Capital Redeployment in the Equity Market *

Transcription

1 Capital Redeployment in the Equity Market * Huaizhi Chen Harvard Business School This draft: January 22, 2018 First draft: August 31, 2017 * I thank Lauren Cohen, Robin Greenwood, Dong Lou, Christopher Malloy, Christopher Polk, Andrei Shleifer, Erik Stafford, René Stulz, and Luis Viceira for their valuable comments and suggestions. I would also like to thank seminar participants at American University, Ohio State University, and the University of Delaware for their valuable input. This research was conducted during my post-doctoral fellowship supported by the Behavioral Finance and Financial Stability Initiative at Harvard Business School.

2 Capital Redeployment in the Equity Market ABSTRACT Payouts, in the form of dividends and buybacks, reached a height of almost a trillion dollars per annum in the recent years. A large proportion of these dollars are directly reinvested into the stock market. Drawing on data on mutual fund holdings, I show that capital repayments are accompanied by predictable excess returns in stocks connected to these payments, consistent with demand-driven price pressure. Due to the persistence of these capital return programs, abnormal returns accumulate over significant holding periods. Additionally, the exposure to capital redeployment by non-payout firms is associated with firm level issuances. While firms exposed to high levels of capital returns negligibly increase their own buyback and dividend activities, they significantly increase their issuance of equity through seasoned offers relative to other firms. JEL Classification: G10, G14, G23, G31, and G35. Keywords: Mutual Funds, Payout Policy, Dividend Policy, Stock Buyback, and Spillover Effects. 2

3 In recent years, publicly listed firms in the United States have distributed substantial amounts of cash to their investors through dividends, and increasingly through stock buybacks. Some financial market participants have suggested that these payments are fueling the booming stock market. The Wall Street Journal, for example, writes that stock buybacks and dividend payments are a key pillar supporting the bull market. 1 Such statements are puzzling in the context of classical finance literature. Theories of capital structure posit invariance between policies supporting payouts and non-payouts (Miller and Modigliani 1961) and (Black 1976). According to the invariance view, a dollar is worth the same as a dollar outside the firm, and a return of capital to investors is immaterial to value. In practice, cash payouts from equity firms are often redeployed by asset managers back into the stock market. Dividends are automatically disbursed to the portfolios holding the original stock. In mutual funds, dividends from the underlying stocks appear as cash in the portfolio. After a pre-arranged distribution date, the portion of Net Asset Value per mutual fund share (NAV) represented by the dividend dollars is reallocated as new shares if the mutual shareholders elect to keep these dollars within the fund; otherwise the dividend value per share is returned as cash 2. In the event of stock buybacks, where firms repurchase their own shares, participation by investors is less mechanical. However, much like dividends, buybacks transfer cash from public firms to investors, and in practice, these dollars end up reinvested in the stock market. Between 2010 and 2015, $4.25 trillion were distributed by US-allocated common stocks in the form of dividends and buybacks. By way of comparison, over the same period, investors deposited $744 billion into mutual funds. Given substantial evidence that flows influence stock prices 3, a natural hypothesis is that capital redeployment will influence prices through a downward slopping demand. Specifically, in this paper I propose and test a channel whereby capital redeployment leads to predictable patterns in stock returns. The mechanism is as follows: Public firms, by initiating capital return, transfers cash to investors who may be limited in their choice of investable assets. The cash deployment in turn drives up demand for certain stocks. 1 Wallstreet Journal Money Matters: Stock Buybacks Slow. Should We Be Worried? on August 21, Although the Investment Company Act requires funds to redistribute cash from both capital gains and dividends to the ultimate investors, most mutual funds have programs for investors to reinvest these proceeds automatically. Empirically, I find that 84% of these distribution dollars are reinvested in the mutual fund on average in my sample period. The passive outflow from distributions are captured as investor capital outflow. See Section 1.3 and Appendix A5. 3 See for example, (Frazzini and Lamont 2008), (Coval and Stafford 2007), (Lou 2012), and (Edmans, Goldstein and Jiang 2012). 3

4 Whether or not this influences prices is largely a question of how aggressively arbitrageurs can counteract this demand (Shleifer and Vishny 1992), (Shleifer and Vishny 1997). To test my hypothesis, I trace cash flows from dividend and stock buybacks to individual mutual fund portfolios. I estimate the redeployment of cash flow from payouts back into the equity markets through the quarter-to-quarter changes in fund portfolio holdings. I show that cash payouts predictably relate to changes in the mutual fund holdings of certain assets. Appetite for stocks with dividends and buybacks is persistent among mutual funds: a portfolio exposed to significant capital returns will continue to have high cash payouts by its underlying assets for many quarters. When a high capital return portfolio receives dividends, it keeps this cash predominantly invested in a predictable set of holdings similar to their existing assets. Such a fund, also participate in buybacks, and uses the proceeds to purchase other existing assets. In contrast, funds with low capital returns tend to decrease more holdings from quarter to quarter, have higher ActiveShares (Cremers and Petajisto 2009), and are much more likely to be benchmarked to a Growth Index. The aggregation of this trading effect to a stock is related to the investor flow induced price pressure shown in the literature (Coval and Stafford 2007), (Lou 2012), and (Edmans, Goldstein and Jiang 2012), but is much more persistent, and predicts cross sectional spread in asset price at a significant horizon. In summary, this paper finds that capital returns generate cross sectional demand due to constraints on the set of investable assets by asset managers. This source of demand has significant implications on secondary financing, a central role facilitated by the equity markets. Stocks associated with the highest demand from redeployment cash flows tend to persistently issue more common stocks than their peers; indicating that long term stock demand is central to a firm s financing decisions. The timing of capital redeployment is as follows. Following a payment of a dividend, a mutual fund receives cash, which is immediately recorded as an increase in net asset value (of course, if the stock drops in price ex-dividend, this offsets the change in NAV). On average, the dividend inflow is most correlated with purchase decisions within the following 4 to 5 quarters. A similar logic operates with respect to stock buybacks. A buyback program creates an exchange between asset holders and public firms on average through market clearing. The percentage decrease in the aggregate mutual fund holdings corresponds at a one to one ratio to the percentage decrease in shares outstanding for each stock during each quarter. While only some funds may choose to sell to a buyback, a stock s repurchase will transfer cash to the funds that 4

