Decentralized finance (DeFi) markets for startups: search frictions, intermediation, and the efficiency of the ICO market

Momtaz, Paul P. Article — Published Version Decentralized finance (DeFi) markets for startups: search frictions, intermediation, and the efficiency of the ICO market Small Business Economics Provided in Cooperation with: Springer Nature Suggested Citation: Momtaz, Paul P. (2024) : Decentralized finance (DeFi) markets for startups: search frictions, intermediation, and the efficiency of the ICO market, Small Business Economics, ISSN 1573-0913, Springer US, New York, NY, Vol. 63, Iss. 4, pp. 1415-1447, https://doi.org/10.1007/s11187-024-00886-3 This Version is available at: https://hdl.handle.net/10419/315614 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/s11187-024-00886-3 RESEARCH ARTICLE Decentralized finance (DeFi) markets for startups: search frictions, intermediation, and the efficiency of the ICO market Paul P. Momtaz Accepted: 22 January 2024 © The Author(s) 2024 Abstract This paper examines the efficiency of the Initial Coin Offering (ICO) market through a searchtheoretical lens. Search intensity associated with the process of identifying valuable startups is increasing in market granularity. DLT increases market granularity because asset tokenization lowers entry barriers. Lower-end entrants, however, increase aggregate search intensity but may lack search skills. The resulting search-related inefficiency creates a niche for intermediaries or institutional investors that specialize on search. Consistent with the theory, specialized crypto funds increase ICO market efficiency by reducing search frictions, inter alia, by shortening the timeto-funding and increasing the funding amount. At the same time, crypto funds extract sizable economic rents for their intermediation services. Overall, the study relates to the general trade-off between centralization and decentralization in entrepreneurial finance. It suggeststhatmarketfrictionsspecifictoearly-stagecrowdfunding of entrepreneurship may prevent “perfectly” Decentralized Finance (DeFi) markets from functioning efficiently. Plain English Summary Decentralized Finance (DeFi)marketsmay require a substantial degreeof centralization to function efficiently. We show that centralization in the form of institutional investors that P.P. Momtaz (B) TUM School of Management, Technical University of Munich, Arcisstr. 21, 80333 Munich, Germany e-mail: [email protected] intermediate Initial Coin Offerings (ICOs) lead to, first, shortertimeperiodstoreachfundraisinggoalsand, second, higher valuations. In a search-theoretical model, we quantify the extent to which centralization mitigates frictions in a decentralized market. Centralization reducestradingdelaysandimprovesdecentralizedmarket efficiency especially in times of market downturns and when there is uncertainty about the team or product quality. Thus, the principal implication of our study is that decentralized markets for startups may not be optimal for society. Centralization is valuable because it improves the speed with which entrepreneurs and investors meet, and because it mitigates market frictions arising from asymmetric information. Keywords Entrepreneurial finance ·Blockchainbased crowdfunding ·Initial Coin Offering (ICO) · Tokenization ·Crypto funds ·Decentralized Finance (DeFi) JEL Classification G23 ·G24 ·L26 1 Introduction New technologiesare continuouslychangingthenature of entrepreneurial finance. The trend goes toward a decreasing degree of intermediation. The rationale is, inter alia, that disintermediation increases the economic transaction surplus that entrepreneurs and investors get to enjoy. For example, equity-based 123 / Published online: 23 February 2024 Small Bus Econ (2024) 63:1415–1447 crowdfunding and its related forms (for reviews, see Moritz and Block, 2016; Mochkabadi and Volkmann, 2020; Block et al., 2021) have partially eliminated Venture Capitalists (VCs) from more traditional venture financing, which has substantially increased the potential return on investment that investors receive,1 and it has also expanded the supply-side market for venture financing to previously underserved individual investors. The crowdfunding revolution continues to have a dramatic impact on how ventures raise financing, and it also challenges classic scholarly paradigms in the entrepreneurial finance literature, which resulted in some of the most impactful research in economics and management of the last decade (e.g., Ahlers et al., 2015; Belleflamme et al., 2014; Mollick, 2014). Decentralized Finance (DeFi) markets for startups are the next stage in the evolution of entrepreneurial finance (Bellavitis et al., 2021; Block et al., 2021; Chen & Bellavitis, 2020; Kher et al., 2021). DeFi markets for startups refer to Distributed Ledger Technology- (DLT-)based crowdfunding (more commonly known as token offerings or Initial Coin Offerings, ICOs, see Fisch (2019), for seminal work), which further economize on intermediation costs by replacing crowdfunding platforms, such as Kickstarter, with smart contracts (Adhami et al., 2018; Bellavitis et al., 2020,2021; Benedetti & Kostovetsky, 2021; Boreiko & Risteski, 2021; Campino et al., 2022; Chalmers et al., 2022; Fisch, 2019; Fisch et al., 2021; Howell et al., 2020; Momtaz, 2020a,2021c). Therefore, we follow Schueffel (2021) in defining DeFi as a paradigm of peerto-peer financial service provision without a centralized intermediary. Smart contracts are computer protocols that automate the exchange of investors’ financial contributions to ICOs and tokens that often represent claims on ventures’ future assets at a predefined exchange rate. The only fee incurring in the execution of a smart contract is the fee to operate the blockchain network. For example, the average transaction cost on the most popular blockchain, Ethereum, was less than $2 during January 2022, which reduces transaction costs for crowdfunding to a minimum.2 This paper argues both theoretically and empirically that, despite its transactional efficiency, the ICO mar1VCs typically charge performance fees of 20% and annual management fees between 1 and 2%. 2In comparison, equity-based crowdfunding platforms typically charge fees around 7%. ket is relatively inefficient with respect to “search.” Search broadly refers to the process of finding a matching transaction counterparty. The intuition is straightforward: DeFi markets for startups are very granular; that is, they have high levels of market participation (anyone with internet connectivity may participate) and market completeness (everything can be tokenized). Smart contracts enable that anyone can trade anything with anybody at almost no cost (transactional efficiency).The flipsideis, however, thatmarketgranularity is proportionate to the required search effort (see, for a recent survey among individual investors, Ante et al., 2022). More individual agents and traded products and services mean that agents wishing to transact have to screen deeper markets, which takes more time, in order to avoid resource misallocations through suboptimal transactions (search-related inefficiency). Theproblemisplausiblyparticularly pronouncedinthe ICO market because DLT and smart contracts promote marketgranularityand marketsegmentation(Benedetti &Nikbakht,2021), while theydonotoffer a technological solution to facilitate search. For this reason, critics showcase the ICO market to argue that perfectly decentralized fundraising is utopian given the pervasive search frictions, and that entrepreneurial finance may revert back to intermediated markets (e.g., Zetzsche et al., 2020). Consequently, this paper aims to advance the literature on search in entrepreneurial finance by addressing the following, overarching research question: How efficient is the ICO market and do search frictions reduce aggregate market efficiency? The question is important because the current state of the literature on ICOs is ripe with efficiency losses due to market design problems (e.g., Bellavitis et al., 2021; Hornuf et al., 2021;Momtaz,2021c), but fails in large part to provide an explanation for why novel, specialized intermediaries, so-called “crypto funds,” are rapidly entering the ICO market (Fisch & Momtaz, 2020). Crypto funds are a blend of venture-style hedge funds that pool retail investors’ funds and channelthem throughsophisticated tradingstrategiesto tokenized startups in liquid secondary markets for tokens. Crypto funds plausibly have emerged as a response to search frictions in markets for tokens, which are very pronounced due to the high levels of asymmetric information in ICOs (Block et al., 2021; Boreiko & Vidusso, 2019; Zetzsche et al., 2020). This resonates with an 123 1416 P.P. Momtaz established literature that intermediaries extract rents from reducing search frictions in decentralized markets (Demsetz, 1968; Rubinstein & Wolinsky, 1987). Of course, search-related arguments are implicit in many existing works in entrepreneurial finance, and not an innovation of this study. However, to our best knowledge, search has never been explicitly modeled in extant theory nor tested empirically in the context of entrepreneurial finance, which is the principal contribution we claim for this study. For instance, a vast literature examines signaling (for a review, see Colombo, 2021), e.g., in IPOs (Arthurs et al., 2009; Colombo et al., 2019), crowdfunding (Ahlers et al., 2015;Vismara,2018b,2016),andICOs (An et al., 2019; Belitski & Boreiko, 2021; Bellavitis et al., 2020;Fisch,2019; Giudici & Adhami, 2019; Lee et al., 2022), as well as adjacent arguments, such as information cascades (e.g., Vismara, 2018a). While all these studies implicitlyassumesearch frictions to be animportantreasonas to why signaling is the prime determinant of success in the competition for entrepreneurial finance, they never make search frictions explicit; in fact, none of these studies mentions “search” at all. Another example is the literature on institutional investments in startups, with a focus on venture capital (Bertoni et al., 2011; Colombo et al., 2010) and ICOs (Fisch & Momtaz, 2020). These studies test whether there is a selection effect in the form that institutional investors are able to pick startups with more favorable growth prospects than non-institutional investors. Again, at the root of the selection effect is search (i.e., institutional investors possess better skills and more resources to screen the market and negotiate deals), albeit the precise nature of search in these markets is never made explicit. Our eclectic theory draws upon multidisciplinary search theory in decentralized markets, financial intermediation, as well as asymmetric information and limits to signaling literatures in entrepreneurship to propose two overarching hypotheses. The first hypothesis, the Decentralized Inefficiency Hypothesis (DIH), posits that search-related frictions render the ICO market relatively inefficient. Specifically, excessive search in the ICO market reduces the market’s aggregate efficiency in at least three distinct ways. First, search frictions imply that the ICO market involves two-sided matching: Startups conduct ICO campaigns to attract investors and investors, in turn, screen the market to identify attractive startups for investment purposes. The time to conduct ICO campaigns often takes several months (Momtaz, 2020a), which is time in which startup-investor matches are delayed. Thus, the first way in which search frictions impede ICO market efficiency is through a delay in token allocations. Second, search is costly. It is costly for startups to market ICO campaigns to investors, and it is costly for investors (interms of both time and financial resources) to perform a due diligence on potentially interesting investment targets. These search-related costs imply that some investments that would be socially optimal in a frictionless economy do not take place if search costs exceed the anticipated transaction surplus. Therefore, thesecondwayinwhichsearchfrictionsimpedemarket efficiency is in terms of an aggregate underinvestment in high-quality, tokenized startups. Third, because the ICOmarketischaracterizedbyhigh levels of asymmetric information and there are limits to signaling, there is substantial uncertainty in the ICO market, which can cause a misallocation of financial resources to undeserving tokenized startups. One way for this to occur is through adverse selection (Hornuf et al., 2021)or moral hazard (Momtaz, 2021c). Thus, the efficiency of the ICO market is also impeded by overinvestments in low-quality, tokenized startups. The second hypothesis, the Intermediated Efficiency Hypothesis (IEH), posits that new DeFi intermediaries, in particular crypto funds, increase ICO market efficiency by reducing search-related frictions. Intermediaries have long been known for extracting rents by reducing search frictions in decentralized markets (Demsetz, 1968; Rubinstein & Wolinsky, 1987; Schueffel, 2021). Crypto funds develop a competitive advantage in search through economies of scale in crypto-specific human capital investments. Crypto funds screen the market and invest in the best startups, signaling startup quality to the market and certifying project legitimacy (Fisch & Momtaz, 2020). They also reduce search frictions related to post-ICO information production. Given the salient manifestations of moral hazard in the ICO market (Hornuf et al., 2021;Momtaz, 2021c), investors need to monitor startups postfunding and coordinate collective actions against shirkingteams, which isproblematicfor individualinvestors becausetheymaynotbeabletodetectmanifestationsof moral hazard or coordinate collective actions directed against such behavior. Crypto funds not only have the skills and resources to search for indicators of startup teams’ effort provisions, the threat of exit in liquid sec123 1417Decentralized finance (DeFi) markets for startups: search frictions... ondary markets for tokens may prevent shirking and other forms of detrimental behavior in the first place. Testing the DIH and IEH is challenging. The key difficulty is that both hypotheses are related to ICO market efficiency, which is a relative construct, and a perfectly efficient market is a counterfactual benchmark that is not observed in reality. For this reason, our empirical approach is twofold. The first empirical part involves reduced-form regression analyses of two testable relations that are related to our overall argumentation that the ICO market has pronounced search frictions, which intermediation via crypto funds help reduce. Specifically, we test (i) whether intermediated ICOs are more efficient in terms of the time it takes to achieve the crowdfunding goal, and (ii) whether entrepreneurs in non-intermediated ICOs need to sell their tokens at a discount to attract enough investors. The two empirical relations are not free of endogeneity concerns. For example, it is possible that only ICOs with strong success prospects are able to secure intermediation services (selection effect), rather than it is the intermediation that shortens the time-to-funding or increases the token value (treatment effect). To this end, we employ several two-stage and instrumental variable approaches to disentangle the true effects of ICO intermediation. The results suggest that (i) intermediated ICOs achieve the crowdfunding goal 25% faster and (ii) non-intermediated ICOs have to offer tokens at a substantial discount of 57%. These results are in line with anecdotal evidence, in particular that non-intermediated ICOs offer tokens at discounts in the range of 50 to 70%. Therefore, these results jointly suggest that ICO intermediation makes the market more efficient in terms of time-to-funding, while ICO intermediaries plausibly are able to extract substantial rents for their services. In the second empirical part, given the challenging nature of the DIH and IEH, we juxtapose the reducedform regression-based evidence with structural estimates from a simple model of the ICO market. The model allows to estimate the market’s aggregate efficiency, which is a novelty in the entrepreneurial finance literature. In the model, there are individual investors and intermediaries. Only individual investors enjoy a utility from holding tokens (because the token ownership enables them, for instance, to partake in an online gaming community), while intermediaries have no utility from holding tokens, but they extract rents from trading tokens. Startup firms are heterogeneous in the model with respect to their underlying platform sizes. Intuitively, tokens of large platforms are more valuable than tokens of smaller platforms. We calibrate the model with actual ICO market data covering the 2017–20 period. The model predicts several aggregate quantities very well. Consistent with our reduced-form estimates and findings in related studies (e.g., Bellavitis et al., 2021; Fisch and Momtaz, 2020), the structural estimation of the model shows that intermediaries help reduce trading delays and that search costs are pronounced in the ICO market. Importantly, individual sellers and buyers share the transaction surplus more equally in non-intermediated ICOs than in intermediated ICOs, in which intermediaries pocket the transaction surplus almost exclusively. Overall, the model-implied estimates suggest that the ICO market creates onlye one-third to one-fifth of thewelfareitcouldpotentiallycreateifitwereperfectly efficient, with the loss stemming from search-related inefficiency. Theoretical contributions, practical implications, limitations and avenues for future research are discussedinSect.8.Precedingthat,weprovidesomeinstitutional background on DeFi, ICOs, and crypto funds in Sect.2, derive overarching hypotheses in Sect.3,discuss data and regression results in Sects.4and 5,the formal model in Sect.6, and the structural estimation in Sect.7. 