From investment discipline to intelligent agents
A Field Ripe for Automation
Automating Optimal Investment Decision Making
October 2018 Prime Capital AG Page 2
A well structured process in terms of environment: market information & portfolio accounting, buy and sell decisions, risk-return target and restrictions
[ … we’ll be mostly talking about quantitative/empirical finance rebranded as AI]
A device that perceives its environment and takes actions that maximize its chance of success at some goal
[T2 sensing its environment and seeking to eliminate Sarah Connor]
When we know how a machine does something “intelligent”, it ceases to be regarded as intelligent…it’s perceived as doing “only” data mining or optimization
[Face recognition by cameras rarely associated with AI]
Ever larger disruptions: games, spam filters and cats!
The Investment Process as an Artificial Intelligence Problem
Major Achievements of AI
Page 3 October 2018 Prime Capital AG
Intelligent behaviour by machines
The Investment Process as an Artificial Intelligence Problem
Major Goals of AI
Machine Learning Finding patterns, predicting outputs, predicting optimal actions
Reasoning, problem solving Optimal decision making under uncertainty
Planning, scheduling Find optimal path to achieve a set goal
Natural language processing Read and understand human language
Perception Use inputs from sensors
Motion and manipulation Localization, mapping and motion planning
Page 4 October 2018 Prime Capital AG
Focus on machine learning and optimization
The Investment Process as an Artificial Intelligence Problem
Major Goals of AI Applied to Investment Management
Machine Learning Learn from data to predict returns or buy/sell decision
Reasoning, problem solving Design optimal target portfolio
Planning, scheduling Optimal trade scheduling to move from current to target portfolio
Natural language processing Most information is already numeric
Perception Mostly no need for sensors
Motion and manipulation Mostly achieved by book-keeping
Page 5 October 2018 Prime Capital AG
Both disciplines significantly converged over the past decade
Related Disciplines
Machine Learning vs. Statistics
October 2018 Prime Capital AG Page 6
Often doing the same things
Naming it differently
A simple systematic/automated trading framework to test various methods
Case Study
Market Neutral Long/Short Stock Selection
October 2018 Prime Capital AG Page 7
Simulation UK FTSE 100 stocks since early 00’, fundamental, price and volume data GBP100m trading capital, transaction cost estimates, daily trading Rolling forward analysis, 1 year calibration period
Objectives Market neutral long/short stock selection Maximize Sharpe ratio Targeting 10% volatility p.a.
Test impact of various ML/AI approaches
Supervised learning for return forecast Unsupervised learning for stock clustering and portfolio construction Dynamic programming for optimal trading moving from current to target portfolio
Established and well-defined
Case Study
Investment Management Process
October 2018 Prime Capital AG Page 8
Information
Historical & live prices
Balance sheet data
Earning estimates
Macro data
Alternative data like sentiment, satellite imagery, etc.
Active views
Views on asset returns expressed as rank or expected returns
View on as asset risk profile expressed as covariates, sector classification, etc.
