The literature on finance and innovation treats the relationship as monotone: more capital directed at innovative firms should raise long-run productivity growth. This paper shows why that need not hold. A Schumpeterian growth model is developed in which financiers screen heterogeneous candidate innovations before funding them, subject to a genuine cost-speed tradeoff: stricter screening raises the average quality of funded projects but slows the rate at which capital reaches the innovation frontier. The balanced-growth rate is a strictly concave, single-peaked function of screening intensity — an Allocation Frontier — with a technologically determined peak and a decentralized equilibrium that always falls strictly short of it, by an amount governed by the cost of screening relative to the value of getting it right. A calibration combining OECD productivity data, the markup literature, and an illustrative target for screening intensity finds this shortfall costs the calibrated economy roughly four percent of its attainable growth rate. Comparative statics further show that “more innovation finance” is not one thing: cheaper screening and greater entrant heterogeneity both raise growth by moving the economy toward its own frontier peak, while a larger raw flow of candidates raises growth by moving it away from an unchanging one.
We present the first formal treatment of \emph{yield tokenization}, a mechanism that decomposes yield-bearing assets into principal and yield components to facilitate risk transfer and price discovery in decentralized finance (DeFi). We propose a model that characterizes yield token dynamics using stochastic differential equations. We derive a no-arbitrage pricing framework for yield tokens, enabling their use in hedging future yield volatility and managing interest rate risk in decentralized lending pools. Taking DeFi lending as our focus, we show how both borrowers and lenders can use yield tokens to achieve optimal hedging outcomes and mitigate exposure to adversarial interest rate manipulation. Furthermore, we design automated market makers (AMMs) that incorporate a menu of bonding curves to aggregate liquidity from participants with heterogeneous risk preferences. This leads to an efficient and incentive-compatible mechanism for trading yield tokens and yield futures. Building on these foundations, we propose a modular \textit{fixed-rate} lending protocol that synthesizes on-chain yield token markets and lending pools, enabling robust interest rate discovery and enhancing capital efficiency. Our work provides the theoretical underpinnings for risk management and fixed-income infrastructure in DeFi, offering practical mechanisms for stable and sustainable yield markets.
Companies continue to explore the marketing potential of non-fungible tokens (NFTs). To succeed, they must understand the core motivations of NFT buyers. This explanatory study provides descriptive insights about NFT buyers and identifies key operational principles within the NFT market. It addresses the critical questions: What NFT buyer segments exist? Which segments should companies target, and how? Surveying 703 NFT buyers, the study identifies five distinct NFT buyer segments: curious speculators (18%), cautious investors (5%), utility-driven buyers (35%), tech-savvy investors (29%), and status-seeking socializers (13%). The first two segments are primarily motivated by investment, purchasing NFTs mainly for resale. In contrast, the remaining segments are driven by ownership motives, purchasing NFTs not only for financial potential but also for personal use, technological interest, and their social values. Drawing on customer engagement theory, the study highlights these three ownership-oriented segments as particularly attractive for companies. Beyond a high willingness to buy branded NFTs, these segments actively recommend NFTs to new buyers and show substantial engagement in NFT communities, a critical success factor for NFT markets. The study further shows that companies can enhance NFT buyer engagement through incentivized referrals and provide marketers with a strategic guide for effectively targeting these segments.
Amidst the frenzy surrounding Non-Fungible Tokens (NFTs) in 2021, the concept of digital assets and trading was redefined. Although the initial hype may have subsided, NFTs continue to drive innovation in ownership, with substantial revenue streams flowing through the market. This transformative shift underscores the importance of discerning the factors that shape this ecosystem. This paper delves into the intricate dynamics of the NFT market, particularly focusing on the impact of creation methods—whether hand-drawn or artificial intelligence (AI)-generated—on market behavior. In a comprehensive analysis of the NFT market, we have analyzed a vast dataset comprising 1,478,556 transactions of NFT art from the OpenSea marketplace in 2023 to explore correlations and patterns between key transactional features. Furthermore, we employed regression models to predict the sales of an NFT and classification models to distinguish between hand-drawn and AI-generated NFTs. Finally, by comparing different machine learning models, we identified the most appropriate model for analyzing the market, considering the non-linear relationships and complex nature of the NFT market. Overall, the results provided in this research can lead to making more informed decisions regarding investment, creation, and trading.
