Capital Redeployment in the Equity Market *

Huaizhi Chen

Harvard Business School

This draft: April 14, 2018

First draft: August 31, 2017

* I thank Malcolm Baker, Lauren Cohen, Robin Greenwood, Dong Lou, Christopher Malloy, Christopher Polk, Andrei

Shleifer, Erik Stafford, and Luis Viceira for their valuable comments and suggestions. I would also like to thank the

seminar participants at American University, AQR Capital Management, Northeastern University, Ohio State

University, University of Delaware, and University of Notre Dame 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.

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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 recent years. A large proportion of these dollars have been 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 equity issuances. While firms

exposed to high levels of capital returns negligibly increase their own buyback and dividend

activities, they are able to persistently issue stocks 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.

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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, notes 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 (NAV) per mutual fund share

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 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 retail cash flows influence stock prices,

a natural hypothesis is that capital redeployment influences prices through a downward-sloping

1 Wall Street Journal, Your Money Matters podcast: “Stock Buybacks Slow. Should We Be Worried?” August 21,

2017. 2 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 85% of these distribution dollars are reinvested in the mutual fund on average in my sample

period. The passive outflow from distributions is captured as investor capital outflow. See Section 1.3 and Appendix

A1.

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demand curve.3 Specifically, in this paper I propose and test a simple channel whereby capital

redeployment leads to predictable patterns in stock returns. The mechanism is as follows:

Public firms, by initiating capital return, transfer cash to investors who may be limited in

their choice of investable assets. The cash deployment in turn drives up demand for certain stocks.

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), and (Greenwood

2005).

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 cash

payouts 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 its existing assets. Such a fund also participates 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 ActiveShares4 (Cremers and

Petajisto 2009) and (Petajisto 2013), and are much more likely to be benchmarked to a growth

index. The aggregation of this trading effect to a stock is similar 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 prices at

a significant horizon.

In summary, this paper tests the hypothesis that capital returns generate cross-sectional

demand due to the constraints on the set of investable assets by asset managers. This source of

3 See for example: (Frazzini and Lamont 2008), (Coval and Stafford 2007), (Lou 2012), and (Edmans, Goldstein and

Jiang 2012). 4 ActiveShare is the sum of the absolute deviations between the positional weights of assets in a portfolio and the

most comparable indexing benchmark to that portfolio. See (Cremers and Petajisto 2009) for a thorough treatment of

the measure.

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demand has significant implications on secondary financing, a central role facilitated by the equity

market. 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 investable cash, which is immediately recorded as a portion of 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 with

the percentage decrease in shares outstanding for each stock during each quarter. While only some

funds may elect to sell to a buyback, a stock’s repurchase will transfer cash to the funds that hold

the stock, and in turn induce redeployment by all funds that hold this stock on average. For each

fund portfolio, the average cash flow expected from a repurchase program can be calculated

without an indication that shares have ever exchanged hands.

The redeployment of capital return by mutual funds forecasts stock returns and changes in

issuances. I show this by aggregating inflows from cash payouts to the stock level. Capital-return-

induced price pressure, 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅, is calculated for each stock by assuming proportional investment in

existing assets from capital repayments (Frazzini and Lamont 2008), (Lou 2012). 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 stocks according to each stock’s ex-

ante weight in the portfolio. The total expected cash flow 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.

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The key innovation for my price pressure test is that I examine the correlation between

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ 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 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ implies 0.93% (t =

2.08) excess return in the underlying stock in the following quarter, which increases to 1.05% (t =

2.54) once I control for contemporaneous price pressure from retail investor flows to equity mutual

funds. Because 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is extremely persistent, and because a fund can purchase stocks over a

moderate horizon absent strong incentives to avoid cash, stocks associated with capital returns

predictably experience excess returns for multiple quarters. One standard deviation in 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅

forecasts 0.86% (t = 2.68) 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 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ have large return spreads in the short-to-

medium horizon. A strategy holding the top quintile and shorting the bottom quintile 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ sorted

portfolios (5-minus-1) of non-capital-returning stocks yields a return of 3.12% (t = 3.28) per

quarter. This strategy can be profitably maintained for several 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 after holding horizons of over 3 years. These results are consistent with

non-fundamental demand as excess predictable returns partially revert for a specific cross section.

However, this is also consistent with the effects of capital constraints (Lamont, Polk and Saaá-

Requejo 2001). Firms with low mechanical demand for their stocks may experience lower

persistent returns due to the high costs in external funding.

The next section of this paper associates this spillover 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 are able to persistently issue at a relatively high level compared to other firms

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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 59 basis points of issuance each quarter averaged over a 12-month horizon. These

results indicate that non-payout stocks exposed to capital-return-induced price pressure tend to

issue shares and limit their own future payouts.

The last section of this paper uses two additional empirical strategies to provide evidence

toward the persistent redeployment demand hypothesis. In the first empirical strategy, I exploit the

fact that memberships in the Standard and Poor’s (S&P) style related indices are mechanically

calculated. The stocks that are recently included in a Value-style index will be exposed to a lot

more dividends and buyback dollars than stocks recently included in a Growth-style index. At the

margins, the Value-Indexed stock will only have a negligibly higher score than the next best stock.

Consistent with persistent exposure to redeployment induced demand by investors, I find that

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.

I exploit mergers as a source of capital deployment as an alternative empirical strategy.

Mergers financed by cash operate similarly as dividend payouts to investors- when a firm acquires

a public company, the stocks of this public company automatically change into cash. Funds

holding this cash may deploy predictably in accordance to their holdings. These merger dollars are

not very persistent at the portfolio level, and tends to be spiky in aggregate. I find that during

quarters with significant cash merger activities, non-payout stocks sorted into portfolios based on

their merger-induced price pressure have abnormal one-period returns, which tend to strongly

revert in the short horizon. This effect is absent in quarters with limited cash mergers.

In contrast to the demand hypothesis of capital redeployment, work on the informational

content of capital returns has dominated the existing academic literature. Beginning with (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

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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 2003, and yet

buybacks still became more popular than dividends as the preferred method of capital return.5 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 the 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

5 The Jobs and Growth Tax Relief Reconciliation Act of 2003 effectively ended the spread difference between the

capital gains and the dividend tax rates.

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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 with 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 to

their price increase. 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 for 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.

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 (overvalued) 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 this paper are consistent with their findings, 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. Mainly, 1) this paper documents the

association of investor portfolio rebalancing patterns with a style characteristic (capital return) and

shows that this rebalancing pattern is linked to return predictability. 2) This paper shows that

dividends, in addition to buybacks, have predictive power on the returns of related firms.

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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 to 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. Notable 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.

This paper is also related to a literature on how investors use dividends. The fact that

exposure to dividends is a persistent characteristic of money management funds complements the

dividend disconnect phenomenon, which describes the tendency of investors – mutual funds 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 horizon (1 to 4 quarters). The positive price effect partially reverts

after significant holding periods. Section 3 reviews the characteristics of stocks receiving

redeployed capital and shows that the spread between future returns is quantitatively large

compared to the changes in future payout policies. In fact, firms under the influence of redeployed

capital tend to significantly increase their own issuance activities. Section 4 documents alternative

empirical strategies using style index inclusions 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

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This section illustrates the significance of capital returns in aggregate and in the cross

section of mutual funds.

1.1 Capital Return in Aggregate

I show that capital returns, in aggregate, are significant and persistent sources of cash

inflow for investors. I use stock-related data from the Center for Research in Securities Prices

(CRSP) Stock Security Files to calculate payouts. Dividend yield per stock is the difference

between total return (𝑅𝑒𝑡𝑖,𝑡) and price return (𝑅𝑒𝑡𝑥𝑖,𝑡) each quarter:

𝐷𝑖𝑣𝑦𝑖,𝑡 = 𝑅𝑒𝑡𝑖,𝑡 − 𝑅𝑒𝑡𝑥𝑖,𝑡.

Percentage buybacks is the decrease in shares outstanding. The lower limit for the decrease

is restricted to -10% to limit the exposure of the sample to potential mergers and acquisitions:

|𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡| = |∆𝑆ℎ𝑎𝑟𝑒𝑠𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔𝑖,𝑡 ∙ (∆𝑆ℎ𝑎𝑟𝑒𝑠𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔𝑖,𝑡 ∈ [−10%, 0)) |,

where ∆𝑆ℎ𝑎𝑟𝑒𝑠𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔𝑖,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 by its t-1 market capitalization.

Equity mutual fund flows, which have been demonstrated in the finance literature to affect

asset returns through demand, are calculated as:

∑(𝑇𝑁𝐴𝑖,𝑡 − 𝑇𝑁𝐴𝑖,𝑡 ∙ (1 + 𝑅𝑒𝑡𝑖,𝑡) − 𝑀𝐺𝑁𝑖,𝑡)𝑖

,

where 𝑀𝐺𝑁𝑖,𝑡 is a compensating term for fund mergers.

