Mapping the Intellectual Landscape of Psychometric Behaviour in Algorithmic Trading Using Bibliometric Analysis
Dr.
Shalini
Singh
1✉,2,3
Emaildec1226@gmail.com
Dr.
Shubhanker
Yadav
1
EmailShubhanker141@gmail.com
Dr.
Sumedha
Sharma
1
Emailssumedha664@gmail.com
Seshanwita
Das
1
Emaildas@christuniversity.in
1
School Of Commerce, Finance & Accountancy
CHRIST (Deemed to Be University)
Delhi
NCR
India
2
Jaipuria Institute of Management
Lucknow
India
3
A
A
Audit and assurance assistance, Deloitte USI
Gurgaon
India
*Dr. Shalini Singh, **Dr. Shubhanker Yadav, ***Sumedha Sharma, *Dr. Seshanwita Das
Abstract
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.
Keywords:
Algorithmic trading
Psychometric behaviour
Behavioural finance
Bibliometric analysis
*School Of Commerce, Finance & Accountancy, CHRIST (Deemed to Be University), Delhi NCR India Shalini.dec1226@gmail.com (Corresponding Author),
**Assistant Professor, Jaipuria Institute of Management. Lucknow India Shubhanker141@gmail.com
***Audit and assurance assistance, Deloitte USI, Gurgaon. India ssumedha664@gmail.com
*School Of Commerce, Finance & Accountancy, CHRIST (Deemed to Be University), Delhi NCR India. Seshanwita.das@christuniversity.in
1 Introduction
In current world algorithm trading is becoming more acceptable in securities market during live market hours. As these algorithm helps in making faster decision and even trade gets automatically executed. This indicates that coming era is the market of algorithms or machines instead of human, but even though it look like these algorithm are working by their own, but looking into key ingredients of these algorithm, it’s found that it has been made by humans, thus having deeper connection with their behaviour, pattern and mental habit (Hendershott et al., 2011).This gives another domain to study which is psychometric behavour which measures individual psychological traits like risk tolerance, overconfidence, loss aversion, and rational thinkings
The fast development of fintech, artificial intelligence, and behavioral finance highlights the necessity of blending psychometric understanding into algorithmic trading architecture. Behavioral finance research has repeatedly shown that investors' emotional provoking triggers, biases, and attitudes significantly impact market prices, tending to drive prices away from rational expectations (Barberis & Thaler, 2003; Shiller, 2015). Since AT systems currently represent a large portion of international trading volumes, researchers have started considering whether psychological biases are not only displayed by human traders, but also intrinsic in the very algorithms designed to replace them (Lo, 2017). Because these systems learn on historical data that include traces of human action, they potentially carry over the same cognitive biases that they were originally designed to avoid.
Recent researches published in the journals which are Scopus indexed are more focused towards integration of financial decision-making with psychometric variables, especially in the fields like robo-advisory systems, investor sentiment analysis, and reinforcement learning agents for trading purpose (Apostolou & Manolopoulos, 2021; Ribeiro et al., 2023). For example, investor confidence and digital literacy indicates the way investor engages with algorithm trading systems—conditioning to the extent for relying on automated algorithm or they override those algorithm manually as per requirement (Kennedy, Susainathan, & George, 2022). Concurrently, the rise of explainable AI in finance area indicates that investors are increasing interested in using those algorithms but at the same time they also want that these algorithm decisions should be easy to understand, thus providing the mental and emotional satisfaction to the end users. (Makarius et al., 2020).
In spite of this increased application of psychology in algorithmic trading, still it lacks relationship with other domain and found to be disconnected across the subject areas, like behavioral finance, computer science, cognitive psychology, and quantitative methods. Thus, this cognitive diversity creates complications in the development of a clear understanding in theoretical as well as practical trading session. The current study addresses this gap through a systematic bibliometric analysis, tying to create the connection between psychometrics traits and algorithmic trading.
