Univesal Alpha Factory Crafting Portable Excess Return By Investing In Liquid Commodity Futures Mitev

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Case Study Presentation at the European Alternative Investment Summit on 5th – 7th November 2008 Fairmont Le Montruex Palace, Montreux, Switzerland

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Univesal Alpha Factory Crafting Portable Excess Return By Investing In Liquid Commodity Futures Mitev

  1. 1. s Universal Alpha Factory: Crafting Portable Excess Return by Investing in Liquid Commodity Futures European Alternative Investment Summit a marcusevans summit series event 5-7 November 2008 | Fairmont Le Montreux Palace | Montreux | Switzerland
  2. 2. s Disclaimer 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 November 2008 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: info@fin4cast.com, www.fin4cast.com/indices. 11 08 2
  3. 3. s 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 11 08 3
  4. 4. s Definition of Beta In 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) 11 08 4
  5. 5. s Stock Market Beta 11 08 Source: Thomson Reuters 5
  6. 6. s Bond Market Beta 11 08 Source: Thomson Reuters 6
  7. 7. s Commodity Market Beta 11 08 7 Source: Thomson Reuters
  8. 8. s Currency Market Beta 11 08 Source: Thomson Reuters 8
  9. 9. s Alternative Beta 11 08 Source: Thomson Reuters 9
  10. 10. s 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 11 08 10
  11. 11. s Commodity Alpha 11 08 Source: fin4cast 11
  12. 12. s Beta and Alpha Sources Source: Thomson Reuters 11 08 12
  13. 13. s Separating Alpha from Beta Yt = α + β * X t + ε t Traditional beta: Stock Market Return Return Alpha = Skill = Residuals Market Risk Return of different Alternative Pure Asset Classes Return Residuals Alpha = Beta Traditional Beta Yt = α + δ * At + β * X t + ε t Alternative Beta: Yt = α + β1 * X 1 t + β 2 * X 2 t + β 3 * X 3 t + β 4 * X 4 t + L + β k * X k t + ε t Commodity Bonds Stocks Currency Hedge Funds Commodity Alpha Risk Risk Risk Risk Risk 11 08 13
  14. 14. s Interdependences between the asset classes (March 1999 – Sep 2008) Rotated Matrix of the Principal Components a Components 1 2 3 DJIA .797 10 year US -.720 T-Note CS HFI .565 .518 EURUSD .831 DJ AIGCI .642 .374 FIN4CAST .952 Method: Principal Components Analyse. Rotation: Varimax with Kaiser-Normalisation. a. The rotation converged after 7 iterations. 11 08 Multi – Correlation Coeffitien represents the average correlation to all other Betas and Alpha 14 Value Added Coeffitient = ABS (Sharpe Ratio/Multi-Correlation Coeffitien)
  15. 15. s Interdependences between the asset classes (March 1999 – March 2003) 11 08 15
  16. 16. s Interdependences between the asset classes (April 2003 – July 2007) 11 08 16
  17. 17. s Agenda Interdependences between the asset classes (July 2007 – September 2008) 11 08 17
  18. 18. s Maximizing returns through commodity exposure Agenda 11 08 18
  19. 19. s 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: Metal: Energy: Corn (CBoT) Copper (COMEX) Natural Gas (NYMEX) Soybean (CBoT) Gold (COMEX) Light Sweet Crude Oil (NYMEX) Wheat (CBoT) Silver (COMEX) Coffee (NYBoT) Palladium (COMEX) Cotton (NYBoT Platinum (COMEX) Sugar (NYBoT) Lean Hog (CME) Live Cattle (CME) 11 08 19
  20. 20. s Asset allocation as of 27th October 2008 11 08 20
  21. 21. s Commodity long/short exposure YTD 2008 11 08 21
  22. 22. s Performance attribution YTD 2008 (Agriculture) 11 08 22
  23. 23. s Performance attribution YTD 2008 (Agriculture) 11 08 23
  24. 24. s Performance attribution YTD 2008 (Live Stock) 11 08 24
  25. 25. s Performance attribution YTD 2008 (Metals) 11 08 25
  26. 26. s Performance attribution YTD 2008 (Metals) 11 08 26
  27. 27. s Performance attribution YTD 2008 (Energy) Agenda 11 08 27
  28. 28. s Measuring the effect of porting Alpha to the core investment 11 08 28
  29. 29. s Thanky you very much for your attention! Q&A 11 08 29
  30. 30. s Appendix: Alpha-generation process Forecasting Selection of leading indicators Evaluation of forecasts Selection of forecasts Portfolio construction Trading 11 08 30
  31. 31. s Modelbuilding & Forecasting Process From Data Acquisition to Forecasts Generation 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 11 08 31
  32. 32. s Input Selection for the Mathematical Forecasting Models 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 11 08 etc. Reduction 32
  33. 33. s Building & Evaluating of the Mathematical Forecasting Models 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 11 08 33
  34. 34. s Selecting of the best Mathematical Forecasting Models 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 11 08 model is adjusted periodically to the changing market environment! 34
  35. 35. s Portfolio Construction Process From Forecasts Generation to Asset Allocation 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 11 08 35
  36. 36. s Strategy Implementation Process From Asset Allocation to Order Execution & Portfolio Analysis in-house or external Application Server 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 Private Network Brokers FIX Engine Exchange(s) reject 10 5 Consistency Checks Confirmation Orders of the 9 6 Execution Trading System 7 Interfaces 8 11 08 36
  37. 37. s Commodity indices used in the presentation 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 diversified across 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. 11 08 37
  38. 38. s Biography Dr. Miroslav Mitev Siemens AG Österreich Siemens IT Solutions and Services PSE/fin4cast Phone: +43 (0) 51707 46253 Fax: +43 (0) 51707 56465 Mobile: +43 (0) 676 9050903 Email: miroslav.mitev@siemens.com 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. 11 08 38

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