An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F25%3A43925113" target="_blank" >RIV/62156489:43110/25:43925113 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s10614-024-10605-5" target="_blank" >https://doi.org/10.1007/s10614-024-10605-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10614-024-10605-5" target="_blank" >10.1007/s10614-024-10605-5</a>
Alternative languages
Result language
angličtina
Original language name
An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns
Original language description
In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard & Poor's 500 (S&P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student's distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.Byla vydána korekce článku pod UT WoS 001320224300001 a EID 2-s2.0-105002042651.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computational Economics
ISSN
0927-7099
e-ISSN
1572-9974
Volume of the periodical
65
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
35
Pages from-to
1819-1853
UT code for WoS article
001220298900002
EID of the result in the Scopus database
2-s2.0-105001569882