Equity Portfolio

Long Short Combined

Sharpe: 2.65

Sharpe: 1.68

Sharpe: 0.74

This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved.


Key Risks

29

Leverage and Correlation Risk

The strategy will aim to deliver high and consistent risk-adjusted returns by combining an important number of Premia strategies exhibiting low correlation to each other. Through this diversification, high risk-adjusted returns are achieved through reduction of the volatility. Leveraging the economic exposure is therefore critical in order to deliver a certain level of volatility (and associated levels of potential absolute returns). Combining both leverage and diversification makes the strategy particularly sensitive to (i) asymmetries in distributions, volatilities and correlations, (ii) potential regime shifts over time in correlation levels, (iii) risks of correlation estimation errors and (iv) economic leverage.

The ability to maintain leverage could be impaired if counterparties change their margining methodologies or impose credit limitations or restrictions, which could result in forced liquidation of positions at disadvantageous prices. High leverage risk might lead to increase capital lost.

Mitigants: thorough analysis of distribution, volatility and correlation asymmetries at the heart of the strategy; daily VaR monitoring with internal and regulatory limits; stress tests to model extreme events, especially large and correlated adverse moves; internal market risk limits; attractive margining methodology obtained from counterparties with “lock-ups”; diversification across multiple counterparties.

Performance Risk

Individual sub strategies are based on persistent risk or anomaly Premia identified by AXA Investment Managers Chorus Limited (“AXA IM Chorus”). Long-term history and back-testing is necessary to statistically and economically validate the opportunities and avoid strategies that appear to generate stable returns based on recent history but also represent bets on extreme events. Past performance not being necessarily a good indicator of future performance, there is a risk that the Premia strategies identified by AXA IM Chorus do not perform. Additionally, most Premia are cyclical in nature, making it difficult to differentiate between a cycle and a change in pattern.

Mitigants: reliable process to select the Premia, continuous monitoring and research on the factors and regular improvements, diversification across the Premia.

Counterparty Risk

The strategy will primarily use derivatives in order to obtain exposure to the desired investment strategies. These derivatives fall into three broad categories: listed derivatives, cleared OTC derivatives, non-cleared OTC derivatives.

Mitigants: AXA IM Chorus has obtained attractive initial margining terms from the counterparties; AXA IM counterparty risk framework will apply (counterparty selection, documentation negotiation, calculation of counterparty exposure, setting up of counterparty limits); exposure will also be monitored at AXA level.

Model Risk

AXA IM Chorus proprietary decision making tool (“DMT”) will cover data integration, trade signal generation and strategy construction. The research and modeling process engaged by AXA IM Chorus is complex and involves financial, economic, econometric and statistical theories. The DMT relies extensively on quantitative models, information and data supplied by third parties. When such quantitative models, information and data prove to be incorrect or incomplete, any decisions made in reliance thereon will expose the strategy to potential risks and losses.

Mitigants: reliable and systematic front office processes (code quality, peer reviews, data integrity checks), model risk review by AXA IM Risk Management, application of AXA IM standards (model validation policy, incident escalation and error correction policy).

Tax Risk

Changes in taxation (such as on investment income e.g. section 871(m) in the United States, on transactions e.g. the financial transaction tax in Europe) might impair the ability to generate performance on certain investment strategies.

Mitigants: the AXA IM Chorus strategy takes into account all economics when selecting or allocating to individual Premia signals, including costs such as taxes. Adverse changes in taxes could impair the expected performance of Premia signals leading AXA IM Chorus to allocate less or remove exposure to these signals. Whilst being a challenge, AXA IM Chorus does not anticipate this to be constraining as other signals have good capacity, and many new signals are being researched.

Annex and Fund Information


The Long Short Approach

Source: AXA IM Chorus proprietary data, October 2017. This chart is for illustration only. The above chart may not represent actual strategies. The concept of benchmark is purely illustrative and does not revisit a particular index. From August 1999 to August 2017. Performance results of the past are no indicator for any future returns or trends. The value and in-come derived from investments may go down as well as up. This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved.

