An‐Pin Chen
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Communication top 5%
- Knowledge Management and Sharing
Papers in
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- Stock Market Forecasting Methods 35
-
- Complex Systems and Time Series Analysis 18
- Co-authors
- Mu‐Yen Chen (4 shared papers)Yu‐Chia Hsu (9 shared papers)Chang-Chun Lin (3 shared papers)Szu-Hao Huang (5 shared papers)Chun‐Hung Wang (1 shared paper)Chunping Huang (1 shared paper)Wei‐Lun Chen (1 shared paper)Chia‐Chen Chen (2 shared papers)
- Journals
- Expert Systems with Applications (5 papers)The Electronic Library (3 papers)Soft Computing (2 papers)IEEE Access (2 papers)Applied Soft Computing (2 papers)
- Partner nations
- TaiwanJapanUnited States
In The Last Decade
An‐Pin Chen
65 papers receiving 649 citations
Peers
Comparison fields: 5 of 98
- Management Science and Operations Research 239
- Communication 94
- Finance 100
- Strategy and Management 116
- Artificial Intelligence 165
Countries citing papers authored by An‐Pin Chen
This map shows the geographic impact of An‐Pin Chen'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 An‐Pin Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites An‐Pin Chen more than expected).
Fields of papers citing papers by An‐Pin Chen
This network shows the impact of papers produced by An‐Pin Chen. 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 An‐Pin Chen. The network helps show where An‐Pin Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside An‐Pin Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 119 | |
| 2 | 2016 | 97 | |
| 3 | 2008 | 71 | |
| 4 | 2005 | 47 | |
| 5 | 2001 | 36 | |
| 6 | 2007 | 23 | |
| 7 | 2007 | 19 | |
| 8 | 2018 | 18 | |
| 9 | 2003 | 16 | |
| 10 | 2014 | 15 | |
| 11 | 2017 | 15 | |
| 12 | 2006 | 14 | |
| 13 | 2007 | 14 | |
| 14 | 2014 | 13 | |
| 15 | 2008 | 13 | |
| 16 | 2016 | 11 | |
| 17 | 2008 | 10 | |
| 18 | 2016 | 10 | |
| 19 | 2007 | 9 | |
| 20 | 2007 | 7 |
About An‐Pin Chen
An‐Pin Chen is a scholar working on Management Science and Operations Research, Economics and Econometrics, Artificial Intelligence, Finance and Electrical and Electronic Engineering, having authored 70 papers that have together received 708 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (35 papers), Complex Systems and Time Series Analysis (18 papers), Financial Markets and Investment Strategies (11 papers), Metaheuristic Optimization Algorithms Research (10 papers), Evolutionary Algorithms and Applications (10 papers), Neural Networks and Applications (7 papers), Financial Risk and Volatility Modeling (6 papers) and Energy Load and Power Forecasting (6 papers). The work is most often cited by research in Management Science and Operations Research (239 citations), Communication (94 citations), Finance (100 citations), Strategy and Management (116 citations) and Artificial Intelligence (165 citations). An‐Pin Chen has collaborated with scholars based in Taiwan, Japan and United States. Frequent co-authors include Mu‐Yen Chen, Yu‐Chia Hsu, Chang-Chun Lin, Szu-Hao Huang, Chun‐Hung Wang, Chunping Huang, Wei‐Lun Chen, Chia‐Chen Chen, Mei‐Chih Chen and Chien‐Hua Huang. Their work appears in journals such as Expert Systems with Applications, The Electronic Library, Soft Computing, IEEE Access and Applied Soft Computing.
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.