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Machine Learning-Based Stock Price Prediction for Business Intelligence
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Author(s): Bhavya K. R. (Reva University, India), Malla Sudhakara (Reva University, India), G. Ramasubba Reddy (Sai Rajeswari Institute of Technology, India), L. N. C. Prakash K. (CVR College of Engineering, India), Rupa Devi B. (AITS, India)and Sangeetha M. (Reva University, India)
Copyright: 2023
Pages: 18
Source title:
AI-Driven Intelligent Models for Business Excellence
Source Author(s)/Editor(s): Samala Nagaraj (Woxsen University, India)and Korupalli V. Rajesh Kumar (Woxsen University, India)
DOI: 10.4018/978-1-6684-4246-3.ch013
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Abstract
The act of digital marketing uses a variety of traditional methods such as analyst consensus, earnings per share estimation, or fundamental intrinsic valuation. Also, social media management, automation, content marketing, and community development are some of the most popular uses for digital marketing. Stock price prediction is a challenging task since there are so many factors to take into account, such as economic conditions, political events, and other environmental elements that might influence the stock price. Due to these considerations, determining the dependency of a single factor on future pricing and patterns is challenging. The authors examine Apple's stock data from Yahoo API and use sentiment categorization to predict its future stock movement and to find the impact of “public sentiment” on “market trends.” The main purpose of this chapter is to predict the rise and fall with high accuracy degrees. The authors use an artificial intelligence-based machine learning model to train, evaluate, and improve the performance of digital marketing strategies.
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