2015-20
Unique Dividends for Retail Shareholders:
Evidence from Shareholder perks
Katsushi Suzuki

Unique Dividends for Retail Shareholders: Evidence from Shareholder Perks

Katsushi Suzuki
Graduate School of Business Administration,
Kobe University, Japansuzukatsu@b.kobe-u.ac.jp
First version dated February 11, 2014
Version H dated June 2, 2015

Abstract
Previous studies indicate that retail investors are thought of as noise traders, price-takers, and freeriders. However, many managers worldwide embrace the expense and treat their retail shareholders
favorably. We examine why firms provide small individual stockholders with special treatment byusing Japanese shareholder perks, which resemble dividends that are only for small individual
shareholders. We find that firms with a low number of individual shareholders and high board
ownership tend to initiate shareholder perks. The number of individual shareholders increases
dramatically after perk initiation. The high attractiveness of perks for individual investors is
positively associated with the stock return on the initiation announcement day and negatively
associated with the cost of capital after initiation. The ex-perk day return is significantly negative,
and the trading volume around the ex-perk day is significantly positive. The number of individual
shareholders is positively associated with the discount premium and the abnormal volume aroundthe ex-day. These results imply that firms introduce perks to attract individuals and that apreference clientele effect clearly exists in dealings around the ex-day.

JEL classification: G14, G30, G35
Keywords: Shareholder perks, Retail investors, Clientele effect, Ex-day return1. Introduction
Numerous existing studies indicate that small individual investors and shareholders are thought ofas proverbial noise traders, price-takers, and free riders (e.g., Grossman and Hart (1980), Kyle
(1985), Black (1986)). However, many firms and managers assume significant expense and treat
their small shareholders favorably by providing, e.g., an information session, a factory tour or ashareholder perk.1 Why do firms provide small shareholders with special treatment? How does
this special treatment affect the shareholder clientele and firm value? Shareholder perks provide a
unique opportunity to examine these questions.
A shareholder perk is a gift to shareholders. Such gifts are not cash; instead, they consist of
products either from the firm or from other firms.2 Although these perks are uncommon in the
U.S., many companies worldwide provide shareholder perks.3,4 At the end of 2013, approximately
28% of all public companies in Japan, 17% of the FTSE 100 companies on the London Stock
Exchange, and 12% of the ATX 100 companies on the Australian Securities Exchange had adopted
shareholder perks.5 Because shareholder perks of approximately the same value are given to every
shareholder, retail shareholders have advantages related to the value of the shareholder perk overtheir investment value.6 From 2001 to 2011, the average perk yield for the smallest shareholders

Some countries’ governments have also adopted preferential treatment for retail shareholders. For example, the U.K. has
introduced the individual saving account, ISA, which makes income and capital gains tax-exempt for investors with investment
capital of 11,280 pounds or less. Japan introduced a system similar to the U.K. version of the ISA in 2014.
2 Other firms’ products include food, gift cards, etc. For example, NIPRO, which is the largest producer of medical equipment inJapan, provides a ¥15,000 ($150) JCB gift card to every shareholder as a shareholder perk. The minimum trading size of NIPRO
investors is ¥901,000 (approximately $9,010), and the perk yield for minimum-size shareholders is approximately 1.66% based on
NIPRO’s stock price on February 28, 2014.
3 The Wall Street Journal examined some perks from U.S. companies on December 13, 2013.
(http://online.wsj.com/news/articles/SB10001424052702303330204579250763759331636)
4 Tirole (2005) shows the ownership of common stock for listed companies in Germany, Japan, the U.K. and the U.S. Whileindividual ownership in the U.S. was 42.5%, individual ownership in other countries was less than 20%. One reason thatshareholder perks are widely used among countries other than the U.S. may be that those countries have stronger incentives toencourage individual investors to participate in the market than the U.S. does.
5 Fama and French (2001) report that the proportion of U.S. dividend payers was only 20.8% in 1999 and has declined
substantially in the last two decades.
6 Some business reports argue that perks affect stock returns. For example, consider the case of Coles Myers, an Australiancompany. After introducing a shareholder perk (a shareholder discount card), the number of shareholders swelled from 68,000 to
580,000. When Coles Myers cancelled its discount card in 2005, the company’s share price fell by nearly 17% the following day(http://www.theage.com.au/articles/2004/07/29/1091080376943.html?from=storylhs).
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in Japanese public companies is approximately 2.0% in our sample, which is larger than the average
dividend yield (approximately 1.6%). We thus consider shareholder perks to be a pseudo-dividend
only for retail shareholders.7 The aggregate value of the perks that firms offer is markedly smaller
than a total dividend payment. The average total payment for perks is approximately 0.1% of firm
value in our sample. It would be difficult to use shareholder perks as a means to signal future
performance or to alleviate a free cash flow problem, such as a cash dividend.
To examine the role of shareholder perks, we offer the following three hypotheses: (1) theInvestor base hypothesis, (2) the Entrenchment hypothesis and (3) the Advertising hypothesis. The
introduction of perks may attract attention from individual investors who face informational
problems, e.g., the winner’s curse problem, because adopting shareholder perks only increases anindividual's yield or improves their recognition. 8 Merton (1987) argues that an increase in therelative size of a firm’s investor base reduces the firm’s cost of capital, i.e., the cost of information
completion, and increases its market value. The Investor base hypothesis is that firms introduce
perks as a tool to increase the number of retail shareholders. The hypothesis predicts that firms
with low attention from individuals will be likely to introduce perks, and the number of individual
shareholders will increase after the introduction. The hypothesis also predicts that the initiation ofa shareholder perk will reduce the firm's cost of capital after perk adoption and raise the firm's
stock price on the announcement day of adoption.
The introduction of shareholder perks may change a firm’s ownership structure because
shareholder perks encourage investments by individual shareholders. Grossman and Hart (1980)
and Shleifer and Vishny (1986) argue that retail shareholders are not likely to play a monitoringrole because their ownership is too small. Further, because shareholder perks increase the payout

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Investors are not taxed on shareholder perks.
Rock (1986) and Beatty and Ritter (1986) argue that newly listed firms pay sweeteners to uninformed investors who face thewinner's curse problem to encourage their participation in an offering. Barber and Odean (2008) and Lou (2014) indicate that
individual investors are likely to purchase attention-grabbing stocks.
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yield only for retail shareholders, the special treatment transfers wealth from large shareholders toretail shareholders. Large shareholders who have a monitoring role and dislike such a transfer willsell off their positions after shareholder perks are initiated. Therefore, managers may introduce
shareholder perks to entrench themselves (Entrenchment hypothesis). Several existing papers showthat manager entrenchment destroys firm value (e.g., Stulz (1988), Morck, Shleifer, and Vishny,(1988), McConnell and Servaes, (1990), Barclay, Holderness, and Sheehan (2007), and Faleye(2007)). The Entrenchment hypothesis predicts that the stock return on the initiation announcement
day will drop and that institutional ownership and operating performance will decrease after the
introduction.
In many cases, firms pay out their own products as perks. Such firms may use their perks as
an effective advertising tool because the opportunity for their individual shareholders to use theirproducts increases. The Advertising hypothesis predicts that a business-to-consumer company(hereafter, a B-to-C company) will be likely to introduce its product as perks. Furthermore, theoperating performance of a B-to-C company that uses its own products as perks will increase after
adoption, and this firm's stock return on the announcement day of adoption will be positive.
All of the above-mentioned hypotheses are based on the premise that small investors have ahigher interest in shareholder perks than large investors. The difference in preferences among
investors has attracted attention from many researchers. Numerous existing papers examine thedifference in tax and dividend preferences among investors on ex-dividend day, i.e., the tax anddividend clientele effect (e.g., Elton and Gruber (1970), Kalay (1982), Eades, Hess, and Kim (1984),
Michaely (1991), Michaely, Thaler, and Womack (1995), Green and Rydqvist (1999), Seida (2001)
and Graham, Michaely, and Roberts (2003)). However, these studies provide mixed evidence about
whether the clientele effect exists. Shareholder perks have a stronger influence on the difference inpreferences among shareholders than a change in dividends or capital gain taxes. Furthermore, as3

mentioned above, the total value of shareholder perks is low. If the difference in preferences among
investors does not affect stock trading, the ex-perk day return and volume will be not significantly
changed, unlike ex-dividend day stock behavior. 9 The stock behavior around the ex-perk day
provides an opportunity to clearly examine whether the clientele effect exists.
This paper uses Japanese data to examine these predictions, which provides several benefits.
We can collect extensive perk data because many Japanese firms have introduced shareholder perks.
In addition, Japanese public companies are required to release the number of individual
shareholders each year. Therefore, we are able to examine the behavior of individual shareholders
carefully.
We find the following. First, firms that have a low number of individual shareholders, havehigh board ownership, and operate as B-to-C companies tend to initiate shareholder perks. Second,
the number of individual shareholders increases dramatically after perk initiation. Although theaverage institutional ownership does not change around the perk initiation, the average individualownership increases by approximately 3%. Third, operating performance does not change aroundperk initiation. Fourth, the announcement day return is significantly positive, at approximately 3%.
The increasing ratio of the number of individual shareholders around initiation is positively
associated with the announcement return. However, B-to-C companies do not significantly
associate with the announcement return. Fifth, the average cost of capital decreases significantly
after perk initiation. Additionally, the decrease in the cost of capital after perk initiation is positively
associated with Merton’s shadow costs and negatively related with the increasing ratio of thenumber of individual shareholders. Finally, the price drop on the ex-perk day is nearly equal to theperk yield for retail shareholders. The average abnormal trading volumes around the ex-perk day
are significantly positive. The number of individual shareholders is positively associated with the9

Even if the market is perfect, the ex-dividend day return will be negative.

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price drop and the trading volume around the ex-perk day.
These findings are consistent with the Investor base hypothesis and inconsistent with boththe Entrenchment and the Advertising hypotheses. Our findings imply that special treatment
provided to individual stockholders is in the interest of all current shareholders.10 Merton (1987)
argues as follows: “If an increase in the size of a firm’s investor base is in the best interest of thecurrent stockholders, then management should expend resources of the firm to induce investors
who are not currently shareholders to incur the necessary costs of becoming aware of the firm
(p.500).” Our findings regarding stock behavior around the ex-perk day are also consistent withthe dynamic dividend clientele model of Michaely and Vila (1995) and Michaely and Vila (1996),
in which the expected price drop and the trading volume around the ex-day are reflected by thedifference in preference among investors.
Our results contribute to the existing literature in several ways. First, our paper is the first thatexamines the role of shareholder perks as special treatment for retail shareholders. Because
shareholder perks are used around the world, our results will provide many firms in these countries
with a suggestion, e.g., the drivers of adoption, regarding the effect of perks on ownership change,
firm value, and stock behavior around the ex-perk day.
Second, our paper contributes to the Investor base literature. A large number of studies
examine various events or variables to test Merton’s (1987) Investor base model, e.g., exchangechanges (Kadlec and McConnell (1994)), cross-listings for foreign companies (Foerster and
Karolyi (1999), changes in minimum trading units (Amihud, Mendelson, and Uno (1999)), theinitiation of analyst coverage (Irvine (2003)), advertising expenditures (Grullon, Kanatas andWeston (2004) and Lou (2014)) and company name fluency (Green and Jame (2013)). Most
existing studies, excluding Amihud et al. (1999) and Green and Jame (2013), use the number of

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B-to-C companies may not use their products as perks for advertising but instead for cost savings.

