Pension Fund Trading and Stock Returns. Russell Jame * May Abstract

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1 Pension Fund Trading and Stock Returns Russell Jame * May 2012 Abstract Managers investing on behalf of pension plan sponsors (i.e. pension funds) face greater fiduciary responsibilities and more stringent investment mandates than mutual funds. These constraints may reduce the information content of pension fund trading. Consistent with this view, we find that the stocks most heavily bought by pension funds subsequently significantly underperform the stocks most heavily sold by pension funds. We find no such pattern for mutual funds. Moreover, we identify a subset of managers who trade on behalf of both pension plan sponsors and other clients (e.g. retail investors), and we find that their trading on behalf of plan sponsors is significantly less informative than their trading for other clients. JEL Classification: G11, G12, G23 Keywords: Pension Funds, Stock Returns, Institutional Trading * School of Banking and Finance, University of New South Wales, Gate 2 High Street, Sydney, NSW r.jame@unsw.edu.au. Telephone: I would like to thank Allison Keane and ANcerno Ltd. for making available the institutional transaction data and answering all data-related questions. I would also like to thank David Feldman, Clifton Green, Byoung-Hyoun Hwang, Mark Kamstra, Andy Puckett, Konark Saxena, Mark Seasholes, and seminar participants at the FIRS conference in Sydney, the Deakin Finance Colloquium, the University of New South Wales, and the University of Technology Sydney.

2 Pension Fund Trading and Stock Returns May 2012 Abstract Managers investing on behalf of pension plan sponsors (i.e. pension funds) face greater fiduciary responsibilities and more stringent investment mandates than mutual funds. These constraints may reduce the information content of pension fund trading. Consistent with this view, we find that the stocks most heavily bought by pension funds subsequently significantly underperform the stocks most heavily sold by pension funds. We find no such pattern for mutual funds. Moreover, we identify a subset of managers who trade on behalf of both pension plan sponsors and other clients (e.g. retail investors), and we find that their trading on behalf of plan sponsors is significantly less informative than their trading for other clients. JEL Classification: G11, G12, G23 Keywords: Pension Funds, Stock Returns, Institutional Trading

3 Introduction U.S. pension funds manage over $7 trillion in assets, roughly 50% of which is invested in equities (Pension & Investments (2008)). The sheer size of the pension fund industry suggests that pension fund trading may have a large impact on stock prices. If pension funds are savvy investors who trade primarily on value-relevant information, then their trading should lead to more informative stock prices. In contrast, if much of pension fund trading is driven by preferences for stocks with certain characteristics (e.g. Del Guercio (1996)) or reputational concerns (e.g. Scharfstein and Stein(1990)), then their trading may push prices away from their fundamental value and exacerbate stock market volatility. A large literature has explored the impact of institutional trading on stock prices, however the literature has focused primarily on mutual funds (e.g. Chen, Jegadeesh, and Wermers (2000) or Wermers (1999)) or aggregate institutional investors (e.g. Nofsinger and Sias (1999) and Sias (2004)). Most of the existing work finds that mutual fund trading and aggregate institutional trading positively forecasts stock returns, which suggests that institutional trading speeds up the price-adjustment process. 1 However, a number of recent papers present evidence consistent with institutional trading destabilizing stock prices. 2 In contrast to most of the existing literature on institutional trading, this paper focuses primarily on the trading of pension funds. In particular, this paper explores whether pension fund trading has a different impact on stock prices than trading by other institutions. There are several reasons to believe that pension fund trading may be significantly 1 Other papers that find evidence of a positive correlation between institutional demand and future returns include Bennet, Sias, and Starks (2003), Chen, Hong, and Stein (2002), Gompers and Metrick (2001), Cohen, Gompers, and Vuolteenaho (2002), Grinblatt, Titman, and Wermers (1995), and Sias, Starks, and Titman (2006). 2 See e.g. Dasgupta, Prat, and Verado (2011), Brown, Wermers, and Wei (2009), Guiterez and Kelley (2009), and Puckett and Yan (2008). 1

4 less informative than aggregate institutional trading. First, internally managed pension funds tend to have more limited resources and greater legal restrictions, which may hinder their ability to attract and retain the most skilled fund managers. 3 Second, external fund managers face different incentives when trading on behalf of pension plan sponsors versus other clients (e.g. retail investors). For example, pension funds have greater exposure to legal liability and more stringent investment mandates than mutual funds (see e.g. Del Guercio (1996) and Del Guercio and Tkac (2002)). In addition, Lakonishok, Shleifer, and Vishny (herafter LSV) (1992) argue that the additional layers of delegation found in the pension fund industry encourage pension funds to focus less on performance and more on implementing investment strategies that can easily be justified to superiors. The more hierarchical structure may also reduce pension fund managers' incentives to collect 'soft information' (see e.g. Stein (2002) and Massa and Zhang (2009)). Similarly, the organizational structure can result in greater bureaucracy, which may limit pension funds ability to trade on short-lived information. Pension funds and mutual funds also face a different performance-flow relationship. Del Guercio and Tkac (2002) find that the performance-flow relationship in the pension fund industry is essentially linear, while the performance-flow relationship in the mutual fund industry is convex. 4 This indicates that pension funds are more likely to be punished for bad performance and less likely to be rewarded for good performance. Thus, risk-averse pension fund managers have a stronger incentive to mimic their peers, even if they believe such strategies are unlikely to generate superior returns. The primary constraint to studying pension fund trading is data availability. Unlike 3 See e.g.: Primack, Dan "How to stop the public pension brain drain" CNN Money, March 22, A number of papers document a convex performance-flow relationship in the mutual fund industry (see e.g. Ippolito (1992) and Sirri and Tufano (1997). However, Huang, Wei, and Yan (2007) find that the convexity, though still present, has declined in more recent years. 2

