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Company Investment Recommendation Based on Data Mining Techniques

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Book cover Business Information Systems Workshops (BIS 2019)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 373))

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Abstract

There are about seventy thousand companies listed on various stock markets worldwide and there is public information on about three hundred thousand companies on Wikipedia but that is only a small fraction of all companies. Among the millions others are hiding the future technological innovators, market disruptors and best possible investments. So, if an investors has an example of the kind of company they are interested in, how can they successfully find other such investment options without sifting through millions of options?

We propose non-personalized recommendation approach for alternatives of company investments. This method is based on data mining techniques for investment behaviour modelling. The investment opportunities are discovered using the idea of transfer learning of indirectly associated company investments. This allows companies to diversify their investment portfolio. Experiments are run over a dataset of 7.5 million companies, of which the model focuses on startups and investments in the last 3 years. This allows us to investigate most recent investment trends. The recommendation model identifies top-N investment opportunities. The evaluation of the proposed investment strategies show high accuracy of the recommendation system.

This research is partially supported by projects that received funding from the European Union Horizon 2020 Research and Innovation Programme – euBusinessGraph (Grant Agreement no. 732003) and InnoRate (Grant Agreement no. 821518).

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Notes

  1. 1.

    GraphDB web page. https://www.ontotext.com/products/graphdb/ Accessed 12 Jun 2019.

  2. 2.

    Open–Source Data Mining Library SPMF. http://www.philippe-fournier-viger.com/spmf/index.php Accessed 12 Jun 2019.

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Author information

Authors and Affiliations

  1. Sirma AI trading as Ontotext, Sofia, Bulgaria

    Svetla Boytcheva & Andrey Tagarev

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetla Boytcheva

Authors
  1. Svetla Boytcheva
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  2. Andrey Tagarev
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Corresponding author

Correspondence to Svetla Boytcheva .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. University of Seville, Seville, Spain

    Rafael Corchuelo

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Boytcheva, S., Tagarev, A. (2019). Company Investment Recommendation Based on Data Mining Techniques. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems Workshops. BIS 2019. Lecture Notes in Business Information Processing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-36691-9_7

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  • DOI: https://doi.org/10.1007/978-3-030-36691-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36690-2

  • Online ISBN: 978-3-030-36691-9

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