The hidden cost of underwriting
Nicholas Pricha, Sean Foley*, Graham Partington and Jiri Svec
University of Sydney
30 April 2014
Abstract
We examine agency costs around underwritten seasoned equity offerings (SEOs),
focusing on underwritten dividend reinvestment plans (DRIPs). The underwriters have an
incentive to sell stock during the pricing period for the issue. This reduces the price at which
shares are issued and can increase the returns to underwriting. Using data for individual
brokers’ transactions, we show that underwriting brokers engage in an abnormally high level
of selling during the issue pricing period. Comparison of pricing period returns between stock
with underwritten DRIPs and a matched sample of non-underwritten DRIPs shows that
significantly more negative returns accrue to firms that have their issues underwritten.
JEL classification: G14
Keywords: Agency Conflict, Reinvestment plans, DRIP, Underpricing, Underwriting,
SEO
*
Email: sean.foley@sydney.edu.au. The authors thank the Securities Industry Research Centre of Asia-Pacific
(SIRCA) for the provision of data and the Capital Markets CRC Limited (CMCRC) and the Centre for
International Finance and Regulation (CIFR) for financial support. The authors would also like to thank the
participants at the JCF Schulich conference on market misconduct as well as Ryan Davies, Terry Walter, Alex
Sacco, Reuben Segara and Angelo Aspris for their thoughtful comments.
1
1
Introduction
Conflicts of interest and the consequent agency costs are regularly encountered in
corporate finance. Such agency conflicts include management manipulating earnings to
maximize the value of funds raised from seasoned equity offerings (SEOs), 1 executives
manipulating the timing of information releases to maximize the value of issued stock
options2 or to meet analyst expectations3 and underwriters over-allotting initial public
offering (IPO) stock to profit from the “Green Shoe” option (Fishe (2002), Aggarwal (2003),
Zhang (2004), Jenkinson and Jones (2007)).
In this paper we examine agency conflicts arising from underwriting in the context of a
seasoned equity offering (SEO). We examine a unique institutional setting where the issue
price is based on an average of the prices at which the stock trades prior to the issue and
where the underwriter has the opportunity to influence this price by trading during the period
over which the price is determined. Furthermore, during the pricing period the underwriter
knows how much stock they will be called upon to take up.
At the time the underwriting agreement is struck the underwriter faces the risk of having
to take up stock if there is a participation shortfall, but typically they are not prohibited from
trading during the pricing period. The underwriters have an incentive to temporarily depress
the stock price during the pricing period. This will lower the issue price and thus provide
extra profit on the underwriter’s stock allocation assuming the price bounces back from a
temporarily depressed state. Our hypotheses, therefore, are that underwriters engage in
abnormal selling activity over the pricing period and that the stock price is abnormally
depressed during this period.
We focus on new issue dividend reinvestment plans (DRIPs), a subset of SEOs, which
provide the unique institutional setting described above. We utilize a dataset which identifies
the buying and selling broker for every trade, allowing the buying, and selling behavior of all
brokers to be identified. We test our hypotheses in the Australian market where DRIPs are
invariably new issue DRIPs and are an important source of funds. In 2009, 230 ASX listed
companies raised $11.4 billion using DRIPs, representing 18% of total secondary offerings
1
Many firms have documented the subsequent underperformance of SEOs due to accrual management (Rangan,
1998, Teoh, Welch and Wong, 1998), real earnings management (Cohen and Zarowin, 2010), and liquidity risk
(Lin and Wu, 2013).
2
For more on executive options timing see Yermack (1997) and Chauvin and Shenoy (2001).
3
Marciukaityte and Varma (2013) document executive management of earnings to meet analyst expectations.
2
by large corporations.4 Such is the importance of DRIPs as a source of finance that some
firms choose to have their DRIPs underwritten in order to guarantee the amount of capital to
be raised.
In support of our first hypothesis, we observe aggressive selling by the underwriting
brokers during the pricing period. Abnormal volume is 236% higher than during the
preceding benchmark period. Whilst it is not possible to determine from our data whether the
trades by underwriting brokers are proprietary or client facilitation, we only observe
significant abnormal selling in the pricing period by the underwriting broker. No such selling
is observed for non-underwriting brokers in the underwritten DRIPs, or by brokers in our
matched sample of non-underwritten DRIPs. The results are consistent with manipulation of
the share price by the underwriter during the pricing period in order to generate additional
profit. However, we cannot rule out the alternative motivation of sales to hedge the price risk
of the allocation. If hedging is the motive, the hedging is only partial since on average less
than half of the underwriter’s allocation is sold during the pricing period. This leaves stock
available for resale and provides the potential to profit from a price rebound at, or subsequent
to, allocation. Whatever the underwriters motive, the consequence is clearly abnormal sales
and, as discussed below, a depressed issue price.
In support of our second hypothesis, we find that underwritten DRIPs have negative
abnormal returns of 4% during the pricing period, which is significantly worse than the
negative abnormal returns of 2.3% experienced by non-underwritten DRIPs. The temporary
decrease in the market price of the stock during the pricing period leads to a reduction in the
issue price of the DRIP shares, resulting in a benefit to all participants in the DRIP
(particularly the underwriter). However the adverse impact on the share price and the lower
issue price is to the detriment of non-participating shareholders.
This paper proceeds as follows. In section 2 we review the DRIP issue process, the
incentives created by the process and how, consistent with their incentives, underwriters can
manipulate prices. Section 3 describes the data and method. Section 4 provides a discussion
of the results while section 5 concludes.
4
ASX Annual Report, 2010.
3
2
2.1
Dividend reinvestment plans, agency conflicts and underwriter incentives
Australian Institutional Details
A new issue DRIP is a type of seasoned equity (SEO) offering. DRIPs enable
shareholders to automatically re-invest all, or part, of their dividend entitlements in additional
shares of the issuer’s stock. New-issues under a DRIP resemble rights issues, in that the right
to participate is based pro rata on the size of the existing holdings, and the capital
raised/retained depends on both the participation rate and whether the issue is underwritten.
