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The Impact of FinTech Regulation: Underpricing and Secondary Market Flipping in Marketplace Lending
Shyam Venkatesan Ivey Business School Western University
Brian Wolfe
School of Management University at Buffalo
The State University of New York
Woongsun Yoo
College of Business & Management
Saginaw Valley State University
Abstract
Marketplace lending platforms (MLPs) function similar to IPO underwriters by matching capital demanders with suppliers of capital and extracting a flat fee as compensation. Using the lens of the underwriter literature, we examine motives of MLPs and find evidence that they underprice the primary offering of debt securities. This discount appears to be driven by the need to circumvent complex regulatory restrictions on investor participation. Our evidence suggests this discount allows primary market investors to flip securities into the secondary market for notes, thus providing the platform a mechanism to access restricted investors. We provide evidence that the MLP discount is unwound as investor restrictions are removed. Our results underscore the parallels between new financial technology (FinTech) firms and traditional agents such as underwriters but also the unintended consequences of complex FinTech regulation.
JEL Classifications: G21, G23, L81, D53, G28
Keywords: IPO, Underwriter discount, Peer-to-peer lending, FinTech, Marketplace Lending,
Crowdfunding, financial intermediation
*We thank seminar participants at the Toronto FinTech conference for their helpful feedback. This paper was previously titled “The Nexus of Marketability, Market Segmentation, and Platform Pricing Mechanisms in Peer-to-peer Lending”. All errors are our own. Address correspondence to B. Wolfe by mail, 264 Jacobs Management Center, University at Buffalo, Buffalo, NY 14260, phone (716)-645-3260, or email: [email protected]
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The primary offering of securities was once allocated almost exclusively to institutional investors
(Lowry et al., 2017), but now through technological progress retail investors have direct access to the
initial offering of many securities. Financial technology (FinTech) firms focused on disintermediating
markets enable retail investors to fund debt and equity in the primary market for these assets. For
example, member payment dependent notes (MPDN) offered through marketplace lending platforms
allow retail investors to directly fund a debt issue by an individual borrower. The primary offering of
equity assets can be acquired through initial coin offerings (ICOs) or crowdfunding portals. This
democratization of investing is widespread, touching on many asset classes from consumer debt to
startup equity. It has also experienced rapid growth in the past decade. A recent TransUnion report
suggests marketplace lending platforms comprise one third of the personal unsecured lending origination
in the United States. At the same time, ICO activity in 2017 outpaced initial round venture capital
investment by almost $3.5 billion.
While the FinTech industry has grown rapidly and opportunities to invest in the initial offering of
securities abound, regulators charged with protecting investors scramble to keep pace with the
innovation. Because FinTech securities fail to fit squarely into traditional asset classes, in some cases
they have drawn the ire of regulators1 while in other cases they have expanded rapidly without much
supervision.2 It is also unclear whether these newly issued securities should fall under state or federal
purview. The complexity of FinTech regulation is burdensome for FinTech firms and at the same time
difficult to adequately regulate. This tension is echoed by the high priority regulatory alignment has
taken in the FinTech market summary penned by the Department of Treasury (Mnuchin and Phillips,
2018). In order to create regulation that strikes a balance between investor protection and financial
innovation, it is important to understand the incentives of FinTech platforms. Relatively little work
exists on platform incentives and this paper takes a step toward understanding the behavior (incentives) 1 http://www.nasaa.org/5622/prosper-marketplace-inc-enters-settlement-with-state-securities-regulators-over-sales-of-unregistered-securities/ 2 By the time the SEC released a press statement in July 2017 to reign in ICOs as securities (https://www.sec.gov/news/press-release/2017-131) the ICO market had already scaled up to compete in size with initial round VC funding.
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of intermediaries in these FinTech markets especially in light of some of the complexities caused by
FinTech regulation.
In this paper, we examine the pricing behavior of one of the more mature FinTech segments,
marketplace lending, and compare it to that of an equity underwriter. Marketplace lending platforms
(MLPs) distribute the vast majority of the loans originated and extract origination fees as their main
source of revenue. This encourages MLPs to focus on origination volume in a way that is very similar to
the IPO underwriter’s focus on deal volume (to drive fee revenue). In the equity underwriting literature,
the underwriter has to resolve the information sharing issues and the contracting concerns to maximize
the deal volume. They reduce these agency concerns by underpricing the primary security offering
and/or preferentially allocating securities (Benveniste and Spindt, 1989; Degeorge et al., 2007). Control
over the pricing mechanism affords them the opportunity to influence the pricing and allocation
decisions. A large literature suggests that the global drift toward a fixed price mechanism (book
building) and away from auction pricing is driven by the need, on the part of the underwriter, to employ
these tools (Jagannathan and Sherman, 2006). We observe a similar drift away from auction pricing
toward fixed pricing on the part of MLPs, but, as we will discuss later, the above mentioned motivation
for such a shift appears irrelevant in the context of marketplace lending.
Given the similarities between marketplace lending platforms and IPO underwriters, but the ill fit
of traditional underwriter motives for fixed pricing mechanism selection, what incentives could drive
platforms to switch to a fixed price mechanism? We find evidence that marketplace lending platforms
use pricing control to impose a security discount (interest rate premium) which gives primary market
investors a large enough spread to act as market makers in a secondary market. By providing primary
market investors a discount, the MLPs can create room to flip securities from the primary market to a
secondary market even after transaction fees. MLPs are motivated to encourage this flip due to a
regulation on primary market participation which restricts some retail investors from participating in
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primary MDPN issuance, but not the secondary market.3 Effectively, this should allow MLPs to access
more investment capital to originate loans and thus collect more origination fees.
Our story is similar to Fishe (2002) where underwriters (platforms) are able to derive a benefit by
encouraging flipping activity in the secondary market that outweighs the cost from discounting the
primary offering. Our results underscore the unintended consequences of security regulation in FinTech
markets but also point to the incentive parallels that can be drawn from the traditional underwriter
literature.
To show that marketplace lending platforms underprice the primary offering of MPDN, we use
state level security registration changes that affect investors’ ability to participate in the primary offering
of MPDN. The current regulatory structure forces marketplace lending platforms to obtain security
registration for every state investors reside in before investors can participate in the primary offering of
MPDN. As state security registrations are approved, investors can fund loans in the primary offering of
MPDN. Assuming demand for credit is elastic and the platforms would originate more loan volume if
they offered lower interest rates to borrowers, we should observe an interest rate decrease, i.e. an
unwinding of the underwriter discount, as investors are permitted to participate in the primary offering.
Using these primary market participation changes as a staggered event study, we indeed observe
that the platforms decrease interest rates (increase prices) by an average of 1.5-10.2 BP on LendingClub
and 20-24 BP on Prosper in 30 day windows surrounding investor participation changes. We show that
these initial results are robust to multiple specifications after including a rich set of borrower
characteristics, loan credit grades, personal loan benchmark interest rates, and even daily time fixed
effects. We also examine two large changes where multiple states simultaneously alter their stance to
allow (restrict) investor participation on LendingClub in December 2014 (October 2008) and show a 25
3 Secondary market trading of MPDN is conducted through an alternative trading system (ATS) called FolioFN and is not restricted by geography. This is because state level security regulators focus on restricting the issuance of particular securities within their state, but once a MPDN is issued on a primary market, security regulators’ ability to restrict investment is limited.
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BPS interest rate drop (88-131 BPS increase) occurs around such an event.4
Our first block of tests show that when platforms are able to manipulate the security price, they
adjust prices around shifts in the supply of investors. However, this could simply be an artifact of
investor competition. That is, as the number of investors increases, the platform may adjust the price
because a more competitive supply of capital allows them to spur demand through interest rate
discounts. To show the changes in security prices are not simply driven by a shift in competition among
investors, we use an identical setup but during the time period when the platform cannot control the
price. The Prosper platform opened in 2006 using an auction pricing mechanism which it maintained
until December 2010 (Wei and Lin, 2016). If increases in investor competition would result in investors
bearing a lower equilibrium interest rate we should observe a similar interest rate decrease during the
auction period. Instead, we show that as investor competition increases due to shifts in investor supply
in the primary market, no changes in interest rates are observed.
The pricing movements around shifts in primary market investor restrictions suggest the platform
is manipulating price specifically around these investor supply change dates. In order to tie these interest
rate changes with flipping activity, we examine secondary market data of MPDN. We show that there is
substantial quoting activity of flipped MPDN, notes less than 30 days old and priced with a markup that
yields a net gain on the asset sale after secondary market fees, over the period December 2012 to
December 2016. On average 8.24% of the daily quote volume are notes that fall under our definition of a
flipped note. Using quoting activity of secondary market investors, we show that secondary market
flipping activity decreases after the LendingClub IPO which was a large shift in primary market investor
participation. This shift in flipping activity coincides with the LendingClub 25 BP interest rate decrease
in December 2014.
