From Market Anomalies to Machine Learning: A Systematic Review of Stock Market Winners, Indices, and Digital Transformation
Abstract
The study set out to revisit the anatomy of stock market winners by conducting a systematic literature review covering evidence from 1988 to 2023. The review synthesized thirty two peer-reviewed studies drawn from finance, economics, information systems, and behavioral science to assess how traditional anomalies, institutional mechanisms, and artificial intelligence-driven approaches explain superior stock performance. Guided by the PRISMA framework, inclusion criteria focused on journal articles and scholarly books with clear empirical methodologies, while opinion papers and non-financial studies were excluded. Data were extracted into a coding matrix and analyzed thematically. The findings indicate that early anomaly-based studies highlighted financial attributes such as low price-to-book ratios, accelerating earnings, and strong relative strength as enduring predictors of winners, though replication work shows diminishing abnormal returns under modern efficiency conditions. A second thematic stream revealed that institutional and structural mechanisms—including index construction, platform recognition, and data sovereignty—play a decisive role in amplifying visibility and liquidity, often shaping winners independent of fundamentals. A third theme identified artificial intelligence and behavioral dynamics as critical in contemporary markets. While deep reinforcement learning and machine learning models improved prediction and portfolio performance, concerns regarding robustness, reproducibility, and costs persist. In parallel, gamblified investing platforms were found to expose retail investors to systematic underperformance through design features that encourage excessive risk-taking. The review concludes that the anatomy of stock market winners is co-produced by financial attributes, institutional frameworks, and technological plus behavioral dynamics. It recommends that scholars integrate anomaly-based and artificial intelligence approaches, regulators promote transparent index and data governance practices, and financial platforms adopt responsible design principles to protect investors. The study contributes to ongoing debates on market efficiency and provides a multi-dimensional framework for understanding stock market winners in the era of digital transformation.
Keywords: Stock market winners; Market anomalies; Efficient Market Hypothesis; Index construction; Platform recognition; Artificial intelligence in finance; Machine learning trading; Deep reinforcement learning; Data sovereignty; Gamblification of investing; Systematic literature review; Digital transformation of markets
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