无形资产时代的股权投资

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无形资本创造支出有所增加，但会计准则却未能跟上。我们调查了这是否影响了账面价值和收益的价值相关性。我们构建了一个无形资产强度的综合指标，用于对行业进行分类。该指标基于资产负债表中资本化的无形资产、研发支出以及销售、一般及管理支出。我们发现，对于美国及其他国家/地区无形资产强度较高的公司而言，账面价值和收益的价值相关性有所下降；而对于无形资产强度较低的公司而言，其账面价值和收益的价值相关性在美国保持稳定，而在国际范围内则有所提升。

编者注：

提交日期：2020 年 10 月 19 日

Stephen J. Brown 于 2020 年 12 月 30 日接受

披露： 作者报告没有利益冲突。

本文采用我们的双盲同行评审流程进行外部评审。文章被接受发表后，作者在致谢中对审稿人表示感谢。本文的审稿人包括 John Adams 和一位匿名审稿人。

自20世纪90年代以来，用于创造无形资本的支出有所增加，但会计准则并未随之调整。在我们为本文研究的非美国公司样本中，资本化无形资产（不包括商誉）占总资产的比例在1994年至2018年间从0.2%上升至2.2%，而对于美国公司而言，这一比例则从2.75%上升至6.12%。同期，未资本化的无形资本支出（例如研发支出）也有所增加。对于我们为本文研究的非美国公司样本，研发支出占总收入的比例在1994年至2018年间从1.52%上升至2.20%，而对于美国公司而言，这一比例则从9.47%上升至14.36%。

最近，几位作者研究了无形资产相对于实物资产重要性的提升对美国价值投资策略的影响（Arnott、Harvey、Kalesnik 和 Linnainmaa，2021；Amenc、Goltz 和 Luyten，2020；Lev 和 Srivastava，2019）。他们调整了市净率 (B/P)，以解释未入账的无形资本造成的偏差，但他们一致认为，美国价值型投资策略有效性的下降不能归咎于无形资产引发的结构性经济变化或会计准则未能适应这些变化。Li（2020）将他们的研究扩展到其他一些国家——英国、欧洲大陆国家、日本以及日本以外的亚洲地区。

我们的研究并非预测美国价值策略的业绩是否会或何时复苏，并非提倡使用一种或多种优先价值指标，亦非建议调整估值比率以弥补为了追求更高的股票回报而遗漏的无形资产。相反，我们提供了一种方法，使传统的股权投资分析能够处理不同公司和行业之间无形资产强度差异的影响，并将美国在该领域的研究扩展到全球14个最大的国际经济体——李（2020）研究的8个发达市场和6个新兴市场。我们发现，对于美国国内外无形资产强度较高的公司而言，财务变量与同期股价之间的关联性已显著减弱，投资者再也无法忽视无形资产带来的经济环境变化。

全球会计准则要求公司将用于以下目的的金额作为费用而不是资本化：创造无形资本的活动。这一要求导致了系统性和持续性的低估股权的账面价值。其他一些价值指标可以用来识别价值股票的收益与价格、现金流与价格比率也会受到会计失真的影响。原因是，创造无形资本所产生的成本立即计入费用，而资产产生的相应收入/现金流入通常发生在未来一个或多个期间。其结果是损益表上的支出与收入不匹配。 1 例如，Lev 和 Sougiannis (1996) 指出在化学和制药行业，研发的初始支出可以立即费用化可以对长达九年的收入和收益产生有益的影响。此外，资本化的无形资产资产在长期内会影响报告收益，因为它们会逐渐摊销（费用化），尽管摊销做法因无形资产类型、行业和国家而异。

Arnott 等人（2021）和 Amenc 等人（2020）的研究表明，考虑无形资产的影响而对账面价值进行的调整确实提高了美国公司 B/P 的回报预测能力，Li（2020）也证实了多个国际市场公司的研究结果。然而，Arnott 等人（第 61、65 页）也承认，

这一改进的价值衡量指标近期也遭遇了大幅回落，在 2007 年之后仍然不如 S/P（销售额与市值之比）或 E/P（收益与市值之比）。或许，无形资产调整后的 B/P 仍然缺少一些重要的东西……未来研究的有趣课题将是：哪些指标在生成更优的 HML（高账面市值比减低账面市值比）价值因子或预测未来企业利润方面表现最佳，以及这些指标的最佳设置是否因行业、领域或国家而异。

我们通过探索其他价值衡量指标以及这些指标在不同行业和国家之间的差异，对这些领域的研究进行了补充。我们提出了一个衡量无形资产强度的综合指标，该指标可以反映三类无形资本对财务报表影响的行业间差异：资产负债表上报告的无形资产（不包括商誉）、创新（研发）资本以及组织（销售、一般和管理）资本。

利用该综合指标，我们将行业划分为高、低无形资产强度组，并分析了美国和国际公司在每个强度组内（1）股价与（2）账面价值和收益之间的同期关系。我们发现，在高无形资产强度组中，美国和国际公司的账面价值和收益的综合价值相关性在1994年至2018年间均有所下降。相比之下，对于低无形资产强度组，这些变量的价值相关性在美国保持稳定，但在国际上同期有所上升。我们发现，高、低无形资产强度组之间账面价值和收益的价值相关性的差异，对于国际公司而言更大，且在国际市场的差异大于在美国市场的差异。我们的研究结果对于那些寻求构建投资策略的国际股票投资者尤为重要，这些策略需要考虑到无形资产强度对估值比率和其他用于评估公司盈利能力、质量、增长和风险特征的财务指标的影响。

研究动机

我们的研究动机包括：(1) 无形资产对股权估值比率的潜在影响，该比率通过 Ohlson (1995) 的剩余收益估值模型将账面价值和收益与公司内在价值联系起来；(2) 无形资产对其他对股权投资者重要的投资指标的潜在影响。无形资产及其不充分的会计处理会影响公司价值和风险因素，例如资产增长率或债务权益比率。通过影响报告收益，无形资产及其不充分的会计处理还会影响盈利能力特征，例如股本回报率。Arnott 等人 (2021)、Amenc 等人 (2020) 和 Li (2020) 建议的账面价值与利润率调整并未提供有关此问题的信息。我们对无形资产的兴起是否影响了基本财务变量（例如账面价值和收益）与同期股价之间的关系的广泛研究可以为这些方面提供见解。

美国市场关于无形资本投资如何影响价值的先前研究证据账面价值与收益的相关性尚无定论。Lev 和 Gu (2016, p. 89) 指出，相关性同期股票价格与账面价值和收益之间的差距从 1950 年到 2013 年，由于无形资本投资增加，经济增长放缓。Barth、Li 和 McClure（2018）认为，然而，尽管某些项目（如净收入）的价值相关性已经下降，但他们所审查的“会计金额”的总价值相关性保持不变从 1962 年到 2014 年。2 Collins、Maydew 和 Weiss (1997) 得出的结论是，美国上市公司的盈利和账面价值的综合价值相关性无形资产密集型公司在 1953 年至 1993 年期间并没有衰落，但是 Ciftci、Darrough、和 Mashruwala (2014) 通过对一组类似公司的分析得出了相反的结论 Core、Guay 和 Buskirk (2003) 研究了 1975 年至 2007 年间的同一问题。对美国公司广泛样本和它们认为具有象征意义的子样本进行了一段时间的研究。 “新经济”。他们发现，他们的模型的解释力下降了新经济时期（1995-1999 年）所有类型的公司都存在同样的问题。这些相互矛盾的发现先前的研究表明，无形资产对账面价值和收益的价值相关性尚不明确。在美国市场，相关性股票价格与这两个财务变量之间的关系可能随着时间的推移而波动。