5 hold this stock, and in turn, induce redeployment by these funds on average. The fact that only some funds may sell a stock during a buyback event contains independent information from the average cash-flow expected from with these repurchases. The redeployment of capital return by mutual funds forecasts stock returns. I show this by aggregating capital returns to the stock level. Capital return induced price pressure, CIPP, is calculated for each stock by assuming proportional investment in existing assets (Frazzini and Lamont 2008)/ (Lou 2012) from capital repayments. This simple measure of cash induced price pressure assumes that the expected cash-flow from capital returns to each mutual fund portfolio is apportioned to the underlying according to each stock s ex-ante weights. The total expected cashflow from all mutual fund portfolios for each stock is then aggregated. The numeraire is chosen as the total holdings of each stock within the mutual fund industry. The final measure approximates the total redeployment cash flow from payouts to individual stocks in the aggregate US equity market using observable mutual fund holdings. The key innovation, for my price pressure test, is that I examine the correlation between CIPP and the returns of stocks that do not conduct capital returns. These growth stocks are attractive laboratories as they share redeployment inflows through investors but have not explicitly changed dividend or repurchase policies. Stocks associated with large amounts of capital returns tend to appreciate in the following quarters. One standard deviation in CIPP implies 0.93% (t = 2.08) excess return in the underlying stock in the following quarter, which increases to 1.03% (t=2.44) once I control for contemporaneous price pressure from investor flows. Because CIPP is extremely persistent and that, absent of strong incentives to avoid cash, a fund can purchase stocks over a moderate horizon, stocks associated with capital returns predictably experience excess returns for multiple quarters. One standard deviation in CIPP forecasts 0.85% (t = 2.67) increase in excess returns over an entire year. The price predictability associated with capital returns indicates a potential calendar time trading strategy. Quintile portfolios sorted on lag CIPP have large return spreads in the short to medium horizon. A strategy holding the top quintile and shorting the bottom quintile CIPP sorted portfolios (5 minus 1) of non-capital returning stocks yields a return of 3.01% (t = 3.16) per quarter. This strategy can be held for multiple quarters. The average quarterly excess returns for the 5 minus 1 and 5 minus 3 portfolios at varying holding period horizons are plotted in Figure 3. Both figures show that the excess returns of these long short portfolios revert to statistical insignificance 5

6 after holding horizons of over 3 years. These results are consistent with non-fundamental demand as excess predictable returns do partially revert for a specific cross section. However, this is also consistent with results on capital constraint and real effect (Lamont, Polk and Jesus Saa-Requejo 2000). Firms with low mechanical demand for their stocks may experience lower future returns through the same channel as captured by financing constraints. The next section of this paper associates this spill-over channel of return predictability with future issuance and payout changes. Non-payout stocks with the highest capital return induced price pressure do not significantly increase their own buyback and dividend payouts. Instead, I find that these stocks increase their own issuances relative to other firms at the 12 quarter to 48 quarter horizons. One standard deviation of measurable price pressure from capital returns is associated with an increase of 1 basis point of buybacks, 2 basis points of dividends, and 55 basis points of issuances each quarter averaged over a 12 month horizon. These results indicate that nonpayout stocks exposed to capital return induced price pressure tend to issue shares and limit their own future payouts. This is consistent with the view that issuances in the equity market are associated with long term persistent demand for certain stocks. The last section of this paper provide additional evidence for persistent redeployment demand hypothesis using two other empirical strategies. These strategies examine style investment indexing and cash mergers. In the first empirical strategy, I document the cumulative returns of stocks that are added to the S&P 1500 Value vs those that are added into the S&P 1500 Growth indices. An inclusion in the Value (Growth) Index indicate that this stock has a marginal higher (lower) value as calculated by a mechanical formula than the dropped stock. Stocks that are added to the S&P 1500 Value Index have higher persistent abnormal returns far after the inclusion date than the stocks that are added to the S&P 1500 Growth Index. In the second empirical strategy, I show that during quarter with significant aggregate cash merger activities, there is a predictable split in the returns of stocks exposed to these mergers and the returns of stocks not exposed to these mergers. Cash mergers operate similar to cash payouts; however these merger dollars are not very persistent at the portfolio level. I show that despite having abnormal one period returns, portfolios sorted on merger induced price pressure tend to strongly revert in the short horizon. This effect is absent in quarters with limited cash mergers. Contrasting the demand hypothesis of capital redeployment, works studying the informational content of capital returns dominate the existing academic literature. Beginning with 6

7 (Ross 1977) and (Bhattacharya 1979), many argue that capital return policy signals information about the underlying firm. The idea is that in order to overcome information asymmetry with investors, firms with strong expected cash flows commit to payouts because payouts are costly signals of these firms future opportunities, while firms with weak expected cash flows cannot commit to a return policy. In equilibrium, investors price firms by their future cash flow as implied by their current payout policy. Consistent with these theories, empirical research finds that stocks appreciate significantly during announcements of capital return (Vermaelen 1981). However, these stocks also tend to have abnormal returns long into the future- up to 4 years (Ikenberry, Lakonishok and Vermaelen 1995), which indicates either that investors underreact to the information content of payouts or that signaling may not fully explain the price discovery mechanism. These signaling theories also neglect the popularity of buybacks as a form of capital return. The tax shield advantages of buybacks over dividends were eliminated in , and yet buybacks still became more popular than dividends as the preferred method of capital return. In surveys of CEOs, there is a widespread consensus among executives of public firms that stock buybacks are advantageous over dividends because they are a more flexible way of returning capital (Graham and Harvey 2002) and (Brav, et al. 2005)- firms can decrease their buyback activities without suffering significant investor outcry. These stylized facts suggest the existence of unexplored mechanisms originating from capital payouts. Signaling alone cannot rationalize price predictability, and must be joined with a form of investor under-reaction to explain the main empirical facts outlined in the paper- that stocks connected to capital payouts tend to outperform into the future. If firms signal their type through payout policies, investors must also underreact to this signal because prices are very slow to adjust. While I cannot rule out all potential mechanisms involving signaling and investor under-reaction that are consistent with observed price effect on stocks, the results in this paper can reject several explicit versions of this mechanism. The most basic form of signaling requires firms to pay dividends or conduct stock buybacks to signal their own underlying fundamentals. Since capital return implied price pressure affects firms that do not return capital, this version of the signaling hypothesis cannot explain the documented return predictability. Another version of the signaling/under-reaction hypothesis states that the capital return program by one firm signals 4 The Jobs and Growth Tax Relief Reconciliation Act of 2003 effectively ended the spread difference between the capital gains and the dividend tax rates. 7

