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Categories and evaluation bias in valuation of technological innovation.

机译:技术创新评估中的类别和评估偏见。

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摘要

This dissertation examines the perceptual bias of investors and securities analysts (the "audience" in the stock market) in their valuation of public firms' innovative activities. I suggest that such bias occurs because the audience views a firm's innovation through the prism of the firm's categorization in product markets---its industry category---which may only loosely conform to the technological interrelationship among firms in knowledge space. I explore theoretically the conditions under which evaluation bias is most likely to occur---notably, due to innovations that defy the existing categorical structure used by the audience.I further argue that reliance on categorical identity for information cues may reduce the audience's ability to adequately assess the value-relevance of innovations that deviate from the technological norm in an industry. By analyzing the absolute forecast errors in security analysts' reports, I find confirming evidence that deviant innovations increase the forecast bias. I further argue and demonstrate that such bias is less prevalent for diversified firms and in industries with less stable categorical "norms," two conditions in which the audience is less likely to rely on categorical information and more likely to employ firm-specific information. Finally, I discuss the contribution and implication of the findings to studies of categorization and value-relevance of technological innovations.Based on this theoretical framework, I develop hypotheses for empirical tests. I first argue that both technological opportunities and technological threats residing outside a firm's industry are more likely to be underestimated by the stock market than those residing within the industry. In a sample of large U.S. manufacturing firms covering the years 1980 through 2000, I collected patent data to reflect innovative activities of firms. I compare the effect of a firm's innovative activities on its current market valuation and its future cash flows. Consistent with my predictions, I find that firms capitalizing on cross-industry opportunities are more likely to be undervalued, while firms facing cross-industry technological competition are more likely to be overvalued.
机译:本文研究了投资者和证券分析师对上市公司创新活动进行估值时的感知偏差(股票市场中的“受众”)。我建议之所以会出现这种偏见,是因为听众通过公司在产品市场中的分类(即行业类别)来看待一家公司的创新,而这种创新只能大致符合知识空间中公司之间的技术相互关系。我从理论上探讨了最有可能发生评估偏差的条件-尤其是由于创新违背了受众所使用的现有类别结构,我进一步认为,依靠信息线索的类别身份可能会降低受众的认知能力充分评估偏离行业技术规范的创新的价值相关性。通过分析安全分析师报告中的绝对预测错误,我发现了确凿的证据表明,过时的创新会增加预测偏差。我进一步论证并证明,这种偏见在多元化的公司和类别“规范”较不稳定的行业中不那么普遍,这两种情况下,受众不太可能依赖类别信息,而更有可能采用公司特定信息。最后,我讨论了研究结果对技术创新的分类和价值相关性研究的贡献和意义。在此理论框架的基础上,我提出了用于实证检验的假设。首先,我认为,与行业内存在的可能性相比,股票市场对存在于公司所在行业之外的技术机会和技术威胁的估计都更容易被低估。在一个涵盖1980年至2000年的大型美国制造公司的样本中,我收集了专利数据以反映公司的创新活动。我比较了一家公司的创新活动对其当前市场估值和未来现金流量的影响。与我的预测一致,我发现利用跨行业机会的公司更有可能被低估,而面临跨行业技术竞争的公司则更有可能被高估。

著录项

  • 作者

    Geng, Xuesong.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Business Administration Management.Economics Finance.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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