2 Institutional background: DeFi, ICOs, and crypto funds 2.1 Decentralized finance (DeFi) and the pursuit of disintermediation DeFi markets may have several advantages over traditional finance markets. First, DeFi may improve market participation. More individuals and small enterprises may gain (equitable) access to finance because DeFi reduces the entry frictions, such as, for example, through a mitigation of local bias in venture financing (Sorenson et al., 2016) or lending (Becker, 2007). Second, DeFi may make markets more complete by facilitating financial innovations. This could be spurred by the open-source character of DeFi, paired with its lack of borders and focus on interoperability standards (Harveyetal.,2021).Third,DeFipromisesasignificant reduction in transaction costs stemming from multiple 123 1418 P.P. Momtaz sources (Gao & Li, 2021). For example, disintermediation increases the share of the transaction surplus from which transaction parties can exclusively benefit, the transparency of public ledgers significantly reduces auditing costs, and the deterministic and trustless character of smart contracts minimizes the execution risk. At the same time, DeFi has yet to address a number of novel and idiosyncratic risks that fall broadly into two categories: intra-protocol and inter-protocol risks. Intra-protocol risks include consensus failures, such as 51% attacks on Proof-of-Work (PoW) blockchains and validator cartels on Proof-of-Stake (PoS) blockchains, as well as intra-protocol arbitrage on automated market maker (AMM) exchanges, known as miner extracted value (MEV) (Daian et al., 2020). Inter-protocol risks include so-called oracle attacks, in which biased or fake outside information is fed into smart contracts, and “flash loans” that pave the way for inter-contract arbitrage (Wang et al., 2021).3Both intraand interprotocol risks have a common attribute in that they represent technical vulnerabilities that are extremely difficult for individual platform users to detect or even understand. Therefore, these risks distinguish DeFi from intermediated financial markets. The consequences of these risks may be salient in crowdfunding markets, such as the ICO market, because individual backers may not possess the technological knowledge to adequately evaluate the novel protocol risks. 2.2 The DeFi market for startups: initial coin offerings (ICOs) Token offerings (or initial coin offerings, ICOs) are an entrepreneurial finance mechanism that shares some common features with crowdfunding, venture capital, and initial public offerings (for an excellent recent review, see Brochado and Troilo, 2021). Specifically, ICOs have evolved from crowdfunding by employing DLT to both issue and exchange stakes in startup firms (Bellavitiset al., 2021;Fisch,2019;Howelletal.,2020; Momtaz, 2020a). ICOs are peer-to-peer startup financing transactions that rely on smart contracts to automate trustless transactions between entrepreneurs and investors (Fisch et al., 2022; Rawhouser et al., 2023). In an ICO, an entrepreneur raises venture financing 3See Carter and Jeng (2021) for an overview of DeFi protocol risks. by selling cryptographically protected digital assets, known as tokens or coins, to investors. Tokens can represent different types of value and rights. Cryptocurrency tokens are mere mediums of exchange, such as Bitcoin; security tokens may include voting and control rights; and utility tokens are payment instruments (Howelletal.,2020; Lambert et al., 2021). Utility tokens are the most frequently issued token type in ICOs (Bellavitis et al., 2020), though developments in ICO regulation have initiated a gradual shift to security token offerings (Lambert et al., 2021). Utility tokens are voucher-like assets that can be redeemed for one unit of the venture’s future product or service. The reliance on DLT means that ICO investors require not only business skills but also a great deal of technological knowledge (Bellavitis et al., 2021;Fisch,2019). Unlike other entrepreneurial financing mechanisms, ICOs integrate the full spectrum of “ticket sizes”, ranging from micro-cap ICOs (<$100,000) to mega-cap ICOs (>$1,000,000, such as the EOS campaign, with more than $4 billion raised). The first ICO (MasterCoin) took place in July 2013, and the market has steadily evolved since then. Figure1 shows the evolution of the market for token offerings over the 2017–2020 period. During that time, roughly 5,500 token offerings were completed, with the majority in 2018. Bellavitis et al. (2021) estimate that 2,598 token offerings raised an aggregate funding amount of $12.3 billion in 2018 alone. Utilitytokenofferings areoften thought to beperfectly disintermediated peer-to-peer transactions and issued tokens are typically traded post-ICO in liquid secondary markets. Smart contracts allow entrepreneurs and investors to automate the transaction in a trustless way, thereby redistributing the transaction surplus exclusively to entrepreneurs and investors; in contrast, intermediaries in crowdfunding or initial public offerings typically charge a fee of 5–7%. Disintermediation might also democratize entrepreneurial finance markets by lowering both supplyand demand-side entry barriers (Butticé & Vismara, 2022; Fisch et al., 2022; Meoli et al., 2022; Rawhouser et al., 2023), leading to more complete markets with higher participation. Moreover, because tokens can be traded at closeto-zero transaction costs and limited trading delays through DLT, ICO aftermarkets are highly liquid. Liquid post-ICO token exchange markets reduce startup firm discounts associated with illiquidity (Barg et al., 123 1419Decentralized finance (DeFi) markets for startups: search frictions... Fig. 1 Evolution of the ICO market 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 2017 2018 2019 2020 2021 # ICOs 2021) and provide investors with rapid exit opportunities (Fisch & Momtaz, 2020;Momtaz,2020a). Facilitated trades of stakes in startups might make entrepreneurial finance markets more efficient (e.g., by means of (fair) token valuations obtained from equilibrium prices in liquid token exchange markets that are informative for the market; see Momtaz, 2021c), thereby improving capital allocation to the best entrepreneurial projects and potentially promoting long-term economic growth (Acs & Szerb, 2007; Audretsch, 2018; Bellavitis et al., 2020). Empirical works are mostly concerned with success determinants of token offerings (e.g., Adhami et al., 2018; Belitski and Boreiko, 2021; Bellavitis et al., 2020; Bellavitis et al., 2021;Fisch,2019; Giudici and Adhami, 2019; Hornuf et al., 2021;Howelletal.,2020; Momtaz, 2020a). The roles of search, intermediation, and aggregate token market efficiency represent a void in the literature. Fig. 2 Evolution of intermediation in the ICO market 0 50 100 150 200 250 300 350 2017 2018 2019 2020 # Intermediated ICO 123 1420 P.P. Momtaz 2.3 New intermediaries entering the ICO market: crypto funds Structural problems in the ICO market, such as the systematic manifestation of moral hazard (Hornuf et al., 2021;Momtaz,2021c) and regulatory (Cumming et al., 2019) and informational frictions (Bourveau et al., 2022), have led to the emergence of a novel, specialized intermediary: the “crypto fund.” Most crypto funds resemble venture capital or hedge funds, with the importantdifference that theytrade in “non-securities.” The number of crypto funds is rapidly growing. More than 800 crypto funds are active and have aggregate assets under management to the amount of $57 billion in the first quarter of 2021. The average crypto funds gross return in the first quarter of 2021 was 98%, slightly below Bitcoin’s 103%. Like most hedge funds, a large portion of crypto funds are domiciled in the British Virgin Islands or Cayman Islands for tax, legal, or other regulatory reasons, although half of them hold primary offices in the U.S.4Figure2illustrates the penetration of the market for token offerings by crypto funds over the 2017–2020 period. Crypto funds are an intriguing asset class because they differ from traditional venture capital funds in several important ways. First, crypto funds mostly trade in non-securities, avoiding much of the regulation traditional funds face. Trading in non-securities largely exemptsthemfromtheInvestment Company Act,which enables them to cater to a new market of small and individual investors, who are not accredited or qualified in thelegalsense(Mokhtarian&Lindgren,2018).Indeed, crypto funds attract small investors with significantly lower minimum investment requirements. According to Crypto Fund Research (2021), the median minimum fund investment amounts to $100,000. Moreover, crypto funds are largely exempted from the Advisers Act. This lifts limits on performance fees that can be charged to small investors, making crypto funds more financiallyattractive(albeitraisingconcernsaboutmisalignment of incentives). Second, DLT saves crypto funds time and fees that would otherwise be incurred for third-party custodians pursuant to the Advisers Act. Third, with some exceptions, tokens are taxable only in the case of “recognition events,” i.e., if they are exchanged for fiat money. This allows investors to opti4See https://cryptofundresearch.com/q1-2021-crypto-fundreport/. mize both the timing and the amount of their personal tax liabilities in coordination with their overall portfolios (Mokhtarian & Lindgren, 2018). Finally, the liquidity of tokens lifts venture capital funds’ burden to identify and invest in “unicorns” to compensate for the relatively large number of failed projects, because liquid token markets allow crypto funds to exit at any time (Kastelein, 2017). 