8 future capital return by other firms operating in closely related industries (Massa, Rehman and Vermaelen 2007). There is return predictability on stocks connected to capital returns because they are undervalued in accordance to the available information about their peers, but investors are slow to react to this information. There are multiple reasons why the price effect found in this paper is incompatible. Here are a few first order ones: 1) I show that the degree of future capital return by firms exposed to repurchases and dividends is negligible and quantitatively miniscule with their respective increase in prices. 2) Since announcements to change dividend policy and to conduct open market buybacks occur potentially a year prior to the actual program, the signaling hypothesis indicates that the timing of the price predictability on the connected firms should follow the timing of the firm announcements. The documented price effect coincides with the timing of the actual cash redeployment activity. 3) If stock payouts are a signal of the profitability of related firms, then this signal is available to all investors. Instead, I observe that investors with capital return inflows significantly scale up these holdings over investors with low capital return inflows. 4) If the return predictability of stocks associated with capital returns is simply an under-reaction to information contained in the buybacks of similar firms, then the price effect should be strictly positive. A set of related studies investigates the timing of stock issuance and buybacks, the latter of which is a large component of capital return. These works conclude that firm managers initiate stock repurchases (issuance) when they believe their firms are undervalued (overvalue) or when they have incentive misalignment with investors (Loughran and Ritter 1995), (Baker and Wurgler 2000), and (Kahle 2002). I abstract from the timing of buybacks by focusing on firms that do not conduct stock buybacks and shed light on the mechanism of capital redeployment. However, a study in the field of stock market timing that is particularly related to this paper is (Greenwood and Hanson 2012), which finds that firms with negligible buybacks and issuances have factor returns correlated with the net issuance pattern of firms with similar characteristics. The empirical results presented in paper are consistent with their finding as investors tend to have style portfolios related to stock characteristics; however, with a bottom up approach, this paper sheds light on the underlying pricing mechanism in several ways. 1) It documents the association of investor portfolio rebalancing patterns with a style characteristic (capital return), and that this rebalancing pattern is linked to return predictability. 2) It shows that dividends, in addition to buybacks, have 8

9 predictive power on the returns of related firms. 3) It documents the very long-term reversal of excess returns in these non-capital-returning firms. A well-developed stream of corporate finance literature argues that a firm s internal capital markets may not efficiently allocate resources to the most profitable divisions. Internal segments that best use capital tend arrive at suboptimal allocations. The resources given to an internal division depends on the profitability of other internal segments. Profitable segments, in effect, subsidize unprofitable ones. Noted papers in this literature include (Berger and Ofek 1995), (Shin and Stulz 1998), (Scharfstein and Stein 2000), and (Ozbas and Scharfstein 2009). This paper follows literature to investigate the quantitatively significant reallocation of corporate profits outside of the firms through investors. While it may not be an extreme surprise to the followers of this corporate finance literature that external capital reallocation may also be inefficient for portfolio management, by studying the external reallocation of corporate payoffs, I extend boundaries of this channel to investor ownership and show that this redeployment is associated with significant price dispersion. Mutual funds tend to purchase stocks that conduct little cash payouts using the cash flows from stocks that do it extensively. This paper is also related to a literature on how investors use dividends. The fact that exposures to dividends is a persistent characteristic of money management funds complements the dividend disconnect phenomenon, which describes the tendency of investors- mutual fund and otherwise- to treat dividend returns differently than price returns, documented in (Hartzmark and Solomon 2017). The rest of this paper is divided into five sections. The next section analyzes the capital redeployment mechanism at the investor level and shows how dividend and buyback cash are channeled through mutual funds. Section 2 demonstrates price predictability by aggregating the capital return variables into the stock level and calculating the capital return implied price pressure on each stock. I show that this variable is extremely persistent and particularly informative about stock returns at the medium one to four quarter horizons. The positive price effect reverts after significant holding periods. Section 3 reviews the characteristics of the stock receiving redeployed capital and show that the spread between returns is quantitatively large compared to the changes in future payout policies. However, firms under the influence of redeployed capital tend to significantly increase their own issuance activities. Section 4 documents empirical strategies using 9

10 Style Indexing and Cash Mergers that provide additional evidence toward the redeployment demand hypothesis. Section 5 concludes and discusses the results of the paper. 1. Capital Return and Mutual Funds This section examines the economic magnitudes of capital returns and how this source of cash flow is related to investment by money managers. 1.1 Capital Return in Aggregate In aggregate, capital returns are significant and persistent sources of cash inflow for investors. To show this, I obtain stock-related data from the Center for Research in Securities Prices (CRSP) Stock Security Files. Dividends and buybacks are calculated quarterly. Dividend yield per stock is calculated as the difference between total returns (Ret i,t ) and price returns (Retx i,t ) each quarter, that is Divy i,t = Ret i,t Retx i,t. Percentage buybacks is calculated as the decrease in shares outstanding. The lower limit for the decrease is set to -10% to restrict the exposure of the sample to potential mergers and acquisition. Specifically, this is Buyback i,t = SharesOutstanding i,t ( SharesOutstanding i,t [ 10%, 0)), where SharesOutstanding i,t is the percentage change in split adjusted shares outstanding. The dollar values of dividends and buybacks per stock are calculated by multiplying the stock s buyback and dividend yields with its t-1 market capitalization. Equity mutual fund flows, which have been previously demonstrated in the finance literature to affect asset returns through demand, are calculated as (TNA i,t TNA i,t (1 + Ret i,t ) MGN i,t ), i where MGN i,t is a compensating term for fund mergers. Figure 1 plots the aggregate capital returns from common stocks traded on the AMEX, NASDAQ, and NYSE exchanges and the aggregate net investor capital flow for equity funds. 10

11 Between 1990 and 2002, annual buyback cash flow ranged from $17 to $159 Billion, while dividend payouts ranged from $92 to $161 Billion. Capital return programs increased dramatically after , paying investors $160 to $497 Billion with buyouts and $172 to $419 Billion with dividends. Investor flows to equity funds, which have been demonstrated to affect stock prices through demand (Coval and Stafford 2007) and (Lou 2012), is plotted as a benchmark. The aggregate capital return from payouts accumulates at a yearly horizon to a significantly larger magnitude when compared to investor flow to equity, which flattens out at the yearly horizon. For instance, between 2010 and 2015, 4.25 trillion dollars of capital returns accumulated in net from public firms compared to 744 billion dollars of investor inflow to mutual funds. The relative magnitude of firm payouts during this period is almost 6 times as high as investor capital flow. In Figure 2, we also observe that most of the capital returned come from only a small percentage of publicly traded firms. The top panel of Figure 2 shows that 80% of the stocks traded participate in quantitatively insignificant amount of capital return; and on average, 5% of stocks conduct more than 50% of the capital return to investors in the financial sector. The main implication of the demand hypothesis of redeployment is that non-payout stocks most exposed to capital return dollars will have higher predictable demand and command higher returns than those are the least exposed to these dollars through investor portfolios. I will be conducting return predictability tests on specifically assets that do not return capital to get a clean test of demand driven price pressure. In summary, payouts from public firms are significant and large sources of inflow capital into the equity markets. The amount of cash being contributed from these capital return programs are considerably more persistent and less volatile in aggregate than measures of investor demand. The cumulative magnitude of capital return sizably surpasses capital flows from investors are a significant source of aggregate demand. 1.2 Exposure to Payouts by Individual Mutual Funds In this section, I show that professional investors (open-ended mutual funds) vary significantly in their exposure to capital returns. This capital return exposure is a persistent 5 The Jobs and Growth Tax Relief Reconciliation Act of 2003 reduced the overall tax rate for capital gains and dividends. 11

