Factors, Forecasts, and Simulations of Volatility in the Stock Market Using Machine Learning
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".