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Review of Accounting Studies

03.07.2024

The changing nature of financial analysis in the presence of ETFs

verfasst von: Russell Lundholm, Xin Zheng

Erschienen in: Review of Accounting Studies

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Abstract

We study how the launch of an electronically traded fund (ETF) that holds firms in a specific industry changes the behavior of analysts who follow that firms in that industry. An industry ETF allows investors to trade the firm-specific payoff separately from the industry payoff. This causes significant changes in the value of different types of information. In particular, following an increase in a firm’s industry ETF coverage, the firm’s analyst coverage increases in the following year, and this holds after controlling for changes in institutional investment and other characteristics. We also find that, following an increase in ETF coverage, analyst recommendations are more likely to include an industry recommendation separate from the firm-specific recommendation, and the latter is more likely to be stated in relative terms. Our results strengthen when the new ETF is a better hedge against the industry payoff factor and when we introduce a plausible control for endogeneity.

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Israeli, Lee, and Sridharan (2017) find limited evidence of a negative relation between the percentage of shares held by all ETFs and future changes in analyst coverage. As discussed later, our measure of ETFs is much more focused on industries than their broad measure of ETF holdings. And, as Bhojraj, Mohanram and Zhang (2020) show, industry and sector ETFs have very different informational properties than broad market ETFs. Further, we use the introduction or dissolution of an industry ETF, rather than percentage of shares held, as our treatment variable because we are interested in changes in trading opportunities.

The addition of industry recommendation data in IBES follows rule changes prompted by the Global Settlement. Among other things, these rules required brokers to “clearly define in each research report the meaning of each rating in the system, including the time horizon and any benchmarks on which a rating is based (FINRA Rule 2241 C(2)).”.

Specifically, Kadan et al. (2020, p. 3) report that the 24 percent of stated benchmarks are the industry, 15 percent are the market, and 20 percent are some fixed total return, leaving 41 percent of the benchmarks to be either too complex or confusing to categorize.

Our proxy for whether the analyst communicates in a relative or absolute manner is certainly noisy. Some analysts could simply be known to forecast relative performance without explicitly communicating this through the IBES system.

Jegadeesh et al. (2004) find that the analysts’ focus on glamour stocks causes the level of recommendation to poorly predict future returns. However, they show that changes in recommendations do predict future returns.

Crawford, Roulstone, and So (2012) add nuance to this observation, showing that the first analyst to follow a firm tends to collect industry-level information, with the mix of information switching to firm level as more and more analysts follow the firm.

There are myriad reasons why an analyst recommends a stock. For a comprehensive list, see Huang, Lehavy, Zang, and Zheng (2018).

The results of Israeli et al. (2017) are consistent with our results or the other cited papers. The introduction of an industry ETF could lower noise trading in the firm asset, consistent with their finding of a reduction in liquidity and, at the same time, increase trading opportunities on firm-specific information, with conflicting impacts on the value of analysts’ information and equilibrium price efficiency.

We are assuming equal weighting of firms in the ETF. Our argument would still hold with value-weighting or any other weighting scheme that allows large numbers to eliminate exposure to individual firm variation. In addition, there is not a clear optimal number of firms to hold in an ETF, hence the variation in the data. The number of firms in an ETF trades off two forces—a few firms allows the sponsor to select those firms with the most exposure to the industry component, while many firms is better for averaging over unknown firm-specific noise.

While analysts in the IBES system make recommendations at the firm level Y_i, Bradshaw, Ertimur, and O’Brien (2017) argue that their historical comparative advantage is in finding \({Y}_{i}^{s}\). They state: “The key to success, then, will be the analyst’s ability to extract idiosyncratic information that they can use to distinguish the mispricing opportunities in a field of generally well-priced companies (p. 15).”.

How many firms an ETF holds trades off two forces: 1) a few firms allows the ETF sponsor to pick only those firms that have a large exposure to the industry factor, but 2) many firms provides more power to average out the firm noise in each firm’s payoff. Relatedly, the math above assumes equal weighting, but in practice ETFs deploy many different weighting schemes. We have no predictions regarding the optimal size or weighting system for an ETF.

Firms not included in an industry ETF might nonetheless still be part of a hedged strategy with an ETF if they have exposure to the ETF’s industry component. However, we have no way of identifying these firms. Hence we create our sample using those firms that are actually held by an ETF at some point.

As discussed by Kadan, Madureira, Wang, and Zach (2009), we eliminate duplicate recommendations that were re-issued in 2002 when brokerages changed their rating systems from five categories to three. We also eliminate duplicate recommendations that were re-issued by Barclays when it took over Lehman in 2008.

As is often the case, the change in analyst following may suffer from endogeneity issues. We address this issue in Section 5.2.1 but acknowledge that we do not have a classic identifying event to control for endogeneity. The other two dependent variables are less likely to suffer from this problem.

This is a slight abuse of notation because in the recommendation-level data time t is the specific date the recommendation was made, while in the aggregated data time t refers to a calendar year.

An alternative definition of the annual variables would be as the percentage of the firm’s recommendations that are relative or include an industry recommendation. However, the total number of recommendations in a year is highly correlated with the number of analysts following the firm in the year, which is our third dependent variable. Consequently, scaling by the number of recommendations would essentially be scaling by the number of analysts. Despite these concerns, our results are very similar for ΔRelative_t and ΔIndustry_t if we define them as changes in the percentage of recommendations that are relative or include an industry recommendation.

Our results across all dependent variables and models have similar levels of significance when clustering by analysts rather than by firms. Note also that brokerage-level clustering is not available for data at the firm-year level; some firm-years have no analyst coverage and hence are not associated with any brokerage, while others are associated with multiple analysts at a number of different brokerages. However, the finer data used in the levels analysis in Table 10 can and does cluster by brokerage.

Of our three dependent variables, it seems less likely that ΔRelative_t or ΔIndustry_t are affected by a hot industry effect, but we include them in Table 6 for completeness.

Anecdotal evidence suggests that brokerages do not impose these types of constraints on their analysts. Of the 20 largest brokerages (by number of recommendations issued), only the analysts at Barclays uniformly stated all recommendations in relative terms, and only the analysts at Sidoti uniformly stated all recommendations in absolute terms. Much more common are the examples of JP Morgan, First Boston, and Raymond James, where the fraction of relatively stated recommendations bounced around between 40 and 80 percent of analyst recommendations in any given year. We find similar variation in the mix of recommendations that include an industry recommendation. Of the 20 largest brokerages, only the analysts at Morgan Stanley uniformly provided industry recommendations. It does not appear that the style and industry content of analyst recommendations is commonly a brokerage-level decision.

The control variables are measured once, at the beginning of the calendar year, to be consistent with our firm-year regressions in the previous sections.

We thank Professor Jiang for sharing this data with us.

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Titel: The changing nature of financial analysis in the presence of ETFs
verfasst von: Russell Lundholm
Xin Zheng
Publikationsdatum: 03.07.2024
Verlag: Springer US
Erschienen in: Review of Accounting Studies
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-024-09841-9