Institutional investors and long-run return reversals: Insights into post-seo underperformance

Transcription

1 Institutional investors and long-run return reversals: Insights into post-seo underperformance Roger M. Edelen, Özgür Ş. İnce, and Gregory B. Kadlec * Abstract Several studies document a positive relation between changes in institutional ownership ( IO) and short-run returns following seasoned equity offerings (SEOs) and attribute it to an informational role for institutions. However, we find that IO is negatively related to both long run returns and operating performance following SEOs. Indeed, SEO underperformance is almost entirely confined to stocks in the top two quintiles of IO. This result echoes recent findings of long run return reversals in the context of institutional herding, and suggests that the positive link between IO and short run SEO returns found in other studies is a manifestation of destabilizing institutional herding rather than information. More broadly, our evidence establishes a central role for IO in SEO underperformance. * Edelen is from UC Davis, Ince is from University of South Carolina, and Kadlec is from Virginia Tech. We thank seminar participants at University of Oregon and Virginia Tech for helpful comments. Thanks to Brad Barber, Dave Denis, and Huseyin Gulen for particularly useful suggestions. The authors bear full responsibility for errors. 1

2 1. Introduction An emerging literature documents an unexpectedly rich temporal pattern to stock returns following changes in institutional ownership (which we abbreviate IO). Studies have long found that IO is positively related to future returns in the short run (three to six months), and generally concluded that the effect is permanent and indicative of informed trading. 1 However, more recent studies have found that IO is negatively related to future returns over the long run (one year or more). For example, Gutierrez and Kelley (2009) and Dasgupta, Prat, Verardo (2011) examine price effects of institutional herding and find a long-run return reversal following IO. Taken together, a positive short-run and negative long-run relation is consistent with institutions having a destabilizing impact on asset prices (Barberis and Shleifer, 2003; Vayanos and Woolley 2013). These conflicting views on the role of institutional investors in financial markets are particularly relevant to the case of seasoned equity offerings (SEOs). On the one hand, institutions are dominant participants in primary equity markets (SEOs in particular, see Chemmanur, He, and Hu, 2009) who are usually cited as playing an informative role (e.g., IPO bookbuilding models). On the other hand, many studies argue that SEO underperformance reflects exploitation of overvalued stock by issuing-firm managers [Loughran and Ritter (1995), Daniel, Hirshleifer, and Subrahmanyam (1998), Brav, Geczy, and Gompers (2000), and Baker and Wurgler (2002)]. Putting the two together implies that institutions are both informed and exploited. Several studies reconcile these contradictory views by arguing that institutions are indeed sophisticated, only buying the right SEOs. This assertion is substantiated with evidence of a positive relation between IO and post-seo returns [see e.g. Gibson, Safieddine, and Sonti (2004) 1 For evidence of a positive relation between institutional ownership and short-horizon returns, see, e.g., Wermers (1999), Coval and Moskowitz (2001), Badrinath and Wahal (2002), Cohen, Gompers, and Vuolteenaho (2002), Parrino, Sias, and Starks (2003), Gibson, Safieddine, and Sonti (2004), and Alti and Sulaeman (2012). 1

3 and Chemmanur, He, and Hu (2009)]. 2 Two considerations suggest caution regarding this conclusion. First, the return horizon considered in these studies (generally the quarter of or following the SEO, with robustness checks out to four quarters) does not fully account for the three to five year horizon that is the norm for underperformance in the SEO literature. Second, as referenced above, theoretical and empirical studies of herding suggest that the positive relation between IO and short-horizon returns is followed by a negative relation over longer horizons. Thus, this study is motivated by the observation that high institutional participation in SEOs (suggesting herding) combined with a positive relation between IO and short-run SEO returns (suggesting price impact of the herd) supports an alternative, destabilizing hypothesis regarding institutions role in SEOs. Under this alternative hypothesis, institutional buying around an SEO should be associated with a long-run reversal to fair value as the destabilization runs its course. Under the informational hypothesis espoused in the aforementioned studies, the relation between IO and long run abnormal returns should be non-negative. This sets up the primary aim of our study: to analyze the relation between IO and long run post-seo performance. We conduct our analysis using a variety of methodologies, and find that not only do institutions exhibit a herding-like participation in SEO stocks leading up to the SEO (as found in other studies), they tend to buy SEO stocks that subsequently underperform the most over the long run. Indeed, virtually all long-run post-seo underperformance occurs in the top two quintiles of stocks sorted by IO in the year prior to the SEO -- there is no long-run post-seo underperformance for stocks in the bottom three quintiles of IO. Our evidence therefore supports the destabilizing herd rather 2 Alti and Sulaeman (2012) examine the relation between changes in institutional holdings and three and five-year post-seo abnormal returns and conclude that there is little evidence of stronger underperformance for issuers with high institutional demand. 2