关于无形资产对账面价值和收益价值相关性影响的国际证据极其稀缺，且通常仅限于英国和澳大利亚市场。Silva（2012）指出，在英国市场，账面价值能够更好地预测无形资产密集度较低的行业的股价，而收益则对无形资产密集度较高的行业更为有效。Goodwin 和 Ahmed（2006）发现，在 2005 年澳大利亚实施相当于国际财务报告准则的准则之前，当允许无形资产费用化和资本化时，澳大利亚公司的收益价值相关性有所下降，但对于确认无形资产的公司（“资本化公司”）而言，这种下降幅度较小。资本化公司中，来自无形资产密集型行业的比例显著较高。 Fraser、Tarbert 和 Tee (2009) 证明，在英国市场，在无形资产投资相对较高的行业中，股价对中期报告、初步盈利报告和年度股东报告披露的反应不那么显著，这表明财务变量对无形资产密集型公司的价值相关性低于其他公司。

因此，过去关于美国市场账面价值和收益的价值相关性的研究结论是混合的，并且不适用于2012年以后的情况。我们使用了最近时期的证据来重新审视这个问题，并评估由于美国市场无形资产强度增加而导致的账面价值和收益的价值相关性的变化是否也延伸到国际市场。

过去的（单变量）方法侧重于特定类型的非资本化支出，这些支出会产生无形资产资本不足以衡量企业支出对各类无形资本以及此类支出影响产生的行业间差异的评估估值比率或其他投资指标。3 一些研究人员（例如 Israel、Laursen 和 Richardson 2020）建议对公司应考虑股权估值中无形资本类型和金额的差异各行业。我们的综合无形资产强度衡量指标为投资者提供了一种捕捉不同行业无形资本变动的财务报表效应，并比较其不同的效果。我们证明了我们的无形强度测量可以随着时间的推移保持一致，并且涉及美国和国际投资领域。

这一主题的研究也面临着数据不确定的障碍。Arnott 等人（2021）使用了公司层面的 Peters 和 Taylor (2017) 提供的用于调整账面价值的无形资本估计值美国公司。Amenc 等人（2020 年）利用长期财务数据进行了类似的调整以及某些特定数据项（例如，美国统计局特定行业的研发折旧率经济分析和公司成立年份）。由于国际关于这一主题的研究，是否有足够的广度和历史的基本面和宏观经济数据目前还不清楚是否能够允许在所有或大多数国际市场上进行此类调整。 4 使用来自无论是在发达市场还是新兴国际市场，我们都证明了我们提出的无形强度指标，这些指标足够稳健，可用于美国和国际股票的财务报表分析和估值。

无形强度指标

我们衡量无形资产强度的综合指标包含三个部分：（1）资产负债表上报告的无形资产总额（不包括商誉）；（2）研发费用；以及（3）销售、一般及行政管理费用 (SG&A)。我们将在以下小节中讨论选择这些指标的原因及其衡量方法。

我们利用综合指标来确定各行业公司的无形资产强度除银行、保险和多元化金融行业集团外美国及海外均采用全球行业分类标准（GICS）体系。我们排除了这三个行业组，因为我们用来衡量无形资产强度的指标是受到其非典型财务报告实践的影响。5 例如，由于业务性质，银行会捆绑并报告几种类型的经营活动销售、一般及行政费用属于销售、一般及行政管理类别，而且在全球范围内，几乎没有银行或保险公司披露研发费用。

可辨认无形资产。

我们将公司除商誉外的资本化无形资产总额称为可辨认无形资产 。理论上，资产负债表上报告的任何无形资产都已计入账面价值。即便如此，我们仍将其作为单独的组成部分纳入综合无形资产强度指标，原因有三。

首先，对于可产生无形资本的支出，其资本化的会计准则并不一致。例如，在美国，内部开发专利的成本必须予以冲销（即在损益表中计入费用），但如果这些未记录的专利的所有权随后因企业收购或合并而转移，则必须按其公允价值在收购方的资产负债表上予以资本化。因此，忽略已资本化的无形资产可能会低估通过收购而非有机增长的公司的总体无形资产强度。从总体层面来看，经历过整合期的行业似乎比其他行业的无形资产强度更低。

Second, we intended to compare the effects of intangible intensity on the value relevance of financial variables in the US and international markets. Hence, we noted that cross-country differences in accounting standards that govern the choice of expensing versus capitalizing expenditures to create intangible assets (and any change in those standards over time) can lead to problems in comparability. For example, some researchers have found changes in the value relevance of capitalized intangible assets before and after the introduction of International Financial Reporting Standards (IFRS), and some have found issues in subsamples of companies that made different financial reporting choices in regimes where both expensing and capitalization were permitted.Footnote⁶

Third, evidence from around the globe demonstrates that several types of intangible expenditures that have been capitalized and reported on the balance sheet were value relevant, both in aggregate and individually: Oliveira, Rodrigues, and Craig (2010) examined Portuguese companies, Ritter and Wells (2006) and Dahmash, Durand, and Watson (2009) studied companies in Australia, and Aboody and Lev (1998) evaluated the equity market effect of capitalized software development costs in the United States.

For these three reasons, we included capitalized identifiable intangible assets in our intangible-intensity composite.

We excluded goodwill from our measurement of the capitalized intangible assets for two reasons. First, our primary objective was to examine the stock market effects of various forms of intangible capital investments that have gained in importance because of the rapid transformation in corporate investment and business models since the 1990s. Since goodwill is simply an accounting byproduct of business combinations, it is unclear if it meets this criterion. Second, prior evidence regarding the value relevance of goodwill is mixed. The findings vary not only for the US and international equity markets but also for different time periods in the same market. The reason is that differences in the rules for writing off goodwill by market and over time have led to subjective assessments of the fair value of goodwill. Managerial discretion in applying goodwill valuation rules has exacerbated the problems that affect accurate measurement of goodwill (Dahmash et al. 2009).

R&D Expenses.

US accounting standards require the cost of both research and development to be expensed, but IFRS are a bit less restrictive, allowing the capitalization of development costs if certain criteria are met. In-process R&D (consisting of R&D assets acquired in business combinations or asset acquisition transactions) can also be capitalized. Thus, we can capture the effect of in-process R&D on financial statements in our first intangible capital metric—identifiable intangible assets—which was discussed previously. Although US accounting rules that require R&D costs to be expensed have remained consistent since 1974, international accounting guidance on this subject exhibits considerable variation and has continued to evolve. Footnote⁷ Some countries have been edging into greater conformity with the United States at an uneven pace as they move to IFRS.

Research evidence from the United States and abroad indicates that R&D expenditures create intangible innovation capital that is reflected in equity market values. This research includes that of Ahmed and Falk (2006), who examined Australian companies; a study of companies in France, Germany, the United Kingdom, and the United States by Zhao (2002); and findings by Smith, Percy, and Richardson (2001) from studying the Australian and Canadian markets. Lev and Sougiannis (1996) estimated the R&D capital of a sample of more than 800 US manufacturing companies, of which about half belonged to five high-intangible-intensity industries (chemicals and pharmaceuticals, machinery and computer hardware, electrical and electronics, transportation vehicles, and scientific instruments). The authors demonstrated that adjusting earnings and book values of companies for the capitalized value of R&D makes those variables more value relevant.

SG&A Expenses.

SG&A expenses have been used by several authors to proxy for another type of intangible company capital, namely, organization capital. Lev and Radhakrishnan (2005) characterize organization capital as the set of “unique systems and processes employed in the investment, production, and sales activities of the enterprise, along with the incentives and compensation systems governing its human resources” (p. 73). They used annual sales and general and administrative expenses reported in income statements to estimate changes in companies’ organization capital and showed that such changes explain differences between the market values and book values of US companies.