3 Theory and hypotheses 3.1 Intuition Beforedeveloping ourconceptualframeworkmore formally, we preface the theoretical discussion by stating our two overarching hypotheses and providing some intuition behind them. The first hypothesis, the Decentralized Inefficiency Hypothesis (DIH), posits that search-related frictions render the ICO market relatively inefficient. The second hypothesis, the Intermediated Efficiency Hypothesis (IEH), posits that new DeFi intermediaries, in particular crypto funds, increase ICO market efficiency by reducing searchrelated frictions. The intuition is simple: the ICO market is very granular; that is, it has high levels of market participation (anyone with internet connectivity may participate) and market completeness (everything can be tokenized).5Smart contracts enable anyone to trade anything with anybody at almost no cost (transactional efficiency). However, smart contracts do not provide technological solutions to facilitate searching for matching transaction counterparties. Therefore, because the number of trading agents and traded claims potentially reaches a maximum in the ICO market and agents bear the burden of finding the agent with the perfectlymatchingclaimfortrade,theICOmarketmaynot achieve its welfare potential when many socially optimal trades do not occur if the expected transaction surplus does not compensate for the expected search costs (search-related inefficiency). In the ICO market, these search frictions are plausibly even more pronounced due in large part to the highly asymmetric information andthelimitstoeffectivesignaling(Hornufetal.,2021; Momtaz, 2021c). Therefore, a perfectly decentralized 5Of course, there are limitations to tokenized market participation and tokenization, such as the technical sophistication that is required from individuals. 123 1421Decentralized finance (DeFi) markets for startups: search frictions... ICOmarketmayberelativelyinefficient(i.e.,theDIH), and reintroducing a certain degree of intermediation improves themarket’soverallefficiency (i.e.,the IEH). The following section introduces the building blocks for our theory and formally derives the hypotheses. 3.2 Search-related frictions and ICO market efficiency Search-related frictions refer to economic costs stemming from market imperfections that impede the efficient matching of transaction counterparties in decentralized markets (e.g., Duffie et al., 2005). As such, search frictions are proportionate to the degree of market decentralization. In principle, market failure may occur if search costs exceed the welfare arising from the exchange of assets (Weill, 2020). Therefore, the probability of market failure increases in the degree of market decentralization. As we discuss in Sect.3.3 below, decentralized markets that face salient searchrelated inefficiency often revert back to intermediated market microstructures, in which intermediaries offer services that reduce search frictions (Gavazza, 2016; Rubinstein & Wolinsky, 1987). The following explains why search frictions in the ICO market are plausibly verypronounced.ICO-specificsearchfrictionsinclude, inter alia, (1) protocol-interface risks, (2) protocolimmanent risks, (3) smart-contract risks, (4) oracle risks, and (5) governance-related risks (Harvey et al., 2021). The ICO market is prone to search frictions by design, largely because it improves on both market participation and market completeness. Market participationreferstothenumberofagentsthatcanaccessamarket. DLT has significantly lowered the entry barriers to entrepreneurial finance markets, inter alia, through a dramatic reduction of the transaction costs for crowdfunding campaigns (demand-side entry barrier) and a reduction in the minimum investment amount thanks to fractional token ownership (supply-side entry barrier) (Bellavitis et al., 2021;Fisch,2019; Huang et al., 2020; Lambert et al., 2021; Zetzsche et al., 2020). Indeed, Fisch et al. (2022) report that the ICO market has democratized entrepreneurial finance, evidenced, e.g., by the increased number of investors from ethnic minorities (see, also, Butticé and Vismara, 2022; Meoli et al., 2022; Rawhouser et al., 2023). Market completeness refers to the variety of assets in a market. Asset heterogeneityalsocreates search problems becauseitis proportionate to the investors’ effort required to determine the relative fit of a focal asset in the light of an investor’s subjective preferences (Rubinstein & Wolinsky, 1987). Smart contracts have increased asset heterogeneity substantially because they allow the tokenization of any claim. For example, Fisch and Momtaz (2020) report that the ICO market’s demand side is very competitive, with often more than 1,000 competing token offerings present at the same time. Given this high intensity, it is evident that market completion exacerbates search frictions. The high level of asymmetric information in the ICO market further aggravates the search problem for investors (Bellavitis et al., 2020; Block et al., 2021;Fisch,2019; Hornuf et al., 2021). Asymmetric information is a pervasive problem in entrepreneurial finance (Colombo et al., 2019;Vismara,2018b). At its core, the problem with asymmetric information is that financial investors lack the information to gauge the true quality of an investment, resulting in equilibrium prices that are based on the population average instead of a more discriminatory pricing mechanism based on the underlying investment value (Jensen & Meckling, 1976; Leland & Pyle, 1977). Consequently, high-quality investments could sell at a discount, deterring issuers from putting those investment opportunities on the market entirely, which may create a market for lemons (Akerlof, 1978). Informational asymmetries are salient in the ICO market, inter alia, because blockchain-savvy entrepreneurs are typically young and lack a track record (An et al., 2019;Fisch, 2019); the tokens sold are for yet undeveloped, future products (Fisch, 2019;Howelletal.,2020;Momtaz, 2020a); there are little mandatory disclosure laws (see Bellavitis et al., 2021; Boreiko et al., 2019); and token issuers are known to embellish the information disclosed in ICO whitepapers (Momtaz, 2021c). These problems increase aggregate uncertainty in the ICO market, which accordingly exacerbates search-related inefficiency. Finally, search-related inefficiency is also partly driven by the limits to signaling in the ICO market. Several studies argue that the absence of an institutional framework for ICOs may create a moral hazard in signaling (Hornuf et al., 2021;Momtaz,2021c). For example, Momtaz (2021c, p. 2) argues that “issuers 123 1422 P.P. Momtaz in Sect.5.2, and for the impact of non-intermediation on token value discounts in Sect.5.3. 5.1 Econometric specification: two-stage approaches to mitigate endogeneity Several two-stage approaches help mitigate concerns about potential endogeneity pertaining to sample selectivity,9In particular, our approach to debias the treatment effects of intermediation mitigates selection based on unobservables, which are potentially pronounced confounders in entrepreneurial finance.10 The general approach is to estimate a first-stage model that predicts the probability that an ICO is intermediated or non-intermediated. These “selection probabilities” are then transformed and included in the second-stage models, which estimate the treatment effects. Specifically, we are interested in two distinct treatment effects; namely, the treatment effect of intermediationonthe time it takesICO firms toachievetheir crowdfunding goals and the treatment effect of the lack of intermediation on token valuation. Equations1and 2 represent the potentially confounded OLS models: ICO durationi=β×1Intermediatedi+Ωiγ+εi(1) Token valuationi=β×1Non-intermediatedi+Ωiγ+εi(2) where iindexes ICOs, 1Intermediatediand 1Non-intermediatedirepresent indicator variables for whether or not ICO iis intermediated, and irepresents a vector of control variables. For the models in Eqs. 1and 2to estimate an unbiased treatment effect of ICOs’ intermediation status, it is necessary to assume that the independent variables are orthogonal to the error term, i.e., E[i,ε i]=0. This condition is violated if selectivity is present; for example, in the case that only high-quality ICOs are able to secure intermediation services. Therefore, the 9The techniques used in our study have been employed before in similar contexts (e.g., Fisch and Momtaz 2020; Bertoni et al., 2011; Colombo et al., 2010; Colombo et al., 2021; Cumming et al., 2023). 