Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis
Deep learning techniques predict stock prices using combined numerical and sentiment data for improved accuracy and profitability.
What it examines
This paper explores the use of deep learning and machine learning techniques to predict stock market prices and trends by combining numerical/economical data with textual/sentimental data. The study aims to enhance stock Technical Analysis using Sentiment Analysis, focusing on AAPL, GOOG, NVDA, and S&P 500 Information Technology.
What it concludes
The study concludes that integrating Sentiment Analysis with Technical Analysis enhances stock market predictions. Potential applications include more accurate financial forecasting and investment strategies. Future research could explore additional data sources and further refine the models.
Evidence objects
The study concludes that integrating Sentiment Analysis with Technical Analysis enhances stock market predictions. Potential applications include more accurate financial forecasting and investment strategies. Future research could explore additional data sources and further refine the models.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis | SN Computer Science Skip to main content Log in Menu Find a journal Publish with us Track your research Search Cart Home SN Computer Science Article Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis Original Research Published: 18 April 2024 Volume?5 , article?number 446 , ( 2024 ) Cite this article SN Computer Science Aims and scope Submit manuscript Georgios-Markos Chatziloizos 1 , Dimitrios Gunopulos 2 & Konstantinos Konstantinou 2 208 Accesses Explore all metrics Abstract Machine learning and deep learning techniques are applied by researchers with a background in both economics and computer science, to predict stock prices and trends. These techniques are particularly attractive as an alternative to existing models and methodologies because of their ability to extract abstract features from data.?Most existing research approaches are based on using either numerical/eco
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