Using SG&A expenses to estimate the stock of organization capital for a sample of US companies, Eisfeldt and Papanikolaou (2013) concluded that companies with a high ratio of organization capital to book assets exhibit higher annual average market returns. Using an industry-relative measure of SG&A expenses, Angelopoulos, Giamouridis, and Vlismas (2012) showed that intangible organization capital is helpful in predicting stock returns for US companies. Comparable results for international companies, however, are sparse. Tronconi and Marzetti (2011) reported a positive link between an SG&A-based measure of organization capital and certain financial performance metrics for European companies.

Because the validity of SG&A expenses as a proxy for intangible organization capital has been confirmed by multiple studies, we used the same approach. Note that Arnott et al. (2021) and Amenc et al. (2020), however, considered only 30% of the total SG&A expenses reported on income statements to be capitalizable intangible assets. Our research was unaffected by this design choice because we used SG&A expenses to rank and classify companies according to their organization capital rather than attempt to assess its value relevance or adjust book values by the amount of unrecorded organization capital.

Other Types of Intangible Capital.

Two other financial statement items—advertising expenses and labor costs—have also been posited to create intangible capital. Advertising expenses are considered to be a gauge of intangible brand capital, and labor costs are regarded as an indicator of intangible human capital. We did not include these items in our composite measure of intangible capital for the following reasons.

Both US and international evidence corroborating the value relevance of advertising expenditures for UK companies (e.g., Shah and Akbar 2008; Shah, Stark, and Akbar 2009) is weak. Footnote⁸ Moreover, Govindarajan, Rajgopal, Srivastava, and Wang (2019) showed that in the United States, advertising has stayed constant since the 1980s at very low levels compared with expenditures on other forms of intangible capital, such as R&D.Footnote⁹ Finally, advertising expenses are a subcomponent of sales and marketing expenses, which are included in the aggregate SG&A expenses figure usually reported in income statements. Because we used aggregate SG&A expenses to represent organizational capital in our intangible-intensity composite, either sales and marketing expenses or, ideally, advertising expenses should be excluded from the aggregate SG&A expenses figure to avoid double counting of advertising expenses in the composite. Most companies, however, do not disclose either of these items separately. Because convincing evidence about the value relevance of intangible brand capital is lacking and in light of the practical difficulties related to its measurement, we did not include it in our composite measure of intangible intensity.

Prior evidence supporting the value relevance of human capital includes Angelopoulos et al. (2012), who reported that long–short portfolios based on an industry-relative human capital measure provide statistically significant risk-adjusted returns for only the first year after portfolio formation, and Pantzalis and Park (2009), who found that arbitrage portfolios based on a market valuation measure of intangible human capital provide excess returns only for small companies. Edmans (2011) considered employee satisfaction to be a type of intangible asset and demonstrated that for a limited number of companies (the “100 Best Companies to Work for in America”), it is positively correlated with shareholder returns. He acknowledged that the companies in his sample are unusually large and exhibit notably better earnings performance than other companies. In fact, not all the companies in his sample are publicly traded, which further limits data availability for comparable studies. A common theme underlying all studies in this area is that, unlike R&D and SG&A expenses that link intuitively to, respectively, innovation capital and organization capital, investments in human capital are notoriously difficult to measure, which prompts researchers to use indirect, output-based estimates. Because investments in intangible human capital assets are difficult to quantify and data to estimate such investments are hard to obtain in all 15 of the markets we studied, we did not include such investments in our intangible-intensity composite, but these assets remain a topic for future investigation.

Data and Methodology

Our sample consisted of companies based in countries ranked by 2018 GDP among the top 15 in the world according to the World Bank (2019).Footnote¹⁰ We obtained the requisite financial and market data for these companies from the Standard & Poor’s Xpressfeed database. We used data for fiscal years between 1994 and 2018 because that database is sparsely populated before 1994, especially for international companies.

For each year, we included companies that reported the required financial data (described later) for an annual financial reporting period that ended during that year. For our value-relevance tests, we used stock price data up to the end of 2019. We retained small and loss-making companies in our sample because prior research (Darrough and Ye 2007; Collins et al. 1997; Joos and Plesko 2005) indicated that such companies are often persistently unprofitable entities that tend to invest more heavily in R&D activities that create intangible capital than do larger and profitable companies.

For each company, we computed three metrics of intangible intensity: Footnote¹¹(1) total intangible assets, excluding goodwill, relative to total assets, (2) R&D expenses relative to total revenues, and (3) SG&A expenses relative to total revenues. Footnote¹² and in Appendix A show changes in the data availability for these metrics over time in, respectively, the US universe and international universe. Because the first of our three intangibility metrics is derived from balance sheet items and the other two from income statement items, the presentations in and are structured accordingly.

Except for some narrowly focused studies in Australia and the United Kingdom, past research on this topic has focused on US companies, so our work adds to this literature by reporting on the relative availability of data to construct the aforementioned three metrics of intangible intensity in both the United States and 14 other countries. Footnote¹³ Note that on average, the information required to compute capitalized intangible assets was available for 52% (67%) of the companies in the US (international) universe that reported total assets and 33% (32%) of all US (international) companies that disclosed information about goodwill. SG&A expenses were available for 97% (95%) of US (international) companies; data on R&D expenses were available for 31% (30%) of US (international) companies that reported total revenues.Footnote¹⁴

Most of the previous work on this subject has focused on specific types of intangible capital (primarily, innovation capital created by R&D activities) one at a time. A drawback of such univariate approaches is that innovation capital dominates in certain industries, such as pharmaceuticals, because it is widespread and of large magnitude, but in other industries, different types of intangible capital may be more significant and value relevant than R&D. Investors who prefer to hold broadly diversified portfolios rather than a narrow selection of companies from specific industries can gain comprehensive insights about the effects of intangible capital investment by using our intangible-intensity composite to make investment decisions for all types of companies. By aggregating the impact of the main types of intangible capital that the literature has linked to stock prices and returns, the composite enables investors to classify and compare companies belonging to different industries on common ground.Footnote¹⁵ We expected the availability of financial data for computation of each of our three intangible capital metrics to vary according to the nature of a company’s business, which might, in turn, depend on its industry membership. Footnote¹⁶

For every year during our sample period, we computed the median intangible intensity for all companies within each of 21 four-digit GICS industry groups (which excludes Banks, Insurance, and Diversified Financials) for each of our three intangible-intensity metrics. Next, we ranked the 21 industries annually according to median intangible intensity, measured independently for each of the three metrics. Finally, we combined every industry’s annual rank on the three intangible-intensity metrics to obtain its equally weighted composite intangible-intensity rank for that year. Footnote¹⁷ Thus, we calculated a set of 21 annual composite intangible industry ranks for each of the 25 years in our sample period.

Applying this procedure independently to the US and international universes, we obtained two sets of ranks. In , we present the 25-year average composite intensity rank of each industry for the two investment universes. We used these average composite ranks to classify the 10 lowest-ranked industries into the “ low-intangible-intensity category” and the remaining 11 industries into the “high-intangible-intensity category.” The order of industries that resulted is remarkably similar for the two universes. Indeed, except for two differences (Energy and Retailing), the set of high- and low-intangible-intensity industries in the United States and abroad is identical.

