Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
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Abstract (arXiv)
Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 3; 2026: 1.
7 papers.
- 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 65
- 2025FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMscited by 4
- 2021Stock Market Analysis with Text Data: A Reviewcited by 1
- 2025Multimodal Stock Price Predictioncited by 1
- 2024Hierarchical Organization Simulacra in the Investment Sectorcited by 0
- 2025A three-step machine learning approach to predict market bubbles with financial newscited by 0
- 2026FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learningcited by 0
- 2026FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning30 Jul 2026
- 2025A three-step machine learning approach to predict market bubbles with financial news18 Oct 2025
- 2025FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs24 Jul 2025
- 2025Multimodal Stock Price Prediction23 Jan 2025
- 2024Hierarchical Organization Simulacra in the Investment Sector1 Oct 2024
- 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Models19 Jul 2023
- 2021Stock Market Analysis with Text Data: A Review23 Jun 2021