Since dividends are paid regularly, the DRIP is analogous to predictable small periodic rights
issues which are capped by the total dollar amount of dividends. There are no disclosure
requirements and all brokerage fees are absorbed by the issuer. For small equity raisings
DRIPs can be an alternative, low cost form of SEO compared to rights issues and placements.
Internationally, Eckbo and Masulis (1992) highlight the growing prevalence of DRIPs for
raising equity in the US market.
The attractiveness of the DRIP is often increased by issuing the shares at a discount,
typically around 2.5% and in some cases up to 10%, from the stock’s market price during the
pricing period.5 However, as the DRIP participation rate is uncertain prior to the dividend
being declared, the cash to be retained by the firm is unknown. To eliminate this uncertainty
companies can elect to use an underwriter.
In an underwritten DRIP (UDRIP), the underwriter (typically an investment bank) enters
into an agreement to purchase an agreed proportion of the DRIP shares, which could cover up
to 100%, of the new dividend. If the participation rate of existing shareholders is lower than
the proportion guaranteed, the underwriter will buy enough new shares to cover the shortfall.
Investors choosing not to take up the shares will still receive their cash dividend, however
new shares, equivalent in cost to the value of that dividend, will be issued to the underwriter
instead. The participation shortfall (and hence the underwriter’s commitment) is known by
the dividend record date, as eligible shareholders must register for the DRIP prior to that date.
The DRIP issue price is generally computed as the arithmetic average of the daily
volume-weighted average sale price (VWAP) of all ordinary shares sold on the exchange in
the ordinary course of trading during the pricing period (typically 5 to 15 days), less any
applicable discount. The timing of the pricing period is fixed and occurs sometime after the
5
Such discounts can provide another source of profit for the underwriting broker.
4
ex-dividend date. Since the pricing period spans the record date the shortfall will be known
before the pricing period ends.
There is evidence that the management of some companies have concerns over price
pressures caused by UDRIPs. For example, Orica Ltd increased their DRIP pricing period
from 7 to 12 trading days when an underwriter was appointed “...so that the [issue] price was
impacted less by short term variations in the company’s share price.” Orica Ltd. (ASX:ORI),
23/10/2007.6 More generally, we find that management increase the length of the pricing
period upon the appointment of an underwriter in 46 out of our original sample of 126
UDRIPs (36.51%).
2.2
Agency Conflicts and Underwriter Incentives
This study relates to the broader literature on the link between underwriters’ incentives
and equity issue underpricing. In IPOs, the existence of the “Green Shoe” option to oversell
shares in the IPO is shown by Fishe (2002) to create an agency conflict between the
underwriter and the issuing firm, which results in IPO underpricing. The underwriter is able
to “oversell” the issue, selling a greater number of shares than are actually on offer. Such a
practice necessitates that the underwriter covers this short position. This can be accomplished
either by on-market purchase or through the use of the Green Shoe option.7 Fishe (2002)
shows that this is analogous to a call option, allowing the underwriter to purchase short-sold
shares at the market price if the price subsequently falls below the issue price, or by using the
Green Shoe option should the price rise. The structure of this call option combined with the
impact of stock-flippers (traders who purchase in the IPO and sell immediately in the
secondary market) results in the underwriter underpricing the issue, to the detriment of the
issuing firm. Empirical support for the model of Fishe (2002) is documented by Aggarwal
(2003) and Ellis, Michaely, and O’Hara (2000), with Green Shoe options found to be fully
utilized for issues with prices that rise and avoided when post-issue prices fall. This body of
literature on IPO underpricing suggests the presence of an agency conflict as underwriters
maximize their own profit instead of acting in the best interests of the firm. Similarly, in an
underwritten DRIP there is an incentive for the underwriter to manipulate the DRIP issue
price to extract an increased profit, to the detriment of the issuing firm’s shareholders.
6
Firms that have either terminated their underwriting agreement or replaced it with a private placement include
SuncorpMetway Ltd (ASX:SUN), 19/09/2008 and Transpacific Industries Group Ltd. (ASX:TPI), 03/10/2008.
7
See Aggarwal (2003) for a detailed discussion of the Green Shoe option. On NASDAQ this option is restricted
to 15% overallotment, and the option must be exercised within 30 days.
5
In a typical DRIP agreement the price at which shares are issued is determined during the
pricing period, hence underwriters, who will receive a predetermined number of stock at an
as yet undetermined price, have an incentive to put temporary downward pressure on the
price of the stock during the pricing period. Similar behavior has been documented by
Chauvin and Shenoy (2001) for firm executives seeking to maximize the value of options on
the day they are granted. Whilst the incentive structure for managers and underwriters is
similar, the strategy differs in its execution. Executives, unable to trade during the pricing
period achieve their objective by opportunistically manipulating the flow of good and bad
information to temporarily depress the stock price. Underwriters on the other had are able to
temporarily depress the share price by selling the stock during the pricing period. Once the
pricing period concludes, the removal of this selling pressure allows the underwriter to profit
on the as yet unsold portion of the underwritten allocation.
The stock sold during the DRIP pricing period is fully hedged as long as the underwriter
achieves execution at VWAP. On allocation of stock at issue, the underwriter will pay the
VWAP minus any discount. Selling during the pricing period engenders little execution risk
and simultaneously mitigates the economic risk of the underwriter’s commitment. The
incentives of the underwriter can be analyzed by examining the payoffs provided to the
underwriter. The underwriter profit per share is:
𝑈𝑛𝑑𝑒𝑟𝑤𝑟𝑖𝑡𝑒𝑟 𝑝𝑟𝑜𝑓𝑖𝑡 𝑝𝑒𝑟 𝑠ℎ𝑎𝑟𝑒 = 𝐹 + 𝛼(𝑉) + (1 − 𝛼)𝑃 – (𝑉 − 𝐷)
(1)
Where F is the underwriting fee in dollars per share, α is the proportion of shares of the
shortfall sold by the underwriter during the pricing period at a price of V. V is the arithmetic
average of the daily VWAP of all ordinary shares during the pricing period, D denotes the
issue price discount per share in dollars and P is the price at which the share is sold on issue,
or thereafter.