4 than informational view of institutions role in an SEO setting. Indeed, destabilizing institutional herds appear to be at the heart of the long run SEO underperformance phenomenon. In further support of our assertion that IO plays a central role in long-run SEO underperformance, we examine non-seo stocks matched to SEO stocks on the basis of IO. We find long-run return underperformance in the non-seo sample that is generally statistically indistinguishable from that in the SEO sample (the difference is significant at 10% in one quintile). Impressively, we find similar (though somewhat weaker) results for operating performance. Thus, IO is both necessary for long-run underperformance following an SEO, and nearly sufficient for long-run underperformance without an SEO. An important consideration in interpreting our results is the holding period of institutional investors. A negative relation between IO and long-run post-seo returns rejects an informational role for institutions only if IO persists throughout the long-run post-seo return window. We find that it does (see Figure 1). We emphasize that our analysis operates on aggregate IO. It may be that there are transfers within the institutional investor universe that benefit an informed subset of institutions at the expense of others. Even so, our evidence implies that the overall impact of institutions net of these intra-institutional is destabilizing, not informational. A key consideration in the SEO literature is operating performance. The herding literature generally does not considered this dimension of performance, but changes in institutional ownership could impact a firm s discount rate, and thus the investment decisions of the firm s managers. For example, if herding drives up the share price then the firm s equity cost of capital is lower, ceteris paribus. This would imply a lower hurdle rate on real investment. Unfortunately, internal hurdle rates are not observable. However, if a lower hurdle rate triggers investment and the firm faces a declining marginal product, then the implied reduction in operating performance 3

5 is observable. We use this reasoning to conduct a parallel analysis of the relation between IO and operating performance. We find that long-run operating performance of SEO stocks is abnormally low (as in Loughran and Ritter, 1994), and that high IO firms experience the largest decline in operating performance; particularly when the firm undertakes real investment along with that IO. As previously noted, a number of studies suggest that SEOs are used by managers to exploit equity mispricing. One interpretation of our evidence is that managers time that exploitation to coincide with the arrival of an institutional herd. This hypothesis dovetails nicely with the analysis in Alti and Sulaeman (2012), but the interpretation in their study is very different. Alti and Sulaeman document that a run-up in share price leads to an SEO only when that run up is accompanied by an increase in institutional ownership. They interpret this as indicating that institutional certification is a precondition for an offering to be well received. An alternative interpretation, more consistent with our evidence of long run reversals following institutional buying, is that market-timing managers need more than just overvalued shares to motivate an SEO; they also need a readily identifiable target to unload those shares upon. Recent herding in the firm s stock by institutions would surely satisfy that identification requirement. Irrespective of the role that IO plays in SEOs informed certification or destabilizing herd our evidence makes it clear that firms undertaking an SEO in the presence of high IO enjoy a low cost of capital, where low means relative to standard return benchmarks. What is not clear is whether or not institutions have rational expectations about those low returns. Rational expectations would imply that the true required return of a very large segment of the market (institutions in aggregate) is lower than that indicated by standard benchmarks. In other words, the benchmarks are wrong. But it may be that the benchmarks accurately reflect institutions required returns; they re just not aware that they ve pushed prices too high (and expected returns too low). 4

6 In other words, prices are wrong. While we do not claim to resolve this manifestation of Fama s joint hypothesis, we provide several analyses to shed some light on the matter. One possible benchmark oversight is an improvement in stock liquidity. Lin and Wu (2013) find that increases in liquidity around SEOs are related to increases in institutional investors, and Bilinski, Liu, and Strong (2012) find that post-issuance return performance is related to changes in liquidity. We confirm this link between ΔIO, liquidity, and post-seo performance. However, ΔIO remains strongly related to long run performance after controlling for liquidity using both Amihud (2002) and Pastor and Stambaughs (2003) measures. Another change in condition that could warrant a lower required return is corporate investment, e.g., the exercise of real options as in Carlson, Fisher, and Giammarino (2006), or productivity shocks as in Li, Livdan and Zhang (2009). If institutions are attracted to such firms and benchmarks are slow to adjust to the change in firm risk, a spurious relation could arise between ΔIO and benchmarking errors. We observe a small decline in beta estimates following SEOs as in Carlson et al. (2010), but that decline is uncorrelated with ΔIO. The effect is partly explained by an asset growth factor, but ΔIO remains statistically significant, whereas asset growth is only weakly so. Finally, Lehavy and Sloan (2008) and Edelen, Ince, and Kadlec (2015) argue that changes in institutional ownership might relate to lower required returns by way of a change in market segmentation (as in Merton (1987), Allen and Gale (1994), Basak and Cuoco (1998), and Shapiro (2002)). However, calibration suggests that this market-segmentation channel could not generate the reduction in discount rate we find for high- IO SEO firms. A mispricing interpretation of our evidence seems to contradict the widely held prior belief that institutions are sophisticated investors. However, even sophisticated investment managers might play a destabilizing role if forced to do so by investor flows, as in Coval and Stafford (2007), 5

