Data driven company valuation and IPO performance prediction

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Type and Duration

FFF-Förderprojekt, April 2018 until March 2020 (finished)


Hilti Chair of Business Process Management

Main Research

Business Process Management

Field of Research

Big Data Analytics


An important part of every initial public offering (IPO) is an accurate valuation of the company planning to sell shares. Most established valuation methods only take hard financial facts into account. However soft facts, like management experience, were shown to play an important role in the success of companies, especially for start-ups and SMEs. In practice it is common to use a mix of several valuation models, which makes the process less transparent for investors.
This project aims to develop a company valuation model that makes the valuation process more efficient as well as more accurate and transparent for both investors and companies. The project evaluates existing valuation models and explores technologies to integrate further information into the valuation process like consumers' opinion on the company, the experience of the managerial staff, expert opinions as well as a more accurate prediction of the company's future performance. To this end state of the art algorithms in the fields of text-mining, sentiment analysis, machine learning and crowdsourcing are investigated. These methods will be used to facilitate the automatic extraction of financial data from existing records, analyze a company's public reputation from (social) media data and predict its future performance.
By partnering with Own a startup company aiming to disrupt the equity market with a block chain based sales platform for company shares, we ensure that our work will have a direct impact on the equity market. This cooperation further allows us to profit from market insight and an environment to evaluate the use of our model.


Data analytics


  • Forschungsförderungsfonds der Universität Liechtenstein


  • Werner, F., Basalla, M., Schneider, J., Hays, D., & vom Brocke, J. (2021). Blockchain Adoption from an Interorganizational Systems Perspective - A Mixed-Methods Approach. Information Systems Management, 38(2), 135-150. (ABDC_2022: B; ABS_2021: 2; VHB_3: C)

  • vom Brocke, J., Basalla, M., Kaiser, L. F., Schneider, J., Ragtschaa, S., Batliner-Staber, F., & Dzinic, E. (2018). Own - The Case of a Blockchain Business Model Disrupting the Equity Market. CONTROLLING - Zeitschrift für erfolgsorientierte Unternehmenssteuerung, 30(5), 19-25. (VHB_3: D)