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Top Down Investing

Top Down Investing. Bottom-up Approach Choose under-valued securities Buying performance cheaply Top-Down Approach Build the ideal portfolio Structure portfolio to investor needs May have “expensive” & “cheap” stocks. The Ideal Portfolio. High return Low risk Inexpensive to manage.

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Top Down Investing

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  1. Top Down Investing • Bottom-up Approach • Choose under-valued securities • Buying performance cheaply • Top-Down Approach • Build the ideal portfolio • Structure portfolio to investor needs • May have “expensive” & “cheap” stocks

  2. The Ideal Portfolio • High return • Low risk • Inexpensive to manage

  3. Measures of Return • Total return • After tax income and appreciation • Geometric mean = growth • [V(T) / V(o) ](1/T) - 1

  4. Measures of Risk • Volatility • 2 = variance: average squared deviation from historical mean • Downside Risk • semi-variance: average squared negative deviation from historical mean • Value-at-Risk • Minimum expected loss for a given horizon and probability level

  5. Measures of Expense • Transactions • Information acquisition • Monitoring costs • Probability of losing client

  6. Technology of Return and Risk • Harry Markowitz , 1959 • Reduced investment to two dimensions • Showed that portfolio mix matters most • Turned investing into statistics

  7. Mean and Standard Deviation • Mean measures expected return • Standard deviation measures investor risk • Example: six asset classes 1970 - 1996

  8. Correlation: the Third Statistic • Correlation and co-movement • One asset “hedges” the other • Two assets are better than one

  9. Gold and the Stock Market • Correlation of -.3 since 1970 • Hedged 70’s crash

  10. Gold in the Portfolio? • 25% risk reduction • 3/4 stocks, 1/4 gold • Is gold dominated?

  11. The Efficient Frontier • More assets move frontier • Frontier is a continuous set of efficient portfolios • Highest return for each level of risk

  12. The First Frontier • Markowitz took stocks from the NYSE • Mixed them with cash • Created the first frontier

  13. Risk Reduction by Adding Assets

  14. International Equity Groups

  15. Risk and Return Inputs N Periods Geometric Arithmetic Standard Mean (%) Mean (%) Deviation (%) MSCI Automobiles Cap App 324.00 8.16 9.97 20.07 MSCI Banking Cap App 324.00 10.74 12.56 20.64 MSCI Chemicals Cap App 324.00 7.94 9.34 17.53 MSCI Energy Sources Cap App 324.00 9.18 10.84 19.33 MSCI Gold Mines Cap App 324.00 7.67 15.91 46.01 MSCI Telecomm Cap App 324.00 6.68 7.62 14.25 MSCI Textiles & Apparel Cap App 324.00 6.20 8.16 20.88 MSCI Transport - Airlines Cap App 324.00 7.40 10.25 25.51 MSCI Utilities - Elec&Gas Cap Ap. 324.00 5.97 7.03 15.29

  16. Capital Appreciation Indices

  17. Results

  18. Minimum Variance Portfolio

  19. Value at Risk • How much do I expect to lose 1 in 20 times? • E.G. VAR for a $100 million portfolio with a std. of 12% at the 1/20 confidence level is: • VAR = $100m * 1.64 * 12% - 8% = 12%

  20. Value at Risk Minimum expected annual loss at a 95% confidence level for the lowest risk portfolio = -12%

  21. New Risk Technology • VAR with simulations • VAR with non-normal returns • VAR with derivatives • VAR with chaotic systems

  22. Systematic Risk • Non-diversifiable risk • Market risk • Beta risk

  23. Portfolio Investors • Diversify away most risk • Demand return only for residual • Have advantage over non-diversified investors • Can bid more for risky assets • Have less volatile portfolios

  24. Portfolio Investors’Expected Returns • Only market exposure matters • Higher  means higher expected return

  25. Factor Models • Assume price-setters are diversified • Ignore diversifiable risk • Expected return must compensate remaining risk • “Factors” are risk sources

  26. Developing a Top-Down Portfolio • Assess sensitivity of client to: • inflation shocks • interest rate shifts • GDP shocks • Tilt portfolio away from stocks matching firm sensitivity • Capture factor exposure with minimum transactions costs

  27. Example • Client is the pension fund of an international oil company • defined benefits • ability to contribute depends upon oil prices • Exposure to oil shocks • APT allows them to “hedge” oil shocks • Analysis lets them tilt towards risks they care less about

  28. Measuring Beta • Linear “Response” to Factor Returns • Example: MSCI is about a 50% “hedge” of the S&P 500. • Better Fit = Better Hedge

  29. Multi-Factor Models • APT = Macro-economic risk factors • BARRA = Security-specific risk factors • Fama-French = Size and book to market ratio as risk

  30. Arbitrage Pricing Theory • Chen, Roll and Ross factors are: • Production risk • Inflation shocks • term structure shifts • investor confidence • Explanatory factors • Fundamental economic forces drive stocks

  31. BARRA factor models • Stock characteristics: • Earnings • Leverage • Growth • Sales • No common factors • model works to explain returns

  32. Fama and French Factors • Size • Small stocks have a premium • Book to market • A “distress” premium? • These beat S&P beta

  33. Fma & French (1992) Results

  34. New Directions in Asset Pricing • Statistical methods for identifying factors • Style analysis • Economic modeling of risk • International Factors • Diversity of markets • Diversity of environments • Diversity of historical experiences

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