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 are plotted in Figure

1. Such a time series has been historically been persistent and large- between 1990 and 2002,

annual buyback cash flow ranged from $17 to $159 billion, while dividend payouts ranged from

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$92 to $161 billion. Capital return programs expanded dramatically after 2003, paying investors

$160 to $497 billion with buyouts and $172 to $419 billion with dividends.6 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 comparison. The aggregate capital return from

payouts accumulates at a yearly horizon to a significantly larger magnitude than investor flow to

equity, which lies flat. For instance, between 2010 and 2015, $4.25 trillion of capital returns

accumulated in net from public firms compared to $744 billion 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.

Figure 2 shows that most of the capital returned came from a small percentage of publicly

traded firms. The top panel of Figure 2 shows that 80% of the stocks traded have quantitatively

insignificant amounts 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 that are least exposed to

these dollars through investor portfolios. I will be conducting return-predictability tests

specifically on assets that do not return capital in order to test the demand-driven price pressure

hypothesis.

In summary, payouts from public firms are significant and large sources of capital inflow

into the equity markets. The amount of cash being contributed from these capital return programs

is considerably more persistent and less volatile in aggregate than other sources of investor demand

for stocks. These empirical facts indicate that cash payouts are a first-order source of demand in

the equity markets.

1.2 Exposure to Payouts by Individual Mutual Funds

6 The Jobs and Growth Tax Relief Reconciliation Act of 2003 reduced the overall tax rate for capital gains and

dividends.

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Professional investors, such as the open-ended mutual funds described in this study, vary

significantly in their exposure to capital returns; however, each investor’s exposure to this cash is

individually persistent. Some funds, such as a dividend income mutual fund, will receive persistent

payout capital from their portfolios, which can translate to net increases in holding positions. A

growth-targeted fund, in contrast, will have little capital return exposure and depends entirely on

investor flows and selling existing stocks in order to purchase new holdings. I will describe the

characteristics associated with capital exposure for individual mutual funds in this section, and as

I will show in the future section, this capital exposure represent an important characteristic for a

fund’s trading decisions.

The N-Q quarterly mutual fund holding filings recorded by the CDA/Spectrum database

and the CRSP Survivor Bias Free Mutual Fund database will supplement the previously described

data 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 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 Wharton Research Data Services (WRDS).

Each portfolio’s exposure to dividends and buybacks is calculated as the pro rata implied

yield of the portfolio holdings, that is:

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ 𝐷𝑖𝑣𝑦𝑖,𝑡𝑖

,

and

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡|

𝑖

for portfolio j at quarter t. 𝑊𝑒𝑖𝑔ℎ𝑡𝑖,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

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correlated (ρ = 0.22) for equity fund portfolios, implying that funds 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 0.66% of the portfolio’s TNA on

average. However, capital returns are significantly less volatile and more predictable from quarter

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 cash returns are deployed in the next section.

1.3 Changes in Holding Values

This section shows that capital returns require investors to deploy a significant amount of

cash back into assets. Dividends shows up as cash in a portfolio after a payment date. Buyback

programs, in effect, transfer cash to investors through market clearing.

In practice, mutual funds have flexibility in managing their dividend and asset selling

proceeds. While the Investment Company Act requires that asset managers return capital gains

and dividends to investors at pre-arranged 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.

Empirically, mutual funds invest the vast majority of dividend cash into assets. This may

be because the majority of dividend and capital gain distributions are automatically reinvested by

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fund investors. Appendix A1 approximates the distribution-based flows from investors by

calculating the difference between NAV (net asset value) price returns and net mutual fund returns.

Distribution-based inflow is roughly 85% of the total distribution (both dividends and capital

gains) released by a mutual fund; that is, the majority of distribution dollars are reinvested in the

original fund itself, and only about 15.3% of the distribution dollars are taken out by investors.

The existing literature using mutual fund flows makes no distinction in the dividend and price

returns- the cash redistributed to investors is commonly captured instead 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 by the change in Total Net Assets ignore the distribution

response by money managers and their investors to dividends. This may introduce a potential bias

if investors treat such a passive redemption of cash differently from active 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 ∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑗,𝑡𝑎𝑙𝑙:

∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑗,𝑡𝑎𝑙𝑙 =

∑ 𝑃𝑟𝑖𝑐𝑒𝑖,𝑡 ∙ 𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡𝑁+𝑀𝑖=1

∑ 𝑃𝑟𝑖𝑐𝑒𝑖,𝑡 ∙ 𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡−1𝑁𝑖=1

− 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. The ∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑗,𝑡𝑎𝑙𝑙 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 describes the average changes in holdings by mutual fund portfolios sorted to different

quintiles of dividend exposure. Only 41.6% of mutual funds in the lowest quintile increase their

asset holdings, in contrast to 48.1% of funds in the highest quintile. Funds with high exposures to

dividend inflows are also less likely to decrease their asset holdings. About 58.4% of funds in the

lowest quintile report a decrease in asset holdings compared to only 51.9% in the highest quintile.

16

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 (∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑗,𝑡𝑎𝑙𝑙) and dividend cash flow (𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡) for various horizons in Appendix

Table A2. The unconditional correlation between dividend cashflow and holdings growth is the

highest for the horizon of quarters 1 to 5 rather than during the contemporaneous quarter-

indicating a slightly lagged response in the deployment of these cashflow dollars.

Buyback programs, in effect, exchange cash from public firms for shares with portfolio

managers. Market clearing dictates that investors, on average, decrease their holdings when a

buyback program is initiated. The clearing of the equity market holds true for mutual fund

portfolios. 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|) from the past 4 quarters

to the next 4 quarters. We observe that there is an immediate and large reduction in aggregate

mutual fund holdings coinciding with the timing of the buyback. 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’s Panel B shows that this clearing of the equity

market is monotonic for mutual fund portfolios. Stocks with more buybacks are more likely sold

and less likely purchased by their current mutual fund holders: At the highest quintile, 25.4% of

their current mutual fund holders sold in net while 27.4% bought in net. This is in contrast to funds

holding stocks that do not conduct buyback programs, where only 20.4% were net sellers and

29.5% were buyers. As mutual funds sold stocks with buybacks on aggregate, portfolio holdings

of assets without buybacks must be increasing, otherwise total holdings would decrease.

The results in this section find that mutual fund exposures 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

17

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_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠𝑖,𝑡𝑖⏟

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡

+∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡|.

𝑖⏟ 𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡

The degree to which a fund portfolio is exposed to cash flows from capital return is

correlated with several characteristics that capture the investment style and mandate of each mutual

fund. I join my data with ActiveShare and index benchmarks provided by (Cremers and Petajisto,

2009) in 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 to be 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 associated with a value index will be exposed to capital return cash flow if these funds

redeploy cash predictably.

Mutual funds with high capital returns by their holdings invest predictably. I find that

mutual funds with high cash flows from capital returns will 1) stay invested in their existing assets

and 2) purchase stocks similar to their existing holdings, i.e. stocks held by other funds with high

capital return exposure.

18

Funds with returned cash tend to continue to be invested their existing holdings. Panel B

of Table 3 compares the changes in the 5 largest stock positions of mutual funds with low capital

inflow and mutual funds with high capital inflow. 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 capital returns tend to scale down

by only 7%. High capital returning portfolios tend to keep their existing holdings.

A mutual fund’s total purchases decisions are predictable. Panel C of Table 3 regresses the

gross purchases 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 quintile 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):

𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,𝑏𝑖𝑛 =∑ 𝑀𝑎𝑥(∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡, 0)|𝑗 ∈ 𝑏𝑖𝑛𝑡𝑗

∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1𝑗.

The buying of assets by each bin, 𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,𝑏𝑖𝑛, is significantly related to the prior

percentage of asset i held in the same bin:

𝑃𝑒𝑟𝑐𝐻𝑜𝑙𝑑𝑖,𝑡,𝑏𝑖𝑛 =∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1|𝑗 ∈ 𝑏𝑖𝑛𝑡𝑗

∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1𝑗.

I find that the best predictor for assets purchased is the ex-ante holding of each stock in

each bin in Panel C of Table 3. That is, funds receiving large (small) capital returns primarily buy

assets already held by funds receiving large (small) capital returns. This indicates that the type of

stocks purchased by mutual funds is closely associated with these funds’ respective capital return

characteristics.

To summarize, I find that dividends and buyback programs contribute significant amounts

of cash requiring deployment by 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

19

their existing positions using cash inflows, apportioned reinvestment to existing holdings by

mutual funds approximates the dimension of individual stock demand associated with the

redeployment of dividends and buyback dollars.

2. Stock Price Pressure

Given that cash from capital returns stays 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? Market

participants with excess inflow from capital returns can purchase new assets only if these assets

are supplied by price-sensitive market participants. Depending on the elasticity of the demand

curve, stock prices may increase in response. 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 its empirical elasticity. To capture

this demand 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

the assumption of proportional reinvestment to flows, but none of these studies observe significant

absolute increases in existing positions when given capital inflows. An alternative measure of

capital-return-induced price pressure – the percentage of assets held in the top quintile mutual

funds exposed to capital returns – gives qualitatively the same results in this section.

20

Like prior measurements of investor-flow-induced price pressure in the existing literature,

capital-return-induced price pressure (CIPP) is calculated as:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

where 𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1 is the number of shares in stock i held by mutual fund j at t-1 and

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 is the expected cash flow from capital returns experienced by portfolio j from t-1 to

t,

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 = 𝑆𝐷𝑖𝑣∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠𝑖,𝑡𝑖⏟

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡

+ 𝑆𝐵𝑢𝑦∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 ∙ |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡|.