Our results hold important policy implications and underscore the influence of regulatory
restrictions in the expanding FinTech markets. In light of our results, the call for regulatory 4 We describe the events in more detail later in Section 1. The large shift in investment restrictions is tied to two events. The initial restrictions created in 2008 were the result of platform conversations with the SEC while the lifting of restrictions involve the LendingClub equity IPO.
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restructuring seems justified as old systems of security regulation appear to have new implications such
as underpricing in FinTech markets. Given the infancy of other FinTech markets like equity
crowdfunding and initial coin offerings, our results imply that a better understanding of all FinTech
platforms and their incentives is merited.
Our paper joins the emerging literature on financial technology platforms with the underwriter
literature. The underwriter literature is vast and explores multiple underwriter behaviors such as
underpricing (Benveniste and Spindt, 1989; Sherman and Titman, 2002), preferential allocation
(Goldstein et al., 2011; Sherman, 2000), pricing mechanism selection (Sherman, 2005), and even
investor behavior under different pricing mechanisms (Chiang et al., 2010). There are multiple papers
that would suggest an auction mechanism is less costly in terms of underpricing and direct fees
(Degeorge et al., 2010; Derrien and Womack, 2003; Lowry et al., 2010). One paper similar to ours
(Fishe, 2002) discusses benefits the underwriter might receive from encouraging flipping in the
secondary market. He shows evidence that underwriters might profit as market makers providing price
support to the newly issued security. Our story also revolves around the notion that the MLP might
benefit from encouraging flipping activity, but in our setting the benefit is more direct in that MLPs
unlock secondary market capital to generate more primary market origination and side step state level
regulation.
The literature on marketplace lending and, more generally, FinTech platforms is a new and
growing. Early work on marketplace lending focused on borrower characteristics that influence lending
outcomes (Lin and Viswanathan, 2015; Ravina, 2018; Senney, 2016), investor bias/behavior (Agrawal et
al., 2015; Lin et al., 2015) and more recently drivers for platform growth (Buchak et al., 2017; Butler et
al., 2017; Havrylchyk et al., 2016). Reward based crowdfunding such as capital raised on platforms like
Kickstarter have also been studied for characteristics that influence funding outcomes and
entrepreneurial success (Mollick and Nanda, 2015). Our paper is unique relative to the above papers in
that it examines the behavior of the MLP and tries to understand incentives that may produce such
platform behavior.
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We are aware of two papers that consider platform incentives and attempt to explain platform
behavior. A recent paper by Vallee and Zeng (2018) examines the incentives platforms have to produce
borrower information for investors that could lead to greater adverse selection issues and potentially
decrease overall platform origination volume. A second paper by Chiu et al. (2018) also looks at the
incentives of MLPs but examines preferential allocation decisions by the platforms. They find that
platforms preferentially allocate loans to investors to resolve adverse selection and investor clientele
issues. Our paper is unique in that it examines pricing behavior of the platforms from the perspective of
the underwriter literature and the channels that may drive the pricing mechanism drift toward a platform
pricing mechanism.
1 Marketplace Lending Background
In this section, we provide some of the history and background surrounding the marketplace
lending industry used in this study. The structure of MLPs and the marketplace lending industry is very
dynamic and has evolved multiple times. We will cover the evolution of each platform individually and
then the regulatory structure that has given rise to primary market restrictions. Before we delve into
platform specific details, we briefly review the marketplace lending process.
Generically, FinTech platforms use technology to match capital demanders such as individual
borrowers/firms with capital suppliers. The platforms use technology to broker these transactions and
(generally) do not participate in the capital provision. In other words, the platforms do not typically
supply any capital to fund the capital demands of borrowers and bear no credit risk from
default/bankruptcy. Platforms earn origination fees and servicing fees on the capital requests. Debt
platforms appear to take these fees out of the funding proceeds while crowdfunding platforms are often
compensated with shares of equity in the issuing firm.
The process for marketplace lending platforms works as follows and is summarized in Figure 1.
Borrowers submit a loan request to the platform and the platforms provide an initial screening based on
the applicant’s creditworthiness. After a loan request passes a credit screen, the platform posts the loan
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request in front of investors to fund. If enough investors choose to fund the loan, the borrower receives
funds and investors receive a promissory note tied to the payments of the borrower. Investors may
choose to sell the promissory note in a secondary market (FolioFN) after its primary offering on the
MLP.
1.1 Prosper Marketplace / Prosper Funding LLC
Prosper was the first marketplace lending platform to emerge in the mid-2000’s in the United
States. Prosper initially selected an auction pricing mechanism where borrower loan requests passing
the initial credit screen were posted electronically and available for investors to competitively fund
through a multiunit uniform price auction (Wei and Lin, 2016). Investors could provide capital in small
increments ($25 minimum) to fund a loan and once a loan was fully funded, the platform would
originate a loan. Thus, investors in this early period specified both a price (interest rate) and quantity of
capital for a particular loan request. In some cases, the auction remained open for a prescribed window
so that investors could outbid each other and borrowers obtain more competitive interest rates. Because
funding occurred in small increments, initial investors were largely retail investors and all investors were
pooled together in one funding market.
In 2008, the platform made a format change whereby it ceased creating the loans and allowed an
industrial chartered bank to originate the loans (Rigbi, 2013). The switch allowed Prosper to export
interest rates of the originating bank nationwide and avoid usury caps that were restricting origination in
some states. Following the change, the platform would purchase the borrower’s loan from the bank
shortly after origination (2-3 days) and hold the loan on its balance sheet. Instead of selling the loan to
investors, the platform would now issue a separate security, referred to as a member payment dependent
note (MPDN), to investors that bears all of the borrower credit risk. In conjunction with the change, the
platform also created a secondary market for MPDN which was operated by a third party, FolioFN, so
that retail investors could liquidate notes. While these changes were occurring the platform ceased
operation in October 2008 but reopened in July 2009.
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The following year, in December 2010, the platform announced it would switch from an auction
pricing mechanism to a posted pricing mechanism (Wei and Lin, 2016). This pricing mechanism switch
came as a surprise to investors. From that point onward, the platform would price the loan contracts and
investors would make loan participation decisions. As the popularity of the market grew, it began to
attract institutional investors. Anecdotes at the time would suggest these institutional investors began to
impose adverse selection issues on the retail investors because of their speed. To maintain both groups
of capital providers, Prosper added a second funding market for institutions. This second funding
market required whole loan funding, i.e. one investor per loan. It is our understanding that
simultaneously the fractionally funded market began imposing a cap on the proportion of a loan
investors could fund to limit institutional investor interest in the fractional market. The platform claims
to randomly assign loans between the fractional and whole loan market, although, recent evidence
suggests there may be preferential allocation on the part of the platform (Chiu et al., 2018). Figure 2
provides a summary of the timeline of events for Prosper.
1.2 LendingClub
Similar to Prosper, LendingClub was founded in 2006 and its early evolution mirrored much of
Prosper’s development. One exception is that when LendingClub began operation, it used a posted
(fixed) pricing mechanism identical to the one elected by Prosper in 2010. Similar to Prosper,
LendingClub underwent format changes in the spring/summer of 2008 and began using an industrial
bank to originate loans. Beginning in April 2008 the platform ceased lending operations until federal
regulators were satisfied and the platform could reopen in October 2008. The platform would also sell
MPDN to investors while holding the original loan on its balance sheet. LendingClub also opened a
secondary market in October 2008 using the same alternative trading system (FolioFN) to host the
market transactions. In first quarter 2013, LendingClub also added a second funding market for
institutional investors and required participants in the new funding market to fund loans in their entirety.
In December 2014, LendingClub became a publicly traded company. This IPO process is
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significant as we will later discuss. The IPO of equity on a national market exchange (NYSE) removed
multiple primary market restrictions. A summary of the LendingClub events is presented in Figure 3.
1.3 Marketplace Lending Regulatory Background
The format switch in 2008 by both platforms was precipitated by conversations between the
MLPs, the Securities and Exchange Commission (SEC), and state security regulators beginning in late
2007. Under the new MPDN structure in early 2008, platforms were issuing a new security to retail
investors and forced to (federally) register the MPDN with the SEC. This registration process is similar
in spirit to the registration firms undergo during an IPO of equity securities. However, because the
MPDN did not trade on a national market exchange, the platforms could not benefit from traditional
Blue Sky (state security registration) exemptions that typically come with federal security registration.
Instead, the platforms were forced to seek security registration from each state before investors residing
in a state could participate in the funding process.
Effectively, the need to register MPDN at the state level split the primary market for MPDN.
Following their SEC mandated quiet periods in 2008, both platforms emerged with more restricted
investor pools. The platforms applied for security registration within states during their respective quiet
periods, but many states delayed security registration approval while state level regulators reviewed the
platforms’ operations. We take advantage of this staggered regulatory approval in our empirical design
as groups of investors are permitted to renew participation in the primary offering of MPDN. We note
that the states did not uniformly approve MLPs and the timing of security registration approval during
the period between 2008 and 2014 is different for each platform. This suggests the platforms had little
ability to influence the timing of security registration approval other than compliance with security
regulator requests.