As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.
We study how trading fees and continuous-time arbitrage affect the profitability of liquidity providers (LPs) in Geometric Mean Market Makers (G3Ms). We use stochastic reflected diffusion processes to analyze the dynamics of a G3M model under the arbitrage-driven market [Milionis et al. 2022a. “Automated Market Making and Loss-Versus-Rebalancing.” arXiv e-prints]. Our research focuses on calculating LP wealth and extends the findings of Tassy and White [Tassy and White. 2020. “Growth Rate of a Liquidity Provider's Wealth in xy = c Automated Market Makers.”] for the constant product market maker (Uniswap v2) to a broader range of G3Ms, including Balancer. This allows us to calculate the long-term expected logarithmic growth of LP wealth, offering new insights into the complex dynamics of AMMs and their implications for LPs in decentralized finance.
In this paper, we studyhow to optimize existing non-fungible token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we uncover an unexpected finding: current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. To address this, we propose, analyze, and implement a novelreference incentivemodel, inherently structured as a directed acyclic graph (DAG)-based NFT network. Leveraging the Stackelberg game framework and deep reinforcement learning (DRL), this model aims to maximize connections (or references) between NFTs, enabling isolated NFTs to expand their networks and accumulate rewards from subsequent or subscribed ones. Through both theoretical and practical analyses, we demonstrate the optimal utility of the proposed model.
Viktor Ström, Nima Sanandaji, Saeid Esmaeilzadeh, M Esmaeilzadeh
Purpose The purpose of this paper is to investigate the potential link between Sweden’s high reliance on equity capital financing among small and medium-sized enterprises (SMEs) and its recognition as the most innovative economy in Europe according to the European Innovation Scoreboard (EIS). This paper examines the idea that the high levels of trust within Swedish society can explain why private equity financing is more prevalent among Swedish SMEs. Design/methodology/approach To test these ideas, the authors use data from the Survey on Access to Finance for Enterprises to measure the private equity reliance of firms. The authors also use the EIS to measure the innovation capacity of nations and various aspects of SMEs’ innovation activities. Finally, societal levels of trust are measured through the World Value Survey. Findings First, the authors find that European countries with a higher proportion of SMEs relying on equity financing tend to be ranked as more innovative by the EIS. Second, the authors find that the correlation between a nation’s share of SMEs relying on equity financing and their level of innovation activities is marginally stronger for product innovations than for business process innovations. Third, the authors find that countries with higher levels of trust tend to have higher equity capital reliance among SMEs. Originality/value This study builds upon previous research on equity capital and SMEs’ innovation activity while introducing new insights into the relationship between societal trust and equity financing.
Blockchain technology facilities multi-party applications that do not require the parties to trust each other, that are failure-resistant due to their decentralized nature, and that provide a consistent view on the transaction history. These properties make blockchains attractive for decentralized finance (DeFi), and in particular for trade finance, where parties do not necessarily trust each other and aim at reducing their financial risks.Traditionally, intermediaries like banks or fiduciaries provide such services – along with several inconveniences like the increased risk of fraud due to antiquated systems and processes, considerable settlement delays, and high costs.In this work, we focus on the financial instrument Letter of Credit (L/C), which is used to secure payments in international trade. We propose a method for evaluating blockchains for DeFi based on this use case. We adapt existing catalogues of criteria for platform evaluation to fit the development and operation of DeFi applications. After discussing and designing a prototype of a typical L/C workflow, we implement it on selected blockchain platforms. The evaluation rates the feasibility and usability of the development process.
There is disagreement in the literature concerning the impact of regulations on firms’ development. While some researchers believe that regulation impedes companies’ development (Poel et al., 2014; Jalilian et al., 2007), others argue that regulations enable companies’ development (Peck et al., 2018; Mayson et al., 2014). This paper aims to contribute to a better understanding of the impact of financial regulations on development of Distributed Ledger Technology (DLT) firms. In-depth semi-structured open-ended interviews have been conducted with 20 Small Medium Enterprise (SME) DLT companies in Europe during April and May 2019. Our results show that the expected impact of financial regulation can be ambiguous as it can both enable and constrain a firm’s development. This is in line with Kitching et al. (2015) theory of regulation as a dynamic force.