5 most institutional investors, pension funds typically do not report 13F filings with the SEC. Internally managed pension funds with greater than $100 million in total assets do directly report their holdings, but the overwhelming majority of pension fund assets are managed by external money managers (e.g. Goldman Sachs Asset Management). 5 These money managers do report their holdings to the SEC, but these holdings are an aggregation of pension fund holdings, mutual fund holdings, hedge fund holdings, proprietary holdings, etc. As a result, it is not possible to directly examine their trading on behalf of pension plan sponsors. We circumvent this data constraint by obtaining a proprietary dataset of pension fund trading from ANcerno Ltd, a transaction cost consulting firm. The dataset spans from and includes the complete transaction history for all pension plan sponsors that subscribe to ANcerno. The dataset also includes a manager code, which allows us to identify specific external money managers trading on behalf of pension plan sponsors. In addition, there are many cases where a plan sponsor hires a money manager who also subscribes to ANcerno (e.g. CalPERS hire Putnam Investments, and Putnam Investments also subscribes to ANcerno). This provides a unique opportunity to compare the aggregate trading of a money management firm (hereafter: money manager trading) to their trading on behalf of pension plan sponsors (hereafter: pension fund trading). Additionally, the dataset includes the exact date and execution price of all trades. Thus, unlike 13F filings, which only report quarterly holdings, this dataset allows us to examine the price effects of pension fund trading over short horizons (e.g. one week) as well as longer horizons (e.g. one quarter). We begin by examining the relationship between the weekly trading of pension funds and stock returns. We find pension fund order imbalances are strongly positively related to 5 Bauer, Frehen, Lum, and Otten (2007) find that 85% of all pension fund assets in the CEM Benchmarking database are managed externally. 3

6 contemporaneous (i.e. formation period) stock prices. More interestingly, we find pension fund trading negatively forecasts returns over the subsequent six months and year. We also examine the relationship between the quarterly trading of pension funds and stock returns. Pension fund trading over quarterly horizons generates larger formation period returns and larger subsequent reversals. A portfolio that went long the quintile of stocks most heavily sold by pension funds over the previous quarter and short the quintile of stocks most heavily bought would earn gross returns of 1.48% over the subsequent quarter and 6.66% over the subsequent two years. Characteristic-adjusted returns based on size, book-to-market, and momentum reduce the estimates to 1.28% and 6.46%, respectively, but both estimates remain highly significant. The results also hold in cross-sectional regressions where we control for a variety of firm characteristics such as size, book-to-market, past returns, turnover, age, volatility, and net share issuance. The reversals are strongest in small stocks, but are also present in large stocks. In sharp contrast to our pension fund findings, we find no evidence that money manager trading negatively forecasts returns. In additional analyses, we examine changes in quarterly holdings based on 13F filings, and continue to make similar observations. In particular, we find that the trading of internally managed pension funds negatively forecasts stocks returns, while the trading of investment companies (mostly mutual funds) and investment advisors is unrelated to future stock returns. We also find that trading by banks and insurance companies negatively forecast returns. Since pension funds, banks, and insurance companies have greater fiduciary responsibilities (see e.g. Del Guercio (1996)), this finding is consistent with the view that legal liability concerns contribute to uninformed trading. An alternative view of our findings is that pension funds (as well as banks and 4

7 insurance companies) are simply less skilled or have fewer resources than other money managers. To test this conjecture, we repeat the analysis on the sub-sample of managers who appear in the ANcerno dataset as both pension funds and money managers. Even amongst this subset of identical money management firms, we continue to find that pension fund trading leads to significant price reversals, while money manager trading does not. This finding suggests that differences in skill are unlikely to fully explain the differential price effects associated with pension fund and money manager trading. Instead, the findings point to the possibility that the incentives in the pension industry encourage pension fund managers to engage in greater amounts of uninformed trading, which generates significant price reversals. 6 This paper adds to the literature that explores the stock price consequences of pension fund trading. To the best of our knowledge, only two papers examine the relationship between pension fund trading and future stocks returns: LSV (1992b) and Puckett and Yan (2008). LSV (1992b) examine the trading behavior of 335 pension funds from Although not the focus of their paper, in footnote 1 they investigate the relationship between pension fund trading and subsequent one quarter stock returns. They find some evidence that trading in smaller stocks is profitable over the subsequent quarter, but find no relationship for larger stocks. One potential explanation for the difference in our findings is that they analyze an earlier time period. 7 Puckett and Yan (2008), using the same data used in this study, find evidence of return reversals following weekly sell herds and return continuations following weekly buy herds for both pension funds and money managers. However they do not examine the relationship between pension fund trading and longer-horizon returns. Our paper 6 We note that these incentives need not be suboptimal from the plan sponsor's perspective. For example, imposing narrow tracking error constraints may reduce the information content of pension funds' trades. At the same time, plan sponsors may believe this is a necessary cost in order to ensure proper diversification across multiple external money managers, or as a way to limit agency costs (see e.g. He and Xiong (2011)). 7 We explore this possibility further in Section III.G. 5

