# Security Issuance, Institutional Investors and Quid Pro Quo: Insights from
SPACs††thanks: We would like to thank Christian Opp, Joanna Wu, Michael
Gofman, and Bill Wilhelm for their helpful comments and suggestions. We thank
the 3Cavaliers at the University of Virginia for their generous research
support and Lam Bui and Hengxiang Cao for their excellent research assistance.

Gaurab Aryal Zhaohui Chen Yuchi Yao Chris Yung Department of Economics,
Washington University in St. Louis<EMAIL_ADDRESS>School of
Commerce, University of Virginia<EMAIL_ADDRESS>School of Business,
University of Rochester<EMAIL_ADDRESS>School of Commerce,
University of Virginia<EMAIL_ADDRESS>
###### Abstract

Security issuance is subject to informational and agency-related frictions.
However, it is difficult to separate their effects. We consider SPACs and
assess those effects separately. To this end, we identify premium investors
who produce value-relevant information: their participation is associated with
higher SPAC success and announcement-day returns. Non-premium investors,
however, engage only in quid pro quo arrangements. Their high returns today
from issuers (quid) mean they are more likely to participate in low-returns
future deals initiated by those issuers (quo). Thus quid pro quo is not pure
agency cost but partly a transfer that enables more firms to go public.

Keywords: Security Issuance, Institutional Investors, SPAC, Underpricing JEL
classification: G14, G32, G34.

##  1 Introduction

Firms issuing securities through intermediaries face two types of friction.
One, there may be an informational gap between them (issuers) and the
investors, which may prevent securities from being sold at full value. Second,
there is an agency problem if intermediaries do not maximize sale proceeds but
instead engage in quid pro quo arrangements with investors. Specifically,
intermediaries may favor certain buyers (investors) at the expense of the
issuers, expecting their favoritism to be returned through profitable future
transactions.

Ritter and Welch (2002) point out that both frictions coexist and emphasize
the need and difficulty in separating those frictions. In a survey paper,
Lowry et al. (2017) similarly pose this separation as an unresolved question,
using IPO underpricing as an example:

> Does [IPO underpricing] reflect a reward to institutions for sharing value-
> relevant information with underwriters, or does it reflect a quid pro quo
> arrangement where the bank is rewarding its best clients? [..] This is an
> immensely important issue and one that we hope future research will be able
> to address.

We revisit this problem in the context of special purpose acquisition
companies (SPACs) and assess and separate the effects of these frictions on
security issuance.111We focus on broader issues in security issuance rather
than questions specific to SPACs and their regulatory oversight. Thus we
sidestep questions about the issuer’s choice between traditional IPO and SPAC,
whether SPACs have misled public investors, and how SPACs should be regulated.
See Gahng, Ritter and Zhang (Forthcoming) and Klausner, Ohlrogge and Ruan
(2022) for comprehensive reviews. To this end, we identify two types of
institutional investors: “premium” and “non-premium” (defined shortly below).
We find evidence consistent with premium investors acting in the role of
information production and non-premium investors engaging in only quid pro quo
arrangements. However, unlike the extant literature that identifies any quid
pro quo as purely value-destroying, our estimates suggest that in SPACs, quid
pro quo can create value by enabling marginal businesses to access public
markets.

We can separate the effects of information and quid pro quo by relying on the
institutional features of SPACs. A SPAC is a publicly traded shell (i.e.,
blank check) corporation formed by a sponsor and public investors with the
sole purpose of effecting a merger with a private firm to enable the latter to
go public. As such, SPACs offer four research design advantages over a
traditional IPO.

First, unlike traditional IPOs, a SPAC divides the process of going public
into two distinct phases that are affected by either informational or agency-
related friction, but not both. In the initial stage, the _sponsor_ of the
SPAC raises capital from investors. The SPAC starts as a shell company without
an operating business, which limits the amount of information asymmetry
compared to a traditional IPO.222Consistent with this, in our sample, we find
a small (1.3%) underpricing at SPAC IPO. The next phase consists of a
“business combination” in which the SPAC acquires or merges with a private
business.

Second, during the business combination phase, SPACs report the allocations to
each institutional investor or the _private investment in public equity_
(acronym, “PIPE”) investor. These data enable us to measure the investment
return history for every sponsor-PIPE pair and create in-sample measures of
past “relationships” based on their track records. In traditional IPOs,
allocations to institutional investors are not reported, so one cannot
credibly measure past relationships between underwriters and investors.333In a
traditional IPO, although one can read quarterly 13F filings after the IPO, it
may be insufficient to quantify favoritism if preferred clients are allowed to
“flip” shares in secondary market trading. Furthermore, 13F filings do not
distinguish between shares allocated at the IPO and purchased in the secondary
market.

Third, SPACs are publicly traded firms. Consequently, we observe the effect of
major events (e.g., securing new external capital or identifying a target) on
share prices. Fourth, the quid pro quo channel is easier to observe in SPACs
than in traditional IPOs. Unlike the investment banks in traditional IPOs,
SPAC sponsors typically do not operate in other lines of business (e.g.,
trading and wealth management). So, to the extent that sponsors earn quid pro
quo from investors in future SPACs deals, we can measure those gains.

Once the SPAC finds a target and negotiates a “business combination” deal, the
existing SPAC investors have to approve the deal. Those who do not like the
deal can redeem their investments. Such redemptions reduce the money available
for the post-merger firm. As a safeguard, once the target is identified but
before the target firm begins public trading, the SPAC sponsor brings in PIPE
investors.

These investors that participate in the target announcements are the analogs
to the institutional buyers in the traditional IPO bookbuilding models.
Specifically, the sponsor finds a target and approaches PIPE (at) investors to
solicit their interest. Then the sponsor builds a book of PIPE demand, sets
prices, and allocates shares. Thus, when it comes to the business combination
phase, SPAC and traditional IPO are similar in that they both are subject to
information and agency frictions–the subject of this paper.

Much of our analysis focuses on the role of such PIPE investors. We classify
them into two types of investors$\\--$premium and non-premium$\\--$based on
their participation frequency and investments in SPACs, using k-means
clustering, which is an objective approach to classification. The underlying
assumption is that the PIPE investors who participate frequently and invest
large amounts are likely more reputable than others (Benveniste and Spindt,
1989), which behooves them to conduct due diligence and evaluate the proposed
merger deal.

Using data on all 1,072 SPACs from January 2010 to December 2021, we find
evidence consistent with premium investors offering value-relevant information
and playing certification roles. In particular, the participation of premium
investors is associated with significantly less share redemption by public
SPAC investors and a large positive announcement effect on the SPAC stock
price. The estimates suggest that one standard deviation (32.85 pps) increase
in premium PIPE investors’ participation is associated with a seven pps
decrease in the redemption rate (which is 12.4% of the sample mean) and a four
pps increase in the SPAC’s announcement-day return (which is 108.8% of the
sample mean).

Creating a SPAC and finding a suitable target falls on the sponsors, who act
as intermediaries between the target (who issues shares) and PIPE investors
(who buy shares). Sponsors profit an average of $126.9 million when the merger
is successful: otherwise, the SPAC is liquidated, their securities expire
worthlessly, and they lose their initial investment, which, on average, is
$8.2 million. Therefore, the sponsors benefit from participation by PIPE
investors which improves the chances of a successful merger.

So, the sponsors may have incentives to offer discounted shares to PIPE
investors in strong deals in exchange for a tacit promise to help out in
(future) weaker deals. Under such a quid pro quo arrangement, high-quality
firms (inadvertently) transfer to (in part) low-quality targets by allowing
the latter to go public.444This transfer between issuers is reminiscent of the
mechanism described in Benveniste, Busaba and Wilhelm (2002), who argue that
underwriters cross-subsidize issuers depending on their position in a wave.
There, the leader is subsidized, whereas, in SPACs, the follow-on SPACs (by
the same sponsor) are subsidized.

Such a quid pro quo arrangement suggests the following testable predictions.
First, for each sponsor-PIPE pair, money left on the table (acronym, “MLOT”)
should be mean-reverting, i.e., high profits in a current deal predict low
profits in a future deal, and vice versa. As is standard in the literature
(e.g., Lowry and Schwert, 2004), we measure MLOT using the first-day closing
price, taken as a proxy for the true value. Second, when the deal is weak, the
sponsor allocates shares disproportionately to those who earned strong returns
from that sponsor in the past. Third, when a sponsor with strong relationships
with investors, accumulated through past deals, leads a SPAC, that SPAC takes
fewer days to find a target and is less likely to be liquidated.

These predictions have no analog in the information production paradigm:
investors who did well in the past receive new signals of the current deal’s
quality and are under no obligation to support future weak deals. If anything,
the MLOTs of the premium investors should exhibit positive dependence if
investor skill is persistent.

We find evidence consistent with all three quid pro quo predictions, but
_only_ when considering non-premium investors. Specifically, non-premium
investors profit less in the current deal when they have high returns on past
deals. In particular, one dollar _above_ average MLOT in the last deal leads
to a $2.72 _below_ average MLOT from the current deal. We interpret this
_mean-reversion_ as the sponsor building a relationship and drawing it down,
likely for weaker deals.

We refer to weaker deals as “cold” deals and use the difference between the
offer share price of the SPAC, i.e., the share price at SPAC IPO, and the
share price on the closing day of the merger to measure it. Indeed, we find
that investors with a stronger relationship are more inclined to participate
in weaker deals. In particular, non-premium investors with a strong
relationship with a sponsor (with a total MLOT from this sponsor that is one
standard deviation above the sample mean) are 3.54 pps more likely to
participate in deals with the cold measure that are in the 90th percentile of
sample than deals with the cold measure that are at 10th percentile.
Conversely, non-premium investors without a prior relationship with the
sponsor are less likely to participate in cold(er) deals. Moreover, we observe
that when a non-premium investor with a strong relationship does participate,
the sponsor allocates a larger portion of securities to them in weaker deals
than in stronger deals.

We contribute to the literature by establishing a new institutional theory in
security issuance based on a sponsor’s relationship with institutional
investors. We provide evidence that the quid pro quo arrangement enables
cross-deal subsidies that enable weaker firms to go public by reducing the
SPACs’ liquidation risk. In contrast, the existing literature treats quid pro
quo as pure agency costs. More precisely, we find that if sponsors allocate
one standard deviation more MLOT above average ($300 million) to non-premium
investors, the liquidation probability of the current deal falls by 48.93 pps.
As a reference, the average liquidation rate in our sample is 17.82%, so this
decrease is economically meaningful. However, the MLOT allocated to the
premium investors is uncorrelated with the liquidation risk.555In Appendix A,
we verify that our main findings are robust to the presence of outlier (large
and small) investors and placebo assignments.

We also contribute to the information production mechanism by measuring the
information content of institutional investors. The traditional IPO literature
argues that price-limited bids are likely more informative than market-order
bids (Cornelli and Goldreich, 2001; Jenkinson, Jones and Suntheim, 2018).
SPACs are publicly traded and thus afford us the advantage of quantifying
price reactions to information conveyed by PIPE investments.

Overall, premium and non-premium investors play different but complementary
roles in SPACs and are paid accordingly. In our sample, each premium investor,
on average, is paid $6.96 million, and a non-premium investor $3.17 million.
Moreover, their observable differences can only explain $0.04 million of the
$4.79 million gap between their pay. We interpret this result as evidence of
their different “business models.” That is, a premium investor is paid $6.96
million to produce information for the current target, and a non-premium
investor is paid $3.17 million to reduce liquidation risk for the future SPAC.

Related Literature. Our research contributes to a long line of research that
uses underpricing to infer the importance of agency and informational
problems. For example, Benveniste and Spindt (1989) predict positive average
underpricing because investors are rewarded for value-relevant information.
The implication is that in a traditional IPO, when prices rise from the
midpoint of the filing range until the offer price (i.e., when demand is
high), there should be further price increases when the stock starts trading.
In other words, underwriters partially adjust to good information revealed by
investors but fully adjust to bad information.666See Hanley (1993) for
evidence of partial adjustment. Bradley and Jordan (2002) and İnce (2014)
argue that prices respond to public information, which is inconsistent with
bookbuilding models, whereas Lowry and Schwert (2004) argue that the IPO price
is “almost efficient” with respect to public information. In contrast, Beatty
and Ritter (1986); Loughran and Ritter (2002) and Loughran and Ritter (2004)
argue that underpricing is pure unproductive rent.

We complement this literature by looking at a broader range of outcomes:
redemption, announcement price reactions, liquidation, and allocations, and
show that both information and quid pro quo arrangements matter.

We also contribute to a nascent literature that seeks to understand the role
and functioning of SPACs. For instance, Gahng, Ritter and Zhang (Forthcoming)
study the cost and benefit of using SPACs to go public; Gofman and Yao (2022)
study the conflict of interest and associated harm from directors serving on
the boards of multiple SPACs; and Klausner, Ohlrogge and Ruan (2022) analyze
the structure of SPACs and the associated costs.

Our findings about the differential effects of premium and non-premium
investors complement the findings in Wang and Yung (2011) that partial
adjustment is present only in the subset of highly reputable underwriters. In
particular, they show that low-reputation underwriters cluster heavily on
exactly zero-dollar price revision. This behavior is consistent with no
information being produced or revealed. We also complement Chemmanur, Hu and
Huang (2010), who study the role of institutional investors in IPOs.

Our finding that information production coexists with quid pro quo is
reminiscent of the findings in Jenkinson, Jones and Suntheim (2018). Using
data on the initial allocations of investment banks participating in
traditional IPOs, they find evidence that investors are rewarded more when
they submit price-limited bids and participate more actively in the meetings
before or during bookbuilding. Both behaviors are consistent with an
informational role. However, they also find that allocations are affected by
how much revenue the investor generates for the bank, which is consistent with
the quid pro quo argument.

As previously mentioned, SPACs offer an opportunity to examine this issue in a
context where we can directly observe the effect of PIPE investor
participation on redemption decisions, announcement period returns, and the
likelihood of liquidation, none of which are observable in a traditional IPO.
The notion of quid pro quo employed in our papers is slightly different.
Jenkinson, Jones and Suntheim (2018) document that investors build a
relationship with a bank to get underpriced shares. We instead document that
_sponsors_ build relationships with non-premium PIPE investors to use them to
complete future deals.