10 For example, unobserved heterogeneity in startups’ time-tofunding by venture capitalists can be so pronounced that it biases common time-to-event models (Momtaz, 2021b) first stage explicitly models the selective matching between ICO firms and intermediaries. Specifically, we predict that ICO iis intermediated, 1Intermediatedi,by a vector of exogenous control variables that possibly influence the selection mechanism, Ω(s) i: 1Intermediatedi=Ω(s) iδ+ξi(3) Estimates from Eq. 3help control for unobserved heterogeneity as follows. We use Generalized Residuals (GRs) both as explicit controls for selection-based endogeneity, and as instrumental variables for ICOs’ intermediation status (Gourieroux et al., 1987), which controls for unobserved heterogeneity by explicitly modeling any endogeneity in the error term, defined as follows: GRi=1Intermediatedi× φ−Ω(s) iδ 1−−Ω(s) iδ +(1−1Intermediatedi) × −φΩ(s) iδ −Ω(s) iδ(4) where φ(.)and (.) denote the probability density and the cumulative density functions of the standard normal distribution, respectively. We restrict the standard deviation of the error term for intermediated (σε, 1Intermediatedi)tobeequaltothatofnon-intermediated ICOs (σε,1Non-intermediatedi). The restriction ensures that GRican be added as an instrumental variable to Eq. 1. For Eq. 2, the adjustments are identical, with the exception that “selection probabilities” are estimated for the case that ICO iis not intermediated, i.e., 1Non-intermediatedi. 5.2 Regression results: intermediation and ICO duration Table 3shows the regression results for how ICO intermediation impacts ICO duration. All models include quarter-year and country fixed effects to absorb both time-related and geographical variation. All reported standard errors are robust. The selection model for Eq. 3is in column (1), with an indicator variable for 123 1429Decentralized finance (DeFi) markets for startups: search frictions... Table 3 Two-stage analysis of search frictions in intermediated vs. non-intermediated ICOs Stage: 1st 1st 2nd 2nd Model: Selection Control GR IV Dependent variable: 1Intermediated Duration Duration Duration Key variable: 1Intermediated −0.273*** −0.273*** −0.287*** (0.076) (0.077) (0.079) ICO-related controls: Ethereum 0.030 −0.032 −0.032 −0.032 (0.041) (0.069) (0.069) (0.069) Pre-sale −0.013 0.090* 0.090* 0.090* (0.030) (0.050) (0.050) (0.050) # competing offerings (log.) −0.098** 2.616*** 2.616*** 2.615*** (0.039) (0.066) (0.066) (0.066) Soft cap, in $ mil. (log.) −0.001 −0.268*** −0.268*** −0.268*** (0.041) (0.068) (0.068) (0.068) Hard cap, in $ mil. (log.) −0.006 0.007 0.007 0.007 (0.009) (0.015) (0.015) (0.015) Whitelist 0.036 −0.038 −0.038 −0.038 (0.026) (0.044) (0.044) (0.044) KYC −0.007 −0.027 −0.027 −0.027 (0.033) (0.054) (0.054) (0.054) Team-related controls: Expert rating 0.023 −0.015 −0.015 −0.014 (0.026) (0.043) (0.043) (0.043) # team members 0.001 −0.001 −0.001 −0.001 (0.002) (0.003) (0.003) (0.003) % team with technical degree 0.207*** −0.154 −0.154 −0.151 (0.078) (0.130) (0.131) (0.130) % team with rypto experience 0.008 0.042 0.042 0.042 (0.064) (0.106) (0.106) (0.106) % team with Ph.D. 0.348* −0.784*** −0.784*** −0.779*** (0.177) (0.295) (0.297) (0.296) Population weights ✓✓✓✓ Generalized Residuals ✗✗✓✗ Instrumental Variable ✗✗✗✓ Country fixed effects ✓✓✓✓ Quarter-year fixed effects ✓✓✓✓ # Obs. 567 567 567 566 Adjusted R20.018 0.823 0.823 0.823 Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively whether an ICO is intermediated as the dependent variable. Our baseline regression results for Eq. 1are presented in column (2), with the natural logarithm of the timebetweenICOstartandendindaysasthedependent variable. Thesecond-stageregressionresultsareshown in columns (3) and (4), with the generalized residual, as measured in Eq. 4, as an added control and as an instrumental variable for the intermediation indicator, 123 1430 P.P. Momtaz respectively.Inparticular,theseapproachesoutperform matching-on-observables in our context because they do not require ICO intermediation to be independent of unobserved factors and the marginal probability of ICO intermediation does not need to equal the average probability. Finally, given the sample population differences reported in Table 1, we weight all regression coefficients by the inverse of the (absolute value) sum of relative deviations from population means for each token offering to move our estimates of the local treatment effect in our sample closer to the average treatment effect in the population of ICOs. The results suggest that the effect of ICO intermediation on ICO duration is significantly negative throughout all models in columns (2) to (4). The coefficients range from −0.273 to −0.287, statistically highly significant with p-values consistently below 1%. The similarity of these coefficients may imply that selectivity does not significantly bias the causal effect of ICO intermediation on ICO duration. Indeed, the adjusted R2for the selection model in column (1) is relatively low (1.8%). Overall, the marginal effect of ICO intermediation on ICO duration is a relative decrease in the time it takes for intermediated ICO firms to achieve their crowdfunding goals of −24.9% (exp(−0.287)– 1). This strongly supports the overarching hypothesis that intermediaries help reduce search frictions in the ICO market. For the selection model, the coefficients of the control variables largely show plausible effects, suggesting that (i) the number of competing offerings is negatively associated with the probability of ICO intermediation, while (ii) the relative amount of team members with a technical background or (iii) a Ph.D. degree have positive effects. For the key models in columns (2) to (4), the coefficients of the control variables also appear plausible, suggesting that (i) pre-sales and (ii) the number of competing offers increase the time it takes ICO firms to achieve the crowdfunding goal, while (iii) the size of the soft cap and (iv) the percentage of team members with a Ph.D. degree decrease the duration. It is noteworthy that the coefficients of the control variables are consistentinterms of both thesignsandthe magnitudes across columns (1) to (3). Overall, the results in Table 3suggest that ICO firms have an economic motive to secure intermediation for their offerings to reduce their time-to-funding. 5.3 Regression results: lack of intermediation and discount on token value Table 4presents regression results for the effect of ICO intermediation on token valuations. In particular, we test whether tokens offered in non-intermediated ICOs trade at a relative discount. The tests are similar to those in Table 3, with the difference that the dependent variables are replaced for an indicator variable for non-intermediated ICOs in column (1) and the natural logarithm of token valuations in $ million in columns (2) to (4). The models also include quarteryear and country fixed effects, and the standard errors are robust. Columns (3) and (4) show second-stage regressions that control for unobservable heterogeneity by controlling for the generalized residuals in column (3) and for instrumenting the indicator variable for non-intermediated ICOs with the generalized residuals in column (4). The results suggest that tokens offered in nonintermediated ICOs trade at a significant discount. Specifically, the average non-intermediated ICO offers tokens at a discount of up to −57.7% (exp(−0.861)–1) inthe IVmodel incolumn (4).Thecoefficientestimates for our key variable ranges from −0.793 to −0.861, which are highly statistically significant, with p-values consistently below 1%. It is noteworthy that the key coefficient of the control model (−0.793) is clearly different from that in the GR and IV models (−0.850 and −0.861, respectively), indicating that the selection of intermediated vs. non-intermediated ICOs would underestimate the token valuation effect in the absence of the adjustments in columns (3) and (4). Note that the adjusted R2is similarly high as in related studies (Bellavitis et al., 2020;Fisch,2019), and the control variables also seem to be consistent. Specifically, (i) expert ratings, (ii) soft cap amounts, (iii) hard cap amounts, (iv) whitelists, (v) the number of ICO team members, and (vi) the relative amount of team memberswithpriorcrypto industry experienceare positively related to ICO token valuations. In contrast, (vii) only