Table 1. Average Composite Intangible-Intensity Ranks, 1994–2018

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To evaluate the consistency of the US and international composite intangible-intensity rankings, we calculated and present in the Kendall’s coefficient of concordance—a W-statistic with a χ² distribution (Zar 1999)—for each of the 25 years in our sample.Footnote¹⁸ Note that for all years, the W-statistics are highly significant. When we divided our 25-year study period into two subsamples, 1994–2006 and 2007–2018, we found similar results for the two subperiods that were also consistent with the full-sample results. For investors, the implication of the findings reported in is that the composite measure of intangible intensity that we propose is built on pervasive intangibility metrics. The composite can be used to classify industries by their intangible intensity in the United States and internationally in a similar fashion—an important consideration for investors who wish to use the composite to construct global investment strategies or compare factor performance among investment universes.

Table 2. Consistency of US and International Composite Intangible-Intensity Ranks, 1994–2018

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In addition to assessing cross-universe consistency, we also evaluated the consistency of the annual intangible-intensity ranks across industries over time within each investment universe for each of the three intangible-intensity metrics and for the intangible-intensity composite. We, again, relied on Kendall’s concordance statistic to compare the relative annual intangible-intensity ranks for the 21 industries over the 25-year sample period (with slight exceptions for the intensity of R&D expenses because sufficient data were lacking for certain industries in early years). The results, provided in , show that for each of three types of intangible capital and for the intangible-intensity composite, relative industry ranks have remained stable over time at a statistical level of confidence exceeding 99%. Because the pace of evolution of intangible intensity among industries and various types of intangible capital varies, this finding is important. The time-series persistence of our composite measure of intangible intensity provides assurance that investment strategies based on the choice or weighting of factors that drive investment returns according to intangible intensity are likely to be stable and replicable.

Table 3. Consistency of Intangible-Intensity Ranks over Time, 1994–2018

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Combined Value Relevance of Book Value and Earnings

We used our composite intangible-intensity measure to study the effect of investments in intangible capital on the value relevance of book value and earnings, which are often used to construct valuation ratios, as well as other financial metrics that investors use to evaluate the profitability, quality, growth, and risk characteristics of companies. Prior studies of this issue have defined intangible intensity in an ad hoc manner, typically with a focus on intangible innovation capital created by R&D and ignoring the identifiable intangible assets reported on the balance sheet. The reason is that most previous researchers adopted the following definition of intangible intensity, which was initially proposed by Collins et al. (1997, p. 51, footnote 16):

Note that intangible intensity does not refer to the presence of large amounts of recorded intangibles because the concerns raised in the literature relate more to unrecorded intangibles. Consequently, we define companies as intangible intensive when their production functions likely contain large amounts of unrecorded intangibles. We recognize that any such classification is somewhat ad hoc. We define intangible intensive as being companies in the two-digit SIC codes 48 (electronic components and accessories), 73 (business services), and 87 (engineering, accounting, R&D and management related services) and three-digit SIC codes 282 (plastics and synthetic materials), 283 (drugs), and 357 (computer and office equipment).

Since the time of the Collins et al. (1997) study, however, the relative importance of other types of intangible capital, especially organization capital, has grown and additional intangible-intensive industries, such as Media & Entertainment, with new types of intangible capital, like subscriber lists, have emerged. Moreover, as Lev and Gu (2016) showed, corporate investment in intangible assets has increased so much faster than the investment in tangible assets that since the mid-1990s, it has overtaken investment in tangible assets. As discussed in the section “Data and Methodology,” ignoring intangibles already recorded on the balance sheet may produce a misleading or inconsistent intangible intensity–based classification of industries. Therefore, ranking and classifying industries by a broader set of intangible-intensity metrics is warranted. Doing so may lead to conclusions that differ from prior work about interindustry variations in the combined value relevance of book value and earnings.

Oddly, the limited international research in this area has also relied on the Collins et al. (1997) categorization of intangible-intensive industries, although the system was initially conceived for the US universe. Hence, the implications of using our proposed composite intangible-intensity measure to study the effects of intangible intensity on the value relevance of book value and earnings in the international universe are unknown and deserve investigation.

Following previous research, we used regression analysis to investigate the impact of intangible intensity on the value relevance of earnings and book value. For each investment universe, we regressed contemporaneous share price on net income per share and book value per share for companies in each intangible-intensity category (based on the ranks of industry groups in ). For all sample companies, we obtained book values, net income, and the outstanding number of shares for each fiscal year between 1994 and 2018. We also extracted, using the filing dates for the reports provided by Xpressfeed, the month-end share price for the month in which the financial report containing book value and net income became publicly available. To match book value and net income with contemporaneous share prices, we excluded observations for which the month-end date of the share price was more than six months beyond the end of the annual fiscal period covered by the financial report.

We estimated all regressions annually and computed the R² values for each regression; higher R² values denote more combined value relevance for net income and book value. We made the following hypotheses: (1) If investments in intangible capital affect the value relevance of financial statements of companies in the high-intangible-intensity group more unfavorably than in the low-intangible-intensity group, the combined R² of book value and earnings should be lower for the high-intangible-intensity group, and (2) if the adverse effect of intangible capital investment on the value relevance of earnings and book value for high-intangible-intensity companies has intensified over time, regression R²values for that group should be shown to decline gradually over time and should slide below that for the low-intangible-intensity group.

The R²values obtained from our annual regressions are plotted in for the US universe and for the international universe, where the dashed lines are the linear trends. These data tend to support our hypothesis, although the inference is weaker for US companies than for international companies.

Figure 1. R² Values from Annual Regressions of Share Price on Book Value per Share and Net Income per Share: US Universe, 1995–2019

Figure 1. R2 Values from Annual Regressions of Share Price on Book Value per Share and Net Income per Share: US Universe, 1995–2019

Figure 2. R² Values from Annual Regressions of Share Price on Book Value per Share and Net Income per Share: International Universe, 1995–2019

Figure 2. R2 Values from Annual Regressions of Share Price on Book Value per Share and Net Income per Share: International Universe, 1995–2019

Overall, our findings are consistent with the results of comparable analyses conducted by Ciftci et al. (2014) and Core et al. (2003) for US companies. First, in and , we detect a declining linear trend for the value relevance of earnings and book value among companies in the high-intangible-intensity group in both the US and international universes over the sample period. Second, we note a sharp drop in the value relevance of these financial variables for both groups during the 1995–99 so-called New Economy period in both investment universes.Footnote¹⁹ Thus, our objective and comprehensive methodology for classifying industries into low- or high-intangible-intensity categories leads to conclusions that are comparable to the conclusions from previous research for companies in the United States. As for the international universe, we believe our study is the first to document that similar relationships exist between intangible intensity and the value relevance of earnings and book values for international companies.

Nevertheless, we also note some differences in results from analogous past research on US companies. indicates that the value relevance of book value and earnings for companies in the low-intangible-intensity group increased from 2009 on; this relatively recent period was not included in the Ciftci et al. (2014) and Core et al. (2003) studies. For companies in the high-intangible-intensity group, a similar upswing is visible beginning in 2014.

To evaluate the statistical significance of the change in R² values over time, we conducted two types of tests. First, to determine the trend of annual R² values, we computed the Theil–Sen Footnote²⁰ slope of each of the four sets of 25 (for each year) R² values (i.e., for the two intangible-intensity groups in each investment universe) and the related z-statistic for each slope estimate. These results are reported in .

Table 4. Trend of R²Values from Regressions of Share Price on Book Value per Share and Net Income per Share, 1995–2019

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For the US universe, shows that the trend was strongly negative and statistically significant for the high-intangible-intensity group and slightly positive but insignificant for the low-intangible-intensity group. The 95% confidence intervals for the two trend estimates do overlap slightly. Footnote²¹ Taken together, these findings imply that in the United States, the combined value relevance of book value and earnings has decreased over time for companies in the high-intangible-intensity group but this decrease has not occurred for companies in the low-intangible-intensity group. From , a clear divergence in value relevance for high- and low-intangible-intensity industries is evident in the US universe after 2008, but the magnitude of the difference between the two groups fluctuates over time.

