generating alpha based on forecasts integrated active asset management mitev kuehrer

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Alpha Generation based on Forecasts –Intergrated Active Asset Management

European fund of hedge funds summita marcusevans FoF summit series event2 - 4 June 2008 | Le Méridien Beach Plaza | Monte-Carlo | Monaco

May 08

2

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QuantitativeAnalysis &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 gratefullyacknowledged.

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: [email protected], 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.

Disclaimer

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Agenda

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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Introduction of Siemens fin4cast

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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INNOVEST Kapitalanlage AGINNOVEST Kapitalanlage AG

PSE Services provided to Siemens Groups and Divisions

PGPower GenerationPGPower Generation

PTDPower Transmission andDistribution

PTDPower Transmission andDistribution

TSTransportation SystemsTSTransportation Systems

SVSiemens VDOAutomotive AG

SVSiemens VDOAutomotive AG

SFSSiemens FinancialServices GmbH

SFSSiemens FinancialServices GmbH

Siemens AG ÖsterreichInternal contractsSiemens AG ÖsterreichInternal contracts

SDSiemens Dematic AGSDSiemens Dematic AG

SBTSiemens BuildingTechnologies AG

SBTSiemens BuildingTechnologies AG

ICNInformation andCommunication Networks

ICNInformation andCommunication Networks

Otherregional companiesOtherregional companies

Fujitsu SiemensComputersFujitsu SiemensComputers

A&DAutomation and DrivesA&DAutomation and Drives

I&SIndustrial Solutionsand Services

I&SIndustrial Solutionsand Services

ICMInformation anCommunication Mobile

ICMInformation anCommunication Mobile

SBSSiemens Business ServicesGmbH & Co. OHG

SBSSiemens Business ServicesGmbH & Co. OHG

MEDMedical SolutionsMEDMedical Solutions

InfineonInfineon Technologies AGInfineonInfineon Technologies AG

Osram GmbHOsram GmbH Central unitsCentral units

PSEProgram andSystem Engineering

PSEProgram andSystem Engineering

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Introduction Siemens fin4cast

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 complexquantitative 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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New Trends in Active Asset Management

• Target Analysis

• Input Pre-selection

• Input Selection

• Forecasting

ResearchPortfolio

ConstructionStrategy

Implementation

• Maximize Return• Minimize Risk• Risk/Return Optimization• Optimal Asset Allocation• Portfolio Analysis

• Order Generation

• Order Execution

• Risk Management

• Slippage Analysis

Integration of Research, Portfolio Construction & Strategy Implementation

Slippage Analysis & Back-propagationPortfolio Analysis & Back-propagation

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DataAcquisition

• Reuters• Thomson

Financial

Input pre-selection

Criteria:• economical• statistical

Data storage,processing &

cleaning

Input Selection

Search Algorithms:• Neighborhood search• Iterative improvement

approaches• Genetic Algorithm

Non Linear Models

• Single & Multi Output MLP

Learning Algorithms• Steepest Descent• Quick prop

Forecast Post analysis

Comparative in sample and out of sample tests(Forecast Statistics)

Evaluationrejected

Forward tests(Forecast Statistics)

Forecasts

fin4cast Integrated Research ProcessFrom Data Acquisition to Forecasts Generation

Linear Models

• ARIMA/SARIMA• VAR/VARX• Factor Models• ARCH/GARCH

Estimation methods:AOLS, WOLS, SUR, ML.

Evaluationrejected

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Integration of Research - Input Selection for the Mathematical Forecasting Models

OriginalInput Set

app.. 2000 Time Series

EconomicalCriteria


Macro Economic

Interest Rates

Price Data

Currency Rates

etc.

StochasticOscillators

RelativeDifferences

(Exponential) MovingAverage

etc.

TechnicalAnalysis


StatisticalAnalysis

Lags

Stationarity

Correlation

Dynamic Correlation

Normality

Granger Causality

Input Set


Search Algorithm

Correlation & Regression Analysis

AN Algorithm

Generic Algorithm

Economical Selection Grading

Sensitivity Analysis

Optimized Input Set


Principal Component & Factor Analysis

Cluster Reduction

max. 20 most important driving factors of the future returns of a pre-specified asset, e.g. S&P 500 Future

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Integration of Research - Building & Evaluating of the Mathematical Forecasting Models

OptimizedInput Set

Linear Modeling

Non Linear Modeling

ARIMA/SARIMA VAR & VARX

Factor ModelsARCH/GARCH

Single Output MLPMulti Output MLP

Network Topology and Parameter Tuning

• Correlation• R2 &

extended R2• Hitrate• Residual

Analysis• Normality

Tests• etc.

