Mapping the Intellectual Landscape of Psychometric Behaviour in Algorithmic Trading Using Bibliometric Analysis

In today’s fast-evolving financial market, integrating human choices with the power of computation is creating new arena in decision-making in securities markets. Algorithmic Trading (AT) has now been center of attraction among the global researchers, but its effectiveness, efficiency and accuracy are boosted when integrated with investors psychometric profiling and dynamic optimization of algorithms for trading purpose. Intersections among the various domains like decision science, behavioural finance, psychology, and data science states the greater roles of cognitive biases, risk perception, and financial self-efficacy on the design and operation of automated trading systems. To understand this integration of multi-domain area, researcher constructed bibliometric review of 470 Scopus-listed articles which were downloaded for the time frame 2015 to 2025, words were researched on Scopus platform by adding the set: to "psychometric" AND ("algorithmic trading" OR "behavioural finance" OR "automated trading" OR "fintech"). Through performance analysis, co-authorship mapping, keyword co-occurrence, thematic evolution, and citation-impact statistics, the study founded that: (1) there had been exponential growth in the research articles from 2015 on an average 4 to 8 per year; (2) authorship has been from diverse geographical arena specially from the United States and India; (3) the emergence of four robust thematic clusters—Risk Tolerance and Bias Calibration, Digital-Financial Literacy, Psychometric Scale Engineering, and Machine-Learning Behavioural Modelling; and (4) there has been huge shift from building psychometric scale for measuring financial behaviour and personality, to artificial intelligence for understanding the emotion of people and their actions in the stock market. The findings from this research study have strong contributions to theoretical aspect and also in real life for those individuals who design algorithm trading system for investors who manage portfolios and for government who involves in regulations. There is also scope of further research which this study provides, like explainable AI, cross-cultural psychometrics and green algorithm.