8 extends this literature by documenting a robust and economically meaningful relationship between pension fund trading and returns for up to two years in the future. The paper also contributes to the literature that compares pension funds to other institutional investors. For example, LSV (1992) argue that additional layers of delegation found in the pension fund industry results in additional agency problems. Building off this work, Del Guercio and Tkac (2002) argue that differences in client sophistication and agency problems generate differences in the determinants of fund flows across the pension fund and mutual fund industries, and Del Guercio (1996) finds that differences in fiduciary responsibilities across the two industries influence pension funds and mutual funds preferences for stocks with certain characteristics. Our findings suggest that pension fund trading is also motivated by different factors. Specifically, compared to mutual funds and other investment advisors, pension fund trading appears to be more motivated by non-informational reasons, such as fiduciary considerations and career concerns. I. Data and Descriptive Statistics A. The ANcerno Data We obtain data on institutional trading from January 1, 1999 to December 31, 2008 from ANcerno Ltd. (formerly the Abel Noser Corp). 8 ANcerno is a consulting firm that works with institutional investors to monitor their trading costs. The ANcerno data include the complete transaction history for all of its institutional clients. Each observation corresponds to an executed trade. For each execution, the database reports the date of the trade, the execution price of the trade, the stock traded, the number of shares traded, whether the trade was a buy or a sell, and identity codes for the institution making the trade. For each stock traded in the ANcerno dataset, 8 Other papers that use ANcerno data include Anand, Irvine, Puckett, and Venkataraman (2011), Busse, Green, and Jegadeesh (2010), and Puckett and Yan (2011). 6

9 we collect returns, share price, trading volume, shares outstanding, and a factor to adjust shares outstanding from CRSP, and we collect book value of equity from Compustat. The institutions in the ANcerno dataset are anonymous; however the dataset does include permanent numeric identifier codes. This allows us to track a given institution both in the cross-section and throughout time. There are three main identifier variables: an institution type identifier, a client identifier, and a manager identifier. The institution type identifier allows us to distinguish between clients that are pension plan sponsors (e.g. CalPERS and United Airlines) and clients that are money managers (e.g. Putnam Investments and Lazard Asset Management). Pension plan sponsors include both corporate and public pension funds, as well as both defined benefit and defined contribution plans. There is no way to distinguish amongst different types of pension plan sponsors. Money managers include mutual funds, hedge funds, banks, and insurance companies. The distinction between institution types reflects whether the ANcerno client is a pension plan sponsor or a money manager and is unrelated to whether a money manager is trading on behalf of a pension plan sponsor. For example, if Putnam Investments is an ANcerno client and Putnam Investments makes trades on behalf of a pension plan sponsor (e.g. CalPERS), the trades would not be classified as pension fund trades. This is because the client (Putnam Investments) is a money manager. In contrast, if CalPERS becomes an ANcerno client and hires Putnam Investments as a money manager, those (perhaps identical) trades would be considered pension fund trades since the client (CalPERS) is a pension plan sponsor. The client identifier corresponds to the pension plan sponsor or money manager who is subscribing to the ANcerno consulting services. The manager code identifies a specific money 7

10 management company. 9 The data generally do not distinguish between different products within a money management firm. For example, if Putnam Investments offered a small-cap growth fund and a large-cap value fund, both of these products would have the same manager code. 10 The manager code is constant across different clients. For example, if CalPERS and United Airlines both hired Putnam Investments, the manger code would be identical (although the client code would be different). Similarly, if Putnam Investments subscribed to ANcerno, it would be given the same manager code. This structure allows us to examine the aggregate trading of Putnam Investments as well as their trading for specific pension plan clients. The central unit of analysis for this study is the manager code. The Appendix provides a more detailed description of the database. Table I provides descriptive statistics for the sample. Panel A reports the results for the full sample of pension funds. From , the sample includes 590 different pension funds who are responsible for over $3 trillion in trading volume. We also track the number of pension funds trading in a given week or quarter. We define 'week' as five trading days, while quarters are based on actual calendar time. In the average week (quarter) during 1999, 327 (409) different pension funds traded at least one stock. There were 1926 (4052) stocks that were traded by at least two pension funds in a given week (quarter) and 744 (2738) stocks that were traded by at least five pension funds in a given week (quarter). The number of pension fund managers in the sample is declining over time; however, there is still a sizeable amount of trading activity in the last years of our sample. For example, in the average quarter of 2008, there were 213 pension fund managers, who traded over 3000 different stocks and 9 In some cases, ANcerno cannot reliably identify the money management firm in which case ANcerno assigns a made code value of either -1 or 0. These observations are excluded from the analysis. 10 Some prior studies using ANcerno data have interpreted the manager code as reflecting a specific product. Discussions with ANcerno representatives suggest that this is generally not the case. 8