For the international universe, the z-statistic for the trend of the combined value relevance of earnings and book value is negative but statistically insignificant for the high-intangible-intensity group but positive and significant for the low-intangible-intensity group. Furthermore, note that the 95% confidence intervals for the trend of R²values for the high- and low-intangible-intensity groups do not overlap.Footnote²² The nonoverlapping confidence intervals allow us to infer that the difference between the slight downward trend for the high-intangible-intensity group and the upward trend for the low-intangible-intensity group is statistically meaningful.

The R²values for the international universe plotted in show a steady decline beginning in 2006 in the combined value relevance of book values and earnings for the high-intangible-intensity companies but a gradual increase in value relevance for low-intangible-intensity companies. This increasing divergence between the two groups for international companies may be the result of standardization of accounting policies governing the capitalization of intangibles after the widespread adoption of IFRS in 2005. In the pre-IFRS period, legacy accounting standards in several countries—notably, Australia, France, and the United Kingdom—permitted both capitalization and expensing of the costs incurred to create intangible capital assets. Goodwin and Ahmed (2006) and Oswald, Simpson, and Zarowin (2017) provided evidence that in the more permissive pre-IFRS regime, capitalization was informative to investors for companies that were intangible intensive. IFRS adoption compelled international companies to hew more closely to US accounting provisions, which tend to prohibit capitalization of intangibles. Scaling back the capitalization option may have caused the informativeness of book value and earnings to drop after implementation of IFRS, especially for highly intangible intensive international companies.

and also highlight the differences between the high- and low-intangible-intensity groups in how the combined value relevance of book value and earnings changed during and immediately after the New Economy period. For both US and international companies, the value relevance of book value and earnings fell more sharply for high-intangible-intensity industries than low-intangible-intensity industries during the dot-com bubble of 1995–1999, but as noted by Core et al. (2003) and Ciftci et al. (2014), the cause may have been temporary overoptimism about companies that represented the New Economy. Furthermore, for international companies, the extraordinary increase in intangible investment in the mid- and late 1990s seems to have reversed course in 2000, leading to a correspondingly greater rebound in value relevance of book value and earnings for high-intangible-intensity industries. For US companies, the period of excessive optimism seems to have been longer, the decline in value relevance more gradual, and the subsequent rebound more muted and occurring over a shorter period.

To gain assurance that these empirical findings were not driven by a few industries in either of the two intangible-intensity groups or by systematic interindustry differences in the relationship between the fundamental financial variables and stock prices, we conducted an additional test. We regressed the contemporaneous share price on net income per share and book value per share for companies in each of the 21 industries and estimated the time trend of the 25 annual R²values for each industry. For each investment universe, we then calculated the correlation between the 21 industry time trends and the corresponding 25-year average composite intangible intensities for the 21 industries. We found that for the US (international) universe, the correlation between the time trend of R²values and composite intangible intensity across all 21 industries was –0.59 (–0.62). Both correlations are significant at the 99% level of confidence. They confirm the existence of a strong negative relationship between intangible intensity and the value relevance of book value and earnings for industries around the world.

In addition to analyzing the trend of R²values for each intangible-intensity group, we used the methodology in Ciftci et al. (2014) to test for differences in the combined value relevance of book value and earnings between the high- and low-intangible-intensity groups. For each investment universe, we estimated the following panel regression of the 25 annual R²values for both intangible-intensity groups together. We used dummy variables to designate time, intangible intensity, and the interaction of time and intangible intensity, and we included certain scale control variables.

The regression was $\begin{matrix} R_{g t}^{2} = a + b_{1} T I M E + b_{2} I N T_D_{h} + b_{3} I N T_D_{h} \times T I M E_{t} \\ + b_{4} C V_P_{g t} + b_{5} C V_B V P S_{g t} + e_{\begin{array}{l} g t \\ , \end{array}} \end{matrix}$ (1) where

$R_{g t}^{2}$ = the R²for the regression of share price on earnings and book value for each year, t, and intensity group, g

TIME = a variable with values between 1 and 25 depending on the year of the regression

$I N T_{-} D_{h}$ = a variable with the value of 1 if the R² was for an observation in the high-intangible-intensity group and 0 otherwise

$I N T_{-} D_{h} \times T I M E_{t}$ = the value of $I N T_{-} D_{h}$ multiplied by TIME

$C V_{-} P_{g t}$ = the coefficient of variation of share price for each year and intensity group

$C V_{-} B V P S_{g t}$ = the coefficient of variation of book value per share for each year and intensity group

$e_{g t}$ = the regression error for each year and intensity group

Ciftci et al. (2014) and Brown, Lo, and Lys (1999) emphasized that R²values are not comparable across regressions conducted on subsamples of companies because of differences in scale. To control for such differences, they recommended that certain additional independent variables be included when combining or comparing regression results for different samples of companies or time periods. In fact, Ciftci et al.’s replication of the Collins et al. (1997) study that included controls for differences in scale led to conclusions about changes in the value relevance of book value and earnings over time that were opposite to those of Collins et al. Therefore, we included the two independent scale control variables in our panel regressions, the coefficient of variation of share price and the coefficient of variation of book value per share, as suggested by Brown et al. Our results for regressions that did not include the scale control variables (not reported for brevity) are, however, qualitatively similar.

Regression results for both the US and international universes are reported in . The coefficient b₃ for the variable $I N T_{-} D_{h} \times T I M E_{t}$ is of particular interest because it captures the difference in slopes between the low- and high-intangible-intensity groups. For both the US and the international universes, this regression coefficient is negative and statistically significant, at confidence levels exceeding, respectively, 99% and 93%. This finding indicates that for US and international companies, the slope of R² values representing the combined efficacy of book value and earnings in explaining contemporaneous share prices has been dropping over time for the high-intangible-intensity group relative to the low-intangible-intensity group.Footnote²³ This trend is visually depicted in and and confirms our previous analysis of the nonparametric trend of R² values.Footnote²⁴

Table 5. Trend of R² Values from Regression of Share Price on Book Value per Share and Net Income per Share, 1995–2019

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Finally, from and , we also conclude that the declining linear trend of the (combined) value relevance of book value and earnings for high-intangible-intensity companies has been slightly greater in the United States than internationally. Over the full period of our study, the R² of the regression fell by about 30% (from 0.55 to 0.385) for the high-intangible-intensity group of US companies as opposed to a 22% drop (from 0.75 to 0.585) for the high-intangible-intensity group of international companies.

To our knowledge, the findings we report here for international companies constitute a new contribution to the literature because no past studies have examined how intangible intensity affects the relationship between financial statement variables and stock prices in multiple countries.

Conclusions

Earnings and book value are of interest to investors because these variables underlie two corresponding valuation ratios—earnings to price and book to price—that are the basis of popular value investing and other types of investment strategies based on the profitability, quality, growth, and risk characteristics of companies. However, the efficacy of value-investing strategies has fallen precipitously in recent years. A possible reason is that the volume and variety of corporate expenditures on activities that create intangible capital have increased, albeit unevenly, over time and for different industries but financial reporting standards did not accommodate such structural economic changes. Our primary conclusion is that intangible capital intensity is, in fact, related to changes in the value relevance of earnings and book value, as reflected in the power of these financial variables to explain contemporaneous movements and cross-sectional variation in stock price in the 1994–2019 time period for the companies in our global sample.

