The value of publicly available, textual and non-textual information for startup performance prediction
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DOI: 10.1016/j.jbvi.2020.e00179
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- Kaiser, Ulrich & Kuhn, Johan M., 2020. "The value of publicly available, textual and non-textual information for startup performance prediction," ZEW Discussion Papers 20-012, ZEW - Leibniz Centre for European Economic Research.
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- Coad, Alex & Karlsson, Johan, 2022. "A field guide for gazelle hunters: Small, old firms are unlikely to become high-growth firms," Journal of Business Venturing Insights, Elsevier, vol. 17(C).
- Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
- Khanindra Ch. Das, 2023. "What Affects Startup Acquisition in Emerging Economy? Evidence from India," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 22(2), pages 111-134, June.
- Dafei Yin & Jing Li & Gaosheng Wu, 2021. "Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods," Papers 2112.07985, arXiv.org.
- Kim, Jongwoo & Kim, Hongil & Geum, Youngjung, 2023. "How to succeed in the market? Predicting startup success using a machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
- Lele Cao & Gustaf Halvardsson & Andrew McCornack & Vilhelm von Ehrenheim & Pawel Herman, 2023. "Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems," Papers 2309.16888, arXiv.org, revised Jun 2024.
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More about this item
Keywords
Startup; Performance; Prediction; Text as data; Algorithmic scoring;All these keywords.
JEL classification:
- L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Statistics
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