11 executed over $50 billion worth of total trading volume. Panel B repeats the analysis for the full sample of money managers. The sample consists of 207 money management firms who are responsible for over $25 trillion in trading. While there are fewer money managers in the sample, they account for nearly ten times the trading volume of pension funds. Panel C repeats the analysis for the subset of pension fund managers who also subscribe to ANcerno. Of the 590 fund managers in Panel A, 143 also subscribe to ANcerno at some point in the sample period. Although this reflects only 24% of the sample of pension funds, it accounts for nearly 50% of pension fund trading. Most of the money managers in the sample trade on behalf of pension plan clients. For example, of the 75 money managers trading in the first quarter of 2004, 56 traded on behalf of a pension plan client at some point in the sample. Panel D reports the number of pension fund managers who appear in the dataset simultaneously as pension fund managers and money managers. 11 In the full sample, there are 124 different pension funds who appear as both pension funds and money managers at a given point in time. However, in any one quarter, the number of funds that appear as both pension funds and money managers is relatively small. For example, there are only 40 such funds in an average quarter of These 40 managers executed nearly $420 billion worth of trading, of which roughly 5% (24/419) was on behalf of pension plan sponsors who subscribe to 11 To understand the distinction between Panel C and Panel D, suppose CalPERS subscribed to ANcerno services from and hired Putnam Investments and Lazard Asset Management. At the same time, Putnam Investments subscribed to ANcerno from , and Lazard Asset Management subscribed to ANncerno from In this case, since both Putnam Investments and Lazard Asset Management appear as pension fund managers and money managers at some point in the sample, both would be included in Panel C. However, Lazard Asset Management would not be included in Panel D, because there is no overlap between when they appear as a pension fund manager and when they appear as a money manager. 9

12 ANcerno. As noted earlier, the ANcerno dataset only includes institutional investors that chose to become ANcerno clients. One concern is that ANcerno clients might differ systematically from the typical institutional investor. In the Appendix we examine how our results might be influenced by possible selection biases. We conclude that such biases are unlikely to alter our central findings. B. Thomson Reuters Data (Quarterly Holdings) We also examine institutional trading using quarterly holdings data from Thomson Reuters (formerly known as CDA/Spectrum S34). The holdings data reported in Thomson Reuters are obtained from the 13F filings that all institutional investors with greater than $100 million in total assets must report to the SEC. As noted earlier, a limitation of Thomson Reuters is that it is not possible to identify money manager trading on behalf of pension plan sponsors. However, the 13F filings offer a number of advantages relative to the ANcerno data. First, the data allow us to examine the trading of all internally managed pension funds. Second, by examining the trading of all management companies with greater than $100 million in total net assets, we alleviate the concern that our money manager results are limited to the subsample of companies that subscribe to ANcerno. Third, the sample includes many more managers. This results in more precise trading estimates and increases the power of our tests. Fourth, the quarterly holdings are available beginning in 1980 which enable us to examine a much longer time series. Lastly, the 13F filings allow us to classify institutions based on investor type. We consider three groups of institutional investors. First, we classify all investors with a type code equal to 3 or 4 as Investment Companies/Advisors. This group includes mutual 10

13 funds, asset management companies, investment banks, private wealth management companies, and hedge funds. This group tends to have fewer investment restrictions and typically has a more convex performance-flow relationship. We classify all investors with a type code equal to 1 or 2 as Banks/Insurance Companies. Like pension funds, banks and insurance companies often face stringent investment mandates and have greater exposure to legal liability (Del Guercio,1996). Finally, we classify all other institutions (institutions with a type code of 5) as Pension Funds. This groups includes internally managed pension funds as well as other plan sponsors such as foundations and endowments. 12 The type code variable is inaccurate beginning in Specifically, many investment advisors (type code = 4) are mistakenly classified as other institutions (type code =5). To correct for this error, we follow the approach outlined in Agarwal et al. (2011). Specifically, we reassign an institution which has type code 5 after 1998 to an earlier code, if available and if different from 5. We manually classify management companies that enter the database after Panel E of Table 1 provides summary statistics for the Thomson Reuters data. Although the quarterly holdings data are available starting in 1980, trading activity is very sparse in the earlier years of the sample, particularly for pension funds. 13 We examine quarterly holdings starting in 1985, which corresponds to the first year of the sample in LSV (1992). In our full sample, we have 200 different pension funds who are responsible for roughly $5.7 trillion in trading. Our sample includes 729 different banks and insurance companies and over 4000 different investment companies/advisors. The investment 12 The number of investors with a type code of 5 is relatively small, prohibiting us from separately examining pension funds vs. other plan sponsors. 13 For example, in the first quarter of 1980, fewer than 250 stocks were traded by at least 5 different pension fund managers. 11

