Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50
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...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2227-7390/12/24/3918 |
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| author | Javier Parra-Domínguez Laura Sanz-Martín |
| author_facet | Javier Parra-Domínguez Laura Sanz-Martín |
| author_sort | Javier Parra-Domínguez |
| collection | DOAJ |
| description | 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 R<sup>2</sup> 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. |
| format | Article |
| id | doaj-art-3c28952b16564e9bbcc5c6f81942aa43 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-3c28952b16564e9bbcc5c6f81942aa432025-08-20T02:43:43ZengMDPI AGMathematics2227-73902024-12-011224391810.3390/math12243918Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50Javier Parra-Domínguez0Laura Sanz-Martín1BISITE Research Group, University of Salamanca, 37007 Salamanca, SpainBISITE Research Group, University of Salamanca, 37007 Salamanca, SpainThis 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 R<sup>2</sup> 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.https://www.mdpi.com/2227-7390/12/24/3918artificial intelligencefinanceprediction modelsfinancial decision-makingneural networks |
| spellingShingle | Javier Parra-Domínguez Laura Sanz-Martín Artificial Intelligence in the New Era of Decision-Making: A Case Study of the Euro Stoxx 50 Mathematics artificial intelligence finance prediction models financial decision-making neural networks |
| title | 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_short | 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 |
| topic | artificial intelligence finance prediction models financial decision-making neural networks |
| url | https://www.mdpi.com/2227-7390/12/24/3918 |
| work_keys_str_mv | AT javierparradominguez artificialintelligenceintheneweraofdecisionmakingacasestudyoftheeurostoxx50 AT laurasanzmartin artificialintelligenceintheneweraofdecisionmakingacasestudyoftheeurostoxx50 |