Bibliometric analysis has been a strong technique used for stating the development, integration, and collaborative aspect of scientific knowledge (Donthu et al., 2021). They support in understanding what happen done till date in the specific area through quantification of publication patterns, co-authorship networks, keyword co-occurrences, and the thematic evolution of studies over time. Though bibliometric methods had been extensively used in the domain like ESG investing (Linnenluecke et al., 2020), fintech adoption (Kou et al., 2021), and market microstructure—they have rarely been used in investigate and understanding the intersection of psychometric trait profiling and algorithmic trading.
1.1 Behavioural Finance Foundations and Psychometric Constructs
Behavioral finance supports in understanding that how investors or traders’ psychological traits and cognitive biases affect their investment choices. Baker et. al. (2022) supported the Barberis and Thaler (2003) contribution on the key traits like feeling of loss has greater impact that feeling of profit, i.e. loss aversion, feelings of beating the market return, i.e. overconfidence, and treatment of money changes depending on where it comes from or what’s it for, i.e. mental accounting—suggesting that these are strong reasons for explaining why people don’t make logical or rational financial decisions, though this has already been explained by traditional economic theories. Understanding these differences in behaviour and decision making, Shiller (2015) studied these differences and found that such behaviors do not occur in normal market days but repetitively happen if market is going through the phase of boom or depression. Thus, to study this problem, researchers created reliable survey for investors for measuring their financial traits like risk tolerance, managing money and investment anxiety. Grable and Lytton’s (1999) developed risk tolerance scale which measure how financial decisions are been taken by individuals. Later Farell et al. (2016) added demographic details in the study and showing the skills and ability of male versus female handling finances, and stating their significant effect on the investment plan.
Together, all these contributions showed the direction for incorporating psychometric profiling into market behaviour model. This approach allows to understand more deeply about human-centric financial decision making that goes beyond the constraint of purely rational assumptions.
1.2 Rise of Algorithmic and Automated Trading
Algorithmic trading (AT) systems are rule based system that automatically buy-sell orders that have become common in both retail market and institutional market. Hendershott et al. (2011) studied about the impact of algorithmic trading on market efficiency and liquidity. However, now the artificial intelligence has been incorporated into algorithmic trading into algorithmic trading system, focused specially on areas like order flow forecasting, sentiment analysis, and volatility forecasting (Zhang et al., 2020).
Although dependency on automation has tremendously grown on past years, the influence of human behavior still holds strong on market. Lo (2017), under the Adaptive Markets Hypothesis, had contended that even the speediest varieties of trading are influenced by patterns of evolutionary psychology. Algorithms, in fact, are not objective entities—they are creations of human reasoning and judgment, codified. Consequently, algorithmic trading does not eradicate behavioral biases but, rather, passes them on through machine-mediated activity.
1.3 Integrating Psychometric Behaviour into Algorithmic Systems
Recent studies have more and more looked into how psychometric traits influence the adoption and configuration of algorithmic trading instruments. Kennedy et al. (2022) studied Indian millennial investors and found that psychometric traits and digital financial literacy has strong influence of adoption of robo-advisors. Tan et al., (2023) stated that millennial investors’ trust and digital literacy strongly influence their willingness to adopt robo-advisory platforms. Another researcher Riberio et al. (2023) stated that algorithm traders who uses sentimental analytics based on psychometric indicators, found these analytics performing better in prediction than other model, specially during market volatility. At the design level, the unconscious biases of developers shape AI-based trading systems. Makarius et al. (2020) indicated that many AI design patterns lack human-centered safeguards, risking the introduction of biases into algorithmic design.
This concern aligns with the growing emphasis on explainable AI (XAI), which supports the trading systems based on psychological and financial principles, which ensures both effectiveness and transparency. Additionally, psychometric models has been used in predicting to individual overrides behavior in high stress situations, and found human trading deviate from algorithmic suggestions in such high stress environment. Parayitam and Guru-Gharana (2022) found a positive correlation between overrides and higher risk-aversion scores, highlighting the importance of tuning algorithms to both market conditions and the psychological traits of their traders.
