univesal alpha factory crafting portable excess return by investing in liquid commodity futures...

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Universal Alpha Factory: Crafting Portable Excess Return by Investing in Liquid Commodity Futures

European Alternative Investment Summita marcusevans summit series event5-7 November 2008 | Fairmont Le Montreux Palace | Montreux | Switzerland

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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 Alternative Investment summit on 5-7 November2008 in Fairmont Le Montreux Palace, Montreux, Switzerland only. Research support from fin4cast is gratefully acknowledged.

Dr. Miroslav Mitev - 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.

Disclaimer

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Agenda

Definition of Beta and Alpha

Separating Alpha from Beta

Inter-dependences between different asset classes

Maximizing returns through commodity exposure

Generating Alpha from long & short exposure to commodities using liquid futures

Measuring the effect of porting Alpha to core investment

Conclusion and Q&A

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Definition of BetaIn general Beta represents the market return (Risk Premium) of an

asset class

Depending on investor’s objectives the Beta could be defined as:

the return of the stock market (DJ Industrial Average Index)

the return of the bond market (U.S. Treasury Note)

the return of the commodity market (DJ AIG Commodity Index)

the return of the currency market (EUR/USD Exchange Rate)

the return of investor‘s liabilities (Liability Index = Zero Coupon Bonds)

Depending on the way investors take exposure to Beta we could distinguish between:

Traditional Beta, i.e. the long exposure through buy and hold of futures, ETFs, etc.

Alternative Beta, i.e. the rotation between the traditional betas and taking advantage of short exposure (CS Tremont Hedge Fund Index)

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Stock Market Beta

Source: Thomson Reuters

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Bond Market Beta


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Commodity Market Beta


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Currency Market Beta


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Alternative Beta


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Definition of Alpha

In general Alpha represents the excess return vs. a given benchmark

Per definition Alpha can not be replaced or explained by the existing traditional and alternative Betas, i.e. it has a very low correlation to Beta

Alpha can only be generated by taking active bets and is subject to manager’s skills, i.e. Know-How and technology

Depending on investor’s objectives we can distinguish between:

Relative Alpha, i.e. the relative out-performance against a given benchmark which is usually measured by the information ratio

Absolute Alpha, i.e. the absolute excess return above a pre-defined threshold return usually measured by the Sharpe Ratio

An example for a commodity Alpha prepared for this presentation is the fin4cast Commodity Index which benefits from long and short positions in 13 liquid commodity futures

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Commodity Alpha

Source: fin4cast

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Beta and Alpha Sources


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Separating Alpha from Beta

Return Alpha = SkillStock Market Return

=Market Risk

Residuals

ttXY εβα ++= *t

tttt XAY εβδα +++= **

Pure Alpha

AlternativeBeta

Return

Return of different Asset Classes

=Traditional Beta

Residuals

CommodityRisk

CurrencyRisk

StocksRisk

ttkkttttt XXXXXY εβββββα +++++++= ***** 44332211 L

Alternative Beta:

Traditional beta:

Hedge FundsRisk

CommodityAlpha

BondsRisk

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Rotated Matrix of the Principal Components a

.797

-.720

.565 .518 .831 .642 .374 .952

DJIA10 year UST-NoteCS HFIEURUSDDJ AIGCIFIN4CAST

1 2 3Components

Method: Principal Components Analyse. Rotation:Varimax with Kaiser-Normalisation.

The rotation converged after 7 iterations.a.

Multi – Correlation Coeffitien represents the average correlation to all other Betas and Alpha

Value Added Coeffitient = ABS (Sharpe Ratio/Multi-Correlation Coeffitien)

Interdependences between the asset classes (March 1999 – Sep 2008)

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Interdependences between the asset classes (March 1999 – March 2003)

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Interdependences between the asset classes (April 2003 – July 2007)

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Interdependences between the asset classes (July 2007 – September 2008)Agenda

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Maximizing returns through commodity exposureAgenda

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Generating Alpha from long/short commodity exposure

Case study: fin4cast Commodity Index

benefiting from the most liquid commodity futures across agriculture & live stock, metal and energy sectors by combining long and short futures positions.

Eligible commodity futures:

Agriculture & Live Stock:

Corn (CBoT)

Soybean (CBoT)

Wheat (CBoT)

Coffee (NYBoT)

Cotton (NYBoT

Sugar (NYBoT)

Lean Hog (CME)

Live Cattle (CME)

Metal:

Copper (COMEX)

Gold (COMEX)

Silver (COMEX)

Palladium (COMEX)

Platinum (COMEX)

Energy:

Natural Gas (NYMEX)

Light Sweet Crude Oil (NYMEX)

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Asset allocation as of 27th October 2008

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Commodity long/short exposure YTD 2008

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Performance attribution YTD 2008 (Agriculture)

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Performance attribution YTD 2008 (Agriculture)

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Performance attribution YTD 2008 (Live Stock)

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Performance attribution YTD 2008 (Metals)

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Performance attribution YTD 2008 (Metals)

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Performance attribution YTD 2008 (Energy)Agenda

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Measuring the effect of porting Alpha to the core investment

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Thanky you very much for your attention!

Q&A

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Appendix: Alpha-generation process

Forecasting

Selection of leading indicators

Evaluation of forecasts

Selection of forecasts

Portfolio construction

Trading

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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

Modelbuilding & Forecasting 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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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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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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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)

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

Strategy Implementation ProcessFrom Asset Allocation to Order Execution & Portfolio Analysis

in-house or external institutions

Confirmed weights & number of contracts

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

Private Network

Internet(128 Bit SSL)

FIX Engine

13

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Goldman Sachs Commodity Index: The S&P GSCI™ is a composite index of commodity sector returns representing an unleveraged, long-only investment in commodity futures that is broadly diversifiedacross the spectrum of commodities (Energy 73.86%, Metals 8.73%, Agriculture 13.14%, Live Stock 4.26%) . The returns are calculated on a fully collateralized basis with full reinvestment. The combination of these attributes provides investors with a representative and realistic picture of realizable returns attainable in the commodities markets. Individual components qualify for inclusion in the S&P GSCI™ on the basis of liquidity and are weighted by their respective world production quantities. The principles behind the construction of the index are public and designed to allow easy and cost-efficient investment implementation. Possible means of implementation include the purchase of S&P GSCI™ related instruments, such as the S&P GSCI™ futures contract traded on the Chicago Mercantile Exchange (CME) or over-the-counter derivatives, or the direct purchase of the underlying futures contracts.

The Dow Jones - AIG Commodity Index (DJ-AIGCI)® is composed of futures contracts on 19 physical commodities. The component weightings are also determined by several rules designed to insure diversified commodity exposure (Energy 33%, Metals 26.2%, Agriculture 30.3%, Live Stock 10.5%). Investors may invest in the Dow Jones AIG Commodity Index buy purchasing futures contracts traded on CBOT (Chicago Board of Trade). Alternatively, they may also purchase Pimco Commodity Real Return Fund, which mimics the returns of the Dow Jones AIG Commodity Index.

The PHLX Gold and Silver Index is a capitalization-weighted index composed of the common stocks of nine companies in the gold and silver mining index. The index is a product of the Philadelphia Stock Exchange and began trading in January 1979 with an initial value of 100.

Commodity indices used in the presentation

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Biography

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]