The profit maximizing underwriter thus faces an optimization problem. The problem is to
sell sufficient stock during the pricing period so as to reduce VWAP whilst still retaining
enough shares to profit from their subsequent sale. The underwriter clearly prefers a higher
market price for the subsequent sale (P) and a lower VWAP (V) during the pricing period.
The underwriter is unlikely to be able to successfully manipulate the price at which the shares
are sold post-issue due to the unwinding problem documented by Aggarwal and Wu (2006).
Thus the likely strategy is trading to depress the price during the pricing period.
6
On the basis of the discussion in this section we propose the following hypotheses:
H1: Underwriting brokers will exhibit unusual selling behavior during the pricing period.
H2: Underwriting a DRIP will lead to lower prices and consequent negative abnormal
returns during the pricing period.
3
Data and Method
3.1
Data
The data identifying DRIP announcements is provided by the Securities Industry
Research Centre of Asia-Pacific (SIRCA). We identify 2771 DRIP announcements, 126 of
which are underwritten, between January, 2007 and December, 2011. For each announcement
we collect the date, dividend type, ex-dividend date, record date, and payment date as well as
the ASX stock code and the GICS industry sector classification. The DRIP prospectus and
ASX Appendix 3B documents are used to determine share allotments (both to participating
shareholders and underwriters), along with the corresponding issue prices and the identity of
the underwriting lead manager.8 Details of the pricing period start and end dates are also
obtained from these documents. Stock price and the market index (All Ordinaries) data are
also supplied by SIRCA. Order level data is obtained from SIRCA’s Australian equities
database which contains all orders and trade executions submitted in the Australian equity
market. For each order, this data set contains ASX stock codes, times, dates, volume, prices
and the broker identification codes of both the buyer and the seller. There are 93 unique
brokers trading in the UDRIP and DRIP stocks during the sample period. We remove
UDRIPs where we cannot identify the underwriting broker, or which have price sensitive
announcements during the pricing period.9 Of our initial sample of 126 UDRIPs, 39 UDRIPs
are removed leaving us with a final sample of 87 UDRIPs.
3.2
Matched Sample Construction
To identify the impact of underwriting a DRIP, UDRIPs are matched to comparable DRIPs.
Matched DRIPs are selected according to Equation (2), which gives a scaled sum of squared
differences between pairs of DRIP and UDRIP firms, across the market capitalization of the
firm and the size of the issue.
8
Appendix 3B documents are necessary whenever new shares are issued on the ASX and identify the number,
price and reason for the new issue.
9
These include 11 operational results, 9 DRIPs with an unidentifiable underwriting broker, 8 M&A
announcements, 6 earnings updates, 3 credit rating changes and 2 asset sales.
7
2
2
𝑀𝑎𝑡𝑐ℎ𝑖𝑛𝑔 𝑆𝑐𝑜𝑟𝑒𝑈,𝐷 =
𝑥𝑖𝐷
∑( 𝐷
𝑥𝑖
𝑖=1
−
+
2
𝑥𝑖𝑈
)
𝑥𝑖𝑈
(2)
where, 𝑥𝑖𝐷 and 𝑥𝑖𝑈 denote the firm market capitalization and issue size for DRIP and
UDRIP firms, respectively.
In the matching process we ensure that during the pricing period the DRIP does not have
any price sensitive announcements. We select the DRIP and UDRIP pairs with the lowest
matching score within four months of the UDRIP (Time-Match). For robustness testing, we
create a second set of matched firms, based on the lowest matching score within the same
industry, whilst relaxing the contemporaneous time period constraints (Industry-Match). This
generates a new set of matched firms whose fundamental characteristics more closely
resemble their UDRIP counterparts, but which may be drawn from different time periods
within the sample.
The summary statistics for UDRIPs and time-matched and industry-matched DRIPs are
shown in Table 1. Panel A groups UDRIPs by year. The financial crisis of 2008 resulted in a
significant increase in UDRIPs, both by number and dollar value. This reflected the greater
demand for funding certainty during difficult market conditions. As the economic conditions
improved, the number of UDRIPs declined. While a similar pattern is observed in the
matched DRIPs depicted in Panel B, it is evident that the UDRIPs and the matched DRIP
samples do not have identical numbers of observations by year. This is because the four
month matching period for the time-matched DRIPS spans the year end. Comparing the
equity capital raised across the samples the medians are reasonably similar, but due to large
bank UDRIPs in 2007 and 2008 the means show some substantial differences.
< Insert Table 1 here >
3.3
Broker Trading Behavior
As the underwriting broker is identified in the disclosure documents, we can identify all
trades made under the underwriting broker’s ID. Overall volume for underwriting brokers is
higher than that of unaffiliated brokers trading the same UDRIP stock. This is not surprising.
There are fewer small brokers acting as underwriters. Underwriting brokers are generally
larger in size and command greater market share. We account for the size difference by using
each broker as their own control in constructing trading metrics.
8
Knowing the identity of the broker on the buy and sell side of every trade allows us to
identify the purchasing and selling behavior of all brokers. Trades in UDRIP stocks and the
matched DRIP stocks were analyzed across trading windows, before, during and after the
pricing period. The first day of the pricing period is defined as day 0 and our analysis focuses
on the pricing period window [0, End], where End denotes the end of the pricing period. The
returns during the pricing period are then compared to a 5-day and 10-day pre-pricing and
post-pricing period ([-5, -1], [End+1, +5] and [-10, -1], [End+1, +10]). We note that, in the
windows [-5, -1] and [-10, -1], the cause of trading volume should be interpreted with
caution.10 Short term trading about the ex-dividend date by both dividend capture traders and
dividend avoidance traders may substantially affect the volumes observed.
Two volume metrics are used to analyze the extent of abnormal trading. The first metric
developed by Chordia, Roll and Subrahmanyam (2002) is used to measure the imbalance
between buying and selling orders that become trades. The second metric is an abnormal
volume metric which is used to measure abnormal volumes separated by whether the broker
acts as the buyer or seller.