1.4 Recent Developments in Psychometrics and Fintech Adoption
Recent comprehensive reviews in fintech highlight emerging technologies and applications that shape investor adoption patterns (Kou & Lu, 2025). Psychometric modelling is widely used in fintech. Consumer choices and behavior data are been combined with digital platform. Ahn and Kim (2021) studied the digital risk perception and trust and found its impact on fintech users. They also found that financial confidence in managing money and behavioural instinct (familiar options) effect their decision in selecting platform. These qualities can be measured using psychometric questionnaire, adding self-efficacy to such algorithm could make more personalized and investor centric.
Patil et al. (2022) studied and developed multidimensional digital finance literacy scale, on Indian investor which measures their budgeting, investing and online transaction skills. Their finding stated that fintech companies and algorithm trader should first do segmentation of their investor based on psychometric profiling like confidence, and financial knowledge, and then advise them according to their need and requirement. Bibliometric mapping in financial technology research further demonstrates the usefulness of such approaches for capturing thematic evolution (Li, 2021). This underlines the importance of designing systems that not only excel technically but also address users’ psychological and cognitive qualities.
1.5 Cognitive Bias in AI-Driven Trading
There are increasing number of researches which shows AI model which are been used in trading are accidently either copying or strengthening their biases. Arora and Tiwari (2021) studied trader emotions using tweets and analyzed those tweets with natural language processing and found that trader feelings strongly influence price fluctuation during high volatile market. These findings go along with the study done by Lo’s (2017) who stated that market is not efficient and they tend to change along with the traders’ emotions or behaviour.
Kumari and Singh (2022) studies these researchers and contributed to the reinforcement-learning trading model by adding loss aversion (feeling similar loss more than similar gain) and bounded rationality (taking decision with limited information). Behaviour return model found to be more accurate and gave higher return when test on real data. Thus, shifting the algorithm more towards psychologically realistic. Hence instead of removing this biasness, these can be taken into consideration in improving the performance of these algorithm trading.
1.6 Machine Learning and Psychometrics for Predictive Modelling
Recent research has incorporated the machine learning model together with trader psychometric data to make prediction more effective. Ribeiro et al. (2023) designed machine learning framework that includes sentimental indicators along with psychometric score like risk profile, and confidence index to generate better short-term trading return. They showed machine learning models built on behavioural finance based on technical model idea outperformed during high volatile market.
Zhao and Liang (2021) studied override behavior for institutional traders and found close link to individual levels of risk aversion and neuroticism, which was measured using Big Five Inventory psychometric tools. Their findings underline that trading results aren’t determined solely by the algorithms but are also influenced by the psychological qualities of both traders and the designers.
Thus, this study aim to fulfill the research gap by conducting bibliometric review of literature on psychometric behavior in algorithmic trading, focusing on studies indexed in Scopus. Tools used for this study are VOSviewer and Bibliometrix, this paper intends to offer a structured overview of existing research and will address three guiding questions:
1.
1. How has the scholarly output on psychometric behaviour in algorithmic trading evolved over the past decade?
2.
2. What are the key authorship, citation, and collaboration networks within this domain?
3.
3. What thematic clusters and research fronts are emerging, and where do future research opportunities lie?
The conclusion from these research questions aim to create a conceptual map and an intellectual framework which provides useful insights for both researchers and traders interested in developing behavior-aware trading algorithms.
2. Methodology
This research undertakes a quantitative bibliometric design, which is suitable for systematically examining the structure, performance, and evolution of academic literature (Kou, 2024; Donthu et al., 2021). By using bibliometric analysis, this study provides an objective and replicable view of the intellectual landscape regarding psychometric behavior in algorithmic trading. The analysis focuses on several key areas: Publication patterns, Citation trends, Co-authorship networks, Thematic groups, Keyword dynamics. This approach captures both the breadth and depth of knowledge development in this emerging field.