While the IPO of LendingClub likely occurred for multiple reasons (capital requirements, founder
exit, etc…), the offering of a national market system traded (equity) security allowed the platform to
circumvent the remaining state security registration requirements. LendingClub obtained a legal opinion
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that suggested it’s publically traded equity preempted state security registration requirements and should
clear the way for investors in all states to invest on the platform. The majority of states accepted the
legal opinion although a few states have refuted this interpretation and investment is still restricted in
some states according the LendingClub’s prospectus filings with the SEC. In Figure 2 and Figure 3, we
report the state level additions for each platform which we use in the initial tests as a staggered event
study.
2 Hypothesis Development
On the surface, the drift in market pricing mechanisms toward platform (fixed) pricing and away
from auction pricing mechanisms is a curious choice. Auction mechanisms should provide more
accurate listing prices according to theory and empirical evidence (Derrien and Womack, 2003; Lowry
et al., 2010). There is even empirical evidence that auctions in the marketplace lending environment are
not prone to retail investor bias (Lin et al., 2015) making the shift even more of a puzzle. This migration
in pricing mechanism bares a strong resemblance to the global equity IPO trend (Kutsuna and Smith,
2003; Sherman, 2005) away from auction pricing and toward book building.
At the risk of oversimplification, we consolidate the incentives behind pricing mechanism
reported in the literature into two groups. These are summarized in Lowry et al. (2017) who suggests
the typical explanation for the global drift in pricing mechanism away from auctions and toward a fixed
price mechanism is information collection in the vein of Benveniste and Spindt (1989). Under the
information collection channel, underwriters have a motive to accurately price securities and investors
have private information that is valuable to the underwriter to incorporate in the pricing. Preferential
allocation, underpricing, and soft benefits are rewards granted by the underwriter, at the expense of the
firm and atomistic investors, for this information. Such rewards are only available if the underwriter
controls the pricing and allocation of the security, thus the shift away from auction pricing where
underwriters have no ability to control price or allocation.
The second channel to explain pricing mechanism selection suggests that firms are willing to pay
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underwriters directly through underwriting fees but also indirectly in the form of underpricing to obtain
post-IPO analyst coverage or similar post-IPO benefits (Degeorge et al., 2007). Underwriters thus select
a book building or other fixed price mechanism for the ability to charge this indirect fee to firms.
Because in the marketplace lending environment the platform sets interest rates before allocating
loans to investors no information loop can exist in the Benveniste and Spindt (1989) sense. In the
marketplace lending market, borrowers have no channel to accrue soft benefits like the Degeorge et al.
(2007) story either. Thus these principal channels are unlikely to drive platform pricing behavior in the
case of marketplace lending.
Because the traditional underwriter explanations cannot explain this pricing mechanism drift, we
fall back to a model of the marketplace lending platform suggested by Wei and Lin (2016). They
present a theoretical model to characterize the contracted interest rate in two pricing mechanisms
(auction and posted price). In a posted price format, the interest rate is endogenous to the platforms
profit function. Wei and Lin (2016) describe the platforms’ expected profit structure by the following
equation:
Eπp = α · Q · P r(WN :Q< γ(p)) (1),
where α is the fixed fee charged by the platform; Q is dollar amount requested by the borrower; WN
:Q is the Qth lowest interest rate, out of N , that investors are willing to lend at; and γ(p) is loans rate of
return in excess of expected default rate. Pr(WN :Q < γ(p) is essentially the probability of full funding.
In their model, the platform is going to set an interest rate that maximizes the above expected profit
function subject to non-negativity constraints. It is clear from Equation 1 that the profit function of the
platform hinges on the volume of loans (Q). In this way, platforms are similar to underwriter models.
As discussed in the background section, the primary market for a security may be segmented, i.e.
not all investors are able to participate, for one of two reasons. In the IPO literature, underwriters may
preferentially allocate the initial offering to select investors which restricts other investors from
participating in the primary offering. In our setting, restricted participation occurs in the primary
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offering because of regulation that limits investor participation based on investor state of residence
(Cornaggia et al., 2018).
Given the profit function of the platform hinges on the volume of loans (Q), the platform has a
strong incentive to provide all investors access to platform investment. In the absence of a secondary
market for the MPDN, regulatory limits on investor participation should restrict the supply of capital to
the platform and lower aggregate origination volume. However, when a secondary market exists, the
(primary market) excluded investors may still hold notes if the platforms can induce primary market
investors act as market makers and flip securities to the secondary market. With the ability of the posted
price platform to coordinate prices, we hypothesize that under segmented primary markets and the
existence of a secondary market, the platform will discount (increase) loan prices (interest rates).
However, if the segmentation in the primary market is dissolved, the platform will remove this
segmented market discount to encourage greater loan demand. Thus interest rates should decrease if
primary market segmentation is eliminated. We state this formally:
Hypothesis 1: The interest rate of the security offered by a posted price platform should decrease
when primary market segmentation is dissolved (H1A) relative to its segmented interest rate (H10).
Hypothesis 2: The interest rate of the security offered by a posted price platform should increase
when primary market segmentation is introduced (H1A) relative to its unsegmented interest rates
(H10).
Wei and Lin (2016) argue that, under mild conditions, the platform will set the interest rate very
close to the borrowers outside opportunity. The equilibrium interest rate for an auction market is also
discussed and shown to be strictly lower than the posted price market. In their model, because auction
market participants lack the ability to coordinate in a competitive market, they compete away the rents
captured by the fixed price investors.
Wei and Lin (2016) suggests that a platform employing a posted price mechanism has the
incentive to set prices (interest rates) lower (higher) than the auction market, absent any secondary
market conditions. However, the implicit assumption in the Wei and Lin (2016) model is the absence of
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a secondary market for the security, given the one period nature of the model. We suggest that platforms
have an additional incentive to discount security prices when a segmented primary market exists in
conjunction with a secondary market. We anticipate that auction investors would face an inability to
coordinate similar to the Wei and Lin (2016) model when presented with a secondary market for notes.
Thus, under auction pricing the contracted interest rate only reflects the compensation for interest rate
and credit risk. Formally,
Hypothesis 3: The interest rate of the security offered by an auction price platform should decrease
when primary market segmentation is dissolved (H3A) relative to its segmented interest rate (H30).
Cornaggia et al. (2018) identify multiple state security registration approvals between 2008 and
2016. As states transition from primary market exclusion to inclusion, two things occur simultaneously.
First, supply in the primary market increases. Second, demand in the secondary market should decrease
as investors are now able to earn the market making premium even if they pursue a buy and hold
strategy. In this scenario, as market segmentation is slowly removed and with no changes to probability
of funding the only way to maximize profit is create more loans by attracting more borrowers.5
Combining these observations we test the following:
Hypothesis 4: Given the decrease in secondary market participation, the fraction of flipped notes
that are quoted should decrease (H4A) relative to the higher level of primary market segmentation
(H40).
3 Data Description
We have gather loan data for MPDN issued on LendingClub and Prosper from a variety of
sources. First, we obtain loan data and borrower information directly from the platforms. Both
LendingClub and Prosper provide a wealth of information on the borrower creditworthiness including
characteristics such as credit score, income, outstanding debt, years of employment, occupation, public
records, delinquencies, borrower state of residence, and credit queries from 2007 until 2017. We also
5 In the case of LendingClub, the probability of funding is always 100% after loans pass the initial credit screen. On LendingClub, once the credit check is done and the borrower’s request is accepted by the platform, all the loans are funded. Typically it is funded by the investors but in some odd cases the platform directly funds loans.
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obtain information on the loan contract that describes interest rates, loan size, and term. For Prosper
we are also able to collect the date of loan origination.
We augment loan information provided by the platform with information provided by the SEC
on loan listings for LendingClub. While LendingClub only provides the month of loan issue, we are
able to gather loan origination dates from SEC filings and match them to loan data.
In order to compare interest rate information for the MPDN, we obtain benchmark interest rate
data. We obtain personal unsecured loan data from The Bankrate Monitor which is a publication that
surveys commercial banks and credit unions across the United States each week and reports the
average interest rate on multiple debt securities (real estate, auto, personal). Bankrate Monitor surveys
banks for the best rate they would provide for a borrower with a 700 FICO score for a 3 year, $3000
loan with no discounts for accompanied deposit accounts.
Table 1 below provides a summary of the loan and borrower data. Data from LendingClub
MPDN are provided in Panel A while data on Prosper MPDN are given in panel B.
To analyze investor propensity to flip securities from the primary market to the secondary
market, we obtain secondary market data for LendingClub notes from Interest Radar.6 Interest Radar
was one of the original third parties enabling retail investors to automate investment on the
marketplace lending platforms. As secondary markets were added in 2008/2009, Interest Radar added
functionality to invest on the secondary market and tools to help guide pricing decisions. As a result,
Interest Radar began collecting the notes available for sale on the secondary market starting in
December 2012.
