Applications of Mathematical Optimization Approaches to Portfolio

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10249562" target="_blank" >RIV/61989100:27510/19:10249562 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf" target="_blank" >https://mme2019.ef.jcu.cz/files/conference_proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Applications of Mathematical Optimization Approaches to Portfolio

Popis výsledku v původním jazyce

In this paper the portfolio optimization problem is solved by applying mathematical optimization approaches. We apply the naive strategy to obtain a portfolio with equal weights. The efficient portfolios are also obtained by considering the standard deviation and the mean absolute deviation as the risk measure separately. In our empirical analysis, based on the in-sample data, we construct the efficient frontiers by applying the Mean Variance approach and the Mean Absolute Deviation approach, and then we make the back-test of these efficient portfolios in the out-of-sample period to verify whether the strategies obtained by the optimization approaches work efficiently. In the back-test, the main performance measure of the portfolio is the Maximum Drawdown. To make the verification conclusive, we also generate random-weights portfolios and make hypothesis tests. By comparing the results, we conclude that for both of Mean Variance approach and Mean Absolute Deviation approach, they each obtains portfolios with efficiency in our empirical analysis.

Název v anglickém jazyce

Applications of Mathematical Optimization Approaches to Portfolio

Popis výsledku anglicky

In this paper the portfolio optimization problem is solved by applying mathematical optimization approaches. We apply the naive strategy to obtain a portfolio with equal weights. The efficient portfolios are also obtained by considering the standard deviation and the mean absolute deviation as the risk measure separately. In our empirical analysis, based on the in-sample data, we construct the efficient frontiers by applying the Mean Variance approach and the Mean Absolute Deviation approach, and then we make the back-test of these efficient portfolios in the out-of-sample period to verify whether the strategies obtained by the optimization approaches work efficiently. In the back-test, the main performance measure of the portfolio is the Maximum Drawdown. To make the verification conclusive, we also generate random-weights portfolios and make hypothesis tests. By comparing the results, we conclude that for both of Mean Variance approach and Mean Absolute Deviation approach, they each obtains portfolios with efficiency in our empirical analysis.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA17-19981S" target="_blank" >GA17-19981S: Finanční aplikace stochastického uspořádání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

MME 2019 : 37th International Conference on Mathematical Methods in Economics 2019 : conference proceedings : České Budějovice, September 11-13, 2019

ISBN

978-80-7394-760-6

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

439-444

Název nakladatele

Jihočeská univerzita v Českých Budějovicích

Místo vydání

České Budějovice

Místo konání akce

Ceske Budejovice

Datum konání akce

11. 9. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000507570400073