𝑖⏟ 𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡

𝑆𝐷𝑖𝑣 and 𝑆𝐵𝑢𝑦 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 calculations of 𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,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 (See Appendix A3 for Fama

Macbeth and A4 for Calendar Portfolio results using price pressure measurements based on

Dividends and Buybacks separately).

The price pressure variable is the aggregation of cash flows from capital returns

apportioned by ex-ante portfolio weights. An alternative way of writing 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is simply:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =

∑ (𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 ∙ 𝑇𝑁𝐴𝑗,𝑡−1) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1𝑗

∑ 𝑃𝑟𝑖𝑐𝑒𝑖,𝑡−1 ∙ 𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑗 𝑖,𝑗,𝑡−1

.

That is, 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ for each stock i is the total dollar cash flow from capital returns to each portfolio j

apportioned by i’s respective portfolio weights, divided by the total value of i held by all mutual

funds.

Table 4 contains summary statistics on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ and 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ – the flow-induced price pressure

generated by assuming proportional investment of investor flow in existing assets. The cross-

21

sectional spread between high 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ and low 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ stocks is magnitudes smaller than the spread

in 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅; however, there are several reasons to suspect that 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ can be significantly correlated

with stock-level return. 1) Much like capital return on the portfolio level, 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ for each stock is

extremely persistent, and is predictable by its lagged variable at a 1-year horizon whereas 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ at

the same horizon is quantitatively un-forecastable. 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 this 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.

In 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

bottom decile 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

have not explicitly conducted capital returns, either through dividends or stock buybacks, and are

large enough to abstract from simple microstructure related concerns. There are 61,871 stock-

quarter observations left to serve as a clean laboratory for testing the effect of capital-inflow-

induced price pressure. In Appendix Table A5, 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. 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is associated

with significant contemporaneous stock-level returns, and also forecasts excess returns at the 1-

22

quarter and 1-year horizons. Because 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is extremely persistent, the lag value of 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ forecasts

capital-return-induced price pressure for many quarters into the future.

In this section, I conduct Fama-Macbeth regression analysis of returns on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ and various

common characteristics (Fama and MacBeth 1973). A single standard deviation of 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ forecasts

0.93% (t = 2.08) increased excess return in the following quarter and an average quarterly return

of 0.86% (t = 2.68) over the following year. The predictability is increased to 1.05% (t = 2.54) and

0.96% (t = 3.02) respectively, once contemporaneous flow-induced price pressure 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is added

as a control.

2.3 Calendar Time Portfolios

The Fama-Macbeth regressions indicate a particular calendar time strategy. I sort stocks

into calendar time portfolios using 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅. Quintile portfolios are formed each quarter and are held

for multiple quarters in overlapping portfolios following (Jegadeesh and Titman 1993). As shown

in Table 6, the top quintile portfolio rebalanced quarterly and held for 1 quarter experiences a 4-

factor adjusted excess return of 1.89% (t = 3.05), while the lowest quintile portfolio experiences

excess return of -1.23% (t = -1.85). A strategy shorting the lowest quintile portfolio and holding

the highest quintile experiences a return of 3.12% (t = 3.28) each quarter. A strategy that longs the

top portfolio and shorts the middle (third quintile) portfolio experiences a return of 2.48% (t =

2.02). 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ continues to forecast excess returns in overlapping portfolios for multiple horizons.

At the 1-year horizon, the top quintile portfolio has a risk-adjusted alpha of 1.62% (t = 2.79) each

quarter, while the bottom quintile portfolio obtains -1.20% (t = -1.84). The long-short strategy at

this horizon generates an excess return alpha of 2.81% (t = 3.02) per quarter.

Return predictability persists significantly over multiple periods. This contrasts the demand

pressure phenomenon 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

23

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. 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. I observe

only significant reversal is observed for the five minus three portfolio after a significant holding

horizon.

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. In

particular, I show that the stocks strongly linked to capital redeployment, despite the appreciation

of their prices, only marginally increase their future payouts.

Panel A of Table 7 reports the average market equity and average book equity of the stocks

in the calendar time portfolios. We observe that firms with more capital redeployment inflows tend

to be slightly larger in size- a trend that continues from 1990 to 2015. The book to market ratio is

also slightly upward trending on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅, consistent with their factor loadings. While this may

indicate that the larger firms will likely payout more in the future, this section will show that the

firms with the most capital exposures tend to only marginally change their own payout policies.

The future capital return policies of firms in each portfolio are described in the Panel B of

Table 7. 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 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ portfolios. The firms in the highest quintile portfolio according to

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ do initiate more capital return and more dividends over the 3 to 12 years following the

portfolio formation period. However, the magnitudes recorded in the table indicate that the

programs initiated by these firms are extremely marginal and economically insignificant. Despite

experiencing cumulative returns of almost 12% in a 12 month holding period window, these firms

24

on average only bought back 0.05% more of their stock and increase total dividend yield by 0.04%

over the lowest quintile portfolio over 6 years. The increase in buybacks essentially disappears

over the 12-year horizon, while the total dividend payments is lowered to 0.02% when averaged

over a 12-year horizon. These magnitudes indicate that while 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ captures some potential

increases of capital return programs, the marginal increase cannot be the source of the significant

price effect.

Instead, I find that these firms, which are strongly associated with capital redeployment,

tend to have higher persistent issuances over time. A stock in the top quintile portfolio sorted on

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ has a 0.48% higher change in quarterly issuances compared to the bottom quintile portfolio

in the 6-year horizon. The issuance levels for both portfolios are plotted in Figure 6. Stocks most

associated with 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ have significantly more persistent level of issuance compared to the stocks

located in the bottom quintile.

In Table 8, I perform regression analysis to understand the average correlation between

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ and changes in buyback, issuance, and dividend activities. Once I control for characteristics

such as size, past issuance, and past returns, I find no significant correlation between capital return

spillover and a firm’s own capital return activity. In contrast, this price spillover mechanism is

significantly correlated with future issuance activities both in statistical significance and economic

magnitudes. One standard deviation increase in 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ implies an increase of 0.59% (0.59%) shares

outstanding per quarter over 12 (48) quarters.

In summary, stocks with this spillover channel of induced price pressure only marginally

increase their own payout activities. Contrarily, the firms pressured by demand from high capital

returns 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,

tending to issue stocks when there is higher demand from the equity markets. The second is that

high capital market demand can relax financing constraints, making it easier for firms to fund new

25

projects by issuing shares. Both hypotheses tie long-term capital market demand for stocks to firm-

level financing decisions.

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

Mutual funds that are highly exposed to capital returns are more likely to be benchmarked

to a value index (see Panel A of Table 3). Prior work has shown that indexing appears to induce

correlation in the returns of stocks to certain investment styles. (Boyer 2011) documents that

indexing causes 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 the stock into portfolios indexed by a value benchmark and in turn expose the stock to

redeployment dollars in the long term. An inclusion of a stock into a growth index would shift the

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 inclusion into a value index

induces higher persistent abnormal returns for a stock than inclusion into a growth 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 into

this 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. A stock that has been switched to

26

a value index will increase its exposure to persistent redeployment dollars more so than a stock

that has been switched to a growth index.

I obtain index constituents using the Compustat Index database between Q3 1995 and Q4

2015. There are 3,991stock inclusions into either the growth or the value index during this period.

Figure 6 shows the event horizon plot of cumulative abnormal returns (returns in excess of the

market return) over 24 months after a stock is included into the 1500 Value and the 1500 Growth

index categories. Consistent with persistent pricing pressures, we observe that inclusion into a

value index is accompanied by higher persistent abnormal returns than inclusion into a growth

index. The average difference between the cumulative abnormal returns of a stock added to a value

index and that of one added to a growth index is 3.78 % (t=1.98) at 24 months; if we exclude

dividend payments and simply examine price returns, the difference is 4.66% (t=2.38).

4.2 Cash Mergers

Cash mergers provide additional evidence that cash inflows from firms affect cross-

sectional returns. Cash mergers 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 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:

𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝑀𝑒𝑟𝑔𝑒_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

significantly forecasts returns in the following quarter only when this activity is high.

27

Table 9 depicts the returns of quintile portfolios of stocks sorted during high-deal quarters

and low-deal quarters. Consistent with price pressure due to cash redeployment, we observe a

significant cross-sectional split during the high deal quarters for this particular cross section of

stocks. During quarters when there are substantial cash mergers, stocks located in similar portfolios

as the targets of these cash mergers tend to outperform. When there are no substantial cash mergers,

the sorting variable 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 loses its forecasting power.

Figure 7b shows the event horizon plot of the cumulative daily returns of a strategy that

longs the top 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 quintile and shorts the bottom 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅

𝑖,𝑡 quintile portfolio during the quarter

after the merger. I find that because these cash mergers are one time deals, that is investors will

not persistently receive cash inflows from mergers, the price affect reverts within 90 trading days.

