Case Study Presentation at the FoHF Summit 2nd – 4th June 2008 Le Meridien Beach Plaza, Monte-Carlo, Monaco

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Alpha Generation based on Forecasts –
Intergrated Active Asset Management
European fund of hedge funds summit
a marcusevans FoF summit series event
2 - 4 June 2008 | Le Méridien Beach Plaza | Monte-Carlo | Monaco

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Disclaimer
Quantitative
Analysis &
Optimization
The views and opinions expressed in this presentation are those of the authors
only, and do not necessarily represent the views and opinions of Siemens AG,
or any of its employees. The authors make no representations or warranty,
either expressed or implied, as to the accuracy or completeness of the
information contained in this presentation, nor is he recommending that this
presentation serves as the basis for any investment decision. This presentation
is prepared for the European fund of hedge funds summit on 2 - 4 June 2008 in
Monte-Carlo, Monaco only. Research support from Fin4Cast is gratefully
acknowledged.
Dr. Miroslav Mitev & Dr. Martin Kuehrer - Siemens AG Österreich, Siemens IT
Solutions and Services, Program and System Engineering, Fin4Cast,
Gudrunstrasse 11, 1100 Vienna, Austria, Phone: +43 (0) 517 07 46253, Fax: +43
(0) 517 07 56256, email: info@fin4cast.com, www.fin4cast.com/indices.
The corresponding paper “New trends in Active Asset Management: Integration
of Research, Portfolio Construction and Strategy Implementation for Systematic
Investment Strategies in the Time of Algo-Trading” is available upon request.
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Quantitative
Agenda
Analysis &
Optimization
Introduction of Siemens fin4cast
New trends in Active Asset Management – Integration of
Research, Portfolio Construction and Strategy Implementation
Siemens fin4cast Integrated Active Asset Management
Approach
Case Study – fin4cast Income Index
Conclusion and Q&A
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Quantitative
Introduction of Siemens fin4cast
Analysis &
Optimization
fin4cast has its roots in an internal project for Siemens pension and
treasury department in 1995. fin4cast with 50 staff is based in
Vienna, Austria.
fin4cast is part of the Program and System Engineering (PSE) division
of Siemens AG Österreich (SAGÖ). SAGÖ group with 30.000 staff is
headquartered in Vienna, Austria.
PSE with 7 000 staff and locations in 10 countries is headquartered in
Vienna, Austria.
PSE offers hardware and software solutions, selected products, as well
as a broad range of services for the entire field of information and
communications technology, primarily to Siemens in-house groups
and divisions.
fin4cast is a provider of quantitative and pure systematic investment
strategies, designed to adapt to the current market environment.
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Quantitative
PSE Services provided to Siemens Groups and Divisions
Analysis &
Optimization
PG A&D ICN Siemens AG Österreich
PG A&D ICN Siemens AG Österreich
Power Generation Automation and Drives Information and Internal contracts
Power Generation Automation and Drives Information and Internal contracts
Communication Networks
Communication Networks
PTD I&S ICM Other
PTD I&S ICM Other
Power Transmission and Industrial Solutions Information an regional companies
Power Transmission and Industrial Solutions Information an regional companies
Distribution and Services Communication Mobile
Distribution and Services Communication Mobile
TS SD SBS
TS SD SBS
Transportation Systems Siemens Dematic AG Siemens Business Services
Transportation Systems Siemens Dematic AG Siemens Business Services
GmbH & Co. OHG
GmbH & Co. OHG
SV SBT MED PSE
SV SBT MED PSE
Siemens VDO Siemens Building Medical Solutions
Siemens VDO Siemens Building Medical Solutions Program and
Program and
Automotive AG Technologies AG
Automotive AG Technologies AG System Engineering
System Engineering
SFS Osram GmbH Central units
SFS Osram GmbH Central units
Siemens Financial
Siemens Financial
Services GmbH
Services GmbH
Infineon
Fujitsu Siemens INNOVEST
Infineon
Fujitsu Siemens INNOVEST
Infineon Technologies AG
Computers Kapitalanlage AG
Infineon Technologies AG
Computers Kapitalanlage AG
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Quantitative
Introduction Siemens fin4cast
Analysis &
Optimization
For its own use Siemens monitored currencies, commodities and -
especially for its pension funds – stock and bond markets.
Siemens also developed quantitative tactical asset allocation strategies
for its own requirements.
fin4cast was established in 1995 to develop and to apply complex
quantitative methods for predicting returns and estimating risks of
individual financial instruments, and for optimizing of investment
portfolios.