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analysts, institutional shareholders, and total shareholders in recognizing investors and do not focus
on the number of retail shareholders. Further, Bondnruk and Östberg (2013) show that theshareholder base affects the payout policy. Our paper extends these papers by using the uniquepayout and focusing on the number of retail shareholders.
Finally, our paper contributes to the clientele literature on payout policy. Numerous previous
papers examine the clientele effect through the ownership shift around dividend adoption anddividend-related taxation changes (Michaely et al. (1995), Grinstein and Michaely (2005), Graham
and Kumar (2006), and Korkeamaki, Liljeblom and Pasternack (2010)), stock return around exdividend day (Elton and Gruber (1970), Kalay (1982), Eades et al. (1984), Michaely (1991), Greenand Rydqvist (1999), and Graham et al. (2003)), and trading volume around ex-dividend day(Michaely et al. (1995), Seida (2001) and Rantapuska (2008)). Using the unique payout event, ourpaper provides findings that are consistent with the clientele effect in these studies.
The remainder of the paper is organized as follows. Section 2 describes the features ofJapanese shareholder perks. Section 3 describes our data. In Section 4, we report our empiricalresults and discuss our findings. Section 5 contains our conclusions.

2. Shareholder perks in JapanA shareholder perk is a gift to shareholders. The gift is not in the form of money but consists of thefirm’s products or those of another firm. For example, ANA, a famous Japanese airline, provides a
50% discount coupon for airline tickets to shareholders who hold more than 1,000 shares (theminimum trade unit). The stock price of ANA on Feb 28, 2014 is ¥227. Therefore, investors who
invest ¥227,000 (approximately $2,270) in ANA stock can receive the discount coupon as a perk.
Figure 1 indicates the number and percentage of Japanese companies with shareholder perks.
The percentage of public companies with shareholder perks has increased gradually, exceeding
28% in 2011. During our sample period, the average percentage of public companies with6

shareholder perks is 24.75%. The number of firms with shareholder perks increased through 2008
but decreased as the total number of public firms in Japan decreased due to mergers, acquisitions
and delistings.
We examine the 6-digit industry classifications (Nikkei 139 small-size industry
classifications) of firm-year samples with shareholder perks from January 2001 to December
2011.11 The number of B-to-C companies is 15,246 firm-year samples (approximately 38.87% of
the total 39,228 firm-year samples), which is fewer than the number of business-to-businesscompanies (hereafter B-to-B companies). B-to-C companies (6,413 firm-year samples and 66.03%
of the total sample of 9,712) are more likely to adopt shareholder perks than B-to-B companies(3,299 firm-year samples and 33.97% of the total sample). Among B-to-C companies, 86.13% of
the firms that offer shareholder perks use their own products or services as perks (only 871 of the
B-to-C companies use other firms’ products, e.g., gift cards or food, as perks). Of the B-to-B sample,
46.98% use products that are relevant to their company as shareholder perks.12 These results are
consistent with the advertising tool concept, which states that firms tend to use perks as a marketingstrategy.
In approximately 50% of all companies that have adopted shareholder perks, the value of theperk that investors receive increases as the number of shares held increases. However, because therate of increase in perk value is quite low, in nearly all cases, minimum-unit stockholders receive
the highest value. For example, Toyo Suisan, which is a major food company, provides two typesof shareholder perks. The first is a gift of its food with a value of ¥3,000 for shareholders who holdbetween 1,000 (the minimum trade unit) and 5,000 shares. The second is a similar gift with a valueof ¥5,000 for shareholders who hold more than 5,000 shares. For large stockholders, the yield of
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We cannot use the 3-digit industry classification system to classify B-to-C companies. For example, auto parts suppliers andauto dealers are both classified as belonging to the auto industry. However, auto parts suppliers are B-to-B companies, whereas
auto dealers are B-to-C companies.
12 For example, although a parts company is a B-to-B company, when a parts company uses end products made using their parts
as shareholder parks, we classify such perks as their own product perks.

7the shareholder perk is quite small and nearly valueless.
Shareholders commonly receive their perks at the time that the company’s financial book closes.
If a firm pays its perks twice per year, shareholders can receive a perk at both book and interim
closings. If a firm also pays a dividend, the timing of the perk is the same as the timing of thedividend payment, i.e., the ex-dividend day and the ex-perk day are the same day.

3. Data
We use shareholder perk data collected from the Japan Company Handbook (in Japanese, Kaisha
Shiki Hou), for all publicly traded firms in Japan for the period from January 2001 to December
2011. The Japan Company Handbook provides data on the name and ticker of companies that haveadopted perks, the values of those perks, the minimum number of trading shares required to receive
shareholder perks, the types of perks offered, and the timing of perk payment.
Announcement dates of the initiation of shareholder perks are obtained from the eol ESPer
database. When a Japanese company introduces a stockholder perk, it is not required to make anofficial announcement through a press release. 13 Therefore, we collect information regarding
announcement days only from companies that voluntarily issued press releases. The eol ESPer
database contains voluntarily issued press releases announcing the initiation of perks. We obtained
429 samples of the announcement date of shareholder perks.
Financial data, the number of shareholders, the ownership of firms, the names of the top 10
shareholders, the value of dividends, the existence of anti-takeover defenses, and industry
classifications are obtained from the Nikkei NEEDS Financial Quest database. We collect data
related to stock prices, stock returns, and three-factor portfolio returns from the NPM portfolio
master database. We also obtain the liquidity factor of Pastor and Stambaugh (2003) from Professor
Hitoshi Takehara of Waseda University. Information on the number of directors on the board and13

See the Tokyo Stock Exchange home page (http://www.tse.or.jp/sr/faq/index.html#title_6_4).

8the number of outside directors is obtained using Board data from Toyo Keizai Inc.

4. Examination
4.1. The determinants of perk initiation
We begin by examining the determinants of shareholder perk initiation. Our sample for this analysis
includes public firm-years from 2001 to 2011 unless (1) the firm had already adopted a shareholder
perk, (2) the firm is a financial firm, or (3) the data used in the analysis are unavailable. The numberof firms in our firm-year sample is 27,196, and the number of firms that initiated perks in the next
year during the sample period is 544.14 We use the sample to examine the drivers of shareholder
perk initiation.
We first compare firm characteristics between the Initiation and Non-Initiation sample. Table
1 presents descriptive statistics for the sample. Column one shows the summary statistics for our
total sample; summary statistics for the Initiation and Non-Initiation samples are separately
documented in columns two and three, respectively. The total number of individual shareholders,# of individual shareholders, and the individual shareholder ratio, which is defined as thepercentage of individual shareholders to total shareholders, are the proxies for the number ofindividual investors. We find that the # of individual shareholders and the individual shareholder
ratio for the initiation sample are significantly lower than for the non-initiation sample. These
results are consistent with the idea that the initiation sample has a low number of individual
shareholders.
We compare ownership and governance structures. To calculate total retail investor
ownership, we define individual ownership, which deducts the ownership of individual
shareholders who are Top 10 shareholders from total individual ownership. Comparing individual14

To examine the determinants of perk initiation, we also use a sample constructed using initiation and matching samples (one to
one matching). We select as matching companies non-adopting firms where the ROA is nearest to the adopting firm in the rangeof 80% to 120% of scale (book assets). We arrive at nearly the same results.

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ownership, we find that the initiation sample tends to be much lower than the non-initiation sample.
These results imply that the initiation sample is less attractive to individual investors. We define
Top 10 ownership, Board ownership and Institutional ownership as the sum of the top 10
shareholders’ ownership, board members’ ownership and institutional ownership, respectively. The
mean Top 10 ownership for the initiation sample is approximately 4% larger than that for the noninitiation sample. In particular, the mean Board ownership for the initiation sample isapproximately 7% larger than that for the non-initiation sample. However, the mean Institutional
ownership in the initiation sample is approximately 2% lower than that in the non-initiation sample.
Companies in which management intentions are easily adopted may have introduced shareholder
perks. We use the % of outside directors and anti-takeover defense as proxies for corporate
governance. The variable % of outside directors is defined as the percentage of the number ofoutside directors to the total number of directors. Anti-takeover defense is a dummy variable thatis equal to one if the firm adopts an anti-takeover defense and zero otherwise. Although there is no
significant difference in % of outside directors between the initiation and the non-initiation samples,
the anti-takeover defense for the initiation sample is lower than that for the non-initiation sample.
To compare the other characteristics between the initiation and non-initiation samples, we
use several proxies: Market Asset, Leverage, ROA, Excess Cash, Tobin’s Q, Dividend, and B-to-C
company. Market Asset is a size proxy and is defined as the market capitalization at the closing
price of the previous fiscal year plus the debt book value of the firm from the previous fiscal year.
The number of shareholders may be strongly correlated with firm size. Leverage is defined as thesum of short- and long-term leverage over book assets of the previous fiscal year. There is no
significant difference in Market Asset and Leverage between the initiation and the non-initiation
samples. ROA is the operating profit over book assets of the previous fiscal year. Excess Cash is
the residual from regressing cash holdings on firm-specific characteristics and represents the firm’s
10excess cash holdings.15 TobinQ is a proxy of growth opportunities and is defined as the sum of
market capitalization and book debt over book assets. The mean ROA and TobinQ in the initiation
sample are larger than in the non-initiation sample. Dividend is a dummy variable that is equal to
one if a firm pays a dividend and zero otherwise. B-to-C company is a dummy variable that is equal
to one if the firm is a business-to-consumer company and zero otherwise. B-to-C companies tend
to initiate shareholder perks, which is consistent with the idea of shareholder perks as marketing
tools.
We next conduct multivariate logistic regression analyses of the determinants of shareholder
perk initiation. The dependent variable in the regression is Initiation, which is a dummy variableequal to one if a firm initiates a shareholder perk and zero otherwise. We focus on the independent
variable of ln(# of individual shareholders) and Individual shareholder ratio as the proxies for theInvestor base hypothesis. If companies with a low number of individual shareholders tend to initiate
shareholder perks to attract individual investors, the predicted signs of ln(# of individual
shareholders) and Individual shareholder ratio will be negative. We also focus on the ownershipof retail shareholders as a proxy of the incentive to change ownership. If the governance mechanism
discourages managers working toward entrenchment from adopting the perk, the proxies for thegovernance mechanism will be negatively associated with perk initiation. We use Institutional
ownership, Board ownership, % of outside directors, and Anti-takeover as the governance proxies.
The Entrenchment hypothesis predicts that these variables are negatively associated with perk
initiation. To examine the advertising concept, we use the B-to-C company variable. If firms
introduce a perk as an advertising tool, a B-to-C company will tend to introduce the perk. We
therefore predict that the coefficient of B-to-C company will be positive. As control variables, weused the following variables: ln(Market Asset), ROA, Leverage, Excess Cash, TobinQ, Dividend,
The residual from regressing cash holdings on firm-specific characteristics represents the firm’s excess cash holdings. The firststep regression is as follows: (Cash/Book Asset) = 0.55+0.1*Cash flow + 0.65*Volatility - 0.22*Leverage +1.64*Dividend 0.06*CapEx + 0.03*TobinQ - 0.01*ln(Market Asset)+ Year dummy + Industry dummy.
15

