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A deep fusion model for stock market prediction with news headlines and time series data.

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Title: A deep fusion model for stock market prediction with news headlines and time series data.
Authors: Chen, Pinyu, Boukouvalas, Zois, Corizzo, Roberto
Source: Neural Computing & Applications; Dec2024, Vol. 36 Issue 34, p21229-21271, 43p
Subject Terms: STOCK prices, MARKET timing, PORTFOLIO performance, TIME series analysis, PORTFOLIO management (Investments)
Abstract: Time series forecasting models are essential decision support tools in real-world domains. Stock market is a remarkably complex domain, due to its quickly evolving temporal nature, as well as the multiple factors having an impact on stock prices. To date, a number of machine learning-based approaches have been proposed in the literature to tackle stock trend prediction. However, they typically tend to analyze a single data source or modality, or consider multiple modalities in isolation and rely on simple combination strategies, with a potential reduction in their modeling power. In this paper, we propose a multimodal deep fusion model to predict stock trends, leveraging daily stock prices, technical indicators, and sentiment in daily news headlines published by media outlets. The proposed architecture leverages a BERT-based model branch fine-tuned on financial news and a long short-term memory (LSTM) branch that captures relevant temporal patterns in multivariate data, including stock prices and technical indicators. Our experiments on 12 different stock datasets with prices and news headlines demonstrate that our proposed model is more effective than popular baseline approaches, both in terms of accuracy and trading performance in a portfolio analysis simulation, highlighting the positive impact of multimodal deep learning for stock trend prediction. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: A deep fusion model for stock market prediction with news headlines and time series data.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Pinyu%22">Chen, Pinyu</searchLink><br /><searchLink fieldCode="AR" term="%22Boukouvalas%2C+Zois%22">Boukouvalas, Zois</searchLink><br /><searchLink fieldCode="AR" term="%22Corizzo%2C+Roberto%22">Corizzo, Roberto</searchLink>
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  Data: Neural Computing & Applications; Dec2024, Vol. 36 Issue 34, p21229-21271, 43p
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  Data: <searchLink fieldCode="DE" term="%22STOCK+prices%22">STOCK prices</searchLink><br /><searchLink fieldCode="DE" term="%22MARKET+timing%22">MARKET timing</searchLink><br /><searchLink fieldCode="DE" term="%22PORTFOLIO+performance%22">PORTFOLIO performance</searchLink><br /><searchLink fieldCode="DE" term="%22TIME+series+analysis%22">TIME series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22PORTFOLIO+management+%28Investments%29%22">PORTFOLIO management (Investments)</searchLink>
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  Data: Time series forecasting models are essential decision support tools in real-world domains. Stock market is a remarkably complex domain, due to its quickly evolving temporal nature, as well as the multiple factors having an impact on stock prices. To date, a number of machine learning-based approaches have been proposed in the literature to tackle stock trend prediction. However, they typically tend to analyze a single data source or modality, or consider multiple modalities in isolation and rely on simple combination strategies, with a potential reduction in their modeling power. In this paper, we propose a multimodal deep fusion model to predict stock trends, leveraging daily stock prices, technical indicators, and sentiment in daily news headlines published by media outlets. The proposed architecture leverages a BERT-based model branch fine-tuned on financial news and a long short-term memory (LSTM) branch that captures relevant temporal patterns in multivariate data, including stock prices and technical indicators. Our experiments on 12 different stock datasets with prices and news headlines demonstrate that our proposed model is more effective than popular baseline approaches, both in terms of accuracy and trading performance in a portfolio analysis simulation, highlighting the positive impact of multimodal deep learning for stock trend prediction. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00521-024-10303-1
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      – Code: eng
        Text: English
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        PageCount: 43
        StartPage: 21229
    Subjects:
      – SubjectFull: STOCK prices
        Type: general
      – SubjectFull: MARKET timing
        Type: general
      – SubjectFull: PORTFOLIO performance
        Type: general
      – SubjectFull: TIME series analysis
        Type: general
      – SubjectFull: PORTFOLIO management (Investments)
        Type: general
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      – TitleFull: A deep fusion model for stock market prediction with news headlines and time series data.
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            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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