31

• Some portfolios grouped based on common characteristics have outperformed others. An example is Momentum: stocks that performed well in the past are likely to perform well in the future

• In this illustration, the high Momentum portfolio outperforms the benchmark, while the low Momentum portfolio underperforms the benchmark

• By not being constrained to be long-only, we can create long/short portfolios that capture these specific factor returns while being market neutral. Going short the low momentum portfolio to hedge the beta of the high momentum portfolio will generate superior returns versus hedging with the indicator

Investment Universe

High Momentum Stocks

Low Momentum Stocks

Long High Momentum

Stocks

Short Low Momentum

Stocks

Cumulative Returns (%) of European Momentum Portfolios

0%

100%

200%

300%

400%

500%

600%

700%

1999 2002 2005 2008 2011 2014 2017

Low Momentum Basket High Momentum Basket Indicator


History of Thought: An Economics Nobel Timeline

32

Daniel Kahneman 2002 Loss aversion and other biases Failures in updating Beliefs correctly

Eugene Fama 2013 Clean financial data Value and size factors

Bob Shiller 2013 Excess volatility Sentiment Bubbles

Richard Thaler 2017 Anomalies Over- and under-reaction Psychology & financial prices Reference points, Mental accounting

Herbert Simon 1978 “Bounded Rationality”: The brain neglects smaller effects


* Expected Volatility can change over time. It is presented here only for illustration purposes and is not mentioned in the prospectus. This strategy presents risk of capital loss and there is no guarantee that the objectives of the investment strategy described herein will be achieved. Details judged in the future as well as future performance expectations are based on assumptions only. Such assumptions, forecasts and details may represent the present opinion about future events, and they therefore contain considerable risks and uncertainties. Thus, the real performance may be higher or lower. Investors should be conscious that the actual performance may considerably differ from former results. The future performance in this respect cannot be assured of.

Key Features of the Sub-Fund

- Name of the Sub-Fund: AXA World Funds (“AXA AWF”) Multi Premia

- Legal Structure: The Sub-Fund is part of the AXA AWF Luxembourg UCITS SICAV

- Management Company: AXA Funds Management S.A.

- Reference Currency: USD

- NAV Calculation Frequency: Daily

- Expected Volatility: 8 – 10%*

- Swing Pricing: applies

- Reporting: monthly report, portfolio holdings may be available upon request

- Synthetic Risk and Reward 5 Indicator (SRRI):

Regulatory Regime

- The AXA WF SICAV is registered by the CSSF

- AXA IM Chorus is incorporated in Hong Kong, majority owned by AXA IM SA, and licensed by the SFC, the frontline regulator for AXA IM Chorus

Fund Information

AWF Multi Premia Sub-Fund Structure

33


Fund Information

AWF Multi Premia Share Classes

1 “High Water Mark” reflects the highest level reached by the Share Class Net Asset Value at which a Performance Fee was paid (or the initial share class’ Net Asset Value if no Performance Fee has ever been paid). 2 The Benchmark Fund is equal to the Share Class’ Net Asset Value at inception and has the same performance as the Benchmark Index. The Maximum Subscription fee for the A Class is 5.50% and for the F class is 2.00%. * Effective Management Fees are as of October 2017

34

ISIN Code Class Type Currency Effective Management Fees* Minimum Initial Subscription Performance Fees

LU1575039877 A Capitalization USD 2.20% 100,000

15% of the annual performance ranging from 0 to 10% and 20% for performance above 10%, subject to

“High Water Mark”1

LU1575039950 F Capitalization USD 1.30% 500,000

LU1575040024 I Capitalization USD 1.20% 5,000,000

LU1626186875 I Capitalization EUR 1.20% 5,000,000

LU1575040370 I Distribution USD 1.20% 5,000,000

LU1575040453 ZI Capitalization USD 1.00% 30,000,000

LU1626187337 ZI Capitalization EUR 1.00% 30,000,000

LU1575040537 ZI Distribution USD 1.00% 30,000,000

LU1626186107 A Capitalization EUR(H) 2.25% 100,000

20% of the annual performance in excess of the difference between the Share Classes’ Asset Value (net of all

fees and costs but accrued Performance Fees ) and highest value between the Benchmark Fund2 and

the “High Water Mark”1 value.