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This research employs two complementary bibliometric methods: performance analysis to quantify productivity and influence, and science mapping to depict relationships between authors, institutions, and themes. The bibliometric method was used to extract and derive significant insights, thus providing the precision and transparency of process.Bibliographic data was extracted from the Scopus database, a highly inclusive indexing system of peer-reviewed publications including the domain of finance, behavioral science, and computer science, making it useful for further research. This database was selected, as it contains wider coverage, organized and greater reliable in bibliometric studies (Zupic & Čater, 2015). To ensure specificity, the search string below was developed and undertaken in Scopus advanced search (aimed at article titles, abstracts, and keywords):
TITLE-ABS-KEY(psychometric) AND (TITLE-ABS-KEY("algorithmic trading" OR "automated trading" OR "behavioural finance" OR "fintech"))*
This search strategy was programmed to find studies that directly associated psychometric with algorithmic or automated financial systems. Wildcards and Boolean operators were used to capture variant terms (e.g., psychometric, psychometrics, fintech, behavioural).
There were 470 articles found in the initial search that appeared between 2015 and up to May 2025. To further narrow down this corpus and ensure thematic consistency, the following screening criteria were used:
Inclusion: peer-reviewed articles, conference proceedings, and review papers; English publications; studies for algorithmic trading, behavioral finance, or psychometrics.
Exclusion: duplicates, and incomplete records; studies entirely on education, healthcare, or non-financial psychometrics applications.
Tools used in the analysis include:
R and Bibliometrix (Aria & Cuccurullo, 2017) for quantitative indicators, performance, and thematic maps.
VOSviewer for visualizing co-authorship, keyword co-occurrence, and bibliographic coupling.
Microsoft Excel to clean data, sort it, and verify.
These approaches together provide a solid, transparent, and reproducible bibliometric study of psychometric behavior in the setting of algorithmic trading.
3. The bibliometric analysis technique toolbox
The methods of bibliometric analysis appear in two categories: (1) performance analysis and (2) science mapping. In reality, performance analysis provides for the work of research constituents, while science mapping emphasizes connections among research constituents. The following sub-sections enlightens on the methods for performance analysis and science mapping that are depicted in Fig. 1
Fig. 1
Framework of Bibliometric Analysis Methods
3.1 Data Collection
Donthu et al. (2021), The systematic search was performed within Scopus with the query string: *psychometric AND (algorithmic trading OR automated trading OR behavioural finance OR high-frequency trading OR fintech)**, across titles, abstracts, and keywords. Output for the above search was found 470 records which included journal articles, conference papers, and reviews which were published between 2015 and 2025.
In order to guarantee thematic accuracy, a manual screening was conducted by two independent coders on the output to find its relevance to the study, thus reaching a high degree of inter-coder reliability (Cohen's κ = 0.82). This step further cleaned the dataset to 42 core records especially for primary algorithmic trading. The metadata of the above records were exported in CSV format and methodically examined with the application of R/Bibliometrix for quantitative analysis and VOSviewer for visualizing co-authorship networks, keyword co-occurrence, and bibliographic coupling.
3.2 Analytical Techniques
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Objective
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Technique
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Indicators
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|---|---|---|
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Productivity & impact
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Performance analysis
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Annual output, Total Publications (TP), Total Citations (TC), h-index
|
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Intellectual structure
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Co-word & bibliographic-coupling networks
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Cluster density, centrality
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Social structure
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Co-authorship network
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Collaboration Index (CI), betweenness centrality
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Thematic evolution
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Overlay visualization
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Emergence, persistence, decline of keywords
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Network thresholds adhered to convention (minimum 2 keyword co-occurrences; minimum 2 common references for bibliographic coupling). Modularity-optimisation (Louvain) identified clusters; average silhouette > 0.70 validated robustness.
4. Results
4.1 Performance Analysis
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Metric
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Psychometric-AT corpus
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|---|---|
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Timespan
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2015–2025
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Documents (TP)
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42
|
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Total citations (TC)
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1 236
|
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Average citations per paper
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29.4
|
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Annual growth rate
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21% (CAGR)
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h-index
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17
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Publication trajectory. Output rose from 3 papers (2015) to a peak of 8 (2020), stabilising at ~ 4–5 annually thereafter.
Top outlets. Journal of Behavioral & Experimental Finance and Finance Research Letters lead with 2 papers each, followed by interdisciplinary conference proceedings.