To investigate the value relevance of earnings and book value, we proposed and validated a composite measure of intangible intensity that captures the financial statement impact of three types of intangible capital: intangible assets reported on the balance sheet (excluding goodwill), innovation capital created by R&D expenditures, and organization capital resulting from SG&A expenses. We first showed that our composite intangible-intensity measure is consistent over time and across the US and international investment universes in its ability to rank and classify industries by their intangible intensity. We then analyzed the contemporaneous relationship between stock price and the two financial variables of interest, book value per share and net income per share, for US and non-US companies. We formed these subsamples on the basis of the intangible intensity of the industry to which the companies belong. As we hypothesized, we did find a decline in the combined value relevance of earnings and book value of companies in the high-intangible-intensity group in both the US and international universes, but we did not find such a decline for companies in the low-intangible-intensity group.

Our approach to this issue differs from that of Arnott et al. (2021), Amenc et al. (2020), and Li (2020), who attempted to adjust book values for the impact of unaccounted intangible capital, and from that of Angelopoulos et al. (2012), who estimated industry-relative intangible intensity for companies in each industry. Although such methods can compensate for biases in valuation metrics that result from inadequate accounting of intangible capital, company-level estimates of intangible capital may be volatile and fraught with measurement error. The industry-level methodology for gauging intangible intensity that we used in this study can mitigate both problems. Moreover, industry-level measures of intangible intensity can capture macroeconomic aspects of intangible intensity—such as industry concentration (see Crouzet and Eberly 2019) and product/market competition (see Gu 2016)—that industry-relative estimates of the intangible intensity of individual companies are unable to incorporate. Nevertheless, we acknowledge that all these alternatives are imperfect ways to address this important but complex issue.

Our conclusions held for both US and international companies in the largest 14 economies of the world. Importantly, our conclusions about the impact of intangible intensity on the value relevance of earnings and book value are stronger for international companies, in that the divergence between the low- and high-intangible-intensity groups of industries is greater in the international arena and has continued to increase over time. For investors who aim to build and use value investing or other types of strategies that rely on book values and earnings, the implication is that such strategies may benefit from taking variations in intangible intensity into account. Our primary objective in this study, however, was to measure intangible intensity and establish that it is relevant for investors as a step toward building a robust and consistent investment framework. Therefore, we did not attempt to investigate whether value investors can enhance the return prediction ability of their valuation models or whether equity investors in general can improve their assessments of the profitability, quality, growth, and risk of companies by accounting for cross-sectional variations in intangible intensity. We leave these issues to future research.

The authors gratefully acknowledge the suggestions of Executive Editor Stephen J. Brown and Co-Editor Steven Thorley, CFA. They also thank their colleagues at Bridgeway Capital Management, especially Andrew Berkin (head of research) for his guidance and encouragement and other members of the investment management team for helpful comments.

Notes

¹ The mismatch may be more acute for business entities that are in the early stages of their life cycle, when spending on activities that create intangible assets (e.g., research and development or customer acquisition) is high.

² In addition to net income and book value, they studied cash flow from operations, cash, total assets, intangible assets, sales, sales growth, R&D expenses, advertising expenses, cost of goods sold, capital expenditures, other comprehensive income, and special items.

³ Intangible capital items may include computerized information, innovation (including both scientific R&D and nonscientific discovery and development; Corrado, Hulten, and Sichel 2005), human resources (Pantzalis and Park 2009), organizational competencies (Lev and Radhakrishnan 2005), customer franchises (Bonacchi, Kolev, and Lev 2015), and brand values (Barth, Clement, Foster, and Kasznik 1998).

⁴ Li (2020) argued and showed that, at least in developed international markets, book value can be successfully adjusted without relying on the complex procedures suggested by Peters and Taylor (2017).

⁵ Of the 236,008 (411,330) company-year observations in our US (international) sample for which ubiquitous financial statement items, such as total assets, were available in the Xpressfeed database, 63,602 (46,160) company-year observations were for companies in the Banks, Insurance, and Diversified Financials industry groups.

⁶ In a pre-IFRS reporting regime, certain countries (including Australia, the United Kingdom, and France) permitted both expensing and capitalization of R&D expenditures. Oswald, Simpson, and Zarowin (2017) found differences in (1) the value relevance of the capitalized versus expensed development costs in a pre-IFRS regime and (2) changes in the value relevance of R&D expenditures before and after IFRS adoption for UK companies that switched from expensing to capitalization. Jaafar (2011) showed that the adoption of Australian-equivalent IFRS led to an increase in the value relevance of identifiable intangible assets.

⁷ For example, Lee and Lee (2020) stated that prior to 1999 in South Korea, R&D expenditures were classified as either ordinary or extraordinary depending on the characteristics of the activities; R&D expenditures that occurred in the ordinary course of business were expensed, whereas those not meeting this criterion were capitalized.

⁸ Most prior studies on this subject (e.g., Bublitz and Ettredge 1989) have shown that the life of brand value assets created by advertising expenditures is no more than one to two years.

⁹ In unreported results, we found that for our sample of US companies, advertising expenditures dropped from about 3.6% to 1.6% of total revenues while R&D expenditures rose from about 9.5% to more than 14.0% in the 1994–2018 period.

¹⁰ In descending order of GDP, the top 15 countries in the world are the United States, China, Japan, Germany, the United Kingdom, France, India, Italy, Brazil, Canada, Russia, South Korea, Australia, Spain, and Mexico. The complete list by GDP ranking is available from the World Bank (2019).

¹¹ Xpressfeed reports R&D expenses and SG&A expenses as separate components of the income statement item under “Other Operating Expenses.” Our three metrics had to have nonnegative values, and their intensity could not be computed if the scaling variable (total assets or total revenues) was missing or zero. Such cases (amounting to fewer than 0.4% of all available company-year observations) were treated as data errors and excluded from our sample.

¹² Relative rankings for intangible intensity based on alternative measures (total assets or total expenses) to scale R&D expenses and SG&A expenses were similar and are not reported here for brevity.

¹³ in Appendix A provides some summary information about the availability of such data separately for developed and emerging countries.

¹⁴ In light of our focus on interindustry differences in intangible intensity, we provide, in and in Appendix A, information about the availability of the requisite data at the industry level for, respectively, the United States and other countries.

¹⁵ We recognize that this approach is imperfect. Within the four-digit GICS classifications that we used, intangible intensity can vary at the subindustry level; for example, within the Utilities sector, the wind and solar power sectors are likely to differ from those that rely on fossil fuels and nuclear energy. A more granular industry classification approach would yield additional insights but would come at the expense of reduced sample sizes at the industry level.

¹⁶ and confirm this conjecture.

¹⁷ We acknowledge that our assumption that all sources of intangible capital are equally important contributors to intangible intensity is subjective. The three metrics of intangible capital have different useful lives, and they differ in the amount and timing of the cash flows they generate. Accurate measurement of these attributes would enable assignment of more appropriate (unequal) weights to different sources of intangible capital.

¹⁸ We required at least three companies in an industry for estimation of the median intangible intensity, and we required that intangible intensity medians for at least two of the three metrics be available for computation of the composite intangible intensity in any year.

¹⁹ According to Core et al. (2003), this period was marked by several unusual economic developments, including high stock market returns, high valuations, and increased productivity driven by the declining price of computing power and investments in information technology and modern manufacturing facilities that benefit from information technology.

²⁰ The Theil–Sen estimator (Theil 1950; Sen 1968) is a nonparametric technique for estimating a linear trend by choosing the median of the slopes of all lines through pairs of points in the sample. This procedure produces a (statistically) efficient estimator that is insensitive to outliers. It can be significantly more accurate than a nonrobust simple linear (least-squares) regression for skewed and heteroskedastic data.

²¹ For the US universe, the 95% confidence intervals for, respectively, the high- and low-intangible-intensity groups are (–0.011, 0.000) and (–0.002, 0.007). For the international universe, the 95% confidence intervals are (–0.005, 0.004) and (0.005, 0.012).

