Machine learning and Deep learning applications in Stock Price Prediction: Top trend and bibliometric analysis
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
Arash Salehpour
1
,
Karim Samadzamini
2
1 - Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
2 - Department of Computer Engineering, University College of Nabi Akram, 51839-18993, Tabriz, Iran
الکلمات المفتاحية: Stock Price Prediction, Bibliometric, Financial Markets, Deep Learning, Machine Learning,
ملخص المقالة :
This paper conducts an exhaustive bibliometric examination of Machine Learning and Deep Learning applications in Stock Price Prediction from 2013 to 2022. The stock market's economic significance and volatility underscore the interest in predictive models. Numerous studies use techniques like Support Vector Machines, Neural Networks, and Reinforcement Learning to forecast market trends. Complexities, including uncertainty and data density, pose challenges in financial forecasting. The study employs Scopus and advanced visualization tools for trend analysis. Queries focusing on "Deep learning," "Machine learning," and "Stock price prediction" yielded 131 papers from 93 sources. The analysis reveals a 36.55% annual growth rate, with China, India, and South Korea as leading contributors. The recurring and co-citation networks illuminate influential papers and prominent themes. "Automated news reading: Stock price prediction based on financial news using context-capturing features" emerges as a pivotal document. Thematic maps highlight evolving trends like "neural networks" and "financial markets." Applying Lotka's Law uncovers uneven author contributions. The paper emphasizes electronic trading, deep learning, and investment themes in stock price prediction research. This analysis offers insights into research trends, influential authors, and emerging themes. It is a valuable resource for researchers, practitioners, and policy-makers exploring the fusion of machine learning, deep learning, and stock price prediction.