14 companies/advisors group experiences substantial growth during our sample period. In 1985, there are 328 investment companies responsible for $115 billion in trading. By 2008, the sample includes over 2500 investment companies responsible for over $2.5 trillion in trading. C. The Persistence and Determinants of Pension Fund Demand Using both transaction level data from ANcerno and changes in quarterly holdings from Thomsons, we construct two measures of demand for pension funds and other institutional investor. Following LSV (1992), our measures of demand for stock i in quarter t are defined as follows: NRatio it # net buyers # traders it = (1) it DRatio it = $ Buyit $ Sell $ Buy + $ Sell it it it (2) Most of the tests will focus on NRatio it, which measures the proportion of pension funds that are net buyers of stock i in quarter t. Our emphasis on the order imbalance of traders, rather than the dollar volume order imbalance, is motivated by two factors. First, a number of studies find that the total number of transactions is more influential in determining stock price movements than trading volume. 14 Second, career concerned managers are likely to choose strategies that are followed by a number of different funds (as opposed to one very large fund). Thus if a large fraction of pension fund trading is motivated by reputational concerns, order imbalances in the number of traders may be a better measure of uninformed trading. Section III.D investigates the relative impact of these two measures on stock prices. The impact of pension fund trading on stock prices likely depends on the persistence of 14 See for example, Jones et al. (1994), Chordia and Subrahmanyam (2004), Coval and Stafford (2007), and Gutierrez and Kelley (2009). 12

15 pension fund demand. To investigate the persistence of pension fund demand, we analyze the evolution of NRatio over time by ranking stocks into quintiles based on NRatio in week t. We require that a stock be traded by at least two pension funds to be included in the sample. Figure 1 plots the average NRatio of the buy (quintile 5) and sell (quintile 1) portfolios over the subsequent 25 weeks. In the formation week, the spread between buys and sells is 83%. The spread remains 28% after the first week, 18% after the second week, and continues to exceed 5% for 10 weeks after portfolio formation. We also estimate that the cross-sectional correlation between pension fund NRatio (PF NRatio) this week and last week is roughly 35%. Following Sias (2004) we decompose this correlation into the portion that is due to pension funds following their own trades and the portion that is due to following other pension funds. 15 We find that roughly 80% of the correlation (28/35) is driven by pension funds following their own trades. However, the remaining 20% is driven by pension funds following other pension funds, and the estimate is highly significant. 16 Figure 1 also plots the persistence of money manager NRatio (MM NRatio). Money manager demand is also highly persistent. The cross-sectional correlation between money manager demand this week and last week is roughly 32%. However, the Sias (2004) decomposition indicates that roughly 99% of this persistence is driven by money managers following their own trades. The persistent demand suggests substantial dispersion in pension fund (and money manager) demand over longer horizons, such as one quarter. Before we investigate whether cross-sectional dispersion in demand is related to stock returns, it is useful to examine whether 15 A more detailed description of the methodology is discussed on pages of Sias (2004). 16 We estimate statistical significance from the time-series standard deviation. We use a Newey-West (1987) correction with the lag length equal to 10 weeks. 13

16 certain firm characteristics are significantly associated with pension fund and money manager demand. Each quarter, we compute NRatio it for all stocks traded by at least five pension funds and five money managers. We then regress NRatio it on the following firm characteristics: Size the natural log of the market capitalization of the stock in the quarter prior to the formation period Book-to-market (BM) - the ratio of the book value of equity from the fiscal year-end in the prior calendar year to the market value of equity form the prior December. S&P 500 a dummy variable which equals one if the firm belongs to the S&P 500 and zero otherwise. Turnover - the natural log of the average monthly turnover over the prior year. 17 Volatility - the natural log of the standard deviation of monthly returns over the prior year. Age - the natural log of the number of months since the firm first appeared in the CRSP dataset. Net Issuance - net shares issued over the prior 12 months as constructed in Pontiff and Woodgate (2008). Mom1 - the returns over the quarter prior to the formation period. Mom2_4 - return in the year prior to the formation period, excluding the quarter prior to the formation period Mom5_16 - return in the fours year prior to the formation period, excluding the year prior to the formation period. To facilitate comparison across all variables, each quarter, we standardize all variables (except S&P 500) to have a mean of 0 and a variance of 1. We estimate the regression as a panel and cluster standard errors by firm and quarter. Table 2 presents the results of the regression. In general, pension fund demand and money manager demand are associated with similar characteristics. For example, both pension funds and money managers are net buyers of smaller stocks, stocks with low turnover, value stocks, and stocks with high returns over the past quarter. Since most money managers have pension plan sponsors as clients, it is not surprising that pension fund and money manager 17 We scale NASDAQ volume by two so that turnover is comparable across all exchanges. 14

