风险对冲风险投资建议

Xiaoxue Zhao, Weinan Zhang, Jun Wang
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引用次数: 13

摘要

随着风险融资交易数据的日益可及性,风险投资公司(vc)在开发量化工具以识别新的投资机会方面面临巨大挑战。推荐技术有可能帮助风投公司做出数据驱动的投资决策,它可以根据过去的投资数据,对不同领域的大量初创公司提供自动筛选过程。先前的一项研究表明,使用协同过滤来捕捉和预测风险投资行为具有潜在的优势。然而,风险融资中的两个基本挑战使得传统的推荐技术难以应用。首先,在进行投资时应谨慎考虑风险因素:对于一家潜在的初创公司,风投需要具体评估这项新投资在其持有的投资组合中所占的比例,以对冲投资风险。其次,投资行为比传统的推荐应用少得多,VC的投资通常仅限于少数行业类别,因此不可能使用主题多样化方法来对冲风险。本文从风险管理的角度来解决创业推荐问题。我们提出了5种风险感知型创业公司选择和排名算法,以捕捉风险投资公司的投资行为并预测他们的新投资。除了对新的风险感知推荐模型的贡献之外,我们在收集的CrunchBase数据集上的实验显示,在强基线上,我们的性能有了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk-Hedged Venture Capital Investment Recommendation
With the increasing accessibility of transactional data in venture finance, venture capital firms (VCs) face great challenges in developing quantitative tools to identify new investment opportunities. Recommendation techniques have the possibility of helping VCs making data-driven investment decisions by providing an automatic screening process of a large number of startups across different domains on the basis of their past investment data. A previous study has shown the potential advantage of using collaborative filtering to catch and predict the VCs' investment behaviours. However, two fundamental challenges in venture finance make conventional recommendation techniques difficult to apply. First, risk factors should be cautiously considered when making investments: for a potential startup, a VC needs to specifically estimate how well this new investment can fit into its holding investment portfolio in such a way that investment risk can be hedged. Second, The investment behaviours are much sparser than conventional recommendation applications and a VC's investments are usually limited to a few industry categories, making it impossible to use a topic-diversification method to hedge the risk. In this paper, we solve the startup recommendation problem from a risk management perspective. We propose 5 risk-aware startup selection and ranking algorithms to catch the VCs' investment behaviours and predict their new investments. Apart from the contribution on the new risk-aware recommendation model, our experiments on the collected CrunchBase dataset show significant performance improvements over strong baselines.
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