12 characteristic of each portfolio. A fund exposed to large amounts of cash payouts continues to be exposed to new cash payouts, while a fund with little capital return continues to have little capital return exposure. In addition to the previously described data, I use the N-Q quarterly mutual fund holding filings recorded by the CDA/Spectrum database and the Center for Research in Security Prices (CRSP) Survivor Bias Free Mutual Fund database for this section. Domestic open-ended mutual funds are required to disclose their equity holdings each quarter in the N-Q filings and these filings are captured in the CDA/Spectrum database. The holdings data is matched to the Center for Research in Security Prices (CRSP) Survivor Bias Free Mutual Fund database for information on fund style, total net assets, monthly returns, expense ratios, and other fund characteristics. The matching between CDA/Spectrum and CRSP is conducted using MFLinks provided by the Wharton Research Data Services (WRDS). Each portfolio s exposures to dividends and buybacks are calculated as the pro rata implied yield of the portfolio holdings, that is and Div_Flow j,t = Weight i,j,t 1 Divy i,t, i Buy_Flow j,t = Weight i,j,t 1 Buyback i,t i for portfolio j at quarter t. Weight i,j,t 1 is the weight of asset i in portfolio j at t-1. The calculation can be interpreted as the dollar dividend return and dollar pro rata buyback for each portfolio as a percentage of the portfolio s Total Net Assets. These two measurements are significantly correlated (ρ = 0.22) for equity fund portfolios implying that a fund exposed to dividends are also exposed to buyback dollars. Table 1 describes the summary statistics on the capital return exposure experienced by equity funds in my sample. An average inflow from dividends is 0.34% of a mutual fund s TNA each quarter, while the pro-rata dollar amount of buyback dollars is 0.42%. These sources of cash are larger in the second half of the sample. The average exposure to these capital return programs is similar in magnitude to the average investor flow, which is 0.65% of the portfolio s TNA on average. However, capital returns are significantly less volatile and more predictable from quarter 12

13 to quarter. The autocorrelation coefficients show that exposure to capital return in each mutual fund can be forecasted up to 1 year with significant accuracy. In summary, there is significant heterogeneity among investors in the amount of cash payouts they receive. This heterogeneity is persistent for individual mutual funds. I investigate where all the cash returns are deployed in the next section. 1.3 Changes in Holding Values This section shows that capital returns require investors to redeploy a significant amount of cash into assets. Dividends are invested into holdings in the same quarter as they are recorded. Buyback programs through market clearing redeems shares from these asset managers at roughly a one to one basis. Cash from firm payouts are redeployed by investors into assets. In practice, mutual funds have flexibility in managing their dividend and asset selling proceeds. While the Investment Company Act regulates asset managers to return capital gains and dividends to investors at prearranged distribution periods for taxation purposes, each individual fund has its own private distribution management methods. A single fund can keep dividends invested in cash prior to a distribution event; it can invest immediately and re-obtain the needed distribution cash by selling assets before a set distribution date; or it can hedge its cash obligations with option instruments. I show in this section that, empirically, mutual funds invest dividend cash into assets as this cash arrives, and leave insignificant amounts as precautionary cash for distributions. This may be because the majority of dividend and capital gain distributions are automatically reinvested by fund investors. Appendix A5 approximates the distribution based flows from investors by calculating the difference between NAV (net asset value) price-returns and net mutual fund returns. The amount of distribution based inflow is roughly 84.1% of the total distribution (both dividends and capital gains) released by a mutual fund; that is, the majority of distribution dollars are re-invested in the original fund itself, and only about 15.9% of the distribution dollars are taken out by investors. In the existing literature on mutual fund flows, the cash redistribute to investors are commonly captured as investor outflows. For instance, (Coval and Stafford 2007), (Frazzini and Lamont 2008), and (Lou 2012) make no distinction between capital outflow due to distribution and outflow due to investor redemption of fund shares. Outflows as measured through the 13

14 difference in Total Net Assets ignore the distribution response by money managers and their investors to dividends. This may introduce a potential bias if such a passive redemption of cash is different from the active mutual fund shares redemptions. I calculate the change in asset holdings per portfolio to describe its reinvestment process. The change in the CDA/Spectrum reported equity holdings of fund j between quarter t-1 and t is calculated as Holding all j,t, Holding all j,t = N+M i=1 N i=1 Price i,t Shares i,j,t 1, Price i,t Shares i,j,t 1 where stocks 1 through N exist in the portfolio at t-1 and stocks N+1 through M are added between t-1 and t. all The Holding j,t variable can be naturally interpreted as the percentage difference between the value of total assets held at time t and the value of total assets held at time t-1 if these assets were held to t. Dividends captures a significant source of cash flow for mutual fund portfolios. Panel a of Table 2 shows that sorting mutual funds on their dividend exposures captures a dimension of portfolio growth that isn t captured by investor flow alone. Only 34.7% of mutual funds in the lowest quintile of dividend exposure increase their asset holdings, contrasting 39.7% of funds in the highest quintile. Funds with high exposures to dividend inflows also are less likely to decrease their asset holdings. About 52.7% of funds in the lowest quintile reports a decrease in asset holdings compared to 46.7% in the highest quintile. When an investment company receives dividends from its underlying holdings, it can increase its holdings immediately, or wait to invest this cash. The average reinvestment timing of this cash flow is largely an empirical question. I tabulate the average correlation between growth in holdings ( Holding all j,t ) and dividend cash flow (Div_Flow j,t ) for various horizons in Appendix Table A8. The unconditional correlation between dividend cash flow and holdings growth is the highest at the quarters 1 to 5 horizon rather than during contemporaneous quarter. Buyback programs, in effect, exchange cash from public firms for shares with portfolio managers. Portfolios generally decrease their holdings of the company shares when buyback programs are conduct. A stock that is currently conducting a large buyback program is more likely to be sold by its existing shareholders than a stock with small or no buyback programs. Table 2 14

15 panel b shows that for a stocks currently in the highest quintile of buyback size, 31.5% of their current mutual fund holders sold in net, while only 25.6% bought in net. This is in contrast to funds holding stocks that do not conduct buyback programs, where only 25.9% were net sellers and 28.6% were buyers. Figure 5 plots coefficients from the panel regression of the change in aggregate mutual fund holdings of stocks and the decrease in shares outstanding ( Buyback ) over the past 4 to the next 4 quarters. Not surprisingly, the equity market clears and there is an immediate and large reduction in aggregate mutual fund holdings coinciding with the timing of the buyback. As mutual funds sold stocks with buybacks on aggregate, portfolio holdings of assets without buybacks must be increasing else total holdings would decrease. This indicates that all of fund proceeds from selling stocks to buyback programs are reinvested into other assets. The results in this section finds that mutual fund exposure to dividends and buyback dollars are significant sources of cash flow for reinvestment. If such investors are constrained in their reinvestment decisions, then these cash-flows will drive demand in the cross section of equities. I investigate where these dollars are invested in the next section. 1.4 Proportional Investment into Assets If there are no trading costs or market frictions, we expect portfolio managers to optimize entirely based on their expectation of risk and return on assets. The degree of capital return based inflow should have very little information on the type of assets a fund purchases. A stock revealed to be undervalued would likely be acquired by a fund regardless of the fund s exposure to payout programs. However, given that funds have individual style mandates and there are liquidation costs to rebalancing, a practical benchmark, which is formally tested in this section, may be that capital return inflow is invested into a portfolio s existing holdings. I combine the cash-flows from both dividend payouts and buyback payouts into a single variable for each mutual fund. That is: Cap_Flow j,t = Weight i,j,t 1 Dividends i,t + Weight i,j,t 1 Buyback i,t. i Div_Flow j,t Buy_Flow j,t The degree to which a fund portfolio is exposed to cash flows from capital return is correlated to several characteristics that captures the investment style and mandate of each mutual fund. I join my data with ActiveShare and index benchmarks provided by (Cremers and Petajisto, 15 i