²² See previous note.

²³ We repeated the analyses in and for a full “global” sample of companies. For this test, we combined US and international companies in the high-intangible-intensity groups, added an indicator variable to distinguish whether a particular company belonged to the US or international universe, and ran our primary annual cross-sectional regression of stock price on book value and earnings for this “global” sample of high-intangible-intensity companies. We obtained 25 R² values. We repeated the same procedure for the low-intangible-intensity companies. The Theil–Sen’s slopes (z-statistics) for the high- and low-intangible-intensity groups were, respectively, –0.00 (0.26) and 0.007 (3.10), and the coefficient (t-statistic) for $I N T_{-} D_{h} \times T I M E_{t}$ was –0.007 (–3.49).

²⁴ The coefficient on the TIME variable is positive and significant for both the US and international regressions, indicating that the combined value relevance of earnings and book value has increased for companies in the low-intangible-intensity group for the time period and sample of companies included in our study.

References

Aboody, David, and Baruch Lev. 1998. “The Value Relevance of Intangibles: The Case of Software Capitalization.” Journal of Accounting Research, vol. 36 (Supplement: Studies on Enhancing the Financial Reporting Model): 161–91 https://doi.org/10.2307/2491312(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Ahmed, Kamran, and Haim Falk. 2006. “The Value Relevance of Management’s Research and Development Reporting Choice: Evidence from Australia.” Journal of Accounting and Public Policy, vol. 25, no. 3: 231–64 https://doi.org/10.1016/j.jaccpubpol.2006.03.002(open in a new window).
(Open in a new window)Google Scholar
Amenc, Noel, Felix Goltz, and B. Luyten. 2020. “Intangible Capital and the Value Factor: Has Your Value Definition Just Expired?” Journal of Portfolio Management, vol. 46, no. 7: 83–99 https://doi.org/10.3905/jpm.2020.1.161(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Angelopoulos, Georgios, Daniel Giamouridis, and Orestes Vlismas. 2012. “Inferring the Value of Intangible Assets.” Working paper (1 March). Available at https://ssrn.com/abstract=1786285.(open in a new window) https://doi.org/10.2139/ssrn.1786285(open in a new window).
(Open in a new window)Google Scholar
Arnott, R. D., C. R. Harvey, V. Kalesnik, and J. T. Linnainmaa. 2021. “Reports of Value’s Death May Be Greatly Exaggerated.” Financial Analysts Journal, vol. 77, no. 1: 44–67.
updates
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Barth, Mary, Michael B. Clement, George Foster, and Ron Kasznik. 1998. “Brand Values and Capital Market Valuation.” Review of Accounting Studies, vol. 3, no. 1-2: 41–68 https://doi.org/10.1023/A:1009620132177(open in a new window).
(Open in a new window)Google Scholar
Barth, Mary E., Ken Li, and Charles G. McClure. 2018. “Evolution in Value Relevance of Accounting Information.” Working paper, Stanford Graduate School of Business (May).
(Open in a new window)Google Scholar
Bonacchi, Massimiliano, Kalin Kolev, and Baruch Lev. 2015. “Customer Franchise—A Hidden, Yet Crucial, Asset.” Contemporary Accounting Research, vol. 32, no. 3: 1024–49 https://doi.org/10.1111/1911-3846.12095(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Brown, S., K. Lo, and T. Lys. 1999. “Use of R2 in Accounting Research: Measuring Changes in Value Relevance over the Last Four Decades.” Journal of Accounting and Economics, vol. 28, no. 2: 83–115 https://doi.org/10.1016/S0165-4101(99)00023-3(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Bublitz, B., and M. Ettredge. 1989. “The Information in Discretionary Outlays: Advertising, Research and Development.” Accounting Review, vol. 64, no. 1: 108–24.
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Ciftci, Mustafa, Masako Darrough, and Raj Mashruwala. 2014. “Value Relevance of Accounting Information for Intangible-Intensive Industries and the Impact of Scale: The US Evidence.” European Accounting Review, vol. 23, no. 2: 199–226 https://doi.org/10.1080/09638180.2013.815124(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Collins, Daniel W., Edward L. Maydew, and Ira S. Weiss. 1997. “Changes in the Value-Relevance of Earnings and Book Values over the Past Forty Years.” Journal of Accounting and Economics, vol. 24, no. 1: 39–67 https://doi.org/10.1016/S0165-4101(97)00015-3(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Core, John E., Wayne R. Guay, and Andrew Van Buskirk. 2003. “Market Valuations in the New Economy: An Investigation of What Has Changed.” Journal of Accounting and Economics, vol. 34, no. 1-3: 43–67 https://doi.org/10.1016/S0165-4101(02)00087-3(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Corrado, C. A., C. R. Hulten, and D. Sichel. 2005. “Measuring Capital and Technology: An Expanded Framework.” In Measuring Capital in a New Economy edited byHaltiwanger, John C., Carol Corrado, and Daniel E. Sichel: 11–45Chicago: University of Chicago Press https://doi.org/10.7208/chicago/9780226116174.003.0002(open in a new window).
(Open in a new window)Google Scholar
Crouzet, Nicolas, and Janice C. Eberly. 2019. “Understanding Weak Capital Investment: The Role of Market Concentration and Intangibles.” NBER Working Paper 25869 (May) https://doi.org/10.3386/w25869(open in a new window).
(Open in a new window)Google Scholar
Dahmash, Firas N., Robert B. Durand, and John Watson. 2009. “The Value Relevance and Reliability of Reported Goodwill and Identifiable Intangible Assets.” British Accounting Review, vol. 41, no. 2: 120–37 https://doi.org/10.1016/j.bar.2009.03.002(open in a new window).
(Open in a new window)Google Scholar
Darrough, Masako, and Jianming Ye. 2007. “Valuation of Loss Firms in a Knowledge-Based Economy.” Review of Accounting Studies, vol. 12, no. 1: 61–93 https://doi.org/10.1007/s11142-006-9022-z(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Edmans, Alex. 2011. “Does the Stock Market Fully Value Intangibles? Employee Satisfaction and Equity Prices.” Journal of Financial Economics, vol. 101, no. 3: 621–40 https://doi.org/10.1016/j.jfineco.2011.03.021(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Eisfeldt, Andrea L., and Dimitris Papanikolaou. 2013. “Organization Capital and the Cross-Section of Expected Returns.” Journal of Finance, vol. 68, no. 4: 1365–406 https://doi.org/10.1111/jofi.12034(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Fraser, Ian, Heather Tarbert, and Kai Hong Tee. 2009. “Do the Financial Statements of Intangible-Intensive Companies Hold Less Information Content for Investors.” Applied Financial Economics, vol. 19, no. 17: 1433–38 https://doi.org/10.1080/09603100902902212(open in a new window).
(Open in a new window)Google Scholar
Goodwin, John, and Kamran Ahmed. 2006. “Longitudinal Value Relevance of Earnings and Intangible Assets: Evidence from Australian Firms.” Journal of International Accounting, Auditing and Taxation, vol. 15, no. 1: 72–91 https://doi.org/10.1016/j.intaccaudtax.2006.01.005(open in a new window).
(Open in a new window)Google Scholar
Govindarajan, Vijay, Shivaram Rajgopal, Anup Srivastava, and Ye Wang. 2019. ““R&D Spending Has Dramatically Surpassed Advertising Spending.” Harvard Business Review20 (May) https://hbr.org/2019/05/rd-spending-has-dramatically-surpassed-advertising-spending(open in a new window) .