Optimal portfolio
Target portfolio maximizing investment objectives
Subject to various constraints
Implementation
Path to optimal portfolio
Buy/sell decision on path to optimal portfolio Investment
Objectives
Book-keeping and risk reporting: profit & loss, balance sheet, VaR, trade details (costs, implementation, lag)
Applying AI across the investment process
Case Study
Basic Modelling Framework
October 2018 Prime Capital AG Page 9
Fundamentals
Volume
Price
Estimate
Select
Predict
Target portfolio
Simple heuristics, buy top 20%, sell bottom 20% of forecasts
Create clusters of stocks to increase numbers of independent portfolios and thus reduce risk
Trade scheduling
Optimally rebalance towards the target portfolio in light of transactions costs and signals of various speeds
Supervised learning Linear and nonlinear regression
Data collection Filtering and feature extraction
Statistical and industry classification
Smoothing portfolio
rebalancing
More sophisticated learners do not always perform better
Case Study
Supervised Learning
October 2018 Prime Capital AG Page 10
0
100
200
300
400
500
600
Jun 2
001
Fe
b 2
002
Okt
20
02
Jul 2
00
3M
rz 2
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Mai 20
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No
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pr
2016
Dez
201
6
Sim
ula
ted
pe
rfo
rmance
OLS Ridge regression AdaBoost
Simulation parameters
GICS sector neutral book
Buy/sell top/bottom 10%
Gross of transaction costs
No trade scheduling
Simulation parameters
Sharpe ratio = 1.0
Realized volatility = 10%
Gross exposure = 2 x
Turnover = 50% daily
Optimal trading towards target portfolio to minimize transaction costs
Case Study
Dynamic Programming
October 2018 Prime Capital AG Page 11
0
20
40
60
80
100
120
140
160
Dez
201
1
Mrz
20
12
Jul 2
01
2
Okt
20
12
Dez
201
2
Ap
r 2
013
Jul 2
01
3
Se
p 2
013
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201
3
Mrz
20
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20
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Sim
ula
ted
pe
rfo
rmance
basic system gross basic system net
basic system net optimized
Initial ridge regression/sector neutral model gross and net of transactions
Further optimization of transaction costs through trade scheduling
Key Takeaways
Relax!
October 2018 Prime Capital AG Page 13
Old stuff AI has been applied to investment decisions for such a long time that it is seen
there as mere optimization, statistics or economic analysis
Supervision & expert knowledge
Learning is mostly supervised and requires serious expert knowledge
Noisy & limited dataset Classical linear statistical models are most often sufficient to capture the bulk of
alphas
Key Takeaways
Industry Outlook
October 2018 Prime Capital AG Page 14
Skynet (Terminator) supposedly became self-aware on August 29th 1997
Key Takeaways
Industry Outlook
October 2018 Prime Capital AG Page 15
Machines Best at quickly analyzing linear relationships in high dimensions Focus on liquid and standardized markets Handling of large portfolios and frequent transactions
Future Shift to automated strategies is already happening Driven by investors demand for lower costs and diversifications, e.g. ETF flow Driven by regulators; what can be codified can be automated
Humans Best at quickly finding complex non-linear patterns in low dimensions Focus on specialized and illiquid opportunities Handling of bespoke and infrequent transactions
Skynet (Terminator) supposedly became self-aware on August 29th 1997
October 2018 Prime Capital AG Page 16
This document does not constitute any advice, recommendation or investment proposal; was issued for information purpose only, has no contractual value; and may contain
errors and/or omissions. This document does not create any legally binding obligations on the part of Prime Capital AG and/or its affiliates (“Prime Capital”) and nothing
contained herein shall in any way constitute any offer by Prime Capital to provide any service or product, or an offer or solicitation of an offer to buy or sell any securities or
other investment product. This document is not intended for distribution or use by any person or entity who is a citizen or resident of or located in any jurisdiction where such
distribution, publication or use would be prohibited. Past performance is not indicative of future results. The value of an investment in the fund may go up as well as down and
can result in losses, up to and including a total loss of the amount initially invested. No representation or warranty, express or implied, is made as to the accuracy,
completeness or correctness of the information contained in this document and Prime Capital assumes no responsibility or liability for any of the contents, errors and/or
omissions herein, nor for any use thereof or reliance placed thereupon by any person. In case of any inconsistency between this document and the latest prospectus pertaining
to the fund, that prospectus shall prevail. A decision to invest in the fund should only be taken after careful consideration of that prospectus and the legal information contained
therein. The prospectus can be obtained from the fund’s Administrator, registered office or representative (where applicable). You should consult a lawyer, an accountant or
other financial advisor as to the fund’s suitability for you, prior to any investment in the fund.
