Post-merger Performance Modeling

Post-Merger Performance Modeling is a systematic analytical framework within merger modeling that quantitatively and qualitatively forecasts or evaluates the operational, financial, and strategic outcomes of a corporate merger after its execution, distinguishing itself by its exclusive focus on post-integration performance dynamics relative to pre-deal projections and synergy targets.

59

Publications

4.9K

Citations

122

Authors

81

Institutions

Publications per year

2017–2026

16

Authors

122

  1. EM
    Enrico Moretti

    University of California, Berkeley, United States

    2 publications · 165 citations

    2

    h-index

  2. KR
    Kannan Ramaswamy

    Birla Institute of Technology and Science - Hyderabad Campus, India

    2 publications · 255 citations

    2

    h-index

  3. AW
    Andrew W. Stark

    University of Manchester, United Kingdom

    2 publications · 272 citations

    2

    h-index

  4. RP
    Ronan Powell

    UNSW Sydney, Australia

    2 publications · 272 citations

    2

    h-index

  5. CP
    Craig Peters

    United States Department of Justice, United States

    2 publications · 360 citations

    2

    h-index

1–5 of 122

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 215 citations

    4

    h-index

  2. Journal of Banking & Finance

    2 publications · 228 citations

    2

    h-index

  3. Journal of Financial and Quantitative Analysis

    JFQA · 2 publications · 460 citations

    2

    h-index

  4. RePEc: Research Papers in Economics

    Repository · 2 publications · 31 citations

    2

    h-index

  5. Journal of Industrial Economics

    1 publications · 11 citations

    1

    h-index

1–5