##  2 Institutional Details

This section introduces important institutional details about SPACs. In
particular, these features of SPACs are important for understanding how they
differ from traditional IPOs and the measurement of underpricing in the data
that follow in the next section.

###  2.1 A Primer on SPACs

A SPAC is formed when a sponsor begins the SEC-mandated initial public
offering process. A SPAC is, first and foremost, an IPO and must comply with
all SEC IPO regulations. In addition, SPACs are subject to additional
requirements as “blank check company”, including how they use their escrow
fund (described below). The SPAC’s purpose is to identify and acquire a
private company, thereby bringing it public. During its IPO process, the SPAC
usually announces its intended target industry. This industry is meant to
reflect the expertise of its management team and founders and to inform
potential investors of its acquisition strategy. More often than not, these
industries are broad, e.g., financial services, healthcare, and technology, so
as not to constrain the search. A SPAC, however, is not obligated to pursue a
target in the announced industry, although most do.777For example, _Hunter
Maritime Acquisition Corp._ announced it would target the international
maritime shipping industry but merged with a fintech company named _NCF Wealth
Holdings_.

During their IPOs, SPACs offer securities to public investors called _units_ ,
typically priced at $10 per unit.888One exception is _Periphas Capital
Partnering Corporation_ , which priced its unit at $25. A unit consists of one
share of Class A common stock and a proportion (e.g., 1, 1/2, 1/3) of one
redeemable warrant. Initially, these units trade as a whole. However, usually
around the 52nd calendar day following the IPO, owners have the option to
either continue to hold units as a whole or trade shares and warrants
separately.999After the separation, SPACs do not issue fractional warrants and
only whole warrants trade. In other words, the number of warrants issued after
separation equals the fraction rounded down to the nearest whole number. For
example, if a unit contains one share and 1/3 of a warrant, then an investor
with 1 unit receives no warrant, but an investor with 30 units receives ten
warrants. Once the merger deal between the SPAC and a target is closed, a
warrant gives an investor the right to purchase additional shares of common
stock. Typically, the strike price is set well above the original SPAC price;
a common choice is $11.50 per share. Any shares created in this manner
automatically convert into ordinary shares after a merger.

Sponsors purchase shares before SPACs go public. These shares are called the
“founder shares” or the sponsor’s “promote”. These founder shares are
identical to the Class A common shares included in the units owned by public
investors, except that only the founder shares (i) contain the voting rights
to elect directors, (ii) are subject to transfer restrictions (“lock-up”), and
(iii) are irredeemable and become worthless if the SPAC liquidates.

A SPAC generally has 24 months from its IPO to identify a target company,
negotiate a deal, and complete the merger. Failing to complete the merger on
time leads to its liquidation, and the SPAC has to redeem all public shares
and pay its investors with the fund held in the trust account, including any
interest earned.

Once the SPAC locates a target, it uses proceeds from the trust fund for the
merger. Until then, the trust fund cannot be touched except to pay public
shareholders who redeem. To meet operating expenses, the sponsor creates a
“risk capital” by selling private placement warrants. These warrants are
irredeemable when the SPAC liquidates and are subject to transfer
restrictions.

Next, SPAC shareholders vote on the deal, and the transaction occurs only if a
majority votes in favor. Shareholders also can redeem their shares, typically
having at least 20 business days to redeem their shares before the merger is
complete. The merger agreement with the target company requires that the SPAC
have a minimum net worth or a certain amount of cash (as per the negotiation)
as a closing condition. Thus, if too many public shareholders redeem, the SPAC
may not meet the closing conditions of the merger. These financing shortfalls
create a need for _private investments in public equities_ (PIPE) investors.

###  2.2 PIPE Investment

Sometimes the funds raised in the SPAC IPO are sufficient to purchase the
target located by the sponsor. More typically, the sponsor needs (or chooses)
to raise additional funds. Because the SPAC is a publicly traded firm, these
funds are referred to as PIPEs.

PIPE investors are accredited institutional investors, e.g., hedge funds and
mutual funds, that agree to purchase securities issued by a company. Such an
agreement requires the company to file a resale registration statement (in the
future) to allow investors to resell their securities. Compared to raising
capital in the public market, PIPE investments tend to have lower transaction
costs, and companies can disclose transaction details _after_ receiving
commitments from PIPE investors.

We categorize PIPE transactions based on the timing of their commitment:
before, at, and after the business combination. In particular, the sponsor can
invite PIPE investors before the SPAC IPO to commit to making certain
investments in connection with the business combination. The PIPE transactions
formed at the time of the IPO but before a target is identified are called
forward purchase agreements; henceforth, PIPE (pre). The second type of PIPE
transaction is the one announced along with the news about the business
combination; henceforth, PIPE (at). The third type of PIPE transaction occurs
after the target is announced but before the merger is completed; henceforth,
PIPE (post).

Table 1: Example of Key Events and Timeline for a SPAC

Key Events | Dates
---|---
S-1 Filed and PIPE (pre) Announced | 08-Jan-2021
IPO | 29-Jan-2021
Deal Announced, PIPE (at) Announced | 06-Jul-2021
Proxy Filed | 12-Aug-2021
PIPE (post) Announced | 18-Jan-2021
Shareholder Vote | 24-Jan-2022
Deal Closed | 25-Jan-2022
Liquidation Deadline | 02-Feb-2023

Note: Key events and the dates for the SPAC _CF Acquisition Corp V_.

In principle and practice, a SPAC can have any combination of these three
types of PIPEs. For example, 1 shows an example SPAC, CF Capital Acquisition
Corp V, which had all three types of PIPEs. The PIPE (at) investors are
similar to the institutional investors in traditional IPO. After finding a
target, sponsors engage in an “roadshow” to raise PIPE investments. During
these roadshows, sponsors share material and proprietary information about the
target, creating a scope for information production.

###  2.3 SPAC vs. Traditional IPO

SPAC IPOs are different from traditional IPO in that SPAC is a blank-check
company without a business model. However, the merger between a SPAC and a
target, or de-SPAC transaction, is analogous to the traditional IPO because,
during the de-SPAC process, a private company transitions to public trading
and receives new financial capital in exchange for newly created
securities.101010For more about differences and similarities see the SEC
report “ _Special Purpose Acquisition Companies, Shell Companies, and
Projections_ ” available at
https://www.sec.gov/rules/proposed/2022/33-11048.pdf. In Figure 1, we compare
the timeline of a SPAC and a traditional IPO.

Figure 1: Events timing for Traditional IPO and SPAC

Note: Illustration of the timelines of a traditional IPO (top) and a SPAC
(bottom). Here, the acronym “BC" stands for a business combination.

A private operating company’s path to becoming a publicly listed company
through a SPAC begins when the SPAC considers it a candidate for the business
combination. The sponsors perform due diligence and negotiate a letter of
intent with the target company. Once the letter is signed, the second type of
PIPE fundraising process begins. In particular, sponsors and the target
company management consult with potential PIPE investors, pitch the company’s
business plan, and take subscriptions for shares from PIPE investors. Thus,
PIPE investors are similar to institutional investors in traditional IPOs.

After PIPE investments are secured, the deal is announced to the public
investors, and the de-SPAC process starts. Private companies become publicly
listed on the secondary market when the business combination is closed.

Finally, the _Private Securities Litigation Reform Act_ provides a safe harbor
for forward-looking statements made by SPACs in their de-SPAC transactions.
There is no such safe harbor for the traditional IPO. Thus, the private
company’s performance projections can be reported to investors during the de-
SPAC announcement.

##  3 Testable Hypotheses

We develop testable hypotheses based on two competing paradigms – the
information production and quid pro quo arrangements – that we assess in our
data.

Information Production. Under the first paradigm, investors have or obtain
private information about the issuer and earn rents according to the value of
their information. We refer to this paradigm as “information production”
because it views the central goal of the “going public” process as producing
or revealing value-relevant information about the issuer.

The process of going public via SPACs has three instances that involve
investors (see Figure 1). So, information production will have different
implications for those steps.

In particular, information production predicts zero underpricing during the
SPAC IPO because there is yet to be a target identified and, thus, no due
diligence to perform. There is virtually no scope for investors to have
private information; they exchange cash for a proportion of a trust fund of a
fixed value.

In the business combination or de-SPAC phase, investors that participate in
the target announcement (PIPE (at)) are the analogs to the buyers in the IPO
bookbuilding models. Specifically, the sponsor finds a target and approaches
PIPE (at) investors to solicit their interest. Then the sponsor builds a book
of PIPE demand, sets prices, and allocates shares.

However, these investors may differ in their abilities to produce information.
For instance, Benveniste and Spindt (1989) suggest that premium investors who
frequently access the market are likely more informed than others. In our
context, such investors have private information about the quality of the
issuing (target) firm and overall conditions affecting the market valuation.
Conversely, non-premium investors are less informed and consequently have less
impact on prices. Under these assumptions, the securities of the target firm
may be underpriced to compensate premium investors for truthfully disclosing
their private information about the target.

Furthermore, a premium PIPE (at) investor is incentivized to purchase more
shares when its private information indicates the de-SPAC deal is profitable.
Additionally, sponsors prioritize allocating to premium investors to encourage
them to disclose their information. Consequently, a larger allocation to
premium investors signals the good quality of the deal, which reduces
redemption by public investors and leads to a positive market reaction.
However, as non-premium PIPEs have no certification role, their participation
should neither affect redemption nor lead to higher announcement-day returns.

Conversely, we posit that PIPE (pre) and PIPE (post) investors do not play
certification roles. In the case of PIPE (pre), there has yet to be any
information about the target, so there is nothing to certify. PIPE (post)
investors are used less often than PIPE (at) investors and can be used to fill
the financing gap caused by redemption. If the sponsor intended a PIPE
investor to be used for certification purposes (as opposed to “gap filling”),
the sponsor would have announced the participation of the PIPE
contemporaneously when the deal was announced rather than waiting until after
public investors reacted.

Finally, suppose a premium PIPE (at) investor acts purely as an information
producer and is under no obligation to the sponsor to maintain a relationship.
In this case, the PIPE will participate in deals it likes and be absent from
deals otherwise. Such an arrangement should not generate any mean reversion
when the unit of observation is sponsor-PIPE.

######  Hypothesis 1.

Under the information production paradigm, the following should hold:

  1. 1.

Allocations to premium PIPE (at) investors are associated with lower
redemption and positive announcement-day returns.

  2. 2.

For a sponsor-premium PIPE (at) pair, MLOT is not mean-reverting.

Quid Pro Quo. We now discuss the implications of the quid pro quo paradigm.
Under this paradigm, the seller of securities acts through a self-interested
intermediary. Rather than selling securities at full price, say, to the
premium investors, the intermediary gives discounts to some buyers, with an
implicit understanding that this favor is rewarded through profits on future
SPAC transactions.

The SPAC IPO presents the underwriter with such an opportunity. Underwriters
likely deal with institutional investors more frequently (and in more varied
capacities) than sponsors. As an intermediary, the underwriter may choose to
favor institution investors (the buyers) over sponsors (the sellers), just as
in a traditional IPO. If so, the SPAC offering will be underpriced.

The business combination phase presents another opportunity for us to observe
favoritism, except the actors are swapped. The issuer is the private firm
rather than the SPAC because it is the private firm, not the SPAC, that issues
new securities. The buyers are the PIPE investors who infuse new capital in
exchange for securities. The intermediary is the sponsor that negotiates the
terms under which PIPE investors buy these securities and profits only if the
deal is completed.

Suppose that the underpricing at the business combination phase is not because
of information production but because the sponsor allocates rents to PIPE (at)
investors. Then, the sponsor should receive returns from these investors in
the future. If so, then including PIPE investors in a SPAC would not benefit
the SPAC. Therefore, higher allocations to any PIPE (at) investors should
_not_ be associated with either lower redemption rates or higher announcement-
day returns.

In any given deal, sponsors can favor particular PIPE investors and insist on
favorable terms from the target firm. Favoritism may manifest differently at
this stage than in traditional IPOs. In a traditional IPO, underwriters
operate in many lines of business, including trading and wealth management. An
institutional investor receiving underpriced shares in a traditional IPO may
repay the underwriter by providing business through other channels. In
contrast, the sponsors operate only in the SPAC industry, and the primary
objective of a SPAC sponsor is to close the existing fund and start future
SPACs

We hypothesize that quid pro quo arrangements are employed only with non-
premium PIPE investors. A sponsor showing favoritism to a particular non-
premium PIPE investor expects reciprocity in future deals. Specifically, non-
premium PIPE investors participate in an underpriced deal (acting as a tax on
the current private firm) only in exchange for implied participation in a
future weak deal. If a PIPE is involved only in “strong” deals, then the
relationship is one-sided and involves “quid” with no “quo.”

While a long-term relationship (e.g., Levin, 2003) between a non-premium PIPE
investor and a sponsor can provide necessary incentives to the investors to
participate in weaker future deals, the observed interactions in any data are
likely finite. Then the question remains if non-premium investors have
incentives to participate in weaker future deals. In our context, it is
appropriate to view non-premium investors are long-lived players who interact
with sponsors who are short-lived players. In such a setting, results from
Fudenberg and Levine (1994) indicate that an investor does not need to have
repeated interactions with a particular sponsor for the sponsor to have the
incentive to honor their commitment to participate in weak future deals. That
is because an investor’s participation is publicly observed. Suppose they
refrain from participating in a weak deal led by a sponsor from whom they have
received favorable returns. In that case, they risk losing their market-wide
reputation and opportunities to participate in future SPACs.

Furthermore, under quid pro quo arrangements, the sponsor benefits in creating
goodwill among investors, hoping to “draw down” on this goodwill in the future
when the deals are weak. So, we hypothesize that the liquidation risk of a
SPAC is negatively correlated with the amount of MLOT investors have received
from this SPAC’s sponsor in the past.

######  Hypothesis 2.

Under the quid pro quo paradigm, the following should hold:

  1. 1.

Allocations to non-premium PIPE (at) investors are uncorrelated with
redemption and announcement-day returns.

  2. 2.

For a given sponsor -non-premium PIPE (at) pair, MLOT is mean-reverting.

  3. 3.

When a deal is weak, the sponsor is likelier to allocate shares to non-premium
PIPE investors who earned large past MLOT.

  4. 4.

SPACs are less likely to liquidate when the sponsor has earned goodwill
through high past MLOT. This connection occurs through only non-premium and
not premium PIPEs.