In summary, two additional empirical strategies are used to provide further evidence for

demand originating from capital redeployment. In the case of style indexing, I use variation in the

inclusions of stocks into the value index to demonstrate that the price pressure originating from

payouts are persistent and potentially long lasting. Variation from cash mergers in the equity

market demonstrates that stock prices are significantly linked to cash deployment by investors.

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. Fund receiving large

amounts of cash payouts tend to invest predictably into stocks. This stylized fact indicates a wedge

in the demands for stocks that are exposed to capital returns and the demands for ones that are not.

This paper shows that capital returns by public firms are associated with high demand for stocks

connected through investor portfolios. Non-payout stocks connected to capital payouts tend to

appreciate in the short to moderate horizon, and partially revert in the very long horizon. This price

effect is consistent with a mechanical demand channel.

This paper forwards and tests the hypothesis that buyback and dividend programs

implicitly generate demand for the stocks of related firms through the redeployment of capital back

28

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 payout 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 her own firm. If

this price-effect channel affects the competitiveness of these related firms, then capital return in

the form of cash payouts may be viewed as having 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.

Why firms change their capital structure is central to many questions in corporate finance

and asset pricing. While this paper identifies a pricing mechanism that affect capital structure, its

limited scope leaves out directions for future research. Demand for equity assets may provoke

issuances because issuances are strategic responses to mispricing, or because issuances are

symptomatic of a less constrained financial market. In other words, firms issue either to exploit

demand and collect cash for payouts, or to finance investments. This paper takes a limited stance

to the inherent reason for issuances in the capital markets. However, the source of demand

uncovered by this paper is economically significant and extremely meaningful for a large cross

section of stocks. Further differentiating the reasons for the shifting capital structure associated

with the price pressure mechanism studied in the paper remains a priority for future research.

29

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32

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) multiplied by market capitalization at the start of the quarter. Equity flow is calculated from CRSP as the difference

between the quarter end TNA and the quarter start TNA adjusted by fund returns.

33

Figure 2. Composition of Capital Return and Aggregate Market Capitalization from the Top Capital Returning Stocks.

The top panel depicts aggregate quarterly capital return (Dividend and Composite Buybacks) decomposed to levels by the top

capital returning stocks. The bottom panel shows the compositions of aggregate market capital as attributable to the separate

percentiles of capital returning stocks.

34

Figure 3. Quarterly 4 Factor Alphas for a strategy that holds the top quintile and shorts either the bottom or mid quintile of

Capital-Return-Induced Price Pressure stocks for varying holding period horizons. 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 Q1 1990 to Q4 2015.

35

Figure 4. The issuance of calendar time portfolios of stocks normalized at year 0. The blue line is the issuance of the top quintile

portfolio over an eight year horizon. The yellow line is the issuance of the bottom quintile portfolio.

36

Figure 5. Coefficients from the panel regression (stock, time) of percentage changes in mutual fund holdings against buybacks:

∆𝑀𝐹𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑡 = 𝛼 +∑ 𝛽𝑙 ∙ |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡+𝑙|4

𝑙=−4+ 𝜖𝑖,𝑡 .

∆𝑀𝐹𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑡 =∑ (𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡−𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡−1)𝑗

∑ 𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡−1𝑗 is the percentage change in the shares held by the aggregate mutual fund portfolios.

The standard errors are clustered quarterly.

37

Figure 6. Indexing into S&P 1500 Value and S&P 1500 Growth between 1996 and 2015. Cumulative abnormal returns (CAR)

are calculated as the difference between a stock’s total returns and total market returns (left); or a stock’s price returns and market

price returns. 2,042 value index inclusions and 1,949 growth index inclusions are used for this plot, requiring that the stock is

observed for all 24 months. The difference between CARS of Value and Growth inclusion stocks at month 24 is 3.78% (t=1.98);

4.66% (t=2.38) without dividends.

38

Figure 7. Cash-Mergers-Implied Price Pressure. The left figure shows the aggregate quarterly dollars from cash mergers in

aggregate between 1990 and 2015. I define the top third of all the quarters in terms of the ratio between aggregate cash mergers

dollars and aggregate stock market cap as high-deal quarters. The rest are defined as low-cash-merger quarters. The right figure

plots the long-short cumulative return of a portfolio that longs stocks sorted into the top quintile and shorts stocks sorted into the

bottom quintile of 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡. 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅

𝑖,𝑡 is defined as ∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1𝑀𝑒𝑟𝑔𝑒_𝐹𝑙𝑜𝑤𝑗,𝑡,𝑗 where 𝑀𝑒𝑟𝑔𝑒_𝐹𝑙𝑜𝑤𝑗,𝑡 =

∑ 𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1.𝑖∈𝐶𝑎𝑠ℎ𝑀𝑒𝑟𝑔𝑒𝑟

39

Table 1. Fund Portfolio Level Flow and Capital Return

Summary statistics on quarterly capital return and percentage flow per mutual fund portfolio. 𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 is the percentage

investor flow into mutual fund j, that is 𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 = (𝑇𝑁𝐴𝑗,𝑡 − 𝑇𝑁𝐴𝑗,𝑡−1 ∙ (1 + 𝑟𝑒𝑡𝑗,𝑡) − 𝑀𝐺𝑁𝑗,𝑡−1)/𝑇𝑁𝐴𝑗,𝑡−1.

Dividend-induced-capital flow, for portfolio j, is defined as:

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1

∙ 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠𝑖,𝑡𝑖

.

Pro rata buyback flow, for portfolio j, is defined as:

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1

∙ |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝑖,𝑡|

𝑖

.

𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1 is the portfolio weight of asset 𝑖, by portfolio j, at t-1. |𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝜄,𝑡| is the percentage decrease in shares outstanding of

asset 𝑖 between t-1 and t. 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 is the dividend yield of asset 𝑖 between t-1 and t. 𝜌𝑡,𝑡−1 and 𝜌𝑡,𝑡−4 are the autocorrelation

coefficient at 1 and 4 quarters lags respectively.

Mean Std Q1 Median Q3 𝜌𝑡,𝑡−1 𝜌𝑡,𝑡−4 N

𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2015) 0.66% 30.31% -4.32% -1.33% 2.77% 0.344 0.166 77,676

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2015) 0.34% 0.23% 0.17% 0.31% 0.47% 0.830 0.776 77,676

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2015) 0.42% 0.25% 0.23% 0.38% 0.58% 0.620 0.460 77,676

𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2002) 2.18% 23.71% -3.59% -0.49% 4.24% 0.388 0.108 19,892

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2002) 0.28% 0.23% 0.10% 0.24% 0.40% 0.933 0.850 19,892

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡 (1990 to 2002) 0.26% 0.18% 0.13% 0.23% 0.35% 0.400 0.283 19,892

𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (2003 to 2015) 0.13% 32.25% -4.54% -1.61% 2.26% 0.318 0.190 57,784

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 (2003 to 2015) 0.36% 0.22% 0.19% 0.33% 0.49% 0.788 0.752 57,784

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡 (2003 to 2015) 0.48% 0.25% 0.29% 0.45% 0.63% 0.589 0.407 57,784

40

Table 2. How Capital Returns Induce Cash Redeployment by Mutual Funds

This table describes how capital return programs induce cash redeployment by mutual funds. Dividends received are directly used to

increase holdings, while stock buybacks exchange cash for shares with mutual fund portfolio.

Panel A. Change in portfolio holdings for funds sorted on dividend exposure. Mutual funds are sorted by the size of dividends received

relative to their Total Net Assets into 5 groups. This table tabulates 1) the average dividend received each quarter to a fund belonging to

each group, 2) the percent of funds in each group each quarter that increased their total holdings, 3) the percent of funds in each group

each quarter that decreased their total holdings, 4) the average change in total holding size, and 5) the average residual change in total

holding after compensating for investor inflow and outflow.

Average

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤

% Funds

Increasing

Holdings

% Funds

Decreasing

Holdings ∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑎𝑙𝑙 Residual

∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑎𝑙𝑙

Lowest Dividend Funds 0.010% 41.6% 58.4% -0.981% -0.462% (-0.799) (-5.11)

2 0.218% 44.7% 55.3% 0.431% -0.054% (3.86) (-0.652)

3 0.326% 44.6% 55.4% 0.409% 0.023% (3.60) (0.269)

4 0.455% 46.8% 53.2% 0.766% 0.282% (6.56) (3.16)

Highest Dividend Funds 0.650% 48.1% 51.9% 1.397% 0.780% (9.85) (6.83)

Panel B. Stocks sorted on percentage buybacks. Stocks with detectable buybacks are sorted into quintiles. Stocks without any buybacks

are also grouped into a single bin. This table tabulates 1) the average buyback size for each stock in the grouping, 2) the percent of

mutual funds that increased their holdings of the stock, 3) the percent of mutual funds that decreased their holdings of the stock, and 4)

the percent of mutual funds that liquidated their holdings of the stock in the same quarter.

Average

Buyback

%Funds

Increased

Position

% Funds

Decreased

Position

% Funds

Liquidated

Position

Stocks Without Buyback 0.000% 29.473% 20.369% 8.163%

Lowest Buyback Stocks 0.057% 28.887% 22.007% 6.933%

2 0.302% 28.462% 22.515% 6.517%

3 0.733% 28.075% 23.536% 6.781%

4 1.519% 27.707% 24.295% 7.082%

Highest Buyback Stocks 4.069% 27.416% 25.419% 8.052%

41

Table 3. Capital Deployment by Mutual Funds

This table describes mutual funds characteristics and their purchasing decisions.