The secondary market for MPDN allows investors to post non-marketable limit sell orders on
the trading platform. Sellers select a price for the note and an expiration period with a maximum of 7
days. Secondary market investors are presented with the original information on the loan from
origination in addition to updates on credit score and payment information. Interest Radar collected
information on all available notes every 2 hours over this time period. Thus, as notes were
purchased/expired/cancelled, Interest Radar would mark their removal date but the nature of the note’s
exit is unknown. We refer to this as the quote data from the Interest Radar.
6 https://app.interestradar.com/app
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The quote data contains unique identifiers on the loan id and the note id, the latter which
identifies which one of the MPDN for a particular loan is being sold (since each loan is funded through
multiple notes – one for each investor). Importantly we use the markup/discount to identify MPDN
being sold at a premium greater than the FolioFN transaction fee of 1% principal. We match loan
identifiers to SEC data to obtain the actual date of issue of the notes. Actual size/price of the note is
not provided, but Interest Radar does provide a price range. We assume each note takes the average of
each price range when calculating volume posted each day.
While it is possible for primary market investors to flip loans at any time in their history, the
return on the investor capital will be greatest if the investor immediately flips a loan into the secondary
market. We identify notes from the secondary market quote data as flipped notes if their markup
exceeds the 1.0% transaction fee and they are posted for sale less than 30 days (15 days) from their
origination date. This helps to avoid misclassifying notes posted for sale due to informational changes
as flipped notes. No information updates are given on the loan until borrowers post their first payment
on the MPDN 30 days after origination. This should make notes inside this window relatively free of
such misclassification issues.
We aggregate the daily volume of new notes identified as flipped notes and scale it by the daily
volume of notes posted for sale on the secondary market as our measure of flipping activity. Figure 4
graphs this measure over time for our entire sample period. As is evident from the figure, retail
investors post a significant amount of flipped notes early in the sample but this activity diminishes over
time. The sample suggest that an average of 8.24% of note volume posted for sale falls within our
categorization of flipping. This is consistent with the idea that as states approve investors to participate
in the primary issuance of a MPDN, the need for investors to flip notes is diminished.
4 Empirical Results 4.1 Evidence from state registration
We begin our empirical analysis by showing how changes in market segmentation have
ramifications for the yield set by MLP platforms. After emerging from the quiet period, both platforms
were forced to register securities in many states. Different states in U.S. have different processes and
processing times for security registration. On account of this, the state approvals were staggered
throughout the post quite period. Figures 2 and 3 clearly show the timeline of the registration process
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for each platform.
As more states approve the registration, more investors enter the primary market and make it
less segmented. The timing of these approvals are outside LC’s control and hence these events are truly
exogenous. This is evident from the order of state registrations – it is platform specific which suggests
platforms had little control over timing of the registration process other than through their response to
regulators. We exploit each of these separate changes in registration to empirically test the effect of
diminished market segmentation on the yields the platforms set.
4.1.1 Regression evidence
With the addition of each state we perform a short window event study. As the segmentation
drops, with the addition of each state, the platform has lesser of an incentive to discount the MPDNs
and have the primary market participants to flip the loans in the secondary market. Therefore, we
expect a negative coefficient for the dummy variable that switches on when the state registration is
approved.
We begin with LendingClub where there are 8 different state registration events in our sample
between October 2008 and December 2014. We use a +/-15 day window around these events to test for
a difference in interest rates. To increase the power of our test we use the event time approach. We use
the pooled ordinary least square (OLS) regression estimator to estimate the following specification:
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛽𝛽𝑡𝑡 + 𝛽𝛽𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑡𝑡𝑖𝑖𝑙𝑙𝑙𝑙 + 𝛽𝛽1 ∙ 𝑃𝑃𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑡𝑡 + 𝑥𝑥′𝑏𝑏𝑙𝑙𝑏𝑏𝑏𝑏𝑙𝑙𝑏𝑏𝑏𝑏𝑏𝑏𝛽𝛽𝑙𝑙 + 𝑥𝑥′𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝛽𝛽𝑙𝑙 + 𝜖𝜖𝑖𝑖,𝑡𝑡 (2)
In the above specification we control for loan level characteristics like term of the loan (term),
log of loan amount (LnAmount), and the grade of the loan (Grade). Grade is a categorical variable
where Grade A represents a highest quality and Grade G the lowest quality. Further, borrower
characteristics like debt-to-income ratio (dti), log annual stated income (LnIncome), log monthly debt
payment (LnDebtPmt), the number of hard credit inquiries in the past 6 months (inqLast6mths),
number of open accounts (openAcc), number of delinquencies in the past two years (delinq2yrs), public
records on file (pubRec), length of employment (empLength), and logged revolving balance
(LnRevolving) are also used as control variables.
We expect the interest rates to increase in term, dti, inqLast6mths, and delinq2yrs as these
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represent a higher risk for investors in the note. A high LnIncome and a longer empLength mitigates
some these risks and hence we expect that these would have a negative marginal effect on the set
interest rate.
Obviously, price of risk in the aggregate economy changes over time. To address this problem
we use information on the date of issuance of the note to create a day fixed effect. This should capture
the average price of risk across all notes issued on a particular day. To control for unobserved
heterogeneity in the interest rates between the states all the specifications in Table 2 include a borrower
state fixed effect. The standard errors reported below the coefficients are also clustered at the state
level.
Table 2 presents the results from Equation 2. The unit of observation is at the contract (loan)
level. In column (1) of panel A, the coefficient on PostRegistration dummy is our key variable. This
variable is a dummy variable which takes the value of one for loans made after a state registration
occurs and zero otherwise. The coefficients on the day dummy variables (unreported) capture the
changes in daily yield due to shifts in macroeconomic conditions. Although the sign on the
PostRegistration coefficient is negative, there is considerable sampling variation that prevents us from
finding any statistical significance. In column (2) of panel A we allow the impact of registration to vary
across different loan credit grades by interacting the PostRegistration dummy with the loan credit
grade. The pattern of declining interest rates is clearly visible here across the different loan grades.
Readers should note that although Grade A loans, given its limited credit risk, would be the ideal
security to flip in the secondary market the demand for credit among borrowers in that credit grade is
very limited. Therefore, it is not a surprise to find that the effects are highlighted much more in Grade
B and Grade C instruments. Consistent with our expectations we also find that the interest rates are
increasing in dti, inqLast6mths, and delinq2yrs and decreasing in LnIncome.
To bolster our evidence above we also look at the loan data from Prosper, the other lending
platform. After Dec 20, 2010 Prosper also used a posted price mechanism to set the interest rates on the
loans they made. We repeat the specification in Equation (2) on the contract data obtained from Prosper.
The relevant results are presented in Column (1) and (2) of panel B in Table 2. These results show a very
similar pattern to the LendingClub results. Prosper is considerably smaller in size and in the number of
contracts they originate. The smaller sample size increases the sampling variation and the effects can be
seen on the estimated standard errors. However, the overall results are quite consistent and point to a
considerable decline in the contracted rates after the reduction in primary market segmentation. Thus we
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reject the null for Hypothesis 1.
4.2 Supply effects
A simple alternate story also explains why we observe the decline in interest rates following
state security registration. Market segmentation leads to a drop in supply of investors (capital) that in
turn drive interest rates higher. Subsequently, when the states are added back to the primary market the
supply of capital increases. This should also result in the interest rates going down. However, this
would not explain a difference in the secondary market trading activity; evidence for which is shown
below. In this section we show that the observed effects in section 4.1 are not purely because of shock
to the competitive supply of capital but to the pricing mechanism of the platform and its revenue
function.
Earlier, we mentioned that Prosper also had a segmented primary market and faced many of the
same market conditions as LendingClub. However, until December 2010 Prosper used an auction pricing
mechanism as opposed to posted-price mechanism. Subsequently, in December 2010 they moved to a
posted-price mechanism identical to LendingClub. During the time that Prosper was using an auction
mechanism it successfully sought security registration approvals. A detailed timeline of events for
Prosper is provide in Figure 3.
Hypothesis 3 suggests that the changes in interest rate occur only at the nexus of market
segmentation and posted-price market mechanism. In an auction framework, there is no possibility of a
strategic action or collusion between the investors. Therefore any excess rents, conditional on credit
market forces, should be competed away. Therefore, while Prosper was in the auction regime, where
the loan interest rates are not controlled by the platform, we expect there to be no significant changes in
interest rate following the increase in primary market investor participation.