##  4 Data

We consider all 1,072 SPACs traded in major exchanges between January 2010 and
December 2021. See Figure A4 for the monthly scatter plot in Figure A4, which
includes the so-called boom and bust cycle for SPACs. We construct our sample
by merging five datasets: (i) the _SPAC Research_
,111111https://www.spacresearch.com/. (ii) the _PrivateRaise_ –SPACs
Platform,121212https://www.privateraise.com/about/platform_spac.php. (iii) the
_PrivateRaise_ –PIPEs Platform (iv) _Bloomberg_ data on SPAC prices, and (v)
Super 8-K filings from SEC.131313https://www.sec.gov/edgar/search-and-
access.,141414Thus, we exclude SPACs traded in the over-the-counter markets.
After a SPAC’s merger with a target is complete, the SPAC is delisted. The new
(merged) company may be assigned a new ticker symbol. We use Bloomberg-
assigned unique identifiers for matching across samples.

The SPAC Research database and the PrivateRaise’s SPACs platform database
contain information on SPACs, including dates for important events, and the
PrivateRaise’s PIPEs platform database contains comprehensive information on
PIPE transactions. PrivateRaise’s PIPEs platform covers more than 20,000
placement profiles of PIPEs transactions. It also includes contract terms,
investment amounts, types of transactions, prices, identities, and allocations
of the PIPE investors.

For these 1,072 SPACs, we manually merge the first two databases. The issuer’s
name recorded in the PIPE transactions can be either that of SPAC or that of
the post-merger company. In those cases, we use both names to match the SPAC
sample with the PrivateRaise database. To ensure match accuracy, we also use
the total PIPE amount from PrivateRaise to cross-check the match. If there are
inconsistencies, we use the SPAC’s SEC filings documents to record the correct
amount. We then merge our sample with Bloomberg’s pricing data; warrant prices
are unavailable in CRSP. Our matching process yields detailed information on
transactions for all three types of PIPE investments for each SPAC in our
sample.

In summary, we observe each SPAC’s life-cycle status, IPO structure, IPO
outcomes, sponsor and management team, redemption, merger details, merger
outcome, PIPE transactions, and price movement across events. Out of these
1,072 SPACs, as of February 17, 2023, 473 have successfully merged, 128 have
announced that they have identified a target, 280 are in the process of
identifying targets, and 191 have failed and liquidated.

The number of SPAC shares changes over time on account of i) investors
exercising warrants, ii) redemption of shares, iii) securities purchased by
PIPES, and iv) the fact that founders sometimes voluntarily forfeit
securities. Consequently, the number of shares listed in the IPO prospectus is
no longer current as of the business combination date. We use Super 8-K (Super
20-F for foreign issuer) filings to collect information on the number and
types of outstanding securities on the first listing day of the post-merger
company. Information about securities at the time of the SPAC IPO may not be
comprehensive either because the sponsors have forfeited their founder shares
and private placement securities or the units that contain rights would have
become common shares or both. To accurately measure the number of outstanding
securities post-merger, we manually collect information from the Super 8-K (or
Super 20-F) filings. SEC mandates that a SPAC file Super 8-K within four
business days since the merger is complete, and it records the shares and
warrants held by sponsors and public investors.

Table 2 provides summary data. On average, SPACs raise $272.42 million
proceeds during their IPO, with a median of $240 million and 90th percentile
at $440 million. In terms of securities, typically, a SPAC unit contains 0.44
warrants and 0.01 rights. During the IPO process, SPACs’ underwriters choose,
on average, to exercise the 11.59% over-allotment option to meet the
additional demand.

SPACs take, on average, 361 days to find a target and an additional 168 days
to complete the merger process. After targets’ announcements, 56.54% of SPAC’s
public shareholders redeem their shares on average. Table 2 also displays
information about the participation of PIPE investors for 473 SPACs that
completed the merger process during our sample period. At the time of their
IPOs, 10% of SPACs have pre-committed investment from PIPE investors, i.e.,
PIPE (pre). When SPACs announce their targets, 66% of them also announce
investments from PIPE (at), and 12% of SPACs obtain additional investments
from PIPE (post) after the target announcement.

Returns. We measure SPAC returns at its IPO and when it announces a target.
Following the literature on IPO underpricing, we measure SPAC’s IPO return as
$\text{Listing-day return}=\frac{\text{First-Trading Day Closing
Price}}{\text{Offer Price}}-1,$ and announcement-day return as
$\text{Announcement-day return}=\frac{\text{Announcement Day Closing
Price}}{\text{One-day-before-announcement Day Closing Price}}-1.$ As seen in
Table 2, the average return for SPAC IPO investors on the first trading day is
1.3%, and when the target is announced, SPAC investors receive an average
return of 3.74%.

Money Left on the Table. To measure underpricing, i.e., the return earned by
investors, we use _money left on the table_ (“MLOT”), which is the difference
between the value of securities on the first day of listing the post-merger
company and their initial investment. We begin with the total investments made
by public investors, which we denote using superscript $\mathfrak{pu}$. Their
MLOT is the sum of the value of their shares and the value of their warrants
minus their leftover investments during IPO, i.e.,
$\text{MLOT}^{\mathfrak{pu}}=\text{Shares}^{\mathfrak{pu}}\times\text{P}_{share}+\text{Warrants}^{\mathfrak{pu}}\times\text{P}_{warrant}-\text{IPO
Proceeds}\times(1-\text{Redemption Rate}).$

Table 2: Summary Statistics

| Mean | Std.Dev. | P10 | P50 | P90 | Obs.
---|---|---|---|---|---|---
SPAC Measures |  |  |  |  |  |
IPO proceeds (mm USD) | 272.42 | 218.22 | 100.00 | 230.00 | 440.00 | 1,072
No. of Warrant | 0.44 | 0.24 | 0.20 | 0.50 | 0.75 | 1,072
No. of Right | 0.01 | 0.03 | 0.00 | 0.00 | 0.00 | 1,072
Overallotment (%) | 11.59 | 5.37 | 0.00 | 15.00 | 15.00 | 1,072
Listing-day return (%) | 1.30 | 3.02 | -0.70 | 0.40 | 4.00 | 857
Days searching | 361.71 | 215.79 | 114.00 | 329.50 | 677.00 | 614
Days remaining | 340.98 | 189.87 | 120.00 | 303.00 | 611.00 | 614
Announcement-day return (%) | 3.74 | 12.56 | -0.68 | 0.34 | 9.95 | 586
Redeemption(%) | 56.54 | 37.49 | 0.00 | 70.00 | 97.00 | 445
Days deSPAC | 167.99 | 67.06 | 100.00 | 160.00 | 241.00 | 474
PIPE Participation |  |  |  |  |  |
$\mathbbm{1}${PIPE (pre)} | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 473
$\mathbbm{1}${PIPE (at)} | 0.66 | 0.47 | 0.00 | 1.00 | 1.00 | 473
$\mathbbm{1}${PIPE (post)} | 0.12 | 0.33 | 0.00 | 0.00 | 1.00 | 473
$\mathbbm{1}${Premium PIPE (at)} | 0.50 | 0.50 | 0.00 | 1.00 | 1.00 | 473
$\mathbbm{1}${Non-Premium PIPE (at)} | 0.65 | 0.48 | 0.00 | 1.00 | 1.00 | 473
No. of PIPE (at) | 10.76 | 11.83 | 0.00 | 8.00 | 26.00 | 472
No. of Premium PIPE (at) | 3.02 | 4.36 | 0.00 | 1.00 | 10.00 | 472
No. of Non-Premium PIPE (at) | 7.75 | 8.64 | 0.00 | 6.00 | 18.00 | 472
PIPE Investment/IPO Proceeds |  |  |  |  |  |
PIPE (pre) (%) | 2.93 | 11.22 | 0.00 | 0.00 | 0.00 | 473
Premium PIPE (at) (%) | 17.84 | 32.85 | 0.00 | 0.00 | 52.39 | 473
Non-Premium PIPE (at) (%) | 41.94 | 58.64 | 0.00 | 28.57 | 101.16 | 473
PIPE (post) (%) | 7.45 | 30.39 | 0.00 | 0.00 | 22.68 | 473
MLOT (mm USD) |  |  |  |  |  |
Public Investors | 113.22 | 433.26 | -3.76 | 11.24 | 226.41 | 433
Sponsors | 126.93 | 324.28 | 15.66 | 64.32 | 195.48 | 425
PIPE | 201.22 | 1,200.54 | -25.60 | 3.75 | 248.17 | 358
PIPE (pre) | 29.37 | 120.76 | -10.52 | 3.17 | 35.70 | 45
PIPE (at) | 193.07 | 1,135.58 | -25.36 | 4.77 | 248.17 | 303
PIPE (post)∗ | 8.07 | 42.62 | -15.74 | 0.25 | 31.76 | 56
Premium PIPE (at) | 76.63 | 374.15 | -10.22 | 1.68 | 129.46 | 231
Non-Premium PIPE (at) | 134.65 | 838.67 | -17.73 | 3.40 | 138.48 | 303
MLOT/Investment (%) |  |  |  |  |  |
Public Investors | 182.10 | 898.42 | -7.89 | 24.87 | 179.08 | 433
Sponsors | 1,400.46 | 2,570.29 | 370.04 | 861.15 | 2,220.70 | 324
PIPE | 91.26 | 472.71 | -16.50 | 4.47 | 85.80 | 358
PIPE (pre) | 70.33 | 289.37 | -8.05 | 4.36 | 91.80 | 45
PIPE (at) | 63.74 | 307.47 | -16.00 | 4.20 | 80.90 | 303
PIPE (post)∗ | 70.74 | 285.16 | -36.02 | 0.80 | 110.60 | 56
Premium PIPE (at) | 64.05 | 291.75 | -13.00 | 5.30 | 94.90 | 225
Non-Premium PIPE (at) | 64.10 | 308.46 | -16.00 | 4.20 | 80.90 | 301

Note: The sample includes 1,072 SPACs that went through the IPO process
between January 2010 and December 2021. All variables are defined in Table B1.
$(^{*})$ We exclude a PIPE (post) investor named _Quinpario Acquisition
Corporation 2_ , with unusually large returns.

Here Sharespu is the total number of outstanding shares held by public
investors on the first-listing day of the post-merger company. Thus, Sharespu
includes non-redeemed public shares, and shares are automatically converted
from rights included in the SPAC unit. Warrantspu is the total number of
warrants held by public investors on the first-listing day of the post-merger
company. Similarly, $\text{P}_{share}$ and $\text{P}_{warrant}$ denote the
price for shares and warrants of the post-merger company at the end of the
first trading day.

For the sponsor’s MLOT, we can replace the IPO proceeds that were not redeemed
in the above definition of $\text{MLOT}^{\mathfrak{p}}$ with risk capital,
i.e.,
$\text{MLOT}^{\mathfrak{s}}=\text{Shares}^{\mathfrak{s}}\times\text{P}_{share}+\text{Warrants}^{\mathfrak{s}}\times\text{P}_{warrant}-\text{Risk
Capital},$ where Sharess measures the total number of shares held by the
sponsor on the first-listing day of the post-merger company. Thus Sharess
includes the founder shares and private placement shares purchased using the
sponsor’s “risk capital” and excludes shares forfeited by the sponsor during
the de-SPAC process. Warrantss measures the private placement warrants
purchased by the sponsor and excludes warrants forfeited by the sponsor.

For a PIPE investor’s MLOT, a similar definition applies. However, instead of
risk capital, we subtract the issuance amount, i.e.,
$\text{MLOT}^{\mathfrak{pi}}=\text{Shares}^{\mathfrak{pi}}\times\text{P}_{share}+\text{Warrants}^{\mathfrak{pi}}\times\text{P}_{warrant}-\text{Issuance
Amount}^{\mathfrak{pi}},$ where Sharespi, Warrantspi are the number of shares
and warrants purchased by PIPE investors, and Issuance Amountpi is the dollar
amount of the issuance. We calculate MLOTpi for all three types of (pre, at,
and post) PIPE transactions.

Table 2 highlights the MLOT for various investor types. Investing in a SPAC
generates an average of $113.22 million for public investors, $126.93 million
for sponsors, and $201.22 million for PIPE investors. Out of the 45 SPAC deals
in which they participate, PIPE (pre) investors make $29.37 million on
average. From the 303 SPAC deals in which they take part, PIPE (at) investors
earn $193.07 million per deal. PIPE (post) investors participate in 56 deals
and earn $8.07 million per deal on average.

On average, a SPAC deal generates 182.1% return for public investors and
1,400% for the sponsors. PIPE investors get an average return of 91.26%, where
PIPE (pre) investors earn 70.33%, PIPE (at) investors earn 63.74%, and PIPE
(post) investors earn 70.74%.

Premium PIPE Investors. In total, there are 2,005 PIPE investors in our
sample. Out of them, 76 invested in PIPE (pre) transaction, 249 in PIPE
(post), and 1,826 invested in at least one PIPE (at) transaction, but only a
few large institutional investors participated frequently and invested
significantly more than others. To capture this distinction, we use k-means
clustering on the total investments in all SPACs and the number of SPACs to
classify PIPE investors into two groups. We refer to a subset of PIPE
investors who frequently participate as “premium” PIPE investors and the rest
as non-premium PIPE investors. There are 34 premium PIPE investors in our
sample (Table 3).151515As a way to corroborate this classification, we counted
the number of articles written about each of these 34 premium investors in
_The New York Times_ and found that these premium investors have the highest
counts. For example, excluding research universities, e.g., Columbia
University, and non-financial companies, e.g., General Motors and Microsoft
Inc., top ten investors include Morgan Stanley, Fidelity, USB, Wells Fargo,
Blackstone, Citadel, Bain Capital, BlackRock, T. Rowe Price, and SoftBank.