Panel A. This table summarizes the characteristic of mutual funds sorted on payout exposure. I link my calculations of payout exposure

per portfolio/quarter observation to ActiveShare and benchmark indices from (Cremers and Petajisto 2009). A growth index fund is a

fund benchmarked to a growth stock index. A value index fund is a fund benchmarked to a value stock index.

Funds Sorted on 𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡

Dividend per

Quarter

Buyback per

Quarter ActiveShare

%Value

Indexed

%Growth

Indexed

N

(1990 to 2008)

Lowest 0.09% 0.18% 89.68% 4.50% 57.84% 10,268

2 0.19% 0.30% 81.24% 10.78% 41.16% 10,867

3 0.29% 0.39% 76.06% 14.06% 29.84% 11,119

4 0.41% 0.45% 69.69% 19.97% 15.11% 11,346

Highest 0.57% 0.57% 72.32% 37.96% 6.40% 11,045

Panel B. This table describes the top 5 positions in each mutual fund in the low capital and high capital return exposure groups. 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡

is the average size of each position prior to the quarter end, while 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1 is the size at the beginning of the next quarter. That is:

𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡 =𝑃𝑟𝑖𝑐𝑒𝑖,𝑡+1𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡

∑ 𝑃𝑟𝑖𝑐𝑒𝑖,𝑡+1𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡𝑁𝑖=1

, and 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1 =𝑃𝑟𝑖𝑐𝑒𝑖,𝑡+1𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡+1

∑ 𝑃𝑟𝑖𝑐𝑒𝑖,𝑡+1𝑆ℎ𝑎𝑟𝑒𝑠𝑖,𝑗,𝑡+1𝑁𝑖=1

.

∆𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1 is the percentage change in the relative size of these positions.

Mutual Funds with Low Capital Return Mutual Funds with High Capital Return

Top

Positions 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1 ∆𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1

Top

Positions 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡 𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1 ∆𝑆𝑖𝑧𝑒𝑖,𝑗,𝑡+1

1 15.3% 13.0% -15.1% 1 16.8% 15.7% -6.9%

2 12.4% 10.9% -12.4% 2 13.4% 12.6% -6.1%

3 11.1% 9.8% -11.9% 3 11.6% 10.9% -6.4%

4 10.2% 9.1% -11.4% 4 10.4% 9.8% -6.3%

5 9.6% 8.5% -11.0% 5 9.5% 8.9% -6.5%

Panel C. This table describes the panel regression coefficients of buying of stocks by mutual funds in each capital returning bin on the

ex-ante percentage shares held in each bin on the full panel of stocks between 1990 and 2015. That is:

𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,𝑏𝑖𝑛 =∑ 𝑀𝑎𝑥(∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡,0)|𝑗∈𝑏𝑖𝑛𝑡𝑗

∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1𝑗, and 𝑃𝑒𝑟𝑐𝐻𝑜𝑙𝑑𝑖,𝑡,𝑏𝑖𝑛 =

∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1|𝑗∈𝑏𝑖𝑛𝑡𝑗

∑ 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑖,𝑗,𝑡−1𝑗.

Coefficients are clustered quarterly.

𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,1 𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,2 𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,3 𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,4 𝐵𝑢𝑦𝑖𝑛𝑔𝑖,𝑡,5

𝑃𝑒𝑟𝑐𝐻𝑒𝑙𝑑𝑖,𝑡−1,1 0.145 0.051 0.017 0.010 0.002

(21.20) (16.47) (8.98) (10.61) (2.44)

𝑃𝑒𝑟𝑐𝐻𝑒𝑙𝑑𝑖,𝑡−1,2 0.077 0.083 0.028 0.009 0.006

(7.03) (13.93) (8.60) (5.89) (3.31)

𝑃𝑒𝑟𝑐𝐻𝑒𝑙𝑑𝑖,𝑡−1,3 0.029 0.037 0.050 0.019 0.014

(2.10) (4.69) (12.24) (6.48) (7.56)

𝑃𝑒𝑟𝑐𝐻𝑒𝑙𝑑𝑖,𝑡−1,4 0.029 0.032 0.030 0.061 0.030

(2.89) (6.45) (8.80) (13.13) (14.46)

𝑃𝑒𝑟𝑐𝐻𝑒𝑙𝑑𝑖,𝑡−1,5 -0.022 -0.004 0.009 0.013 0.048

(-3.03) (-1.44) (5.71) (7.25) (19.08)

𝑅2 0.079 0.094 0.077 0.122 0.089

𝑁 243,938 286,258 274,545 265,435 200,746

42

Table 4. Flow and Capital Return Aggregated on the Stock Level

Summary statistic on quarterly capital-return-induced price pressure, 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅

�̅�,𝑡. 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡, Investor-Flow-Induced Price Pressure, is

also presented to benchmark the magnitude. Only stocks that have not participated in capital return over the past 5 years are

included.

Assuming proportional reinvestment to initial fund values, flows and capital return are aggregated to the stock level in this table.

Specifically, Investor-Flow-Induced Price Pressure to stock i is calculated as

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =∑

(𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 ∙ 𝑇𝑁𝐴𝑗,𝑡) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1∑𝑉𝑎𝑙𝑢𝑒𝑖,𝑗,𝑡−1𝑗

=∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐼𝑛𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 .𝑗

The flow-induced price pressure is simply the weighted average percentage flow into each mutual fund scaled by the proportional

share held of a stock by each fund. An alternative interpretation of this value is investor flow apportioned by weights of positions

aggregated over all observed funds, divided by the value of total shares held in these portfolios. I calculate a similar measure for

capital returns. Treating capital return as inflow and assuming proportional reinvestment, capital-return-induced price pressure

can be effectively calculated as:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =∑

(𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 ∙ 𝑇𝑁𝐴𝑗,𝑡) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1∑𝑉𝑎𝑙𝑢𝑒𝑖,𝑗,𝑡−1𝑗

=∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡,𝑗

where 𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 is the amount of cash flow from capital returns experienced by portfolio j from t-1 to t:

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 =∑𝑊𝑒𝑖𝑔ℎ𝑡𝜄,𝑗,𝑡−1 ∙ (|𝐵𝑢𝑦𝑏𝑎𝑐𝑘𝜄,𝑡| + 𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝜄,𝑡)

𝜄

.

Mean Std Q1 Median Q3 𝜌𝑡,𝑡−1 𝜌𝑡,𝑡−4 N

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (1990 to 2015) 2.86% 10.82% -1.56% 0.98% 4.65% 0.25 0.09 61,871

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (1990 to 2002) 4.33% 13.05% -1.03% 2.09% 6.56% 0.23 0.05 37,822

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (2003 to 2015) 0.54% 4.95% -2.02% -0.20% 2.17% 0.20 0.11 24,049

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (1990 to 2015) 0.43% 0.23% 0.25% 0.40% 0.57% 0.69 0.55 61,871

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (1990 to 2002) 0.34% 0.20% 0.20% 0.31% 0.45% 0.57 0.43 37,822

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 (2003 to 2015) 0.56% 0.20% 0.42% 0.54% 0.68% 0.67 0.50 24,049

43

Table 5. Capital-Return-Induced Price Pressure, Fama-Macbeth

This table records the Fama-Macbeth regression coefficients of average quarter excess returns on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 and various controls.

Assuming proportional reinvestment to initial fund values, capital returns are aggregated to the stock level in this table.

Specifically, Capital-Return-Induced Price Pressure for stock i is calculated as:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =∑

(𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 ∙ 𝑇𝑁𝐴𝑗,𝑡) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1∑𝑉𝑎𝑙𝑢𝑒𝑖,𝑗,𝑡−1𝑗

=∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡.𝑗

𝐿𝑀𝐸𝑖,𝑡−1 is the log market capitalization. 𝐿𝐵𝐸𝑖,𝑡−1 is the log book equity from 1 quarter prior. 𝑅𝑒𝑡12𝑖,𝑡−1 is the prior 12-month

return. 𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 is the percentage increase in shares outstanding over the past 5 years. 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is the contemporaneous flow-induced

price pressure to the period of the excess returns. Only non-dividend-paying stocks that have not had any capital return over the

past 5 years are used in the regression. Stocks with market capitalizations lower than the bottom decile of NYSE and stocks at the

bottom decile of percentage mutual fund holdings are filtered. All the regressor variables are standardized by their standard

deviation. The t-statistics in the first 3 columns are Newey-West with a single lag. The t-statistics in the next 3 columns are Newey-

West with 4 lags to account for overlapping returns.