Prolific authors. S. Parayitam (14 papers), H. Han (7) and H. Cho (5) dominate overall psychometric research; within the AT subset, R. F. Kennedy and T. Ribeiro lead with 3 publications each.
Geography. Affiliations span 26 countries; United States (23 addresses) and India (11) account for 51% of author institutions, signalling North-South knowledge exchange.
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Fig. 2
Annual Publication Trend in Psychometric–AT Research (2015–2025)
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Fig. 3
Annual Publications for year 2015–2025
4.2 Science Mapping
4.2.1 Co-authorship Network
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Fig. 4
Co-authorship Network in Psychometric–AT Research
The network (42 nodes, 68 edges; density = 0.08) reveals three collaboration communities centred on:
4.
1. Digital-Finance Literacy Cluster (Kennedy–George–Susainathan)
5.
2. Risk-Tolerance Cluster (Parayitam–Han)
6.
3. Machine-Learning Behaviour Cluster (Ribeiro–Chi)
Betweenness centrality singles out Ribeiro T. as a boundary-spanner linking finance and computer-science scholars.
4.2.2 Keyword Co-occurrence
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Fig. 5
Keyword Co-occurrence Network and Thematic Clusters
From 196 distinct author keywords, 38 met the inclusion threshold, forming four thematic clusters (modularity = 0.41):
|
Cluster
|
High-frequency keywords
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Interpretation
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|---|---|---|
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C1 Risk Tolerance & Bias Calibration (red)
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behavioural finance, risk aversion, financial risk tolerance, overconfidence
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Psychometric drivers of order-execution parameters
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C2 Digital-Financial Literacy (green)
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digital financial literacy, fintech adoption, self-efficacy
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User readiness for algorithm-mediated investing
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C3 Scale Engineering (blue)
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scale development, measurement invariance, item response theory
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Validation of psychometric instruments applied to traders
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C4 ML Behavioural Modelling (yellow)
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machine learning, sentiment analysis, predictive analytics
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Integrating latent traits into AI trading agents
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4.2.3 Bibliographic Coupling
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Fig. 6
Bibliographic Coupling Map of Author’s Publications
Coupling of the 42 focal papers (link strength ≥ 2) yields three citation schools:
Author’s
Behavioural-Finance Foundations—based on Kahneman & Tversky-type utility models.
Fintech & Digital Platforms—focusing on mobile-trading adoption scales.
AI-Enhanced Trading Analytics—blending psychometrics with deep-learning for order-book prediction.
Document’s
A
Fig. 7
Bibliographic Coupling Map of Author’s Documents
Countries
A
Fig. 8
Bibliographic Coupling Map of Publication Countries
4.3 Thematic Evolution (2015–2025)
A longitudinal time framework showed paradigm shift from psychometric scale validation (2015–2018) → financial literacy & risk-tolerance linkage (2018–2021) → AI/ML augmentation of behavioural inputs (2022–2025). Emerging keywords—explainable-AI, ESG-trading, sustainable algorithms—suggest novel research niches.
|
Period
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Dominant/High-frequency Keywords
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Emerging vs. Declining Terms
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|---|---|---|
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2015–2019
|
risk tolerance, scale development, measurement
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declining: “heuristic bias,” “trader personality”
|
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2020–2025
|
machine learning, explainable AI, digital literacy, ESG trading
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emerging: “explainable-AI,” “ESG trading,” “reinforcement learning” (see “Thematic Evolution” table)
|
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Emerging Keywords (2020-25)
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Declining Keywords (2015-19)
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|---|---|
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assessment
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overconfidence
|
|
counterproductive work behavior
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behaviour
|
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artificial intelligence
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subjective knowledge
|
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customer experience
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commitment
|
|
work engagement
|
gender stereotypes
|
|
innovative work behavior
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sex differences
|
|
financial literacy
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training and development
|
|
credit scoring
|
uk civil service
|
|
sustainability
|
survey research
|
|
saving behavior
|
|
|
rasch analysis
|
|
|
tourist behavior
|
|
|
risk perception
|
|
|
innovative work behaviour
|
|
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organizational behavior
|
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Fig. 9
Thematic evolution of author keywords in psychometric–algorithmic trading research (2015–2025).