The main objective of fin4cast project was to adapt the already existing
load forecasting and power plant optimization Siemens technology to
the global financial markets and to leverage the existing quantitative
Know-How.
As result, the unique fin4cast technology emerged providing Siemens
with a strong competitive edge and ability to develop innovative,
quantitative and pure systematic investment strategies.
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Quantitative
New Trends in Active Asset Management
Analysis &
Optimization
Integration of Research, Portfolio Construction &
Strategy Implementation
Portfolio Strategy
Research Construction Implementation
• Order Generation
• Target Analysis • Maximize Return
• Minimize Risk • Order Execution
• Input Pre-selection
• Risk/Return Optimization • Risk Management
• Input Selection
• Optimal Asset Allocation
• Slippage Analysis
• Forecasting
• Portfolio Analysis
Portfolio Analysis & Back-propagation Slippage Analysis & Back-propagation
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fin4cast Integrated Research Process
Quantitative
Analysis & From Data Acquisition to Forecasts Generation
Optimization
Data storage,
Data Input pre-selection Input Selection
processing &
Acquisition
cleaning
Criteria: Search Algorithms:
• Reuters • economical • Neighborhood search
• Thomson • statistical • Iterative improvement
Financial approaches
• Genetic Algorithm
Linear Models
Forecast Post analysis
• ARIMA/SARIMA
Comparative in sample and out of
• VAR/VARX
sample tests
• Factor Models
(Forecast Statistics)
• ARCH/GARCH
Evaluation
rejected
Estimation methods:
AOLS, WOLS, SUR, ML.
Forward tests
(Forecast Statistics) Non Linear Models
• Single & Multi Output MLP
Evaluation
rejected
Learning Algorithms
Forecasts • Steepest Descent
• Quick prop
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Integration of Research - Input Selection for the
Quantitative
Analysis &
Mathematical Forecasting Models
Optimization
Original Economical Technical Statistical Input Set Search Optimized
Input Set Criteria Analysis Analysis Algorithm Input Set
app.. 2000 app.. 800 app.. 3500 app.. 100 app.. 20
Time Series Time Series Time Series Time Series Time Series
Macro
gs
Correlation &
La
Economic
Stationarity Regression
Interest Analysis
Correlation
Rates
AN Algorithm
Dynamic
Price Data
Correlation Generic
Currency Algorithm
Normality
Rates
Economical
Granger
etc. Selection
Causality
Grading
Sensitivity
Stochastic max. 20 most
Analysis
Oscillators important
driving factors
Relative
Principal of the future
Differences
Component & returns of a pre-
(Exponential) Factor
specified asset,
Moving Analysis
e.g. S&P 500
Average
Cluster Future
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Integration of Research - Building & Evaluating of the
Quantitative
Analysis &
Mathematical Forecasting Models
Optimization
Linear Modeling
Forecasts
Internal Selection of
Model &
Number of Factors and
Method
Inputs Forecast
Post-analysis
ARIMA/SARIMA
Optimized
Input Set VAR & VARX • Correlation
Factor Models • R2 &
ARCH/GARCH extended R2
• Hitrate
• Residual
Non Linear Modeling
Analysis
• Normality
Model &
Network Topology and Tests
Method
Parameter Tuning • etc.
Single Output MLP
Multi Output MLP
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Integration of Research – Selecting of the best Mathematical
Quantitative
Analysis &
Forecasting Models
Optimization
Use of
Model
In Sample Out of Sample Forward
Combination Models
500.000 Models 200.000 Models 50.000 Models
today live calculation of the mathematical models
1. Nov 2003
1. Jan 2000
(model compilation)
Evaluation of Selecting the Continuos
Postanalysis of accuracy
Model building
best
of forecasts accuracy of adjustment
• Building the basic model forecasting
min. 30 weeks forecasts and
Models
• linear vs. non linear
min. 4 weeks optimization
• stability of the model •Baysian
• can take several weeks
in real environment Model
• Adjusting and
to find optimal model
Averaging
Optimizing
•AIC & BIC
• real testing
Model
Combination
During the „Out-of-Sample“, „Forward“, and „Use of Model“ Process the mathematical
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model is adjusted periodically to the changing market environment!
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fin4cast Integrated Portfolio Construction Process
Quantitative
Analysis & From Forecasts Generation to Asset Allocation
Optimization
Actual Portfolio Objective Function
Weights Maximize
φ(x) = pTx – ½ R xTQx – SC(x0, x)
Forecast for each Maximization of expected portfolio
asset return by simultaneous minimization
of expected portfolio risk and
Inputs for the Portfolio Construction
return forecasts implementation costs for the
Long/Short
respective coming period
directional forecasts
Asset Allocation
forecasts of the returns’
distribution
e.g.