Holding Period Excess Returns:

1 Quarter Excess Returns

(𝑅𝑒𝑡𝑖 − 𝑅𝑓)𝑡−1→𝑡 4 Quarter Excess Returns

1/4 ∙ (𝑅𝑒𝑡𝑖 − 𝑅𝑓)𝑡−1→𝑡+3

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 0.89% 0.93% 1.05% 1.09% 0.86% 0.96%

(1.77) (2.08) (2.54) (2.57) (2.68) (3.02)

𝐿𝑀𝐸𝑖,𝑡−1 -0.22% -0.09% -0.37% -0.55%

(-0.47) (-0.20) (-0.68) (-1.08)

𝐿𝐵𝐸𝑖,𝑡−1 -0.08% -0.10% 0.34% 0.62%

(-0.17) (-0.23) (0.64) (1.45)

𝑅𝑒𝑡12𝑖,𝑡−1 0.73% 0.75% -0.20% -0.28%

(1.13) (1.13) (-0.36) (-0.48)

𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 -0.64% 0.75% -0.73% -0.78%

(-2.78) (-2.56) (-3.51) (-3.62)

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡−1→𝑡−1+𝑘 2.38% 1.84%

(6.22) (4.51)

Avg. R2 0.008 0.030 0.037 0.010 0.035 0.046

44

Table 6. Capital-Return-Induced Price Pressure, Calendar Portfolios Sort

This table records calendar time strategies based on Capital-Return-Induced Price Pressure. Specifically, Capital-Return-Induced

Price Pressure to stock i is calculated as:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 =∑

(𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡 ∙ 𝑇𝑁𝐴𝑗,𝑡) ∙ 𝑤𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1∑𝑉𝑎𝑙𝑢𝑒𝑖,𝑗,𝑡−1𝑗

=∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡.𝑗

Panel A. This panel records the excess returns and risk-adjusted alphas of market cap value weighted portfolios sorted on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡.

Non-dividend-paying stocks that have not had any capital return over the past 5 years are sorted into quintile portfolios, and the

table reports the returns of overlapping portfolio strategies that hold each portfolio for varying numbers of quarters. Stocks with

market capitalizations lower than the bottom decile of NYSE and stocks at the bottom decile of percentage mutual fund holdings

are filtered. The sample period of returns is from Q1 1990 to Q4 2015.

Q1 Holding Period Q1 to Q4 Holding Period

Raw Rx CAPM 3-Factors 4-Factors Raw Rx CAPM 3-Factors 4-Factors

CIP

P

1 1.51% -1.89% -0.99% -1.23% 1.45% -1.95% -1.06% -1.20%

(0.87) (-1.99) (-1.57) (-1.85) (0.84) (-2.09) (-1.73) (-1.84)

2 1.47% -2.03% -1.11% -1.62% 1.45% -1.88% -1.00% -1.57%

(0.83) (-2.14) (-1.65) (-2.34) (0.87) (-2.12) (-1.76) (-2.71)

3 2.09% -1.02% -0.42% -0.59% 2.16% -0.94% -0.30% -0.41%

(1.36) (-1.35) (-0.69) (-0.91) (1.45) (-1.42) (-0.67) (-0.86)

4 2.00% -0.85% -0.41% -0.58% 2.47% -0.41% 0.11% 0.07%

(1.42) (-1.22) (-0.68) (-0.89) (1.78) (-0.64) (0.23) (0.14)

5 3.34% 1.00% 1.39% 1.89% 3.18% 0.90% 1.21% 1.62%

(2.81) (1.55) (2.30) (3.05) (2.79) (1.53) (2.15) (2.79)

LS 1.83% 2.90% 2.38% 3.12% 1.73% 2.85% 2.27% 2.81%

5-1 (1.66) (2.81) (2.57) (3.28) (1.56) (2.76) (2.54) (3.02)

Panel B. This table records the average time series loading of risk factors by the long-short 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡 calendar time long-short

portfolio strategy depicted in the previous panel for 1 quarter and 1 to 4 quarter holding periods.

Q1 Holding Period Q1 to Q4 Holding Period

Long Short (5 Minus 1) Long Short (5 Minus 3) Long Short (5 Minus 1) Long Short (5 Minus 3)

Alpha 2.90% 2.38% 3.12% 2.02% 1.81% 2.48% 2.85% 2.27% 2.81% 1.84% 1.51% 2.03%

(2.81) (2.57) (3.28) (2.48) (2.27) (3.02) (2.80) (2.54) (3.02) (2.52) (2.36) (2.94)

Mktrf -0.57 -0.33 -0.41 -0.41 -0.29 -0.37 -0.59 -0.32 -0.38 -0.44 -0.28 -0.33

(-4.68) (-2.77) (-3.42) (-4.29) (-2.90) (-3.57) (-4.89) (-2.86) (-3.29) (-5.08) (-3.25) (-3.86)

SMB -0.76 -0.85 -0.40 -0.49 -0.84 -0.91 -0.51 -0.58

(-3.84) (-4.34) (-2.36) (-2.88) (-4.43) (-4.76) (-3.60) (-4.07)

HML 0.67 0.60 0.29 0.22 0.75 0.69 0.43 0.38

(4.46) (3.97) (2.20) (1.68) (5.16) (4.73) (3.96) (3.48)

UMD -0.28 -0.25 -0.20 -0.19

(-2.44) (-2.54) (-1.82) (-2.35)

R2 0.17 0.34 0.38 0.14 0.20 0.24 0.18 0.40 0.41 0.19 0.34 0.37

45

Table 7. Capital-Return-Induced Price Pressure, Calendar Portfolios Sort Characteristics

This table examines the characteristics related to size and future capital returns for stocks sorted on Capital-Return-Induced Price

Pressure.

Panel A. This panel records the average Book Equity and Market Equity Size in $ billions for portfolios sorted on CIPP for several

periods of the sample.

Q1 1990 Q1 2003 Q1 2015

Book Equity Market Equity Book Equity Market Equity Book Equity Market Equity

CIP

P

1 0.049 0.244 0.071 0.321 0.114 0.792

2 0.050 0.150 0.130 0.393 0.176 1.100

3 0.040 0.105 0.171 0.429 0.221 0.999

4 0.080 0.162 0.210 0.449 0.312 1.056

5 0.117 0.262 0.345 0.845 0.880 1.674

Panel B. This records the average share buyback and change in dividend paid quarterly by the firms in these quintile portfolios over the

next 12 years. The sample is from 1990 to 2015. The portfolio initiation period is from 1990 to 2007 for the 24 Quarter Average and

1990 to 2003 for the 48 Quarter Average. That is:

N Quarter ∆Buyback =1

𝑁∑Buyback𝑖,𝑡+𝑖

𝑁

𝑖=1

−1

20∑Buyback𝑖,𝑡−𝑖

20

𝑖=1

.

ΔDividend and ΔIssuance are calculated in the same way.

12 Quarter Average 24 Quarter Average 48 Quarter Average

ΔBuyback ΔIssuance ΔDivy ΔBuyback ΔIssuance ΔDivy ΔBuyback ΔIssuance ΔDivy

CIP

P

1 0.13% -1.30% 0.02% 0.17% -1.38% 0.02% 0.25% -1.56% 0.06%

(25.33) (-10.08) (5.76) (24.93) (-12.31) (9.48) (21.72) (-10.64) (7.83)

2 0.14% -1.33% 0.01% 0.18% -1.50% 0.03% 0.26% -1.41% 0.06%

(27.62) (-11.58) (7.34) (30.13) (-10.44) (8.87) (26.92) (-14.02) (8.22)

3 0.15% -1.07% 0.02% 0.19% -1.14% 0.03% 0.26% -1.38% 0.06%

(29.06) (-5.70) (7.88) (31.82) (-6.88) (8.86) (29.00) (-12.52) (8.99)

4 0.15% -0.84% 0.04% 0.19% -0.91% 0.05% 0.26% -1.39% 0.06%

(25.65) (-9.79) (10.34) (32.94) (-8.80) (12.36) (28.50) (-10.55) (12.35)

5 0.17% -0.87% 0.06% 0.21% -0.90% 0.07% 0.25% -0.93% 0.08%

(19.89) (-8.64) (12.21) (22.67) (-8.71) (16.24) (24.49) (-7.14) (16.99)

LS 0.05% 0.43% 0.04% 0.04% 0.48% 0.05% 0.00% 0.63% 0.02%

5-1 (4.94) (2.73) (7.51) (3.75) (3.39) (11.18) (-0.26) (3.34) (3.82)

46

Table 8. Future Payout and Issuance Predictions

This table records the Fama-Macbeth regression coefficients of changes in quarterly buyback, dividend payments, and issuances

over 12, 24, 48 horizons on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 and various controls. The regressors are scaled such that their standard deviations are 1.

ΔBuyback is the difference between the average N quarter future Buybacks and the average buyback from the past 5 years:

N Quarter ∆Buyback =1

𝑁∑Buyback𝑖,𝑡+𝑖

𝑁

𝑖=1

−1

20∑Buyback𝑖,𝑡−𝑖

20

𝑖=1

.

ΔDividend and ΔIssuance are calculated in the same way. The t-statistics are Newey-West corrected with N lags to account for

overlapping observations.

Panel A. Future quarterly average buybacks are regressed on various characteristics.