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Year indicator dummy variables and Industry indicator dummy variables.16
Table 2 shows the logistic regression results. The coefficients of ln(# of individual
shareholders) and Individual shareholder ratio are negatively associated with the probability ofperk initiation in all of the models that include those variables. The ln(# of non-individual
shareholders) is not negatively associated with the probability of perk initiation in Models 2 and 5.
These results imply that firms with low attraction for individual shareholders tend to initiate
shareholder perks and are consistent with the Investor base hypothesis. Although the coefficient of
Individual ownership is negative in Model 3, it is not significantly negative in the other models. In
all of the models, the coefficient of Board ownership is significantly positive. Thus, managers offirms with high board ownership tend to introduce perks. The other governance proxies are notsignificantly associated with the probability of perk initiation. These results are not consistent withthe predictions of the Entrenchment hypothesis. The coefficient of B-to-C company is significantly
positive in all of the models. The results are consistent with the advertising tool concept.
Firms may use shareholder perks to signal future performance or to alleviate a free cash flow
problem, similar to a cash dividend. The signaling idea predicts that firms with high informational
asymmetries will be likely to initiate shareholder perks. We use ln(Market Asset) as a proxy for
informational asymmetries. The idea behind alleviating a free cash flow problem is thatoverinvestment problems are likely to be more pronounced in cash-rich firms without many growth
opportunities. If the idea is supported, TobinQ will be negatively and Excess Cash will be positively
associated with perk initiation.17 We find that ln(Market Asset) is positively and Excess Cash isnegatively associated with the probability of perk initiation. TobinQ is not significantly or
marginally associated with perk initiation. These findings are not consistent with the signaling and16

We use the 33 Nikkei medium industry classifications.
Lang and Litzenberger (1989) use Tobin’s Q as a proxy for growth opportunities and examine the relation between Tobin’s Q
and stock performance after an increase in dividends. They find that the performance of high Tobin’s Q firms after an increase individends is stronger than the performance of low Tobin’s Q firms. Lie (2000) examines the relation between excess cash and firm
payouts. He finds that cash-rich firms are likely to increase dividends and repurchases.
17

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free cash flow idea that shareholder perks are used as a dividend.

4.2. Number of shareholders, ownership and operating performance around perk initiation
In this section, we examine the number of shareholders, the ownership, and operating performance(ROA, ROS, and SOA) around the time of shareholder perk initiation using a sample of 536 perk
adoptions. We exclude the 8 samples from the 544 perk initiation sample in the previous section
because we cannot use financial statistics for firms after delisting. ROA is operating profits overbook assets, ROS is operating profits over sales, and SOA is sales over book assets. The Investor
base hypothesis predicts that the number of individual shareholders will increase after shareholder
perk initiation. If shareholder perks encourage managerial entrenchment, the ownership of retailshareholders will increase and institutional ownership will decrease after perk initiation. The
Entrenchment hypothesis also predicts that operating performance will decrease after perk
initiation because agency problems worsen. The Advertising hypothesis predicts that operating
performance, particularly in B-to-C companies with perks tied to their own products, will improve
following the initiation of shareholder perks.
To control for various features of the initiation firms (specifically, firm size, growth
opportunities, profitability, and risk), we use a difference-in-difference (hereafter DID) estimatorof the number of shareholders, ownership and ex-post operating performance variables. The DID
estimator is defined as follows:

𝐶
𝑇
𝐷𝐼𝐷𝑁 = (𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑡+𝑁− 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑡𝑇 ) − (𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑡+𝑁− 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑡𝐶 )where Variable indicates the number of shareholders, ownership, or performance variables; and T
and C denote the treatment and control groups, respectively. Treatment and control observations
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are matched to examine the change in the variables around the time of perk initiation. The lower
case t represents the fiscal period immediately before perk initiation; N is the fiscal period covering
two or three years after perk initiation. The method of control group selection is explained inAppendix A.
Table 3 shows the DID results for the number of shareholders, ownership and operating
performance around the time of perk initiation. The number of individual and non-individual
shareholders significantly increases from t to t+3.18 These results imply that recognition frominvestors improves after perk initiation. The Individual shareholder ratio increases after perk
adoption. These results imply that there may be greater improvement in individual recognition thanin non-individual recognition. We also find that ownership per individual shareholder decreases
after perk initiation. These results support the clientele effect and the Investor base hypothesis.
We find that although Individual ownership increases by approximately 1.5% between t andt+3, Institutional ownership does not significantly change around the time of perk initiation. Not
all performance variables decrease around perk initiation. These results are not consistent with theEntrenchment hypothesis. Board ownership decreases by approximately 0.9% from t to t+3. This
result implies that a decrease in board ownership is covered by an increase in individual ownership.
The operating performance variables of B-to-C companies that use their products as perks do not
improve following perk initiation. These results are inconsistent with the Advertising hypothesis.

4.3 Announcement-day return
The results of Sections 4.1 and 4.2 imply that firms with a low number of individual shareholders
are likely to initiate shareholder perks and that the number of individual shareholders increases
following perk initiation. According to Merton’s (1987) Investor base model, an increase in the
Using the panel regression followed by Grullon et al.’s (2004) specification, we examine the effect of shareholder perks on thenumber of individual investors and find that shareholder perks are positively associated with the number of individual
shareholders and individual ownership.
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number of shareholders decreases a firm’s cost of capital and raises its stock price. The Investor
base model predicts that the announcement of a perk should raise the stock price in firms withshareholder perks, which attract individual shareholders. The Entrenchment hypothesis predicts
that the stock return on the announcement day of a perk initiation will be negative. The Advertising
hypothesis predicts that the stock return on the announcement day will be positive in a B-to-C
company.
In this section, we examine stock returns on the announcement day of perk initiation. To
examine abnormal returns on adoption announcement days, we collect data from 429 firms thatannounced perk adoption from January 2001 to December 2011. We exclude non-public firms inprevious book closings, financial companies, firms that announced a stock split or changed the
trading unit on the same day, and firms for which 150 days or more had passed since their IPO.
Because 122 samples are excluded, we use the remaining 307 samples to examine announcement
day returns.
To examine abnormal stock returns on the announcement day, we use the market model.
Abnormal returns are computed as follows:

𝐴𝑅𝑖,𝑡 = 𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 − 𝛼̂𝑖 − 𝛽̂𝑖 𝑅𝑀𝑡
𝑇

𝐶𝐴𝑅𝑖 [𝑑, 𝑇] = ∑ 𝐴𝑅𝑖,𝑡𝑡=𝑑where Returni,t is the stock return on day t for firm i, and RM is the value-weighted return for all
listed firms. ARi,t is the abnormal return for firm i on day t. Coefficient estimates are obtained using
an OLS regression on estimation period returns. The estimation period is -150 days to -11 days
before the announcement day. CARi[d,T] is the cumulative abnormal return for firm i from day d
15

to day T.
Table 4 present the abnormal returns and cumulative abnormal returns around theannouncement day of the initiation of perks. We find that cumulative abnormal returns from day 1 to day +1 are significantly positive—approximately 2%. These results are consistent with theInvestor base and the Advertising hypotheses and inconsistent with the Entrenchment hypothesis.
To examine the relationship between the increasing number of individual shareholders andcumulative abnormal returns, we use the variables IR_IND and ΔIndividual shareholder ratio.
IR_IND and IR_NON_IND denote increasing ratios for the number of individual and institutional
shareholders, respectively, from year t to t+2 (i.e., IR_IND = # of individual shareholderst+2 / # ofindividual shareholderst – 1, IR_NON_IND = # of non_individual shareholderst+2 / # of
non_individual shareholderst – 1).19 Year t is the accounting period immediately preceding perk
initiation. ΔIndividual shareholder ratio is the difference in the Individual shareholder ratio fromthe fiscal period immediately before perk initiation to the fiscal period two years after perk
initiation. The Investor base hypothesis predicts that the IR_IND and ΔIndividual shareholder ratio
are positively associated with the cumulative abnormal return around the announcement day. To
examine the Advertising hypothesis, we use the product dummy, OWN, which is equal to one if thefirm uses its products as shareholder perks and zero otherwise. The Advertising hypothesis predicts
that OWN is positively associated with the cumulative abnormal return around the announcement
day.
Panel A of Table 5 shows the summary statistics for the announcement sample. The mean
(median) IR_IND and IR_INST are 1.335 (0.408) and 0.174 (0.059), respectively. 20 The mean
ΔIndividual shareholder ratio is 0.022. Of the B-to-C companies, 62.7% use their own products asperks in the announcement day return sample.
19

When we use increasing ratios from t to t+1, the results are qualitatively similar. In this paper, we only report results forincreasing ratios from t to t+2.
20 IR_IND and IR_NON_IND are winsorized at the upper and the lower 1% level.

16Panel B of Table 5 presents the OLS regression results for cumulative abnormal returns
around the announcement day of the perk initiation. The dependent variable is the cumulative
abnormal return from AD–1 to AD+1, CAR[–1,+1]. The independent variables that we most focus
on are IR_IND, ΔIndividual shareholder ratio, and OWN. We find that the coefficients of IR_IND
and ΔIndividual shareholder ratio are significantly positive in all of the models. The results imply
that the high attractiveness of perks for individual shareholders raise the stock price on theannouncement day, which is consistent with the Investor base hypothesis. The dummy variable of
B-to-C company is not significantly associated with cumulative abnormal returns. The coefficientof OWN in Models 5 and 6 is not statistically significant. These results are not consistent with theAdvertising hypothesis.
As control variables, we use IR_INST, Ownership structure, Market Asset, Leverage, ROA,
and TobinQ. The coefficient of IR_INST is significantly negative in Models 3 and 4, although thecoefficient in Model 2 is not statistically significant. These results are inconsistent with theinstitutional investor base idea in which firms introduce shareholder perks that attract institutional
investors to increase their stock price. Although the coefficients of individual ownership and board
ownership are not statistically significant, institutional ownership is negatively related withcumulative abnormal returns. The coefficient of ln(Market Asset) is not statistically significant.
Because we can consider the market asset variable to represent informational asymmetry, the resultshows that the market does not evaluate the signal as an alleviation of informational asymmetry,unlike cash dividend results. Moreover, TobinQ, which is the proxy of growth opportunities, is not
associated with announcement returns. These results imply that the perk is not evaluated as
fulfilling the role of easing a free cash flow problem in the markets.