17 demand are related. In fact, the correlation between PF NRatio and MM NRatio is However, there are also some significant differences between pension fund and money manger demand. Specifically, relative to money managers, pension funds are significantly less likely to be buyers of small stocks, low-turnover stocks, and stocks with strong past returns over the prior two to four quarters. Interestingly, all of these characteristics tend to be associated with higher expected returns. This finding suggests that pension fund trading may be less informative about future returns than money manager trading. We investigate this possibility next. III. Pension Fund Trading and Stock Returns In this section, we investigate the two central research questions of the paper. First, does pension fund trading affect stock prices? Second, does pension fund trading have a different impact on stock prices than the trading of other money managers? A. Weekly Trading and Stock Returns We first examine the impact of pension fund and money manager trading over weekly horizons. Each week we sorts stocks based on PF NRatio and MM NRatio. We require that a stock be traded by at least two pension funds and two money managers. We examine the returns on each portfolio around several holding periods, ranging from the formation period (0,0) to the subsequent 50 weeks after the formation period (1,50). Panel A of Table III reports the time-series average (across the 503 weeks in our sample) of the buy-and-hold gross return for each portfolio. Statistical significance is estimated from the time-series standard deviation of returns. To account for serial correlation due to overlapping holding periods, we compute Newey-West (1987) standard errors. The lag length is set equal to the length of the holding period. The first column indicates that the heavy buys (i.e. quintile 5) of pension funds significantly outperform their heavy sells (quintile 1) during the formation period. This is 15

18 consistent with price pressure from pension funds moving stock prices. However, this is also consistent with short-term momentum trading or pension fund trading forecasting short-term stock returns. 18 More interestingly, we find that pension fund trading has forecasting power for subsequent stock returns. Over the subsequent week, heavy buys outperform heavy sells by 28 basis points (hereafter bps). However, in the Appendix we show that this short-term continuation is sensitive to our measure of excess demand. 19 Looking at longer horizons, we find the heavy buys of pension funds underperform their heavy sells. Over the subsequent 13 weeks, heavy buys underperform heavy sells by a statistically significant 42 bps. This difference grows to a 152 bps over the subsequent 50 weeks. 20 The short-term price continuations and longer-term reversals are consistent with the model of Dasgupta, Prat, and Verardo (2011b). In their model, reputational concerns of fund managers imply an endogenous tendency to imitate past trades, which results in short-term continuations followed by eventual reversals. Interestingly, we find no evidence that trading by money managers generates significant reversals over longer horizons. Panel B presents a similar analysis using the characteristics adjustment proposed by Daniel, Griblatt, Titman and Wermers (1997) and Wermers (2004) (hereafter DGTWadjusted returns). 21 DGTW-benchmark portfolios are constructed by first sorting all stocks into quintiles based on market capitalization. Then within each size quintile, stocks are 18 In unreported results, we find that pension fund trading is positively related to returns at shorter horizons. For example, the price at which a buy is executed is roughly 12 bps higher than the price at the time of the order, while the price at which a sell is executed is roughly 25 bps less than the price at the order. 19 The appendix also reconciles our short-term findings with Puckett and Yan (2008) who find that the stocks most heavily sold by pension funds and money managers experience significant reversals in the following week. 20 We have also examined the seasonality of our findings (unreported). We find no evidence that the effects are greater in December or January. This suggests that "window dressing" (e.g. Lakonishok et al. (1991), "portfolio pumping" (e.g. Carhart et al. (2002)) and tax-loss selling (e.g. Sias and Starks (1997)) are not driving our findings. 21 We also examine alphas from a five factor model which include the Fama-Frnech (1993) factors, the Carhart (1997) momentum factor, and the Pastor and Stambaugh (2002) liquidity factor. Our findings are robust to this alterantive risk adjustment. 16

19 sorted into quintiles based on book-to-market ratio, resulting in 25 different portfolios. Within each portfolio, stocks are once again sorted into quintiles based on prior 12 month returns, resulting in 125 portfolios. 22 The benchmark for each stock is the portfolio to which it belongs. The DGTW-adjusted return for each stock is the difference between the stock return and the equally weighted benchmark portfolio return over a particular holding period. Relative the gross returns reported in Panel A, the DGTW-adjusted returns reported in Panel B generate similar coefficients and slightly smaller standard errors. Thus, the reversals associated with pension fund trading are robust to controlling for size, book-to-market, and momentum. B. Quarterly Trading and Stock Returns Given the persistence of pension fund and money manager demand (see Figure 1), one might expect trading over longer horizons to have a more pronounced impact on prices. To explore this idea, we repeat the above analysis, substituting weekly trading for quarterly trading. Specifically, each quarter, we compute PF NRatio and MM NRatio for each stock traded by at least five pension funds and five money managers. We sort stocks into quintiles and examine the returns on each portfolio around several event periods, ranging from the formation period (0, 0), to eight quarters after the formation period (1, 8). Panel A of Table IV reports the time-series average (across the 40 quarters in the sample) of the buy-and-hold gross returns for each portfolio. The return patterns around quarterly trading tend to be of similar sign, but substantially larger than the patterns around weekly trading. For example, pension funds heavy buys outperform their heavy sells by 1035 bps during the formation quarter. Not surprisingly, we are unable to detect the short-term 22 For more details on the DGTW-benchmark construction procedure see DGTW (1997). The DGTW benchmarks are available via 17