Benchmark index: - EONIA capitalized for EUR(H)

shareclass - Overnight depot bank in CHF for

CHF(H) shareclass - SONIA capitalized for GBP(H)

shareclass

LU1626186289 A Capitalization CHF(H) 2.30% 100,000

LU1626186362 F Capitalization EUR(H) 1.35% 500,000

LU1626186792 F Capitalization CHF(H) 1.40% 500,000

LU1626186529 F Capitalization GBP(H) 1.30% 500,000

LU1626186958 I Capitalization EUR(H) 1.25% 5,000,000

LU1626187253 I Capitalization CHF(H) 1.30% 5,000,000

LU1626187170 I Capitalization GBP(H) 1.20% 5,000,000

LU1626187097 I Distribution EUR(H) 1.25% 5,000,000

LU1626187501 ZI Capitalization EUR(H) 1.05% 30,000,000

LU1626187840 ZI Capitalization CHF(H) 1.10% 30,000,000

LU1626187766 ZI Capitalization GBP(H) 1.00% 30,000,000

LU1626187683 ZI Distribution EUR(H) 1.05% 30,000,000


Biographies

Information about the staff team in AXA IM Chorus is only informative. We do not guarantee the fact that staff remain employed by AXA IM Chorus and exercise or continue to exercise in the AXA IM Chorus team.

35

Pierre-Emmanuel Juillard, Chief Executive Officer

Pierre-Emmanuel Juillard joined the Company as Managing Director in April 2016. He previously worked at Goldman Sachs in Hong Kong where he was a Partner, Head of Structuring and Responsible Officer in the Securities Division in Asia Pacific since 2010.

Prior to this he was Head of Structured Finance at AXA IM having joined AXA IM in 1999 as Head of Financial Engineering and Asset and Liability Management Studies. From 2001 to 2004, he was Head of Securitization and Structured Credit of AXA IM.

Before joining AXA IM, Pierre-Emmanuel spent two years as a Senior Derivatives Marketer covering financial institutions at Chase Manhattan Bank in London. Prior to that, he worked at Aurel in Paris as Head of Structured Derivatives and at Paribas Capital Markets in London as an Executive Quantitative Researcher and Marketer covering French financial institutions.

Pierre-Emmanuel was a member of the Board of Directors of the International Swaps and Derivatives Association (ISDA) from 2009 to 2010. Pierre-Emmanuel studied at ENSAE and ISUP in France, majoring in mathematics and economics.

Jérôme Brochard, Co-Chief Investment Officer

Jérôme Brochard joined the Company as co-CIO in July 2016. He previously worked at Goldman Sachs where he was a Managing Director in the Equities Division, heading the Systemic Trading Strategies team. Prior to this, Jérôme was a Founder of Nexgen Group (now a subsidiary of Natixis) developing the Corporate Derivatives and Reinsurance business activities in Europe from 2001 to 2005. He started his career in 1999 as an underwriter in AXA Re Paris.

Jérôme graduated from Ecole Polytechnique in France and holds an MSc eq. in Economics, Finance and Statistics from ENSAE in Paris. He is a member of the French Institute of Actuaries.

