Igor Halperin
Impact in
- Finance top 5%
- Stochastic processes and financial applications
- Credit Risk and Financial Regulations
- Financial Markets and Investment Strategies
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- Stock Market Forecasting Methods
- Risk and Portfolio Optimization
Papers in
- Finance 11
- Stochastic processes and financial applications 9
- Credit Risk and Financial Regulations 2
-
- Stock Market Forecasting Methods 5
- Co-authors
- Matthew Francis Dixon (11 shared papers)Paul Bilokon (10 shared papers)Andrey Itkin (2 shared papers)
- Journals
- Quantitative Finance (2 papers)Physica A Statistical Mechanics and its Applications (2 papers)International Journal of Theoretical and Applied Finance (1 paper)Neural Computing and Applications (1 paper)The Journal of Computational Finance (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Igor Halperin
15 papers receiving 279 citations
Peers
Comparison fields: 5 of 50
- Finance 129
- Management Science and Operations Research 73
- Artificial Intelligence 63
- Management Information Systems 18
- Accounting 12
Countries citing papers authored by Igor Halperin
This map shows the geographic impact of Igor Halperin'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 Igor Halperin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor Halperin more than expected).
Fields of papers citing papers by Igor Halperin
This network shows the impact of papers produced by Igor Halperin. 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 Igor Halperin. The network helps show where Igor Halperin may publish in the future.
Co-authors
The 3 scholars most cited alongside Igor Halperin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 170 | |
| 2 | 2008 | 45 | |
| 3 | 2020 | 26 | |
| 4 | 2019 | 22 | |
| 5 | 2019 | 10 | |
| 6 | 2020 | 7 | |
| 7 | Machine Learning in Finance: From Theory to Practice | 2020 | 7 |
| 8 | 2010 | 3 | |
| 9 | 2013 | 2 | |
| 10 | 2020 | 2 | |
| 11 | 2020 | 2 | |
| 12 | 2021 | 2 | |
| 13 | 2013 | 1 | |
| 14 | 2020 | 1 | |
| 15 | 2023 | 1 | |
| 16 | 2020 | 0 | |
| 17 | 2020 | 0 | |
| 18 | 2020 | 0 | |
| 19 | 2020 | 0 | |
| 20 | 2022 | 0 |
About Igor Halperin
Igor Halperin is a scholar working on Finance, Management Science and Operations Research, Artificial Intelligence, Management Information Systems and Computational Theory and Mathematics, having authored 20 papers that have together received 301 indexed citations. Recurring topics across this work include Stochastic processes and financial applications (9 papers), Stock Market Forecasting Methods (5 papers), Reinforcement Learning in Robotics (3 papers), FinTech, Crowdfunding, Digital Finance (2 papers), Credit Risk and Financial Regulations (2 papers), Adaptive Dynamic Programming Control (1 paper), Gaussian Processes and Bayesian Inference (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Finance (129 citations), Management Science and Operations Research (73 citations), Artificial Intelligence (63 citations), Management Information Systems (18 citations) and Accounting (12 citations). Igor Halperin has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Matthew Francis Dixon, Paul Bilokon and Andrey Itkin. Their work appears in journals such as Quantitative Finance, Physica A Statistical Mechanics and its Applications, International Journal of Theoretical and Applied Finance, Neural Computing and Applications and The Journal of Computational Finance.
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.