Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F23%3A43923360" target="_blank" >RIV/62156489:43110/23:43923360 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.frl.2023.103814" target="_blank" >https://doi.org/10.1016/j.frl.2023.103814</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.frl.2023.103814" target="_blank" >10.1016/j.frl.2023.103814</a>
Alternative languages
Result language
angličtina
Original language name
Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios
Original language description
This work aims to compare the performance of the traditional portfolios of the S&P500, Markowitz, and Sharpe with the multifractal trend fluctuation portfolios (MF-DFA) and portfolios of artificial neural networks with Student's asymmetric probability classification (ANN-t). In this study, we use daily data for S&P500 stocks between January 18, 2018, and July 12, 2022, where we backtest return and risk metrics such as annual volatility, Value at Risk, Sharpe Ratio, Sortino Ratio, Beta, and Jensen's Alpha. For both return and risk, we obtain the results confirming that the ANN-t technique might indicate better investment entries, which contradicts the Efficient Market Hypothesis (EMH).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
<a href="/en/project/GA22-34451S" target="_blank" >GA22-34451S: New Methods in Pricing Government Debt: Uncertainty and Policy Implications</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Finance Research Letters
ISSN
1544-6123
e-ISSN
1544-6131
Volume of the periodical
55
Issue of the periodical within the volume
July
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
103814
UT code for WoS article
001060836200001
EID of the result in the Scopus database
2-s2.0-85153328042