4.4.Cost of Capital17

Next, we examine the cost of equity capital around the time of the initiation of a shareholder
perk. Merton (1987) develops a model of capital market equilibrium in an incomplete information
market. Merton’s model assumes that each investor purchases only shares for which they can obtain
the information. Under this assumption, each investor cannot diversify the idiosyncratic risk intheir portfolio. In equilibrium, a security’s expected return depends on the fraction of investors thatpurchase the security. Merton formally derives the shadow cost, λ, and the expected return, E(Ri),of incomplete information for a security as follows:

𝜆𝑖 = (1− 1) 𝛿𝑥𝑖 𝜎𝑖2
𝑞𝑖

𝐸(𝑅𝑖 ) − 𝐸(𝑅𝑖∗ ) = 𝜆𝑖

𝐸(𝑅𝑖∗ )
𝑅𝑓

where qi is the size of the investor base of firm i relative to the total number of investors, δ is theparameter of the investor’s risk aversion, xi is the market value of firm i in proportion to the total
market value of all traded securities and σi2 is the diversification of idiosyncratic risk. E(Ri) is the
expected return on firm i with incomplete information, E(R*) is the expected return on firm i withcomplete information, and Rf is the risk-free rate.
In Merton’s model, an increase in the size of the investor base and in the shadow costdecreases the cost of equity capital. If the investor base concept is supported, the introduction of aperk that attracts individual investors will reduce the cost of equity capital. We examine the effectof a change in the number of individual shareholders on the cost of capital by using the changes
in Merton’s shadow cost, λ, around perk initiation. Following Kadlec and McConnell (1994), we
estimate the change in Merton’s shadow cost, Δλ:18

𝑅𝑉𝐴𝑅𝑝𝑜𝑠𝑡 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑝𝑜𝑠𝑡
𝑅𝑉𝐴𝑅𝑝𝑟𝑒 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑝𝑟𝑒∆𝜆 = [()−()] ∗ 1,000,000
𝑁𝐼𝑁𝐷𝑝𝑜𝑠𝑡
𝑁𝐼𝑁𝐷𝑝𝑟𝑒where NINDpre and NINDpost are the number of individual shareholders of the end of the most recent
fiscal year prior to the perk initiation announcement date and at the second fiscal year end after theannouncement date, respectively. RVARpre and RVARpost are the stock’s residual variance calculated
from the daily date over the interval [-150, -11] prior to the announcement date and over the interval[11, 150] following the second fiscal year end after the announcement date, respectively.
RELCAPpre and RELCAPpost are the firm’s market capitalization divided by the sum of the market
capitalization of all stocks available on the NPM portfolio master data base, calculated at the endof the month prior to the announcement date and the end of the second fiscal year following theannouncement date, respectively.
To examine whether the cost of capital is reduced through perks that attract individual
investors, we use Fama and French’s three-factors and Pastor and Stambaugh’s (2003) liquidity
factor.21 We run the following time-series regression for each firm that adopted a perk:

𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼𝑖,0 + 𝛼𝑖,1 𝐷𝑡 + (𝛽𝑖,0 + 𝛽𝑖,1 𝐷𝑡 )(𝑟𝑚,𝑡 − 𝑟𝑓,𝑡 ) + (𝛾𝑖,0 + 𝛾𝑖,1 𝐷𝑡 )𝑆𝑀𝐵𝑡+ (𝛿𝑖,0 + 𝛿𝑖,1 𝐷𝑡 )𝐻𝑀𝐿𝑡 + (𝜎𝑖,0 + 𝜎𝑖,1 𝐷𝑡 )𝐿𝐼𝑄𝑡 + 𝜀𝑖,𝑡where 𝑟𝑖,𝑡 is the monthly return of adoption firm i at time t, and 𝑟𝑓,𝑡 is the risk-free rate at time t.
𝐷𝑡 = 1 if t is in the post-adoption period and 𝐷𝑡 = 0 otherwise. 𝛽𝑖,0 , 𝛾𝑖,0 , 𝛿𝑖,0 , and 𝜎𝑖,0 are thepre-adoption factor loadings on the three Fama-French factors and Pastor and Stambaugh’s
liquidity factor. 𝛽𝑖,𝑖 , 𝛾𝑖,𝑖 , 𝛿𝑖,1 , and 𝜎𝑖,1 indicate the differences between the post- and preadoption factor loadings on the four factors. 𝛼𝑖,0 is the pre-adoption abnormal return. Our main
21

Using the relative volume and the ILLQ measure, as in Amihud, Mendelson, and Lauterbach (1997), we also examine liquidity
around the initiation of a perk. However, we do not find an average improvement in liquidity associated with shareholder perks.

19variable of interest is 𝛼1 , which is the difference between post- and pre-adoption abnormal returns.
To avoid an announcement effect, we run the regression for each firm for a 46-month period, frommonth – 24 to month – 2 prior to the shareholder perk declaration month and from month + 2 tomonth + 24 after the shareholder perk declaration month.
Table 6 presents the summary of the cost of equity capital (𝛼1 ) calculated by FF3/LIQ, theFF3- and one-factor models and the difference tests of the cost of equity capital using the change
in Merton’s shadow cost (Δλ). We winsorize each variable at the 1st and 99th percentiles. We
divide the total sample into four groups based on Δλ to examine the effect of the change inshadow costs on the change in the cost of capital. The increasing rate of the number of individual
shareholders, IR_IND, and the change in the individual shareholder ratio, ΔIndividual
shareholder ratio, are negatively associated with Δλ. We find that the difference between postand pre-adoption abnormal returns in the lowest Δλ subgroup is significantly lower than that in
the highest Δλ subgroup. These results are consistent with the Investor base hypothesis.
Table 7 presents the OLS regression results for the difference between post- and pre-adoption
abnormal returns. The dependent variable is 𝛼1 calculated using the FF3/LIQ model. Our
independent variables of interest are Δλ, IR_IND and ΔIndividual shareholder ratio. The
recognition concept predicts that the coefficient of Δλ will be positively and IR_IND and
ΔIndividual shareholder ratio will be negatively associated with 𝛼1 . We also use IR_NON_IND,
B-to-C company, ln(Market Asset), Unsystematic risk, Leverage, ROA, and TobinQ as control
variables. Unsystematic risk is the mean square error computed as the deviation of the stock pricefrom the value predicted by the market model for the period from -150 days to -11 days before theannouncement day. We find that the coefficient of Δλ is positively and the coefficients of IR_IND
and ΔIndividual shareholder ratio are negatively associated with the difference between post- andpre-adoption abnormal returns in Models 2 to 5. The results in this section suggest that shareholder
20

perks that attract individual shareholders reduce the cost of equity capital. These findings are
consistent with the Investor base hypothesis.22

4.5.Ex-perk day return and volume
4.5.1. Ex-perk day return
As mentioned above, the shareholder perk yield is different for retail and large shareholders. We
find that ownership changes around shareholder perk initiation. These findings are consistent withthe dividend clientele effect. The ex-perk day analysis also provides an opportunity to examine
whether the dividend clientele effect exists, for example, based on the dividend-related tax
difference between retail and large shareholders. We examine stock returns and trading volumes
around the ex-perk day.
The ratios of the total value of shareholder perks to capitalization are close to zero. In a
perfect market, the ex-perk day return will also be close to zero. The Dynamic dividend clientele
model of Michaely and Vila (1995) and Michaely and Vila (1996) indicates that the expected pricedrop on the ex-dividend day reflects the average preference of all traders.23 The Dynamic dividendclientele model predicts that if retail investors participate in ex-perk day trading, their preference
will have a significant effect on the ex-perk day return.
We obtain a sample of 8,163 ex-perk days from 2001 to 2011. There are many firms thathave released the value of their perks. We collect from the sample those for which we can calculate
the shareholder perk yield. The number of ex-perk days for which we can specify perk values (e.g.,
a $100 Amazon gift card for each investor holding more than 100 shares) is 3,279. Among the3,279 ex-perk days, 2,844 are on the same day as the ex-dividend day, and the remaining 435 ex-

22

We also use 𝛼1 calculated using the FF3-factor and the one-factor models as a dependent variable. The results are qualitatively
similar.
23 The model also incorporates the risk involved in the ex-day transaction and transaction costs. Kalay’s (1982) and Boyd andJagannathan’s (1994) models incorporate the transaction costs into the ex-dividend day return.

21

perk days are not on an ex-dividend day. In addition, we collect a sample of 21,631 ex-dividenddays that are not also ex-perk days to compare the ex-perk day return.
Perk values differ for retail and non-retail shareholders. We calculate the perk’s yield as
follows:

𝑃𝑉𝑃𝑆𝑟𝑒𝑡𝑎𝑖𝑙,𝑖 =𝑃𝑒𝑟𝑘 𝑣𝑎𝑙𝑢𝑒 𝑝𝑒𝑟 𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑠ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑒𝑟𝑖
𝑇ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑡𝑟𝑎𝑑𝑖𝑛𝑔 𝑠ℎ𝑎𝑟𝑒𝑖

𝑃𝑉𝑃𝑆𝑡𝑜𝑡𝑎𝑙,𝑖 =𝑇𝑜𝑡𝑎𝑙 𝑃𝑒𝑟𝑘 𝑣𝑎𝑙𝑢𝑒𝑖
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚 𝑜𝑓 𝑜𝑢𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 𝑠ℎ𝑎𝑟𝑒𝑠𝑖
𝑃𝑌𝑟𝑒𝑡𝑎𝑖𝑙,𝑖 =𝑃𝑉𝑃𝑆𝑟𝑒𝑡𝑎𝑖𝑙,𝑖𝑃𝑐𝑢𝑚,𝑖

𝑃𝑌𝑡𝑜𝑡𝑎𝑙,𝑖 =𝑃𝑉𝑃𝑆𝑡𝑜𝑡𝑎𝑙,𝑖𝑃𝑐𝑢𝑚,𝑖𝐷𝑌𝑖 =𝐷𝑖
𝑃𝑐𝑢𝑚,𝑖

Total yieldi = PYretail,i + DYi

PVPSretail,i is the perk value per share for minimum-unit investors in firm i. 24 PVPSretail is thehighest perk value per share for firm i’s investors. PVPStotal indicates the average perk value pershare. Pcum,i is the closing price of firm i on cum-day. PYretail and PYtotal represent the perk yields
for retail investors and average investors, respectively. Di is the dividend per share of firm i, and
DY is the dividend yield. If trading by retail investors affects the ex-perk day return, the ex-day
return will more closely approximate Total yieldi than the sum of PYtotal and DYi. We also calculate
the premium on ex-day returns, Premium, as follows:

24

In Japan, all firms have minimum trading units. We adjust the minimum trading unit to examine PVPSretail. For example,
suppose the minimum trading size of company A is 100 shares and company A’s perk is a $100 Amazon gift card for each investor
who holds more than 100 shares. If the investor purchases only 100 shares, he/she can obtain the $100 Amazon gift card. Thus,
PVPSretail = $100/100 shares.

