Investment Analytics: A Bibliometric Review

This study aimed to conduct a mapping of the research field of investment and analytics from 2017 to 2026. The study’s premise is the notion that analytics is used to inform investment decisions and is an area that organisations must invest in. The authors intended to explore how research on these two concepts has developed, which topics it encompasses, who leads the field and how its language has evolved. To achieve this goal, the authors performed a Scopus database search on 11 September 2026 with the terms “Investment AND Analytics” following the PRISMA approach, resulting in a final set of 2,071 documents, including 1,874 articles and 197 reviews. The data were analysed using bibliometric and VOS viewer. The present review has achieved its intended purpose as a consolidated overview of a fragmented body of work that spans a range of sectors and uses a variety of terms. The findings are summarised below according to the three research questions asked in the Introduction. The first research question asked about the trends in publications and citations. The findings suggest that there has been a significant rise in the research on investment and analytics. The number of annual publications rose from 59 in 2017 to 483 in 2026, an annual growth rate of 26.32% with an R2 value of 0.898231. Remarkably, 2025 and 2026 account for 938 publications, or 45.3% of the dataset, while 2026’s figure is expected to continue to climb as the year is incomplete. The literature has been cited 44,409 times, with an average of 21.44 citations per document, with (Sivarajah et al., 2017) being cited 1,736 times. Thus, while the literature is well-cited, early influential works have had a significant impact on the field while more recent writings have had less time to accumulate citations. The second research question examined the main themes, sources, authors and countries. The field is globally diverse, with 7,755 authors, 1,190 sources and 116 countries, an average of 3.97 authors per article and 31.39% international collaboration. Output is concentrated, as the top ten countries account for 63.4% of mentions, led by the US (1,024), China (591), India (542), the UK (368) and Australia (332). Keyword maps at thresholds of 5, 10, 15 and 25 show “investments” as the top keyword (554 occurrences). An investment, prediction and machine learning cluster appears at every level, while the analytics and AI group is less stable, with seven clusters at the lowest level. Finally, general terms like "human," "article," and "female" are consistently grouped together across all levels and should not be interpreted as a theme of the field. The third research question analysed how terms and themes evolved over time and identified what gaps remain. “Big data” dominated author keywords from 2017 to 2020, “machine learning” and “data analytics” led from 2021 to 2024, and “artificial intelligence” became prominent in 2025-2026. Ethics (106 publications) and ESG (55 publications) surged in the last two years reflecting a shift toward AI-driven applications and their societal and environmental impacts. Gaps still remain including inconsistent analytics definitions, a disconnect between investment appraisal and financial machine learning research, and uneven geographical output. This review is useful to a number of different groups. For academics it provides a coherent research map that brings together a disparate body of literature, helping both established and new researchers to see the major streams, sources and countries of contribution. The core of the research is clearly investment, prediction and machine learning, with AI, ethics and ESG all growing significantly in 2025-2026. It also has some clear methodological implications for future bibliometric work. For managers, investors and decision makers the consensus is that the value of analytics is highly context dependent. Müller et al. (2018) found productivity gains only in IT-intensive or highly competitive industries and Mikalef et al. (2020) found firm skills and resources to be important. Managers should evaluate projects in stages using multi-criteria methods (Yalcin et al., 2022) and investment managers using machine learning should factor in trading costs as Ma et al. (2021) show that high turnover can effectively eliminate much of the return. For society Bindeeba et al. (2025) combine 59 studies on digital change and ESG performance and Giordino et al. (2026) find a greater AI focus is associated with better ESG performance (which may be of benefit in areas such as energy use, health care and food). There are however some significant caveats. Galaz et al. (2021) point to the dangers of algorithmic bias and unequal access to AI and the IEA (2026) report that data centre electricity use is rising rapidly so the benefits of AI need to be weighed against these costs. For policymakers this is all evidence based guidance. Narayan et al. (2025) find that poor ESG data leads to higher financing costs for SMEs in emerging markets so standardised ESG data and reporting could improve transparency. Support for adoption should be sectoring specific and responsible AI requirements, risk assessment mechanisms and investment support policies should be developed in parallel. Given that only 31.39% of the publications involve authors from more than one country, there is a case for funding to encourage cross-country collaboration. This review has some limitations and each of them points to a chance for future research. The study was done within the time and resources of an academic project, so the authors could not add more databases, more checks or more advanced methods. The authors kept only English articles and reviews from journals. Other records such as conference papers and book chapters were left out hence the data set may be smaller than the full field. The search words were broad, and the method does not report a check of each record for relevance. The general words found in the keyword maps show that some records may not be closely linked to the topic. The data were taken on 11 September 2026, so 2026 is not a full year. The output of 483 documents in 2026 covers only a part of the year. In the same way the new papers have had less time to gain citations that is why the citation counts favour older papers. Finally, this review is descriptive. It did not use co-citation analysis, bibliographic coupling or thematic evolution analysis, and it did not judge the quality of the studies.

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