16 2009) in the panel a of Table 3. I observe that funds most exposed to capital returns tend to have lower ActiveShare measures and are more likely to be benchmarked to a Value Index; funds having the least capital return exposure tend to have higher ActiveShare measurements and are more likely benchmarked to a Growth Index. The results indicate that the ex-ante variation in asset portfolio weights is associated with benchmarking and indexing activities. A non-payout stock strongly linked to a value index will be exposed to capital return cash-flow if these funds redeploy cash predictably. I ll show in Table 3, panels b and c, that mutual funds with high capital returns invest predictably in stocks. I find that mutual funds with high cash flows from capital returns will 1) keep invested in their existing assets and 2) purchase stocks similar to their existing holdings, that is stocks held by other funds with high capital return exposure. To show that funds with returned cash tend to keep their existing investments, Table 3 panel b compares the changes in the top 5 largest stock positions from mutual funds with low capital inflow with those held by mutual funds with high capital inflows. Although funds in both groups tend to scale down their existing positions on average, there is a large differential in scaling between the two types of funds. On average, mutual funds with the lowest capital returns tend to scale down their largest positions by over 15%, whereas funds with the highest tend to scale down by only 7%. High capital returning portfolios tend to keep their existing holdings. To show that a mutual fund s total purchases are predictable, Table 3 panel C regresses the gross buying of stocks, indexed by i, by the ex-ante percent of assets held by other mutual funds with low to high capital return exposures. Here I group mutual funds into 5 bins based on their exposure to capital returns. I calculate the gross buying of each stock in each bin as the total positive change in holdings by the mutual funds in each bin, similar to the buying measure in (Coval and Stafford 2007). Specifically, Buying i,t,bin = j Max( Holding i,j,t, 0) j bin t. j Holding i,j,t 1 The buying of assets by each bin, Buying i,t,bin, is significantly related to the prior percentage of asset i held in the same bin, that is: PercHold i,t,bin = j Holding i,j,t 1 j bin t. j Holding i,j,t 1 16

17 In the panel C of Table 3, I find that the best predictor for asset purchased is the ex-ante holding of each stock in each bin. That is, funds receiving large (small) capital returns primarily buy assets already held by funds receiving large (small) capital returns. To summarize, I find that dividends represent significant cash inflows to and that buyback programs contribute significant amount of cash requiring deployment from mutual funds. This cash is deployed into assets already held by similar funds based on their cash return characteristic. Although funds do not literally scale up their existing positions using cash inflows, apportioned reinvestment to existing holdings by mutual funds of a certain exposure to cash returns is a reasonable approximation to gauge individual stock demand. 2. Stock Price Pressure Given that cash from capital returns stay predominantly invested (in net) in stocks linked by existing mutual fund holdings, it is natural to ask, is there a price effect on these stocks? For market participants with excess inflow from capital returns to keep purchase new assets, they will only be able to scale up these holdings through purchases if these assets are supplied by price sensitive market participants. This occurs when stock prices increase to the increased demand. In this section, I show that stock prices predictably correlate with this capital redeployment based inflow mechanism. 2.1 Capital Return Induced Price Pressure The results in Section 1 demonstrate that portfolios exposed to capital returns predominantly invest in assets held by similar portfolios. Stocks held on average by investors with high (low) levels of capital return, should experience high (low) levels of investor demand. The degree to which price correlates with this demand depends on the stocks price. To empirically proxy this price effect, I aggregate capital return based inflow to the stock level by assuming proportional investment in assets. While mutual fund portfolios do not literally reinvest proportionally into their existing assets, as there is significant turnover and investment into new positions, this is a simple and commonly used assumption in prior measures of flow exposure by stocks; for example, (Frazzini and Lamont 2008), (Lou 2012), and (Coval and Stafford 2007) use 17

18 the assumption of proportional flow exposure by assets but none of these studies observe significant increases in existing positions when given capital inflows. An alternative measure of capital return induced price pressure- as the percentage of assets held in the top quintile mutual funds exposed to capital returns- gives qualitatively the same results in this section. Like prior measurements of investor flow induced price pressure in the existing literature, Capital-return Induced Price Pressure is calculated as CIPP i,t = j SharesHeld i,j,t 1 SharesHeld i,j,t 1 Cap_Flow j,t where SharesHeld i,j,t 1 is the number of shares in stock i held by mutual fund j at t-1 and Cap_Flow j,t is the expected cash flow from capital returns experienced by portfolio j from t-1 to t, Cap_Flow j,t = S Div Weight i,j,t 1 Dividends i,t + S Buy Weight i,j,t 1 Buyback i,t. i Div_Flow j,t i Buy_Flow j,t S Div and S Buy are scaling coefficients chosen to be 1 and 1 respectively. I assume that there isn t quantitatively significant overlap between the stocks that are sold off during a buyback and dividend returns. Alternative calculation of Cap_Flow j,t using different positive scaling coefficients of dividend and buyback exposure do not change the results qualitatively. This is because both dividends and buybacks individually forecast returns (Appendix A3). The price pressure variable is the aggregation of cash flows from capital returns apportioned by ex-ante portfolio weights. An alternative way of writing CIPP is simply: CIPP i,t = j (Cap_Flow j,t TNA j,t 1 ) weight i,j,t 1. j Price i,t 1 SharesHeld i,j,t 1 That is, CIPP for each stock i, is total the dollar cash flow from Capital Returns to each portfolio j apportioned its respective portfolio weight, divided by the total value of this stock held by all mutual funds. Table 4 contains summary statistics on CIPP, and FIPP - the flow induced price pressure generated by assuming proportional investment of investor flow in existing assets. The crosssectional spread between high CIPP and low CIPP stocks is magnitudes smaller than the spread 18