Following Chordia et al (2002) the order imbalance metric for each broker is computed as
follows:
𝑃(𝑉𝑂𝐿𝑗,𝑡 ) =
𝑆
𝐵
𝑉𝑂𝐿𝑗,𝑡
− 𝑉𝑂𝐿𝑗,𝑡
(3)
𝑆
𝐵
𝑉𝑂𝐿𝑗,𝑡
+ 𝑉𝑂𝐿𝑗,𝑡
where j indexes the broker and t indexes the trading day, the
S
and
B
superscripts represent
seller and buyer respectively. A metric greater (less) than one indicates excess sales
(purchases) made by a broker on a particular day while zero implies that order are in balance.
Order imbalance metrics for each day are then averaged across all brokers for the UDRIP
sample and for the matched DRIP samples, and then further averaged across the trading
window.
Following Henry and Koski (2010), the abnormal volume metric is measured as follows:
𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝑉𝑜𝑙𝑢𝑚𝑒𝑗,𝑡 =
𝑉𝑜𝑙𝑢𝑚𝑒𝑗,𝑡
−1
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑉𝑜𝑙𝑢𝑚𝑒𝑗
(4)
where Volumej,t is the total abnormal buying/selling volume of broker j on each day t during
the event period and Average Volumej is the average buying and selling volume of each
10
In 69 out of the 87 UDRIPs the pricing period starts within 10 days of the ex-dividend date.
9
broker j during a “clean period” measured between 60 and 10 days prior to the start of the
pricing period ([-60, -11]). The abnormal volume metrics are then averaged across all brokers
in each category for each event-day, and then further averaged across the trading window, in
the same way as the order imbalance metric.
3.4
Calculation of abnormal returns
A standard event study method is used to examine the share price response to UDRIPs
during the pricing period. The event windows are the same as those used for the analysis of
volume, [-10, -1], [-5, -1], [0, End], [End+1, 5] and [End+1, 10]. The daily abnormal returns
𝐴𝑅𝑖,𝑡 are determined from the market model as follows:
𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡 − 𝔼[𝑅𝑖,𝑡 ]
(5)
where 𝑅𝑖,𝑡 is the observed return for security 𝑖 on day 𝑡 and 𝔼[𝑅𝑖,𝑡 ] is the market model return
for security 𝑖 on day 𝑡, with betas constructed over the period [-180,-11]. Cumulative
abnormal returns are calculated as follows:
𝑡0 +𝑛
𝐶𝐴𝑅𝑖,[𝑡0 −𝑚,𝑡0 +𝑛] = ∑ 𝐴𝑅𝑖,𝑡
(6)
𝑡0 −𝑚
where 𝐶𝐴𝑅[𝑡0 −𝑚,𝑡0 +𝑛] is the CAR for firm 𝑖 over period [𝑡0 − 𝑚, 𝑡0 + 𝑛] and m and n are the
starting and ending days of the event window, respectively. These CARs are then averaged
for across firms for each event day, and then averaged again across days in the window of
interest. As a robustness test, and following the Australian DRIP studies of Chan et al. (1993,
1996), we also employ the zero-one market- model, where 𝔼[𝑅𝑖,𝑡 ] is equal to the market
return on day t.
3.5
Regression analysis
To analyze the differences between the returns of UDRIP and DRIP samples in the
pricing period we use the following regression:
𝐶𝐴𝑅𝑖 = 𝛽0 + 𝛽1 𝑈𝐷𝑅𝐼𝑃𝑖 + 𝛽2 ln(𝑆𝑖𝑧𝑒)𝑖 + 𝛽3 𝐷𝑖𝑣_𝑌𝑖𝑒𝑙𝑑𝑖 + 𝛽4 ln(𝑉𝑎𝑙)𝑖 + 𝛽5 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑖 + ε
where i is a firm subscript. UDRIP indicates whether the dividend is underwritten and takes a
value of one if the DRIP is underwritten and zero otherwise. We also utilize an alternative
specification for the regression in which an interaction variable U_Sfall is substituted for
UDRIP. U_Sfall is the product of the UDRIP dummy and the percentage of shares taken up
10
(7)
by the underwriter. The other four variables, measured one month prior to the start of the
pricing period, control for firm-specific factors. ln(𝑆𝑖𝑧𝑒) is the natural logarithm of the
market capitalization of the firm, 𝐷_𝑌𝑖𝑒𝑙𝑑, is the dividend yield and is a measure of the
relative size of the issue. ln(𝑉𝑎𝑙) is the average daily traded value of the stock. 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 is
the size of the percentage discount applied to the VWAP in order to determine the issue price.
Table 2 reports the descriptive statistics of the DRIP plan structure and the firm
characteristics that are used as control variables. On average, UDRIP plans exhibit a longer
plan pricing period than both time-matched and industry-matched DRIPs. Table 2 also shows
that the participation rate for UDRIPs is lower than for both groups of matched DRIPs. This
is consistent with the literature on rights issues, which shows that rights issues are more likely
to be underwritten when the expected take-up in the offer is low (Bøhren, Eckbo and
Michalsen, 1997). The dividend yield is slightly lower for UDRIPs, while the discount
applied to the new shares issued under the UDRIPS and DRIPs is similar. Indeed the median
discounts are identical across all samples at 2.5%.
< Insert Table 2 here >
4
4.1
Results
Broker Trading Behavior
Figure 1 plots Chordia et. al’s (2002) order imbalance, for the underwriting brokers, the
unaffiliated brokers and brokers in the matched DRIPS. Since the length of the pricing period
varies across firms, we present the order imbalance of each broker group by aligning the
metric by both the start (Panel A) and end (Panel B) of the pricing period. Panel A starts at
day -10 so that it does not overlap with the benchmark period and symmetrically ends at day
+10. Panel B can extend back to day -20 without overlapping the benchmark period and
extends symmetrically to day +20.