Table 3: Premium PIPE Investors

Investor | #SPAC | Volume (million USD)
---|---|---
Fidelity Management & Research Company | 69 | 4,342
BlackRock, Inc. | 57 | 2,317
Capital Research and Management Company | 29 | 2,231
Alyeska Investment Group L.P. | 106 | 1,704
Morgan Stanley | 14 | 1,238
Millennium Management, LLC | 77 | 1,059
Luxor Capital Group | 52 | 972
Blackstone Inc. (f/k/a The Blackstone Group Inc.) | 34 | 965
Koch Industries Inc. | 21 | 926
Third Point, LLC | 12 | 910
Hedosophia Services Limited | 23 | 910
T. Rowe Price Group, Inc. | 27 | 899
SoftBank Group Corp. | 16 | 795
Franklin Resources, Inc. (d/b/a Franklin Templeton Investments) | 24 | 741
Senator Investment Group LP | 36 | 713
Moore Capital Management, Inc. | 66 | 629
Neuberger Berman Group LLC | 20 | 626
Baron Capital Group, Inc. | 25 | 621
Citadel LLC | 51 | 604
Hudson Bay Capital Management LP | 56 | 577
Suvretta Capital Management, LLC | 31 | 576
UBS AG | 57 | 494
Wellington Management Company, LLP | 26 | 437
Heights Capital Management, Inc. | 65 | 431
Magnetar Capital LLC | 39 | 424
D.E. Shaw & Co., L.P. | 38 | 411
Monashee Investment Management LLC | 61 | 408
Ghisallo Capital Management LLC | 62 | 389
Jane Street Group, LLC | 49 | 368
Park West Asset Management, LLC | 27 | 342
Kepos Capital LP | 51 | 286
Linden Advisors LP | 43 | 282
Schonfeld Strategic Advisors LLC | 32 | 224
BlueCrest Capital Management Ltd. | 31 | 181

Note: This table lists the name of premium PIPE investors identified using
k-means clustering based on the number of SPAC deals and total investment
volumes using data on SPACs that went through an IPO from January 2010 to
December 2021.

Table 2 provides summary statistics for premium and non-premium PIPE
investors. A typical SPAC deal includes 10 PIPE (at) investors, with around
three premium and seven non-premium investors. In the 231 SPAC deals in which
they participate, premium investors earn an average of $76.63 million (MLOT)
with an average return of 64.05%. Non-premium investors participate in 303
SPACs and make an average of $134.65 million (MLOT) and earn an average return
of 64.10%. As the standard deviations suggest, the high average returns for
the non-premium PIPEs are driven by five outlier SPACs with extreme returns.
We revisit this difference in premium and non-premium investors’ MLOTs in
Section 6.

##  5 Empirical Results

In this section, we investigate whether the data are consistent with the
hypotheses stated in Section 3. We begin measuring underpricing at the SPAC
IPO level and compare it with the traditional IPO underpricing. Then, we show
that the premium PIPE investors who participate in business combinations
generate value-relevant information, whereas non-premium PIPE investors engage
in a quid pro quo relationship. We end by quantifying the benefit of this quid
pro quo relationship in terms of lowering the liquidation risk of future
SPACs.

###  5.1 Underpricing

####  5.1.1 Underpricing during SPAC IPO

We first examine underpricing during the SPAC IPO. Table 2 reports the average
equal-weighted first-day return of SPACs is 1.3%. Although our data start from
2010, most SPAC IPOs occurred between 2020 and 2021. In 2020, 248 SPACs went
public, with an average equal-weighted first-day return of 1.3%; in 2021, 613
SPACs went public, and their average equal-weighted first-day return was
1.51%. Although these measures are statistically different from zero (p-values
$\approx 0$ in both cases), they are small.161616For comparison, the average
equal-weighted first-day return for traditional IPOs was 41.6% in 2020 and 32%
in 2021. For more on the IPO underpricing, see
https://site.warrington.ufl.edu/ritter/files/IPOs-Underpricing.pdf, accessed
09/22/2022. In addition, SPACs almost always trade at $10 per share until the
target announcement date. This uniformity is inconsistent with any information
about sponsor quality having been revealed during the SPAC IPO.

####  5.1.2 Underpricing during de-SPAC

This section focuses on the underpricing associated with the de-SPAC stage. We
say that a private company’s shares are underpriced if the first-day trading
value of the post-merger company’s securities held by all SPAC investors
(including sponsors, PIPEs, and original SPAC investors who did not redeem)
exceeds the proceeds invested in the private firm.

There are at least two sources of asymmetric information at this stage. First,
the target company may be better informed about its business prospect than
anyone else (Smith Jr, 1986; Booth and Smith II, 1986). Second, the PIPE
investors may also have better information about factors affecting the target
firm than the public investors. In response to PIPE investors’ information,
sponsors may underprice the securities issued to them as an incentive payment
for their value-relevant information. We focus on the second type of
information.

From Table 2, we see that, on average, a private company that uses the SPAC
route to access the public market leaves $126.93 million MLOT to the sponsors
and $201.22 million to the PIPE investors. Furthermore, we also see that PIPE
investors who participate in different stages of SPAC are compensated
differently. During the SPAC IPO, PIPE (pre) investors earn an average of
$29.37 million through forward-purchase agreements, but PIPE (at) investors
get, on average, a total of $193.07 million per deal. After the target is
disclosed, PIPE (post) investors who participate receive an additional $8.07
million MLOT. Even among PIPE (at) investors, approximately one-third of the
MLOT accrues to 34 premium investors. This difference suggests that the two
types of investors have different business models, which we return to later in
Section 6.

Overall, the SPAC shares offered to PIPE (at) investors, especially the
premium PIPE investors, during the de-SPAC phase are underpriced. These
results are consistent with Hypothesis 1 that PIPE (at) investors
participating in the de-SPAC stage are privately informed about the quality of
the target company.

###  5.2 Information Production

####  5.2.1 Redemption Rates

To test the first part of Hypothesis 1-1, we use the following regression:

$\displaystyle\text{Redemption rate}_{i}$ $\displaystyle=$
$\displaystyle\alpha_{0}+\alpha_{1}\times\%\text{Premium PIPE
(at)}_{i}+\alpha_{2}\times\%\text{Non-Premium PIPE (at)}_{i}$ (1)
$\displaystyle+\alpha_{3}\times\%\text{PIPE
(pre)}_{i}+\alpha_{4}\times\%\text{PIPE (post)}_{i}+{\bf
X}^{\top}\alpha_{5}+\varepsilon_{i},$

where $\text{Non-Premium PIPE (at)}_{i}$ denotes the investment allocated to
premium PIPE (at) investors as a fraction of the total IPO proceeds in SPAC
$i$, similarly for other allocations, and ${\bf X}$ is a vector of covariates
including total IPO proceeds and fixed effects that may vary across
specifications. If premium investors generate value-relevant information, the
higher the share allocated to premium investors lower the redemption rate,
i.e., $\alpha_{1}<0$.

Table 4 provides evidence consistent with the hypothesis. In column (1), we
include only the allocations to four types of PIPE investors. In column (2),
we control for the size of the SPAC IPO and the liquidation risk of the SPAC
when the target is announced using Days Remaining until the expiration. In
column (3), we control for the quarter-fixed effects. In column (4), which is
also our preferred specification, we additionally control for the target
company’s sector fixed effects to alleviate the problem that investors may
have varying investment preferences across industries. In all these
specifications, we cluster the robust standard errors at the sponsor level to
capture correlations within a sponsor but across investors and SPACs.

We find that the allocations to premium PIPE (at) investors negatively
correlate with the redemption rates, and this result is robust across all
these specifications. The estimates from Table 4 column (4) suggest that one
standard deviation (32.85 pps) increase in premium PIPE investors’
participation, is associated with a 7.03 pps decrease in the redemption rate,
which equals 12.4% of the sample mean.

Furthermore, we find no association between non-premium PIPE (at) or PIPE
(pre) allocations and redemption rates, as in Hypothesis and Hypothesis 2-1.
These results are consistent with the hypothesis that premium PIPE (at)
investors are better informed about the deal quality than either the non-
premium PIPE (at) investors or PIPE (pre).

Table 4: Redemption Rates

| (1) | (2) | (3) | (4)
---|---|---|---|---
PIPE (pre) (%) | 0.125 | 0.192 | 0.148 | 0.156*
| (0.123) | (0.119) | (0.116) | (0.095)
Premium PIPE (at) (%) | -0.419*** | -0.380*** | -0.366*** | -0.214***
| (0.069) | (0.064) | (0.059) | (0.050)
Non-Premium PIPE (at) (%) | -0.013 | -0.009 | -0.002 | -0.037
| (0.034) | (0.035) | (0.034) | (0.030)
PIPE (post) (%) | 0.120*** | 0.103*** | 0.114*** | 0.148***
| (0.035) | (0.031) | (0.033) | (0.035)
Log IPO proceeds | ✗ | ✓ | ✓ | ✓
Days Remaining | ✗ | ✓ | ✓ | ✓
IPO Month FEs | ✗ | ✗ | ✓ | ✓
Target Sector FEs | ✗ | ✗ | ✗ | ✓
Adj. $R^{2}$ | 0.161 | 0.182 | 0.183 | 0.400
Obs. | 444 | 444 | 444 | 430

Note: This table shows the results from estimating (1) using SPACs that went
through an IPO from January 2010 to December 2021. The independent variable is
the investment amount as a percentage of the SPAC’s IPO proceeds for all PIPE
(pre) investors, premium PIPE (at) investors, non-premium PIPE (at) investors,
and all PIPE (post) investors. Premium PIPE (at) investors are listed in Table
3. All other variables are defined in Table B1. All unbounded continuous
variables are winsorized at their $1^{st}$ and $99^{th}$ percentile values.
Robust standard errors are clustered at the sponsor level and are reported in
parentheses. ${}^{*},^{**}$, and ∗∗∗ denote p-values less than 0.1, 0.05, and
0.01, respectively.

The negative association between premium PIPE (at) allocation and redemption
rates is consistent with premium investors producing information. Suppose not,
and instead, suppose that sponsors underprice and distribute them to the
premium investors in exchange for a promise to help them in the future on an
unrelated SPAC. In that case, because the timing of the investors should be
immaterial, we should also expect the allocations to PIPE (post) investors to
correlate negatively with the redemption rate. However, as we see from Table
4, the two are positively correlated, further strengthening our finding.

Lastly, we note that almost all (231 out of 25) PIPE (post) investors are non-
premium investors. In fact, among the top 20 PIPE (post) investors by
investment size, only Luxor Capital Group is a premium investor. Thus, it is
reasonable to assume that sponsors include premium investors at the time of
the announcement as certification and use non-premium investors later as
needed to backfill the trust fund.

####  5.2.2 Announcement-day Return

To test the second part of Hypothesis 1-1 about the announcement-day return,
we use the following regression:

$\displaystyle\text{Announcement-day return}_{i}$ $\displaystyle=$
$\displaystyle\beta_{0}+\beta_{1}\times\%\text{Premium PIPE (at)}_{i}$ (2)
$\displaystyle+\beta_{2}\times\%\text{Non-Premium PIPE (at)}_{i}$
$\displaystyle+\beta_{3}\times\%\text{PIPE (pre)}_{i}+\varpi_{i}.\qquad$

The estimation results are shown in Table 5. There is a strong positive
association between announcement-day returns (%) and allocations to premium
PIPE (at) investors. In particular, the estimates in Table 5 column (4)
suggest that one standard deviation (32.85 pps) increase in premium PIPE (at)
investors’ participation is associated with a 4.07 pps increase in the SPAC’s
announcement-day return, which is 108.8% of the sample mean of the
announcement-day returns. Thus the effect of premium investors on
announcement-day return is economically substantial and statistically
significant.

Furthermore, the announcement-day returns are uncorrelated with the
allocations to non-premium PIPE (at) investors. Again, this finding is
consistent with the hypothesis that larger investment by premium PIPE (at)
investors acts as a certification of the quality of the deal. In contrast,
investment by non-premium PIPE (at) is non-informative.

We also observe a negative correlation between announcement-day returns and
allocations to PIPE (pre) investors. Specifically, the estimates in column (4)
suggest that one standard deviation (11.22 pps) increase in PIPE (pre)
investors’ participation is associated with 0.81 pps lower in announcement-day
return, which equals 21.7% of the sample mean.

Table 5: Announcement-day Return

| (1) | (2) | (3) | (4)
---|---|---|---|---
PIPE (pre) (%) | -0.118*** | -0.119*** | -0.099*** | -0.072
| (0.042) | (0.042) | (0.037) | (0.044)
Premium PIPE (at) (%) | 0.148*** | 0.140*** | 0.136*** | 0.124***
| (0.041) | (0.041) | (0.038) | (0.040)
Non-Premium PIPE (at) (%) | -0.003 | -0.007 | -0.010 | -0.002
| (0.014) | (0.014) | (0.013) | (0.015)
Log IPO proceeds | ✗ | ✓ | ✓ | ✓
Days Remaining | ✗ | ✓ | ✓ | ✓
IPO Month FEs | ✗ | ✗ | ✓ | ✓
Target Sector FEs | ✗ | ✗ | ✗ | ✓
Adj. $R^{2}$ | 0.099 | 0.104 | 0.098 | 0.095
Obs. | 457 | 457 | 431 | 417

Note: This table shows the results from estimating (2) using SPACs that went
through an IPO from January 2010 to December 2021. The key independent
variable is the investment amount as a percentage of the SPAC’s IPO proceeds
for all PIPE (pre) investors, premium PIPE (at) investors, non-premium PIPE
(at) investors, and all PIPE (post) investors. Premium PIPE (at) investors are
listed in Table 3. All other variables are defined in Table B1. All unbounded
continuous variables are winsorized at their $1^{st}$ and $99^{th}$ percentile
values. Robust standard errors are clustered at the sponsor level and are
reported in parentheses. ${}^{*},^{**}$, and ∗∗∗ denote p-values less than
0.1, 0.05, and 0.01, respectively.