Consistent with our analysis in section 4.1 we again exploit each of the state-by-state changes in
registration to empirically test the effect of diminished market segmentation on the interest rates of the
loans. In the time that Prosper had an auction pricing mechanism, there were five state registrations
events: Connecticut, Oregon, Missouri, Louisiana, and Arkansas. Like before, these events are truly
exogenous to the platform’s pricing decision. We test for differences in the rates contracted around
these events using a pooled OLS regression. Our hypothesis is that there should be no statistical change
in the interest rates set after states are added to the primary market.
Columns (3) and (4) of panel B in Table 2 present the relevant results. The analysis is done in
20
event time using the loans around all the five state registrations. Consistent with our prior expectation,
in none of the specifications we see any evidence of statistical difference in the interest rates. This
evidence clearly shows that the change in the supply of capital, led by investor participation, is not the
main reason why we observe shifts in contracted interest rates. The results do suggest that the pricing
mechanism and the incentive structure of the lending platform drive the interest rates when there are
changes in investor participation.
4.3 LC IPO
In 2014, LendingClub filed and executed an initial public offering of its equity. When
LendingClub lists its common stock on a national exchange, after the IPO, every security it issues
should qualify for federal preemption allowing LendingClub to sell notes in all 50 states and DC
without state security registration. Thus effective December 20, 2014 LendingClub should not face
segmentation in its primary market. Interviews with state security regulators suggest that LendingClub
has notified states that the IPO of its public equity allows any further note issuance to be exempt from
state regulatory issues under the Blue Sky security exemptions. Thus we conduct an event study around
the IPO.
Consistent with Hypothesis 1, MLPs have an incentive to decreases interest rates as primary
market segmentation is removed. In our regression specification we include a dummy variable, IPO, set
to 1 after the IPO in December 2014 and to 0 otherwise. We focus on an event window that extends to
six months before and after the IPO period. The coefficient on the variable IPO is the key variable of
interest.
We expect the marginal effect on the IPO variable to be negative. Table 3 presents the relevant
results around the IPO. Across the different specifications we consistently notice the IPO dummy to be
negative and statistically significant clearly demonstrating that with market segmentation gone the
platforms set a lower interest rate. We find that the average effect across loan grades in column (1) is a
61 BP decrease. Column (2) shows there is some heterogeneity across the different credit grades. We
augment this approach by substituting the day fixed effect with a benchmark interest rate for personal
loans. We obtain the average monthly interest rate data from BankRate Monitor (BM) which is a
publication that surveys commercial banks and credit unions across the United States each week and
reports the average interest rate on personal unsecured loan interest rates. BM surveys banks for the
best rate they would provide for a borrower with a 700 FICO score for a 3 year, $3000 loan with no
21
discounts for accompanied deposit accounts. We include this in our main specification to control for
changes in the price of risk due to changes in the macro-economic conditions and report the results in
column (3) and (4). The results are very similar to column (2) with some heterogeneity across credit
grades but in almost all cases the coefficients suggest a fall in interest rates following the IPO.
A point of potential concern, regarding the above result, might be that LC’s choice of going
public, for an IPO, is not exogenous to the firm’s decision to set interest rates. After all, LC chose to
make the public offering. Therefore, there could be a confounding variable which effects both the
choice variables, interest rates and the decision to go going public.7 However, the platform’s choice of
going public is not endogenous to the investor’s choice of trading the MPDNs in the secondary market.
We use this to test our hypothesis below.
4.3 Evidence of Secondary Market Flipping
We now complement our previous results with evidence of trading patterns in the MPDN
secondary market. In the section above we discuss how after the IPO of its equity, LendingClub would
be able to sell the MPDNs in all the 50 states. Also note, none of the state registration constraints of the
primary market precludes the investors from participating in the secondary market. Therefore, we
conjecture that investors who have no constraints on their participation in the primary market buy
cheap and sell these assets in the secondary market for a premium, i.e. flip the securities in the
secondary market. Their incentive to do so decreased after the IPO since there were no investors
restricted in the primary market.
To test this hypothesis (H4) we identify flipping quotes in the secondary market data. A
secondary market quote is considered a flip if it occurs within the 30 (15) days of origination of the
MPDN and is priced above the transaction fees imposed by the secondary market platform. We
aggregate flip volume each day and scale it by the daily quote volume. We assume that no new adverse
or favorable news regarding the borrower or loan characteristics emerge within the first 30 days of
security issuance.8 Assuming also that the liquidity motivated trades are evenly distributed in time, we
conjecture that there should be a substantial decline in the flipping trades in the post IPO regime if the
investors no longer have the incentive to make the additional spread.
7 Firm profitability, for example, could influence LC’s decision to go public and also influence its decision to set lower interest rates on loan contracts. 8 For robustness we also use a 15 day window.
22
Figure 4 plots the daily time series of the aggregate flipping activity while Figure 5 shows
flipping activity in the +/- 90 day window around the IPO. It is clear that there is considerable variation
in this activity across time. Table 4 shows the statistical difference in the flipping activity in the pre-
IPO and the post-IPO period. We use a variety of windows around IPO and also vary the definition of
flipping trades from 15 to 30 days post the issuance. However, regardless of these choices it is clear
that the average flipping activity has significantly decreased in the post IPO world. The magnitudes of
the changes are anywhere between 1% and 2.5% of the daily volume depending on the choice of the
testing framework. This result clearly substantiates our earlier claim that investors in the P2P markets
act as market makers and that the lending platforms incentivize them to do so by adjusting the prices
4.4 Robustness
4.4.1 Robustness: Nearest-Neighbor matching
One of the possible reasons for observing these differences in outcomes is that the nature of
contracts and that of the loans may have changed, post-state registrations. If this is truly the case there
would be a sample selection bias which would lead to a biased estimator. Moreover, the direction of
the bias would be unclear.
To overcome this concern we use a matched sample analysis and estimate the average treatment
effect. The ”treated” group in our analysis would include contracts that were made after the state
registration and the ”control” group would include those that were made before. We match the
contracts on the debt-to-income ratio of the borrow, annual stated income of the borrower,
delinquencies in the last two years by the borrower, loan amount, maturity period of the loan, amount
in revolving balance, number of open accounts, and number of hard credit inquiries in the last six
months. In addition, we make sure that when two contracts are matched, the credit rating of the
borrowers in the respective contracts is exactly the same. Conditional on these observable
characteristics we assume that the assignment to treatment and control groups are random
(unconfoundedness).
We use the nearest-neighbor matching method. It is a greedy matching algorithm that chooses
the closest control match, across the different dimensions, for each treated unit. The matching process
overcomes the selection problem by ensuring that the probability of the matched contracts to be in the
treated group is the same as it is to be in the control group.
23
Table 5 presents the results of the average treatment effect (ATE) on the matched sample. We
are testing to see if there are any differences in the contracted spread between the treated and control
group.9 Further, since there are costs to matching on continuous covariates, matching on more than one
continuous covariate causes large-sample bias in the ATE estimator. The estimates reported are after
adjusting for this bias (see Abadie and Imbens (2012)). In the overall matched sample, we find that,
post segmentation, there is a 41 basis points drop in the contracted yields. This decrease in the average
interest rates is highly significant, both statistically and economically. We extend our analysis further
by inquiring if these changes are observable across the different credit ratings. The relevant results are
also provided in Table 5. We continue to find that the interest rates have declined after the reduction in
primary market segmentation. Overall, this evidence reinforces our regression based evidence on the
relationship between yields and level of market segmentation.
4.4.2 Robustness: Quiet period
Earlier, in Section 1, we detailed the important events of 2008 pertaining to the marketplace
lending market. Since the SEC identified the MPDN as a “security”, the platforms were forced to get
approvals from every state before the investors in the respective states could invest in these notes. This
severely segmented the lending market as lenders in cash rich states like California, New York, and
Texas could no longer participate in this market. During this time SEC also mandated the MLPs to open
a secondary market where these financial claims created could be traded. In response, LendingClub went
into a quiet period where they did not create any new loans. Only after coming out this quiet period did
they start the registering with the states.
Importantly, for our empirical design, this SEC mandated intervention is an exogenous event
and we use this to study the causal effect of market segmentation on contracted yields. Our hypothesis
is that, under these conditions, a posted-price platform is incented to lower the price of the asset
(increase yield) and attract investors to the primary market and encourage them to act as market makers
and flip part of the loans to the secondary market (H2). The lower price is to compensate them for
inventory carrying risk and for adverse selection. We run a very similar specification:
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛽𝛽𝑡𝑡 + 𝛽𝛽𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑡𝑡𝑖𝑖𝑙𝑙𝑙𝑙 + 𝛽𝛽1 ∙ Quiet𝑡𝑡 + 𝑥𝑥′𝑏𝑏𝑙𝑙𝑏𝑏𝑏𝑏𝑙𝑙𝑏𝑏𝑏𝑏𝑏𝑏𝛽𝛽𝑙𝑙 + 𝑥𝑥′𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝛽𝛽𝑙𝑙 + 𝜖𝜖𝑖𝑖,𝑡𝑡 (3)
9 The spread here refers to the difference between the posted yield on the contract by LC and the interest rates provided by Bankrate Monitors National Index for unsecured personal loan.