the percentage of technical team members has a negative effect. These estimates are consistent across all model specifications in columns (2) to (4). Overall, the non-intermediated ICOs offer tokens at dramatic ceteris paribus discounts of up to −57.7%, 123 1431Decentralized finance (DeFi) markets for startups: search frictions... Table 4 Two-stage analysis of valuation discount in non-intermediated ICOs Stage: 1st 1st 2nd 2nd Model: Selection Control GR IV Dependent variable: 1Not intermediated Valuation Valuation Valuation Key variable: 1Not intermediated −0.793*** −0.850*** −0.861*** (0.262) (0.262) (0.274) ICO-related controls: Ethereum −0.030 −0.031 −0.011 −0.033 (0.041) (0.236) (0.236) (0.237) Pre-sale 0.013 −0.207 −0.250 −0.206 (0.030) (0.172) (0.173) (0.172) # competing offerings (log.) 0.098 −0.293 −0.278 −0.287 (0.039) (0.226) (0.225) (0.227) Soft cap, in $ mil. (log.) 0.001 0.561** 0.571** 0.562** (0.041) (0.233) (0.232) (0.233) Hard cap, in $ mil. (log.) 0.006 0.431*** 0.439*** 0.432*** (0.009) (0.051) (0.051) (0.051) Whitelist −0.036 0.253* 0.268* 0.250* (0.026) (0.150) (0.150) (0.151) KYC 0.007 −0.083 −0.068 −0.082 (0.033) (0.187) (0.187) (0.188) Team-related controls: Expert rating −0.023 0.347** 0.348** 0.345** (0.026) (0.149) (0.148) (0.149) # team members −0.001 0.038*** 0.036*** 0.038*** (0.002) (0.010) (0.010) (0.010) % team with technical degree −0.207*** −1.533*** −1.663*** −1.547*** (0.078) (0.448) (0.450) (0.448) % team with crypto experience −0.008 0.970*** 0.993*** 0.970*** (0.064) (0.365) (0.364) (0.366) % team with Ph.D. −0.348* 1.242 1.420 1.218 (0.177) (1.018) (1.018) (1.019) Population weights ✓ ✓✓✓ Generalized Residuals ✗✗✓(-**) ✗ Instrumental Variable ✗ ✗✗✓ Country fixed effects ✓ ✓✓✓ Quarter-year fixed effects ✓ ✓✓✓ # Obs. 567 567 567 566 Adjusted R20.018 0.264 0.269 0.262 Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively 123 1432 P.P. Momtaz whichis consistentwithanecdotalevidence thatreports discounts in therange of 50 to 70%. This implies that the average non-intermediated ICO issuer “leaves money on the table” to the amount of $5.34 million. By implication, the causal nature of these results suggests that, in competitive DeFi markets, intermediaries can charge very high fees commensurate with the estimated discounts for their intermediation services, an interpretation that we will use to identify our theoretical model below. To summarize the reduced-form evidence in Sects.5.2 and 5.3, ICO intermediation is causally related to the time it takes ICO firms to achieve their crowdfunding goals and token valuation discounts, respectively. Therefore, the results offer initial supporting evidence for the overarching conjectures that intermediation helps reduce search frictions in ICO markets, and intermediaries can charge substantial fees for their services. Table 5 Post hoc analyses: second-stage regression results for different subsamples Dependent variable: Funding amount, in USD (log.) ICO duration, in days (log.) Panel A: Market phases Bull market Bear market Bull market Bear market 1Not intermediated −1.637∗∗ −0.713∗∗ 0.056 0.282∗∗∗ (0.720) (0.287) (0.391) (0.064) Panel B: Team quality Above-median Below-median Above-median Below-median team rating team rating team rating team rating 1Not intermediated −0.771∗∗ −0.537 0.435∗∗∗ 0.277∗∗ (0.318) (0.472) (0.130) (0.127) Panel C: Product quality Above-median Below-median Above-median Below-median product rating product rating product rating product rating 1Not intermediated −0.767∗∗ −0.427 0.448∗∗∗ 0.167 (0.329) (0.489) (0.134) (0.129) Panel D: Venture’s vision quality Above-median Below-median Above-median Below-median vision rating vision rating vision rating vision rating 1Not intermediated −0.712∗∗ −0.763∗0.494∗∗∗ 0.034 (0.318) (0.418) (0.123) (0.128) Panel E: Entrepreneurial incentivization (token retention) Above-median Below-median Above-median Below-median token retention token retention token retention token retention 1Not intermediated −0.833 −0.838∗∗∗ 0.043 0.351∗∗∗ (0.547) (0.299) (0.108) (0.126) Model information: Other controls ✓✓✓✓ Population weights ✓✓✓✓ Generalized Residuals ✓✓✓✓ Country fixed effects ✓✓✓✓ Quarter-year fixed effects ✓✓✓✓ Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively 123 1433Decentralized finance (DeFi) markets for startups: search frictions... 5.4 Post hoc analyses and robustness checks The identified effects of ICO intermediation might differ in various subsamples, and investigating the sensitivity of the main effects may yield additional insights (Newbert et al., 2022). Table 5shows re-estimated second-stage treatment effects based on the GR model for subsamples based on five different categories, with anindicatorvariablefornon-intermediatedICOs.Panel A shows the main effects for bull or stable versus bear phases in the ICO market. The results suggest that the valuation effectis more pronounced in bullmarkets and the duration effect is more pronounced in bear markets. Panel B, C, and D show the main effects for aboveand below-median subsamples based on ICObench’s team, product,andventurevisionratings.The results indicate that the valuation and duration effects are more pronouncedforabove-medianqualityfirms.PanelCshows the main results for aboveand below-median firms based on their token retention ratios. Retaining more tokens is often regarded as a costly signal of firm quality and associated with stronger entrepreneurial incentivization (e.g., Leland and Pyle, 1977). The findings suggest the valuation and duration effects are more pronounced for the below-median token retaining firms. Further, several additional ad hoc checks confirm the results’ robustness. In particular, the results are robust to (i) excluding soft cap and hard cap controls from the regressions, (ii) adding hype-related variables as controls, such as monthly GoogleTrends for ICO, blockchain, and cryptocurrency search terms, (iii) controlling for project size in absolute (i.e., the total token supply multiplied by the ICO token price) and relative (i.e., the project size as the number of tokens sold divided bythetotaltokensupply)terms,and(iv) adjusting the soft cap and hard cap variables by hypothetical investment amounts by crypto funds in the amount of 10%, 25%, and 50%. Moreover, we also disaggregate the ICO intermediation variable into whether the crypto fund’s investment strategy resembles more venture capital or hedge funds and whether the crypto fund’s investment strategy follows a specialized or a diversified strategy. While these results lack statistical power due to small sample sizes given the granular subcategories, they are nevertheless qualitatively consistent with the main results and show that crypto hedge funds and diversified funds have a stronger effect on valuation, while crypto venture funds and specialized funds have a stronger effect on ICO duration. Finally, we replace the funding amount as the proxy for ICO firm valuation with a relative measure (actual funding divided by hard cap), which leads to a statistically highly significant coefficient on the indicator for non-intermediated ICOs of −0.179, confirming the main results on the ICO firm valuation discount in the absence of ICO intermediation. 6 Model 6.1 Setup: the ICO market Any model is an abstraction from reality. Our model faces the additional constraint that we want to structurally estimate it. This requires further abstraction and may come at the cost of ignoring interesting features of the ICO market that can be considered in a purely theoretical model, such as that by Wang et al. (2022). We model the ICO market as a decentralized search-and-bargaining market populated by individual investors and intermediaries who discount the future at rate ρ>0. The key difference between the two investor types is that only individual investors derive utility from holding tokens (e.g., the token may be used as a membership fee for a video gaming online community, or to purchase cloud storage to save electronic files, or for any other purpose), while intermediaries act as institutional investors that extract economic rents from exploiting market imperfections. The model is set in continuous time with an infinite horizon.11 Individual investors (henceforth, individuals)are assumed to be risk neutral. At every instant, a mass μof high-valuation individuals with token valuation zh>0 enters the ICO market. Their valuation drops to zl<zhand they become low-valuation individuals with intensity λ, obeying a continuous-time Markov chain. Because individuals’ valuations are independent, there is a mass of μ λhigh-valuation individuals in steady state. Intermediaries (indexed by dfor “middlemen”) enter the ICO market as an (endogenous) mass μd, and we assume that market entry is free. μdo(a)and μdn(a) denote the endogenous masses of intermediated and 11 Weill (2020) offers an excellent overview of related work on search theory. Our analysis follows Gavazza (2016), combining elements from Duffie et al. (2005), Rubinstein and Wolinsky (1987). 