Convolutional Ensemble Learning for Stock Price Direction Forecasting and Algorithmic Trading

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dc.contributor.authorAltuntaş, Yahya
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2026-04-04T13:19:00Z
dc.date.available2026-04-04T13:19:00Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractFinancial time series forecasting is challenging due to the noisy, non-stationary, and volatile nature of market data. In this context, predicting short-term price direction is a critical task for algorithmic trading. While deep learning (DL) models have shown promising results, they often suffer from reproducibility issues, limiting their reliability in real-world financial applications. This study proposes a DL-based framework that predicts the short-term direction of stock price movements using image representations of time series data. To address the instability of individual models, an ensemble learning strategy is employed. Specifically, a VGG16 convolutional neural network was fine-tuned 10 times independently with different random weight initializations, and the final prediction was generated by majority voting across model outputs. Results demonstrate that the proposed framework improves reproducibility and predictive reliability while delivering competitive financial performance when integrated into trading strategies. © 2025 IEEE.
dc.identifier.doi10.1109/IDAP68205.2025.11222387
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105025022550
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222387
dc.identifier.urihttps://hdl.handle.net/11616/108076
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectCNNs
dc.subjectensemble learning
dc.subjectfinancial time series forecasting
dc.subjecttransfer learning
dc.titleConvolutional Ensemble Learning for Stock Price Direction Forecasting and Algorithmic Trading
dc.typeConference Object

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