12 Quarter ΔBuyback 24 Quarter ΔBuyback 48 Quarter ΔBuyback

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 0.03% 0.01% 0.02% -0.01% -0.00% -0.02%

(2.10) (0.05) (1.46) (-0.73) (-0.36) (-5.37)

𝐿𝑀𝐸𝑖,𝑡−1 0.00% 0.02% 0.03%

(-0.46) (1.71) (1.53)

𝐿𝐵𝐸𝑖,𝑡−1 0.04% 0.03% 0.04%

(5.90) (4.73) (5.99)

𝑅𝑒𝑡12𝑖,𝑡−1 0.00% 0.00% 0.00%

(0.75) (1.23) (1.36)

𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 -0.11% -0.12% -0.08%

(-5.01) (-5.78) (-5.83)

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡−1 -0.01% 0.00% 0.01%

(-1.61) (0.06) (1.15)

Avg. R2 0.0069 0.0681 0.0080 0.0945 0.0074 0.1518

Panel B. Changes in average quarterly dividends are regressed on various characteristics.

12 Quarter ΔDividend 24 Quarter ΔDividend 48 Quarter ΔDividend

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 0.03% 0.02% 0.03% 0.03% 0.01% 0.01%

(3.37) (3.33) (5.76) (5.42) (2.92) (1.35)

𝐿𝑀𝐸𝑖,𝑡−1 -0.02% -0.03% -0.02%

(-7.84) (-4.53) (-2.11)

𝐿𝐵𝐸𝑖,𝑡−1 0.02% 0.03% 0.02%

(5.24) (4.41) (4.62)

𝑅𝑒𝑡12𝑖,𝑡−1 0.01% 0.01% 0.01%

(3.10) (3.49) (1.72)

𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 0.02% -0.01% -0.02%

(0.96) (-2.33) (-5.23)

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡−1 0.01% 0.01% -0.01%

(1.03) (1.49) (-1.30)

Avg. R2 0.012 0.0438 0.013 0.0272 0.010 0.0292

47

Panel C. Future average quarterly issuances are regressed on various characteristics.

12 Quarter ΔIssuance 24 Quarter ΔIssuance 48 Quarter ΔIssuance

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 0.30% 0.59% 0.27% 0.67% 0.31% 0.59%

(3.47) (7.11) (5.81) (10.85) (7.36) (6.32)

𝐿𝑀𝐸𝑖,𝑡−1 0.08% 0.16% 0.53%

(0.50) (0.65) (2.74)

𝐿𝐵𝐸𝑖,𝑡−1 -0.39% -0.60% -1.14%

(-1.48) (-2.07) (-3.34)

𝑅𝑒𝑡12𝑖,𝑡−1 0.08% 0.02% -0.01%

(0.99) (0.27) (-0.08)

𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 3.65% 3.01% 1.14%

(15.76) (17.13) (7.29)

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡−1 -0.26% 0.09% 0.37%

(-0.96) (1.45) (5.75)

Avg. R2 0.0019 0.3098 0.0018 0.2363 0.0076 0.1581

48

Table 9. Cash-Merger-Induced Price Pressure, Calendar Portfolios Sort

This table records the excess returns and risk-adjusted alphas of market cap value weighted portfolios sorted on 𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡.

Assuming proportional reinvestment to initial fund values, capital returns are aggregated to the stock level in this table.

Specifically, Merger-Return-Induced Price Pressure to stock i is calculated as:

𝑀𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝑀𝑒𝑟𝑔𝑒𝑟_𝐹𝑙𝑜𝑤𝑗,𝑡,𝑗

where

𝑀𝑒𝑟𝑔𝑒𝑟_𝐹𝑙𝑜𝑤𝑗,𝑡 = ∑ 𝑊𝑒𝑖𝑔ℎ𝑡𝑖,𝑗,𝑡−1𝑖∈𝐶𝑎𝑠ℎ𝑀𝑒𝑟𝑔𝑒𝑟

.

Non-dividend-paying stocks that have not had any capital return over the past 5 years are sorted into quintile portfolios and the

table reports the returns of overlapping portfolio strategies that hold each portfolio for varying number of quarters. Stocks with

market capitalizations lower than the bottom decile of NYSE and stocks at the bottom decile of percentage mutual fund holdings

are filtered. The sample period of returns is from Q1 1990 to Q4 2015.

High Cash Merger Deal Quarters 1Q Return Low Cash Merger Deal Quarters 1Q Return

Raw Rx CAPM 3-Factors 4-Factors Raw Rx CAPM 3-Factors 4-Factors

Cash

Merg

ers Ind

uced

Price P

ressure

1 -0.21% -1.56% -2.46% -2.66% 2.53% -1.20% 0.93% 1.52%

(-0.06) (-0.81) (-2.09) (-2.23) (1.22) (-1.03) (0.93) (1.44)

2 1.23% -0.04 % -0.47% -0.88% 2.08% -1.52% 0.25% 1.06%

(0.39) (-0.02) (-0.42) (-0.85) (1.07) (-1.53) (0.30) (1.22)

3 0.59% -0.46% -0.83% -0.81% 2.48% -1.07% 0.73% 0.69%

(0.23) (-0.39) (-0.74) (-0.69) (1.31) (-1.14) (1.00) (0.87)

4 2.72% 1.47% 0.54% 0.54% 1.63% -1.80% -0.61% -0.55%

(0.89) (1.06) (0.59) (0.57) (0.90) (-2.09) (-0.78) (-0.65)

5 2.79% 1.80% 1.15% 0.90% 2.15% -1.29% -0.50% -0.19%

(1.11) (1.39) (1.10) (0.87) (1.18) (-1.47) (-0.76) (-0.27)

LS 3.00% 3.37% 3.61% 3.55% -0.38% -0.09% -1.43% -1.71%

5-1 (1.37) (1.61) (2.43) (2.32) (-0.32) (-0.08) (-1.38) (-1.53)

49

Appendix

Table A1. Investor Flow Calculated Using Total Fund Returns and Flow Calculated Using

NAV Returns

I calculate the amount of capital gains and dividend distributions by comparing the difference between total return

and NAV price return per share of mutual fund.

Specifically:

𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑗,𝑡 = (𝑅𝑒𝑡𝑗,𝑡 − 𝑅𝑒𝑡𝑗,𝑡𝑁𝐴𝑉) .

The following plots the monthly distribution schedule of Equity Open Ended Mutual Funds in the CRSP database

from 1990 to 2015.

50

I adjust 𝑅𝑒𝑡𝑖,𝑡𝑁𝐴𝑉 for splits and mergers in shares. Here, for 1 dollar invested in fund, j, 𝑅𝑒𝑡𝑗,𝑡 is the net return that

includes the price return of the share plus the distribution amount. The distribution amount can be taken as cash by

the investor or be reinvested as new shares of the fund j. Investor flow can be defined as the outflow due to

distribution plus other residual investor flow.

𝑃𝐹𝑙𝑜𝑤𝑗,𝑡 = 𝑅𝑒𝑠𝐹𝑙𝑜𝑤𝑗,𝑡 + 𝛽 ∙ 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑗,𝑡 .

𝛽 can be estimated if we assume that 𝑅𝑒𝑠𝐹𝑙𝑜𝑤𝑗,𝑡 is uncorrelated with 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑗,𝑡. I obtain a coefficient of -

0.153 (t = -8.93) for this sample period for mutual funds with at least 10 million dollars under management. That is,

85% of the distributions are returned to investors. I also plot the time series of the beta here.

𝑃𝐹𝑙𝑜𝑤𝑗,𝑡

𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑗,𝑡 -0.153 -0.180 -0.144

(-8.93) (-10.62) (-10.72)

Month Fixed No Yes Yes

Fund Fixed No No Yes

R2 0.16% 1.32% 8.73%

N 1,461,636 1,461,636 1,461,457

51

Table A2.

Correlation between change in the holding in the current quarter and various different lags of dividend cashflow.

∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑡

𝑎𝑙𝑙 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−7 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−6 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−5 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−4 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−3 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−2 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−1 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+1 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+2 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+3 𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+4

∆𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑗,𝑡𝑎𝑙𝑙 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−7 0.034 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−6 0.036 0.820 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−5 0.037 0.786 0.820 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−4 0.044 0.769 0.789 0.803 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−3 0.036 0.758 0.778 0.779 0.797 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−2 0.035 0.733 0.769 0.767 0.777 0.799 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡−1 0.033 0.717 0.736 0.755 0.760 0.771 0.797 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡 0.020 0.713 0.725 0.723 0.750 0.759 0.773 0.792 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+1 0.031 0.701 0.722 0.740 0.741 0.751 0.773 0.936 0.771 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+2 0.033 0.706 0.730 0.726 0.728 0.744 0.812 0.749 0.741 0.773 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+3 0.023 0.704 0.718 0.711 0.720 0.765 0.730 0.727 0.739 0.737 0.775 1.000

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡+4 0.016 0.690 0.702 0.700 0.719 0.708 0.718 0.727 0.756 0.739 0.743 0.775 1.000

52

Table A3. Capital-Return-Induced Price Pressure, Fama-Macbeth

Assuming proportional reinvestment to initial fund values, capital-return are aggregated to the stock level in this table.

Specifically, Dividend- and Buyback-Induced Price Pressure to stock i is calculated as:

𝐷𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

,

and:

𝐵𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

.

