论文标题
Techrank
TechRank
论文作者
论文摘要
我们介绍了Techrank，这是一种基于带有加权节点的双方图的递归算法。我们根据反思方法开发了Techrank来链接公司和技术。我们允许该算法合并反映投资者偏好的外源变量。我们校准网络安全部门中的算法。首先，我们的结果有助于估计每个实体的影响力，并解释公司和技术的排名。其次，他们为投资者提供了对技术的定量最佳排名，因此可以帮助他们设计最佳投资组合。我们建议这种方法作为传统投资组合管理的一种替代方法，并且在私募股权投资的情况下，作为一种新的价格资产方式，无法观察到现金流量。
We introduce TechRank, a recursive algorithm based on a bi-partite graph with weighted nodes. We develop TechRank to link companies and technologies based on the method of reflection. We allow the algorithm to incorporate exogenous variables that reflect an investor's preferences. We calibrate the algorithm in the cybersecurity sector. First, our results help estimate each entity's influence and explain companies' and technologies' ranking. Second, they provide investors with a quantitative optimal ranking of technologies and thus, help them design their optimal portfolio. We propose this method as an alternative to traditional portfolio management and, in the case of private equity investments, as a new way to price assets for which cash flows are not observable.