123 1434 P.P. Momtaz non-intermediated ICOs of a platform of size a, respectively. We discuss the role of platform size below. Because intermediaries do not enjoy utility from holding utility tokens, they serve as potential transaction parties independent of any token-valuation parameter.12 Intermediaries have operating costs k. Tokens We assume that entrepreneurs launch ICOs such that tokens enter the market at every instant as amassofx<μand transacts at endogenous price p∗. Tokens are heterogeneous with respect to their underlying platform size and are generically ranked, such that a=1...100 (1 =largest platform percentile, 100 =smallest platform percentile). Token value decreases in aand has a salvage value s≥0. A worthless token can also be scrapped free-of-charge at any point in time. Platform size matters because it relates to network effects, which ultimately determine token value (Chen & Bellavitis, 2020;Fisch, 2019). We require μ λ>A, where Arepresents the total amount of platform tokens in the market, so that, in a Walrasian market, the marginal token-holder is of the high-valuation type. Instantaneous flow utility π(z,a)is a function of a token’s platform size aand its holder’s valuation z. Utility is increasing in valuations (π(zh,a)>π(zl,a)), decreasing in token’s platform size ranking (∂π(z,a) ∂a<0) (i.e. smaller platform, lower token value), and it has negative z-acomplementarity such that ∂π(zh,a) ∂a<∂π(zl,a) ∂a. Search Individuals pay a search-related costs csto find token transaction parties at pairwise independent Poisson arrival rates γ>0. Therefore, an individual wishing to sell a token meets a potential buyer at rate γs=γμ b, and an individual looking to buy a platform-size-atoken meets a potential seller at rate γb(a)=γμ s(a). The masses μband μs(a)are determined in equilibrium. Similarly, individuals meet intermediariesatpairwiseindependentPoissonarrivaltimes with intensity γ>0. An individual wishing to purchase a platform-size-atoken meets a platform-size-a token-holding institutional at rate γbd(a)=γμdo(a), and intermediaries meet individuals to sell their tokens at rate αds =γμb.Thesumofγbd (a)and αds rep12 Intermediaries do not invest in more than one platforms at once. This assumption largely simplifies the model, and corresponds closely to statistics about institutional investor involvement in the ICO market, e.g., Fisch and Momtaz (2020). resents the combined search intensity at which buying individuals and selling intermediaries meet. Similarly, γsd and αdb(a)express the search intensities with which selling individuals meet intermediaries and buying intermediaries meet individuals wishing to sell platform-size-atokens. Bargaining Meetings between matching buying and selling individuals or with intermediaries lead to price negotiations according to a generalized Nash bargaining framework. The parameters θs∈{0,1}and θd∈ {0,1}denote the relative bargaining power of the seller in a non-intermediated ICO and of the institutional in an intermediated ICO, respectively. Thus, we assume symmetric information about token quality, which is why we estimate the model based on residual prices after controlling for determinants of token value other than platform size in Sect. 7. 6.2 Value functions 6.2.1 Individual investors We distinguish four types of individual investors in our model.Individualscanhaveahigh(zh)orlow(zl)token valuation, and own or do not own the respective token. Further, individual investors who own a token decide betweenkeepingorsellingit,whileindividualinvestors who do not hold a token decide between actively trying to purchase tokens in the market or not. An individual with valuation zholding a platformsize-atoken continuously chooses between keeping or selling the asset. If she is not seeking to sell the token, she enjoys utility π(z,a)from holding a high-valued token. Her valuation switches to zl<zhat rate λ,at which point she faces the decision problem of choosing between the utility from a low-valued token and the proceedsfromselling it(max{Ulo(a), Slo(a)}).Finally, shemayincur a capital change in theamountofU ho(a), which defines her value function as: ρUho(a)= π(zh,a)+λVlo(a)−Uho(a)+U ho(a). Alternatively, shemayactivelytrytosellthetoken.Shealsoenjoysthe current flow utility π(zh,a), faces the decision problem max{Ulo(a), Slo(a)}when her valuation drops, and sustains capital change due to network effects U ho(a). Moreover, she faces the flow search cost cs. She meets potential buyers at rate γs, at which point 123 1435Decentralized finance (DeFi) markets for startups: search frictions... she has to decide between selling the token at price p(a)and becoming a high-valuation non-holder with value Vhn, or keeping it. Selling it results in the capital gain p(a)+Vhn −Sho(a). Similarly, she meets investors at rate γsd, at which point she has to decide whether to accept the investor’s bid price pB(a). Thus, hervaluefunctionsatisfiesthefollowingBellmanequation: ρSho(a)=π(zh,a)−cs+λVlo(a)−Sho(a)+ γsmax{p(a)+Vhn −Sho(a), 0}+γsd max{pB(a)+ Vhn −Sho(a), 0}+S ho(a). Low-valuation token-holding individuals can also choose between keeping and selling the asset. In the former case, because zlis an absorbing state and valuations cannot switch anymore, her value function satisfies: ρUlo(a)=π(zl,a)+U lo(a). In the latter case, the conditions for the value function follow from the fact that zlis an absorbing state: ρSlo(a)=π(zl,a)−cs+ γsmax{p(a)+Vln −Slo(a), 0}+γsd maxpB(a)+ Vln −Slo(a), 0+S lo(a). Individuals who do not own tokens can either passively remain in their ownership status or actively try to purchase tokens in the ICO market. High-valuation individuals who neither hold a token nor are actively trying to purchase one in the market have zero utility: ρUhn =0. High-valuation individuals who do not hold a token but are actively looking to purchase one in the ICO market pay a search cost csand incur a capital loss of Vln −Shn when their valuation drops. Because they cannot be ex-ante sure of the ultimate platform size athat is up for sale when the individual encounters a potential transaction partner, but which is highly value-relevant, they take the expectations over all platform sizes. They meet a selling individual at rate γb(a)and a selling investor at rate γbd(a)and experiencesacapital gain ofmax{Vho(a)−p(a)−Shn,0}and max{Vho(a)−pA(a)−Shn,0}, respectively. This leads to:ρShn =−cs+λ(Vln−Shn)+γb(a)max{Vho(a)− p(a)−Shn,0}da +γbd(a)max{Vho(a)−pA(a)− Shn,0}da. Similarly to type-ln individuals without token ownership, low-valuation individuals who neither hold a token nor are actively trying to purchase one in the market have zero utility: ρUln =0. Low-valuation individuals who do not hold a token but are actively looking to purchase one in the market pay the deterministic search cost cs. Like the high-valuation non-holders, the lowvaluationindividual takestheexpectationsoverallplatform sizes. She meets a selling individual at rate γb(a) and a selling intermediary at rate γbd(a)and experiences a capital gain of max{Vlo(a)−p(a)−Sln,0} and max{Vlo(a)−pA(a)−Sln,0}, respectively. This yields: ρSln =−cs+γb(a)max{Vlo(a)−p(a)− Sln,0}da+γbd(a)max{Vlo(a)−pA(a)−Sln,0}da. 6.2.2 Intermediaries Because intermediaries do not enjoy flow utility from holdingtokens, thereareonly twotypes: intermediaries that currently hold tokens and those that currently do not hold tokens. A platform-size-atoken-holding intermediary pays the flow operating cost k, and decides between selling the token for ask price pA(a)and realizing capital gain pA(a)+Jdn −Jdo(a)net of any network-related effects on capital or realizing the salvage value (e.g., return tokens in case a hard-cap goal was not met in the offering). The token-holding intermediary’s value function satisfies the following Bellman equation: ρJdo(a)=max{−k+αdspA(a)+ Jdn −Jdo(a)+J do(a), ρ Jdn. In contrast, the value function of the token-nonholding intermediary is characterized by the flow operating cost kthat the intermediary incurs while actively searching for a potential transaction and the expectation over the platform sizes when the intermediary meets a seller at rate αdb(a)and realizes a capital gain in the amount of max{Jdo(a)−pB(a)−Jdn,0}. It satisfies: ρJdn = −k+αdb(a)max{Jdo(a)−pB(a)−Jdn,0}da.Note that the free-entry condition implies that intermediaries’ expected capital gains is exactly offset by their operating cost in the latter equation. 6.3 Prices Following the literature (e.g., Gavazza, 2016; Weill, 2020), we solve for the endogenous price in the non-intermediated ICO market segment via generalized Nash bargaining: maxp(a)[Uho(a)−p(a)− Shn]1−θs[p(a)+Vln −Slo(a)]θssubject to Uho(a)− p(a)−Shn ≥0 and p(a)+Vln −Slo(a)≥0. This yields the endogenous price: p(a)=(1−θs)Slo(a)− Vln+θsUho(a)−Shn. The ask price pA(a)and bid price pB(a)are determined in a similar fashion: pA(a)=(1−θd)Jdo(a)−Jdn+θdUho(a)−Shn and pB(a)=(1−θd)Jdo(a)−Jdn+θdSlo(a)− Vln). 