Hector Chan, Co-Chief Investment Officer

Hector Chan joined the Company as Co-CIO in April 2016. He previously worked at Goldman Sachs in Hong Kong where he was a Managing Director from 2009 to 2015. Hector held several positions at Goldman including Head of Macro Structuring Asia Pacific (2009-2015), Head of Japan Structuring (2013-2015) and Head of Fixed Income, Currencies and Commodities Structuring Asia Pacific (2015). Hector was also a member of the International Swaps and Derivatives Association's (ISDA) Asia Pacific Steering Committee and Board and Executive Committee member of the Asia Securities Industries and Financial Markets Association (ASIFMA).

Prior to that, Hector worked at BNP Paribas helping to build the rates, foreign exchange and credit structured business in India from 2003 to 2006 and in China from 2006 to 2007. He then assumed responsibility for the Asia Pacific ex-Japan Interest Rate and Foreign Exchange Structuring team.

Hector holds a Master’s degree in Finance and a DEA in Economics from Paris Dauphine university.


Biographies


36

Augustin Landier, Head of Research Lab

Augustin Landier joined the Company as Head of Research Lab in July 2016. Augustin is also a professor of Finance at HEC Paris, on leave from the Toulouse School of Economics.

Prior to joining the Company, Augustin was a part-time Research Manager at Capital Fund Management (2012-2016), focusing on long-short equity strategies.

Augustin taught Finance at New York University (2004-2009), at the University of Chicago (2002-2004), and was a Resident Scholar at the International Monetary Fund (2009).

His research is largely focused on behavioral finance, asset management and corporate finance, and his work was published in lead scientific Journals. Augustin was awarded the 2014 “best French young economist” award. He is also active in the public policy debate, was a member of the French Council of Economic Analysis (2010-1012 and 2014-2016) and published several books, among which « Investing for Change » (Oxford Press 2009).

Augustin completed his PhD in Economics at the Massachusetts Institute of Technology in 2002, and graduated from Ecole Normale Superieure in Paris in 1998.

Ahcene Gareche, Head of Quantitative Strategies

Ahcene Gareche joined the Company as a Senior Researcher on July 2016. He previously worked at Marshall Wace in London where he was a Quantitative Researcher since 2013. He was recently promoted to Head of Quantitative Strategies position.

Prior to this, he interned at Capital Fund Management as a researcher in the execution team in 2012. He developed a Fokker-Planck description for the queue dynamics of large tick stocks, under the supervision of Jean-Philippe Bouchaud, the Head of Research. His work has been published in the Physical Review E.

Before joining CFM, Ahcene interned at several places in various positions. He did a six months internship at AXA Private Equity in New-York in 2011. Prior to that he also spent two months as a researcher at UCLA in Los Angeles in the Maths Department and five months in the Risk team at Natixis in Paris in 2010.

Ahcene holds a Master’s degree of Advanced Studies from Paris 6 and Ecole Polytechnique and studied at the Ecole Normale Superieure in Cachan. He also passed all three levels of CFA examination.


Biographies


37

Philippe Muller, Chief Technology Officer

Philippe Muller joined the Company as Chief Technology Officer (CTO) on August 2016. He previously worked at Criteo where he was a Senior Engineering Lead since 2015.

Philippe held several positions at Capital Fund Management including Systems Engineer (2010-2013) and Software Engineer (2013-2015). He contributed to the construction of the research platform and improved software engineering practices and systems.

Prior to this, Philippe was a Security & Systems Engineer since 2001 in technology companies. He designed and built scalable and secure internet platforms both for his employers and their customers.

Philippe holds Master’s degree of Information Technology (Maîtrise en Sciences et Techniques – Technologies de l'Information et de la Communication) from the University of Lorraine (2005-2006).

Arnaud Carcel, Chief Operating Officer

Arnaud Carcel joined the Company as Chief Operating Officer in July 2016. He is also member of the Asian Business Board and Executive Committee of AXA IM Asia. Arnaud has been working for AXA IM since April 2005.

Previously, he was Deputy Chief Executive Officer and Chief Operating Officer at Kyobo AXA Investment Managers Co., Ltd. (“KAIM”), in charge of the general management of the firm, where he was a Board member.