Source of data: Prime Capital AG
Disclaimer
One of the most trendy research topic of hedge fund managers
Alternative data
Prime Capital AGPage 2 October 2018
The Legal Risks of Alternative Data
Investment management industry
JP Morgan estimates the investment management industry’s spend on big data is in the $2-3 billion range, and the number is expected to have double digit annual growth
Quantitative managersAlternative data usage has been concentrated with quantitative investment managers such as Renaissance, Winton, D.E. Shaw, Two Sigma and WorldQuant
Discretionary managersHedge funds with partially discretionary strategies such as Marshall Wace, Point72 or Citadel have also looked into this space
One of the most trendy research topic of hedge fund managers
Alternative data
Prime Capital AGPage 3 October 2018
Innovators
50+ firms
have been
working with
alternative
data for
years
Cutting-edge
hedge funds
are
innovators in
this space
Early adopters
20% of HFs >
$1bn AuM have
personal
dedicated to
alternative data source: Jefferies
24% of
discretionary
managers are
using big data source: Barclays
Early majority
The bulk of
quantitative and
discretionary
managers fall
into this category
Late majority
70% of firms
say the
importance
of big data
will grow for
all firms source:
JPMorgan
80% firms
want greater
access to
alternative
data source:
Greenwich Survey
Late majority
Few skeptics
from
traditional
investment
industry
Source: Source: Eagle Alpha, “Alternative Data: Applications & Case Studies Version 2”
The Legal Risks of Alternative Data
Big data collected, cleaned, packaged, modelled and distributed to generate predictive insights and investment returns
What is alternative data?
Prime Capital AGPage 4 October 2018
Fully diffused /Fully commoditized
Historical EOD and
intraday prices on
equities, FX and listed
derivatives
Balance sheet data
Earnings estimates
Macro data (e.g. FRED)
Diffusing now / Moderately commoditized
Sentiment data
Geo-location
Satellite imagery
Economic data
Transportation
Nascent / Untapped
Nanosatellite (weather,
maritime)
Drone imagery
Internet of things
Wearable tech
The Legal Risks of Alternative Data
Legal risks are less emphasized and potentially overlooked by managers and investors
Legal grey zone
Prime Capital AGPage 5 October 2018
’’
’’
Source: Financial Times.
The Legal Risks of Alternative Data
Various potential legal risks are associated with alternative data
Risks
Prime Capital AGPage 6 October 2018
Source: Integrity Research Associates
Availability on
exclusive basis Insider trading Privacy law Copyright law
Web data Unlikely Lower risk Lower risk Higher risk
Web traffic Unlikely Lower risk Lower risk Higher risk
Sentiment data Unlikely Lower risk Lower risk Lower risk
Credit card transactions Possible Higher risk Higher risk Lower risk
Email Receipts Possible Higher risk Higher risk Lower risk
Geolocation Possible Higher risk Higher risk Lower risk
Satel l ite Possible Higher risk Lower risk Lower risk
The Legal Risks of Alternative Data
Facebook – Cambridge Analytica data scandal
Risks: user privacy violation
Prime Capital AGPage 7
Facebook exposed up to 87 million users’ data to Cambridge Analyticathrough a quiz app
Cambridge Analytica gained access to the data of app user’s facebook friends without their knowledge, consent or authorization
Facebook may initially have been misled, but it failed to notify Facebook users about the situation and to ensure that the stolen data was destroyed after learning the true nature of Cambridge Analytica’s work
October 2018
Source: vox.com
The Legal Risks of Alternative Data
Alternative data = material non-public information?