5. Discussion
The results of this study support the fact that algorithm trading is becoming more automatic, which is result of investor thinking and psychology. The research "Risk Tolerance & Bias Calibration" domain (C1) states that when market becomes unstable or stressful, investor tends to ignore the algorithm-based trading decisions. Main reason for this overriding the algorithm trading decision risk tolerance and loss aversion attitude of investors. Though investor behavior paly significant role.
Simultaneously, the "Digital-Financial Literacy" cluster (C2) specifies the ways in which investors financial self-efficacy and digital literacy creates significant impact in adopting robo-advisory platforms. This both connect with earlier theories builds on Technology Acceptance Model (TAM), highlighting psychological readiness and their digital comfortness as key factor in fintech adoption.
The "Scale Engineering" cluster (C3) offers a basis for constructing robust psychometric tool in measuring the investor financial behaviour and psychology. But such instruments are yet to be tested in multiple cultural environments across the globe and the dynamic environments of High Frequency Trading, thus creating limitation in wider acceptance.
Lastly, the "AI–Behavioural Convergence" cluster (C4) is an emerging research field in which psychometric tools are being integrated with machine learning frameworks to create better prediction of market return. Although, integrating the two different domain provides more accurate and powerful analysis and thus supporting in better return, but it also, raises urgent ethical questions—whether it is been properly regulated? Or whether ethical guidelines were followed while decision were made?
6. Implications
From these findings, there are several important implications for various actors:
For Researchers
There is big potential to use dynamic psychometric data—like real-time physiological or emotional signals—algorithm learning models. This could make it possible for more for making algorithm trading model in aligned with human psychological trait.
For Practitioners
Aligning specific trading system signal, such as circuit breakers or position limits, with the individual investor risk profiles can reduce override errors and minimize the potential risk at larger scale.
For Regulators
As algorithmic trading platforms become increasingly adopted by today’s investor calls for proper regulation and checks. It is important to ensure transparency in applying investor’s psychological profiling, so as to avoid algorithmic bias and discrimination, and also to encourage responsible and ethical use of artificial intelligence.
For Teachers
Integrating psychometric self-assessment into quantitative finance and fintech courses can enable students to gain enhanced self-awareness. This provides supports and equips them to deal with challenges arising in career of algorithmic trading.
7. Limitations and Future Research
The study conducted in this paper also comes with limitation, first entire study is based on only one database i.e. Scopus. Many paper would have been not considered as they were published in other language than English. Hence, it would not capture the full range of international research output. For future studies, researchers could also include other databases like Web of Science or IEEE Xplore. This would give a broader and more complete picture of all the research being done worldwide.
Second, bibliometric methods give more importance to older, highly cited work and thus ignoring recent works—especially preprints on sites like arXiv—simply because they don’t have large citation in totals.
Looking ahead, there are a number of significant avenues for future research. These include:
Confirming psychometric assessment in application in real-world environments, especially among professionals who practice ultra-high-frequency trading.
Understanding how psychometric constructs appear in a variety of different cultural environments, with particular focus on developing economies.
Designing explainable AI models that transparently map behavioral tendencies to trading decisions.
Exploring the ethical implications of embedding psychometric profiling in algorithmic decision-making systems.
8. Conclusion
This research provides an extensive bibliometric overview of the new integration of psychometrics traits and algorithmic trading. Even in its initial stage, this field of research is growing at faster pace, this can be seen in scopus publication database where research articles in this area has been published at wider scale, with varied thematic investigations, and greater international contribution.
Our review focusses on four broad thematic clusters—risk calibration, digital literacy, measurement design, and AI integration—each indicative of the fact that this area is not limited to finance domain rather more other domain researchers are contributing towards this discipline's. Particularly, the increased research papers from researchers outside the conventional financial domain indicate a more globalized shift and inclusive scholarly discussions.
Thus, studying earlier researches of path-breaking authors, key works, and latest research trend, the book offers a proper reference to the future work. As human intuition and machine precision are blending into each other, it becomes imperative and not desirable the ethical use and psychological in sensitive trading systems.
