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Which startup to invest in: a personalized portfolio strategy

  • Data Mining and Analytics
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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.

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Notes

  1. Global Entrepreneurship Monitor, “2014 Global Report”, 2014.

  2. http://www.economist.com/blogs/economist-explains/2014/03/economist-explains-25.

  3. PWC & NVCA et al., “Moneytree Report Q4 2014”, 2014.

  4. https://www.crunchbase.com/.

  5. https://www.spokeintel.com/.

  6. https://www.owler.com/.

  7. When this paper is accepted, we will make our dataset publicly available.

  8. http://en.wikipedia.org/wiki/Discounted_cumulative_gain.

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Acknowledgments

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

Author information

Authors and Affiliations

  1. Management Science and Information Systems Department, Rutgers University, 1 Washington Park, Newark, NJ, 07102, USA

    Hao Zhong, Junwei Zhong & Hui Xiong

  2. Decision Science and Management Information Systems Department, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Chuanren Liu

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  1. Hao Zhong
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  3. Junwei Zhong
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Correspondence to Chuanren Liu.

Appendix

Appendix

The conditional sampling distribution of \(\pmb {\beta }\):

$$\begin{aligned}&\displaystyle P(\pmb {\beta }|\pmb {Y},\varTheta _{-\beta },\varOmega _\beta )=\prod _{i=1}^MP(\pmb {\beta }_i|\pmb {Y},\varTheta _{-\beta },\varOmega _\beta ) \nonumber \\&\displaystyle P(\pmb {\beta }_i|\pmb {Y},\varTheta _{-\beta },\varOmega _\beta )={\mathcal {N}}(\pmb {\beta }_i|\pmb {\mu }_i^*,(\pmb {\varLambda }_i^*)^{-1})\nonumber \\&\displaystyle \pmb {\varLambda }_i^* = \pmb {\varLambda }_\beta +\alpha \sum _{j: I_{ij}=1}d_{ij}^2\cdot \pmb {z}_j\pmb {z}_j'\nonumber \\&\displaystyle \pmb {\mu }_i^* = (\pmb {\varLambda }_i^*)^{-1}\Big (\pmb {\varLambda }_\beta \pmb {\mu }_\beta +\alpha \sum _{j: I_{ij}=1}d_{ij}(\log (y_{ij})-d_{ij}\langle \pmb {u}_i,\widehat{\pmb {v}}_j \rangle )\cdot \pmb {z}_j\Big ) \end{aligned}$$
(20)

The conditional sampling distribution of \(\pmb {V}\):

$$\begin{aligned} P(\pmb {V}|\pmb {Y},\varTheta _{-V},\varOmega _V)&=\prod _{j=1}^NP(\pmb {v}_j|\pmb {Y},\varTheta _{-V},\varOmega _V)\nonumber \\ P(\pmb {v}_j|\pmb {Y},\varTheta _{-V},\varOmega _V)&={\mathcal {N}}(\pmb {v}_j|\pmb {\mu }_j^*,(\pmb {\varLambda }_j^*)^{-1}) \end{aligned}$$
(21)

where

$$\begin{aligned} \pmb {\varLambda }_j^*&= \pmb {\varLambda }_V+\alpha \sum _{i: I_{ij}=1}d_{ij}^2\cdot \pmb {u}_i\pmb {u}_i'\\ \pmb {\mu }_j^*&= (\pmb {\varLambda }_j^*)^{-1}\Big (\pmb {\varLambda }_V\pmb {\mu }_V+\alpha \sum _{i: I_{ij}=1}d_{ij}(\log (y_{ij})-\gamma d_{ij}\langle \pmb {u}_i,\pmb {c}^v_j \rangle -d_{ij}\langle \pmb {\beta }_i,\pmb {z}_j \rangle )\cdot \pmb {u}_i\Big ) \end{aligned}$$

The conditional sampling distribution of \(\pmb {C}\):

$$\begin{aligned} P(\pmb {c}_s|\pmb {Y},\varTheta _{-c_s},\varOmega _C)={\mathcal {N}}(\pmb {c}_s|\pmb {\mu }_s^*,(\pmb {\varLambda }_s^*)^{-1}) \end{aligned}$$
(22)

where

$$\begin{aligned} \pmb {\varLambda }_s^*&= \pmb {\varLambda }_C+\alpha \sum _{\begin{array}{c} i,j: I_{ij}=1\\ s\in \varPhi _j \end{array}}(\frac{d_{ij}}{|\varPhi _j|})^2\cdot \pmb {u}_i\pmb {u}_i'\\ \pmb {\mu }_s^*&= (\pmb {\varLambda }_s^*)^{-1}\Big ( \pmb {\varLambda }_C\pmb {\mu }_C \\&\quad + \alpha \sum _{\begin{array}{c} i,j: I_{ij}=1\\ s\in \varPhi _j \end{array}} \frac{d_{ij}}{|\varPhi _j|}(\log (y_{ij})-d_{ij}\langle \pmb {u}_i,\pmb {v}_i+\frac{\gamma }{|\varPhi _i|}\sum _{\begin{array}{c} l:l\in \varPhi _j\\ l\ne s \end{array}}\pmb {c}_l \rangle -d_{ij}\langle \pmb {\beta }_i,\pmb {z}_j \rangle )\cdot \pmb {u}_i \Big ) \end{aligned}$$

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Zhong, H., Liu, C., Zhong, J. et al. Which startup to invest in: a personalized portfolio strategy. Ann Oper Res 263, 339–360 (2018). https://doi.org/10.1007/s10479-016-2316-z

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