22

𝑃𝑒𝑥,𝑖 + 𝐷𝑖
1 + 𝐸[𝑟𝑖 ]−1𝑃𝑉𝑃𝑆𝑟𝑒𝑡𝑎𝑖𝑙,𝑖
1 + 𝐸[𝑟𝑖 ]𝑃𝑐𝑢𝑚,𝑖 −𝑃𝑟𝑒𝑚𝑖𝑢𝑚𝑖 =where Pex,i is the closing price of firm i on ex-day, and 1 + E[ri] is the prospective return. If a firm
does not pay a dividend, the value of D is zero. To estimate E[ri], we use the market model, which
provides an estimation window of 140 days from -150 to -11 days prior to ex-day. We winsorize
the Premium at the 1st and 99th percentiles to minimize the influence of outliers.
Table 8 presents the summary statistics for the raw return on ex-day, the perk yield, thedividend yield, the total yield, and the discount. Columns 1, 2, and 3 show the summary statistics
for the total ex-perk day sample, only the ex-perk-day sample and a simultaneous ex-day sample,
respectively. Column 4 shows the summary statistics for only the ex-dividend-day sample. The
mean (median) raw return on the ex-day of every ex-perk sample is negative. The average (median)
PYretail is 2.0% (1.2%) in the total ex-day sample. For retail shareholders, the shareholder perk is avery attractive payout. By contrast, other investors will not be attracted by the shareholder perk
because the average (median) perk yield for average shareholders (PYtotal) is 0.1% (0.05%) in the
total ex-day sample.
Clearly, Total yield is closer to the decline in stock returns on all ex-day samples than DY
and than the sum of DY and PYtotal in both the only ex-perk-day and the same-day samples. The
results imply that the perk yield for minimum-unit shareholders significantly affects the ex-day
return. Surprisingly, the mean discount for every ex-perk subsample is significantly positive. These
results imply that retail shareholders have an important effect on the ex-day return and evaluate theperk value higher than the value of the equivalent in money.25

25

Numerous studies find that the premium on the ex-dividend day is less than one (Elton and Gruber (1970), Kalay (1982), Eades
et al. (1984), Kato and Loewenstein (1995) Graham, et al. (2003)).

23

We next conduct multivariate OLS regression analyses of Premium. The dependent variablein the regression is Premium. We focus on the independent variable of ln(# of individual
shareholders) and Individual shareholder ratio. Because shareholder perks are distributed for every
shareholder, these variables are proxies for the individual shareholder’s demand for shareholder
perks. We predict that the ln(# of individual shareholders) and Individual shareholder ratio will bepositively associated with Premium. We also predict that ln(# of non-individual shareholders) will
be negatively associated with Premium because the perk value may not be worth consideration for
non-individual shareholders. Elton and Gruber (1970) and Kaley (1982) find that the dividend yieldis negatively associated with the ex-dividend return. Therefore, we predict that PYretail and DY will
be positively associated with Premium. Institutional investors, who prefer capital gains to perk
yields, may play the role of arbitrager in high PYretail stocks. In this case, we predict that PYretail will
be negatively associated with Premium. Because a gift-card-type perk is more liquid than a productperk, a gift-card-type perk will be evaluated higher than a product-type perk. We predict that Gift
card dummy will be positively associated with Premium. As control variables, we use the Only perk
dummy, ln(Market capitalization), Systematic risk, and Unsystematic risk. Systematic risk isestimated using beta, and Unsystematic risk is defined as the idiosyncratic risk scaled by market
variance in the same time period. Beta and idiosyncratic risk are estimated using the market model,
where the estimation window is 140 days, running from -150 to -11 prior to the ex-day.
Panels A and B of Table 9 show the summary statistics for the ex-perk sample and the resultsof the OLS regression, respectively. We find the ln(# of individual shareholders) is positively
associated with Premium. The coefficient of Individual investor ratio is significantly negative inModels 3, 4, and 7. These results imply that the demands of different investors affect the ex-day
return. The coefficient of ln(# of non-individual shareholders) is significantly negative. We alsofind that PYretail is negatively associated with the discount. These results may reflect the trading of24

non-individual arbitragers. Karpoff and Walking (1988 and 1990) argue that effective arbitrage
trading reduces the excess return on the ex-dividend day. The result is consistent with the effective
arbitrage hypothesis (Karpoff and Walking (1988 and 1990)). The type of perk (gift card vs.
product) does not affect the discount. Why is the discount significantly positive and why is the giftcard-type perk evaluated at the same value as the product-type perk? One reason may be social
norms: in the case of a present, people prefer goods to the same value in cash (Ariely (2008)).26

4.5.2. Ex-perk-day volume
The Dynamic dividend clientele model of Michaely and Vila (1995) argues that when ex-day
returns are affected by varying investor preferences, trading volumes will increase around the exday. In accordance with Dhaliwal and Li (2006), we examine abnormal trading volumes around theex-perk day as follows:𝑁𝑉𝑖 =∑𝑡∈[−150,−11] 𝑇𝑢𝑛𝑜𝑣𝑒𝑟𝑖,𝑡140

We calculate the average turnover ratio for trading between -150 and -11 days before the ex-perk
day:

𝐴𝑉𝑖,𝑡 =

𝑇𝑢𝑛𝑜𝑣𝑒𝑟𝑖,𝑡−1
𝑁𝑉𝑖

Next, we calculate AV and AAV. AVt is turnover on day t divided by NV. AAV is the averageabnormal volume around the ex-perk day.
Table 10 presents summary statistics of AV and AAV. Columns 1, 2 and 3 show the results
26

Many managers who initiate perks say “We would like to send our gratitude to our stockholders.”

25

for the total ex-perk day sample, the only ex-perk day sample, and a simultaneous ex-day sample,
respectively. The AV of all of the columns increases gradually until immediately before the ex-perk
day and decreases gradually after the ex-perk day. AAV[-5,+5] of the total ex-perk day sample isapproximately 0.55 and is significantly positive. These results are consistent with the argument of
Michaely and Vila (1995).
To investigate the relationship between the preference of retail shareholders and AAV, we
examine an OLS regression of the trading volumes around the ex-perk day. The dependent variable
is AAV between -5 to +5 of the ex-perk day. Our independent variables of focus are ln(# ofindividual shareholders), Individual investor ratio, and the perk yield for retail investors. Thesevariables are proxies for the level of preference of retail investors. The Dynamic dividend clientele
model predicts that these variables are positively associated with the trading volume around the experk day. ln(# of non-individual shareholders) is the proxy for the preference of non-retail investors.
We predict that the ln(# of non-individual shareholders) will be negatively related with the trading
volume. Following Michaely and Vila (1996), we add DY, ln(Market capitalization), Systematicrisk, and Unsystematic risk to our empirical models. DY and ln(Market capitalization) are theproxies for dividend preference and trading costs, respectively. Risk reduces trading volume;investors cannot hedge all of the systematic risk when trading costs exist. Therefore, both
Systematic risk and Unsystematic risk may be negatively associated with the trading volume. We
also include Gift card dummy, Only perk dummy, and B-to-C company as control variables.
Table 11 presents the OLS regression results. We find that ln(# of individual shareholders)and Individual investor ratio are positively and that ln(# of non-individual shareholders) is
negatively associated with AAV. These results imply that both the individual investor and nonindividual investor groups receive the benefit from trading around the ex-perk day, which isconsistent with the findings of Michaely and Vila (1995 and 1996). In Models 1, 3, and 5, we also26

find that PYretail and DY are positively associated with AAV; these results are also consistent withthe ex-dividend return results. When ln(# of individual shareholders) and PYretail are included in thesame model, the coefficient of PYretail is not statistically significant. One of the reasons for thisresult may be the correlation of ln(# of individual shareholders) and PYretail. The coefficients of
Systematic risk and Unsystematic risk are negatively associated with AAV, and the coefficient ofln(Market Capitalization) is positively associated with AAV. These results are consistent with thefindings of Michaely and Vila (1996). The coefficients of Gift card dummy and B-to-C company
are not significantly related with AAV and are consistent with the ex-day return results.

5. Conclusion
A unique pseudo-dividend for retail investors, the shareholder perk, is used around the world.
Through analyzing the determinants of perk initiation, the change in a firm’s features around perk
initiation, the stock return on the announcement day of perk initiation, and the stock behavioraround the ex-perk day, this paper examines the role and the impact of shareholder perks on firm
value and stock behavior using Japanese shareholder perk data from 2001 to 2011. We offer threehypotheses to examine why firms introduce shareholder perks that provide individual stockholders
with special treatment, i.e., (1) the Investor base hypothesis, (2) the Entrenchment hypothesis, and(3) the Advertising hypothesis.
We find several results. First, firms that have a low number of individual shareholders, havehigh ownership by board members and operate as B-to-C companies tend to initiate shareholder
perks. Second, the number of individual shareholders increases after perk initiation. The averageownership of individual shareholders increases by approximately 3% and board members’
ownership decreases by approximately 3% after perk initiation. Ownership per individual
shareholder decreases after perk initiation. Third, operating performance does not change after
27

initiation, even if B-to-C companies use their products as perks. Fourth, the announcement day
return is significantly positive, at approximately 3%. Additionally, an attractive perk for individual
investors is positively associated with the announcement return. Board ownership andidentification as a B-to-C company are not significantly associated with the announcement return.
Fifth, the average cost of capital decreases significantly after perk initiation. Additionally, thedecrease in the cost of capital after perk initiation is positively associated with the increase inindividual shareholder numbers. Sixth, the average abnormal return on the ex-perk day issignificantly negative. The perk yield for minimum-unit investors is nearly equal to the price dropon the ex-perk day. The number of individual shareholders is positively and the number of nonindividual shareholders is negatively associated with the premium discount of the ex-perk day
return. Finally, the average abnormal volumes around the ex-perk day are significantly positive.
Additionally, the number of individual shareholders is positively and the number of non-individual
shareholders is negatively associated with the abnormal trading volume around the ex-perk day.
These results are consistent with the Investor base hypothesis that an attractive shareholder
perk for individual investors increases the size of the individual shareholder base, decreases thecost of capital, and increases the stock price and that special treatment provided to individual
stockholders is in the interest of current shareholders. Moreover, our findings imply that stock
returns and volumes around the ex-day are affected by the preferences of retail and non-retailinvestors.
Our results suggest various avenues for future research. Much of the asset pricing literaturefinds that individual and institutional investors have significantly different effects on stock prices
and trading volume (Barber and Odean (2000, 2001, 2008), Kaniel, Saar, and Titman (2008) and
Kaniel, Liu, Saar, and Titman (2012)). There may be methods of financing and defense against
takeovers that harness individual investors and/or their optimism. Further consideration of the28

influence of individual investors on corporate finance events may therefore be warranted.

Appendix A: Matching Procedure
The matching procedure was performed as follows. First, we used a probit estimation to model the
probability of a perk’s initiation in year t conditional on the covariates observed in year t. Firms
that initiated perks (𝐴𝑑𝑜𝑝𝑡𝑖𝑜𝑛𝑡 = 1) were used in treatment observations. Next, we attached apropensity score to each observation. The propensity score 𝑒(∙) was defined ase(𝑋𝑡 ) ≡ Pr(𝐴𝑑𝑜𝑝𝑡𝑖𝑜𝑛𝑡+1 = 1|𝑋𝑡 )(A1)

where 𝑋𝑡 is a vector of the covariates in the probit estimation.
Next, we implemented another set of probit estimations, including cross-terms, which were
multiplied by variables that measured the extent of firms’ external control. For each treatment
observation, we identified matched observations from the sample of firms not issuing securities.
The matched observations are those that demonstrated the closest propensity scores to a particulartreatment observation, and these were labeled control observations. It should also be noted that wewere able to use each non-treated observation more than once as a control; that is, a non-treatment
observation may have been used as a control for more than one treatment observation. Several
matching algorithms were used to find the closest control observations. As a baseline, we employed
five matches by selecting five arbitrarily determined observations whose propensity scores wereclosest to each treatment observation.27
One of the benefits of a propensity-score-matching estimation is that it enables us to matchthe treatment and control observations using the scalar propensity score. The propensity score,27

We find that the results obtained using different matching algorithms (e.g., the ten nearest and the kernel matches) are similar
to those obtained using the five nearest matches.

