Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing predic...

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Vydané v:Scientific reports Ročník 14; číslo 1; s. 23762 - 18
Hlavní autori: Ji, Yi, Luo, Yuxuan, Lu, Aixia, Xia, Duanyang, Yang, Lixia, Wee-Chung Liew, Alan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 10.10.2024
Nature Publishing Group
Nature Portfolio
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Accuracy
Algorithms
Decision making
Deep learning
Experiments
Forecasting
Galformer
Humanities and Social Sciences
multidisciplinary
Natural language processing
Neural networks
Predictions
Science
Science (multidisciplinary)
Securities markets
Stock exchanges
Stock index prediction
Stock market indexes
Time series
Transformer
Trends
Deep learning
Transformer
Stock index prediction
Galformer
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-72045-3