Yan Meng

1.2k total citations
42 papers, 777 citations indexed

About

Yan Meng is a scholar working on Artificial Intelligence, Accounting and Finance. According to data from OpenAlex, Yan Meng has authored 42 papers receiving a total of 777 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 16 papers in Accounting and 8 papers in Finance. Recurrent topics in Yan Meng's work include Corporate Finance and Governance (15 papers), Banking stability, regulation, efficiency (7 papers) and Auditing, Earnings Management, Governance (5 papers). Yan Meng is often cited by papers focused on Corporate Finance and Governance (15 papers), Banking stability, regulation, efficiency (7 papers) and Auditing, Earnings Management, Governance (5 papers). Yan Meng collaborates with scholars based in United States, China and Finland. Yan Meng's co-authors include Emma Y. Peng, Robert DeYoung, Bill B. Francis, Qiang Wu, Iftekhar Hasan, Yuan Xie, Marlene Plumlee, Jeff Jiewei Yu, Jun Yin and Hongliang Guo and has published in prestigious journals such as Bioinformatics, IEEE Access and Neurocomputing.

In The Last Decade

Yan Meng

37 papers receiving 726 citations

Peers

Yan Meng
Comparison fields: 5 of 67
  • Accounting 529
  • Finance 302
  • Economics and Econometrics 226
  • Strategy and Management 117
  • Artificial Intelligence 86
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James Perry United Kingdom View profile →
Citations per field, relative to Yan Meng
Yan Meng · 1×
Citations per year, relative to Yan Meng
Yan Meng · 1×

Countries citing papers authored by Yan Meng

Since Specialization
Citations

This map shows the geographic impact of Yan Meng'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 Yan Meng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yan Meng more than expected).

Fields of papers citing papers by Yan Meng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yan Meng. 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 Yan Meng. The network helps show where Yan Meng may publish in the future.

Co-authorship network of co-authors of Yan Meng

This figure shows the co-authorship network connecting the top 25 collaborators of Yan Meng. A scholar is included among the top collaborators of Yan Meng 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 Yan Meng. Yan Meng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 3
3 0
4 1
5 3
6 1
7 4
8
Radical or Incremental Innovations – R&D Investment Around CEO Retirement
2
9
R&D Cuts and Subsequent Reversals: Meeting or Beating Quarterly Analyst Forecasts
0
10 155
11 23
12 14
13 5
14 1
15 2
16
The value and indicator function of patents
14
17
Pending Approval Patents, Proprietary Information, and Bank Loan Spread
1
18
Executive Compensation and Policy Choices at U.S. Commercial Banks
7
19
Toward Evolving Neural Networks using Bio-Inspired Algorithms.
16
20 8

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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