Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting
Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic...
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MDPI AG
2025-07-01
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| author | Michalis Patsiarikas George Papageorgiou Christos Tjortjis |
| author_facet | Michalis Patsiarikas George Papageorgiou Christos Tjortjis |
| author_sort | Michalis Patsiarikas |
| collection | DOAJ |
| description | Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R<sup>2</sup> scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities. |
| format | Article |
| id | doaj-art-d0bb624674e545afa065ffedaaa6fc32 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-d0bb624674e545afa065ffedaaa6fc322025-08-20T02:45:38ZengMDPI AGInformation2078-24892025-07-0116758410.3390/info16070584Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market ForecastingMichalis Patsiarikas0George Papageorgiou1Christos Tjortjis2School of Science and Technology, International Hellenic University, 57001 Thessaloniki, GreeceSchool of Science and Technology, International Hellenic University, 57001 Thessaloniki, GreeceSchool of Science and Technology, International Hellenic University, 57001 Thessaloniki, GreeceFinancial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R<sup>2</sup> scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities.https://www.mdpi.com/2078-2489/16/7/584machine learningstock marketmacroeconomic indicatorsstock forecastingtechnical analysis |
| spellingShingle | Michalis Patsiarikas George Papageorgiou Christos Tjortjis Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting Information machine learning stock market macroeconomic indicators stock forecasting technical analysis |
| title | Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting |
| title_full | Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting |
| title_fullStr | Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting |
| title_full_unstemmed | Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting |
| title_short | Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting |
| title_sort | using machine learning on macroeconomic technical and sentiment indicators for stock market forecasting |
| topic | machine learning stock market macroeconomic indicators stock forecasting technical analysis |
| url | https://www.mdpi.com/2078-2489/16/7/584 |
| work_keys_str_mv | AT michalispatsiarikas usingmachinelearningonmacroeconomictechnicalandsentimentindicatorsforstockmarketforecasting AT georgepapageorgiou usingmachinelearningonmacroeconomictechnicalandsentimentindicatorsforstockmarketforecasting AT christostjortjis usingmachinelearningonmacroeconomictechnicalandsentimentindicatorsforstockmarketforecasting |