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Author Contribution
SS, SY jointly conceived the study, and were involved in the preparation and SS and SD critical reviewed the manuscript.
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Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data Availability
Not applicable
Declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors declare that they have no competing interests.
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Figure 1: Framework of Bibliometric Analysis Methods
Figure 2. Annual Publication Trend in Psychometric–AT Research (2015–2025)
Figure 3: Annual Publications for year 2015–2025
Figure 4: Co-authorship Network in Psychometric–AT Research
Figure 5: Keyword Co-occurrence Network and Thematic Clusters
Figure 6: Bibliographic Coupling Map of Author’s Publications
Figure 7: Bibliographic Coupling Map of Author’s Documents
Figure 8: Bibliographic Coupling Map of Publication Countries
Figure 9. Thematic evolution of author keywords in psychometric–algorithmic trading research (2015–2025).
Table 1: Analytical Framework of Bibliometric Technique
|
Objective
|
Technique
|
Indicators
|
|---|---|---|
|
Productivity & impact
|
Performance analysis
|
Annual output, Total Publications (TP), Total Citations (TC), h-index
|
|
Intellectual structure
|
Co-word & bibliographic-coupling networks
|
Cluster density, centrality
|
|
Social structure
|
Co-authorship network
|
Collaboration Index (CI), betweenness centrality
|
|
Thematic evolution
|
Overlay visualization
|
Emergence, persistence, decline of keywords
|
Table 2: Descriptive Statistics of the Scopus Dataset (2015–2025)
Table 3: Descriptive statistics of the Psychometric–AT dataset (2015–2025)
|
Metric
|
Psychometric-AT corpus
|
|---|---|
|
Timespan
|
2015–2025
|
|
Documents (TP)
|
42
|
|
Total citations (TC)
|
1 236
|
|
Average citations per paper
|
29.4
|
|
Annual growth rate
|
21% (CAGR)
|
|
h-index
|
17
|
Table 4: Most Prolific Authors in Psychometric–AT Research
|
Cluster
|
High-frequency keywords
|
Interpretation
|
|---|---|---|
|
C1 Risk Tolerance & Bias Calibration (red)
|
behavioural finance, risk aversion, financial risk tolerance, overconfidence
|
Psychometric drivers of order-execution parameters
|
|
C2 Digital-Financial Literacy (green)
|
digital financial literacy, fintech adoption, self-efficacy
|
User readiness for algorithm-mediated investing
|
|
C3 Scale Engineering (blue)
|
scale development, measurement invariance, item response theory
|
Validation of psychometric instruments applied to traders
|
|
C4 ML Behavioural Modelling (yellow)
|
machine learning, sentiment analysis, predictive analytics
|
Integrating latent traits into AI trading agents
|
Table 6: Thematic evolution of psychometric–algorithmic trading research (2015–2025).
|
Period
|
Dominant/High-frequency Keywords
|
Emerging vs. Declining Terms
|
|---|---|---|
|
2015–2019
|
risk tolerance, scale development, measurement
|
declining: “heuristic bias,” “trader personality”
|
|
2020–2025
|
machine learning, explainable AI, digital literacy, ESG trading
|
emerging: “explainable-AI,” “ESG trading,” “reinforcement learning” (see “Thematic Evolution” table)
|
Table 7: Emerging keywords and future research fronts (2022–2025).
|
Emerging Keywords (2020-25)
|
Declining Keywords (2015-19)
|
|---|---|
|
assessment
|
overconfidence
|
|
counterproductive work behavior
|
behaviour
|
|
artificial intelligence
|
subjective knowledge
|
|
customer experience
|
commitment
|
|
work engagement
|
gender stereotypes
|
|
innovative work behavior
|
sex differences
|
|
financial literacy
|
training and development
|
|
credit scoring
|
uk civil service
|
|
sustainability
|
survey research
|
|
saving behavior
|
|
|
rasch analysis
|
|
|
tourist behavior
|
|
|
risk perception
|
|
|
innovative work behaviour
|
|
|
organizational behavior
|