<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of this paper is to create a stable model of investment portfolio optimization through a high degree of diversification and reduction of sudden changes in the allocation with monitoring of the dynamics of the impact factor. In this sense, there is bootstrap application procedure, which, without an excessive number of constraints involved in the optimization process provides solutions based on uncertain information. Thus defined, the optimization method has been patented by Michaud (1999) entitled re-sampled efficiency. Accordingly, this paper offers a comparison of the performance block bootstrap optimization models and traditional Markowitz's model inside and outside of the sample by applying the most frequently traded stocks on the BSE. The results show a better performance out of the sample and the presence of a larger number of shares forming the portfolio through bootstrap methodology. However, only through the traditional optimization process could be attained optimum according to the required limits. Such effects can be observed by comparing the limits of efficiency obtained through these optimization models. However, optimization-based methods bootstrap finds its place in reducing errors of assessment resulting from the limited sample size.</p></abstract>

The aim of this paper is to create a stable model of investment portfolio optimization through a high degree of diversification and reduction of sudden changes in the allocation with monitoring of the dynamics of the impact factor. In this sense, there is bootstrap application procedure, which, without an excessive number of constraints involved in the optimization process provides solutions based on uncertain information. Thus defined, the optimization method has been patented by Michaud (1999) entitled re-sampled efficiency. Accordingly, this paper offers a comparison of the performance block bootstrap optimization models and traditional Markowitz's model inside and outside of the sample by applying the most frequently traded stocks on the BSE. The results show a better performance out of the sample and the presence of a larger number of shares forming the portfolio through bootstrap methodology. However, only through the traditional optimization process could be attained optimum according to the required limits. Such effects can be observed by comparing the limits of efficiency obtained through these optimization models. However, optimization-based methods bootstrap finds its place in reducing errors of assessment resulting from the limited sample size.

Testing The Performance Of The Investment Portfolio Using Block Bootstrap Method

University of Novi Sad, Faculty of Economics in Subotica, Serbia

Economic Themes

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

TESTIRANJE PERFORMANSI INVESTICIONOG PORTFOLIJA PRIMENOM BLOK BUTSTREP METODA

The aim of this paper is to create a stable model of investment portfolio optimization through a high degree of diversification and reduction of sudden changes...

Testing The Performance Of The Investment Portfolio Using Block Bootstrap Method

Data publikacji: 25 mar 2015

Zakres stron: 166 - 183

Otrzymano: 13 mar 2014

Przyjęty: 10 lip 2014

DOI: https://doi.org/10.1515/ethemes-2014-0012

Słowa kluczowe
Investment portfolio, optimization, re-sampled efficiency, bootstrap, efficiency limit

© Boris Radovanov et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Testing The Performance Of The Investment Portfolio Using Block Bootstrap Method

Data publikacji: 25 mar 2015

Zakres stron: 166 - 183

Otrzymano: 13 mar 2014

Przyjęty: 10 lip 2014

DOI: https://doi.org/10.1515/ethemes-2014-0012

Słowa kluczoweInvestment portfolio, optimization, re-sampled efficiency, bootstrap, efficiency limit

© Boris Radovanov et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Investment portfolio, optimization, re-sampled efficiency, bootstrap, efficiency limit