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4340. Bootstrap approach to quantifying uncertainty in index tracking and enhanced indexation
Invited abstract in session WD-6: Advancements of OR-analytics in statistics, machine learning and data science 19, stream Advancements of OR-analytics in statistics, machine learning and data science.
Wednesday, 14:30-16:00Room: 1013 (building: 202)
Authors (first author is the speaker)
| 1. | Hakim Mezali
|
| Department of Computing, Glasgow Caledonian University |
Abstract
The focus of this paper is on demonstrating the creation of portfolio uncertainty bands using a bootstrapped procedure. We restrict attention to the model presented in Mezali and Beasley [1] and use re-sampling statistical techniques to build in-sample portfolio uncertainty bands. Further, we propose a number of ways in which the in-sample bootstrapped portfolios, which collectively form an uncertainty band, can be employed to improve out-of-sample portfolio performance for both index tracking and enhanced indexation.
Our formulation includes transaction costs, a constraint limiting the number of stocks that can be in the portfolio and a limit on the total transaction cost that can be incurred. Numeric results are presented for eight test problems drawn from major world markets, where the largest of these test problems involves over
2000 stocks.
Keywords: enhanced indexation; index tracking; portfolio optimisation; quantile regression; bootstrapping
Keywords
- Continuous Optimization
- Financial Modelling
- Forecasting
Status: accepted
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