7 Frazzini and Lamont (2008), and Khan, Kogan, and Serafeim (2012). Several factors argue against this explanation. First, Khan et al. (2012) show that mutual fund inflows typically go towards expansion of existing positions rather than new positions. We find that long-run returns are more highly related to the number of institutional shareholders (new positions) rather than the fractions of shares held by institutions (expansion of existing positions). More directly, we find that the relation between long-run returns and changes in number of institutions is robust to the exclusion of SEOs with inflow-induced buying pressure and controlling for changes in ownership by mutual funds who are likely to be most sensitive to the effects of flow. Finally, even if the institutional buying were the result of flow, it is hard to see why a sophisticated institutional investor would have to concentrate flow-induced stock purchases in the underperforming (SEO) stocks. For example, we had no trouble finding better performing comparable stock. Our inability to identify an alternative risk metric to explain the link between IO and SEO underperformance, or to attribute it to flow, leaves open the possibility that the link arises from the long-run destabilization that follows in the wake of an institutional herd. While that interpretation contradicts the widespread view that institutions are sophisticated investors, it certainly provides an easy fit to our findings. The fact that we observe similar effects at non-seo firms in the face of high IO adds credence to this conjecture. Still, at the end of the day, our results remain subject to Fama s joint hypothesis caveat. 2. Data sources, variable definitions, and SEO firm characteristics 2.1 Data sources and variable definitions We obtain data for SEOs from Securities Data Corporation. Our initial sample of issuing firms includes all SEOs of NYSE, Amex, and NASDAQ stocks between 1981 (Thomson Reuters' 13F 6

8 institutional holdings coverage begins) and 2010 (to allow four years of post-issuance data). We exclude pure secondary offerings; financial institutions; regulated utilities (SIC codes , ); offerings within one year of initial public offering; offerings by the same firm within five years of a previous SEO; and firm-year observations with incomplete data (must be present on CRSP, Compustat, and Thomson Reuters 13F). 3 Our control sample of non-issuing firms consists of all NYSE, Amex, or NASDAQ firms with no IPO or SEO within five years, excluding financial institutions; regulated utilities; and firm-years with incomplete data. Following Loughran and Ritter (1997) we use OIBD/Assets for operating performance, 4 and Following Cooper, Gulen, and Schill (2008), we use the fiscal-year percent change in total assets (Compustat data item AT) as our proxy for corporate investment. Monthly returns for the market and risk-free asset are from Ken French s website. The liquidity factor of Pastor and Stambaugh (2003) is from Wharton Research Data Services (WRDS). We construct size (SMB), book-tomarket (HML), and the momentum (MOM) factor portfolios as in Fama and French (1993), but purge the portfolios of firms that have issued equity in the past five years as in Loughran and Ritter (2000). We obtain data on the number of institutional investors and fraction of shares held by institutional investors for each stock from Thomson Reuters' 13F institutional holdings database. We focus primarily on the number of institutional investors. The literature uses both measures to 3 Issuers with zero institutional shareholders at the beginning of the fiscal year of the SEO (3.2% of the sample) are excluded from regression analyses because percent change in institutional shareholders is undefined. These issuers are included in quintile sorts by assigning them to the quintile with the highest increase in institutional shareholders if they have non-zero institutional shareholders at the end of the fiscal year. Dropping them does not alter the results. 4 OIBD/Assets is operating income before depreciation and amortization divided by the average of beginning and ending period book assets less cash. We subtract cash from book value of assets to prevent operating performance from declining mechanically as a result of cash savings out of offer proceeds. 7

9 proxy for institutional demand but tends to favor the number of institutional investors. 5 Moreover, a count-based measure (number of institutions) is closer to herding measures than quantity based measure (fraction of shares held). Both measures provide qualitatively similar results but studies find the fraction of shares held by institutions tends to contain little incremental information for stock returns relative to number of institutions investors (see, e.g., Sias, Starks, and Titman, 2006 and Jiang, 2010). Nevertheless, we use the fraction of shares held when it appears more directly connected to the hypothesis being tested. 2.2 Changes in institutional investors surrounding SEOs Figure 1 provides a timeline of changes in number of institutional investors ( #Institutions) surrounding SEOs starting several quarters before the SEO. In particular, we graph the median of (#Insts t #Insts -8 )/#Insts -8 ), where t = 0 represents the SEO quarter. [Figure 1 around here] Figure 1 indicates a substantial increase in number of institutional investors surrounding SEOs [as documented in Lehavy and Sloan (2008) and Alti and Sulaeman (2012)]. While a large portion of the increase occurs during the SEO quarter (quarter 0), roughly half of the change leading up to the end of the SEO quarter occurs prior to the SEO. For example, SEO firms experience a 91% increase in number of institutional investors during the four quarters prior to the SEO (quarters -4 to -1). Note also that this increase in number of institutions persists. There is no evidence of a transitory component (reversal) to changes in institutional investors prior to SEOs. 6 Note that 5 For studies that use number of institutional investors to proxy for institutional demand see, e.g, Lakonishok et al. (1992); Chen et al. (2002); Sias Starks, and Titman (2006); and Sias, Jiang (2010); Alti and Suleaman (2012), and Edelen, Ince, and Kadlec (2015). 6 Figure 4 in Section 6 examines the long-run persistence of the initial expansion in institutional ownership. 8