Before joining KAIM, Arnaud was Global Head of Funds & Fees Servicing of AXA IM between 2009 and 2011, responsible for supervising and managing all specific fee calculation and intra-group transfer pricing activities, as well as the valuation and consolidation of AXA Group Insurance Companies’ dedicated investment vehicles.

Between 2005 and 2009, Arnaud was Strategic Auditor at AXA IM.

Arnaud started his career with Arthur Andersen in 1997, covering financial institutions for statutory audit, transaction support and consulting assignments.

Arnaud holds a Master’s degree in Finance and Accounting from Grenoble School of Management. He also passed CFA level 1 examination.


This is promotional material and not a regulatory mandatory document. We point out that this document does not comply with the requirements of respective applicable Directive 2004/39/EG or 2014/65/EU (MiFID/MiFID II) and the related directives and regulations. Therefore, this document is not suitable for any kind of sales services, consulting or financial services vis-à-vis retail clients. The information in this document is exclusively addressed to professional clients who receive this document directly from AXA Investment Managers Deutschland GmbH / its affiliated companies. It is to be treated strictly confidential. The recipients are not allowed to pass this document and the information contained herein on to a third party, in whole or in part, without the prior written consent of AXA Investment Managers Deutschland GmbH / its affiliated companies.

Please note the following information and read the latest prospectus including the general and special terms and conditions of the fund respectively the articles of association, which are the only relevant basis for the purchase of fund units/shares. Any transaction on the basis of information or explanation which is not contained within the prospectus is carried out exclusively on risk of the buyer.

The data, figures, declarations, analysis, predictions, concepts and any other information provided in this document is provided by AXA Investment Managers Deutschland GmbH / its affiliated companies (“AXA IM DE”) and based on our state of knowledge at the time of its creation.

The mentioned information may be amended at any time without notice. Due to the simplification the information in this document can be subjective. As far as we refer to information of third parties we point out that we do not overtake any liability for its content, for the correctness, completeness, topicality and adequacy of these data even if we use only such data which we consider to be reliable. Nevertheless, unintended erroneous statements or presentations may occur. A liability or guarantee for the up-to-dateness, correctness and completeness of the allocated information cannot be assumed by AXA IM DE.

Performance results of the past are no indicator for any future returns or trends. The value and income derived from investments may go down as well as up and is not guaranteed. Furthermore commissions and costs have an adverse effect on the performance of a fund. Please note furthermore that the information in this document is based on simulate historical view. Such information is no reliable indicator for the future. It is provided for illustrative purposes only. The real performance can considerably differ from the herein represented performance.

Information about AXA IM Chorus’ staff is only informative as of the date of this document. We do not guarantee continues employment of the mentioned staff members.

The information in this document is provided for information purposes only, and shall not be construed as legal or tax advice by AXA IM DE, nor shall it be construed as a solicitation to buy or a recommendation to invest. An investment or other decision may not be made on the only basis of this document. Prior to any transaction potentially interested parties must carefully consider the appropriateness of their investments to their specific situation considering the tax aspects and ensure that they understand the risks involved in their investment decisions. As part of this individual consultation you will also be informed of costs and fees associated with the purchase of one of the mentioned products. While providing investment services, AXA Investment Managers Deutschland GmbH and/or its affiliates may receive or grant benefits (commissions, fees or other payments as well as all monetary benefits).

You will receive the latest prospectus, Key Investor Information Document (“KIID”), and the most re-cent annual and semi-annual report upon request free of charge and in paper form at AXA Investment Managers Deutschland GmbH • Bleichstrasse 2-4 • 60313 Frankfurt/Main or from our distribution partners or under www.axa-im.de.

– for professional clients only / not for retail investors –

DATE: 07.05.2018

Disclaimer

38

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Auf der Suche nach alternativem Ertrag - Multi-Premia-Strategien AIC/BAI_AIC... · 2018-05-30 ·...

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