Panel A shows that sell orders by underwriting brokers jump substantially during the
pricing period. In contrast, for the unaffiliated brokers and the brokers in the matched DRIP
samples the order imbalance fluctuates around zero during the pricing period. Panel B
demonstrates that after the conclusion of the pricing period the order imbalance for the
underwriting brokers falls sharply towards zero, while no substantive order imbalance
changes are observed in the control samples.
11
< Insert Figure 1 here >
Table 3 provides statistics for the order imbalance. The striking and strongly significant
result is for the underwriting brokers during the pricing period. The pricing period shows a
sharp increase in selling orders by the underwriting brokers with an average sell order
imbalance of 28% of total orders. In contrast, the unaffiliated brokers and the brokers for the
time-matched DRIPs have much smaller, but significant order imbalances on the buy side
during the pricing period and no significant results at other times.
< Insert Table 3 here >
Table 4 provide the results of both a parametric and a non-parametric test of differences
between order imbalance measures of underwriting brokers, unaffiliated brokers and matched
DRIP brokers over the pricing periods. Pairwise comparisons show that the only significant
differences, between the order imbalances for the underwriting brokers and for the other
broker groups, occur in the pricing period. In all cases the underwriting broker is doing
significantly more selling.
< Insert Table 4 here >
4.2
Abnormal Buying and Selling
Figure 2 plots the daily abnormal selling activity for each broker group. We measure the
abnormal volume from both the start (Panel A) and end (Panel B) of the pricing period. From
both panels it is apparent that there is a marked difference between the selling behavior of
underwriting brokers and unaffiliated or DRIP brokers. The non-underwriting brokers do not
exhibit much evidence of unusual selling behavior prior to, during, or post the pricing period.
Underwriting brokers, however, exhibit abnormally high levels of selling during the pricing
period. This abnormal selling jumps to between 300%-400% above the average daily clean
period selling volumes at the start of the pricing period, remains elevated for the duration of
the pricing period and then drops markedly about the end of the pricing period. While the
abnormal selling by underwriting brokers is less intense after the end of the pricing period, it
is evident that some abnormal selling is continuing. The rise in abnormal selling in Panel B of
Figure 2, starting about day 15, corresponds to the share allotment dates which typically
occur 15-20 days following the conclusion of the pricing period.
< Insert Figure 2 here >
12
Figure 3 displays the abnormal buying behavior of underwriting brokers, unaffiliated
brokers and DRIP brokers. Panel A shows that unaffiliated brokers and DRIP brokers do not
exhibit any unusual buying behavior before, or after, the start of the pricing period. However
for underwriting brokers abnormal buying is observed in the ten days prior to the pricing
period, averaging 82% higher than the benchmark period. The abnormal buying activity is
substantially lower than the abnormal selling activity observed in Figure 2. It is also evident
that some abnormal buying by underwriting brokers continues into the pricing period. Panel
B reveals the buying activity of the underwriting broker continues to be slightly elevated post
the pricing period while there is little abnormal trading across the control samples. Panel B
demonstrates that the end of the pricing period has little effect on the buying patterns of any
of the brokers.
< Insert Figure 3 here >
Table 5 presents daily averages for abnormal volume. The results are given for buying
and selling volume over five intervals: [-10, -1], [-5, -1], [0, End], [End+1, 5] and [End+1,
10]. It is clear from Table 5 that the abnormal volume (normalized to the clean period) is
most evident amongst the underwriting brokers. For underwriting brokers, with one
exception, both the abnormal buy and sell volume are significant across all trading intervals.
The most striking result is the dominance of abnormal sales during the pricing period with an
abnormal selling volume of 236%. Little abnormal activity is exhibited in Panel B by the
unaffiliated brokers, with the only significant results being abnormal volume prior to the
pricing period.
As panels C and D show, the abnormal volumes for brokers trading in the DRIP stocks
are mostly insignificant. There are three cases of significant abnormal selling for the industry
matched DRIPs, all of which occur prior to the pricing period.
< Insert Table 5 here >
4.3
Analysis of Pricing Period Returns
Figure 4 shows a run-up and reversal pattern in the CAR for both UDRIPs and DRIPs
prior to the pricing period. This is likely to be due to dividend capture trading around the exdividend date. Dividend capture leads to a run up in prices cum-dividend, as documented by
Eades, Hess and Kim (1984), and a reversal ex-dividend.
13
Over the pricing period, Figure 4 shows that both UDRIP stocks and DRIP stocks have
CARs that become negative about the start of the pricing period. However, it is clear that
during the pricing period the UDRIP stocks have a more strongly negative CAR until about
day 10. From about day 10 to day 15 the CAR for the UDRIPs reverses its downward trend
and continues upwards thereafter. Ten days is the median length of the UDRIP pricing period
and there is a batch of UDRIP pricing periods that finish after fifteen days. By the day fifteen
96% of the UDRIP pricing periods are completed. The CAR plot is therefore consistent with
a reversal of price pressure as UDRIP pricing periods conclude.
< Insert Figure 4 here >
Table 6 shows the abnormal CARs over five intervals: [-10, -1], [-5, -1], [0, End],
[End+1, 5] and [End+1, 10], where 0 denotes the start and End denotes the end of the pricing
period. The CARs in the windows before and after the pricing period are not significantly
different from zero, neither are they significantly different between the UDRIP and the timematched and industry-matched DRIP samples. However, during the pricing period both the
UDRIP and the DRIP samples have significant negative CARs. The UDRIP has a mean
(median) CAR of -4.02% (-2.17%) while the time-matched and industry-matched DRIP have
a mean (median) CAR of -2.26% (-1.96%) and 1.02% (1.34%), respectively. The UDRIPs
mean CAR is significantly more negative than the matched DRIPs mean CARs at the 1%
level. While the median is more negative for the UDRIPs than for the matched DRIPs, the
differences are not significant.
