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“Darling, time horizon matters”: applying the CNN-LSTM method for predicting US equity ETFs

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  • Wenguang Lin

Abstract

The paper uses a hybrid model of convolutional neural network and long short-term memory (CNN-LSTM) to examine the impact of the prediction (or input) window length on the prediction accuracy of trading decisions for US equity ETFs. Results demonstrate that the prediction window length plays a vital role in the prediction accuracy of the trading decision. A hump-shaped relationship is observed between prediction accuracy and prediction window length over monthly trading days.

Suggested Citation

  • Wenguang Lin, 2026. "“Darling, time horizon matters”: applying the CNN-LSTM method for predicting US equity ETFs," Applied Economics Letters, Taylor & Francis Journals, vol. 33(6), pages 815-820, March.
    • Handle: RePEc:taf:apeclt:v:33:y:2026:i:6:p:815-820
    DOI: 10.1080/13504851.2024.2396550
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