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Using CNN to Model Stock Prices

Author

Listed:
  • Mitja Steinbacher

    (Faculty of Law and Business Studies, Catholic Institute)

  • Matej Steinbacher

    (Pixlifai)

  • Matjaz Steinbacher

    (Fund for Financing the Decommissioning of the Krško Nuclear Power Plant and Disposal of Radioactive Waste)

Abstract

The paper applies Convolutional Neural Networks to examine whether and to what extent closing stock prices can be predicted during the opening hour of a trading day. In particular, the MobileNet-V2 architecture was implemented, which transforms the financial time series into an image classification problem. We used daily data in a 5-minute time interval of the 1000 largest listings in Nasdaq by market capitalization. Results show that according to a standard performance measures, the MobileNet-V2 achieved a high prediction accuracy and outperformed several alternative deep learning algorithms.

Suggested Citation

  • Mitja Steinbacher & Matej Steinbacher & Matjaz Steinbacher, 2025. "Using CNN to Model Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 5299-5340, December.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:6:d:10.1007_s10614-025-10887-3
    DOI: 10.1007/s10614-025-10887-3
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