< Insert Table 6 here >
4.4
Cross-sectional regression analysis
Cross-sectional regressions are used to examine the impact of underwriting on prices
during the pricing period, while controlling for various firm-specific variables. The CAR in
the first five days of the pricing period is the dependent variable. We chose five days as all
the CARS in the regression should be measured over the same period and 5 days is the
shortest pricing period present in the sample. The regression results are summarized in Table
7. Columns 1 to 4 measure the market impact of underwritten DRIPs using the UDRIP
dummy. In columns 1 and 2 we report the results for sample that includes UDRIPs and a set
of DRIPs matched by firm size, issue size and the time of the issue (Time). We consider
CARs measured using both the market model (column 1) and the zero-one (market-adjusted)
14
model (column 2) as benchmarks for expected returns. In columns 3 and 4 we repeat the
analysis substituting a set of DRIPs matched by firm size, issue size and the industry of the
firm (Industry). In columns 5 through 8 we delete the UDRIP dummy and instead use an
interaction between the UDRIP dummy and the proportion of the DRIP that was taken up by
the underwriter due to a subscription shortfall. This variable is labeled 𝑈_𝑆𝑓𝑎𝑙𝑙.
The effect of the UDRIP variable is negative across all model specifications and
statistically and economically significant. Underwritten plans experience pricing period
returns which are approximately 2.3% lower than non-underwritten plans after controlling for
differences in firm size, dividend yield, the volume of shares traded during the pricing period
and the discount associated with the DRIP. The results are robust to using DRIPs matched by
time or industry and to using different abnormal return benchmarks.
< Insert Table 7 here >
Columns 5 through 8 in Table 7 show that the 𝑈_𝑆𝑓𝑎𝑙𝑙 effect is also consistently negative
and statistically and economically significant. Given that the mean level of underwriter
participation is 61%, the results imply that UDRIP pricing period returns are, on average,
approximately 2.6% lower than their DRIP counterparts. The evidence from the regression
models clearly indicates that choosing to underwrite a DRIP leads to significant negative
abnormal returns during the pricing period, even after controlling for other potential causes of
price movements.
With respect to the control variables, the effect of 𝐷𝑖𝑣_𝑌𝑖𝑒𝑙𝑑, reflecting relatively larger
issues, is generally negative and significant, but the effect is more strongly significant in the
regression specifications containing the 𝑈_𝑆𝑓𝑎𝑙𝑙 variable. The effect of, ln(Size), is positive
and significant across all specifications, although the effect weakens in the regression with
the 𝑈_𝑆𝑓𝑎𝑙𝑙 variable. Consistent with increased trading depressing price, the variable
ln(𝑉𝑎𝑙), has a coefficient that is negative and significant across the majority of
specifications, indicating that stocks with high trading in the pricing period experience more
negative returns. The variable 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡, has an insignificant effect across all specifications.
5
Conclusion
We hypothesize that there are incentives for underwriter trading that depresses prices over
the pricing period for underwritten DRIPs. The empirical results show both abnormal selling
by the underwriting broker and abnormal price movements during the period in which the
15
pricing of the new shares is determined. Over the pricing period the daily volume of sales
made by the underwriting broker increased by between 200% and 400% relative to trading by
the underwriting broker in the benchmark period. In contrast, there is no significant abnormal
selling by non-underwriting brokers during the pricing period. Furthermore, there is no
abnormal selling in the pricing period for matched samples of DRIPs that are not
underwritten.
Both regression analysis and comparison of CARs between underwritten DRIPs and a
matched sample of DRIPS that were not underwritten, suggests that underwriting results in
significantly more negative returns during the pricing period. On average returns for
underwritten DRIPs are about 2% more negative.
It cannot be conclusively determined whether the observed trading behavior is motivated
by a desire to manipulate the issue price downward, or by a desire to hedge the price risk
arising from the underwriting commitment, or both. Whatever the motivation it serves the
interest of the underwriters, adds to selling pressure and depresses prices during the pricing
period, which consequently depresses the issue price. The result is less capital for the firms
and a wealth transfer to the underwriters. We suggest that firms stem the transfer of wealth
from non-participating shareholders to the underwriter by either selecting a pricing period
that is less susceptible to price manipulation, or by inserting a clause into the underwriting
agreement to restrict the trading activity of the underwriter.
16
References
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Ellis, K., Michaely, R. & O’Hara, M., 2000. When the Underwriter Is the Market Maker: An Examination of Trading
in the IPO Aftermarket. The Journal of Finance, 55(3), pp.1039–1074.
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17
Table 1
Summary of UDRIPs from 2007 to 2011
Panel A provides an overview of the characteristics of UDRIPs from January 2007 to December 2011
summarized by year. Panel B and Panel C describe the DRIP sample matched by time and industry,
respectively. Market Cap refers to the average market capitalization of companies in the sample measured one
month prior to the start of the pricing period. Equity Capital Raised is the amount of capital raised by the
DRIPs. All percentages are rounded to the nearest percent.
Panel A: UDRIP Plan Distribution by Year
Year
Frequency
Equity Capital Raised ($ ‘000s)
Percentage
(%)
Market Cap
($ ‘000s)
Mean
Median
Total
2007
16
18%
9,349
199,085
29,644
3,185,366
2008
30
34%
9,612
225,759
56,581
6,772,759
2009
18
21%
2,719
49,666
18,912
893,981
2010
9
10%
419
8,178
5,779
73,602
2011
14
16%
8,588
140,441
25,788
1,966,180
Sample
87
100%
7,022
148,183
27,936
12,891,888
Panel B: Time Matched DRIP Plan Distribution by Year
Year
Frequency
Equity Capital Raised ($ ‘000s)
Percentage
(%)
Market Cap
($ ‘000s)
Mean
Median
Total
2007
17
20%
5,071
35,939
20,025
610,965
2008
26
30%
7,603
66,210
33,585
1,721,462
2009
18
21%
6,752
75,994
17,379
1,367,894
2010
15
17%
8,766
71,541
7,423
1,073,108
2011
11
13%
8,471
62,038
17,160
682,419
Sample
87
100%
7,243
62,711
21,161
5,455,848
Panel C: Industry Matched DRIP Plan Distribution by Year
Year
Frequency
Equity Capital Raised ($ ‘000s)
Percentage
(%)
Market Cap
($ ‘000s)
Mean
Median
Total
2007
13
15%
1,708
14,631
4,122
131,678
2008
17
20%
6,393
47,731
24,508
811,434
2009
18
21%
7,257
80,375
10,572
1,446,750
2010
26
30%
6,519
55,930
16,135
1,454,181
2011
13
15%
14,277
174,609
36,493
1,920,697
Sample
87
100%
7,175
71,170
18,497
5,764,740
18
Table 2
Descriptive Statistics
This table gives descriptive statistics for the UDRIP/DRIP characteristics and the firm characteristics used as control variables, partitioned across the UDRIP sample and
matched DRIP control samples. Each sample includes 87 observations. Pricing Period is the number of days in the period used to determine the plan issue price. Underwriter
take-up is the percentage of the DRIP shares being offered that are subscribed for by the underwriter. Participation is the percentage of shares participating in the DRIP.