10 some of the increase during the offer quarter reflects the SEO-induced increase in float, but a significant amount does not. 2.3 SEO and control-firm characteristics Table 1 presents descriptive statistics regarding various characteristics of issuers and nonissuers (control) firms. [Table 1 around here] Consistent with prior studies, SEOs occur following a period of relatively high stock returns and during years with relatively high operating performance and asset growth. Issuing firms are smaller than non-issuers in terms of both book assets and market capitalization. Issuers also tend to be more liquid and have higher CAPM betas and idiosyncratic volatility than non-issuers. Finally, issuing firms experience a much greater increase in number of institutional investors relative to non-issuers (147.7% vs. 17.8%) during the fiscal year of the SEO. 3. SEO stock return performance and changes in institutional investors 3.1 Post-issuance stock return performance using standard benchmarks Table 2 documents the long-run stock return performance of SEO firms following issuance using standard benchmarks in the literature. In Panel A, we use the time-series factor regression methodology where monthly returns during the 36 months following the SEO are regressed on three (Fama and French (1993)), four (Carhart (1997)), and five (Pastor and Stambaugh (2003)) factor portfolio returns. Following Loughran and Ritter (2000) all factors are purged of firms that have issued equity during the past five years. The regressions are estimated using weighted least squares with weights equal to the number of firms in the portfolio. From Panel A, this approach yields an average abnormal return of about -6% per annum, consistent with the literature. 9

11 In Table 2 Panel B we use the matched-sample methodology to benchmark post-issuance returns to size, book-to-market, and liquidity reference portfolios. Matched-sample returns are calculated as issuers three-year buy-and-hold returns from July of year t+1 through June of year t+4 in excess of the returns earned by a reference portfolio of control firms with similar characteristics but no equity issuance during the prior five years. 7,8 From panel B, this approach yields an average three year buy-and-hold return abnormal return of -9.0%, also consistent with the literature. [Table 2 around here] Table 2, Panel C compares characteristics of SEO and control firms matched on size and bookto-market, as in much of the literature. The comparison indicates that issuing firms have a much larger increase in institutional investors, higher idiosyncratic volatility, and a greater increase in liquidity than non-issuing firms matched on standard dimensions SEO stock return performance and changes in institutional investors In this section we evaluate the stock return performance of equity issuers. In section 3.2.1, we use (i) an event-study approach with reference portfolios, and (ii) calendar-time time-series factor regression approach to investigate the relation between changes in institutional investors and post- SEO stock returns. In section 3.2.2, we use Fama-MacBeth (1973) cross-sectional regressions on 7 Size control portfolios are constructed each June using NYSE market capitalization decile breakpoints. Size and book-to-market (liquidity) control portfolios are constructed using annual, independent sorts on size and book-tomarket (Amihud (2002) illiquidity ratio). Missing monthly returns are set equal to the mean monthly returns of the remaining stocks in the portfolio. Following Lyon et al. (1999), we calculate the benchmark return by first compounding the returns and then summing across securities in the reference portfolio, which prevents new listing and rebalancing biases. We estimate p-values using the bootstrapped distribution of abnormal returns from simulated pseudo-portfolios to avoid skewness bias. 8 The three-year buy-and-hold return window in the matching-firm methodology starts in July after the fiscal year spanning the SEO, which permits the use of the most up-to-date post-seo B/M ratio in constructing the reference portfolios where the book value of equity is from the fiscal year-end following the SEO. Starting the return window earlier would necessitate either using the pre-seo B/M ratio that is out-of-date or the post-seo B/M ratio yet unknown to the market, potentially causing a forward-looking bias. 10

12 the full sample including both equity issuers and non-issuers to investigate whether changes in institutional investors explain the underperformance of equity issuers relative to non-issuers Stock return performance of SEO firms Table 3 presents a matched-sample analysis of post-issuance returns partitioned by the change in number of institutional investors during the fiscal year spanning the SEO ( #Institutions). We report average abnormal returns of SEO firms matched on Size, Size+B/M, and Size+Liquidity partitioned into quintiles by #Institutions. The results show that #Institution provides a near monotonic sort of post-issuance abnormal returns, with the highest #Institution quintiles yielding the lowest returns. In particular, the three-year buy-and-hold abnormal returns for stocks in the highest #Institution quintile are -25.6%, -18.9%, and -25.9% versus 13.4%, 14.5%, and 11.1% for stocks in the lowest quintile using Size, Size+B/M, and Size+Liquidity reference portfolios, respectively. The negative abnormal returns of issuers in the highest quintile are all statistically significant at the 1% level while the positive abnormal returns of issuers in the lowest quintile are significant at the 10% level for Size matching; the 1% level for Size+B/M matching; and the 10% level for Size+Liquidity matching. Thus, conventional matched-samples fail to capture a strong dependence of post-issuance returns on #Institutions. [Table 3 around here] In Table 4 we form calendar-time portfolios of SEO firms sorted by #Institutions over the four quarters prior to the SEO. 9 Each month we form an equal-weighted portfolio of issuers from the past 36 months in each #Institutions quintile. Portfolio returns are then regressed in time series 9 In the calendar-time factor regressions, the use of factor returns instead of firm characteristics as benchmarks enables us to start the performance evaluation window immediately following the SEO (e.g., no gap needed to allow the market to observe the post-seo B/M.) and sort issuers by changes in institutions strictly prior to the SEO. 11