Risks: insider trading
Prime Capital AGPage 8
SEC v. Huang
Two employees of Capital One used the bank’s customers credit card data to predict stock price moves for their personal trading account
The pair made $2.8mn between 2015 and 2017
The court ruled that the credit card transaction data is material non-public information and the use of data is without consent of the bank or the credit card users
The two employees end up paying $4.7mn and $13.5mn fine respectively
October 2018
Source: Integrity Research Associates
The Legal Risks of Alternative Data
Competition in speed and ways of accessing the information
Information edge
Prime Capital AGPage 9 October 2018
1810s Nathan Rothschild: trading the waterloo victory before the Wellington’s envoy delivered the news 1850s Paul Reuter:
accessing financial news using pigeons beating post trains
2000s High frequency trading: using powerful computers to transact trades ahead of other market players
1980s Ivan Boesky: arbitraging stocks based on insider information
2000s Alternative data: using big data to analyzecompany performance prior to official announcements
The Legal Risks of Alternative Data
As investors, we should be aware of the potential risks associated with alternative data
Conclusion - risk awareness
Prime Capital AGPage 10 October 2018
ManagersAmong the managers we spoke to, the more established ones already take legal risks into consideration while selecting and utilizing alternative data
InvestorsInvestors should be aware of the current legal grey zone/future legal risk while carrying out due diligence with managers who utilize alternative data
The Legal Risks of Alternative Data
Prime Capital AG | Hedge Fund Solutions
Page 1 | April 2016 | Strictly Confidential
The Legal Risks of Alternative Data
November 2018
Prime Capital AG | Hedge Fund Solutions
Page 2 | November 2018 | strictly confidential
Content
Executive Summary ................................................................................................................................................. 3
Background ............................................................................................................................................................. 4
What is Alternative Data? ........................................................................................................................................ 5
Legal Grey Zone ...................................................................................................................................................... 5
Concluding Remarks ............................................................................................................................................... 8
Contact and Disclaimer ........................................................................................................................................... 9
Prime Capital AG | Hedge Fund Solutions
Page 3 | September 2016 | Strictly confidential
Executive Summary This paper aims to discuss the potential legal risks of alternative data and to raise awareness among
investors about the issues which are currently in a legal grey zone and are potentially overlooked by many
market participants. The paper begins with a brief background of alternative data, before reviewing several
most prominent legal risks coupled with legal cases associated with alternative data and concludes with a
historical review showing that market players have always sought to gain an informational edge. All data
provided in this note are from Prime Capital AG if not stated otherwise.
Prime Capital AG | Hedge Fund Solutions
Page 4 | November 2018 | strictly confidential
Background The investment landscape is constantly changing and in recent years we start to frequently hear buzz words
like “machine learning”, “artificial intelligence” and “big data / alternative data”. The appearance and
advancements in alternative data provide more information for the machine to learn from and the
developments in alternative data and machine learning is bringing artificial intelligence closer to its goal of
creating intelligent machines. The focus of this paper is on alternative data which enriches the basis for
machine learning and artificial intelligence and has been one of the most trendy research topics among
hedge fund managers.
For the past few years, alternative data usage has been concentrated with cutting edge quantitative
investment managers such as Renaissance, Winton, D.E. Shaw, Two Sigma and WorldQuant. Nowadays
almost all quantitative managers that we talk to speak about alternative data and many discretionary
managers with strong quantitative capabilities such as Point72, Citadel and Marshall Wace have also looked
into this space as well.
The alternative data industry has seen rapid growth over the past 10 years from around 50 data providers to
currently 350 data providers in the market1. JP Morgan estimates that the amount that the investment
management industry spends on big data is in the $2-3 billion range, and this number is expected to have
double digit annual growth.
Source: Eagle Alpha, “Alternative Data: Applications & Case Studies Version 2”
1 Source: alternativedata.org
Innovators
50+ firms have
been working
with alternative
data for years
Cutting-edge
hedge funds are
innovators in this
space
Early adopters
20% of HFs >
$1bn AuM have
personal
dedicated to
alternative data source: Jefferies
24% of
discretionary
managers are
using big data source: Barclays
Early majority
The bulk of
quantitative and
discretionary
managers fall
into this category
Late majority
70% of firms say
the importance
of big data will
grow for all firms source: JPMorgan
80% firms want
greater access to
alternative data source: Greenwich
Survey
Late majority
Few skeptics
from
traditional
investment
industry
Prime Capital AG | Hedge Fund Solutions
Page 5 | November 2018 | strictly confidential
What is Alternative Data? Many of our online and offline activities leave digital fingerprints - the shops we’ve been to, the comments we
post after visiting hotels and restaurants, the credit card transactions we made, etc. Such data collected /
purchased by investment managers to form predictive insights regarding performance of a company or the
economy and to generate investment returns is called alternative data.