While in most cases the underwriter take-up plus the participation sums to 1, if the underwriter does not underwrite 100% of the issue the sum could be less than 1. Dividend
yield is the dividend per share divided by the closing price for the stock one month prior to the start of the pricing period. Discount is the size of the discount applied to new
shares issued under the DRIP and is applied to the VWAP during the pricing period. Size measures the market capitalization of each firm one month prior to the DRIP
announcement. Ln(Size) is the natural logarithm of the Size variable. Traded value is the average daily traded value for each stock during the pricing period. Ln(Traded
Value) is the natural logarithm of the traded value variable.
UDRIP
Mean
Pricing period (days)
Underwriter takeup(%)
Participation (%)
Dividend yield (%)
Discount (%)
Size ($m)
ln(Size)
Traded value ($m)
ln(Traded value)
9.49
60.98
32.92
3.06
2.82
7021.61
7.16
1.75
12.22
Median
10.00
61.88
30.15
2.30
2.50
1298.30
7.17
0.80
13.47
DRIP Time
Std. Dev.
3.96
17.13
14.96
2.61
1.51
12456.57
2.16
2.21
3.83
Mean
Median
7.76
40.91
4.00
2.73
7242.63
7.28
2.10
12.80
19
8
34.20
3.59
2.50
1405.95
7.25
0.85
13.52
DRIP Industry
Std. Dev.
2.96
20.37
2.07
2.07
14081.00
2.04
4.22
3.17
Mean
8.25
39.31
3.70
2.91
7175.20
7.15
1.91
12.11
Median
9
37.11
3.33
2.50
1411.80
7.25
1.35
13.89
Std. Dev.
3.42
18.16
1.71
2.52
13798.05
2.18
2.01
4.53
Table 3
Order Imbalances
This table gives the order imbalance metric over the periods [-10, -1], [-5, -1], [0, End], [End +1, +5] and [End
+1, +10] for underwriting brokers, unaffiliated brokers and matched DRIP brokers. The daily order imbalance
metric per broker is calculated as the difference between sell volume and buy volume divided by the sum of buy
and sell volume. The metric is then averaged across all brokers in each category for each event-day, and then
further averaged across the trading window. A measure of 0 indicates that there is no order imbalance. A
measure greater than 0 indicates abnormal selling whilst a measure less than 0 indicates abnormal buying. ***,
** and * represent significance at the 1%, 5% and 10% levels, respectively.
[-10, -1]
Underwriting
Brokers
0.012
0.013
DRIP Brokers
(Time-Match)
-0.005
DRIP Brokers
(Industry-Match)
-0.006
[-5, -1]
-0.038
0.006
-0.012
-0.018
[0, End]
0.288***
-0.051**
-0.009*
-0.008
[End +1, +5]
0.049
0.004
-0.021
0.006
[End +1, +10]
0.011
0.017
-0.009
0.013
Unaffiliated Brokers
20
Table 4
Order Imbalances between Groups Pairwise Comparisons
This table gives the results of pairwise tests of differences between order imbalance measures of underwriting
brokers, unaffiliated brokers and matched DRIP brokers over the pricing periods [-10, –1], [-5, -1], [0,End],
[End+1, +5] and [End+1, +10]. ^^^ (###) represents statistical significance at the 1%, ^^ (##) at the 5% and ^
(#) at the 10% level for the paired student t and the Wilcoxon matched pairs signed ranks test.
Underwriting vs.
Unaffiliated Brokers
Underwriting vs. DRIP
Brokers (Time-Match)
Underwriting vs. DRIP
Brokers (Industry-Match)
[-10, -1]
-0.001
0.017
0.018
[-5, -1]
-0.044
-0.026
-0.02
^^^
0.339###
0.297^^^
###
[End+1, +5]
0.045
0.07
0.296^^^
###
0.043
[End+1, +10]
-0.006
0.02
-0.002
[0, End]
21
Table 5
Broker Inter-day Trading Activity
This table gives the abnormal volume over the periods [-10, -1], [-5, -1], [0, End], [End+1, 5] and [End+1, 10],
where 0 denotes the start and End denotes the end of the pricing period for buy and sell volumes across broker
groups. Abnormal volume is measured as the ratio of trades by each broker each day to average daily trades by
the same broker in a benchmark period. One is then subtracted from this ratio and the resulting metric (%
abnormal volume) is then averaged across brokers and the event period. Panel A gives abnormal volumes for the
broker underwriting the UDRIP. Panel B gives abnormal volumes for unaffiliated brokers. Panel C and D are
trades in DRIP stocks by all brokers matched by time and industry, respectively. Total is the total abnormal
volume for both buy and sell trades. Sales and Purchases gives abnormal volume conditioned on whether the
broker is selling or buying. A value of 0 implies no abnormal volume. ***, ** and * represent significance at the
1%, 5% and 10% levels, respectively.
