Annals of Operations Research

, Volume 263, Issue 1–2, pp 339–360 | Cite as

Which startup to invest in: a personalized portfolio strategy

Data Mining and Analytics


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.


Venture capital Financial portfolio selection Financial decision-makers’ preferences Recommender system Investment strategy 



This research was supported in part by Natural Science Foundation of China (71329201).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Hao Zhong
    • 1
  • Chuanren Liu
    • 2
  • Junwei Zhong
    • 1
  • Hui Xiong
    • 1
  1. 1.Management Science and Information Systems DepartmentRutgers UniversityNewarkUSA
  2. 2.Decision Science and Management Information Systems DepartmentDrexel UniversityPhiladelphiaUSA

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