Using Machine Learning to Predict Optimal Parameters in Portfolio Optimization Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10432005" target="_blank" >RIV/00216208:11320/20:10432005 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using Machine Learning to Predict Optimal Parameters in Portfolio Optimization Problems
Original language description
We use machine learning methods in portfolio optimization problems. Most portfolio optimization problems require selection of one or more parameters and we create machine learning model to predict the optimal values of such parameter with respect to out-of-sample performance. In this paper we use mean-CVaR portfolio optimization model and xgboost machine learning model. Extensive simulations were performed to create the dataset with the optimal choice of the desired parameter. We explore the dependencies of the optimal choice of minimal in-sample mean on input data, like number of stocks or number of scenarios. Predictor importance and prediction evaluation is presented, showing that the model gives reasonable predictions for parameter that is otherwise very hard to select.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GX19-28231X" target="_blank" >GX19-28231X: DyMoDiF - Dynamic Models for the Digital Finance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
38TH INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ECONOMICS (MME 2020)
ISBN
978-80-7509-734-7
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
307-313
Publisher name
MENDEL UNIV BRNO
Place of publication
BRNO
Event location
Brno
Event date
Sep 9, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000668460800047