19 in FIPP ; however, there are several reasons to suspect that CIPP can be significantly correlated with stock-level return. 1) Much like capital return on the portfolio level, CIPP for each stock is extremely persistent, and is predictable by its lagged variable at a 1 year horizon whereas FIPP at the same horizon, is quantitatively unforecastable. 2) Cash from capital returns stay predominantly invested (in net) in stocks linked by existing mutual fund holdings. 3) While investor flows only affect mutual funds, capital return by firms affect all participants of the financial market. The redeployment by investors and the predictable reinvestment of inflows observed in the paper can very well extend to all existing institutional investors; and the price pressure measure can be interpreted as a rough proxy of capital redeployment demand across all investors. I conduct several return predictability tests using this capital redeployment inflow variable. For these tests, I restrict the sample of public common stocks traded on the AMEX, NASDAQ, and NYSE exchanges in two ways. 1) Only stocks with no dividend payments in the past year and no stock buybacks in the past 5 years are used. 2) Stocks with market capitalization less than the tenth percentile of NYSE firms and the bottom decile of stocks ranked on mutual fund ownership are excluded to eliminate micro capitalization and liquidity issues. The final firms in my sample do not explicitly commit to capital returns, either through dividends or stock buybacks, and are large enough to abstract from simple microstructure related concerns. There are 61,845 stockquarter observations left to serve as a clean laboratory for testing the effect of capital inflow induced price pressure. In the Appendix Table A4, I relax the first restriction on stocks - that the sample filters out firms with significant capital return - to demonstrate that the identified pricing phenomenon is generalizable to the entire cross section of stock returns. 2.2 Fama Macbeth Regressions Stock prices are significantly correlated with capital return inflows. CIPP is associated with significant contemporaneous stock level returns, and also forecasts excess returns at the 1 quarter and 1 year horizons. This is because CIPP is extremely persistent, the lag value of CIPP forecasts capital return induced price pressure for many quarters into the future. In this section, I conduct Fama Macbeth regression analysis of returns on CIPP and various common characteristics (Fama and MacBeth 1973). A single standard deviation of CIPP forecasts 0.93% (t = 2.08) increased excess return in the following quarter, and an average quarterly return 19

20 of 0.85% (t = 2.67) over the following year. The predictability is increased to 1.03% (t = 2.44) and 0.93% (t = 3.47) once contemporaneous flow induced price pressure FIPP is added as a control. 2.3 Calendar Time Portfolios and Reversal The Fama-Macbeth regressions indicate a particular calendar time strategy. I sort stocks into calendar time portfolios using CIPP. Quintile portfolios are formed each quarter and are held for multiple quarters in overlapping portfolios following (Jegadeesh and Titman 1993). As shown in Table 7, the top quintile portfolio rebalanced quarterly and held for 1 quarter experiences a 4- factor adjusted excess return of 1.85% (t = 3.01), while the lowest quintile portfolio experiences excess return of -1.16% (t = -1.76). A strategy shorting the lowest quintile portfolio and holding the highest quintile experiences a return of 3.01% (t = 3.16) each quarter. A strategy that longs the top portfolio and shorts the middle (3 rd quintile) portfolio experiences a return of 2.36% (t = 2.82). CIPP continues to forecast excess returns in overlapping portfolios for multiple horizons. At the one-year horizon, the top quintile portfolio has a risk-adjusted alpha of 1.71% (t = 3.00) each quarter, while the bottom quintile portfolio obtains -1.16% (t = -1.83). The long-short strategy at this horizon generates an excess return alpha of 2.86% (t = 3.14) per quarter. Return predictability persists significantly over multiple periods. This contrasts the immediate demand pressure phenomenon found in the existing literature. The investor flow induced price effect begins reverting immediately after its measurement date (Frazzini and Lamont 2008). The persistence of this price effect is likely due to the length and scale of capital return programs, which usually last multiple quarters if not multiple years. A stock currently receiving capital redeployment induced demand will likely continue to receive this price pressure over a significant horizon. This persistent demand would push stock prices further from some fundamental value for multiple quarters or even years. Reversals of prices toward fundamental value may occur only after a significant run-up or when this source of demand fades. The calendar portfolios experience a reversal of their abnormal return patterns at the very long holding period horizon. Figure 3 records the average quarterly risk-adjusted returns of strategies that long the top quintile portfolio and either short the bottom or the mid quintile portfolio over various holding horizons. For each horizon, no new overlapping calendar portfolio 20

21 is initiated unless it can last the whole holding horizon to Q (for example, a new portfolio is not initiated if the holding horizon is 5 and the formation date is Q1 2015). In summary, the abnormal returns associated with capital returns in the moderate horizon and its long-term reversal is consistent with a demand channel of capital redeployment. 3. Connected Stocks and Corporate Structure This section describes the characteristics of stocks influenced by capital redeployment. First, I summarize the contemporaneous relationship between capital redeployment demand and stock characteristics such as value and size. Second, I summarize the future relationship between the stocks linked to capital redeployment and their future cash payout and issuance patterns. Panel a of Table 7 reports the average market equity and average book equity of the stocks in the calendar time portfolios. Consistent with their factor loadings, the portfolio at the top of the quintile tends to contain larger stocks on average. In 1990, the average market cap of the formation portfolio at the top quintile is $2.079 billion, while that of the lowest quintile is $3.424 billion. The distribution of stock sizes became more skewed to the top quintile over time. In 2003, the average market cap of the formation portfolio at the top quintile increased to $ billion, while that of the lowest quintile decreased to $1.470 billion. This trend is followed to Q Panel b describes the capital return policies of the firms in each portfolio over time. If mutual fund investors are rationally responding to capital payouts in certain firms by purchasing similar stocks with the expectation of future payout, then we should see significant capital return in the high CIPP portfolios. The highest quintile portfolio according to CIPP does initiate more capital return and more dividends over the 6 years and 12 years after the portfolio formation period. However, Panel b of Table 7 indicates that the programs initiated by these firms are extremely marginal, and economically insignificant. Despite experiencing cumulative returns of over 11% in the first 12 months of the holding period, these firms on average only bought back 0.047% more of their stock and increased total dividend yield by 0.048% over the lowest quintile portfolio over 6 years. This is decreased to 0.006% in buybacks and 0.030% in dividend payments over a 12-year horizon. The magnitudes indicate that while CIPP captures some potential increases of capital return programs, the marginal increase cannot be the source of the significant price effect. 21

22 Instead, I find that these firms strongly associated with capital redeployment tend to have higher persistent issuances over time. A stock in the top quintile portfolio sorted on CIPP has 0.52% higher change in quarterly issuances than the bottom quintile portfolio in the 6 year horizon. The issuances levels for both portfolios are also plotted in Figure 6. We observe that stocks in the bottom quintile portfolio significantly decrease their issuance over time compared to a stock at the top quintile portfolio. In Table 8, I perform regression analysis to see the average correlation between CIPP and changes in buyback, issuance, and dividend activities. Once I control for characteristic such as size, past issuance, and past returns, there is no significant correlation between capital return spillover and a firm s own capital return activity. However, in contrast, this price spillover mechanism is significantly correlated with future issuance activities both in statistical significance and economic magnitudes. One standard deviation of CIPP implies an increase of 0.55% (0.57%) shares outstanding per quarter over 12 (48) quarters. This is significantly larger than the economic association of this variable with cash payouts. In summary, stocks with this spillover channel of induced price pressure only marginally increase their own payout activities. In contrast, they significantly increase their equity issuances relative to other non-payout stocks. The empirical facts documented in this section are consistent with two potentially non-mutually exclusive hypotheses on capital market redeployment. The first is that firms are opportunistic, they tend to issue stocks when there is higher demand from the equity markets. The second is that high capital market demand can relax financing constraints, whereby firms issue new shares for potential future projects. Both hypothesis ties long term capital market demand for stocks to firm level decisions on financing. 4. Evidence from Indexing and Cash Mergers This section provides additional evidence for the capital redeployment mechanism through Style Indexing and Cash Mergers. 4.1 Style Indexing 22

