通过可解释机器学习揭示高增长企业的关键预测因子
Uncovering key predictors of high-growth firms via explainable machine learning
AI总结：
本研究结合5071家企业的财务、专利及网络特征，通过集成学习与可解释AI方法，明确了非财务特征对高增长企业的预测价值及特征作用规律，可为投资与技术资源配置提供参考。
摘要：
预测高增长企业已引起技术预测与机器学习领域越来越多的关注。现有多数研究主要利用财务数据开展此类预测，但研究表明，企业的研发活动及其在技术生态系统中的网络位置也可作为有价值的预测指标。为厘清各类特征的相对重要性，本文分析了5071家企业的财务与专利数据，提取了三类特征：财务特征、已授权专利的技术特征，以及基于企业与自身核心技术关联的网络特征。通过集成学习算法我们证实，与仅使用财务特征相比，将财务特征与技术特征、网络特征二者之一或两者结合，能实现更准确的高增长企业预测。为进一步深入探究，我们采用可解释人工智能方法，评估了各类别下单个特征的预测能力。在非财务特征中，企业已授权专利的最高经济价值、与企业核心技术相关的专利数量重要性尤为突出。此外，企业规模与高增长概率在达到特定规模阈值前呈正相关，超过该阈值后关联趋于平稳。与之相反，企业已授权专利的最高经济价值仅在超过阈值后，才与高增长概率呈正相关。这些发现阐明了各类特征在高增长企业预测中复杂的预测作用，可为技术资源配置及投资决策提供参考。
英文摘要：
Predicting high-growth firms has attracted increasing interest from the technological forecasting and machine learning communities. Most existing studies primarily utilize financial data for these predictions. However, research suggests that a firm's research and development activities and its network position within technological ecosystems may also serve as valuable predictors. To unpack the relative importance of diverse features, this paper analyzes financial and patent data from 5,071 firms, extracting three categories of features: financial features, technological features of granted patents, and network-based features derived from firms' connections to their primary technologies. By utilizing ensemble learning algorithms, we demonstrate that incorporating financial features with either technological, network-based features, or both, leads to more accurate high-growth firm predictions compared to using financial features alone. To delve deeper into the matter, we evaluate the predictive power of each individual feature within their respective categories using explainable artificial intelligence methods. Among non-financial features, the maximum economic value of a firm's granted patents and the number of patents related to a firms' primary technologies stand out for their importance. Furthermore, firm size is positively associated with high-growth probability up to a certain threshold size, after which the association plateaus. Conversely, the maximum economic value of a firm's granted patents is positively linked to high-growth probability only after a threshold value is exceeded. These findings elucidate the complex predictive role of various features in forecasting high-growth firms and could inform technological resource allocation as well as investment decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