24
The variable of main interest is Quiet, which is a dummy variable that takes a value of one if the
contracts are made after the quiet period and zero otherwise. We focus on an event window that
extends to six months before and after the quiet period. We predict that the coefficient on Quiet, β0, is
going to be positive and statistically different than zero.
Table 6 reports the estimates of the coefficient and the relevant standard errors from the OLS
regression. The results across the different specifications are consistent and suggest that in a segmented
market the yields set by the platform, on an average, are higher than what it was before the quiet
period. This higher interest rate reduces the current price of the loan. Note, after the quiet period
LendingClub also has a secondary market for the notes that they issue. The secondary market provides
increased liquidity to the investors and this should, ideally, increase the value of the asset. If anything,
this should bias us from finding any results. Overall, our results show that effect of segmentation on
interest rates is higher than the gains made from increased liquidity.
5 Conclusion
The importance of the interaction of marketability and market segmentation highlighted above
becomes a critical topic as the area of Financial Technology (FinTech) continues to grow. The
opportunity for security creation expanded substantially with the passage of the J.O.B.S. Act in 2012.
Specifically Title III (Regulation CF) and Title IV (Regulation A+) of the J.O.B.S. Act expanded the
base of investors able to participate in equity and debt markets and the accelerated the creation of
security platforms to issue equity/debt. In addition, disintermediated finance like marketplace lending
continues to expand rapidly. The unsecured personal debt market in the U.S. has grown from nothing in
2006 to almost $12 billion in loans issued in 2015. This compares to an aggregate loan volume of
approximately $241 billion as reported by the Federal Reserve, suggesting peer-to-peer lenders have
made substantial inroads into this segment. Additional markets have been created for real estate loans,
student loans, automotive loans, and even municipal bonds. The continuing expansion of investment
assets has created new issues for security regulators. Our study highlights how the interaction of
regulatory policies aimed at investor protection can create unintended consequences in the security
creation process.
25
Appendix. Variable Definitions
Variable Definition
InterestRatei
Interest rate of loan i. It is the stated interest rate of LendingClub loan, and is the borrower rate of Prosper loan. Borrower rate of Prosper is the interest rate on Prosper loans which does not include any adjustment for the cost of origination fees incurred by borrowers
FlipFraction15(30)d
Percentage of day d's aggregate dollar amount of loans listed for sale in the secondary market within 15 (30) days after being issued in the primary market to total dollar amount of loans listed for sale in the secondary market on the same day d
BankRatem Average of Bankrate Monitors National Index for unsecured personal loan interest rates in month m
Borrower Credit Information dtii Debt-to-income ratio of the borrower of loan i
inqLast6mthsi Number of credit inquiries on the borrower of loan i's credit report in the six months before listing
LnIncomei Log of annual income of the borrower of loan i
LnDebtPmti Log of debt payment of the borrower of loan i. It is monthly (annual) debt payment if the loan is listed by Prosper (LendingClub).
OpenCreditLinesi Number of borrower's open credit lines when borrower's loan i is listed
delinq2yrsi Number of delinquencies (defined as "over 30 days past-due incidences") of LendingClub borrower of loan i in the last 2 years
currentDelinqi Number of borrower's current delinquencies when borrower's loan i is listed by Prosper
delinq7yrsi Number of borrower's delinquencies in the last 7 years when borrower's loan i is listed by Prosper
pubReci Number of public record of LendingClub borrower of loan i
pubRec10yrsi Number of borrower's public records in the last 10 years when borrower's loan i is listed by Prosper
pubRec12mthsi Number of borrower's public records in the last 12 months when borrower's loan i is listed by Prosper
empLengthi Employment length of LendingClub borrower of loan i in years. Possible values are the integer values from 0 to 10. Employment length less than one year is 0, Ten or more years of employment length is 10
monthsEmployedi The length of borrower's employment status in months when borrower's loan i is listed by Prosper
LnRevolvingi Log of the amount of borrower's revolving credit balance when borrower's loan i is listed by the platform
Loan Information LnAmounti Log of the loan amount (in $US) termi Term of the loan in months. Values are either 36 or 60. Gradei Credit grade of a loan assigned by LendingClub. Possible values are A, B, C, D,
26
E, F, and G.
Ratingi Rating of a loan assigned by Prosper. Possible values are AA, A, B, C, D, E, and HR
LCRate_Graded Daily average interest rate of LendingClub loans by credit grade (e.g., LCRate_Ad is the average interest rate of LendingClub A grade loans on day d)
Platform Characteristics
Quiett An indicator equal to one after LendingClub Quiet Period, and 0 before LendingClub Quiet Period. LendingClub Quiet Period was between April 8, 2008 and October 14, 2008.
IPOt An indicator equal to one after LendingClub IPO on December 10, 2014, and 0 before LendingClub IPO
PostRegistrationt An indicator equal to one after a platform registers MPDN at a state of interest, and 0 before state registration.
27
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Figure 1: Marketplace Lending process Overview The figure above provides an overview of the marketplace lending process. Borrowers submit a loan request to the platform and the platforms provide an initial screening based on the applicant’s creditworthiness. After a loan request passes a credit screen, the platform posts the loan request in front of investors to fund. If enough investors choose to fund the loan, the borrower receives funds and investors receive a promissory note tied to the payments of the borrower. Investors may then hold the note to maturity or choose to sell the note in a secondary market (FolioFN).
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Panel A. Auction Pricing Period
Panel B. Platform Pricing Period
Figure 2: Prosper Marketplace timeline of events In the figure above, we outline the major events in the evolution of Prosper Marketplace. Panel A covers the auction pricing period from July 2009 to December 2010. Over time, states accept security registration applications by the platform which allows residents to invest on the platform. Prosper was initiated as an auction platform for investors to fund loan requests from borrowers (Panel A). However, in December 2010 the platform changed pricing mechanisms to a posted price (platform fixed price) which is shown in Panel B.
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Figure 3: LendingClub timeline of events In the figure above, we outline the major events in the evolution of LendingClub. Over time, states accept security registration applications by the platform which allows residents to invest on the platform. LendingClub listed common stock on the NYSE in December 2014 which allowed residents in multiple states access to the primary market of Member Payment Dependent Notes (MPDN)
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Figure 4: Secondary Market Flipping Activity of LendingClub MPDN The figure above shows the fraction of loan volume posted each day from that we identify as notes that are flipped from the primary market to the secondary market during the period December 2012 to December 2016. A note is identified as a flipped note if it was been issued less than 30 (15) days from the date it is quoted for sale and its price markup is greater than 1.0% (the transaction fee on the secondary market FolioFN).
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Figure 5: Secondary Market Flipping Activity of LendingClub MPDN around LendingClub IPO The figure above shows the fraction of loan volume posted each day from that we identify as notes that are flipped from the primary market to the secondary market during the 90 days before/after the LendingClub IPO. A note is identified as a flipped note if it was been issued less than 30 (15) days from the date it is quoted for sale and its price markup is greater than 1.0% (the transaction fee on the secondary market FolioFN).
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Table 1. Descriptive Statistics This table reports summary statistics for the sample. Panel A reports the mean and standard deviation for Member Payment Dependent Notes (MPDN) originated on the LendingClub platform. Panel B reports mean and standard deviation for MPDN originated on the Prosper platform.
Panel A. LendingClub Variable N Mean Std. Dev.