For example, hedge fund managers can analyse the credit card transaction records in combination with satellite
images that can scan car parks and geolocation data from mobile phones to show how many people are visiting
the stores. All these pieces of information can generate signals of how well a company’s business is doing, long
before the financial results are released.
Legal Grey Zone On hearing that investment managers can gather information regarding our consumption behaviours (even
on an anonymised basis) and use such information to generate investment returns, people may already raise
questions about whether such activities potentially violate their rights to consumer privacy. What if a
manager also claims that they can arrange exclusive deals to make sure that they are the only one who can
receive such data? Just like what Matthew Granade, Point72’s chief market intelligence officer told the LSE
students during the alternative investments conference in the school: "The great thing about this area is you
can arrange deals where you are the only ones who get it.” Wouldn’t there be some risks of trading on
material non-public information? For the readers who are not familiar with Point72, it is a hedge fund
managed by Steven Cohen, whose SAC Capital Advisors hedge fund was shut down by federal authorities
in 2013 over insider trading.
Managers usually place less emphasis on the potential legal risks of using alternative data. Their concerns are
more on the cost side or the quality of the data. At the moment, we don’t have clear answers to those questions
and issues related to alternative data are currently in a legal grey zone. However, several risks that are most
likely to be associated with alternative data have been identified. The table below summarizes the likelihood of
these risks regarding some of the most commonly researched alternative data:
Fully diffused / Fully commoditized
Historical EOD and
intraday prices on
equities, FX and listed
derivatives
Balance sheet data
Earnings estimates
Macro data (e.g. FRED)
Diffusing now / Moderately commoditized
Sentiment data
Geo-location
Satellite imagery
Economic data
Transportation
Nascent / Untapped
Nanosatellite (weather,
maritime)
Drone imagery
Internet of things
Wearable tech
’’
’’
Prime Capital AG | Hedge Fund Solutions
Page 6 | November 2018 | strictly confidential
Availability on exclusive basis Insider trading Privacy law Copyright law
Web data Unlikely Lower risk Lower risk Higher risk
Web traffic Unlikely Lower risk Lower risk Higher risk
Sentiment data Unlikely Lower risk Lower risk Lower risk
Credit card transactions Possible Higher risk Higher risk Lower risk
Email receipts Possible Higher risk Higher risk Lower risk
Geolocation Possible Higher risk Higher risk Lower risk
Satellite Possible Higher risk Lower risk Lower risk
Source: Integrity Research Associates
From the table we can see that the usage of web data and web traffic has a higher risk of violating copyright
law because, for example, information which is distributed on the internet could be unintended for public
distribution or usage. Documents labelled “for client only” or “strictly confidential” ca also end up appearing
among google search results. Another example is sentiment data. Such data extracted from public official’s
speeches or other public comments is more likely to be in compliant with the current copyright laws, while
information derived from blogs and articles might run into copyright issues.
Many of the above listed alternative data also trigger higher risks of insider trading and privacy infringement.
Below we discuss those two types of risks in details.
Privacy violation risk A recent and famous example of the privacy violation risk of using big data is the Facebook – Cambridge
Analytica data scandal where Facebook exposed up to 87 million users’ data to Cambridge Analytica through a
quiz app. Facebook failed to notify its users about the situation and ensure that the stolen data was destroyed
after learning the true nature of Cambridge Analytica’s work. While Cambridge Analytica is accused of
accessing the date of the quiz taker’s Facebook friends without their knowledge, consent or authorization.
