Michael Liebmann

524 citations
11 papers · 345 · h-index 8

Impact in

Papers in

Michael Liebmann

10 papers receiving 326 citations

Peers

Michael Liebmann
Comparison fields: 5 of 57
  • Management Science and Operations Research 214
  • Finance 108
  • Economics and Econometrics 117
  • Artificial Intelligence 112
  • Accounting 38
Replace Hiroki Sakaji with:
Hiroki Sakaji Japan
Marc-André Mittermayer Switzerland
Evangelos Stavroulakis Greece
Anna Pomeranets United States
Alejandro Lopez-Lira United States
Christian González-Martel Spain
Irene Aldridge United States
Moritz Sudhof United States
Charles E. Mossman Canada
Tamal Datta Chaudhuri India
Michael Liebmann relative to Hiroki Sakaji Japan Hiroki Sakaji's profile →
Citations per field
00.5×10×14.5×
Hiroki Sakaji · 1×
Citations per year

Countries citing papers authored by Michael Liebmann

Since Specialization
Citations

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

Fields of papers citing papers by Michael Liebmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Michael Liebmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Michael Liebmann Line = papers co-authored together Michael Liebmann links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2013230
2 201236
3 201521
4 201313
5 201611
6 198611
7 201311
8
Information Processing in Electronic Markets: Measuring Subjective Interpretation Using Sentiment Analysis
20129
9
Towards an Oil Crisis Early Warning System based on Absolute News Volume
20122
10 20111
11 20140

About Michael Liebmann

Michael Liebmann is a scholar working on Finance, Management Science and Operations Research, Artificial Intelligence, Economics and Econometrics and Accounting, having authored 11 papers that have together received 345 indexed citations. Recurring topics across this work include Financial Markets and Investment Strategies (7 papers), Stock Market Forecasting Methods (5 papers), Auditing, Earnings Management, Governance (4 papers), Advanced Text Analysis Techniques (3 papers), Market Dynamics and Volatility (3 papers), Complex Systems and Time Series Analysis (1 paper), Advanced Computational Techniques and Applications (1 paper) and Biochemical and Molecular Research (1 paper). The work is most often cited by research in Management Science and Operations Research (214 citations), Finance (108 citations), Economics and Econometrics (117 citations), Artificial Intelligence (112 citations) and Accounting (38 citations). Michael Liebmann has collaborated with scholars based in Germany and United States. Frequent co-authors include Dirk Neumann, Michael Hagenau, Alexei G. Orlov, Matthias Hauser, Liane Häußler, Brigitte Voit, Doris Pospiech, Dieter Jehnichen, Andreas Korwitz and Hartmut Komber. Their work appears in journals such as Decision Support Systems, International Review of Financial Analysis, Macromolecular Chemistry and Physics, International Conference on Information Systems and SSRN Electronic Journal.

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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