InterestRatei 1,248,719 13.177 4.571 dtii 1,248,719 18.296 17.699 inqLast6mthsi 1,248,719 0.652 0.960 LnIncomei 1,248,719 11.114 0.522 LnDebtPmti 1,248,719 11.390 0.752 OpenCreditLinesi 1,248,719 11.730 5.486 delinq2yrsi 1,248,719 0.333 0.897 pubReci 1,248,719 0.209 0.612 empLengthi 1,248,719 6.107 3.554 LnRevolvingi 1,248,719 9.268 1.167 LnAmounti 1,248,719 9.41 0.68 termi 1,248,719 42.992 10.905
Panel B. Prosper
Variable N Mean Std. Dev. InterestRatei 675,952 15.201 6.381 dtii 675,952 37,095.61 188,989.10 inqLast6mthsi 675,952 1.079 1.427 LnIncomei 675,952 11.027 0.743 LnDebtPmti 675,952 6.722 0.818 OpenCreditLinesi 675,952 10.327 5.019 currentDelinqi 675,952 0.303 1.008 delinq7yrsi 675,952 3.604 9.403 pubRec10yrsi 675,952 0.287 0.683 pubRec12mthsi 675,952 0.006 0.090 monthsEmployedi 675,952 111.092 121.115 LnRevolvingi 675,952 9.256 1.363 LnAmounti 675,952 9.255 0.696
35
Table 2. Interest Rate changes around State Registration approval This table reports the results from Equation 2 where interest rates of MPDN (member payment dependent notes) are regressed on an indicator for state registration approval. The sample is composed of 15 day windows around registration acceptance dates shown in Figures 2 and 3. In columns (1)-(2) of panel A, we report the impact of state registration for LendingClub. In columns (1)-(2) of panel B, we report the impact of state registration for Prosper during the period when the platform uses a fixed pricing mechanism. In columns (3)-(4) of panel B, the results are reported for Prosper during the period when the platform uses auction pricing. All the models contain indicators for credit grade, employment status, borrower state of residence. The t-statistics are reported in parentheses. “***”, “**”, and “*” denote statistical significance at the 1%, 5%, and 10% level respectively. Standard errors are clustered at the state of the borrower. Panel A. LendingClub
InterestRate (1) (2) PostRegistration -0.38202 -0.34319
(-0.894) (-0.808)
Grade B × PostRegistration
-0.05051***
(-3.787)
Grade C × PostRegistration
-0.04980***
(-3.248)
Grade D × PostRegistration
-0.01900
(-1.288)
Grade E × PostRegistration
-0.06047***
(-3.229)
Grade F × PostRegistration
-0.10168**
(-2.653)
Grade G × PostRegistration
0.10413
(0.860)
LnAmount -0.01275*** -0.01279***
(-3.655) (-3.696)
term 0.01121*** 0.01121***
(56.806) (56.958)
dti 0.00599*** 0.00599***
(10.501) (10.490)
LnIncome -0.10370*** -0.10364***
(-13.947) (-13.933)
LnDebtPmt -0.00785 -0.00784
(-0.929) (-0.928)
inqLast6mths 0.07484*** 0.07483***
(29.656) (29.604)
OpenCreditLines -0.00489*** -0.00489***
(-13.784) (-13.779)
delinq2yrs 0.02700*** 0.02702***
(11.853) (11.797)
pubRec 0.02020*** 0.02016***
36
(6.711) (6.729)
empLength -0.00047 -0.00046
(-0.892) (-0.892)
LnRevolving -0.01177*** -0.01174***
(-5.590) (-5.584)
Constant 7.92267*** 7.90341***
(18.643) (18.639)
Borrower State FE Yes Yes Employment Status Yes Yes Credit Grade FE Yes Yes Time FE Day Day SE clustered Borrower state Borrower state Number of clusters 50 50 R-squared 0.949 0.949 Adj. R-squared 0.949 0.949 Obs. 206,453 206,453
37
Panel B. Prosper
InterestRate Fixed Pricing Auction Pricing (1) (2) (3) (4) PostRegistration -3.62515*** -3.54522*** 0.12494 0.25001
(-14.281) (-14.546) (0.706) (1.077)
Rating A × PostRegistration
-0.06701
-0.42026
(-0.767)
(-1.305)
Rating B × PostRegistration
-0.00745
-0.23636
(-0.119)
(-0.726)
Rating C × PostRegistration
-0.02966
-0.12992
(-0.458)
(-0.281)
Rating D × PostRegistration
-0.02082
0.07949
(-0.252)
(0.143)
Rating E × PostRegistration
-0.29647***
0.94051
(-2.968)
(1.528)
Rating HR × PostRegistration
-0.40472***
-1.25709**
(-3.631)
(-2.087)
LnAmount 0.30672*** 0.30699*** 0.96696*** 0.96284***
(14.441) (14.517) (6.881) (6.937)
dti -0.00000*** -0.00000*** -0.00000 -0.00000
(-4.425) (-4.577) (-0.242) (-0.247)
LnIncome -0.25566*** -0.25749*** 0.04650 0.05387
(-9.470) (-9.586) (0.219) (0.256)
LnDebtPmt 0.06560*** 0.06635*** 0.17098 0.17183
(3.043) (3.106) (1.503) (1.547)
inqLast6mths 0.03652*** 0.03689*** 0.15904** 0.15742**
(6.118) (6.280) (2.187) (2.064)
OpenCreditLines -0.00577** -0.00584** 0.00160 0.00012
(-2.329) (-2.365) (0.077) (0.006)
curreuntDelinq 0.00286 0.00301 0.08102 0.08204
(0.208) (0.221) (1.122) (1.120)
delinq7yrs 0.00159 0.00161 0.02824** 0.02811**
(1.121) (1.136) (2.547) (2.613)
pubRec10yrs 0.01705 0.01720 0.16521 0.17815
(1.005) (1.013) (0.957) (1.003)
pubRec12mths 0.24023** 0.23934** 0.18794 0.12473
(2.457) (2.391) (0.151) (0.101)
monthsEmployed 0.00018 0.00018 -0.00085 -0.00080
(1.518) (1.558) (-0.678) (-0.623)
LnRevolving -0.00500 -0.00486 -0.02615 -0.02033
(-0.622) (-0.608) (-0.447) (-0.351)
LCRate A
-0.51174** -0.49433**
(-2.128) (-2.061)
LCRate B
0.81686 0.87872
38
(1.556) (1.648)
LCRate C
-0.82277 -0.82140
(-1.644) (-1.660)
LCRate D
0.64327*** 0.68022***
(3.157) (3.224)
LCRate E
-0.08462 -0.08186
(-0.317) (-0.310)
LCRate F
0.09630 0.10203
(0.331) (0.355)
LCRate G
0.40731 0.41552
(1.460) (1.506)
Constant 17.03206*** 17.05279*** -9.34731 -11.25936
(17.463) (17.800) (-0.813) (-0.965)
Borrower State FE Yes Yes Yes Yes Employment Status Yes Yes Yes Yes Credit Grade FE Yes Yes Yes Yes
Time FE Day Day LC Daily Avg Rate by Grade
LC Daily Avg Rate by Grade
SE clustered Borrower state Borrower state Borrower state Borrower state Number of clusters 48 48 48 48 R-squared 0.951 0.951 0.896 0.897 Adj. R-squared 0.951 0.951 0.890 0.891 Obs. 22,465 22,465 1,411 1,411
39
Table 3. Interest Rate changes around Lending Club IPO. The following table is an OLS regression around the Lending Club IPO event. IPO is the dummy variable set to 1 after the IPO in December 2014 and 0 otherwise. The sample includes 280,589 observations from June 20, 2014 to June 20, 2015, which includes 6 months window before and after the IPO date. LnAmount is the natural logged amount of the loan, term is the term of the loan in units of months, dti is the debt-to-income ratio. LnIncome is the natural logged annual stated income, LnDebtP mt is the natural logged monthly debt, inqLast6mths is the number of hard credit inquiries for borrower the in the past 6 months, and OpenCreditLines is the number of open accounts. Also, delinq2yrs is the number of delinquencies in the past two years, pubRec is the public records on file, empLength is the length of employment, and finally, LnRevolving is the logged revolving balance. BankRate is the monthly average of Bankrate Monitors National Index for unsecured personal loan interest rates. The t-statistics are reported in parentheses. “***”, “**”, and “*” denote statistical significance at the 1%, 5%, and 10% level respectively. The errors are clustered by State. InterestRate (1) (2) (3) (4) IPO -0.61490*** -0.43379*** -0.08333*** -0.09608***
(-16.068) (-11.188) (-13.964) (-14.461)
Grade B × IPO
-0.45860*** -0.45460*** -0.33186***
(-41.328) (-40.284) (-26.661)
Grade C × IPO
-0.22833*** -0.22604*** -0.19916***
(-27.873) (-27.376) (-18.171)
Grade D × IPO
0.20444*** 0.20612*** 0.00491
(19.248) (19.246) (0.346)
Grade E × IPO
-0.28926*** -0.28664*** -0.20338***
(-13.016) (-13.181) (-7.186)
Grade F × IPO
-0.04376** -0.04366** -0.04964*
(-2.166) (-2.141) (-1.710)
Grade G × IPO
0.36335*** 0.36147*** 0.02829**
(17.812) (19.107) (2.018)
LnAmount -0.00614 -0.00957** -0.01055** -0.01095***
(-1.481) (-2.362) (-2.606) (-2.727)
term 0.00864*** 0.00914*** 0.00897*** 0.00916***
(44.361) (46.342) (44.169) (45.172)
dti 0.00729*** 0.00715*** 0.00710*** 0.00701***
(15.191) (15.414) (15.490) (15.199)
LnIncome -0.07831*** -0.07681*** -0.07661*** -0.07677***
(-10.458) (-9.977) (-9.967) (-9.865)
LnDebtPmt -0.02418*** -0.02373*** -0.02330*** -0.02282***
(-3.877) (-3.673) (-3.592) (-3.460)
inqLast6mths 0.06947*** 0.06890*** 0.07042*** 0.07026***
(31.604) (32.165) (34.108) (33.236)
OpenCreditLines -0.00592*** -0.00594*** -0.00590*** -0.00589***
(-16.798) (-16.310) (-17.121) (-16.926)
delinq2yrs 0.03065*** 0.03049*** 0.03057*** 0.03048***
40
(17.349) (16.771) (17.123) (16.939)
pubRec 0.03748*** 0.03666*** 0.03659*** 0.03644***
(14.310) (14.062) (13.904) (14.180)
empLength -0.00070 -0.00086 -0.00082 -0.00073
(-1.059) (-1.348) (-1.248) (-1.113)
LnRevolving -0.01257*** -0.01124*** -0.01100*** -0.01061***
(-6.065) (-5.379) (-5.237) (-4.997)
BankRate
0.08574*** 0.07843***
(52.766) (23.867)
BankRate × Grade B
0.06952***
(11.040)
BankRate × Grade C
0.01522***
(3.666)
BankRate × Grade D
-0.11008***
(-22.371)
BankRate × Grade E
0.04459***
(5.198)
BankRate × Grade F
-0.00212
(-0.201)
BankRate × Grade G
-0.17677***
(-16.265)
Constant 8.43844*** 8.29998*** 7.09260*** 7.17689***
(86.880) (86.636) (71.399) (70.628)
Borrower State FE Yes Yes Yes Yes Employment Status Yes Yes Yes Yes
Credit Grade FE Yes Yes BRM BRM × Credit Grade
Time FE Day Day None None SE clustered Borrower state Borrower state Borrower state Borrower state Number of clusters 46 46 46 46 R-squared 0.952 0.953 0.952 0.952 Adj. R-squared 0.952 0.952 0.952 0.952 Obs. 280,589 280,589 280,589 280,589
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Table 4. Change in Flipping Activity around LendingClub IPO We present a two sided t-test comparing the difference in mean flipping volume before and after the LendingClub IPO. The difference in mean is calculated for three different sample windows of +/- 15, 30, and 90 days. FlipFraction30 (FlipFraction15) is the daily fraction of volume in the MPDN secondary market that is identified as flipping. “***”, “**”, and “*” denote statistical significance at the 1%, 5%, and 10% level respectively.