A project on the use of distributed registry technology to improve funding for start-ups in Japan has been considered. The project is based on the application of distributed registry technology, smart contracts, a big database of start-ups credit risk (in Japan CRDS), a local investment fund, a unified marketing and trading platform. The model of investor behavior (taking into account risk and profitability) has made it possible to show, that with growing investor confidence (individuals and households) and a certain level of profitability of startups, investing in startups will be more preferable, than a bank deposit. The features of the project have been highlighted and a conclusion has been made, that under certain conditions, the adduced scheme for financing start-ups can be used in Russia, for example, by the Industry Development Fund.
Pension funds, when they acquire common shares of companies in the capital markets, start to participate more actively in the decision-making of boards of directors and, through their representatives, in the monitoring of managers. The aim of this study is to determine whether pension funds are good monitors. This is done by identifying the influence of the control structure of pension funds over the financial performance and the market value of Brazilian public companies. Using dynamical models of linear and non-linear regressions estimated by GMM-Sys in an unbalanced panel from 1995 to 2015, it is shown that pension funds do not play a good monitoring role, as the control structure of these funds is negatively related to the financial performance of a company or, in other words, the higher the stake, the worse the performance of the company. A possible reason for this is that pension funds invest in the capital markets for portfolio diversification, are not concerned with specific decision-making in companies and have few monitoring skills, thus generating conflicts that go against the objective of maximizing the value of the company. Also, the study identifies the fact that investors give a higher value to the shares of firms in which domestic public funds have investments, even without proof that such funds improve the profitability of companies. Os fundos de pensão, ao adquirirem ações ordinárias de empresas no mercado de capitais, começam a participar mais ativamente nas tomadas de decisão dos conselhos de administração e no monitoramento dos gestores através de seus representantes. Devido a essa questão, o presente estudo buscou verificar se os fundos de pensão são bons monitores através da identificação da influência da estrutura de controle destes no desempenho financeiro e no valor de mercado das empresas de capital aberto brasileiras. Utilizando modelos dinâmicos de regressões lineares e não lineares múltiplas, estimadas pelo GMM-Sys, em um painel não balanceado de 1995 a 2015, foi evidenciado que os fundos de pensão não desempenham um bom papel de monitoramento, já que a estrutura de controle destes fundos possui uma relação inversa com o resultado financeiro tanto interno quanto de mercado, ou seja, quanto maior a participação acionária, menor é o desempenho das empresas. Esse resultado foi encontrado, possivelmente, pois os fundos de pensão investem no mercado de capitais para diversificação de portfólio, não estando preocupados com tomadas de decisão específicas nas empresas, gerando, assim, falta de habilidades de monitoramento adequadas, provocando conflitos que vão contra o objetivo de maximização de valor das empresas. Também, foi identificado que os investidores valorizam as ações de firmas investidas por fundos públicos domésticos, mesmo sem comprovação que tais fundos melhoram a rentabilidade das empresas.
Distributed Ledger Technology (DLT) creates a decentralized system for trust and transaction validation using executable smart contracts to update information across a distributed database. This type of ecosystem can be applied to Commodity Trade Finance to alleviate critical issues of information asymmetry and the cost of transacting which are the leading causes of the Trade Finance Gap (ie. the lack of supply of capital to meet total trade finance demand). The possibility of scaling up such ecosystems with a number of Institutional Investors and micro small medium enterprises (MSME) would be advantageous, however, it brings up its own set of challenges including the stability of the system design. Agent-based modeling (ABM) is a powerful method to assess the financial ecosystem dynamics. DLT ecosystems model well under ABM, as the agents present a clearly defined taxonomy. In this study, we use ABM to assess the Aquifer Institute Platform - a DLT-based Commodity Trade Finance system, in which a growing number of participating parties is closely related to the circulation of utility tokens and transaction flows. We study the system dynamics of the platform and propose an appropriate setup for different transaction loads.