Portfolio Optimization
Risk matrix + 15%
•Quadratic Optimization
- 20%
•Ranking
estimated variance-co-
- 10%
variance matrix (market
risk) + 30%
estimated residual
Constraints
diagonal matrix
(forecasting & model
risk) Market Neutrality, Long/Short,
Exposure, etc.
estimated slippage
(implementation risk) Min. or max. investment to a
single asset or an asset class
Combinatorial constraints
Risk aversion
Turn-over constraints
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fin4cast Integrated Strategy Implementation Process
Quantitative
Analysis &
From Asset Allocation to Order Execution & Portfolio Analysis
Optimization
Siemens in-house or Siemensfin4cast Application Server Siemensfin4cast Thechnology
external institutions 13 Portfolio Reconceliation, Portfolio
Proposed Asset Allocation &
1
Analysis & Risk Management
Consistency Checks
Confirmed weights &
number of contracts •Slippage Analysis
Internet
(128 Bit SSL) •Implementation Short Fall
Pre-Trade Analysis
•Return/Risk Analysis
2
•Stop-Loss
3
•If-than & Stress Tests
12
FIX Engine Scenarios
4 FIX 4.2 11
Radianz Network
Brokers
FIX Engine
Exchange(s)
reject
10
5
Consistency Checks
Confirmation
Orders
of the 9
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Execution
Trading System
7
Interfaces
8
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Case Study – fin4cast Income Index
Quantitative
Analysis &
Objectives
Optimization
The fin4cast Income Index follows a directional long/short investment
strategy. This strategy seeks to profit from price inefficiencies between
the most liquid stock index futures, currency futures, and commodity
futures world wide. Through a combination of long and short positions
the strategy targets to take advantage from market moves and relative
value opportunities. The strategy is characterized through its broad
diversification between regions and asset classes. According to the
forecasts generated by Siemens fin4cast Technology the fin4cast Income
Index I consists of a basket of long positions in those futures with the
highest up wards potential and a basket of short positions in those
futures showing signs of weakness. The strategy aims to achieve an
absolute equity like return at fixed income level of risk.
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Case Study – fin4cast Income Index
Quantitative
Analysis &
Investment Universe
Optimization
The current investment universe consists of the 37 most liquid futures
world wide. Siemens fin4cast is continuously anxious to increase the
investment universe subject to forecast ability, tradability and liquidity
constraints. According to the results of permanent quality checks
Siemens fin4cast might temporarily remove one or more futures from
the investment universe due to forecasting quality concerns.
Stock Index Futures: DJ Euro Stoxx 50 Index, DAX 30 Index, FTSE 100
Index, S&P 500 Index, Nasdaq 100 Index, Nikkei 225 Index, Russell 2000
Index, Hang Seng Index, MSCI Taiwan Index, S&P ASX 200 Index, Tokyo
Price Index, MSCI Singapore Index, Kuala Lumpur Stock Index, Bangkok
S.E.T Index, Kospi 200 Index
Currency Futures: EUR/GBP, EUR/JPY, EUR/CHF, JPY, CHF, GBP, AUD
Commodity Futures: Corn, Soybean, Wheat, Lean Hog, Live Cattle,
Copper, Gold, Silver, Cotton, Sugar, Light Sweet Crude Oil, Cocoa,
Palladium, Platinum
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Case Study – fin4cast Income Index
Quantitative
Analysis &
Portfolio Guidelines
Optimization
fin4cast Income Index can take long or short positions in the underlying futures
The max. allocation to each stock index futures is 50%
The max. allocation to each currency futures is 10%
The max. allocation to each commodity futures is 40%
fin4cast Income Index is rebalanced on a bi-weekly basis, on Monday and Wednesday
fin4cast Income Index does not account for interest gains in local currency resulting from
the margin account
Interest gains on the capital not held in margin account are included. For the interest
calculation 3 months USD LIBOR is used
Transaction costs of 1 basis point for currency and stock index futures and 2 basis points for
commodity futures are included in the index calculation
fin4cast Income Index is adjusted to account for 2% p.a. index calculation fee and FIX-
technology fee
fin4cast Income Index is marked-to-market with close of the day future prices
fin4cast Income Index is USD denominated, margins and daily P&L are converted into USD
on a daily basis
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Case Study – fin4cast Income Index
Quantitative
Analysis &
Performance
Optimization
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Case Study – fin4cast Income Index
Quantitative
Analysis & Comparitive Performance & Asset Allocation