13 on the Fama-French (1994) three-factor model augmented with the Carhart (1997) momentum factor and the Pastor and Stambaugh (2003) liquidity factor. SEO stocks in the lowest #Institutions quintiles do not exhibit significant abnormal returns, whereas SEO stocks in the higher #Institutions quintiles exhibit substantial underperformance. The t-statistic for the abnormal return of the highest #Institutions quintile is -4.2 and the t-statistic for the difference between the abnormal returns of the highest and lowest quintiles is This again demonstrates that the change in institutional investors is an important determinant of post-issuance return performance. Moreover, the fact that changes in institutional investors are measured prior to the SEO establishes that the effects are not due to a reverse causality (i.e., an SEO event causing the change institutional investors). [Table 4 around here] Stock returns of issuers versus non-issuers In Table 5 we present Fama-MacBeth (1973) cross-sectional regressions of stock returns on #Institutions and a dummy indicating that the firm has issued equity during the previous fiscal year, along with a variety of control variables from the literature. Following Cooper et al. (2008), the dependent variable is the compounded raw monthly stock returns between July of calendar year t+1 and June of year t+2, where the SEO indicator equals one if the firm had an SEO during the fiscal year ending in calendar year t. Control variables include asset growth to capture the relation between corporate investment and performance [Cooper et al. (2008) and Lyandres et al. (2009)]; the change in Amihud s (2002) illiquidity ratio and change in share turnover to capture liquidity effects; the change in Dimson s (1979) beta to capture the effect of real options exercise [Carlson et al. (2006, 2010)]; the level of Baker and Wurgler s (2006) sentiment index to capture sentiment-related mispricing and accruals to capture mispricing associated with earnings 12

14 management [Teoh et al. (1998)]. Control variables are observed from the fiscal year ending in calendar year t. We also include but do not tabulate three standard regressors used in the literature to capture expected performance: log market capitalization at the end of June of year t, log book-to-market ratio (book value of equity as of the fiscal year ending in calendar year t divided by market capitalization as of the end of calendar year t); and the six-month buy-and-hold return from January to June in year t (momentum). [Table 5 around here] Regression 1 of Table 5 shows that equity issuers earn 4.4% less during the twelve months starting in July of year t+1 controlling for size, book-to-market, and past returns, with a t-statistic of Comparing regressions 1 and 2 confirms the importance of #Institutions in explaining post-issuance returns. #Institutions is significantly negatively related to future stock returns in the full sample (t-statistic of -2.1) while the SEO indicator variable loses significance in the presence of #Institutions. Moreover, when we interact #Institutions with the SEO indicator in regression 3, the coefficient on the interaction term is indistinguishable from zero, indicating that the impact of a change in institutional investors on long-horizon stock return performance is similar for both issuers and non-issuers irrespective of an SEO event. Finally, Table 5 regression 4 includes all control variables as well as two alternative specifications of investor demand (change in fraction of shares held by institutions and change in the number of all shareholders). The evidence suggests that the relation between #Institutions and long-run returns does not seem to be attributable to standard factor models or standard control variables used in SEO studies Long vs. short-horizon relations between institutions and post-issuance performance 13

15 The negative relation between IO and long-run post-seo returns we document appears to contradict the notion that institutions are relatively informed investors, and the evidence in several studies that institutional investors are "smart" when it comes to SEOs. For example, Gibson, Safieddine, and Sonti (2004), Chemmanur, He, and Hu (2009), and Alti and Sulaeman (2012) all find a positive relation between offer-period institutional demand and post-offer stock returns. However, these studies relate short-horizon changes in institutional holdings to short-horizon returns (horizons of 3-6 months), whereas our analysis relates long-horizon changes in institutional investors to long-horizon returns (horizons of 1-3 years). In Table 6 we examine the effect of time horizon on the relation between changes in institutional holdings and post-seo stock return performance. [Table 6 around here] To allow a more direct comparison with the aforementioned studies we focus on changes in the fraction of shares held by institutions during the offer quarter (denoted %Institutions). 10 We confirm that %Institutions is associated with higher stock returns during the three months immediately following the SEO. However, the relation turns negative for horizons beyond the first quarter (i.e., months 4-6, 7-9, 10-12, and 1-36 post-seo) after the offering. Table 6 Panel B shows that this reversal is most prominent in higher quintiles of %Institutions. Issuers with the largest increases in %Institutions out-perform all other quintiles in the first few months post-seo but under-perform in the long-run; significantly so versus quintile 2. Consequently, the positive short- 10 Gibson et al. (2004) and Chemmanur et al. (2009) focus primarily on changes in the fraction of shares held by institutions whereas Alti and Sulaeman (2012) examine changes in the number of institutional investors. In untabulated results, we find that the relation between institutional interest and returns is positive in the short-run and negative in the long-run using either the percent held by institutions or the number of institutions. 14

