Integrating sentiment analysis with graph neural networks for enhanced stock prediction: A comprehensive survey

Das, Nabanita; Sadhukhan, Bikash; Chatterjee, Rajdeep; Chakrabarti, Satyajit

doi:10.1016/j.dajour.2024.100417

Cited by 8 publications

(1 citation statement)

References 66 publications

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“…Their methodology is based on feeding into a long short-term memory network, which uses the results from the sentiment analysis combined with historical stock data to predict market movements more accurately than previously compared models. Moreover, in [55], researchers integrated sentiment analysis with graph neural networks for stock predictions, highlighting the synergy between graph neural networks' structural data representation capabilities and sentiment interpretation. They explored various graph structures, like stock and investor networks, and how those can incorporate sentiment data extracted from news articles, social media feeds, and financial reports.…”

Section: The Role Of Sentiment Analysis In Stock Market Forecastingmentioning

confidence: 99%

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Papageorgiou,

Gkaimanis,

Tjortjis

2024

Electronics

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Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

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Section: The Role Of Sentiment Analysis In Stock Market Forecastingmentioning

confidence: 99%