Cambridge Analytica here resembles hedge fund managers in the way that they obtained the big data through
a legal contract. However, that contract doesn’t protect the purchasers from the legal risks associated with the
data itself. It is the hedge fund managers’ responsibility to do the proper due diligence on the datasets
purchased and examine them on a case-by-case basis. In addition, even though many managers claim that they
only use data on an anonymised basis, they may still not comply with the privacy law if the users didn’t give
explicit consent for their data to be used. This case also shows how seemingly insignificant information can
provide material information once gathered and structured in a certain way.
Insider trading The SEC has already successfully prosecuted an insider trading case where two employees of Capital One used
the bank’s customer credit card data to predict stock price moves for their personal trading account. The court
ruled the credit card transaction data to be considered material non-public information and that the use of data
was without consent of the bank or the credit card users. The pair made $2.8mn between 2015 and 2017 but
also end up paying $4.7mn and $13.5mn fine respectively.
One defendant settled with a fine of $4,7mn while the other one appealed and argued that the data obtained
was not material because Capital One’s transaction data represented an average of 2.4% of the companies’
revenues. However, the SEC presented evidence that the data had a high correlation to revenues and
convinced the court that the information was material. Similar to the Facebook – Cambridge Analytica case, the
judgement of materiality depends on the predictive power of the data. It is reasonable to assume that
alternative data, which investment managers are willing to pay for, can provide predictive insights for
investment decision making, thereby increasing the likelihood that it will be considered material information.
The next important question to determine if any insider trading risk is involved in the use of alternative data is
whether the data is non-public. The fact that data vendors charge for the packaged data doesn’t make it non-
public as long as the raw data is still accessible for the public. For example, the satellite image of the number of
Prime Capital AG | Hedge Fund Solutions
Page 7 | November 2018 | strictly confidential
cars parked outside a supermarket is public data because one can always go to the parking lot and count how
many cars are parked there. However, availability of data on an exclusive basis potentially heightens the risk of
insider trading by making the information not accessible to the general public.
Prime Capital AG | Hedge Fund Solutions
Page 8 | November 2018 | strictly confidential
Concluding Remarks Throughout the history of finance, people have always sought to gain access to information faster than the
other market participants so as to trade ahead of them. Beginning with the famous story of Nathan Rothschild
trading on the waterloo victory before Wellington’s envoy delivered the news, and then again where Paul
Reuter transferred financial news using pigeons which were faster than the post trains. Then in the 80s we had
Ivan Boesky arbitraging stocks based on insider information and in 2000s where people started to utilize high
frequency trading and alternative data to gain an informational edge in the market competitions.
In this pursuit of gaining a leg up in the market, some engaged in taking advantage of technological advances
while some employed means that were ex-post proven to be illegal. In the cases of the illegal practices, there
has always been a gap between these practices and the regulations that are set in place. Though there are
currently no clear rules or regulations to define the legal risks regarding alternative data and some managers
might put less emphasis on it or overlook the potential legal risks here, but we feel that investors should have
the awareness of this issue and take it into consideration in the due diligence process.
Prime Capital AG | Hedge Fund Solutions
Page 9 | November 2018 | strictly confidential
Contact and Disclaimer
Prime Capital AG
Bockenheimer Landstr. 51-53
60325 Frankfurt am Main
GERMANY
Tel: +49 (0)69 9686 984 0
Fax: +49 (0)69 9686 984 61
21, rue Philippe II
L-2340 Luxemburg
LUXEMBURG
Tel: +352 278 610 84
Fax: +352 278 612 95
Internet: www.primecapital-ag.com
This document is issued and approved by Prime Capital AG, Frankfurt. This information is intended solely for the use of
the person to whom it is given and may not be reproduced or given to any other person. It is not an offer or solicitation
to subscribe for shares in any fund and is by way of information only. Please note that the price of shares and the income
from any fund may go down as well as up and may be affected by the changes in rates of exchange. Past performance is
not indicative of future performance. An investor may not get back the amount invested.