20 continuations found in Table III. However, we continue to find evidence that pension fund trading negatively forecasts returns. Heavy buys underperform heavy sells by 148 bps over the subsequent quarter and this difference grows to 666 bps over the subsequent year. The results from Panel B confirm that the reversals associated with pension funds trading are robust to using DGTW-adjusted returns. Once again, we find no evidence that money manager trading generates significant reversals. 23 C. Cross-Sectional Regression: Baseline Specification To further examine the relationship between pension fund trading and subsequent abnormal returns, we run cross-sectional regressions. This approach allows us to examine the partial effect of pension fund trading on stock returns after controlling for money manager trading, as well as other firm characteristics that may be related to returns, such as turnover, volatility, and net share issuance. The dependent variable is the DGTW-adjusted return measured over various horizons ranging from the formation period (0,0) to the eight quarters after the formation period (1,8). 24 Our independent variables of interest are the decile rankings of PF NRatio and MM NRatio. The regression also contains all firm characteristics included in Table II. As in Table II, all firm characteristics (except S&P 500) are standardized each quarter to have mean 0 and variance 1. Panel A of Table V reports the time-series means from 40 cross-sectional regressions. We again compute standard errors using the Newey-West (1987) adjustment where the laglength is set equal to the length of the holding period. The regression confirms that pension fund trading is positively associated with formation period returns and negatively related to 23 These findings are perhaps surprising in light of Puckett and Yan (2011) who find that the interim trading skill of pension funds is positive and similar in magnitude to the interim trading skill of money managers. The Appendix reconciles these dissimilar results. 24 Using gross returns as the dependent variable generates similar coefficients and slightly larger standard errors. 18

21 returns over the subsequent eight quarters. For example, a one decile increase in PF NRatio is associated with a 14 basis point reduction in DGTW-adjusted returns over the subsequent quarter and a 55 basis point reduction in returns over the subsequent two years. Moreover, all the estimates are statistically significant at a 1% significance level. In contrast, we find no evidence that money manager trading generates significant reversals. In fact, all the estimates are within one standard error from zero. The last row of Table V indicates that the coefficients on PF NRatio are significantly different from the coefficients on MM NRatio. Figure 2 provides some evidence on the effects of pension fund and money manager trading on stock prices throughout the full sample period of The figure plots the average coefficient on PF NRatio and MM NRatio for a quarterly holding period for each year in our sample. PF NRatio is relatively stable over the sample period. The average effect across all years is , with a range of (2001) to (2008). Thus, in all 10 years the coefficient is negative, and in 8 out of the 10 years the coefficient on PF NRatio is less than the coefficient on MM NRatio. We also examine whether the buying and selling of pension funds are differentially informed. Specifically, we replace PF NRatio with PF Heavy Buy and PF Heavy Sell. PF Heavy Buy (Heavy Sell) is a dummy variable which equals one if the stocks is in the top (bottom) 20% of the quarterly distribution of PF NRatio. Similarly, we replace MM NRatio with MM Heavy Buy and MM Heavy Sell, defined analogously. In unreported results, we find that the heavy buys of pension funds underperform by 276 bps (t=-2.60) over the subsequent two years, while the heavy sells outperform by 183 bps (t=1.43). Although the reversals are roughly 50% larger on the buy side, the estimates are not significantly different from each other (after multiplying the coefficient on heavy sell by minus 1). Neither the heavy 19

22 buys nor the heavy sells of money managers earn returns that are significantly different from zero. D. Comparing Trader-Based (NRatio) and Volume-Based (DRatio) Measures of Demand Our primary measure of demand, NRatio, is a measure of order imbalance in the number of traders. An alternative measure of demand is order imbalance in the total dollar volume traded, DRatio. To investigate whether our central findings are robust to using DRatio as our measure of demand, Panel B of Table V repeats the analysis of Panel A, but replaces PF NRatio and MM NRatio with PF DRatio and MM DRatio. Using DRatio, instead of NRatio, yields qualitatively similar findings, although the magnitudes are slightly smaller. For example, a one decile increase in PF DRatio is associated with a 69 bps increase in formation period returns (as compared to a 87 bps for PF NRatio) and a reversal of 42 bps (versus 55 bps for PF NRatio) over the subsequent two years. T h e coefficient on MF DRatio is generally smaller (i.e. more negative) then MF NRatio, and there is a significant negative relationship between MF DRatio and subsequent one quarter returns. The results from Table V indicate that both PF NRatio and PF DRatio negatively forecast stock returns. Since the correlation between the two measures is 0.57, it is not surprising that the two measures yield similar results. A more interesting question is whether each measure of demand has incremental forecasting ability. Holding NRatio constant, a larger DRatio may cause more price pressure and larger subsequent reversals. Similarly, holding DRatio constant, a larger NRatio may generate greater price pressure. Presumably the price impact of a single trader should be lower than the price impact of a number of traders with a similar aggregate trade size, since the single trader can strategically work his order. NRatio is also a better measure of herding, or the degree to which managers follow 20