Likewise, the hypothesis predicts a non-positive market reaction to the
announcement of PIPE (post) investments. To assess this claim, we use an event
study approach. In particular, we use different asset-pricing factor models to
estimate the cumulative abnormal returns (CAR) (Campbell, Lo and MacKinlay,
1997) associated with announcements about PIPE investors in the process of
announcing the target. Specifically, we estimate the following linear
regression model:

$\displaystyle r_{i,t}=\kappa_{i}+\bm{\rho}_{i}\texttt{W}_{t}+\upsilon_{i,t},$
(3)

where $r_{i,t}$ is the share return of SPAC $i$, at date $t$, X includes
portfolio returns of different factor models. For each SPAC, we estimate (3)
using an event window of $[t-\kappa,t+\kappa]$, which is $\kappa$ days around
each PIPE (post) transaction’s announcement date. The estimation period is
chosen to start from the SPAC’s unit separation date and ends before the event
window. Then we obtain the predicted return $\hat{r}_{i,t}$, which gives us
the “abnormal” return as $\hat{\upsilon}_{i,t}\equiv r_{s,t}-\hat{r}_{i,t}$.
The CAR associated with target announcement by $i$ is given by accumulating
$\hat{\upsilon}_{i,t}$ during the event window, i.e.,
$\savestack{\tmpbox}{\stretchto{\scaleto{\scalerel*[\widthof{\texttt{CAR}}]{\kern-0.6pt\bigwedge\kern-0.6pt}{\rule[-505.89pt]{4.30554pt}{505.89pt}}}{}}{0.5ex}}\stackon[1pt]{\texttt{CAR}}{\tmpbox}_{i,t}=\sum_{t=t_{i}-\kappa}^{t_{i}+\kappa}\hat{\upsilon}_{i,t}.$

Table 6 reports the average of the
$\savestack{\tmpbox}{\stretchto{\scaleto{\scalerel*[\widthof{\texttt{CAR}}]{\kern-0.6pt\bigwedge\kern-0.6pt}{\rule[-505.89pt]{4.30554pt}{505.89pt}}}{}}{0.5ex}}\stackon[1pt]{\texttt{CAR}}{\tmpbox}$
and their respective standard deviation across all SPACs using a three-day,
five-day, and 10-day window around the event date. For each event window, we
estimate CAPM, the Fama-French three-factor model, and Fama-French five-factor
model. In all cases, the average
$\savestack{\tmpbox}{\stretchto{\scaleto{\scalerel*[\widthof{\texttt{CAR}}]{\kern-0.6pt\bigwedge\kern-0.6pt}{\rule[-505.89pt]{4.30554pt}{505.89pt}}}{}}{0.5ex}}\stackon[1pt]{\texttt{CAR}}{\tmpbox}$
estimates are not statistically different from zero. This result suggests that
the investors do not respond to the announcement of PIPE (post), likely
because almost all of them are non-premium and unlikely to add information.

Table 6: Cumulative (average) Abnormal Returns

Event Window | CAPM | FF3 | FF5
---|---|---|---
[-3, 3] | 0.71 | 0.74 | 0.75
| (0.80) | (0.79) | (0.78)
[-5, 5] | -1.04 | -1.04 | -1.06
| (1.14) | (1.13) | (1.12)
[-10, 10] | -2.37 | -2.30 | -2.28
| (1.92) | (1.92) | (1.91)

Note: This table reports the SPAC share’s cumulative average abnormal return
in percentage for three different event windows around announcements of 81
PIPE (post) transactions. Here, CAPM stands for capital asset pricing model,
and FF3 and FF5 stand for Fama-French three- and five-factor models,
respectively. The cross-sectional standard deviations of the cumulative
abnormal returns are reported in parentheses. ${}^{*},^{**}$, and ∗∗∗ denote
p-values less than 0.1, 0.05, and 0.01, respectively.

###  5.3 Quid Pro Quo

Next, we test Hypothesis 2 that there is a quid pro quo arrangement between
sponsors and non-premium PIPE (at) investors and that it helps a weaker firm
go public. Recall that under favoritism, the current MLOT should be negatively
correlated with the past MLOT. In particular, if the sponsor allocates
securities to non-premium investors as a favor, then if the MLOT in the past
is high, the sponsor is likely to ask this investor to finance a weak deal
today. We verify that the probability that a sponsor allocates securities to a
“relationship” investor increases if the investor has higher MLOT from the
past deals _and_ the SPAC deal is cold. Lastly, we show that quid pro quo
lowers the liquidation risks.

####  5.3.1 Mean-reverting MLOT

As a first step, we assess if the MLOT exhibits mean reversion for the non-
premium PIPE (at) investors, i.e., MLOT is stable around a mean. In contrast,
under the information production hypothesis, the correlation between the
current MLOT and past MLOT should be zero if the information production across
deals is independent and, as (Benveniste et al., 2003) show, they can be
positively correlated if there is information spillover.

A necessary condition to test favoritism is the presence of repeated deals
between a sponsor and a PIPE investor. In Table 7, we list the top 20 sponsors
in our sample in terms of the total number of initialized and completed SPAC
deals. In our sample, 139 sponsors have initialized more than one SPAC,
totaling 439 SPACs. Furthermore, non-premium PIPE investors are more likely to
participate in repeat deals (with the same sponsor) than premium investors.

To gauge the difference, consider the following. On average, the probability
that a non-premium investor from the previous deal participates in the current
deal is 17%. As there are around eight non-premium investors in a SPAC, one
out of these eight non-premium investors in the current deal is from the
previous deal. If these investors were randomly (uniformly) participating in a
SPAC deal, the probability would be much smaller because there are 1,792 non-
premium investors in our sample. If non-premium investors participate randomly
in a SPAC, then the probability that at least one non-premium investor
participated in the previous deal is negligible.

Table 7: Top 20 Sponsors

Initialized | Completed
---|---
Sponsor | #SPAC | Sponsor | #SPAC
The Gores Group | 13 | The Gores Group | 9
Cohen & Company | 11 | Chardan | 7
Social Capital | 10 | Social Capital | 6
Hennessy Capital Group | 8 | Cohen & Company | 6
Cantor Fitzgerald | 8 | TPG | 5
TPG | 8 | Cantor Fitzgerald | 5
Michael Klein | 8 | Riverstone Investment Group | 5
Jonathan J. Ledecky | 7 | Hennessy Capital Group | 5
Chardan | 7 | Harry L. You | 5
Hedosophia | 6 | Eagle Equity Partners | 5
Harry L. You | 6 | Michael Klein | 4
Riverstone Investment Group | 6 | Jonathan S. Huberman | 4
Fortress Investment Group | 6 | Hedosophia | 4
GigCapital Global | 6 | GigCapital Global | 4
Apollo Global Management | 6 | Chinh Chu | 4
Bill Foley | 6 | Niccolo de Masi | 4
Chinh Chu | 5 | Bill Foley | 4
Jaws Estates Capital | 5 | Apollo Global Management | 4
Craig-Hallum Capital Group | 5 | Union Acquisition Group | 3
Niccolo de Masi | 5 | Casdin Capital | 3

Note: This table presents the top 20 sponsors in terms of the total number of
SPACs initialized and completed.

The probability gap reverses for the premium investors. The probability that a
premium investor from the previous deal participates in the current deal is
36.60%. There are, on average, three premium investors in a SPAC, so a sponsor
includes one premium investor from the previous deal. There are 34 premium
investors in our sample, so if they participate randomly in a SPAC, then the
probability that at least one premium investor in the current deal has
participated in the previous deal by the same sponsor is 90.31%.

Let $\mu$ be the average MLOT for all PIPE (at) investors in our sample. Table
8 is the summary statistics. As we see, on average, 28% of SPACs have at least
one premium PIPE investor. Then we estimate the following linear model,

$\displaystyle\text{MLOT}_{p,i,s}$ $\displaystyle=$
$\displaystyle\theta_{0}+\theta_{1}\times(\text{lag-MLOT}_{p,i,s}-\mu)+{\bf
X}^{\top}\theta_{2}+\eta_{p,i,s},$ (4)

where MLOTp,i,s is the MLOT that PIPE (at) investor $p$ gets in the current
SPAC deal $i$ initialized by the sponsor $s$, and $\text{lag-MLOT}_{p,i,s}$ is
the MLOT from the most recent deal in the past. As before, ${\bf X}$ includes
several covariates, including fixed effects (see Table 9).

Table 8: Summary Statistics of PIPE Investors

| Mean | Std.Dev. | P10 | P50 | P90 | Obs.
---|---|---|---|---|---|---
At Least 1 Premium | 0.28 | 0.45 | 0.00 | 0.00 | 1.00 | 5,812
All PIPE Investors |  |  |  |  |  |
Investment Amount ($mm) | 16.67 | 37.62 | 1.00 | 7.00 | 35.00 | 5,535
MLOT ($mm) | 9.76 | 75.33 | -0.98 | 0.28 | 13.05 | 4,816
No. of Past SPACs | 0.13 | 0.47 | 0.00 | 0.00 | 0.00 | 5,812
MLOT (past) | 2.48 | 23.66 | 0.00 | 0.00 | 0.00 | 5,812
Premium PIPE Investors |  |  |  |  |  |
Investment Amount ($mm) | 20.50 | 38.79 | 3.00 | 10.00 | 45.00 | 1,609
MLOT ($mm) | 11.01 | 45.60 | -1.17 | 0.58 | 23.97 | 1,462
No. of Past SPACs | 0.20 | 0.53 | 0.00 | 0.00 | 1.00 | 1,647
MLOT (past) | 3.98 | 19.82 | 0.00 | 0.00 | 2.55 | 1,647
Non-premium PIPE Investors |  |  |  |  |  |
Investment Amount ($mm) | 15.10 | 37.01 | 0.75 | 5.00 | 30.00 | 3,926
MLOT ($mm) | 9.22 | 85.10 | -0.96 | 0.17 | 10.37 | 3,354
No. of Past SPACs | 0.10 | 0.43 | 0.00 | 0.00 | 0.00 | 4,165
MLOT(past) | 1.88 | 25.00 | 0.00 | 0.00 | 0.00 | 4,165

Note: This table contains summary statistics for SPACs that went through an
IPO from January 2010 to December 2021. Premium PIPE investors are identified
using k-means clustering and listed in Table 3. All variables are defined in
Table B1.

Under information production, the deviation of lagged MLOT from its mean is
either uncorrelated or positively correlated with the current MLOT; hence
$\theta_{1}\geq 0$ in (4). Under quid pro quo arrangements, however, the MLOT
should increase if the past deal was lower than the average and vice versa;
hence $\theta_{1}<0$. The results are in column (1) of Table 9. As expected
under favoritism, MLOT exhibits mean reversion.

Table 9: Mean-Reverting MLOT

| MLOT
---|---
| (1) | (2) | (3)
All PIPE Investors | -0.099* |  |
| (0.052) |  |
Premium PIPE Investors |  | -0.025 | 0.235**
|  | (0.062) | (0.109)
Non-premium PIPE Investors |  | -0.218*** | -2.729***
|  | (0.076) | (0.756)
Instrumental Variables | ✗ | ✗ | ✓
F-stat (first-stage) |  |  | 92.82
Adjusted $R^{2}$ | 0.418 | 0.419 |
Observations | 3,790 | 3,790 | 3,790
|  | Mean | Std.Dev.
$\text{Non-Premium}\times$(lag-MLOT-$\mu$) |  | -8.12 | 5.41
(lag-MLOT-$\mu$)$\times$(lag2-MLOT-$\mu$) |  | 121.96 | 85.54
(lag2-MLOT-$\mu$)2 |  | 135.78 | 5.77
(lag-MLOT-$\mu$)2 |  | 166.93 | 297.54

Note: This table shows results from estimating (5) using SPACs that went
through IPO between January 2010 to December 2021. Each observation is at
(SPAC, sponsor, PIPEat investor) level. The dependent variable, MLOT, measures
the money left on the table the PIPEat investor makes from the SPAC deal. The
key independent variables are the MLOT earned with the same sponsor in the
last deal for all PIPEat investors, premium PIPEat investors, and non-premium
PIPEat investors. Additional controls include the log of IPO proceeds, the
number of PIPE investors, the number of premium PIPE investors, days
remaining, and separate fixed effects for the month, sponsor, and PIPE. All
continuous variables are winsorized at the $1^{st}$ and $99^{th}$ percentile
values. All other variables are defined in Table B1. Robust standard errors
are reported in parentheses. ${}^{*},^{**}$, and ∗∗∗ denote p-values less than
0.1, 0.05, and 0.01, respectively.

Next, we differentiate between premium and non-premium investors and estimate

$\displaystyle\text{MLOT}_{p,i,s}$ $\displaystyle=$
$\displaystyle\delta_{0}+\delta_{1}\times(\text{lag-MLOT}_{p,i,s}-\mu)$ (5)
$\displaystyle+\delta_{2}\times\text{Non-Premium}_{p}\times(\text{lag-
MLOT}_{p,i,s}-\mu)+{\bf X}^{\top}\delta_{3}+\tilde{\eta}_{p,i,s},$

where $\text{Non-Premium}_{p}$ is a binary variable equal to one if the PIPE
(at) investor $p$ is a non-premium investor. The results from the estimation
are in column (2) of Table 9. Comparing the second and third rows, we find
that MLOT exhibits mean reversion for non-premium PIPE investors but not for
premium investors. This result is consistent with the previous results that
only the premium PIPE investors generate information, and Hypothesis 1-2.

A potential concern with the estimate of $\delta_{2}$ in column (2) of Table 9
is that sponsors’ decisions to include non-premium investors may be correlated
with unobserved deal quality. Indeed, favoritism implies that the sponsor is
more likely to involve non-premium investor(s) for a weak deal. If so, the
sample used to identify the parameter is selected and not representative of
the population. Because the sponsor is likely to allocate higher MLOT to non-
premium investors in weak deals, the above estimate can be biased upwards.

We use the instrumental variable (IV) approach to address this problem. The
idea behind our choice of IV is based on the following observation: suppose
$v=v_{1}\times v_{2}$ is endogenous because $v_{1}$ is endogenous, then
$v_{2}$ can be an IV for $v$ under appropriate additional assumptions. For a
recent application of this intuition, see Fabra and Reguant (2014) and Aryal,
Ciliberto and Leyden (2022). In our setting, $v$ is $\text{Non-
Premium}\times(\text{lag-MLOT}-\mu)$ and $v_{1}$ is the Non-Premium dummy, and
given the specification in (5) $(\text{lag-MLOT}-\mu)$ is uncorrelated with
the quality of the current deal.

However, $(\text{lag-MLOT}-\mu)$ may not satisfy the exclusion restriction
because it directly affects the outcome variable. Based on the intuition
above, we propose to use mean deviation from the second-to-last deal, i.e.,
$(\text{lag${}^{2}$-MLOT}-\mu)$ as an excluded IV. Using lagged variables as
IVs is widely used in the dynamic panel data under the Arellano-Bond
estimator. Furthermore, to capture the nonlinearity of the interaction terms,
we include $\\{(\text{lag-
MLOT}-\mu)\times(\text{lag${}^{2}$-MLOT}-\mu),(\text{lag${}^{2}$-MLOT}-\mu)^{2},(\text{lag-
MLOT}-\mu)^{2}\\}$ as additional excluded variables.

