Equity Portfolio Management: Active or Passive? - PowerPoint PPT Presentation

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Equity Portfolio Management: Active or Passive? PowerPoint Presentation
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Equity Portfolio Management: Active or Passive?

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  1. Equity Portfolio Management: Active or Passive? • Passive: • LT buy and hold • Indexation • Replication of an index (broad or specialized • Sampling and Tracking Error •  = 0 • Rebalancing

  2. Equity Portfolio Management: Active or Passive?

  3. Rebalancing an Equity Portfolio • Why? • to manage tracking error (if indexing or not) • to maintain a desired set of weights or risk level • client needs change • Market risk level changes • bankruptcies, mergers, IPOs • Why not? • it’s costly!

  4. Rebalancing: Example 1

  5. Rebalancing: Example 1

  6. Rebalancing: Example 1 • Portfolio is no longer equally weighted • To rebalance: • Sell Y, buy X and Z • Positions must be reset to $10445/3 = $3482 • Sell 4440 - 3482 = $958 of Y (48 shares) • Buy 3482 - 2672 = $810 of X (51 shares) • Buy 3482 - 3325 = $157 of Z (4 shares)

  7. Rebalancing: Example 1

  8. Rebalancing: Example 1 • LT effects of this strategy? • Alternatives? • Example 2: Rebalancing to reestablish a specific level of systematic risk (Target Beta = 1.2)

  9. Rebalancing: Example 2 • Reestablishing a beta of 1.2: • No unique solution for more than 2 securities • Need to sell high  stocks and buy low  stocks • For example, sell Y, buy Z, hold X constant • p = (.256)(1.3)+(WY)(1.7)+(1-.256-WY)(.8) • Find Y such that p = 1.2 • WY = .302 => WZ = 1-.256-.302 = .442 • $3488 in X, $3151 in Y, $4611 in Z

  10. Active Equity Strategies • Beat the market on a risk adjusted basis! • Need a benchmark • More expensive: turnover, research • Must outperform on a fee-adjusted basis

  11. Active Equity Strategies • Styles: • Sector Rotation: move in/out of sectors as economy improves/declines • Earnings Momentum: overweight stocks displaying above average earnings growth • Enhanced Index Fund - majority of funds track index, some funds are actively managed • Quantitative Investment Management

  12. Quantitative Investment Management • How do we forecast performance ? • Screening (Fundamental or Technical factors) • Rank based on some set of factors that correlates with future performance (such as regression analysis) • How do we improve forecasting model? • Add more data (more observations) • Uncover new causal relationships (variables)

  13. Quantitative Investment Management • Regardless of forecast, there are three basic results common to QIM: • 1. Information comes from unexpected events • events with low probability have high info content!

  14. QIM • 2. Profitable QIM techniques won’t be commercialized • Starting with a multifactor model: • Ri = b1F1 + b2F2 + . . . + bkFk + ei • It isn’t easy to get information from these residuals: • 1. patterns are complex • 2. quality of data is limited • 3. outliers may draw undue attention (although irrelevant) • 4. human judgement is superior • 5. analysis must be flexible (more data, constraints) • 6. danger of data mining • 7. even if significant, outliers are too few in number!

  15. QIM • 3. Non-linear models are important • Neural Networks • Genetic Algorithms • Fuzzy Logic • Non-Linear Dynamics • Classification Trees (Recursive Partitioning)