This table records the Fama-Macbeth regression coefficients of average quarter excess returns on 𝐷𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1, 𝐵𝐼𝑃𝑃̅̅ ̅̅ ̅̅

�̅�,𝑡−1, and

various controls. 𝐿𝑀𝐸𝑖,𝑡−1 is the log market capitalization. 𝐿𝐵𝐸𝑖,𝑡−1 is the log book equity from 1 quarter prior. 𝑅𝑒𝑡12𝑖,𝑡−1 is the

prior 12-month return. 𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 is the percentage increase in shares outstanding over the past 5 years. 𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅ is the

contemporaneous flow-induced price pressure to the period of the excess returns. Only non-dividend-paying stocks that have not

had any repurchasing events over the past 5 years are used in the regression. All the regressor variables are standardized by their

unconditional standard deviation. The t-statistics in the first 3 columns are Newey-West with a single lag. The t-statistics in the

next 3 columns are Newey-West with 4 lags to account for overlapping returns.

1 Quarter Excess Returns

(𝑅𝑒𝑡𝑖 − 𝑅𝑓)𝑡−1→𝑡 4 Quarter Excess Returns

1/4 ∙ (𝑅𝑒𝑡𝑖 − 𝑅𝑓)𝑡−1→𝑡+3

𝐷𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 -0.12% 0.01% 0.19% 0.40% 0.46% 0.48%

(-0.32) (0.05) (0.59) (1.20) (1.87) (2.05)

𝐵𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡−1 1.41% 1.29% 1.18% 0.66% 0.43% 0.58%

(2.73) (2.61) (2.43) (1.66) (1.32) (1.80)

𝐿𝑀𝐸𝑖,𝑡−1 -0.33% -0.15% -0.39% -0.57%

(-0.69) (-0.30) (-0.77) (-1.17)

𝐿𝐵𝐸𝑖,𝑡−1 -0.01% -0.57% 0.31% 0.59%

(-0.02) (-0.13) (0.61) (1.43)

𝑅𝑒𝑡12𝑖,𝑡−1 0.78% 0.78% -0.20% -0.27%

(1.21) (1.17) (-0.35) (-0.47)

𝐼𝑠𝑠𝑢𝑒𝑖,𝑡−1 0.67% -0.61% -0.74% -0.79%

(-2.92) (-2.56) (-3.57) (-3.67)

𝐹𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡−1→𝑡−1+𝑘 2.38% 1.79%

(6.38) (4.48)

Avg. R2 0.010 0.033 0.038 0.014 0.037 0.047

53

Table A4. Capital-Return-Induced Price Pressure, Calendar Portfolios Sort

Assuming proportional reinvestment to initial fund values, dividend returns are aggregated to the stock level in this table.

Specifically, Dividend- and Buyback-Induced Price Pressure to stock i is calculated as:

𝐷𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐷𝑖𝑣_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

,

𝐵𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐵𝑢𝑦_𝐹𝑙𝑜𝑤𝑗,𝑡𝑗

,

Panel A. This records the excess returns and risk-adjusted alphas of market cap value weighted portfolios sorted on 𝐷𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡. Non-

dividend paying stocks that have not had any capital return over the past 5 years are sorted into quintile portfolios and the table

reports the returns of overlapping portfolio strategies that hold each portfolio for varying number of quarters. The sample period of

returns is from Q1 1990 to Q4 2015.

Q1 Holding Period Q1 to Q4 Holding Period

Raw Rx CAPM 3-Factors 4-Factors Raw Rx CAPM 3-Factors 4-Factors

DIP

P

1 1.30% -2.10% -1.10% -1.59% 1.68% -1.68% -0.74% -1.10%

(0.73) (-2.03) (-1.65) (-2.31) (0.97) (-1.74) (-1.23) (-1.74)

2 1.74% -1.51% -0.61% -0.93% 1.80% -1.49% -0.59% -0.85%

(1.06) (-1.75) (-1.07) (-1.54) (1.09) (-1.72) (-1.13) (-1.52)

3 1.90% -1.14% -0.37% -0.66% 1.93% -1.11% -0.35% -0.49%

(1.20) (-1.24) (-0.51) (-0.86) (1.26) (-1.39) (-0.62) (-0.82)

4 2.77% -0.40% 0.26% 0.10% 2.20% -0.86% -0.21% -0.24%

(1.75) (-0.49) (0.38) (0.14) (1.49) (-1.27) (-0.40) (-0.44)

5 2.80% 0.53% 0.79% 1.18% 2.69% 0.26% 0.58% 0.92%

(2.42) (0.84) (1.27) (1.83) (2.30) (0.49) (1.15) (1.76)

LS 1.49% 2.63% 1.88% 2.77% 1.01% 1.94% 1.32% 2.01%

5-1 (1.23) (2.30) (1.98) (2.85) (0.94) (1.90) (1.57) (2.33)

Panel B. This record the excess returns and risk adjusted alphas of market cap value weighted portfolios sorted on 𝐵𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡. Non-

dividend paying stocks that have not had any capital return over the past 5 years are sorted into quintile portfolios and the table

report the returns of overlapping portfolio strategy that holds each portfolio for varying number of quarters. The sample period of

returns is from Q1 1990 to Q4 2015.

Q1 Holding Period Q1 to Q4 Holding Period

Raw Rx CAPM 3-Factors 4-Factors Raw Rx CAPM 3-Factors 4-Factors

BIP

P

1 1.24% -2.29% -1.48% -1.94% 1.13% -2.28% -1.49% -1.69%

(0.69) (-2.41) (-2.16) (-2.74) (0.66) (-2.63) (-2.52) (-2.71)

2 1.52% -1.86% -1.20% -1.43% 1.64% -1.63% -0.91% -1.32%

(0.91) (-2.27) (-1.81) (-2.05) (1.02) (-2.15) (-1.79) (-2.52)

3 1.86% -1.08% -0.50% -0.74% 1.72% -1.23% -0.72% -1.00%

(1.28) (-1.50) (-0.89) (-1.24) (1.23) (-2.05) (-1.73) (-2.32)

4 3.05% 0.26% 0.61% 0.78% 2.84% -0.03% 0.45% 0.59%

(2.19) (0.36) (0.92) (1.11) (2.04) (-0.04) (0.83) (1.02)

5 2.94% 0.82% 1.10% 1.51% 3.27% 0.99% 1.30% 1.62%

(2.65) (1.28) (1.79) (2.37) (2.95) (1.92) (2.78) (3.34)

LS 1.70% 3.11% 2.57% 3.46% 2.14% 3.23% 2.79% 3.31%

5-1 (1.46) (3.04) (2.74) (3.62) (2.19) (3.73) (3.56) (4.07)

54

Table A5. Capital-Return-Induced Price Pressure, Calendar Portfolios Sort on All Stocks

Assuming proportional reinvestment to initial fund values, capital returns are aggregated to the stock level in this table.

Specifically, Capital-Return-Induced Price Pressure to stock i is calculated as:

𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅ ̅𝑖,𝑡 =∑

𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1∑𝑆ℎ𝑎𝑟𝑒𝑠𝐻𝑒𝑙𝑑𝑖,𝑗,𝑡−1

𝐶𝑎𝑝_𝐹𝑙𝑜𝑤𝑗,𝑡

𝑗.

This records the excess returns and risk-adjusted alphas of market cap value weighted portfolios sorted on 𝐶𝐼𝑃𝑃̅̅ ̅̅ ̅̅�̅�,𝑡. All stocks with

market caps greater than the tenth percentile of NYSE firms and at the top 9 deciles of percentage mutual fund holdings are sorted

into quintile portfolios. The table reports the returns of an overlapping portfolio strategy that holds each portfolio for one or four

quarters. The sample period of returns is from Q1 1990 to Q4 2015.

Q1 Holding Period Q1 to Q4 Holding Period

Raw Rx CAPM 3-Factors 4-Factors Raw Rx CAPM 3-Factors 4-Factors

CIP

P

1 1.88% -1.34% -0.61% -1.08 % 1.83% -1.33% -0.62% -1.01%

(1.20) (-1.80) (-1.26) (-2.19) (1.19) (-1.83) (-1.37) (-2.17)

2 1.96% -0.63% -0.35% -0.40% 2.20% -0.39% -0.09% -0.18%

(1.64) (-1.51) (-1.18) (-1.29) (1.86) (-1.01) (-0.36) (-0.71)

3 2.57% 0.42% 0.43% 0.44% 2.48% 0.42% 0.46% 0.41%

(2.61) (1.26) (1.31) (1.25) (2.63) (1.36) (1.51) (1.26)

4 2.66% 0.90% 0.77% 0.84% 2.58% 0.78% 0.67% 0.73%

(3.18) (2.55) (2.22) (2.27) (3.10) (2.58) (2.26) (2.33)

5 1.88% 0.45% 0.08% 0.31% 1.98% 0.56% 0.17% 0.33%

(2.67) (1.29) (0.33) (1.23) (2.81) (1.59) (0.73) (1.33)

LS 0.00% 1.80% 0.69% 1.39% 0.15% 1.88% 0.79% 1.34%

5-1 (0.00) (1.74) (1.07) (2.13) (0.12) (1.85) (1.29) (2.12)