The results from this estimation are in column (3) of Table 9. The first-stage
Cragg-Donald Wald F-statistic of 92.82 suggests that our excluded variables
are not weak instruments. Comparing these estimates with the ones in column
(2), we see that, as expected, the fixed-effect estimate for the non-premium
investor is biased upwards. More importantly, and consistent with the quid pro
quo hypothesis, we find that one dollar higher mean deviation of MLOT in the
last deal leads to a $2.729 reduction in the mean deviation from the current
deal and vice versa presumably because now the non-premium investor is “asked”
to participate in tepid deals that have smaller underpricing. In contrast, for
the premium PIPE investors, the effect is the opposite: if the investor made
one more dollar in MLOT above the mean in the last deal, on average, the MLOT
in the next deal with the same sponsor increases by 23 cents consistent with
the hypothesis that premium PIPE investors generate value-relevant
information.171717Some deals have more than one sponsor. In such cases, we
treated each sponsor separately. Although we do not show this here, we verify
that the results are qualitatively the same if we exclude any SPAC with more
than one sponsor or consider only the sponsor with the maximum number of past
SPAC deals.

####  5.3.2 Allocations and Past Relationship

Next, we test the Hypothesis 2-3. A sponsor builds a relationship with a non-
premium PIPE investor by providing MLOT, so when the deal is tepid, it expects
the _relationship investors_ with high past MLOT to return the favor by
participating in the deal. To this end, we evaluate if a sponsor is more
likely to use non-premium PIPE (at) investors if the investor has made larger
returns from the sponsor in the past and the current target is cold.

Although we do not observe the quality of the target, we propose to proxy it
by the difference between the offer share price of the SPAC, i.e., the share
price at SPAC IPO, and the share price on the closing day of the merger. We
denote this gap as “Cold.”

Then, we estimate the probability that a sponsor allocates securities to a
non-premium investor as a function of the past MLOT and a measure of cold,
among other variables. For a sponsor $s$, let ${\mathcal{R}}_{s}$ denote the
set of relationship investors as the set of all non-premium investors that
invested in at least one SPAC with sponsor $s$. Let
$\text{Participation}_{p,i,s}\in\\{0,1\\}$ be a binary variable equal to one
if investor $p\in{\mathcal{R}}_{s}$ is allocated securities from SPAC $i$
started by sponsor $s$, and zero otherwise. Then we estimate the participation
probability under the assumption that the probability takes a logit form,
i.e.,

$\displaystyle\Pr(\text{Participation}_{p,i,s}=1\mid\mathbf{Z}_{p,i,s}^{\top}\bm{\delta})$
$\displaystyle=$
$\displaystyle\frac{\exp(\mathbf{Z}_{p,i,s}^{\top}\bm{\delta})}{1+\exp(\mathbf{Z}_{p,i,s}^{\top}\bm{\delta})},$
(6) $\displaystyle\mathbf{Z}_{p,i,s}^{\top}\bm{\delta}$ $\displaystyle=$
$\displaystyle\mathbf{X}^{\top}\bm{\delta_{0}}+\delta_{1}\times\log(1+\text{MLOT(past)}_{p,s})+\delta_{2}\times\text{Cold}_{i}$
$\displaystyle+\delta_{3}\times\log(1+\text{MLOT(past)}_{p,s})\times\text{Cold}_{i},$

where ${\bf X}$ is the vector of controls shown in Table 9;
$\log(1+\text{MLOT(past)}_{p,s})$ is the log of the total MLOT $p$ made in
past deals with $s$ and measures the past relationship between $s$ and $p$;
and, and as we defined above, $\text{Cold}_{i}$ is the measure of the quality
of the deal $i$.

We estimate (6) using the method of maximum likelihood and present the results
in column (1) of Table 10. The estimated coefficient of the interaction term
(third row) in column (1) is positive (p-value $<0.01$), supporting the quid
pro quo hypothesis that the sponsor first builds a relationship and draws it
down as and when needed.

Table 10: Sponsor and Non-Premium PIPE Investors

| Participation | Allocation
---|---|---
| (1) | (2) | (3) | (4) | (5)
$\log(\text{1+MLOT(past)})$ | 0.085 | -0.004 | -0.088 | -0.335∗∗∗ | 0.019
| (0.136) | (0.123) | (0.098) | (0.081) | (0.010)
|  |  |  |  | [0.013]
Cold | 0.006∗∗∗ | 0.004∗∗∗ | -0.003 | 0.003∗∗ | -0.00003
| (0.001) | (0.001) | (0.002) | (0.002) | (.00006)
|  |  |  |  | [0.0006]
$\log(\text{1+MLOT(past)})\times\text{Cold}$ | 0.008∗∗∗ | 0.011∗∗∗ | 0.011∗∗∗ | 0.008∗∗∗ | 0.0003∗∗∗
| (0.002) | (0.003) | (0.003) | (0.002) | (0.0001)
|  |  |  |  | [0.00012]
Outside Option |  | -0.017∗∗∗ | -0.007∗∗∗ | -0.002 |
|  | (0.003) | (0.002) | (0.002) |
$\widehat{\mathbb{E}(\text{Participation}\mid{\mathbf{Z}})}$ |  |  |  |  | 0.239∗∗∗
|  |  |  |  | (0.018)
|  |  |  |  | [0.024]
Log IPO proceeds | ✗ | ✓ | ✓ | ✓ | ✓
Days Remaining | ✗ | ✓ | ✓ | ✓ | ✓
No. Premium Investors | ✗ | ✓ | ✓ | ✓ | ✗
Sponsor Dummies | ✗ | ✗ | ✓ | ✓ | ✗
Month Dummies | ✗ | ✗ | ✗ | ✓ | ✓
Pseudo/Adj. $R^{2}$ | 0.0098 | 0.0644 | 0.2763 | 0.4061 | 0.8114
Obs. | 10,104 | 10,044 | 7,917 | 7,913 | 7,838

Note: Columns (1) to (4) of this table show the MLE of participation (6), and
column (5) shows the estimates of the allocation (5.3.2). Each observation is
at (SPAC, sponsor, non-premium PIPE investor) level. For each SPAC, non-
premium investors include all relationship investors that participate in at
least one SPAC deal initialized by the sponsor during the sample period. For
the first four columns, the dependent variable, $Participation$, equals one if
the non-premium investor participates in the focal SPAC deal. The dependent
variable for the last column, $Allocation$, is the investor’s allocation as a
fraction of all relationship investors’ allocations. The key independent
variables include the non-premium investor’s MLOT earned with this sponsor in
the past in log form, $\log(1+\text{MLOT ({past})})$, and a measure of the
(reciprocal) of the deal quality, Cold. For the allocation regression, we also
include the estimated probability of participation using estimates from column
(4) as an additional variable. All continuous variables at winsorized at
$99^{th}$ percentile. Robust standard errors are clustered at the sponsor-PIPE
pair level and reported in parentheses. For column (5), bootstrapped standard
errors (500 replications) are reported in the square brackets. ${}^{*},^{**}$,
and ∗∗∗ denote p-values less than 0.1, 0.05, and 0.01, respectively.

One possible concern with the estimation is that it ignores the outside option
of the sponsor, which may be correlated with the unobserved error. A better
outside option lowers the probability of issuing securities to a non-premium
investor and vice versa. Ignoring outside options could introduce attenuation
bias if cold deals have profitable outside options. To alleviate this concern,
we use the highest MLOT among all premium PIPE investors _not included_ in the
current deal as an additional control variable to capture the strength of the
outside option. We also include the log of IPO proceeds, days remaining, and
the number of premium investors as additional covariates.

The estimates from this new model are in column (2) of Table 10. A better
outside option lowers the probability of selecting the non-premium investor,
and our previous finding becomes slightly stronger. The results are robust
when we include dummy variables for the sponsor and the month when the deal is
announced. In all cases, the coefficient for the interaction term is positive
and estimated precisely (p-value $<0.01$).

To help interpret these estimates, we evaluate the probability in (6) at a
different relationship and tepidness while holding other variables at their
average value using the estimates from column (4) of Table 10. We find that
investors with a stronger relationship have higher participation rates in
deals with lower demand. In SPAC deals with Cold at its $90^{th}$-percentile,
non-premium investors who have a strong relationship with the sponsor (one
standard deviation above the mean) are 3.54 pps more likely to participate
than in SPAC deals with Cold at its $10^{th}$-percentile. Conversely, non-
premium investors without a prior relationship with the sponsor are less
likely to participate in cold deals.

Next, we examine whether, conditional on participation, the sponsor allocates
a higher portion of securities to non-premium investors with a stronger
relationship when a deal is cold and vice versa. To this end, we determine the
allocation of SPAC $i$’s securities from sponsor $s$ to PIPE investor $p$
among all relationship investors ${\mathcal{R}}_{s}$,
$\text{Allocation}_{p,i,s}\in[0,1]$, as the ratio of $p$’s shares to the total
shares allocated to ${\mathcal{R}}_{s}$. We set the share to zero if a non-
premium investor does not participate in a deal. We find that the mean
allocation is $2.93$, and the standard deviation of the allocation is $8.77$.
Furthermore, the allocation is skewed towards zero, with the median of $0$ and
the $99^{th}$-percentile at $42.37$. Given that there are 1,792 non-premium
investors, and so the allocation in our sample is skewed to the left with a
mass at zero, we estimate the following Tobit model:

$\displaystyle\text{Allocation}_{p,i,s}^{*}$ $\displaystyle=$
$\displaystyle\mathbf{W}_{p,i,s}^{\top}\bm{\gamma^{*}}+u_{s},$
$\displaystyle\text{Allocation}_{p,i,s}$ $\displaystyle=$
$\displaystyle\min\\{\max\\{0,\mathbf{W}_{p,i,s}^{\top}\bm{\gamma}+\nu_{1}\\},1\\}$
(7) $\displaystyle\mathbf{W}_{p,i,s}^{\top}\bm{\gamma}$ $\displaystyle=$
$\displaystyle\mathbf{X}^{\top}\bm{\gamma_{0}}+\gamma_{1}\times\log(1+\text{MLOT(past)}_{p,s})+\gamma_{2}\times\text{Cold}_{i}$
$\displaystyle+\gamma_{3}\times\log(\text{MLOT(past)}_{p,s}+1)\times\text{Cold}_{i}+\gamma_{4}\times\widehat{\mathbb{E}(\text{Participation}|{\mathbf{Z}})}_{p,i,s},$

where $(\mathbf{W},\text{Allocation}_{p,i,s})$ is always observed but the
latent variable $\text{Allocation}_{p,i,s}^{*}$ is observed only when
$\text{Allocation}_{p,i,s}\in[0,1]$. We further assume that $\mathbf{W}$ is
independent of $(u_{1},]\nu_{1})$ and that $\nu_{1}$ is a mean-zero Normal
random variable with unknown variabce and
$\mathbb{E}(u_{1}\mid\nu_{1})=\rho\nu_{1}$. Furthermore,
$\widehat{\mathbb{E}(\text{Participation}\mid{\mathbf{Z}})}_{p,i,s}$ is the
estimated participation probability of allocation given ${\bf Z}$ and is
determined by the estimates in column (4) of Table 10. Thus, the outside
option serves as our excluded variable, with the underlying exclusion
restriction being that the outside option should not directly affect a non-
premium investor’s allocation.

In Column (5) of Table 10, the estimates indicate a positive association
between a higher probability of participation and an increased allocation of
shares. Moreover, non-premium investors who have previously profited from
deals with the sponsor receive a larger share of allocation than their
counterparts when a deal is considered cold.

In economic terms, a one standard deviation increase in the interaction term
leads to a 5.8% increase in allocation relative to its standard deviation,
amounting to 14.80% of the sample mean. In our sample, an average non-premium
investor is allocated 2.68% of the total non-premium investors’ allocations.
Furthermore, as the non-premium investor’s MLOT (past) times the deal’s Cold
measure increase together by one standard deviation, the allocation fraction
rises by 0.40 pps to 3.08%. In terms of dollar amount, the allocation
increases from $0.40 million to $0.46 million. These findings support the
hypothesis that non-premium investors and sponsors engage in quid pro quo
agreements.

####  5.3.3 Relationship Capital and Liquidation Risk

We have shown that the relationship investors support the sponsor by
participating in a tepid deal and purchasing a larger fraction of shares.
These non-premium investors received larger MLOTs from the same sponsor during
the previous deals. Therefore, it stands to reason that a sponsor builds these
relationships with an eye toward potential tepid deals in the future. If so,
some of the underpricing driven by quid pro quo could be advantageous to
private companies which want to go public.

Next, we verify Hypothesis 2-4 by estimating the liquidation risk and
liquidation probability of SPACs as a function of the total MLOT paid by the
sponsor to non-premium investors. Here, we use the number of days from the
SPAC’s IPO until a target is announced or liquidation is announced to denote
liquidation risk because a SPAC has two years to find a target, or else it
will have to liquidate. That is, the sooner a SPAC can find a deal, the lower
the liquidation risk.

A sponsor with more relationship capital can afford to take more risk with a
potential tepid target because it can draw down the relationship capital to
close the deal and move on to a new SPAC If the risk and the probability
decrease with the MLOT, then we consider that as evidence of a benefit of quid
pro quo.

Table 11: Liquidation and Relationship

| Liquidation Risk | Liquidation
---|---|---
| (1) | (2)
Premium | 0.092 | 0.363
| (0.061) | (0.222)
Non-premium | -0.138∗∗ | -0.406∗
| (0.066) | (0.246)
Log IPO proceeds | ✓ | ✓
No. Past Successful Deals | ✓ | ✓
Obs. | 679 | 680

Note: This table demonstrates that SPACs initiated by sponsors with a history
of stronger relationships with non-premium investors are less likely to
liquidate and face reduced liquidation risk when a target is announced or
liquidated. Column (1) uses a Poisson regression on the dependent variable
_Liquidation Risk_ , which represents the number of days from the SPAC’s IPO
until a target or liquidation is announced. Column (2) uses a Logit regression
on the dependent variable _Liquidation_ , which is equal to one if the SPAC
liquidates and zero if the SPAC successfully completes its merger. The primary
independent variable is the total MLOT earned by premium and non-premium
investors in previous SPACs initiated by the same sponsor. All continuous
variables are winsorized at the $1^{st}$ and $99^{th}$ percentiles. Robust
standard errors, clustered at the sponsor level, are reported in parentheses.
${}^{*},^{**}$, and ∗∗∗ denote p-values less than 0.1, 0.05, and 0.01,
respectively.

