Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review

Abstract

The nonlinear nature of financial data series and the intricate incorporation of data into market prices necessitate a comprehensive exploration of key research findings, prevailing trends, intense debates, and subfields in the market behavior realm. Studies exploring the way in which technical analysis can exploit the deviation from market efficiency in stock markets, based on new prediction techniques (machine learning, deep learning, and artificial intelligence), are lacking. This study presents a comprehensive bibliometric assessment of market behavior using the Scopus database from 1972 to 2022. A thorough assessment process, which included keywords, filters, and data cleaning, was employed to narrow down the literature from 30,551 to 8,289 relevant papers. The research framework delineates seven primary themes that underpin this study: market efficiency, behavioral finance, technical analysis, volatility, fractals, asset pricing, and price discovery. For practitioners, investors, and policymakers, our study presents evidence regarding emerging themes, such as technical analysis, adaptive market hypothesis, and machine learning, which diverges from the findings of the proponents of equilibrium models based on investors’ rationality. Moreover, an in-depth inquiry into the role of technical analysis in shaping portfolio investment presents a promising future research avenue.

Keywords

• bibliometric analysis
• market efficiency
• technical analysis
• forecast
• machine learning
• asset pricing