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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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