29

which is the conditional probability of a treatment given the value of the observed characteristics,
is a useful variable to employ when using vectors of covariates of large dimensionality. Rosenbaumand Rubin (1983) show that treatment observations and control observations with the same
propensity scores have the same distribution as the full vector of covariates. Thus, to obtain thesame covariate probability distributions for the treatment and control observations, it is sufficient
to match firms in terms of their propensity scores. We began with the baseline probit estimation,from which we obtained the conditional probability of a firm initiating a perk in year t given theindustry (2-digit classification) and the values of the observed firm’s characteristics in year t. The
dependent binary variable represents the initiation of the perk in year t. The following explanatory
variables were used. To measure firm performance, we employed ROA, which is defined as theoperating return on total assets. Market Asset, which is defined as the natural logarithm of total
market assets, indicates the extent of asymmetric information among investors. We also used thedividend dummy (Dividend) to represent the payout policy, Leverage to represent the risk ofbankruptcy, and Tobin’s Q to represent the firm’s value gap.

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34

Figure 1. Number and percentage of firms with shareholder perks

35

Table 1.
Summary statistics
This table provides the summary statistics for our sample. The sample consists of 27,196 firm-year observations from2001 to 2011. The first column shows the summary statistics for the total sample. The second and third columns show
the summary statistics for the initiation and the non-initiation of shareholder perks subsamples. # of individual
shareholders is the number of individual shareholders. # of institutional shareholders is the number of institutional
shareholders. # of non-individual shareholders is the number of non-individual shareholders. Individual shareholder
ratio is defined as the number of individual shareholders over the total number of shareholders. Individual ownershipis the total ownership of individual shareholders minus the ownership of the individual shareholders who are Top 10
shareholders. Institutional ownership is the total ownership of institutional shareholders. Top 10 ownership is the sumof the top 10 shareholders' ownership. Board ownership is the sum of the board members’ ownership. % of outside
directors is the number of outside directors divided by the total number of board members. Anti-takeover is a dummy
variable that is equal to one if the firm adopts an anti-takeover defense and zero otherwise. Market Asset is the sum of
the market capitalization at the closing price of the previous fiscal year end and the debt book value of the firm fromthe previous fiscal year. Leverage is the sum of short- and long-term leverage over the book assets of the previous
fiscal year. ROA is the operating profit over the book assets of the previous fiscal year. Excess Cash is the residualfrom regressing cash holdings on firm-specific characteristics and represents the firm’s excess cash holdings. TobinQ
is the sum of market capitalization and book debt over book assets. Dividend is a dummy variable that is equal to one
if a firm pays a dividend and zero otherwise. B-to-C company is a dummy variable that is equal to one if the firm is abusiness-to-consumer company and zero otherwise. Statistical significance levels are based on cross-sectional tstatistics. * and *** indicate significance at the 10% and 1% levels, respectively, in two-tailed tests.

Total sample

Initiation sample

Non-initiation
sampleMeanMedianNumber of shareholders# of total shareholders# of individual shareholders# of non-individual shareholders
Individual shareholder ratio

10,76910,483
286
0.941

3,069
2,887
137
0.955

7,564
7,317
247
0.929

2,088
1,923
111
0.946

10,834
10,547
287
0.941

3,098
2,920
138
0.955

-3,269-3,230-39.3-0.012-1.74 *
-1.74 *
-1.62
-5.60 ***

Ownership & GovernanceIndividual ownership
Top 10 ownership
Institutional ownership
Board ownership% of outside director
Anti-takeover

0.320
0.540
0.196
0.091
0.262
0.058

0.313
0.530
0.166
0.017
0.250
0.000

0.278
0.584
0.177
0.160
0.263
0.028

0.266
0.589
0.149
0.092
0.250
0.000

0.321
0.539
0.197
0.089
0.262
0.059

0.314
0.528
0.167
0.017
0.250
0.000

-0.04
0.04
-0.02
0.07
0.00
-0.03

-10.62 ***3.98 ***-3.26 ***
11.66 ***
0.29
-3.08 ***

Other characteristics
Market Asset (million yen)
Leverage
ROA
Excess CashTobinQ
Dividend
B-to-C company

226,333
0.214
0.044
0.003
1.168
0.804
0.304

26,0930.180
0.040-0.009
0.963
1.000
0.000

156,536
0.213
0.067
0.002
1.387
0.875
0.548

27,0320.178
0.059-0.009
1.051
1.000
1.000

227,758
0.215
0.043
0.003
1.164
0.802
0.299

26,0780.180
0.039-0.009
0.962
1.000
0.000

-71,222
0.00
0.02
0.00
0.22
0.07
0.25

-1.45
-0.16
8.03 ***-0.21
6.60 ***
4.22 ***
12.55 ***

Observations

27,19636

Mediant-statistics

Mean
(A)

544

Median

Mean
(B)

Diff
(A) - (B)

26,652

Table 2.
Logistic regression of the determinants of perk initiation
This table shows the results for the logistic regression of the determinants of perk initiation. The dependent variable isthe initiation dummy variable, which is equal to one if a firm initiates a perk during the fiscal year and zero otherwise.
The independent variables are as follows: ln(# of individual shareholders) is the natural logarithm of the number ofindividual shareholders. Individual shareholder ratio is defined as the number of individual shareholders to the total
number of shareholders. Individual ownership is the total ownership of individual shareholders minus the ownershipof individual shareholders who are Top 10 shareholders. Institutional ownership is the total ownership of institutional
shareholders. Board ownership is the sum of the board members’ ownership. % of outside directors is the number ofoutside directors divided by the total number of board members. Anti-takeover is a dummy variable that is equal to one
if the firm adopts an anti-takeover defense and zero otherwise. Market Asset is the sum of the market capitalization at
the closing price of the previous fiscal year end and the debt book value of the firm from the previous fiscal year.
Leverage is the sum of short- and long-term leverage over the book assets of the previous fiscal year. ROA is theoperating profit over the book assets of the previous fiscal year. Excess cash is the residual from regressing cash
holdings on firm-specific characteristics and represents the firm’s excess cash holdings. TobinQ is the sum of themarket capitalization and book debt over the book assets. Dividend is a dummy variable that is equal to one if a firmpays a dividend and zero otherwise. B-to-C company is a dummy variable that is equal to one if the firm is a businessto-commerce company and zero otherwise. We use industry dummy variables and year dummy variables.*, **, and*** indicate statistical significance at the 0.1, 0.05, and 0.01 levels, respectively.

37

(N = 27,196)

Model 1Model 2ln(# of individual shareholders)-0.31***(-5.33)-0.36***(-5.91)
0.23**(2.07)ln(# of non-individual shareholders)
Individual shareholder ratio
Individual ownership
Institutional ownership
Board ownership% of outside director
Anti-takeover
B-to-C Company
ln(Market Asset)
ROA
Excess Cash
Leverage
TobinQ
Dividend
Year indicators
Industry indicators
Constant
Pseudo R2

Model 3-0.29(-0.64)-0.06(-0.13)
1.86***(5.98)
0.40(1.16)-0.03(-0.10)0.92***(9.27)
0.26***(4.46)
2.07**(2.29)-0.68*(-1.82)-0.01(-0.05)-0.11(-1.63)0.19(1.24)
YesNo-5.14***(-11.06)-0.29(-0.64)-0.28(-0.64)
1.94***(6.14)
0.39(1.12)-0.03(-0.11)0.93***(9.34)
0.17**(2.34)
2.22**(2.44)-0.70*(-1.88)0.12(0.46)-0.09(-1.37)0.22(1.40)
Yes
No-4.98***(-10.56)-2.89***(-4.84)-0.89**(-2.19)-0.30(-0.70)1.91***(6.17)
0.19(0.56)-0.02(-0.06)0.87***(8.80)
0.06(1.47)
2.35***(2.67)-0.74**(-2.00)0.20(0.80)-0.11*(-1.68)0.28*(1.82)
Yes
No-2.58***(-4.26)

0.069

0.069

0.067

38

Model 4Model 5-0.26***(-4.26)-0.28***(-4.33)0.11(0.95)

Model 6-0.10(-0.21)-0.11(-0.26)
1.91***(5.83)
0.47(1.35)-0.03(-0.11)0.55***(2.59)
0.24***(3.91)
2.34**(2.40)-0.74*(-1.86)-0.04(-0.12)-0.06(-0.96)0.19(1.22)
Yes
Yes-3.82***(-7.08)-0.10(-0.23)-0.22(-0.49)1.95***(5.86)
0.47(1.34)-0.03(-0.11)0.55***(2.61)
0.20***(2.62)
2.41**(2.46)-0.75*(-1.89)0.02(0.07)-0.06(-0.85)0.20(1.28)
Yes
Yes-3.75***(-6.92)-1.94***(-2.96)-0.67(-1.55)-0.31(-0.70)1.92***(5.92)
0.28(0.80)-0.02(-0.08)0.51**(2.41)
0.07*(1.70)
2.54***(2.68)-0.80**(-2.05)0.12(0.40)-0.07(-0.99)0.27*(1.71)
Yes
Yes-2.10***(-3.01)

0.097

0.097

0.095

Table 3.
Difference in differences test of ownership and operating performanceThis table shows the difference in differences estimator for the number of shareholders, ownership and operating
performance variables around perk initiation. The sample consists of 536 perk initiation firms from 2001 to 2011. T isthe fiscal period immediately before perk initiation. T+2 and T+3 are fiscal periods of two and three years after the
perk initiation, respectively. # of individual shareholders is the number of individual shareholders. # of non-individual
shareholders is the number of non-individual shareholders. Individual shareholder ratio is defined as the number ofindividual shareholder to the total number of shareholders. Individual ownership is the total ownership of individual
shareholders minus the ownership of individual shareholders who are Top 10 shareholders. Institutional ownership isthe total ownership of institutional shareholders. Board ownership is the sum of the board members’ ownership.
Individual ownership per individual shareholder is defined as the individual ownership divided by the number ofindividual shareholders. ROA is operating profits on book assets. ROS is operating profits on sales. SOA is sales onbook assets. Statistical significance levels are based on t-statistics. ** and *** indicate significance at the 5% and 1%
levels, respectively, in two-tailed tests.

