Adrian Gepp

1.7k total citations
54 papers, 1.0k citations indexed

About

Adrian Gepp is a scholar working on Accounting, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Adrian Gepp has authored 54 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Accounting, 16 papers in Artificial Intelligence and 10 papers in Management Science and Operations Research. Recurrent topics in Adrian Gepp's work include Imbalanced Data Classification Techniques (13 papers), Financial Distress and Bankruptcy Prediction (9 papers) and Stock Market Forecasting Methods (7 papers). Adrian Gepp is often cited by papers focused on Imbalanced Data Classification Techniques (13 papers), Financial Distress and Bankruptcy Prediction (9 papers) and Stock Market Forecasting Methods (7 papers). Adrian Gepp collaborates with scholars based in Australia, United Kingdom and New Zealand. Adrian Gepp's co-authors include Kuldeep Kumar, Martina K. Linnenluecke, Tom Smith, Bruce Vanstone, Sukanto Bhattacharya, Milind Tiwari, Terence J. O’Neill, Steven Stern, Rui Xue and Khaled Halteh and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the Operational Research Society and Decision Support Systems.

In The Last Decade

Adrian Gepp

48 papers receiving 936 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Adrian Gepp Australia	17	487	242	198	170	168	54	1.0k
Carl Pacini United States	11	399 0.8×	155 0.6×	122 0.6×	42 0.2×	132 0.8×	54	1.0k
Sukanto Bhattacharya Australia	11	198 0.4×	192 0.8×	110 0.6×	115 0.7×	41 0.2×	53	756
Marcus D. Odom United States	8	504 1.0×	396 1.6×	99 0.5×	162 1.0×	36 0.2×	18	941
Helmi Hammami France	12	393 0.8×	86 0.4×	306 1.5×	160 0.9×	114 0.7×	33	1.0k
Peter Öhman Sweden	23	814 1.7×	79 0.3×	310 1.6×	49 0.3×	223 1.3×	99	1.5k
Anastassia Fedyk United States	11	226 0.5×	60 0.2×	300 1.5×	155 0.9×	120 0.7×	24	783
Hsin‐Min Lu Taiwan	11	641 1.3×	130 0.5×	180 0.9×	88 0.5×	75 0.4×	26	1.1k
María Jesús Segovia Vargas Spain	11	282 0.6×	175 0.7×	115 0.6×	69 0.4×	48 0.3×	49	569
James Hodson United States	8	203 0.4×	65 0.3×	254 1.3×	143 0.8×	119 0.7×	18	702
Juan Lara‐Rubio Spain	13	252 0.5×	111 0.5×	254 1.3×	30 0.2×	150 0.9×	34	613

Countries citing papers authored by Adrian Gepp

Since Specialization

Citations

This map shows the geographic impact of Adrian Gepp's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adrian Gepp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adrian Gepp more than expected).

Fields of papers citing papers by Adrian Gepp

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adrian Gepp. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adrian Gepp. The network helps show where Adrian Gepp may publish in the future.

Co-authorship network of co-authors of Adrian Gepp

This figure shows the co-authorship network connecting the top 25 collaborators of Adrian Gepp. A scholar is included among the top collaborators of Adrian Gepp based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Adrian Gepp. Adrian Gepp is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Gepp, Adrian, et al.. (2025). Benchmarking deep reinforcement learning approaches to trade execution. Pacific-Basin Finance Journal. 94. 102876–102876.

Tiwari, Milind, Adrian Gepp, & Kuldeep Kumar. (2024). Using data analytics to distinguish legitimate and illegitimate shell companies. SHILAP Revista de lepidopterología. 7. 100123–100123. 1 indexed citations

Todd, James, et al.. (2024). Exploring the Interplay Between Equity Groups, Mental Health and Perceived Employability Amongst Students at a Public Australian University. Research in Higher Education. 65(6). 1316–1339. 2 indexed citations

Gignac, Gilles E., Adrian Gepp, Terence J. O’Neill, & Rui Xue. (2023). The Impact of Housing Tenure on Financial Wellbeing Among Elderly Australians. Social Indicators Research. 171(2). 655–675. 1 indexed citations

Gepp, Adrian, et al.. (2023). Methods of performance analysis in women’s Australian football: a scoping review. PeerJ. 11. e14946–e14946. 1 indexed citations

Bilson, Christopher, et al.. (2022). Empirical validation of ELM trained neural networks for financial modelling. Neural Computing and Applications. 35(2). 1581–1605. 10 indexed citations

Gepp, Adrian. (2021). Advanced Statistics with Applications in R. Journal of the Royal Statistical Society Series A (Statistics in Society). 184(4). 1610–1611. 3 indexed citations

Tiwari, Milind, et al.. (2021). Shell Companies: Using a hybrid technique to detect illicit activities. Bond University Research Portal (Bond University). 4 indexed citations

Gepp, Adrian, et al.. (2020). Estimation of a term structure model of carbon prices through state spaces methods: a pitch. Accounting Research Journal. 34(1). 106–112. 1 indexed citations

10.

Todd, James, Adrian Gepp, Brent Richards, & Bruce Vanstone. (2019). Improving mortality models in the ICU with high-frequency data. International Journal of Medical Informatics. 129. 318–323. 5 indexed citations

11.

Gepp, Adrian, Geoff Harris, & Bruce Vanstone. (2019). Financial applications of semidefinite programming: a review and call for interdisciplinary research. Accounting and Finance. 60(4). 3527–3555. 4 indexed citations

12.

Gepp, Adrian & Raymond N. Johnson. (2019). Audit Data Analytics. Bond University Research Portal (Bond University). 273–324.

13.

Gepp, Adrian, et al.. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature. 40(1). 102–115. 215 indexed citations

14.

Todd, James, Brent Richards, Bruce Vanstone, & Adrian Gepp. (2018). Text Mining and Automation for Processing of Patient Referrals. Applied Clinical Informatics. 9(1). 232–237. 11 indexed citations

15.

Vanstone, Bruce, et al.. (2018). Predicting FTSE 100 returns and volatility using sentiment analysis. Accounting and Finance. 58(S1). 253–274. 30 indexed citations

16.

Halteh, Khaled, Kuldeep Kumar, & Adrian Gepp. (2018). Financial distress prediction of Islamic banks using tree-based stochastic techniques. Managerial Finance. 44(6). 759–773. 37 indexed citations

17.

Gepp, Adrian, Martina K. Linnenluecke, Terence J. O’Neill, & Tom Smith. (2017). Big Data Techniques in Auditing Research and Practice: Current Trends and Future Opportunities. SSRN Electronic Journal. 32 indexed citations

18.

Gepp, Adrian. (2015). Financial statement fraud detection using supervised learning methods.. Bond University Research Portal (Bond University). 5 indexed citations

19.

Gepp, Adrian & Kuldeep Kumar. (2008). The role of survival analysis in financial distress prediction. Bond University Research Portal (Bond University). 54 indexed citations

20.

Gepp, Adrian. (2005). An evaluation of decision tree and survival analysis techniques for business failure prediction. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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