Strategic distortions in analyst forecasts in the presence of short-term institutional investors

Abstract

We document that analysts cater to short-term investors by issuing optimistic target prices. Catering dominates among analysts at brokers without an investment banking arm as they face lower reputational cost. The market does not see through the analyst catering activity and their forecasts lead to temporary stock overpricing that short-term institutional investors exploit to offload their holdings to retail traders. We also report evidence consistent with catering brokers being rewarded with more future trades channelled through them. Our study identifies a new source of conflicts of interest in analyst research originating from the ownership composition of a stock.

Keywords:

• target prices
• earnings forecasts
• strategic distortions
• short-term investors

Acknowledgements

We would like to thank two anonymous reviewers, Jonathan Berk, Francois Derrien, Cristi Gleason, Ole-Kristian Hope, Rick Johnston, Alexander Ljungqvist, Linda Myers, James Ohlson, Tarun Ramadorai, Joel Shapiro, and participants at the 2015 FARS midyear meeting for helpful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

Supplemental data for this article can be accessed https://doi.org/10.1080/00014788.2018.1510303.

Notes

1 Analysts cannot increase optimism in their stock recommendations if the outstanding recommendations are already at the top of the rating scale. Stock recommendation changes are constrained after 2002 because most brokers moved to a three-tier recommendation system and they had to disclose the distribution of their outstanding recommendations in each analyst report, which increases the cost of issuing optimistic recommendations (Kadan et al. 2009).

2 To further support the conjecture that IB analysts face higher reputational costs for issuing biased forecasts, which discourages catering to short-term investors, we also examine analyst Institutional Investor All-America Research Team rankings. All-America (Star) analysts are more likely to be employed by investment banks because their presence has a bearing on the choice of the investment advisor in security offerings (Dunbar 2000, Hong et al. 2000, Hong and Kubik 2003, Loughran and Ritter 2004, Ljungqvist et al. 2006). We find that Star analysts are less likely to bias TPs; any catering to short-term investors comes primarily from non-Star analysts.

3 The collapse of Lehman Brothers not only reduced the pool of brokers through which investors can channel trades, but also put into question survival of many other brokerage houses. Because of the uncertainty about whether some brokers would survive to execute future trades, many investors switched to the safety of large reputable brokers. Thus, the uncertainty created by Lehman Brothers’ collapse generated exogenous variation in the choice of brokers with investors normally choosing less reputable brokers opting for other investment banks (Mackintosh 2008).

4 Our results do not depend on analysts privately communicating their actions to short-term investors, i.e. ‘tipping’, which most brokers proscribe (Irvine et al. 2007). Rather, public disclosure of the target price is necessary to create ‘windows of opportunities’ for short-term investors to off-load their holdings. We assume short-term investors have the sophistication to recognise that analyst TPs are overly optimistic, for example, through comparison with their own valuations. This assumption is consistent with past studies which document that institutional investors, and short-term investors such as hedge funds in particular, are sophisticated investors (e.g. Malmendier and Shanthikumar 2007, 2014, Cella et al. 2013). Further, short-term institutional investors can easily access analyst target prices through analyst reports and morning notes as well as Bloomberg or Thomson Reuters terminals.

5 Because data on how investors allocate trades across brokers is not available, our test provides indirect evidence of short-term investors rewarding catering brokers.

6 Inv_TR and IO capture distinct concepts: IO captures the level of institutional holdings whereas Inv_TR the average holding period of a representative investor in a stock, thus the former variable does not subsume the latter as both capture different economic constructs. The correlation between IO and Inv_TR is only 0.037.

7 The other commonly used source of target price data, First Call, was acquired by Thomson Reuters in June 2001 and was subsequently merged with I/B/E/S. First Call target price data was discontinued in 2004.

8 For comparison, Brunnermeier and Nagel (2004) use a sample of 53 unique hedge funds over period 1998–2000 when they match 13F data with hedge fund information, Brav et al. (2008) use 236 hedge funds, and Cheng et al. (2012) use 435 hedge funds over the period 1994–2008. The sample in Brown and Schwarz (2013) comprises 102 managers in 1999, increasing to 226 managers in 2008.

9 The average investor turnover of 0.213 means that institutional investors hold an average stock in their portfolio for around 28 months (12/0.428 = 28.04).

10 We formally test that the magnitude of the investor turnover effect is higher than the magnitude of the institutional ownership effect by estimating Equation (8) where we first standardise all variables to have a mean of 0 and a standard deviation of 1. We reject the null that the coefficient on Inv_TR equals the absolute value of the IO coefficient, i.e. the absolute magnitudes of the two effects are different.

11 A regression in changes factors out the influence of constant variables, which explains why Inv_bank, is not significant in Models 5 and 6.