We begin with a count (Poisson) regression of the number of days from the
SPAC’s IPO until a target is announced or liquidation is announced on the MLOT
earned by premium and non-premium investors in previous SPACs initiated by the
same sponsor. We also include the log of the IPO proceeds and the number of
past successful SPACs by the same sponsor as additional controls. The results
from this estimation exercise are in column (1) of Table 11, which suggests
that the days it takes for a SPAC to find a target decreases with the MLOT
earned by non-premium investors in the past. In particular, a one standard
deviation increase in the log of MLOT decreases the number of days to find a
target by 64 days, which is 15.17% of the average 422 days it takes to either
find a target or liquidate in the sample, holding all other variables constant
at their respective average values. However, consistent with the information
creation hypothesis, we find that the MLOT earned by premium investors in the
past is uncorrelated with the liquidation risk.

Next, we consider the probability of liquidation. In our sample, we observe
191 liquidations and 473 successful mergers. We verify the extent to which the
difference between these two groups is attributable to the relationship
payments the SPAC sponsor made to non-premium investors. Let
$\text{Liquidation}_{i}\in\\{0,1\\}$ be a binary variable equal to one if the
SPAC $i$ has liquidated and zero otherwise. Under the assumption that we can
use the Logit model to specify the conditional distribution of liquidation, we
estimate the probability of liquidation and present it in column (2) of Table
11. The estimates suggest that SPACs initialized by sponsors who have built a
stronger relationship with non-premium investors are less likely to liquidate.
However, the coefficient is imprecisely estimated (p-value $<0.1$). In
contrast, but consistent with the information production hypothesis, the MLOT
earned by premium PIPE investors from the same sponsor’s deals in the past is
uncorrelated with the success of the current deal.

In terms of economic magnitude, we find that if sponsors had allocated one
standard deviation more MLOT ($300.53 million) to non-premium investors above
average, the liquidation probability of the current deal would fall by 48.93
pps. As a reference, the average liquidation rate in our sample is 17.82%. In
contrast, MLOT allocated to the premium investors is uncorrelated with the
liquidation risk.

In summary, we find evidence consistent with these conditions: (i) premium
PIPE investors generate value-relevant information, (ii) non-premium PIPE
investors are paid agency fees, and (iii) the non-premium investors help close
weaker deals in the future. In Appendix A, we show that these results are
robust to removing outlier investors, placebo treatments, and the definition
of premium PIPE investors.

##  6 Observed and Unobserved Heterogeneity

In this section, we determine how much of the difference in average earnings
between premium and non-premium investors can be explained by differences in
their observable characteristics. If they have different “business models”
(information production vs. agency service), we expect observed heterogeneity
to explain a smaller portion of the difference.

To minimize the effect of outliers in this exercise, we exclude seven out of
473 successful SPACs with closing share prices higher than $100.181818These
SPACs are: Queen’s Gambit Growth Capital, Insurance Acquisition Corp., Matlin
& Partners Acquisition Corporation, Northern Genesis Acquisition Corp. II, VPC
Impact Acquisition Holdings III, Inc., Reinvent Technology Partners Z (f/k/a
Reinvent Technology Partners B), and Ajax I. In Figure 3 (left panel), we
display the histogram of total MLOT paid to all PIPE (at) investors across
SPACs that have successfully merged with a private target firm. The average
MLOT per deal is $62.38 million, with a large standard deviation of $199.07
million. These earnings are divided among, on average, 10.76 PIPE investors
(see Table 2), out of which 3.02 are premium investors. In Figure 3 (right
panel), we show the shares of premium PIPE investors (left vertical line) and
the total MLOT (right vertical line). Averaging these shares, we find that
premium PIPE investors get (around) 26% of the MLOT. In other words, 26% of
the underpricing at the time of the business closing announcement is for
information production, and 74% is for quid pro quo.

Figure 2:  Money Left on the Table

Note: The left panel is the histogram of total MLOT for all
$\texttt{PIPE}_{\texttt{at}}$ investors. The right panel shows the premium
PIPE investors’ share of MLOT (vertical axis on the left) and the total MLOT
in that deal (vertical axis on the right).

Considering each investor separately, the MLOT of a premium investor, on
average, is $6.96 million, and that of a non-premium investor is $3.17
million. Next, we determine what part of this $3.79 million difference in
average MLOT can be explained by differences in the observable characteristics
of these two types of investors.

First, we introduce new variables capturing some of the observed differences
between these two groups. In particular, for each PIPE investor and each SPAC,
we determine the number of SPACs they have participated in before the current
one; how much they have invested; the number of past SPACs that involved a
sponsor different from the current one; total MLOT from other sponsors; and
the number of SPACs with sponsors different from the current one, and whether
the target is in the same industry as the current target. These variables
capture market-wide experience and successes.

We have 4,152 deals at the SPAC and PIPE (at) investor level, of which 1,245
are with premium investors and the rest with non-premium investors. Table 12
presents the descriptive statistics and the results from the tests of mean
differences between premium and non-premium investors.

Table 12: Difference in Characteristics of Premium and Non-premium Investors

|  Premium PIPE Sample |  Non-Premium PIPE Sample |  Difference
---|---|---|---
MLOT ($mm) |  6.95 |  3.17 |  3.78∗∗∗
SPAC Characteristics |  |  |
IPO Proceeds ($mm) |  397.25 |  363.87 |  33.38∗∗∗
Days Remaining |  492.98 |  467.59 |  25.38∗∗∗
PIPE Investor Characteristics |  |  |
No. of past SPACs |  23.99 |  5.51 |  18.48∗∗∗
Past Investment Amount ($mm) |  460.09 |  50.77 |  409.32∗∗∗
No. of SPACs with other sponsors |  24.40 |  5.44 |  18.95∗∗∗
Past MLOT with other sponsors ($mm) |  292.85 |  24.61 |  268.25∗∗∗
No. of SPACs with other sponsors, same industry |  3.59 |  0.80 |  2.79∗∗∗
Past MLOT with other sponsors, same industry ($mm) |  45.83 |  3.65 |  42.18∗∗∗

Note: This table reports means and differences in our sample’s average
characteristics for premium and non-premium investors. ${}^{*},^{**}$, and ∗∗∗
denote p-values less than 0.1, 0.05, and 0.01, respectively.

The two groups display distinct MLOTs and characteristics. Specifically,
premium investors are involved in larger SPACs that take fewer days to
identify a target. Moreover, these investors possess greater experience in the
SPAC market, as evidenced by a higher number of previous SPAC participations
(23.99 vs. 5.51) and a larger investment amount ($460.09 million vs. $50.77
million). They also work with more SPACs (24.40 vs. 5.44) and obtain a higher
MLOT ($292.85 million vs. $24.61 million) from other sponsors. In addition,
premium investors operate more in the same industry with different sponsors
(3.59 vs. 0.80).

Figure 3: Kitagawa-Blinder-Oaxaca Decomposition

Note: The figure displays the explained components of a twofold Kitagawa-
Blinder-Oaxaca decomposition of the premium vs. non-premium MLOT. The x-axis
is the mean difference in MLOT (in millions), and the y-axis lists observable
characteristics.

Next, we use the Kitagawa-Blinder-Oaxaca decomposition to document the extent
and drivers of the MLOT differences between premium and non-premium investors.
In particular, we begin with the following:
$\text{MLOT}_{i,g}={\mathbf{W}}_{g}^{\top}\omega_{g}+\iota_{i,g},\quad
g\in\\{\text{premium, non-premium}\\},$ where ${\bf W}$ is the vector of
observed characteristics in Table 12, and $\iota$ is a mean zero random
variable. Let $\Delta\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a
111{MLOT}=\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a
111{W}_{premium}^{\top}\hat{\omega}_{\text{premium}}-\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a
111{W}_{non-premium}^{\top}\hat{\omega}_{\text{non-premium}}$, where
$\overline{W}_{g}$ denotes the sample average of the group $W_{g}$. We can
then re-write $\Delta\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a
111{MLOT}$ as

$\displaystyle\Delta\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a
111{MLOT}=\underbrace{(\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a 111{\bf
W}_{premium}-\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a 111{\bf
W}_{non-premium})^{\top}\hat{\omega}_{non-premium}}_{\text{explained
portion}}+\underbrace{\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a 111{\bf
W}^{\top}_{premium}(\hat{\omega}_{premium}-\hat{\omega}_{non-
premium})}_{\text{unexplained portion}}.$ (8)

The explained portion is the portion of the MLOT difference explained by
differences in observed characteristics between premium and non-premium
investors. In other words, if we change $\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a 111{\bf
W}_{non-premium}$ to $\macc@depth\char
1\relax\frozen@everymath{\macc@group}\macc@set@skewchar\macc@nested@a 111{\bf
W}_{premium}$, it measures how much additional MLOT a non-premium investor
gets. The unexplained portion is the part driven by differences in the
coefficients.

We find that the mean increase in non-premium investors’ MLOT is $0.04 million
(s.e. $1.38 million) if they had the same characteristics as premium
investors. So the differences in observed characteristics do not account for
the MLOT gap. Figure 3 displays how much each of the eight components in ${\bf
W}$ explains, along with their confidence intervals. Insofar as ${\bf W}$
contains all characteristics that affect MLOT, we find that most of the
difference in average MLOT cannot be explained by differences in average ${\bf
W}$, suggesting that the difference in the business models of these two groups
is important.

##  7 Conclusion

Understanding reasons for underpricing hinges on the relative importance of
information production and quid pro quo. In this paper, we determine the
relative importance within the context of SPACs. Distinguishing between
premium and non-premium PIPE (institutional) investors, we show that the
former produce information and, on average, are paid 26% of the total
underpricing. The latter extracts the remainder 74% as quid pro quo payments.

While we have relied on some institutional features specific to SPACs,
understanding how these features arise to alleviate information and agency
frictions have implications for security issuance under asymmetric information
in general and IPOs in particular. After all, the SPAC business combination
process is also a case of security issuance through sponsors as an
intermediary with PIPE investors as buyers and the target company as the
seller.

There are two main takeaways for IPOs. First, information production and quid
pro quo can play important roles in IPO underpricing. In other words, they are
not necessarily mutually exclusive. Second, non-premium PIPE investors help
sponsors with weak deals, suggesting that, on average, they are value-
enhancing for targets and issuers. This role is unlike the traditional IPO,
where some investment bank clients get favorable allocations of “hot” IPO
shares in exchange for paying high trading fees to the bank.

The benefit of quid pro quo payment is a function of its positive effect on
successful business combinations. However, to determine if quid pro quo
payments enhance welfare, we have to show that the total welfare is higher
when the sponsor is allowed to build and deplete relationships than if not.
These are interesting questions for future research.

## References

  * (1)
  * Aryal, Ciliberto and Leyden (2022) Aryal, Gaurab, Federico Ciliberto, and Benjamin T. Leyden. 2022\. “Coordinated Capacity Reductions and Public Communication in the Airline Industry.” Review of Economic Studies, 89(6): 3055–3084.
  * Beatty and Ritter (1986) Beatty, Randolph P., and Jay R. Ritter. 1986\. “Investment Banking, Reputation, and the Underpricing of Initial Public Offerings.” Journal of Financial Economics, 15(1-2): 213–232.
  * Benveniste et al. (2003) Benveniste, Lawrence M., Alexander Ljungqvist, William J. Wilhelm Jr, and Xiaoyun Yu. 2003\. “Evidence of Information Spillovers in the Production of Investment Banking Services.” Journal of Finance, 58(2): 577–608.
  * Benveniste and Spindt (1989) Benveniste, Lawrence M, and Paul A Spindt. 1989\. “How Investment Bankers Determine the Offer Price and Allocation of New Issues.” Journal of Financial Economics, 24(2): 343–361.
  * Benveniste, Busaba and Wilhelm (2002) Benveniste, Lawrence M., Walid Y. Busaba, and William Wilhelm. 2002\. “Information Externalities and the Role of Underwriters in Primary Equity Markets.” Journal of Financial Intermediation, 11(1): 61–86.
  * Booth and Smith II (1986) Booth, James R, and Richard L Smith II. 1986\. “Capital Raising, Underwriting and the Certification Hypothesis.” Journal of Financial Economics, 15(1-2): 261–281.
  * Bradley and Jordan (2002) Bradley, Daniel J., and Bradford Jordan. 2002\. “Partial Adjustment to Public Information and IPO Underpricing.” Journal of Financial and Quantitative Analysis, 37(4): 595–616.
  * Campbell, Lo and MacKinlay (1997) Campbell, John Y., Andrew W. Lo, and A. Craig MacKinlay. 1997\. The Econometrics of Financial Markets. Princeton University Press.
  * Chemmanur, Hu and Huang (2010) Chemmanur, Thomas J., Gang Hu, and Jiekun Huang. 2010\. “The Role of Institutional Investors in Initial Public Offerings.” Review of Financial Studies, 23(12): 4496–4540.
  * Cornelli and Goldreich (2001) Cornelli, Francesca, and David Goldreich. 2001\. “Bookbuilding and strategic allocation.” Journal of Finance, 56(6): 2337–2369.
  * Fabra and Reguant (2014) Fabra, Natalia, and Mar Reguant. 2014\. “Pass-Through of Emissions Costs in Electricity Markets.” American Economic Review, 104(9): 2872–2899.
  * Fudenberg and Levine (1994) Fudenberg, Drew, and David K. Levine. 1994\. “Efficiency and Observability with Long-Run and Short-Run Players.” Journal of Economic Theory, 62(1): 103–135.
  * Gahng, Ritter and Zhang (Forthcoming) Gahng, Minmo, Jay R. Ritter, and Donghang Zhang. Forthcoming. “SPACs.” Review of Financial Studies.
  * Gofman and Yao (2022) Gofman, Michael, and Yuchi Yao. 2022\. “SPACs’ Directors Network: Conflicts of Interest, Compensation, and Competition.” Available at SSRN 4148668.
  * Hanley (1993) Hanley, Kathleen Weiss. 1993\. “The Underpricing of Initial Public Offerings and the Partial Adjustment Phenomenon.” Journal of Financial Economics, 34(2): 231–250.
  * İnce (2014) İnce, Özgür Ş. 2014\. “Why Do IPO Offer Prices Only Partially Adjust?” The Quarterly Journal of Finance, 04(03): 1450009.
  * Jenkinson, Jones and Suntheim (2018) Jenkinson, Tim, Howard Jones, and Felix Suntheim. 2018\. “Quid Pro Quo? What Factors Influence IPO Allocations to Investors?” Journal of Finance, 73(5): 2303–2341.
  * Klausner, Ohlrogge and Ruan (2022) Klausner, Michael, Michael Ohlrogge, and Emily Ruan. 2022\. “A Sober Look at SPACs.” Yale Journal of Regulation, 39(1): 228–303.
  * Levin (2003) Levin, Jonathan. 2003\. “Relational Incentive Contracts.” American Economic Review, 93(3): 835–857.
  * Loughran and Ritter (2002) Loughran, Tim, and Jay R. Ritter. 2002\. “Why Don’t Issuers Get Upset about Leaving Money on the Table in IPOs?” Review of Financial Studies, 15(2): 413–444.
  * Loughran and Ritter (2004) Loughran, Tim, and Jay R. Ritter. 2004\. “Why Has IPO Underpricing Changed over Time?” Financial Management, 33(3): 5–37.
  * Lowry and Schwert (2004) Lowry, Michelle, and G. Schwert. 2004\. “Is the IPO Pricing Process Efficient?” Journal of Financial Economics, 71(1): 3–26.
  * Lowry et al. (2017) Lowry, Michelle, Roni Michaely, Ekaterina Volkova, et al. 2017\. “Initial public offerings: A synthesis of the literature and directions for future research.” Foundations and Trends® in Finance, 11(3-4): 154–320.
  * Ritter and Welch (2002) Ritter, Jay R., and Ivo Welch. 2002\. “A Review of IPO Activity, Pricing, and Allocations.” Journal of Finance, 57(4): 1795–1828.
  * Smith Jr (1986) Smith Jr, Clifford W. 1986\. “Investment Banking and the Capital Acquisition Process.” Journal of Financial Economics, 15(1-2): 3–29.
  * Wang and Yung (2011) Wang, Wei, and Chris Yung. 2011\. “IPO Information Aggregation and Underwriter Quality.” Review of Finance, 15(2): 301–325.