16 run relations of Gibson et al. (2004), Chemmanur et al. (2009), and Alti and Sulaeman (2012) do not persist over the longer-horizon typically associated with the post-seo puzzle. 4. SEO firm operating performance and changes in institutional ownership An important dimension to the post-seo puzzle is that the poor stock return performance of issuers is accompanied by poor operating performance [Loughran and Ritter (1997)]. In this section we investigate whether the negative relation between ΔIO and subsequent stock return performance documented in Section 3 extends to operating performance Post-issuance operating performance using standard benchmarks Table 7 parallels the analysis of Table 2, focusing on operating performance rather than stock returns. Panel A shows the operating performance of SEO firms from years t-4 through t+4. Two measures of operating performance are reported: OIBD/Assets and return on assets (ROA). 11 Consistent with prior studies, operating performance declines significantly after issuance. In results not tabulated, the median difference between average operating performance in years t-3 through t-1 and years t+1 through t+3 has a Z-statistic of -3.4 for OIBD/Assets and -2.8 for ROA (both significant at less than 1%). [Table 7 around here] Table 7, Panel B presents matched-sample, difference-in-differences analyses of operating performance for SEO firms matched to non-issuing firms on operating and liquidity characteristics. 12 The first row presents the unmatched change in operating performance for SEO 11 Loughran and Ritter (1997) and Denis and Sarin (2001) find that post-seo abnormal operating performance is robust to alternative measures for firms earnings. 12 Following Barber and Lyon (1996), we evaluate abnormal operating performance by matching sample firms to control firms with similar initial operating performance and we use non-parametric Wilcoxon tests to evaluate statistical significance. 15

17 firms year t to t+4 with the remaining rows showing benchmark performance. In all eight matchedsample comparisons the post issuance decline in operating performance is significantly larger for SEO firms than non-issuing firms, consistent with Loughran and Ritter (1997). Table 7, Panel C compares various characteristics of SEO and control firms matched on initial operating performance in year t (as is standard in the literature). As in the case of return performance (Table 2, Panel C), matched firms again fail to capture SEO firms larger increase in institutional investors, higher idiosyncratic volatility, and greater increase in liquidity SEO operating performance and changes in institutional investors In this subsection, we evaluate the abnormal operating performance of SEO firms using (i) matched-sample analysis with portfolio sorts, and (ii) Fama-MacBeth (1973) cross-sectional quantile regressions Matched-sample analysis Table 8, Panel A sorts on #institutions during the fiscal year of the SEO, using two matching controls (operating performance and liquidity). Note that the operating performance of issuers in the low quintile of #institutions does not underperform that of matching non-issuers during the four fiscal years after the offering. By contrast, the operating performance of issuers in the high quintile underperforms substantially (t-statistics ranging from to -4.3). Table 8 Panel B presents the same analysis as Panel A sorting on changes in institutional investors strictly prior to the SEO event. The post-seo decline in operating performance is even more strongly associated with the pre-seo #institutions. For example, the t-statistic on the high-low difference is 3.3 (versus 2.3 in Panel A). [Table 8 around here] 16

18 4.2.2 Regression Analysis Table 9 reports quantile regressions of the change in the OIBD/Assets of all firms (issuers and non-issuers) over four fiscal years (fiscal years 0 through 4) using the Fama-MacBeth methodology. All regressions include the lagged change in operating performance measured during fiscal year -1 to control for mean reversion in accounting data (Barber and Lyon (1996)). [Table 9 around here] Regression 1 of Table 9 uses an indicator variable to show the effect of an SEO event on operating performance. The significantly negative coefficient indicates a decrease in operating performance following SEOs. Regression 2 includes #Institutions and its interaction with the SEO Indicator. The coefficient on #Institutions is significantly negative (t-statistic of -8.5), indicating that changes in institutional ownership are negatively related to future operating performance. Moreover, the coefficient on the #Institutions x SEO ind. interactive term is statistically insignificant, indicating that the impact of #Institutions on operating performance is statistically indistinguishable in the issuer and non-issuer samples. However, the SEO indicator remains significant (t-statistic -2.1). Finally, from regression 3, which includes the same control variables as the Fama-MacBeth stock return regression from Table 5, #Institutions remains statistically significantly negatively related to future operating performance (t-statistic of -6.9). Note that the coefficient on the SEO Indicator turns significantly positive (t-statistic of 2.7), indicating that SEO firms exhibit significantly better operating performance compared to nonissuers after controlling for a host of factors including changes in institutional ownership. 5. Non-issuing Firms and Changes in Institutional Ownership 17