Which is more innovative: the decentralized, diversified firm, or the centralized, more narrowly focused firm? The economics and finance literatures argue that diversified firms have innovation advantages as their operating units have access to an internal capital market. In contrast, the strategy and entrepreneurship literatures argue that managers of these firms suffer from “managerial myopia,” discouraging them from investing in projects with long‐term, uncertain payoffs. We take a fresh look at the relationship between innovation and diversification using a comprehensive sample of diversified and nondiversified firms and a novel approach that teases out the mechanisms influencing the relationship between diversification and innovation. Consistent with conceptual and empirical work in strategy, we find a robust negative correlation between diversification and R&D intensity, suggesting that diversification reduces innovation by discouraging investment. However, our analysis suggests that internal capital market inefficiencies, rather than managerial myopia, is responsible for this observed negative relationship.
This study analyzes which types of firm-level shocks were associated with the centralization of strategic decision-making during the recession of 2008-09. We use a unique survey dataset of more than 14000 manufacturing firms from seven European countries which includes direct information on whether the firms centralized or decentralized their strategic decision-making process. Motivated by theoretical approaches claiming that organizations under considerable stress are more likely to centralize, we use multinomial logit models to test whether firms facing a larger fall in turnover, employment, investment or having to postpone their innovations were more likely to change their decision-making process. We find evidence that employment change and postponing innovations are indeed associated with centralization even when we control for ownership, group structure, financing, management, and strategy.
This dissertation addresses the assembly of organizational resources by technology ventures. We study how innovative firms acquire human and financial capital and then organize those resources, and how public policy affects that capability.\nIn the first chapter, we study the role of information in organizational decision-making for the financing of entrepreneurial ventures. We formally model a decentralized set of agents who vote strategically to allocate resources to a project with unknown outcome; they can each acquire costly information to improve their decision quality. We test our predictions in the setting of venture capital, where partners make their own angel investments outside of their employer. We find that the venture capital partners, acting independently, make riskier investments into younger firms with less educated and younger founding teams, but these investments perform better on some metrics even when controlling for investment size and stage. Geographic distance and liquidity constraints increase the probability the investment is taken up by a partner and not the VC.\nIn the second chapter, we evaluate the impact of skilled immigration on U.S. innovation by exploiting a random lottery in the H-1B visa program. Proponents argue that immigration allows firms to access technical skills and promote innovation, while opponents argue that firms substitute domestic labor for cheaper but equally or less skilled foreign labor. We find that winning an H-1B immigrant does not significantly increase patent applications or grants at the firm level, and there is pervasive use of the program in industries where patenting is not the main value-appropriation strategy.\nIn the third chapter, we study how a firm should organize the diversity of technical experience, contained within its pool of inventive human capital, for firm-level innovation. Using a sample of biotechnology start-ups, we examine the implications of alternate firm-level design regimes, drawing on both a firm-year panel structure and an inventor-year difference-in-differences empirical approach. Organizing a firm's human capital with greater across-team diversity yields increased firm-level innovation benefits as compared to organizing with greater within-team diversity. The benefits of across-team diversity stem mainly from the influence of that regime on team stability.
Using a new and unique dataset dealing with French small and medium-sized enterprise (SME) financing that provides detailed information about 1 116 firm-bank relationships, we test how the number of banks with which a firm works and the organizational structure of its main bank influence its risk-taking behavior. We find evidence that SMEs engaged with a decentralized main bank (a local or mutual one) invest in less risky projects, especially when they work with fewer than three banks (one or two). We also find evidence that single-bank SMEs engaged with a centralized bank (a large or foreign one) take significantly more risks than the others.
This paper gives an interpretation of the recent diffusion of the processes of productive outsourcing founded on two explanatory points. The first is that such processes replace a hierarchical paradigm of information diffusion with a decentralized paradigm in which independent subcontracting firms autonomously collect and process part or all of the prominent information. The second is constituted by the change of the modality of production innovation, becoming the result of autonomously developed inputs that are successively made complementary by the work of the network through an encapsulation process of the information. This is made possible by the fact that after an initial phase in which a new input is jointly projected by the contractor and the subcontractor and in which the information comes shared, a phase follows in which the prominent information for the specification of the characteristics of the product and for the solution of the local and unforeseen problems is collected and processed in a sequential manner and then encapsulated in the input by autonomous production units. In comparison with the vertical integration (make) or the market (buy), this form of governance (subcontract) allows for the organization in more efficient manner of the processing of the information, for the reduction of the informative costs and for the minimization of the risk of spillover.