1 / 22

MS&E 444: Investment Practice Short and long-term prediction combination

MS&E 444: Investment Practice Short and long-term prediction combination. KUMARAGANESH SUBRAMANIAN XIAOLONG TAN PRABAL TIWAREE DIMITRIOS TSAMIS JUNE 3, 2009. Returns Model. Using multiple predictors. Assume that alphas are a linear combinations of factors:

zahina
Download Presentation

MS&E 444: Investment Practice Short and long-term prediction combination

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. MS&E 444: Investment PracticeShort and long-term prediction combination KUMARAGANESH SUBRAMANIAN XIAOLONG TAN PRABAL TIWAREE DIMITRIOS TSAMIS JUNE 3, 2009

  2. Returns Model

  3. Using multiple predictors • Assume that alphas are a linear combinations of factors: • Estimate B using pooled panel regression • Moreover, • is a positive definitive matrix of mean-reversion coefficients

  4. Transaction Costs • Trading shares costs: • Assume that

  5. Optimization Problem • Find the optimal portfolio at each time step by solving the following problem: • Use Dynamic Programming!

  6. Main result • Optimal portfolio is linear combination of previous position and a moving “target portfolio” where and

  7. Simplification • If then

  8. Static model • Solve ie fully discount the future • Solution:

  9. Experiments • Use 6 different commodities futures from London Metal Exchange • Evaluate based on gross and net SR and cumulative returns • Compare optimal, static and no TC strategies • Predictors: normalized averages over 5 days, 1 year and 5 years

  10. Cumulative Returns

  11. Sharpe Ratios • Dynamic strategy: 0.4707 • Static strategy: 0.4618

  12. Effect of lambda

  13. Rebalancing costs

  14. Experiments with shares • Use predictors provided by EvA • Short-term: stat-arb daily predictors • Long-term: EMN monthly predictors • interpolate daily values • There were 1089 securities common across all data

  15. Reduce the size of the portfolio! • Using all the securities produces bad results • Σis essential to the model, but the quality of the estimator deteriorates as the number of securities increases • To evaluate the model try random portfolios and observe their performance

  16. Using all securities

  17. Cumulative Returns with 20 securities

  18. Cumulative Returns with 100 securities

  19. Cumulative Returns with 500 securities

  20. Best portfolio size: 19 securities

  21. Evaluate based on SR

  22. Conclusions • The strategy works better on commodity data • The strategy appears to be self-financing • The strategy does not work well on very large portfolios (probably due to parameter estimation errors)

More Related