Factors, Forecasts, and Simulations of Volatility in the Stock Market Using Machine Learning

Juan Mansilla-Lopez, David Mauricio () and Alejandro Narváez
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Juan Mansilla-Lopez: Facultad de Ingeniería Industrial y de Sistemas, Universidad Nacional de Ingeniería, 210 Túpac Amaru Ave, Lima 15333, Peru
David Mauricio: Facultad de Ingeniería de Sistemas e Informática, Universidad Nacional Mayor de San Marcos, 375 Carlos Germán Amezaga Ave, Lima 15081, Peru
Alejandro Narváez: Facultad de Ciencias Administrativas, Universidad Nacional Mayor de San Marcos, 375 Carlos Germán Amezaga Ave, Lima 15081, Peru

JRFM, 2025, vol. 18, issue 5, 1-25

Abstract: Volatility is a risk indicator for the stock market, and its measurement is important for investors’ decisions; however, few studies have investigated it. Only two systematic reviews focusing on volatility have been identified. In addition, with the advance of artificial intelligence, several machine learning algorithms should be reviewed. This article provides a systematic review of the factors, forecasts and simulations of volatility in the stock market using machine learning (ML) in accordance with PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) review selection guidelines. From the initial 105 articles that were identified from the Scopus and Web of Science databases, 40 articles met the inclusion criteria and, thus, were included in the review. The findings show that publication trends exhibit a growth in interest in stock market volatility; fifteen factors influence volatility in six categories: news, politics, irrationality, health, economics, and war; twenty-seven prediction models based on ML algorithms, many of them hybrid, have been identified, including recurrent neural networks, long short-term memory, support vector machines, support regression machines, and artificial neural networks; and finally, five hybrid simulation models that combine Monte Carlo simulations with other optimization techniques are identified. In conclusion, the review process shows a movement in volatility studies from classic to ML-based simulations owing to the greater precision obtained by hybrid algorithms.

Keywords: volatility; stock market; forecasting; simulation; factors (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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