1 / 15

Equity Portfolio Management: Active or Passive?

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. Equity Portfolio Management: Active or Passive?. Rebalancing an Equity Portfolio. Why?

melodie-black
Download Presentation

Equity Portfolio Management: Active or Passive?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related