2. Lisbon, November 17th 2010. “The Black Swan Markets Conference” Pestana Palace

3. Company’s Key Facts. Asset Management Theory. Portuguese Asset Management: Theory in Practice? Presentation Overview

4. PA Gestão de Patrimónios SA (Asset Management) -Based in the city of Porto -Regulated by Banco de Portugal (Licence number 223) -Regulated by CMVM (Licence number 273) Company Financials (as of September 30th 2010): -Shareholders’ Equity = 6.2 Million Euros -Debt-to-Equity Ratio = 2 % Major shareholders: PA SGPS SA (owns 51%), which in turn is 76% owned by Mr. Pedro Arroja. PA’s Corporate Structure

5. Pedro Arroja, Founder and Chief Research Officer; PhD in Economics, Carleton University (Canada). Ricardo Arroja, Investment Officer; B.S. in Business Administration, Universidade do Porto. Fátima Pereira, Compliance Officer; B.S. in Law, Universidade Lusíada. PA’s Key People

6. Goal? Which Assets? Which Mix of Assets? Asset Management: Theory

7. Goal? Long term, inflation adjusted, preservation and accumulation of wealth. Asset Management: Theory

8. Asset Management: Inflation

9. Which Assets? Traditional Assets: Equities, Long and Short Term Sovereign Debt; Alternative Assets: Real Assets (Real Estate and Commodities), Managed Accounts (or Hedge Funds) and Private Equity. Asset Management: Theory

10. Asset Management: Equities

11. Asset Management: LT Sov Debt

12. Asset Management: ST Sov Debt

13. Long term Inflation rates are proxies for Real Estate’s and Commodity’s Compound Annual Growth Rates. Asset Management: Real Assets Source: Dimson, Elroy and Staunton, ABN Amro/LBS

14. Which Mix of Assets? Asset Management: Theory

15. Typical Asset Mix in Portugal; Asset Management Companies: Typical Business Plan; Alternative Business Plan. Asset Management: Portugal

16. Typical Asset Mix in Portugal

17. Banks’ Private banking divisions and “Gestoras de Patrimónios” (Asset Management companies); Typical Business Plan: Overall, portfolios are highly geared towards low risk/low return traditional asset classes. Typical Business Plan

18. PA Gestão de Patrimónios SA: Alternative Business Plan: Overall, portfolios are geared towards the Yale’s asset class diversification model, with a special emphasis on Managed Accounts and actively managed Long and Short trading strategies. Alternative Business Plan

19. Quantitatively Driven: Trading models are based on Statistics and Econometric algorithms (but NOT High Frequency Trading); PA’s Core Trading Theme: Most short term market fluctuations are mean-reverting; PA’s Main Trading Instruments: Equity Index Futures, Bond Futures, Commodity Futures and FX Futures. PA: Managed Accounts and Trading Philosophy

20. Market: S&P500. Inputs: Daily Highs and Lows; Nature: Counter-trend; Buy/Sell Signals: Four day periods, including one price expansion and three price contractions; Specific Features: “Slow and Steady”, High Probability, Low Turnover model, currently still being back-tested. Price expansion/contraction (Model 3AB)

21. “Back Testing”

23. Market: Dow Jones Industrials Average’s (individual stock components). Inputs: Daily Highs and Lows; Nature: Counter-trend; Buy/Sell Signals: Two day periods, including two price contractions; Specific Features: “Slow and Steady”, High Probability, Low Turnover model. Price expansion/contraction (Model AA)

24. “Back Testing”

26. Market: Nasdaq-100. Inputs: Daily % Change; Nature: Counter-trend; Buy/Sell Signals: Weekly deviations to the mean; Specific Features: “Fast and Furious”, HighTurnover, Low Probability model Price expansion/contraction (Model SJ)

27. “Back Testing”

29. Market: S&P500. Inputs: US Economic Data; Nature: Trend Following (*); Buy/Sell Signals: Economic Estimates versus Actual Data; Specific Features: “Fast and Furious”, High Turnover, Low Probability model. Macro Model

30. “Back Testing”

32. Position Sizing and Recommended Leverage: 1) Model’s t-stat or; 2) Trading Instrument’s Confidence Intervals at the 99% confidence level. Risk Management

33. We believe that in the long run investors benefit by adopting a more diversified (out-of-bonds) portfolio, subject to proper due diligence. We know that statiscally driven trading models can be successfull if given the time to consistently implement a well tested method. Conclusions

34. Thank you very much for your time!

35. Contacts Avenida de Montevideu, 282, 4150-516 Porto. Phone: (+351) 226165220 Fax: (+351) 226165229 Webpage: www.pedroarroja.com Email: r.arroja@pedroarroja.pt