(Open in a new window)Google Scholar
Gu, Lifeng. 2016. “Product Market Competition, R&D Investment, and Stock Returns.” Journal of Financial Economics, vol. 119, no. 2: 441–55 https://doi.org/10.1016/j.jfineco.2015.09.008(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Israel, R., K. Laursen, and Scott A. Richardson. 2020. “Is (Systematic) Value Investing Dead.” Working paper, AQR Capital Management (8 May).
(Open in a new window)Google Scholar
Jaafar, Hartini. 2011. “Accounting for Intangible Assets, Firm Life Cycle and the Value Relevance of Intangible Assets.” Doctoral dissertation, University of Tasmania.
(Open in a new window)Google Scholar
Joos, Peter, and George A. Plesko. 2005. “Valuing Loss Firms.” Accounting Review, vol. 80, no. 3: 847–70 https://doi.org/10.2308/accr.2005.80.3.847(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Lee, Jangwook, and Jiyoon Lee. 2020. “Mispricing or Risk Premium? An Explanation of the R&D-to-Market Anomaly.” Working paper (7 October). Available at https://ssrn.com/abstract=3706541(open in a new window) .
(Open in a new window)Google Scholar
Lev, Baruch, and Feng Gu. 2016. The End of Accounting and the Path Forward for Investors and Managers. Hoboken, NJ:: John Wiley & Sons https://doi.org/10.1002/9781119270041(open in a new window).
(Open in a new window)Google Scholar
Lev, Baruch, and S. Radhakrishnan. 2005. “The Valuation of Organization Capital.” In Measuring Capital in a New Economy edited byHaltiwanger, John C., Carol Corrado, and Daniel E. Sichel: 73–99Chicago: University of Chicago Press.
(Open in a new window)Google Scholar
Lev, Baruch, and Theodore Sougiannis. 1996. “The Capitalization, Amortization, and Value Relevance of R&D Outlays.” Journal of Accounting and Economics, vol. 21, no. 1: 107–38 https://doi.org/10.1016/0165-4101(95)00410-6(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Lev, Baruch, and Anup Srivastava. 2019. “Explaining the Demise of Value Investing.” Working paper, NYU Stern School of Business (25 October) https://doi.org/10.2139/ssrn.3442539(open in a new window).
(Open in a new window)Google Scholar
Li, Feifei. 2020. “Intangibles: The Missing Ingredient in Book Value.” Working paper, Research Affiliates (2 November). Available at https://ssrn.com/abstract=3686595(open in a new window). https://doi.org/10.2139/ssrn.3686595(open in a new window).
(Open in a new window)Google Scholar
Ohlson, J. A.. 1995. “Earnings, Book Values and Dividends in Equity Valuation.” Contemporary Accounting Research, vol. 11, no. 2: 661–87 https://doi.org/10.1111/j.1911-3846.1995.tb00461.x(open in a new window).
(Open in a new window)Google Scholar
Oliveira, Lídia, Lúcia L. Rodrigues, and Russell Craig. 2010. “Intangible Assets and Value Relevance: Evidence from the Portuguese Stock Exchange.” British Accounting Review, vol. 42, no. 4: 241–52 https://doi.org/10.1016/j.bar.2010.08.001(open in a new window).
(Open in a new window)Google Scholar
Oswald, Dennis, Ana Simpson, and Paul Zarowin. 2017. “The Information Benefits of R&D Capitalization.” Working paper (13 April). Available at https://ssrn.com/abstract=2952388(open in a new window). https://doi.org/10.2139/ssrn.2952388(open in a new window).
(Open in a new window)Google Scholar
Pantzalis, C., and J. C. Park. 2009. “Equity Market Valuation of Human Capital and Stock Returns.” Journal of Banking & Finance, vol. 33, no. 9: 1610–23 https://doi.org/10.1016/j.jbankfin.2009.03.008(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Peters, R., and L. Taylor. 2017. “Intangible Capital and the Investment-q Relation.” Journal of Financial Economics, vol. 123, no. 2: 251–72 https://doi.org/10.1016/j.jfineco.2016.03.011(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Ritter, A., and A. Wells. 2006. “Identifiable Intangible Asset Disclosures, Stock Prices and Future Earnings.” Accounting and Finance, vol. 46, no. 5: 843–63 https://doi.org/10.1111/j.1467-629X.2006.00190.x(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Sen, Pranab Kumar. 1968. “Estimates of the Regression Coefficient Based on Kendall’s Tau.” Journal of the American Statistical Association, vol. 63, no. 324: 1379–89 https://doi.org/10.1080/01621459.1968.10480934(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Shah, Syed Zulfiqar Ali, and Saeed Akbar. 2008. “Value Relevance of Advertising Expenditure: A Review of the Literature.” International Journal of Management Reviews, vol. 10, no. 4: 303–25.
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
Shah, Syed Zulfiqar Ali, Andrew Stark, and Saeed Akbar. 2009. “The Value Relevance of Major Media Advertising Expenditures: Some U.K. Evidence.” International Journal of Accounting, vol. 44, no. 2: 187–206 https://doi.org/10.1016/j.intacc.2009.03.004(open in a new window).
(Open in a new window)Google Scholar
Silva, Rita A.. 2012. “Equity Valuation Using Accounting Numbers in High and Low Intangible-Intensive Industries.” Dissertation, Católica Lisbon School of Business and Economics, Lisbon, Portugal.
(Open in a new window)Google Scholar
Smith, Dean T., Majella Percy, and Gordon D. Richardson. 2001. “Discretionary Capitalization of R&D: Evidence on the Usefulness in an Australian and Canadian Context.” Advances in International Accounting, vol. 14: 15–46 https://doi.org/10.1016/S0897-3660(01)14004-3(open in a new window).
(Open in a new window)Google Scholar
Theil, H.. 1950. “A Rank-Invariant Method of Linear and Polynomial Regression Analysis. I, II, III.” Proceedings of the Nederl Akad Wetensch Indag Math, vol. 53: 386–92: 521–25: 1397–412.
(Open in a new window)Google Scholar
Tronconi, Claudia, and Giuseppe Vittucci Marzetti. 2011. “Organization Capital and Firm Performance. Empirical Evidence for European Firms.” Economics Letters, vol. 112, no. 2: 141–43 https://doi.org/10.1016/j.econlet.2011.04.004(open in a new window).
(Open in a new window)Web of Science ®(Open in a new window)Google Scholar
World Bank2019. “Data Catalog: 2018 GDP Ranking.” https://datacatalog.worldbank.org/dataset/gdp-ranking(open in a new window) Accessed 17 December 2019.
(Open in a new window)Google Scholar
Zar, J. H.. 1999. Biostatistical AnalysisFourth editionUpper Saddle River, NJ: Prentice-Hall.
(Open in a new window)Google Scholar
Zhao, Ronald. 2002. “Relative Value Relevance of R&D Reporting: An International Comparison.” Journal of International Financial Management and Accounting, vol. 13, no. 2: 153–74 https://doi.org/10.1111/1467-646X.00082(open in a new window).
(Open in a new window)Google Scholar

Appendix A.

Details of Sample Data

Table A1. Availability of Data Items Required for Computation of Intangible-Intensity Metrics by Country, 1994–2018 (in company-year units)

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Figure A1. Availability of Data Items Required for Computation of Intangible-Intensity Metrics by Year: US Companies, 1994–2018

Figure A2. Availability of Data Items Required for Computation of Intangible-Intensity Metrics by Year: International Companies, 1994–2018

Figure A3. Availability of Data Items Required for Computation of Intangible-Intensity Metrics by Industry: US Companies, 1994–2018

Figure A4. Availability of Data Items Required for Computation of Intangible-Intensity Metrics by Industry: International Companies, 1994–2018

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