19 The results of sections 3 and 4 imply that an increase in number of institutions is a necessary condition for underperformance of SEOs firms. The regressions of Tables 4 and 9 establish that the negative relation between #Institutions and future return and operating performance is not isolated to SEO firms. In this section, we investigate whether #Institutions is a sufficient condition for post-issuance-like underperformance in firms without an SEO. In Table 10 we sort all non-issuing firms (no SEO in the past five years) by #Institutions during the previous year and examine their subsequent long-run stock return and operating performance. We use breakpoints from the population of SEO firms to sort non-issuers. We place non-issuing firms with negative #Institutions in a separate group as they are not directly comparable with issuing firms, which rarely experience a contraction in institutional ownership during the year of the SEO (6.8% of issuers vs. 34.5% of non-issuers). [Table 10 around here] Table 10 documents a significantly negative relation between #Institutions and long-run performance very similar to that of SEO firms, for both return (see Table 3) and operating (see Table 8) performance. Non-issuers with a large increase in institutional investors earn significantly lower three-year buy-and-hold stock returns based on raw as well as size-matched and size+b/m matched abnormal returns compared to non-issuers with a small increase in institutional investors. Similarly, the future four-year operating performance of non-issuers with a large increase in institutional investors during the prior year is significantly worse compared to non-issuers with a small increase. The differences are significant at less than 0.1% level; moreover, they are on a par with that seen at SEO firms. Thus, the anomalous long-run under-performance of SEO firms appears more to do with IO than the SEO itself. This evidence sharpens the conclusion of Bessembinder and Zheng (2013) 18

20 who argue that abnormal performance following corporate events has more to do with firm characteristics than the event itself we show that in the context of SEOs a necessary and sufficient firm characteristic is IO. 6. Implications for the role of institutional investors in asset pricing The analyses of Sections 3-5 suggests that the long-run underperformance following SEOs has little to do with the SEO per se but is instead a manifestation of a more general effect associated with changes in the institutional interest in a firm s stock. This evidence places an important restriction on both mispricing and risk-based explanations of post-seo underperformance: in particular, the explanation should include a central role for IO. In this section we consider potential implications regarding institutions role in asset pricing Mispricing Under a mispricing interpretation of our evidence, agency conflicts, behavioral biases, or investor flow may drive institutional investors to make poor investment decisions. While this interpretation is at odds with the notion that institutions are relatively informed investors, it is consistent with our evidence that institutions increase their participation in SEO stocks with the poorest long-run post-issuance performance, prior to the SEO. An important matter relevant to a mispricing interpretation of our results is the holding period of institutional investors. Our analysis relates IO prior to the SEO to returns over the three-year period following the offering. Perhaps institutions buy prior to the SEO and sell shortly after the SEO capturing the positive short-run returns (as documented in prior studies) while avoiding the negative long-run returns leaving their reputation as sophisticated investors intact. While the evidence in Figure 1 suggests that 19

21 changes in institutional shareholders prior to SEOs are relatively long lived, Figure 2 sheds additional light on this matter. [Figure 2] Figure 2 depicts the long-term cumulative changes in institutional investors for SEO firms sorted by the initial change spanning the fiscal year of the SEO. Two interesting facts emerge from Figure 2: (i) the number of institutional investors in SEO firms continues to expand during the three years following the offering, and (ii) the post-issuance expansion is especially pronounced for firms with large pre-seo expansion the firms with the poorest post-issuance stock return and operating performance. Thus, to the extent that post-issuance underperformance is due to mispricing, our evidence suggests that institutions are particularly prone to these pricing errors. The negative relation between IO and long-run returns we document parallels evidence in several herding studies [Wermers (1999), and Sias (2004), Gutierrez and Kelley (2009) and Dasgupta, Prat, Verardo (2011)]. 13 Collectively, the positive short-run and negative long-run relation between IO and returns is consistent with herding models in which institutions play a destabilizing role in asset pricing. There are many potential motives for such herding, including manager reputation [Scharfstein and Stein (1990)], tracking of common firm characteristics [Lakonishok, Shlefer, and Vishney (1994), Del Guercio (1996), Falkenstein (1999), Barberis and Shleifer (2003)], and correlated investor flow [Coval and Stafford (2007) Frazzini and Lamont (2008) and Khan, Kogan, and Serafeim (2012)]. 13 Evidence that the relation between IO and future returns turns negative over longer horizons also appears in Wermers (1999) [Table VI] and Sias (2004) [Table 5] though it is not a point of emphasis in these studies and no statistics regarding the reversal are provided. 20

22 Prior studies have examined the potential role of investor flow in institutional herding around SEOs. In particular, Frazzini and Lamont (2008) and Khan, Kogan, and Serafeim (2012) find evidence of price pressure prior to SEOs from mutual funds experiencing large investor inflows. In both cases the changes in institutional holdings may correlate with concurrent price pressure and subsequent long-run stock return reversals. Khan et al. (2012) show that mutual fund inflows typically go towards expansion of existing positions (fraction of shares held) rather than new positbions. However, we find that long-run returns are more highly related to the number of institutional shareholders (new positions) rather than the fractions of shares held by institutions (expansion of existing positions). In addition, in untabulated results, we find that the relation between long-run returns and changes in number of institutions is robust to the exclusion of SEOs with inflow-induced buying pressure and controlling for changes in ownership by mutual funds who are likely to be most sensitive to the effects of flow. Hence this evidence suggests casts doubt on flow as a basis for a price pressure explanation. Thus, the negative relation between IO and long-run returns seems more consistent with managerial herding due to agency conflicts or behavioral biases as opposed to mutual fund flow Benchmarking-errors hypothesis An alternative to the mispricing interpretation is that long-run post-seo underperformance reflects benchmarking errors that correlate with institutional preferences. We consider four potential sources: real options, Q-theory, liquidity, and market segmentation. A risk-based interpretation for the negative relation between IO and long-run returns argues that institutions are not duped by the long-run underperformance but rather accept it as a reduction in discount rates that is associated with firms conducting SEO -- a reduction that standard return benchmarks fail to capture. Our evidence narrows this broad explanation to something closely 21