39Difference
periodTreatment

ControlDID

t-statN(T+2) - T
(T+3) - T

2,064
3,500

-170
979

1,917
2,937

6.19 ***
4.25 ***

536
522# of non-individual shareholders(T+2) - T
(T+3) - T

11
16

-11
-1

22
17

3.79 ***
2.43 **

536
522

ln(# of individual shareholders)(T+2) - T
(T+3) - T

0.545
0.693

0.088
0.137

0.458
0.556

13.69 ***
14.42 ***

536
522

ln(# of non-individual shareholders)(T+2) - T
(T+3) - T

0.098
0.101

-0.010-0.039

0.108
0.139

6.30 ***
6.18 ***

536
522

Individual shareholder ratio(T+2) - T
(T+3) - T

0.021
0.029

0.003
0.005

0.018
0.024

6.83 ***
9.57 ***

536
522(T+2) - T
(T+3) - T

0.018
0.029

0.007
0.014

0.011
0.015

2.60 ***
3.45 ***

536
522

Institutional ownership(T+2) - T
(T+3) - T

-0.008-0.013-0.007-0.013-0.001
0.000

-0.41
0.11

536
522

Board ownership(T+2) - T
(T+3) - T

-0.021-0.033-0.017-0.024-0.004-0.009-1.26-2.28 **

536
522

Individual ownership per individual
shareholder (x 10,000)(T+2) - T
(T+3) - T

-0.089-0.103-0.009-0.011-0.794-0.944-7.86 ***
-8.75 ***

536
522

(T+2) - T
(T+3) - T

-0.011-0.017-0.014-0.017

0.003
0.000

0.65
0.09

536
522

ROS

(T+2) - T
(T+3) - T

-0.010-0.019-0.013-0.019

0.003
0.000

0.68
-0.03

536
522

SOA

(T+2) - T
(T+3) - T

0.022
0.016

0.034
0.043

-0.012-0.027

-0.67
-1.36

536
522

ROA of B-to-C company that uses own
product as a perk

(T+2) - T
(T+3) - T

-0.020-0.030-0.019-0.027-0.001-0.002

-0.11
-0.42

199
193

ROS of B-to-C company that uses own
product as a perk

(T+2) - T
(T+3) - T

-0.017-0.024-0.013-0.022-0.004-0.003

-0.46
-0.34

199
193

SOA of B-to-C company that uses own
product as a perk

(T+2) - T
(T+3) - T

0.034
0.012

0.047
0.025

-0.012-0.013

-0.29
-0.41

199
193

Number of shareholders# of individual shareholders

Ownership
Individual ownership

Firm performance
ROA

40

Table 4.
Summary statistics for the announcement return
This table shows the mean abnormal returns and cumulative abnormal returns around announcement day and summary
statistics for the 307 perk initiations sample from 2001 to 2011. AR is abnormal returns. CAR is cumulative abnormal
returns. AD is the announcement day. Statistical significance levels of the mean abnormal returns are based on crosssectional t-statistics. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively, in two-tailed
tests.

(N=307)Meant-statistics

Median# of positive% of positive

AR[AD-5]
AR[AD-4]
AR[AD-3]
AR[AD-2]
AR[AD-1]
AR[AD]
AR[AD+1]
AR[AD+2]
AR[AD+3]
AR[AD+4]
AR[AD+5]0.004-0.378 **
0.012
0.228
0.491 **
1.205 ***
0.365 *
0.212
0.502 ***
-0.011
0.212

0.03
-2.44
0.08
1.44
2.36
4.69
1.86
1.17
2.62
-0.07
1.05

-0.049-0.207-0.048
0.024
0.169
0.456
0.136
-0.024-0.003-0.203-0.160

148
135
139
156
165
186
168
153
153
131
135

48.2%
44.0%
45.3%
50.8%
53.7%
60.6%
54.7%
49.8%
49.8%
42.7%
44.0%

CAR[-5,-2]
CAR[-1,+1]
CAR[+2,+5]
CAR[-5,+5]-0.1332.061 ***
0.915 **
2.843 ***

-0.39
5.02
2.14
3.68

-0.162
1.079
0.108
1.099

141
190
157
173

45.9%
61.9%
51.1%
56.4%

41

Table 5.
OLS regression of the cumulative abnormal return around the announcement day of perk
initiation
This table shows the summary statistics and the results for an ordinary least squares regression of cumulative abnormal
returns around the announcement day. Panel A shows summary statistics for the sample. Panel B shows the ordinary
least squares regression of cumulative abnormal returns around the announcement. The dependent variable is the threeday cumulative abnormal returns around the time of the perk announcement. The independent variables are as follows.
IR_IND and IR_NON_IND denote increasing ratios for the number of individual and institutional investors,
respectively, from the fiscal year prior to the perk announcement to the second fiscal year end after the perk
announcement. ΔIndividual shareholder ratio is the difference in the individual shareholder ratio from the fiscal period
immediately before perk initiation to the fiscal period two years after perk initiation. Individual ownership is the total
ownership of individual shareholders minus the ownership of the individual shareholders who are Top 10 shareholders.
Board ownership is the sum of the board members’ ownership. B-to-C company is a dummy variable that is equal to
one if the firm is a business-to-commerce company and zero otherwise. OWN is a dummy variable that is equal to one
if a B-to-C company uses its products as shareholder perks and zero otherwise. Market Asset is the sum of the market
capitalization at the closing price of the previous fiscal year end and the debt book value of the firm from the previous
fiscal year. Leverage is the sum of short- and long-term leverage over the book assets of the previous fiscal year. ROA
is the operating profit over the book assets of the previous fiscal year. TobinQ is the sum of market capitalization andbook debt over book assets. Heteroskedasticity-adjusted t-statistics are presented in parentheses below the regression
coefficients. *, **, and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
Panel A: Summary statistics of the announcement sample(N = 307)

Mean

MedianIR_IND
IR_NON_IND
Δindividual shareholder ratio
Individual ownership
Board ownership
B-to-C Company
OWN
Market Asset
Leverage
ROA
TobinQ

1.335
0.174
0.022
0.278
0.169
0.577
0.362
139,357
0.207
0.072
1.511

0.408
0.059
0.013
0.266
0.096
1.000
0.000
39,251
0.177
0.059
1.055

42

Std.dev
2.523
0.486
0.042
0.140
0.200
0.495
0.481
304,916
0.184
0.067
1.512

10th
percentile

90th
percentile-0.117-0.182-0.006
0.110
0.002
0.000
0.000
9,330
0.000
0.018
0.799

3.553
0.660
0.054
0.449
0.472
1.000
1.000
353,194
0.463
0.149
2.396

Panel B. OLS regression of the CAR(N = 307)

Model 1

IR_IND

0.40**(2.39)

IR_NON_IND

Model 2Model 3-0.66(-0.88)0.54***(3.00)-1.57**(-2.16)

Δindividual shareholder ratio
Individual ownership
Institutional ownership
Board ownership
B-to-C company

Model 4

Leverage
ROA
TobinQ
Constant
Adjusted R-squared

Model 6

0.54***(3.03)-1.57**(-2.14)4.24(1.05)-8.40**(-2.06)-3.49(-1.08)0.46(0.55)

3.08(0.77)-10.24**(-2.51)-3.58(-1.16)0.29(0.34)

4.64(1.17)-9.52**(-2.34)-3.66(-1.17)0.32(0.39)

24.38**(2.17)
4.25(1.06)-8.75**(-2.12)-3.38(-1.07)0.45(0.54)-0.47(-1.45)3.68*(1.70)-0.25(-0.02)0.36(0.49)
5.96(1.43)-0.48(-1.49)3.56(1.64)
1.54(0.14)
0.46(0.65)
7.21*(1.75)-0.43(-1.33)
3.64*(1.69)
0.41(0.04)
0.42(0.58)
5.68(1.37)

0.057

0.041

0.067

-0.49
(-1.50)3.89*(1.80)
0.58(0.05)
0.46(0.64)
5.95(1.43)

4.64(1.16)-9.53**(-2.32)-3.67(-1.14)0.31(0.34)
0.02(0.02)-0.43(-1.32)
3.64*(1.68)
0.44(0.04)
0.42(0.58)
5.68(1.36)

24.17**(2.16)
4.24(1.05)-8.70**(-2.10)-3.30(-1.02)0.63(0.68)-0.29(-0.25)-0.48(-1.48)3.90*(1.80)
0.24(0.02)
0.46(0.65)
5.91(1.41)

0.049

0.064

0.046

OWN
ln(Market Asset)

Model 5

Table 6.
Changes in the cost of capitalThis table reports the difference in the abnormal return of the following time-series regression, which was run for each perk initiation firm for t from month -24 tomonth -2 prior to the perk initiation declaration month and from month +2 to month +24 after the initiation declaration month:𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼𝑖,0 + 𝛼𝑖,1 𝐷𝑡 + (𝛽𝑖,0 + 𝛽𝑖,1 𝐷𝑡 )(𝑟𝑚,𝑡 − 𝑟𝑓,𝑡 ) + (𝛾𝑖,0 + 𝛾𝑖,1 𝐷𝑡 )𝑆𝑀𝐵𝑡 + (𝛿𝑖,0 + 𝛿𝑖,1 𝐷𝑡 )𝐻𝑀𝐿𝑡 + (𝜎𝑖,0 + 𝜎𝑖,1 𝐷𝑡 )𝐿𝐼𝑄𝑡 + 𝜀𝑖,𝑡where Dt = 1 if t is in the post-adoption period, and Dt = 0 otherwise. αi,0 is the pre-adoption abnormal return, and αi,1 is the difference between the post- and the preadoption abnormal returns. βi,1, i,1, i,1, and σi,1 are, respectively, the differences between the post- and the pre-adoption betas of the market portfolio, SMB, HML and
LIQ factor (FF 3/LIQ model). The table also shows the difference between the post- and pre-adoption abnormal returns by using FF3-factor (rm – rf, SMB and HML)
and one-factor (rm – rf) models. The table shows the αi for the total sample and the individual groups divided by Δλ quartiles.
∆𝜆 = [(

𝑅𝑉𝐴𝑅𝑝𝑜𝑠𝑡 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑝𝑜𝑠𝑡
𝑅𝑉𝐴𝑅𝑝𝑟𝑒 ∗ 𝑅𝐸𝐿𝐶𝐴𝑃𝑝𝑟𝑒)−()] ∗ 1,000,000
𝑁𝐼𝑁𝐷𝑝𝑜𝑠𝑡
𝑁𝐼𝑁𝐷𝑝𝑟𝑒NINDpre and NINDpost are the number of individual shareholders at the end of the most recent fiscal year prior to the perk initiation announcement date and at thesecond fiscal year end after the announcement date, respectively. RVARpre and RVARpost are the stock’s residual variance calculated from the daily date in the interval[-150, -11] prior to the announcement date and in the interval [11, 150] following the second fiscal year end after the announcement date, respectively. RELCAPpreand RELCAPpost are the firm’s market capitalization divided by the sum of the market capitalization of all stocks available on the NPM portfolio master data base,calculated at the end of the month previous to the announcement date and the second fiscal year end following the announcement date, respectively. IR_IND is theincreasing ratio of the number of individual and institutional investors from the fiscal year prior to the perk announcement to the second fiscal year end after the perk
announcement. ΔIndividual shareholder ratio is the difference in the individual shareholder ratio from the fiscal period immediately before perk initiation to the fiscal
period two years after perk initiation. t-statistics with autoregressive error correction standard errors (where errors are deviations from the means) are presented inparentheses. *, **, and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.