23 Mutual funds that are highly exposed to capital returns tend to be more likely benchmarked to a Value Style (see Panel a of Table 3). Prior works have shown that indexing seemingly induces correlation in the returns of stocks to certain styles. (Boyer 2011) documents that index labels cause stocks to co-vary, potentially more so than what fundamentals should dictate, through trading and increased holdings in specific fund portfolios benchmarked to these styles. The implication of benchmarking is that the inclusion of a stock into a value style index, would shift this stock into portfolios indexed by a value benchmark, and in turn expose this stock to redeployment dollars in the long term. An inclusion of stock into a growth index, would shift this stock into growth portfolios, but not expose this stock to redeployment cash flow as much as a switch into a value index. I exploit this variation by showing that the inclusion of stock into a Value-Style Index induces higher persistent abnormal returns than the inclusion into a Growth- Style Index in the months after the inclusion. The empirical strategy is as follows. Standard & Poor s, a provider of indices, uses a mechanical formula for dictating whether a stock constituent in its Composite S&P 1500 Index belongs to the Value or Growth versions of this composite. Several characteristics are inputs to calculate a single value using the defined formula. A stock will be switched to the Value (Growth) Index if its formula value is marginally greater (lower) than the next highest (lowest) stock. As previously described, the stock switched to a Value index will increase its exposure to persistent redeployment dollars more so than a stock switched to a growth index. I obtain index constituents using the Compustat Index database between Q and Q There are 4,692 stock inclusions into either the Growth or the Value index during this period. I plot the event horizon plot of cumulative abnormal returns (returns above the market) for stocks for 48 month after a switch into a Value and a Growth Index category in Figure 6, requiring that the stock can be observed for all 48 months. We observe that an inclusion into a Value index is accompanied by higher persistent abnormal returns than an inclusion into the Growth index, consistent with higher persistent demand based pricing pressure. The average differences between the cumulative abnormal returns of a stock added to a Value index and that of one added to a Growth index is 6.25% (t=1.85) at 48 months; if we exclude dividend payments and simply examine price returns, the difference is significant at 8.27% (t=2.47). 4.2 Cash Mergers 23

24 Cash mergers provide additional evidence for the hypothesis that cash inflows from firms affect cross sectional returns. Cash mergers mechanically exchange stocks from investment portfolios for cash; equity funds receiving these cash windfalls will necessarily reinvest these dollars. One feature of cash mergers, in aggregate, is that they are only substantial during several quarters, and do not persistently drive cash flow into investor portfolios. I plot the aggregate dollars from cash mergers in Figure 7a. I define a quarter as having high cash deals if the ratio of total cash merger dollars to the aggregate market cap is in the top 1/3 of all the quarters during this period. The rest are defined as low cash deal quarters. For the same cross section of stocks that pay no dividends and repurchase zero shares, I find that a measure of merger induced price pressure, MIPP i,t = j SharesHeld i,j,t 1 SharesHeld i,j,t 1 Merge_Flow j,t significantly forecasts returns in the following quarter only when this activity is high. Table 9 depicts the returns of quintile portfolios of stocks sorted on during high deal quarters and low deal quarters. We observe a significant cross sectional split during the high deal quarters for this particular cross section of stocks. During the low cash deal quarters, there is no such predictability. Additionally, I find that the returns from these mergers revert fairly quickly, consistent with non-persistent demand. In Figure 7b, I plot the event horizon plot of the cumulative daily returns of a strategy that longs the top quintile and shorts the bottom quintile portfolio during the quarter after the merger. In summary, I use two additional empirical strategies to provide evidence for demand originating from capital redeployment. In the case of style indexing, I use potentially exogenous variation in inclusions into the Value index to demonstrate a persistent price pressure originating from payouts. In the case of cash mergers, I use a none persistent, but potentially large source of cash flow for investor portfolios. 5. Conclusion This paper examines the redeployment of capital in the equity markets by following the capital return induced trading of asset managers, specifically mutual funds. The funds that receive 24

25 large amounts of capital payouts tend to invest predictably into stocks associated with their current holdings. This indicates a difference in the demand for stocks that are exposed to capital returns and the demand for ones that are not. By taking advantage of a cross section of stocks from firms that do not return capital but vary in their exposure to capital returns, this paper shows that capital return by public firms are associated with high demand for stocks connected through investor portfolios. Stocks connected to capital payouts tend to appreciate in the short to moderate horizon, and partially revert at the very long horizon. This price effect is consistent with a mechanical uninformative demand channel. This paper forwards and tests the hypothesis that buyback and dividend programs implicitly generate demand for related firms through the redeployment of capital back into the equity market. Existing finance literature indicates that the executives of public firms initiate stock repurchases for a variety of purposes- from following the belief that their shares are undervalued to acting on payouts incentives. However, there is very little reason that these executives might consider the stock prices and investment behavior of related firms when directing their own cash distributions. The spillover channel documented in this paper associates the changes in prices and capital structure of related firms to a manager s payout decisions in his own firm. If this priceeffect channel improves the competitiveness of these related firms, then capital return in the form of cash payouts may be viewed as having caused unintended consequences for the manager who initiated the cash payouts. The empirical results outlined here can be a useful account for corporate finance practitioners when they consider future capital repayments. This paper s limited scope also leaves out potential directions for future research. An important topic that remains is how issuances and demand in the financial markets are linked to real investment. Demand for equity assets may provoke issuances because issuances are strategic responses to arbitrage mispricing, or because issuances are symptomatic of a less constrained financial market. Firms issue either to exploit demand and collect cash for payouts, or to finance positive NPV investments. This paper documents a source of demand that is economically significant and extremely persistent for a large cross section of stocks. Given the length of most investment projects in the financial markets, researchers may have greater success in connecting real investment to the demand documented in this paper. 25