123 1436 P.P. Momtaz 6.4 Policies 6.4.1 Individual investors Surplusrentsfromtradearisewhen hn-typeindividuals without token ownership meet lo-type token-holders. Those surplus rents from trade are higher for tokens with relatively large underlying platforms due to the negative complementarity between the input factors of flow utility π(z,a). By implication, not all tokens are reallocated in equilibrium, which helps simplify the analyses. We can reformulate the value functions of hotype token-holding individuals. Vho(a)equals Uho(a) for a<a∗ ho and Shn for a≥a∗ ho. These cases follow from U ho(a)<0. Because the utility ho-type individuals derive from holding platform-size-atokens is decreasing in a, there must be a cutoff a∗ ho at which token-holders decide to realize the salvage value Shn. Therefore, Uho(a∗ ho)=Shn. Likewise, such a cutoff also exists for hn-type token-nonholding individuals at which they decide to purchase a platform-size-a token. Therefore, we have a∗ hn ≤a∗ ho, with a potential wedge from trading frictions. Thus, token-nonholding hn-typeindividualshavethevalue function Vhn =Shn. The value function of token-holding lo-type individuals can be simplified as follows: Vlo(a)equals Slo(a) for a<a∗ l,Ulo(a)for a≤a∗ l<T, and Vln for a=T. Because U lo(a)<0 and csis constant, there exists a cutoff a∗ lsuch that a lo-type individual decides to sell hertokenifit is belowthe cutoffandkeepitif itisabove the cutoff, respectively. If she keeps the token, then she holds it until she can realize the salvage value at T. Further, equilibrium considerations dictate that tokenholdinglo-typeindividualssellto purchase-willinghntype individuals, implying a∗ l≤a∗ hn. Finally, because zlis an absorbing state, the value function of tokennonholding ln-type individuals is Vln =ywhere y represents the value of the smallest platform. 6.4.2 Intermediaries Intermediaries prefer tokens with larger underlying platforms because they trade at greater margins, but intermediaries’ operating costs kare constant. Therefore, intermediaries purchase platform-size-atokens suchthat a≤a∗ dn andrealize thesalvagevalueata∗ dn < a∗ do.Equilibrium considerations alsodictate a∗ do ≤a∗ hn, that is, intermediaries always sell to purchase-willing individuals. 6.5 Distributions of individual investors and intermediaries 6.5.1 Laws of motion for individual investors The mass of platform-size-atoken-holding ho-type individuals evolves over time as non-holding hn-type individuals meet selling platform-size-atoken-holding individuals (γb(a)μhn) or platform-size-atokenholdingintermediaries(γbd(a)μhn),andplatform-sizeatoken-holding individuals switch valuations to the absorbing state zl(λμho(a)); formally: ˙μho(a)= (γb(a)μhn +γbd(a)μhn)−λμho(a)for a<a∗ ho. Similarly, the evolution of platform-size-atokenholding lo-type individuals over time is affected by inflows from token-holding individuals with underlying token-platform sizes a<a∗ ho (because these individuals would just realize the salvage value if their tokens were based on a smaller platform) whose valuations just switched to the absorbing zlstate (λ1(a<a∗ ho)μho(a)), and the outflow of lotype holders of tokens with networks greater than a∗ l(holders of tokens with those platform sizes would prefer to sell rather then keep it) who meet purchase-willing individuals (γs1a<a∗ lμlo(a)) or intermediaries (γsd1a<min{a∗ l,a∗ dnμlo(a)). Technically, for low-valuation owners: ˙μlo(a)= λ1a<a∗ hoμho(a)−γs1a<a∗ lμlo(a)− γsd1a<min{a∗ l,a∗ dnμlo(a)for a<T; for highvaluation non-owners: ˙μhn =(μ −x)+μho(a∗ ho)− λμhn −μhn a∗ l 0γb(a)da −μhn a∗ dn 0γbd(a)da; and for low-valuation non-owners: μln =λμhn + γsa∗ l 0μlo(a)da+γsd min{a∗ l,a∗ dn} 0μlo(a)da+μlo(T). 6.5.2 Laws of motion for intermediaries Similar logic gives the laws of motion for intermediaries in the model: ˙μdo(a)=αdb(a)1a< a∗ dnμdn −αdsμdo(a)for a<a∗ do and ˙μdn = αds a∗ do 0μdo(a)da −μdn min{a∗ l,a∗ dn} 0αdb(a)da + μdo(a∗∗ dn). 7 Structural estimation Thissectiondescribeshow we estimate and identify the model of the ICO market described in Sect. 6.Italso reportskeyparameterestimates,includingsearchcosts, 123 1437Decentralized finance (DeFi) markets for startups: search frictions... transaction surplus, and rent sharing between individual investors and intermediaries. Finally, we benchmark the ICO market to the perfectly efficient Walrasian equilibrium to quantify the effect of market frictions on aggregate welfare created in the ICO market. 7.1 Estimation We the model as follows: The discount rate is ρ= 0.5%, the total mass of ICOs is set to equal the sample median, and the average number of active intermediaries, μd, is set to the sample mean. ICOs differ in their underlying network size. All ICOs’ network sizes are ranked by percentiles from 1 (largest) to 100 (smallest). The unit of time, at which we estimate the model, is set to months, covering 48 months during the 2017–2020 period. The salvage price is S=$0, stipulating that platforms without users are worthless. Tokenholders’ flow payoff equals π(z,a)= ze−δ2a. We estimate the vector ψ= {λ,γs,γ sd,α ds,zh,zl,δ 2,cs,θ s,θ d}. The endogenous contact rates {γs,γ sd,α ds}can be inferred from the data and help identify other parameters of the model, which in turn jointly determine individual investor’ and intermediaries’ policy functions and distributions. The following ICO moments, m1(ψ), are computed based on the model’s solution: (1) The fraction of tokens for sale, m1[1]=a∗ l 0μlo(a)da+a∗ do 0μdo(a)da A; (2) the cumulative number of intermediated ICOs relative to all ICOs, m1[2]=a∗ do 0μdo(a)da A;(3)the fraction of active non-intermediated ICOs, m1[3]= γSa∗ l 0μlo(a)da A; (4) the fraction of active intermediated ICOs, m1[4]=αds a∗ do 0μdo(a)da A; and (5) the average ranking of ICO platform’s underlying network size, m1[5]=a∗ l 0aμlo(a)da+a∗ do 0aμdo(a)da a∗ l 0μlo(a)da+a∗ do 0μdo(a)da . Further, six price moments, m2(ψ) ={β0,β 1,β 2,β 3,β 4,β 5},are obtained from nonlinear least squares from the following two auxiliary regressions: p(a)=β0+β1e−β2a and pB(a)=β3+β4e−β5a. Then, we estimate ψvia the two-step estimator from Hansen (1982): ˆ ψ=arg minψ∈m(ψ) −mS( ˜ ψ)m(ψ) −mS where mSdenotes the simulated token offering and price moments, and ( ˜ ψ) is the consistent estimate of the asymptotic variance-covariance matrix of the moments, and we use m(ψ)−mS mSto have a similar scale. 7.2 Identification Identification follows largely the multidisciplinary search literature, including labor (Eckstein & Van den Berg, 2007) and decentralized real asset markets (Gavazza, 2016). ICO moments identify the transition rates between states. The fraction of non-intermediated ICOs identifies the rate at which individuals meet, γs, while the fraction of intermediated ICOs identifies intermediaries’ contact rates, αds. The cumulative number of intermediated ICOs identifies the rate at which individuals meet intermediaries, γsd. Furthermore, the Markov-chain parameter that governs inventors’ valuation changes, λ, is identified by the total fraction of active ICOs in any given instant. The valuation parameter and the endogenous moments together with the fixed parameters Aand μd and the steady state condition μ λ=μhn +T 0μho(a)da then allow to infer the efficiency parameters of the matching functions, γand γ, and the mass of new market entrants, μ. Specifically, we solve for μho(a), μlo(a), and μdo(a)in the law-of-motions equations in Sect.6.5. To identify the remaining parameters {δ2,zh,zl,θ s,θ d,cs}, we use the price moments and the fifth transaction moment that characterizes the average platform’s underlying network sizes of the marketed ICOs. The price equations identify the value depreciation parameter, δ2, from price variations across tokens of different underlying network size. We follow Gavazza (2016) to identify bargaining and valuation parameters who suggests to exploit differences between the prices in intermediated and nonintermediated ICOs, as well as the vertical heterogeneity of the token network sizes. β0and β3in the price regressionsidentifyhn non-tokenholders’valueofcontinuing to search, Shn, which is their outside option in Nash bargaining with equilibrium prices. Because non-tokenholders do not know the token’s underlying network size of the token of the tokenholder they meet next, Shn does not depend on the token’s underlying network,and hence theinterceptsfrom the price regressions are sufficient for identification. Shn depends on valuations {zl,zh}and bargaining parameters {θs,θ d}. Shn is negative if the transaction surplus is exclusively appropriated by selling individual and intermediaries. Shn increases in the combined buying individuals’ bargaining power, (1−θs)+(1−θd). This also implies 123 1438 P.P. 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