34

N

Mean MeanΔλIR_IND

Mean
ΔIndividual
shareholder
ratio

α1

FF3/LIQ modelFF3 model

CAPM

Total sample

307-0.019

1.335

0.020

-0.890***(-2.58)-1.405*** (-4.38)-0.635**(-2.14)

Δλ quartile
Quartile 1 (least Δλ ）Quartile 2
Quartile 3
Quartile 4 (Most Δλ ）

77
77
77
76

-0.079-0.012-0.001
0.015

3.178
1.594
0.349
0.204

0.044
0.023
0.009
0.006

-4.394***-0.6150.303
1.171**(-6.37)(-0.87)(0.48)(2.10)-4.745***-1.337**-0.556
1.051**(-6.58)(-2.08)(-1.13)(2.12)-3.349***-1.144*0.662
1.318**(-5.41)(-1.85)(1.43)(2.55)

5.565***(-6.26)5.795***(6.60)

4.667***(5.78)

Quartile 4 - Quartile 1

45

Table 7.
OLS regression of the changes in the cost of capitalThis table shows the results for ordinary least squares regressions of the change in the cost of capital around perk
initiation. The dependent variable αi,1 is the difference between post- and pre-adoption abnormal returns. The
independent variables are as follows. Δλ is defined in Table 6. IR_IND and IR_NON_IND denote increasing ratios
for the number of individual and institutional investors, respectively, from the fiscal year prior to the perk
announcement to the second fiscal year end after the perk announcement. ΔIndividual shareholder ratio is thedifference in the individual shareholder ratio from the fiscal period immediately before perk initiation to the fiscal
period two years after perk initiation. B-to-C company is a dummy variable that is equal to one if the firm is abusiness-to-commerce company and zero otherwise. Market Asset is the sum of the market capitalization at theclosing price of the previous fiscal year end and the debt book value of the firm from the previous fiscal year.
Unsystematic risk is the mean square error computed as the deviation of the stock price from the value predictedby the market model for the period from -150 days to -11 days before the announcement day. Leverage is the sumof short- and long-term leverage over the book assets of the previous fiscal year. ROA is the operating profit overthe book assets of the previous fiscal year. TobinQ is the sum of market capitalization and book debt over bookassets. Heteroskedasticity-adjusted t-statistics are presented in parentheses below the regression coefficients. *, **and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.

Model 1ΔλModel 2Model 3Model 4-0.52***(-4.08)-0.41***(-3.19)-1.23*(-1.87)28.08***(3.90)

IR_IND
IR_NON_IND
ΔIndividual investor ratio
B-to-C company
ln(Market Asset)
Unsystematic risk
Leverage
ROA
TobinQ
Constant
Observations
Adjusted R-squared

Model 50.85
(1.46)-0.80***(-3.56)-0.25***(-3.21)-17.08**(-2.55)-0.96(-0.46)0.11(0.23)
11.55***(4.24)

1.03*(1.84)-0.67***(-3.10)-0.26***(-3.46)-14.86**(-2.47)-0.79(-0.38)0.36(0.85)
10.05***(3.83)

0.90(1.58)-0.86***(-3.95)-0.26***(-3.43)-14.81**(-2.38)-0.88(-0.42)0.25(0.57)
12.66***(4.71)

0.82(1.42)-0.87***(-3.96)-0.26***(-3.37)-0.98(-0.48)-14.63**(-2.36)0.30(0.69)
12.71***(4.73)-19.59**(-2.39)0.85(1.47)-0.81***(-3.61)-0.25***(-3.28)-1.09(-0.52)-16.41**(-2.52)0.10(0.23)
12.15***(4.43)

307
0.154

307
0.213

307
0.198

307
0.207

307
0.161

34

Table 8.
Summary statistics of ex-day return, perk yield, and perk premiumThis table shows the summary statistics of the perk yield, the dividend yield, the ex-day return and the ex-day perk
discount. The sample consists of 3,279 ex-perk day firms from 2001 to 2011. Columns 1, 2, and 3 show thesummary statistics for the total ex-perk day sample, the only ex-perk-day sample and a simultaneous ex-day sample,
respectively. Column 4 shows the summary statistics of the only ex-dividend-day sample. Return[Ex-day] is theraw return on ex-day. PYretail is the perk yield for minimum-unit investors. PYtotal is the perk yield based on the
total number of outstanding shares. DY is the dividend yield. Total yield is the sum of PYretail and DY. Discount is
the level of the perk premium on the ex-perk day for minimum-unit investors. t-statistics, presented below thecoefficients, are the t-statistics for the mean difference from zero. *, ** and *** indicate statistical significance atthe 10%, 5% and 1% levels, respectively.

Ex-perk day sample
Total

Only perk

Perk andDividend

Only dividend
sampleReturn[Ex-day]

MeanMedian-0.030-0.023-0.042-0.036-0.028-0.022

PY retail

Mean
Median

0.020
0.012

0.041
0.026

0.016
0.010

PY total

MeanMedian

0.001
0.001

0.004
0.003

0.001
0.001

DY

Mean
Median

0.013
0.011

0.000
0.000

0.015
0.012

0.016
0.013

Total yield

Mean
Median

0.033
0.026

0.041
0.026

0.031
0.025

0.016
0.013

PremiumMeant-statistics

0.284***
4.426

0.643***
5.604

0.229***
3.189

3,279

435

2,844

N

47

-0.010-0.00921,631

Table 9.
OLS regression of the perk discount on ex-perk day
This table shows the results for the ordinary least squares regression of the discount for minimum-unit investors.
Panel A shows the summary statistics for the total ex-perk day sample. Panel B shows the results of the OLS
regression of the perk discount on the ex-perk day. The dependent variable is Discount, which is the level of theperk premium on the ex-perk day for minimum-unit investors. The independent variables are as follows. ln(# ofindividual shareholders) is the natural logarithm of the number of individual shareholders. ln(# of non-individual
shareholders) is the natural logarithm of the number of non-individual shareholders. Individual shareholder ratio
is defined as the number of individual shareholders to the total number of shareholders. Individual ownership isthe total ownership of individual shareholders minus the ownership of the individual shareholders who are Top 10
shareholders. Capitalization is market capitalization as of the last day of the previous month. Systematic risk isestimated using data from the market model, in which the estimation window is 140 days, running from -150 to 11 days from ex-day. Unsystematic risk is the residual of the market model, in which the estimation window is 140
days, from -150 to -11, scaled by the market variance in the same time period. Only perk dummy is a dummy
variable that is equal to one if a firm pays only a perk and zero otherwise. B-to-C company is a dummy variable
that is equal to one if the firm is a business-to-commerce company and zero otherwise. Gift card dummy is adummy variable that is equal to one if the firm offers a gift-card-type perk (high liquidity perk) and zero otherwise.
Heteroskedasticity-adjusted t-statistics are presented in parentheses below the regression coefficients. *, **, and*** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
Panel A. Summary statistics of the ex-perk day sample(N = 3,279)

MeanMedianStd.dev

10th
percentile

90th
percentile# of individual shareholders

7,041

3,403

13,052659

14,430# of non individual shareholders

227

144

302

46

483

Individual shareholder ratio

0.94

0.96

0.06

0.89

0.99

Capitalization (million yen)

44,808

10,560136,859

2,239

102,295

Systematic risk

1.39

1.15

0.64

0.89

2.96

Unsystematic risk

1.69

1.48

0.98

0.80

2.84

Only perk dummy

0.13

0.00

0.34

0.00

1.00

Gift card dummy

0.16

0.00

0.36

0.00

1.00

48

Panel B. OLS regression results of the discount

Model 1ln(# of individual shareholders)Model 2

PY retail
DY
Gift card dummyln(Capitalization)
Systematic risk
Unsystematic risk
Only perk dummy
B-to-C company
Constant
Observations
Adjusted R-squared

Model 4-0.23*(-1.78)0.49***(6.08)-0.44***(-3.34)0.42***(5.31)ln(# of non individual shareholders)
Individual investor ratio

Model 3Model 57.07***(5.43)-21.99*** -27.63*** -21.20*** -27.05*** -24.69***(-11.24)(-13.74)(-10.93)(-13.50)(-12.40)
9.36
2.84
9.78
2.56
1.00
(1.22)(0.36)(1.27)(0.33)(0.13)-0.47*** -0.70*** -0.35*** -0.51*** -0.48***(-7.80)(-9.13)(-3.80)(-5.23)(-8.01)
-0.16
-0.22
-0.18
-0.26
-0.25
(-0.78)(-1.06)(-0.87)(-1.26)(-1.25)-0.35*-0.61***-0.32-0.60*** -0.62***(-1.80)(-3.08)(-1.64)(-3.02)(-3.09)
-0.02
0.08
-0.05
0.05
0.02
(-0.30)(0.97)(-0.58)(0.62)(0.29)
0.44**
0.07
0.54***
0.21
0.22(2.12)(0.35)(2.58)(0.97)(1.06)
0.22
0.20
0.19
0.14
0.18
(1.64)(1.51)(1.42)(1.08)(1.38)
5.10***
4.10***
5.09***
3.93***-1.18(7.77)(6.07)(7.76)(5.83)(-0.89)
3,279
0.051

49

3,279
0.058

3,279
0.051

3,279
0.061

3,279
0.061

Table 10.
Abnormal return and abnormal volume around the ex-day
This table shows the mean abnormal volume (AV) and the mean average abnormal volume (AAV) around ex-perkday. Columns 1, 2, and 3 show the summary statistics for the total ex-perk day sample, the only ex-perk-day sample
and a simultaneous ex-day sample, respectively. The statistical significance levels of the mean AV and AAV are
based on cross-sectional t-statistics. *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively, in two-tailed tests.

Total

Only perk

Perk and dividend

AV/AAVt-statAV/AAVt-statAV/AAVt-statAV
Ex-day -5
Ex-day -4
Ex-day -3
Ex-day -2
Ex-day -1
Ex-day 0
Ex-day 1
Ex-day 2
Ex-day 3
Ex-day 4
Ex-day 50.380 ***
0.424 ***
0.565 ***
0.870 ***
2.259 ***
1.115 ***
0.238 ***
0.073 ***
0.051 ***
0.013
0.037 *

14.77
16.27
19.30
25.45
32.25
25.14
10.59
3.74
2.61
0.69
1.78

0.397 ***
0.408 ***
0.630 ***
0.968 ***
2.502 ***
1.395 ***
0.372 ***
0.121 *
0.032
0.012
0.118

5.05
5.24
7.28
10.02
12.51
10.11
4.78
1.91
0.54
0.20
1.56

0.377 ***
0.426 ***
0.555 ***
0.855 ***
2.221 ***
1.072 ***
0.218 ***
0.065 ***
0.053 ***
0.013
0.024

13.91
15.46
17.88
23.41
29.73
23.04
9.45
3.23
2.62
0.67
1.17

AAV
Ex-day[-5,-1]
Ex-day[-1,+1]
Ex-day[+1,+5]
Ex-day[-5,+5]N0.899 ***
1.204 ***
0.082 ***
0.548 ***
3,279

31.88
30.27
5.54
26.56

0.981 ***
1.423 ***
0.131 **
0.633 ***
435

12.02
11.62
2.53
10.07

0.887 ***
1.170 ***
0.075 ***
0.535 ***
2,844

29.53
27.98
4.93
24.59

50