Panel A: Underwriting Broker Volume during Pricing Periods
Total
Sales
Purchases
[-10, -1]
39%***
57%***
82%***
[-5, -1]
15%
36%**
52%
[0, End]
138%***
236%***
95%***
[End+1, +5]
45%
66%*
57%**
[End+1, +10]
24%
32%*
49%**
Panel B: Unaffiliated Broker Volume during Pricing Periods
[-10, -1]
Total
21%**
Sales
17%***
Purchases
13%*
[-5, -1]
5%
5%
-2%
[0, End]
18%
12%
23%*
[End+1, +5]
7%
5%
5%
[End+1, +10]
4%
6%
0%
Panel C: DRIP Broker (matched by time) Volume during Pricing Periods
Total
Sales
Purchases
[-10, -1]
5%
4%
6%
[-5, -1]
5%
0%
9%
[0, End]
1%
-2%
2%
[End+1, +5]
-2%
3%
-6%
[End+1, +10]
-2%
3%
-7%
Panel D: DRIP Broker (matched by industry) Volume during Pricing Periods
Total
Sales
Purchases
[-10, -1]
15%**
15%***
10%
[-5, -1]
15%*
10%
13%
[0, End]
7%
3%
7%
[End+1, +5]
5%
7%
1%
[End+1, +10]
7%
7%
3%
22
Table 6
Pricing Period CARs
This table gives the CARs from the pricing periods for the UDRIP, time-matched and industry-matched DRIP firms for five event windows [-10, -1], [-5, -1], [0, End],
[End+1, 5] and [End+1, 10], where 0 denotes the start and End denotes the end of the pricing period. CARs are based on the market model as a benchmark return. The CARs
are calculated as the average across all firms of the sum of daily abnormal returns, starting at the beginning of each window. Differences in means and medians depict the
difference between the UDRIP and the respective DRIP matched pairs. ***, ** and * represent statistical significance at the 1%, 5% and 10% levels. Test for differences
between samples are based on the paired student t (mean) and Wilcoxon signed rank test (median).
UDRIP
[-10, -1]
Mean (%)
Median (%)
Mean (%)
-0.322
-0.509
-0.887
Time-matched DRIP
Difference in
Median (%)
means
-0.776
Difference
in medians
Mean (%)
0.565
0.267
0.573
Industry-matched DRIP
Difference
Median (%)
in means
-0.232
Difference
in medians
-0.895
-0.277
[-5, -1]
-0.076
-1.037
-0.396
-0.87
0.32
-0.167
0.024
-0.289
-0.1
-0.748
[0, End]
-4.024***
-2.174***
-2.255***
-1.959***
-1.769**
-0.215
-1.018
-1.336**
-3.006**
-0.838
[End +1, +5]
-0.253
-0.138
-0.559
0.198
0.306
-0.336
-0.095
0.281
-0.158
-0.419
[End +1, +10]
-0.259
-0.085
-0.562
0.028
0.303
-0.113
0.369
0.401
-0.628
-0.486
23
Table 7
Pricing Period Regressions
This table reports the cross-sectional regression results for abnormal returns during the pricing period. The Time
heading indicates regressions with UDRIP firms matched with DRIPs within a four month window surrounding
the UDRIP, as well as firm size and issue size. The Industry heading indicates firms matched based on industry,
firm size and issue size. Market and Zero-one headings denote the use of the market model and the zero-one
(market-adjusted) model, respectively. The dependent variable is the average CAR for the interval [0, +5];
𝑈𝐷𝑅𝐼𝑃 is a dummy variable that equals one if a DRIP is underwritten and zero otherwise (model 1through 4).
U_Sfall is the product of the UDRIP dummy and the percentage of shares taken up by the underwriter (model 5
through 8). ln(Size) is the natural logarithm of the market capitalization of each firm. 𝐷𝑖𝑣_Yield is the dividend
yield calculated as the dividend per share divided by the share price one month prior to the start of the pricing
period. ln(𝑉𝑎𝑙) is the natural logarithm of average daily turnover for each stock during the pricing period.
𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 is the discount rate for each plan for firm 𝑖. Heteroskedasticity consistent standard errors are used.
***, **, * represents significance at the 1%, 5% and 10% level, respectively.
6
Pricing Period CAR with Underwritten Shortfall
Pricing Period CAR with UDRIP Dummy
7
Interaction
Sample
Model
Time
Industry
Time
Industry
Market
Zero-one
Market
Zero-one
Market
Zero-one
Market
Zero-one
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡
0.036
0.040
0.053
0.048
𝑈𝐷𝑅𝐼𝑃
-0.023**
-0.026**
-0.022*
-0.023*
𝑈_𝑆𝑓𝑎𝑙𝑙
0.042
0.047
0.062
0.062
-0.040**
-0.046***
-0.040**
-0.045**
𝐷𝑖𝑣_𝑌𝑖𝑒𝑙𝑑
-0.005*
-0.005*
-0.004
-0.006*
-0.007**
-0.007***
-0.006*
-0.008**
ln(𝑆𝑖𝑧𝑒)
0.012**
0.012**
0.015**
0.013**
0.009*
0.009*
0.012**
0.010*
ln(𝑉𝑎𝑙)
-0.009**
-0.009**
-0.013***
-0.011**
-0.007
-0.007
-0.011**
-0.009*
𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡
-0.144
-0.101
0.058
0.027
-0.316
-0.270
-0.166
-0.209
𝐹
3.49***
3.70***
2.83**
2.75**
3.65***
3.96***
2.75**
2.93***
0.089
0.096
0.081
0.077
0.097
0.107
0.080
0.087
̅2
𝐴𝑑𝑗. 𝑅
24
Figure 1
Broker Order Imbalance
Panel A: Inter-day Order Imbalance aligned by the start of the pricing period
Panel B: Inter-day Order Imbalance aligned by the end of the pricing period
25
Figure 2
Panel A: Inter-day Abnormal Selling aligned by the start of the pricing period
Panel B: Inter-day Abnormal Selling aligned by the end of the pricing period
26
Figure 3
Inter-day Abnormal Buying
Panel A: Inter-day Abnormal Buying aligned by the start of the pricing period
Panel B: Inter-day Abnormal Buying aligned by the end of the pricing period
27
Figure 4
Pricing Period CARs over [-20, +20]
28