Machine Learning Models to Predict Google Stock Prices
The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by explainability and robustness. Our findings show...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/2/81 |
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| author | Cosmina Elena Bucura Paolo Giudici |
| author_facet | Cosmina Elena Bucura Paolo Giudici |
| author_sort | Cosmina Elena Bucura |
| collection | DOAJ |
| description | The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by explainability and robustness. Our findings show that the choice of the best model to employ to predict Google stock prices depends on the desired objective. If the goal is accuracy, the recurrent neural network is the best model, while, for robustness, the Ridge regression model is the most resilient to changes and, for explainability, the Gradient Boosting model is the best choice. |
| format | Article |
| id | doaj-art-41b8604bbfac4be09d93830bd8c3d8d3 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-41b8604bbfac4be09d93830bd8c3d8d32025-08-20T02:44:59ZengMDPI AGAlgorithms1999-48932025-02-011828110.3390/a18020081Machine Learning Models to Predict Google Stock PricesCosmina Elena Bucura0Paolo Giudici1Department of Economics and Management, University of Pavia, 27100 Pavia, ItalyDepartment of Economics and Management, University of Pavia, 27100 Pavia, ItalyThe aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by explainability and robustness. Our findings show that the choice of the best model to employ to predict Google stock prices depends on the desired objective. If the goal is accuracy, the recurrent neural network is the best model, while, for robustness, the Ridge regression model is the most resilient to changes and, for explainability, the Gradient Boosting model is the best choice.https://www.mdpi.com/1999-4893/18/2/81Google stock pricesmachine learning modelsaccuracyexplainabilityrobustness |
| spellingShingle | Cosmina Elena Bucura Paolo Giudici Machine Learning Models to Predict Google Stock Prices Algorithms Google stock prices machine learning models accuracy explainability robustness |
| title | Machine Learning Models to Predict Google Stock Prices |
| title_full | Machine Learning Models to Predict Google Stock Prices |
| title_fullStr | Machine Learning Models to Predict Google Stock Prices |
| title_full_unstemmed | Machine Learning Models to Predict Google Stock Prices |
| title_short | Machine Learning Models to Predict Google Stock Prices |
| title_sort | machine learning models to predict google stock prices |
| topic | Google stock prices machine learning models accuracy explainability robustness |
| url | https://www.mdpi.com/1999-4893/18/2/81 |
| work_keys_str_mv | AT cosminaelenabucura machinelearningmodelstopredictgooglestockprices AT paologiudici machinelearningmodelstopredictgooglestockprices |