Model &Method

Internal Selection of Number of Factors and

Inputs

Forecasts

Model &Method

ForecastPost-analysis

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Integration of Research – Selecting of the best Mathematical Forecasting Models

today(model compilation)

1. Jan 2000 1. Nov 2003

Continuos

adjustment

and

optimization

Model building Postanalysis of accuracy of forecasts

min. 30 weeks

Evaluation of

accuracy of

forecasts

min. 4 weeks

• Building the basic model

• linear vs. non linear

• can take several weeks to find optimal model

• stability of the model in real environment • Adjusting and

Optimizing

• real testing

During the „Out-of-Sample“, „Forward“, and „Use of Model“ Process the mathematicalmodel is adjusted periodically to the changing market environment!

In Sample500.000 Models

Out of Sample200.000 Models

Use of

ModelsForward

50.000 Models

Model Combination

Selecting the best forecasting Models

•Baysian Model Averaging

•AIC & BIC Model Combination

live calculation of the mathematical models

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Constraints

Market Neutrality, Long/Short, Exposure, etc.

Min. or max. investment to a single asset or an asset class

Combinatorial constraints

Turn-over constraints

Portfolio Optimization•Quadratic Optimization•Ranking

Objective FunctionMaximizeφ(x) = pTx – ½ R xTQx – SC(x0, x)

fin4cast Integrated Portfolio Construction ProcessFrom Forecasts Generation to Asset Allocation

Maximization of expected portfolio return by simultaneous minimization of expected portfolio risk and implementation costs for the respective coming period

e.g.

+ 15%

- 20%

- 10%

+ 30%

Actual Portfolio Weights

Inpu

ts f

or t

he

Port

folio

Con

stru

ctio

n

return forecasts

directional forecasts

forecasts of the returns’ distribution

Forecast for eachasset

estimated variance-co-variance matrix (market risk)

estimated residual diagonal matrix (forecasting & model risk)

estimated slippage (implementation risk)

Risk matrix

Risk aversion

Long/Short Asset Allocation

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Siemensfin4cast Thechnology

•Slippage Analysis•Implementation Short Fall•Return/Risk Analysis•Stop-Loss•If-than & Stress Tests Scenarios

fin4cast Integrated Strategy Implementation ProcessFrom Asset Allocation to Order Execution & Portfolio Analysis

Siemens in-house or external institutions

Confirmed weights & number of contracts

Siemensfin4cast Application Server

Proposed Asset Allocation & Consistency Checks

1

Pre-Trade Analysis2

3

Brokers

FIX Engine

Consistency Checks

4 FIX 4.2

5

Trading SystemInterfaces

6

Confirmation of the Execution

Orders

reject

7

8

Exchange(s)

9

10

11

Portfolio Reconceliation, Portfolio Analysis & Risk Management

12

Radianz Network

Internet(128 Bit SSL)

FIX Engine

13

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Case Study – fin4cast Income IndexObjectives

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 IndexInvestment Universe

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 IndexPortfolio Guidelines

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 USDon a daily basis

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Case Study – fin4cast Income IndexPerformance

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Case Study – fin4cast Income IndexComparitive Performance & Asset Allocation

Source: Siemens fin4cast. Correlations, Returns and Standard Deviations are based on monthly returns back to March 1999

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Conclusion and Q&A

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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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

References

May 08

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Biographies

Dr Miroslav Mitev is a managing director and head of quantitative research and strategy development at Siemens/fin4cast. Dr Mitev is responsible for the development of innovative, systematic long-short investment strategies for institutional investors world wide based on Siemens/fin4cast technology. After joining Siemens in 2001 Dr Mitev successfully formed a qualified team of 25 professionals which is continuously developing the Siemens/fin4cast Technology and building mathematical forecasting models for a variety of financial instruments like currency futures, commodity futures, stock index futures, bond futures, single stocks and hedge fund indices. Dr Mitev is in charge of the Siemens/fin4cast’s research cooperation with various universities 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 with main focus on Investment Banking and Capital Markets. Dr Mitev also received a PhD in Economics with main focus on Finance and Econometrics.

Dr. Miroslav MitevSiemens AG ÖsterreichSiemens IT Solutions and Services PSE/fin4castPhone: +43 (0) 51707 46253Fax: +43 (0) 51707 56465Mobile: +43 (0) 676 9050903Email: [email protected]

Dr Martin Kuehrer is a managing director and head of quantitative strategies at Siemens/fin4cast. Dr Kuehrer has beenwith Siemens for 14 years in various different functions. Prior to joining Siemens in 1994 Dr Kuehrer held a number of positions with prominent engineering companies. Dr Kuehrer has steered the quantitative strategies proposition from its beginnings and has formed numerous successful partnerships with financial institutions. Dr Kuehrer is a regular speaker at international conventions on asset management and quantitative investment management. Dr Kuehrer has degrees in engineering and business administration as well as a PhD in finance.

Dr. Martin KuehrerSiemens AG ÖsterreichSiemens IT Solutions and Services PSE/fin4castPhone: +43 (0) 51707 46360Fax: +43 (0) 51707 56465Mobile: +43 (0) 676 3917274Email: [email protected]

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