23 related to institutions, because lower long-run returns (discount rates, under this alternative interpretation) occur only in conjunction with IO. Carlson, Fisher, Giammarino (2006) argue that firm risk decreases following SEOs due to the exercise of real options. If benchmarks are slow to adjust to this decrease in risk it would lead to overstated benchmark returns. Institutions may be attracted to changes in firm characteristics that trigger real option exercise [Falkenstein (1996)], giving rise to a spurious positive relation between changes in institutional investors and these benchmark errors. Under this hypothesis, we should observe a correlation between SEO underperformance and changes in risk. As in Carlson et al. (2010), we find a small decline in beta estimates following SEOs on average [see Table 1]. However, we find no correlation between post-issuance performance and the magnitude of changes in systematic risk [see Table 5]. Related to the real options literature, Li, Livdan, and Zhang (2009) and Chan and Zhang (2010) argue that Q-theory can explain the financing-based anomalies. In particular, a downward shift in the discount rate leads to new financing and investment along with lower future stock returns and operating performance (see also Cochrane (1991, 2011) and Lamont (2000)). Thus, investment forms a proxy for managers perceived discount rate. Consistent with this view, we find evidence that asset growth has a contributing role in SEO firms long-run underperformance (Tables 5 and 9). But, changes in institutional investors remain a significant predictor. Changes in stock liquidity surrounding SEOs is another potential source of benchmarking error related to changes in institutional investors. While asset-pricing theories relate required returns to liquidity [Amihud and Mendelson (1989), Vayanos (1998), Acharya and Pedersen (2005)], this factor is typically omitted from standard performance benchmarks. Lin and Wu (2013) find that increases in liquidity around SEOs are related to increases in institutional investors and Bilinski, 22

24 Liu, and Strong (2012) find that post-issue return performance is related to changes in liquidity. While it is difficult to isolate the effects of variables as empirically close as liquidity and institutional ownership, our tests indicate that changes in institutional investors have stronger effects than that of changes in liquidity. First, our matched-sample analyses in Tables 3 and 8 include matching on liquidity to evaluate abnormal performance using Amihud s (2002) illiquidity ratio and the institutional investor results stand. Second, our calendar-time portfolio abnormal stock return analysis in Table 4 includes the Pastor and Stambaugh (2003) liquidity factor, and again the results stand. Finally, our cross-sectional regressions in Tables 5 and 9 include control variables for changes in Amihud s illiquidity ratio and changes in turnover. Again, the change in institutional ownership appears to be the dominant factor. Finally, Lehavy and Sloan (2008) and Edelen, Ince, and Kadlec (2013) argue that changes in institutional ownership surrounding SEOs might relate to lower required returns by way of a change in market segmentation (as in Merton (1987), Allen and Gale (1994), Basak and Cuoco (1998), and Shapiro (2002)). While our evidence is also consistent with this line of argument, we find that changes in discount rate via this market-segmentation channel do not appear to be capable of accounting for the magnitude of long-run post-seo performance. 7. Conclusion We analyze the relation between changes in institutional ownership and long-run post-seo returns. We find that institutions exhibit a herding-like participation in SEO stocks despite their well-documented long-run underperformance. Moreover, institutions tend to buy SEO stocks with the worst subsequent long-run stock return and operating performance. Indeed, virtually all longrun post-seo underperformance occurs in the top two quintiles of stocks sorted by IO in the year 23

25 prior to the SEO. In short, long-run SEO underperformance occurs only when accompanied by high IO. We find that non-seo firms with large increases in institutional ownership exhibit long-run underperformance (both stock return and operating performance) similar to that of SEO firms. We thus have the surprising conclusion that ΔIO is both necessary for long-run underperformance following an SEO, and sufficient for long-run underperformance without an SEO. This conclusion complements that of Bessembinder and Zhang (2013) who make the general point that long-run abnormal performance following corporate events has more to do with firm characteristics than the event itself. Both the fact that post-seo underperformance is concentrated in high ΔIO stocks and the fact that non-seo firms parallel effect of ΔIO for SEO firms suggests that herding behavior of institutional investors plays a central role in the long-run post-seo underperformance. Whether that herding has its origin in agency conflict or behavioral bias and whether it relates to mispricing or time-varying discount rates is less clear. It is possible that institutional investors have preferences for stock characteristics that identify equilibrium expected returns relating to nonstandard asset-pricing factors (e.g., liquidity or investment). However, we are unable to find strong evidence for a link to expected returns of sufficient magnitude to account for the post-issuance phenomenon. The most plausible interpretation of our results is that institutional herding destabilizes equity prices, with a subsequent long-run reversal to equilibrium valuations. 24