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Which startup to invest in: a personalized portfolio strategy

Author

Listed:
  • Hao Zhong

    (Rutgers University)

  • Chuanren Liu

    (Drexel University)

  • Junwei Zhong

    (Rutgers University)

  • Hui Xiong

    (Rutgers University)

Abstract

Recent years have witnessed a venture capital boom. By offering capitals and mentoring, venture investors would receive high returns if their portfolio companies successfully exit, namely being acquired or going Initial Public Offering. However, the screening and evaluation of startups for investment largely depends on the investors’ personal experiences, social relationships, and qualitative evaluation on firms. The entrepreneurial finance industry thus has a strong call for the methodologically sound, quantitative study of venture capital deals. Plus, more accessible data and more sophisticated analytics techniques signal the opportunity for methodical decision-making in investing the venture financing market. To this end, in this paper, we aim at developing a personalized portfolio strategy for assisting investors to target the right startups and determine the proper amount of capital to fund. Specifically, we first develop a Probabilistic Latent Factor model to estimate the investment preferences of all the investors in a collaborative way. The model is fitted with not only the historical investment records but also the profiles of the venture capitalists and the startups. Then, we assess the startups’ investment outcomes by regressing the potential returns and risks. We improve the regression performances by nonparametric methods. At last, we use modern portfolio theory to optimize the investment strategy over the startups recommended by the investment preference model. As a result, the investment strategy can yield maximized returns with suppressed potential risks, and meanwhile meet the investment preferences of the venture capitalists. The proposed method is evaluated using data from venture financing markets in USA, and the results show that our method outperforms other state-of-the-art methods on various metrics.

Suggested Citation

  • Hao Zhong & Chuanren Liu & Junwei Zhong & Hui Xiong, 2018. "Which startup to invest in: a personalized portfolio strategy," Annals of Operations Research, Springer, vol. 263(1), pages 339-360, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-016-2316-z
    DOI: 10.1007/s10479-016-2316-z
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    References listed on IDEAS

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    Cited by:

    1. Lele Cao & Vilhelm von Ehrenheim & Sebastian Krakowski & Xiaoxue Li & Alexandra Lutz, 2022. "Using Deep Learning to Find the Next Unicorn: A Practical Synthesis," Papers 2210.14195, arXiv.org, revised Jun 2024.
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    3. Shabnam Sorkhi & Joseph C. Paradi, 2020. "Measuring short-term risk of initial public offering of equity securities: a hybrid Bayesian and Data-Envelopment-Analysis-based approach," Annals of Operations Research, Springer, vol. 288(2), pages 733-753, May.
    4. Anita Mezzetti & Loic Mar'echal & Dimitri Percia David & William Lacube & S'ebastien Gillard & Michael Tsesmelis & Thomas Maillart & Alain Mermoud, 2022. "TechRank," Papers 2210.07824, arXiv.org.
    5. Thomas W. Archibald & Edgar Possani, 2021. "Investment and operational decisions for start-up companies: a game theory and Markov decision process approach," Annals of Operations Research, Springer, vol. 299(1), pages 317-330, April.
    6. Venkata Sai Srinivasa Rao Muramalla & Ateeq Mesfer Al-Hazza, 2019. "Entrepreneurial Strategies and Factors Stimulate the Business of Tech Startups," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(3), pages 360-370, May.
    7. Carayannis, Elias G. & Grigoroudis, Evangelos & Wurth, Bernd, 2022. "OR for entrepreneurial ecosystems: A problem-oriented review and agenda," European Journal of Operational Research, Elsevier, vol. 300(3), pages 791-808.
    8. , Aisdl, 2021. "Recipes for a Successful Exit for Clean- and Hard-tech Startups," OSF Preprints k3vey, Center for Open Science.

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