Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50

[EN]This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the si...

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Detalles Bibliográficos
Autores: Parra Domínguez, Javier, Sanz Martín, Laura
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/169162
Acceso en línea:http://hdl.handle.net/10366/169162
Access Level:acceso abierto
Palabra clave:Artificial intelligence
Finance
Prediction models
Financial decision-making
Neural networks
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spelling Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50Parra Domínguez, JavierSanz Martín, LauraArtificial intelligenceFinancePrediction modelsFinancial decision-makingNeural networks[EN]This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model’s responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction.MDPI202620262024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/169162reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésTSI-100933- 2023-0001Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1691622026-06-07T06:28:51Z
dc.title.none.fl_str_mv Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
title Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
spellingShingle Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
Parra Domínguez, Javier
Artificial intelligence
Finance
Prediction models
Financial decision-making
Neural networks
title_short Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
title_full Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
title_fullStr Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
title_full_unstemmed Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
title_sort Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
dc.creator.none.fl_str_mv Parra Domínguez, Javier
Sanz Martín, Laura
author Parra Domínguez, Javier
author_facet Parra Domínguez, Javier
Sanz Martín, Laura
author_role author
author2 Sanz Martín, Laura
author2_role author
dc.subject.none.fl_str_mv Artificial intelligence
Finance
Prediction models
Financial decision-making
Neural networks
topic Artificial intelligence
Finance
Prediction models
Financial decision-making
Neural networks
description [EN]This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model’s responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction.
publishDate 2024
dc.date.none.fl_str_mv 2024
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/169162
url http://hdl.handle.net/10366/169162
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv TSI-100933- 2023-0001
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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