Source of data: Prime Capital AG.
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Tungsten Capital Management GmbH, Hochstraße 35 – 37, 60313 Frankfurt, Germany. Tungsten Capital Management GmbH verfügt über die erforderliche Erlaubnis derBundesanstalt für Finanzdienstleistungsaufsicht ("BaFin") und unterliegt deren Aufsicht.
Gewinnbringende Strategien in turbulenten Märkten
Frankfurt am Main 23.10.2018
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Disclaimer
Dieses Dokument richtet sich ausschließlich an Kunden der Kundengruppe „Professionelle Kunden“ gem. § 31 a Abs. 2 WpHG und/oder „Geeignete Gegenparteien“ gem. § 31 a Abs. 4 WpHG und ist nicht für
Privatkunden bestimmt. Die Verteilung an Privatkunden ist nicht beabsichtigt. Es dient ausschließlich Informationszwecken und stellt keine Finanzanalyse im Sinne des §34b WpHG, keine Anlageberatung,
Anlageempfehlung oder Aufforderung zum Kauf oder Verkauf von Finanzinstrumenten dar. Historische Wertentwicklungen lassen keine Rückschlüsse auf ähnliche Entwicklungen in der Zukunft zu. Diese sind
nicht prognostizierbar. Alleinige Grundlage für den Anteilerwerb sind die Verkaufsunterlagen zum Sondervermögen. Verkaufsunterlagen zu allen Sondervermögen der Universal-Investment sind kostenlos bei
Ihrem Berater / Vermittler der zuständigen Depotbank oder bei Universal-Investment unter www.universal-investment.de erhältlich. Alle angegebenen Daten sind vorbehaltlich der Prüfung durch die
Wirtschaftsprüfer zu den jeweiligen Berichtsterminen. Die Ausführungen gehen von unserer Beurteilung der gegenwärtigen Rechts- und Steuerlage aus. Für die Richtigkeit der hier angegebenen Informationen
übernimmt Tungsten Capital Management keine Gewähr. Änderungen vorbehalten. Quellen: Bloomberg, eigene Berechnungen.
Wertentwicklungen der Vergangenheit sind kein verlässlicher Indikator für die künftige Wertentwicklung.
Stand: 31. Januar 2018
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Gewinnbringende Strategien in turbulenten Märkten
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TUNGSTEN CAPITAL MANAGEMENT
3
Eigentümer Struktur
Corecam Investment GroupGeschäftsführende Partner
Struktur verwalteter Vermögen
Family Offices
Versorgungswerke, Pensionskassen
Banken, Vermögensverwalter, Dachfonds
Gewinnbringende Strategien in turbulenten Märkten
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Referent
LUTZ KLAUSCHIEF INVESTMENT OFFICER UND MANAGING PARTNER, TUNGSTEN CAPITAL MANAGEMENT
SAL. OPPENHEIM, Köln, Portfoliomanagement
PENSIONSKASSE DEGUSSA, Düsseldorf / Essen, Vorstand - Leiter Kapitalanlagen
MAYFAIR FAMILY OFFICE, Hamburg, Leiter liquide Kapitalanlagen
4
Gewinnbringende Strategien in turbulenten Märkten
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Ein alternativer Blick auf Asset AllocationGewinnbringende Strategien in turbulenten Märkten
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.6
Konvergenz Divergenz
• Marktteilnehmer verhalten sich rational – neue Information wird ohne große Preisverwerfungen verarbeitet
• Profitiert von niedrig realisierender Volatilität
• Profitabel in „ruhigen“ Märkten
• Marktteilnehmer verhalten sich irrational - neue Information führt zu großen Preisverwerfungen
• Profitiert von hoch realisierender Volatilität
• Profitabel in „rauhen“ Märkten
Nominal
Aktien StaatsanleihenKredit Equity L/SYield Enhancement
Risiko
Aktien StaatsanleihenKredit Equity L/SYield Enhancement
Alternative Risikosicht
Konvergenz Divergenz
Seite Seite
Histogramme
7
Gewinnbringende Strategien in turbulenten Märkten
- High Yield & Put Write zeigen die typischen Eigenschaften einer Konvergenzstrategie, bspw. mit einer Kurtosis >20
- Aber auch Aktien weisen diese Eigenschaften auf
- Staatsanleihen hingegen nicht, was sie mit ihrer negativen Korrelation zu Aktien zu einem sehr wertvollen Portfolioassetmacht
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.