23 correlated trading strategies. If herding is driven by funds following correlated private signals (e.g Froot, Scharfstein, and Stein (1992)) or inferring value-relevant information from each other s trades (e.g. Bikhchandani, Hirshleifer, and Welch (1992)), then holding DRatio constant, NRatio should not be negatively related to future returns. However, if herding is driven by preferences for stocks with certain characteristics (e.g. Del Guercio (1996)) or reputational concerns (e.g. Scharfstein and Stein (1990)), then NRatio will likely forecast reversals even after holding DRatio constant. To investigate the marginal effects of NRatio and DRatio on stock prices, we repeat the regressions outlined in Table V, but we now include both PF NRatio and PF DRatio as well as MM NRatio and MM DRatio. Table VI reports the results. The regressions include the full list of independent variables from Table V, but for brevity, we only report the coefficients on the measures of pension fund and money manager demand. We find that both PF NRatio and PF DRatio are significantly associated with formation period returns and subsequent reversals, although PF NRatio is associated with larger formation period returns and larger subsequent reversals. We see a very different pattern for money managers. Specifically, there is no evidence that MM NRatio generates reversals, in fact, the coefficients are always positive (albeit statistically insignificant). However, holding MM NRatio constant, MM DRatio leads to significant reversals. The rows in bold indicate that PF NRatio leads to significantly larger reversals than MF NRatio. However, after controlling for NRatio, PF DRatio and MM DRatio have a very similar impact on stock prices. This finding suggests that the larger reversals associated with pension fund trading are not purely driven by large dollar volume order imbalances. Instead, the results suggest that correlated trading by pension funds is significantly less informative than correlated trading by money managers. 21

24 E. Pension Fund Trading and Stock Returns by Firm Size In this section, we investigate the impact of pension fund trading on small and large stocks separately. There are at least two reasons why pension fund trading may have a larger impact on smaller stocks. First, smaller stocks are less liquid and are subject to more arbitrage constraints. Second, reputational concerns and other agency problems are likely to be more severe in smaller stocks. Thus, relative to other institutions, pension fund trading may be particularly uninformed in smaller stocks. We place stocks into market capitalization quintiles based on NYSE breakpoints. We define stocks in the bottom 3 quintiles as small stocks and stocks in the top 2 quintiles as large stocks. We compute the decile ranking of PF NRatio and MM NRatio separately for both small and large stocks. We then run the cross-sectional regressions outlined in Table V on the subset of small and large stocks. Panal A of Table VII presents the results for small stocks. Pension fund trading has a substantial impact on the prices of small stocks. A one decile increase in PF NRatio is associated with a 150 bps increase in returns for small stock during the formation period (as compared to 87 bps for the full sample) and a subsequent two year reversal of 101 bps (as compared to 55 for the full sample). In contrast, the price effects of money manager trading do not appear to be more extreme for smaller stocks. Panel B of Table VII repeats the analysis for large stocks. Even amongst larger stocks, we find significant evidence of reversals after pension fund trading, although the magnitude is more modest. A one decile increase in PF NRatio is associated with a 27 bps reversal over the subsequent year. To further verify that pension fund trading is influential in larger stocks, we repeat the regression on the full sample of stocks, but now value-weight each observation by 22

25 the stock's market capitalization. The results from this regression, presented in Panel C, confirm that pension fund trading has a meaningful impact on the stock prices of large companies. Thus, while the price effects of pension fund trading are more dramatic in small stocks, they are also present in larger stocks. F. Why is Pension Fund Trading Less Informative than Money Manager Trading? The results thus far suggest pension fund trading is less informative than money manager trading. There are (at least) two explanations that are consistent with this finding. The first is the Differences in Resources hypothesis, which argues that pension funds simply have fewer resources (or less skill) than money managers. For example, internally managed pension funds (particularly public pension funds) may spend less money on research and may offer lower salaries to fund managers. If pension funds spend less resources on collecting and gathering information, then in an informationally efficient market (e.g. Grossman and Stiglitz (1980)), pension fund trading should be less informative than money manager trading. The second explanation, the Differences in Incentives hypothesis, argues that pension funds have different incentives than money managers, and these incentives induce them to engage in greater amounts of uninformed trading. For example, pension funds greater exposure to legal liability may result in pension funds buying stocks that appear 'prudent' even if they believe such trading will not generate abnormal returns. Similarly, the less convex performance-flow relationship provides a stronger incentive for career-concerned managers to mimic the strategies of their peers even though such strategies are likely associated with lower-expected returns. 25 To distinguish between these two hypotheses, we restrict our sample to money 25 Koch (2011) and Jiang and Verado (2011) find that the degree to which mutual funds copy their peers is negatively related to fund performance. 23