This study aims to provide a comprehensive synthesis of contemporary paradigms on market anomalies within the framework of the Efficient Market Hypothesis (EMH). Accordingly, the study investigates the extent to which financial anomalies are shaped by asset pricing frameworks and empirical design choices, explores how machine learning methodologies affect the predictability of expected returns, and assesses the role of green finance and environmental, social, and governance (ESG) information within contemporary market efficiency analyses. Factors prominent in the current finance literature, such as the replication crisis, uncertainty arising from multiple testing and model diversity, high-volume data sets, machine learning applications, etc., are fundamentally transforming research on market efficiency and anomalies. At the same time, ESG data related to sustainability and the climate crisis bring different implications for financial pricing.

This study is important in that it goes beyond the narrative of contrast to present current methodological evidence within an integrated framework regarding the relationship between EPT and market anomalies. Market anomalies are addressed in the literature in terms of their behavioral and risk-return dimensions. This study is expected to contribute to the literature by offering a unique synthesis of anomalies as an output of empirical evidence and knowledge structure. Furthermore, the evaluation of ESG data in the context of market efficiency will establish a conceptual bridge between sustainability and asset pricing literature. The main limitation of the study is that it does not focus in depth on specific markets and asset classes in order to synthesize a comprehensive literature, but rather addresses them within a holistic framework. Furthermore, machine learning methods customize the findings on interpretability and measurement differences in ESG data. For future studies, it suggests that the relationship between EPT and market anomalies should be evaluated with current assumptions as a phenomenon that varies depending on the information set, measurement method, market, and investor structure, rather than as a contradiction. In conclusion, it is anticipated that dynamic, conditional, and method-sensitive research with an integrated conceptual and practical perspective in financial markets will create a more realistic and analytical basis for understanding market behavior.