12 Our results from Table 3 do not generalise the findings in Firth et al. (2013) and Gu et al. (2013), who report that Chinese brokers issue optimistic stock recommendations for their mutual fund clients. Their results are unsurprising because institutional investors in China do not moderate optimism in analyst forecasts. Contrary to the US evidence in Ljungqvist et al. (2007), Cheng et al. (2006), and Brown et al. (2014), Gu et al. (2013) report that high mutual fund ownership in China increases optimism in analyst stock recommendations, and the bias is incremental when brokers receive trading commissions from mutual funds. The difference in results in Firth et al. (2013) and Gu et al. (2013) compared to US studies reflect differences in the institutional setup: there are fewer funds and brokers in China compared to the US (e.g. Gu et al. report an average of 51 funds and 69 brokers) and brokers specialise in catering to few select funds (e.g. Gu et al. classify over 70% of brokers’ mutual fund clients as affiliated). The concentrated market structure reduces competition between brokers for new clients and analyst incentives to issue accurate forecasts to attract new clients. Consistent with this prediction, Gu et al. (2013) report that over 85% of stock recommendations in their sample are classified as either strong buy/buy. 

13 The definition of MFFlow follows Edmans et al. (2012), Appendix A. We downloaded this variable from Alex Edman's website: http://faculty.london.edu/aedmans/ (accessed March 2014).

14 The two instruments are valid in our tests. For both models presented in Table 4, the Sargan-Hansen test of overidentifying restrictions does not reject the null that the instruments are valid. Also, the F-statistic of 411.5 comfortably rejects the hypothesis that the instruments are weak (Stock et al. (2002) advocate that the F-statistic should exceed 10 for inference based on the 2SLS estimator to be reliable when there is one endogenous regressor).

15 The non-negative coefficient on ΔTP*Inv_TR is consistent with the evidence in Malmendier and Shanthikumar (2007, 2014), who document that investors, particularly small traders, react more strongly to affiliated analysts’ stock recommendation upgrades (Malmendier and Shanthikumar 2007) and revisions (Malmendier and Shanthikumar 2014), which tend to be biased. They predict that ‘small investors might not seek information about analyst distortions even if the costs of obtaining such information are low. They take recommendations at face value and trust analysts too much’ Malmendier and Shanthikumar (2007, p. 458).

16 One could argue that short-term investors may benefit more from privileged private disclosure of analyst TPs rather than from the analyst attempting to ‘pump’ the market. Three facts counter this argument. First, using daily volume data, Juergens and Lindsey (2009) do not find evidence that analysts working for a market-maker pre-release reports on their stock upgrades to benefit privileged clients. Second, market regulation, e.g. Nasdaq Rule 2110–4 that governs trades in anticipation of analyst reports, may limit private disclosure if this activity can attract the regulator's attention. Third, private disclosure only does not guarantee profitable trades if the market price does not change. Thus, it is unclear how short-term investors benefit from private disclosure of overly biased TPs. Rather, it is public disclosure of optimistic TPs that temporarily increase stock valuations that maximises the likelihood of beneficial trade for short-term investors.

17 The magnitudes of abnormal returns are comparable with other studies that examine returns to trading strategies based on analyst forecasts. To illustrate, Malmendier and Shanthikumar (2007) report daily abnormal returns of −0.04% to −0.07% for a zero-investment portfolio of recommendations issued by affiliated compared to unaffiliated analysts.

18 ‘Soft dollar’ payments is a standard practice where institutional investors commit to (1) allot their trading volume to brokers where sell-side analysts provide valuable service and (2) pay a fixed five to six cent-per-share commission fee that is higher than the typical marginal cost of trading (Goldstein et al. 2009, Juergens and Lindsey 2009, Maber et al. 2014). The UK Financial Conduct Authority estimates that ‘UK investment managers pay an estimated £3bn of dealing commissions per year to brokers, with around £1.5bn of this spent on research.’, (Financial Conduct Authority, 2014b, p. 5). Buy-side institutions that manage portfolios for their clients favour ‘soft dollars’, rather than explicit payments for research reports, since the cost of the former is born by the client, whereas the latter would have to be paid from the buy-side institution's own capital (Maber et al. 2014).

19 The small coefficient on %HF ownership other reflects that hedge funds trade frequently and have holdings across multiple brokers.

20 We expect analyst and broker incentives to maximise fees to align as analysts are compensated from the fees brokers receive from share trading (see also Jackson 2005, Cowen et al. 2006, Beyer and Guttman 2011). Thus, analysts would not object issuing biased research. Further, analysts have an incentive to produce optimistic research as this can lead to more favourable career outcomes (Hong and Kubik 2003, Horton et al. 2017).