APPENDIX

##  Appendix A Robustness

In Section 5, we found evidence consistent with the hypotheses that premium
investors produce value-relevant information and non-premium investors engage
in quid pro quo arrangements. We also found evidence that the quid pro quo
relationship between a sponsor and non-premium investors enables weaker
private firms to go public. In this section, we perform a series of robustness
exercises.

###  A.1 Possible Outliers.

Figure A4: Scatter Plots of SPACs

Note: The figure shows the scatter plot of SPACs by month and year. The top
figure shows the number of SPAC mergers, and the bottom figure shows the deal
sizes (total IPO proceeds) in billions of dollars. The size of each dot is
proportional to the number of deals in a month (top) and the total IPO
proceeds (bottom) in that month.

As we can see from Figure A4, although our sample starts in 2010, most of the
variation comes from SPAC from 2020 to 2022. Although our sample includes the
so-called boom and bust cycle for SPAC, we may worry about the effects of
outliers in our analysis. One such concern is that not all 34 premium PIPE
investors (in Table 3) produce value-relevant information and that our results
are driven by either the large premium PIPE investors or that the small
premium PIPE investors are, in fact, non-premium investors.

To address this concern, we exclude six (large) premium investors with at
least $1 billion invested in SPACs and re-estimate all our models. Then, we
repeat the same exercise, but this time we exclude seven (small) premium
investors with at most $400 million invested in SPACs and re-estimate all
models.

Table A1: Robustness: Excluding Outliers

| Redemption | Announcement-day | Mean Reversion | Liquidation Risk | Liquidation
---|---|---|---|---|---
| Rate (%) | Return (%) | MLOT |  |  |  |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10)
All PIPE Investors (pre) | 0.180* | 0.159* | -0.085* | -0.074* |  |  |  |  |  |
| (0.099) | (0.095) | (0.045) | (0.044) |  |  |  |  |  |
Premium PIPE Investors (at) | -0.241*** | -0.214*** | 0.143** | 0.126*** | 0.153 | 0.149 | 0.114** | 0.080 | 0.423* | 0.356
| (0.082) | (0.051) | (0.060) | (0.041) | (0.104) | (0.099) | (0.058) | (0.061) | (0.226) | (0.222)
Non-premium PIPE Investors (at) | -0.042 | -0.038 | 0.001 | -0.001 | -1.633*** | -2.075*** | -0.155** | -0.126* | -0.448* | -0.391
| (0.030) | (0.030) | (0.014) | (0.015) | (0.531) | (0.615) | (0.062) | (0.065) | (0.235) | (0.241)
All PIPE Investors (post) | 0.152*** | 0.148*** |  |  |  |  |  |  |  |
| (0.036) | (0.036) |  |  |  |  |  |  |  |
Exclude Large Premium Investors | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✗
Exclude Small Premium Investors | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓
Pseudo/Adj. $R^{2}$ | 0.389 | 0.399 | 0.074 | 0.094 |  |  | 0.0248 | 0.0211 | 0.0153 | 0.0133
F-stat (first-stage) |  |  |  |  | 175.54 | 140.73 |  |  |  |
Observations | 430 | 430 | 417 | 417 | 3,487 | 3,578 | 680 | 680 | 681 | 680

Note: This table presents estimates after excluding outliers in premium
investors. Large premium investors are PIPE investors that have invested a
total of more than $1 billion in more than 50 SPACs. They include Fidelity
Management & Research Company, BlackRock, Inc., Capital Research and
Management Company, Alyeska Investment Group L.P., Morgan Stanley, and
Millennium Management, LLC. Small premium investors are those that invested
less than $0.4 billion and participated in at most 50 SPAC deals. They include
Ghisallo Capital Management LLC, Jane Street Group, LLC, Park West Asset
Management LLC, Kepos Capital LP, Linden Advisors LP, Schonfeld Strategic
Advisors LLC, and BlueCrest Capital Management Ltd. Columns (1) and (2) have
the same specification as Table 4, column (4). Columns (3) and (4) have the
same specification as Table 5, column (4). Columns (5) and (6) have the same
specification as Table 9, column (3). Columns (7) and (8) have the same
specification as Table 11, column (1). Columns (9) and (10) have the same
specification as Table 11, column (2). ${}^{*},^{**}$, and ∗∗∗ denote p-values
less than 0.1, 0.05, and 0.01, respectively.

The results are presented in Table A1. In particular, the estimates from
excluding large premium investors are in columns (1), (3), (5), and (7) of
Table A1, and the estimates from excluding small premium investors are in the
remaining columns. Comparing these estimates with those in columns (4), (4),
(3), and (2) of Tables 4, 5, 9, and 11, respectively, we see that our main
findings are neither due only to the large premium investors nor the small
premium investors. The only exception is the liquidation result when we
exclude the large investors (in column (9)), which is less stable.

###  A.2 Placebo Exercises.

Next, we verify that our results are not purely noise and that they capture
the effects of premium and non-premium investors. To this end, we explore the
effect of choosing an arbitrary set of PIPE investors and designating them as
premium PIPE investors on the estimates. If our results were only noise, then
a random group of premium PIPE investors should also give similar estimates.

To implement this “placebo” exercise, we randomly picked 34 out of all 1,826
PIPE investors and labeled them premium investors. Then we estimate all the
models using this new set of premium investors. We repeat this exercise 500
times and present only the coefficients for premium and non-premium investors
and the 95% confidence intervals in Figure A5, in the first and the second
column, respectively. The five rows are redemption rate, announcement day
return, mean-reversion, liquidation risk, and probability of
liquidation.191919For the mean-reversion exercise, we present the first 500
cases with the first-stage F-stat $\geq 50$.

Now, let us consider each row separately. For the redemption rates, comparing
the first row in Figure A5 with the estimates in Table 4 column (4), we see
that the randomly chosen premium PIPE investors do not affect redemption
rates. However, because the non-premium PIPE investors include some (actual)
premium PIPE investors, the redemption rate is low but negatively correlated
with allocations for non-premium PIPE investors. Similarly, comparing the
second row in Figure A5 with the estimates in Table 5 column (4), we find no
effect of premium PIPE investors on the announcement-day return but a small
and positive effect of non-premium PIPE investors on first-day return because
the latter set now contains the premium investors identified in Table 3. These
effects are consistent with only the premium investors identified in Table 3
producing information.

For the mean-reversion (in the third row), some estimates are negative, which
is consistent with the new premium PIPE investor containing non-premium PIPE
investors and vice versa. Finally, for liquidation risk and probability of
liquidation, comparing rows four and five in Figure A5 with Table 11 columns
(1) and (2), we also find that the new estimates are mixed, which is again
consistent with the new premium investors containing non-premium investors,
although the estimates have wider confidence intervals.

Figure A5: “Placebo” Exercises

Note: This figure shows estimation results using randomly generated premium
PIPE identities. We randomly pick 34 PIPE investors out of 1,826, label them
as premium PIPE investors, and re-estimate our models. We repeat this exercise
so that we have 500 sets of randomly chosen premium PIPE samples. The first
column presents the coefficients corresponding to (the new) premium PIPE
investors and the second column corresponds to the (new) non-premium PIPE
investors. The five rows correspond to the (i) redemption rates, (ii)
announcement-day return, (iii) mean reversion, (iv) liquidation risk, and (v)
probability of liquidation. In all these five cases, the specifications are
the same as in Tables 4 and 5, column (4), Table 9 column (3), and Table 11
columns (1) and (2), respectively.

Table B1: Variable Definition

Variable |  Unit |  Definition
---|---|---
Panel A: SPAC-Level Characteristics
SPAC Measures |
IPO proceeds |  MM USD |  Gross proceeds raised in the IPO, including any full or partial exercise of the Greenshoe.
No. of Warrant |  |  Number of warrants included in the unit issued at the SPAC’s IPO.
No. of Right |  |  Number of rights included in the unit issued at the SPAC’s IPO.
Overallotment |  % |  Amount of the overallotment option exercised by the underwriter(s) as a fraction of total IPO proceeds.
Listing-day return |  % |  SPAC IPO investors’ first-day return.
Days searching |  Day |  The number of days between the SPAC’s IPO date and the target announcement date.
Days remaining |  Day |  The number of days between the SPAC’s target announcement date and the liquidation deadline.
Announcement-day return |  % |  One-day return on the SPAC’s shares on the target’s announcement date.
Redemption |  % |  Redeemed SPAC common shares as a percentage of total shares issued at IPO.
Days deSPAC |  Day |  The number of days between the SPAC’s target announcement date and the merger closing date.
Cold |  pps |  The difference between the SPAC’s offer price and the SPAC’s share price on the closing day of the merger.
Liquidation Risk |  Day |  The number of days between the SPAC’s IPO date, the target announcement date, or the liquidation date.
Liquidation |  binary |  One if the SPAC liquidated during our sample period, zero otherwise.
PIPE Participation |
$\mathbbm{1}${PIPE (pre)} |  Binary |  One if PIPE (pre) investors participate in the deal.
$\mathbbm{1}${PIPE (at)} |  Binary |  One if PIPE (at) investors participate in the deal.
$\mathbbm{1}${PIPE (post)} |  Binary |  One if PIPE (post) investors participate in the deal.
$\mathbbm{1}${Premium PIPE (at)} |  Binary |  One if premium PIPE (at) investors participate in the deal.
$\mathbbm{1}${Non-Premium PIPE (at)} |  Binary |  One if non-premium PIPE (at) investors participate in the deal.
No. of PIPE (at) |  |  Number of PIPE (at) investors.
No. Premium PIPE (at) |  |  Number of premium PIPE (at) investors.
No. of Non-Premium PIPE (at) |  |  Number of non-premium PIPE (at) investors.
PIPE Investment/IPO Proceeds |
%PIPE (pre) |  % |  PIPE (pre) investors’ investment as a percentage of total IPO proceeds.
%Premium PIPE (at) |  % |  Premium PIPE (at) investors’ investment as a percentage of total IPO proceeds.
%Non-Premium PIPE (at) |  % |  Non-premium PIPE (at) investors’ investment as a percentage of total IPO proceeds.
%PIPE (post) |  % |  PIPE (post) investors’ investment as a percentage of total IPO proceeds.
Panel B: PIPE-Investor-Level Characteristics
Non-Premium |  Binary |  One if either the PIPE (at) investor’s number of participated PIPE deals or the total commitment amount is not in the top 5th percentile.
Investment Amount |  MM USD |  Dollar amount of the PIPE investor’s investment in the SPAC deal.
MLOT |  MM USD |  The difference between the value of securities held by the PIPE investor on the first day of listing of the post-merger company and the PIPE investor’s initial investment.
lag-MLOT |  MM USD |  The PIPE investor’s MLOT from the previous SPAC deal with the same sponsor.
lag2-MLOT |  MM USD |  The PIPE investor’s MLOT from the one before the previous SPAC deal with the same sponsor.
MLOT(past) |  MM USD |  Total MLOT a PIPE investor made in past deals with the sponsor.
No. of past SPACs |  |  Number of SPAC deals participated by the PIPE investor before the focal SPAC deal.
Past Investment Amount |  MM USD |  The PIPE investor’s total amount of investments in SPAC deals before the focal SPAC deal.
No. of SPACs with other sponsors |  |  Number of other sponsors’ SPAC deals participated in by the PIPE investor before the focal SPAC deal.
Past MLOT with other sponsors |  MM USD |  The PIPE investor’s total MLOT made in other sponsors’ SPACs before the focal SPAC deal.
No. of SPACs with other sponsors, same industry |  |  Number of other sponsors’ SPAC deals that found targets in the same industry as the focal SPAC and participated by the PIPE investor before the focal SPAC deal.
Past MLOT with other sponsors, same industry |  MM USD |  The PIPE investor’s total MLOT made in other sponsors’ SPACs that found targets in the same industry as the focal SPAC.
Outside Option |  MM USD |  Highest MLOT among all premium PIPE investors not included in the current deal.
Participation |  Binary |  One if the sponsor allocates securities to the non-premium PIPE investor, and zero otherwise.
Allocation |  |  Ratio of the non-premium investor’s shares to the total shares allocated to the sponsor’s relationship investors.