英文标题：
《Picking Winners: A Data Driven Approach to Evaluating the Quality of
  Startup Companies》
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作者：
David Scott Hunter, Ajay Saini, Tauhid Zaman
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最新提交年份：
2018
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英文摘要：
  We consider the problem of evaluating the quality of startup companies. This can be quite challenging due to the rarity of successful startup companies and the complexity of factors which impact such success. In this work we collect data on tens of thousands of startup companies, their performance, the backgrounds of their founders, and their investors. We develop a novel model for the success of a startup company based on the first passage time of a Brownian motion. The drift and diffusion of the Brownian motion associated with a startup company are a function of features based its sector, founders, and initial investors. All features are calculated using our massive dataset. Using a Bayesian approach, we are able to obtain quantitative insights about the features of successful startup companies from our model.   To test the performance of our model, we use it to build a portfolio of companies where the goal is to maximize the probability of having at least one company achieve an exit (IPO or acquisition), which we refer to as winning. This $\\textit{picking winners}$ framework is very general and can be used to model many problems with low probability, high reward outcomes, such as pharmaceutical companies choosing drugs to develop or studios selecting movies to produce. We frame the construction of a picking winners portfolio as a combinatorial optimization problem and show that a greedy solution has strong performance guarantees. We apply the picking winners framework to the problem of choosing a portfolio of startup companies. Using our model for the exit probabilities, we are able to construct out of sample portfolios which achieve exit rates as high as 60%, which is nearly double that of top venture capital firms.
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中文摘要：
我们考虑了评估初创公司质量的问题。由于成功创业公司的罕见性以及影响此类成功的因素的复杂性，这可能是相当具有挑战性的。在这项工作中，我们收集了数万家初创公司的数据、他们的业绩、他们创始人的背景以及他们的投资者。我们基于布朗运动的首次通过时间，建立了一个新的创业公司成功的模型。与初创公司相关的布朗运动的漂移和扩散是基于其部门、创始人和初始投资者的特征的函数。所有特征都是使用我们的海量数据集计算的。使用贝叶斯方法，我们能够从我们的模型中获得关于成功创业公司特征的定量见解。为了测试我们模型的性能，我们使用它来构建一个公司投资组合，目标是最大限度地提高至少一家公司实现退出（IPO或收购）的可能性，我们称之为获胜。这个$\\textit{挑选赢家}$框架非常通用，可用于模拟许多低概率、高回报结果的问题，例如制药公司选择开发药物或制片厂选择制作电影。我们将挑选赢家投资组合的构建作为一个组合优化问题，并证明贪婪解具有很强的性能保证。我们将挑选赢家框架应用于选择初创公司投资组合的问题。使用我们的退出概率模型，我们能够构建出样本投资组合，其退出率高达60%，几乎是顶级风险投资公司的两倍。
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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