Feature selection with annealing for forecasting financial time series

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dc.authorid0000-0003-2267-5175
dc.contributor.authorPabuccu, Hakan
dc.contributor.authorBarbu, Adrian
dc.date.accessioned2026-02-28T12:18:08Z
dc.date.available2026-02-28T12:18:08Z
dc.date.issued2024
dc.departmentBayburt Üniversitesi
dc.description.abstractStock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability. However, obtaining accurate and reliable predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies. To mitigate this complexity, this study provides a comprehensive method for forecasting financial time series based on tactical input-output feature mapping techniques using machine learning (ML) models. During the prediction process, selecting the relevant indicators is vital to obtaining the desired results. In the financial field, limited attention has been paid to this problem with ML solutions. We investigate the use of feature selection with annealing (FSA) for the first time in this field, and we apply the least absolute shrinkage and selection operator (Lasso) method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags. Boruta (BOR) feature selection, a wrapper method, is used as a baseline for comparison. Logistic regression (LR), extreme gradient boosting (XGBoost), and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks. The dependent variables consisted of daily logarithmic returns and trends. The mean-squared error for regression, area under the receiver operating characteristic curve, and classification accuracy were used to evaluate model performance, and the statistical significance of the forecasting results was tested using paired t-tests. Experiments indicate that the FSA algorithm increased the performance of ML models, regardless of problem type. The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification. FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification. None of the hybrid BOR models outperformed the hybrid FSA models. Lasso-based models, excluding the LR type, were comparable to the best models for six of the 10 datasets for classification. Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately, providing the field with a useful tool for investors.
dc.description.sponsorshipTHE SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TRKIdot;YE (TUBITAK)
dc.description.sponsorshipNot applicable.
dc.identifier.doi10.1186/s40854-024-00617-3
dc.identifier.issn2199-4730
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85206205460
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1186/s40854-024-00617-3
dc.identifier.urihttps://hdl.handle.net/20.500.12403/6109
dc.identifier.volume10
dc.identifier.wosWOS:001327747800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofFinancial Innovation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260218
dc.subjectFinancial time-series forecasting
dc.subjectFeature selection
dc.subjectMachine learning
dc.subjectCryptocurrency
dc.subjectStock market
dc.subjectReturn forecasting
dc.titleFeature selection with annealing for forecasting financial time series
dc.typeArticle

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