What does this research mean for the field?

Machine learning and deep learning methods generally improve stock market forecasting performance in controlled experiments, achieving a median relative error reduction of 20.3%, though their real-world superiority remains unproven due to methodological and practical limitations. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Synthesize empirical machine learning and deep learning research in financial market forecasting and quantify proposed model performance improvements relative to baseline methods.

September 5, 2026Discover ComputingOpen Access

Machine learning in stock market forecasting: a comprehensive review

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Authors

MKMoksh Khemka KHKamal Haddad TRTanzim RedwanBangladesh Air Force Shaheen College

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Implication

Structured review reveals median error reductions of 20.3% across financial markets, indicating experimental gains lack universal real-world superiority.

Key Points

Synthesize empirical machine learning and deep learning research in financial market forecasting and quantify proposed model performance improvements relative to baseline methods.
Conducted a structured review spanning equities, market indices, commodities, foreign exchange, and cryptocurrencies across classical ML, recurrent networks, CNNs, transformers, and reinforcement learning.
Performed paired-error quantitative evidence mapping evaluating 17 peer-reviewed studies comprising 47 same-dataset, same-horizon proposed-versus-baseline comparisons.
Proposed machine learning models achieved a study-level median relative error reduction of 20.3% (interquartile range: 5.7% to 50.7%; full range: −0.8% to 71.5%) against baseline models across 47 evaluations.
Controlled experimental gains did not establish universal real-world superiority due to unstandardized validation designs, overlooked transaction costs, market slippage, and inconsistent uncertainty reporting.

Cite This Study

Khemka et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd3e16b95aff0620eb1b5 https://doi.org/10.1007/s10791-026-10491-5

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Implication

Structured review reveals median error reductions of 20.3% across financial markets, indicating experimental gains lack universal real-world superiority.

Machine learning in stock market forecasting: a comprehensive review

Discussion

Member takes

Implication

Key Points

Cite This Study

Machine learning in stock market forecasting: a comprehensive review

Discussion

Member takes

Implication

Key Points

Cite This Study

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