Optimization
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Source: Siemens fin4cast. Correlations, Returns and Standard
Deviations are based on monthly returns back to March 1999

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Quantitative
Conclusion and Q&A
Analysis &
Optimization
New trends in Active Asset Management – Integration of
Research, Portfolio Construction and Strategy Implementation
fin4cast Integrated Active Asset Management Approach
Case Study: fin4cast Income Index
Q&A
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Quantitative
References
Analysis &
Optimization
Bessembinder, H. and Seguin, P. J., (1993); Price Volatility, Trading Volume, and Market Depth: Evidence
from Futures Markets; The Journal of Financial and Quantitative Analysis, Vol. 28, No. 1 (pp. 21-39)
Brown, S., Koch, T. and Power, E., (2006); Slippage and the Choice of Market or Limit Orders in Futures
Trading
Gartner, M., Kührer M. and Mitev M., Slippage, (2007); Pre-order and Post-order Analysis in Futures
Trading: An Empirical Study
Grinold, Richard C. and Kahn, Ronald N., (2000); Active Portfolio Management. A quantitative Approach
for Producing Superior Returns and Controlling Risk; 2nd edition McGraw-Hill
Lee, Charles M. C., (1993); Market Integration and Price Execution for NYSE-Listed Securities; The Journal
of Finance, Vol. 48, No. 3 (pp. 1009-1038)
Mitev, Miroslav, (2003); A systematic investment process for alternative and traditional investment
strategy, Dissertation, Institute for Statistics and Operations Research, School of Economics and Social
Sciences, Karl-Franzen-University GRAZ
Perold, Andre F., (1988); The implementation shortfall: Paper versus reality; Journal of Portfolio
Management; Vol 14, pp 4-9
Prix, Johannes, Loistl, Otto and Hütl, Michael, (2007); Algorithmic Trading Patterns in Xetra Orders, The
European Journal of Finance; Vol 13, No 8, pp 717-739
H. Rehkugler, D. Jandra, Kointegrations- und Fehlerkorrekturmodelle zur Finanzmarktprognose
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Biographies
Quantitative
Analysis & Dr. Miroslav Mitev Dr. Martin Kuehrer
Siemens AG Österreich Siemens AG Österreich
Optimization
Siemens IT Solutions and Services Siemens IT Solutions and Services
PSE/fin4cast PSE/fin4cast
Phone: +43 (0) 51707 46253 Phone: +43 (0) 51707 46360
Fax: +43 (0) 51707 56465 Fax: +43 (0) 51707 56465
Mobile: +43 (0) 676 9050903 Mobile: +43 (0) 676 3917274
Email: miroslav.mitev@siemens.com Email: martin.kuehrer@siemens.com
Dr Martin Kuehrer is a managing director and head of
Dr Miroslav Mitev is a managing director and head of quantitative
quantitative strategies at Siemens/fin4cast. Dr Kuehrer has been
research and strategy development at Siemens/fin4cast. Dr Mitev is
with Siemens for 14 years in various different functions. Prior to
responsible for the development of innovative, systematic long-short
joining Siemens in 1994 Dr Kuehrer held a number of positions
investment strategies for institutional investors world wide based on
with prominent engineering companies. Dr Kuehrer has steered
Siemens/fin4cast technology. After joining Siemens in 2001 Dr Mitev
successfully formed a qualified team of 25 professionals which is the quantitative strategies proposition from its beginnings and
continuously developing the Siemens/fin4cast Technology and building has formed numerous successful partnerships with financial
mathematical forecasting models for a variety of financial instruments
institutions. Dr Kuehrer is a regular speaker at international
like currency futures, commodity futures, stock index futures, bond
conventions on asset management and quantitative investment
futures, single stocks and hedge fund indices. Dr Mitev is in charge of
management. Dr Kuehrer has degrees in engineering and
the Siemens/fin4cast’s research cooperation with various universities
business administration as well as a PhD in finance.
and is actively involved in the scientific management of numerous
master thesis and dissertations. Dr Mitev is a regular speaker at
international conventions on liability driven investing, asset
management, hedge funds, portable alpha, advanced quantitative
studies, algo-trading and system research. Dr Mitev’s research is
published on a regular basis in international journals and presented on
international scientific conferences. Prior to joining Siemens Dr Mitev
was at CA IB, the Investment Bank of Bank Austria Group, where he was
in charge of the quantitative research of the securities research division.
Dr Mitev received a Master of Economics and Business Administration
May 08 with main focus on Investment Banking and Capital Markets. Dr Mitev
also received a PhD in Economics with main focus on Finance and 21
Econometrics.