JSAI2020

Presentation information

Interactive Session

[3Rin4] Interactive 1

Thu. Jun 11, 2020 1:40 PM - 3:20 PM Room R01 (jsai2020online-2-33)

[3Rin4-56] Corporate value evaluation using patent document vector

〇Shohei Fujiwara1, Yusuke Matsumoto1, Aiko Suge1, Hiroshi Takahashi1 (1.Keio University)

Keywords:Finance, Valuation, Patent, Natural language processing

Corporate valuation is one of the important financial modeling in IPO and M & A. In particular, at the time of IPO, there is no underlying stock price information, so its price depends on the result of corporate valuation. However, the offer price at the time of the IPO often has an under-pricing in which the public price falls below the initial value, resulting in an opportunity loss for the issuer company. Although many previous studies have referred to this issue, no consensus has yet been obtained. In this study, we consider that there is room for improvement in the corporate value evaluation model and try to evaluate the corporate value by reproducing the initial value of a newly listed company using patent information. Specifically, first, Derwent World Patents Index held by the company is extracted, and the patent document is vectorized by using Sparse Composite Document Vector. Next, the center of gravity of the patent is calculated from the vectorized patent document, and the similarity between the companies is expressed by the distance to select similar companies. Finally, using the selected companies, a comparable peer company multiple method is performed to evaluate the validity of the stock price. In conclusion, it is suggested that the model is more explanatory than using industry average financial figures. In this study, we analyzed feasibility of our proposed method.

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