Through textual analyses of 7.7 million patents, we develop a novel intercompany innovation similarity measure which enables us to find that technologically connected firms cross-predict one another’s returns. Investors impound information about firms’ technological connectedness, although not immediately and fully. Buying (shorting) shares of technological peers earning high (low) returns during the previous month yields a 1.29% monthly return. Firms’ return predictability increases with patent complexity or limited technological disclosures but decreases with better information transparency. Results suggest that investor inattention explains technology momentum. Unlike momentum stemming from simpler, class-based technological links, our Big Data text-based return predictability remains active.

中文翻译：

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创新相似性对资产价格的影响：来自专利大数据的证据

通过对 770 万项专利的文本分析，我们开发了一种新颖的公司间创新相似性度量，使我们能够发现技术相关的公司相互预测彼此的回报。投资者扣押有关公司技术联系的信息，尽管不是立即和完全的。购买（做空）上个月获得高（低）回报的技术同行股票，每月回报率为 1.29%。公司的回报可预测性随着​​专利复杂性或有限的技术披露而增加，但随着信息透明度的提高而降低。结果表明，投资者的注意力不集中可以解释技术发展势头。与源自更简单、基于类别的技术链接的动力不同，我们基于大数据文本的回报可预测性仍然活跃。