26 References Baker, Malcolm, and Jeffrey Wurgler "The Equity Share in New Issues and Aggregate Stock." The Journal of Finance Barberis, Nicholas, and Andrei Shleifer "Style Investing." Journal of Financial Economics Berger, Philip G., and Eli Ofek "Diversification's Effect on Firm Value." Journal of Financial Economics 37 (1): Bhattacharya, Sudipto "Imperfect Information, Dividend Policy, and 'The Bird in the Hand' Fallacy." The Bell Journal of Economics Black, Fischer "The Dividend Puzzle." The Journal of Portfolio Management 5-8. Boyer, Brian H "Style-Related Comovement: Fundamentals or Labels?" Journal of Finance 66 (1): Braun, Matías, and Borja Larrain "Do IPOs Affect the Prices of Other Stocks? Evidence from Emerging Markets." The Review of Financial Studies Brav, Alon, John R. Graham, Campbell Harvey, and Roni Michaely "Payout Policy in the 21st Century." Journal of Financial Economics Brown, Stephen J., and William N. Goetzmann "Mutual fund styles." Journal of Financial Economics Coval, Joshua, and Erik Stafford "Asset fire sales (and purchases) in equity markets." Journal of Financial Economics Cremers, Martin, and Antti Petajisto "How Active Is Your Fund Manager? A New Measure That Predicts Performance." Review of Financial Studies 22 (9): Edmans, Alex, Itay Goldstein, and Wei Jiang "The Real Effects of Financial Markets: Impact of Prices on Takeovers." The Journal of Finance Fama, Eugene F., and James D. MacBeth "Risk, Return, and Equilibrium: Empirical tests." Journal of Political Economy Frazzini, Andrea, and Owen A. Lamont "Dumb Money: Mutual Fund Flows and the Cross-Section of Stock Returns." Journal of Financial Economics Graham, John, and Campbell Harvey "How do CFOs make capital budgeting and capital structure decisions?" Journal of applied corporate finance

27 Greenwood, Robin, and Samuel G. Hanson "Share Issuance and Factor Timing." The Journal of Finance Hartzmark, Samuel M., and David H. Solomon The Dividend Disconnect. Working Paper. Ikenberry, David, Josef Lakonishok, and Theo Vermaelen "Market Underreaction to Open Market Share Repurchases." Journal of Financial Economics Jegadeesh, Narasimhan, and Sheridan Titman "Returns to Buying Winners and Selling Losers:." The Journal of Finance Kahle, Kathleen M "When a Buyback Isn t a Buyback: Open Market Repurchases and Employee Options." Journal of Financial Economics Lou, Dong "A Flow-Based Explanation for Return Predictability." Review of Financial Studies 25 (12): Loughran, Tim, and Jay R. Ritter "The New Issues Puzzle." The Journal of Finance Massa, Massimo, Zahid Rehman, and Theo Vermaelen "Mimicking Repurchases." Journal of Financial Economics Miller, Merton H, and Franco Modigliani "Dividend Policy, Growth, and the Valuation of Shares." The Journal of Business Ozbas, Oguzhan, and David S. Scharfstein "Evidence on the Dark Side of Internal Capital Markets." The Review of Financial Studies 23 (2): Ross, Stephen A "The Determination of Financial Structure: The Incentive-Signalling Approach." The Bell Journal of Economics Scharfstein, David S., and Jeremey C. Stein "The Dark Side of Internal Capital Markets: Division Rent-Seeking and Inefficient Investment." Journal of Finance 55 (6): Shin, Hyun-Han, and René M. Stulz "Are Internal Capital Markets Efficient?" Quarterly Journal of Economics 113 (2): Shleifer, Andrei "Do Demand Curves for Stocks Slope Down?" The Journal of Finance Shleifer, Andrei, and Robert W. Vishny "Liquidation Values and Debt Capacity: A Market Equilibrium Approach." The Journal of Finance

28 Shleifer, Andrei, and Robert W. Vishny "The Limits of Arbitrage." The Journal of Finance Vermaelen, Theo "Common Stock Repurchases and Market Signalling: An Empirical Study." Journal of Financial Economics

29 Figure 1. Annual Aggregate Capital Return and Equity Fund Flow The figure plots the quarterly aggregate capital return (buyback and dividend payments) in the CRSP universe of common stocks traded on the NYSE, NASDAQ, and AMEX exchanges; and net fund flow into the CRSP universe of Equity Funds. Buyback is calculated as the product of adjusted decrease in shares and quarter start prices. Firms whose shares outstanding decreases by more than 10% per quarter are ignored to avoid mergers. Dividend payment is dividend yield (the difference between total and price returns) times quarter start market capitalization. Equity flow is calculated from CRSP as the difference between TNA and quarter start TNA adjusted by fund returns. 29

30 Figure 2. Composition of Capital Return and Aggregate Market Capitalization from the Top Capital Returning Stocks. The top left panel depicts aggregate quarterly capital return (Dividend and Composite Buybacks) decomposed to levels by the top capital returning stocks. The top right panel shows the compositions of aggregate Market Capital as attributable to the top capital returning stocks. The bottom left panel depicts the fraction of aggregate quarterly capital return attributable to levels of top capital returning stocks. The bottom right panel depicts the fraction of aggregate market cap attributable to levels of top capital returning stocks. 30

31 Figure 3. Quarterly 4 Factor Alphas for a strategy that holds the top quintile and shorts either the bottom or mid quintile Capital- Return Induced Price Pressure stocks for varying holding period horizon. Only non-dividend paying stocks that have not had any stock repurchases in the past 5 years are used in the portfolio sort. Calendar time portfolios are only initiated in this figure if it can be held to the full 48 quarters. The sample period of returns is from Q to Q

32 Figure 4. The issuance of calendar time portfolios of stocks normalized at year 0. 32

33 Figure 5. Coefficients from the panel regression (stock, time) of percentage changes in mutual fund holdings against buybacks: 4 MFHolding i,t = α + β l Buyback i,t+l + ε i,t. l= 4 MFHolding i,t = (Shares i,j,t Shares i,j,t 1 ) j Shares i,j,t 1 j is the percentage change in the shares held by the aggregate mutual fund portfolios. The standard errors are clustered quarterly. 33

34 Figure 6. Indexing into S&P 1500 Value and S&P 1500 Growth. Cumulative returns are calculated by taking abnormal returns as the difference between stock total returns (left) and market returns; or stock price returns (right) and market returns. 1,274 value index inclusions and 1,275 growth index inclusions are used for this plot, requiring that the stock is observed for all 48 months. The difference between CARS of Value and Growth inclusion stocks at month 48 is 6.25% (t=1.85); 8.27% (t=2.47) without dividends. 34

35 Figure 7a. Cash Mergers in Aggregate between 190 and The top third of all the quarters in terms of aggregate cash mergers dollars to aggregate stock market cap is defined as high deal quarters. The rest are defined as low cash merger quarters. Figure 7b. Long short cumulative return of a portfolio that longs stocks sorted into the top quintile and shorts stocks sorted into the bottom quintile on MIPP i,t, which is defined as: where MIPP i,t = j SharesHeld i,j,t 1 SharesHeld i,j,t 1 Merge_Flow j,t, Merge_Flow j,t = Weight i,j,t 1. i CashMerger 35