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<p>The financial markets, particularly stock trading, offer a variety of profit-generating opportunities based on complex and volatile behaviour. Investors seek strategies to maximise returns, leading to an investigation of inherent market patterns. Converting OHLC (Open, High, Low, Close) data into transformers-based pre-trained language model compatible text is an innovative method for representing numeric data. Extending the language model’s utility to integrate stock market numeric time-series data incorporates its inherent numeracy in embeddings. Raw data are converted into a format compatible with the pre-trained language model through preprocessing and text templates. Using an ensemble of Bidirectional Encoder Representations from Transformers (BERT), FinBERT (BERT finetuned with the financial corpus), FLANG-BERT (BERT finetuned with the financial corpus) and FLANG-ELECTRA (ELECTRA finetuned with the financial corpus) as feature extractor, historical stock market data are utilised to generate an embedding matrix and fused with established neural network architectures, such as Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to predict stock market trends. The simulation results demonstrate that the proposed integrated approach is preferable to previous methodologies. The significance of the findings is confirmed by statistical validation using the Wilcoxon signed-rank test (<i>p</i> value &lt; 0.01). This study offers a promising approach for improving stock market trend prediction by integrating the ensemble of language model-based numeric embeddings with neural networks.</p>

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Enhancing neural network predictions with finetuned numeric embeddings for stock trend forecasting

  • 期刊论文

  • 发表日期：

    2025年2月17日

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  • Avinash Trivedi,
  • S. Sangeetha

摘要

The financial markets, particularly stock trading, offer a variety of profit-generating opportunities based on complex and volatile behaviour. Investors seek strategies to maximise returns, leading to an investigation of inherent market patterns. Converting OHLC (Open, High, Low, Close) data into transformers-based pre-trained language model compatible text is an innovative method for representing numeric data. Extending the language model’s utility to integrate stock market numeric time-series data incorporates its inherent numeracy in embeddings. Raw data are converted into a format compatible with the pre-trained language model through preprocessing and text templates. Using an ensemble of Bidirectional Encoder Representations from Transformers (BERT), FinBERT (BERT finetuned with the financial corpus), FLANG-BERT (BERT finetuned with the financial corpus) and FLANG-ELECTRA (ELECTRA finetuned with the financial corpus) as feature extractor, historical stock market data are utilised to generate an embedding matrix and fused with established neural network architectures, such as Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to predict stock market trends. The simulation results demonstrate that the proposed integrated approach is preferable to previous methodologies. The significance of the findings is confirmed by statistical validation using the Wilcoxon signed-rank test (p value < 0.01). This study offers a promising approach for improving stock market trend prediction by integrating the ensemble of language model-based numeric embeddings with neural networks.