FlipFraction30 FlipFraction15 Before IPO [-15,-1] 7.396 3.273 After IPO [0,14] 5.543 2.012
Diff. -1.853*** (3.835) -1.261*** (3.264) Before IPO [-30,-1] 7.596 3.504 After IPO [0,29] 5.127 2.300
Diff. -2.469*** (5.008) -1.204*** (3.003) Before IPO [-90,-1] 9.501 4.568 After IPO [0,89] 7.547 3.597
Diff. -1.954*** (4.475) -0.971*** (3.416)
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Table 5. Interest rate changes around LendingClub IPO with Nearest Neighbor Matching This table shows the difference in interest rate paid by borrowers on personal unsecured loans on the LendingClub Platform before and after the IPO of LendingClub using a nearest neighbor matching technique. Using a Mahalanobi distance we match observations 1:1 prior to the IPO maintaining an exact match on credit grade. In the last row we use an additional exact match criteria of borrower’s credit score range in addition to loan credit grade. The matching distance is calculated using creditGrade, term, revolBal, OpenCreditLines, annualInc, delinq2yrs, dti, inqLast6mths, listingAmount, monthlyDebt, pubRec. The creditGrade variable is the t term variable is the term of the loan in units of months, revolBal is the revolving balance openAcc is the number of open accounts annualInc is the annual stated income delinq2yrs is the number of delinquencies in the past two years, dti is the debt-to-income ratio inqLast6mths is the number of hard credit inquiries for borrower in the past 6 months listingAmount is the amount of the loan LnDebtP mt is the monthly debt, pubRec is the public records on file. “***”, “**”, and “*” denote statistical significance at the 1%, 5%, and 10% level respectively.
Estimator : nearest-neighbor matching Matches: requested = 1 Outcome model : matching min = 1 Distance metric: Mahalanobis max = 1
Full Sample or by Credit Grade Coefficient Robust SE z P > |z| 95% Confidence
Interval N
IPO -0.410∗∗∗ 0.004 -99.65 0.000 -0.419 -0.402 303,540 IPO (A) -0.245∗∗∗ 0.008 -30.91 0.000 -0.260 -0.229 51,960 IPO (B) -0.676∗∗∗ 0.010 -69.61 0.000 -0.695 -0.657 78,937 IPO (C) -0.457∗∗∗ 0.006 -71.26 0.000 -0.470 -0.445 85,398 IPO (D) -0.031∗∗∗ 0.009 -3.39 0.001 -0.048 -0.013 50,660 IPO (E) -0.550∗∗∗ 0.016 -33.69 0.000 -0.582 -0.518 26,564 IPO (F) -0.333∗∗∗ 0.026 -12.73 0.000 -0.385 -0.282 7,955 IPO (G) 0.223∗∗∗ 0.023 9.56 0.000 0.177 0.268 2,066
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
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Table 6. Interest Rate changes around Lending Club Quiet period The following table is an OLS regression around the LendingClub Quiet period event. Quiet is the dummy variable set to 1 after the Quiet period in October 2008 and 0 otherwise. The sample includes 3,214 observations from October 8, 2007 to April 14, 2009, which includes 6 months window before and after the quiet period. The LnAmount is the natural logged amount of the loan, dti is the debt-to-income ratio. LnIncome is the natural logged annual stated income, LnDebtP mt is the natural logged monthly debt, inqLast6mths is the number of hard credit inquiries for borrower the in the past 6 months, and OpenCreditLines is the number of open accounts. Also, delinq2yrs is the number of delinquencies in the past two years, pubRec is the public records on file, empLength is the length of employment, and finally, LnRevolving is the logged revolving balance. BankRate is the monthly average of Bankrate Monitors National Index for unsecured personal loan interest rates. The t-statistics are reported in parentheses. “***”, “**”, and “*” denote statistical significance at the 1%, 5%, and 10% level respectively. The errors are clustered by State.
InterestRate (1) (2) (3) (4) Quiet 1.95105*** 1.24147*** 0.71346*** 0.72653***
(29.811) (12.663) (13.021) (13.453)
Grade B × Quiet
1.00417*** 0.85774*** 0.88844***
(12.278) (9.446) (10.026)
Grade C × Quiet
0.85172*** 0.82507*** 0.81611***
(9.800) (10.879) (10.655)
Grade D × Quiet
0.72394*** 0.71307*** 0.69724***
(15.286) (10.808) (10.235)
Grade E × Quiet
0.75474*** 0.73868*** 0.72636***
(9.825) (7.803) (7.706)
Grade F × Quiet
0.81766*** 0.83932*** 0.83295***
(6.352) (7.300) (6.776)
Grade G × Quiet
0.90885*** 0.81796*** 0.79902***
(6.969) (5.885) (5.726)
LnAmount 0.05175*** 0.05931*** 0.07238*** 0.07264***
(4.299) (4.731) (5.286) (5.516)
dti 0.00761*** 0.00840*** 0.00801*** 0.00782***
(4.936) (5.365) (4.016) (4.120)
LnIncome -0.01292 -0.00778 -0.04370 -0.04511
(-0.466) (-0.281) (-1.351) (-1.429)
LnDebtPmt -0.01054 -0.00695 -0.00412 -0.00172
(-1.041) (-0.763) (-0.392) (-0.169)
inqLast6mths 0.00933*** 0.01132*** 0.00493 0.00459
(2.981) (3.486) (1.228) (1.187)
OpenCreditLines -0.00184 -0.00205 -0.00101 -0.00116
(-1.288) (-1.549) (-0.703) (-0.783)
delinq2yrs 0.03963** 0.03978** 0.03058* 0.03287**
(2.457) (2.528) (1.883) (2.091)
pubRec -0.00369 0.00048 0.03284 0.03259
44
(-0.127) (0.016) (0.989) (0.954)
empLength -0.00234 -0.00400 -0.00043 -0.00084
(-0.798) (-1.271) (-0.136) (-0.282)
LnRevolving -0.00918** -0.00939** -0.00396 -0.00383
(-2.280) (-2.063) (-0.665) (-0.610)
BankRate
0.52751*** -0.23238
(6.458) (-1.311)
BankRate × Grade B
1.38432***
(6.170)
BankRate × Grade C
0.93469***
(3.647)
BankRate × Grade D
0.89711***
(4.081)
BankRate × Grade E
0.64654***
(3.110)
BankRate × Grade F
0.15140
(0.376)
BankRate × Grade G
0.48075
(0.646)
Constant 6.99535*** 7.35026*** 0.45105 11.08925***
(24.965) (24.345) (0.341) (4.312)
Borrower State FE Yes Yes Yes Yes Employment Status Yes Yes Yes Yes
Credit Grade FE Yes Yes BRM BRM × Credit Grade
Time FE Month Month None None SE clustered Borrower state Borrower state Borrower state Borrower state Number of clusters 50 50 50 50 R-squared 0.954 0.957 0.947 0.948 Adj. R-squared 0.953 0.956 0.946 0.947 Obs. 3,214 3,214 3,109 3,109
45