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Was kann man tun?
I. Reduktion aggressiver Carry Strategien
Gerade in diesen Strategien erhält man eine attraktive Vergütung für das eingegangene Risiko
II. Kauf von Staatsanleihen mit langer Laufzeit
Einziges long-only Asset mit Divergenz Eigenschaften und positiver Ertragserwartung
III. Tail Risk Hedging
Short Volatility wahrscheinlich attraktivste Risikoprämie am Kapitalmarkt – Wirklich dagegen stellen?
IV. Long Vol-biased Strategies
CTAs, Global Macro und long-biased Volatility Trading – profitieren von hoch realisierender Vol
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.8
Gewinnbringende Strategien in turbulenten Märkten
Seite Seite
Volatility Risk Premium – Standardstrategie „Put Spread“
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.9
Gewinnbringende Strategien in turbulenten Märkten
Put Spread I
-10% -5% +/- 0% +5%
-10,0% -5,4% +1,3% +3,0%
Put Spread II
-10% -5% +/- 0% +5%
-10,0% -10,0% +0,5% +2,3%
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Volatility Trading – Unattraktiv = Opportunität
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.10
Gewinnbringende Strategien in turbulenten Märkten
Kauf Put Spread II
-10% -5% +/- 0% +5%
+10,0% +10,0% -0,5% -2,3%
Verbesserung der Positionskonstruktion
-10% -5% +/- 0% +5%
+1,0% +4,4% -0,5% +3,9%
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Implied/Realized Spread nicht für jeden Gleich
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.11
Gewinnbringende Strategien in turbulenten Märkten
-2,0% -1,5% -1,0% -0,5% +/- 0% +0,5% +1,0% +1,5% +2,0%
+0,8% +0,5% +0,2% +0,05% 0% +0,05% +0,2% +0,5% +0,8%
Bereits kleine Tagesbewegungen reichen aus um negativen Carry signifikant zu mildern
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Volatility Trading Cross Asset – Fixed Income Future
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.12
Gewinnbringende Strategien in turbulenten Märkten
-2,0% -1,5% -1,0% -0,5% +/- 0% +0,5% +1,0% +1,5% +2,0%
-0,3% -0,3% -0,3% -0,2% -0,1% +0,1% +0,5% +1,0% +1,6%
Call Ladder auf 10Y Treasury Future
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Zusammenfassung
Ausschließlich zur Information für "Professionelle Kunden" und "Geeignete Gegenparteien" gemäß §31a Abs. 2 und 4 WpHG.
Long-biased Volatility Trading bietet eine Alternative
Es gibt eine hohe Anzahl an Einzelstrategien im Volatilitätsmarkt, jedoch funktioniert keine in jeder Marktphase
Risikoprämie aus Volatilität wahrscheinlich attraktivste strukturelle Prämie am Markt
Systematisch, passives long Exposure zu Volatilität als Hedge extrem teuer
Investmentportfolios sollen eine Risikoprämie verdienen
Hohe Allokation zu Konvergenz Strategien sinnvoll
13
Gewinnbringende Strategien in turbulenten Märkten
Aktives Management ist der Schlüssel zur Reduktion des negativen Carry
Manager die alle Strategien beherrschen und richtig einsetzen schaffen einen hohen Mehrwert