We analyze how early-stage investors evaluate potential investments by using

ChatGPT to analyze 5,143 expert consultation calls. Companies discussed in these

calls are 15 percentage points more likely to receive financing in the following quarter.

Positive signals about technology integration and customer acquisition increase deal

likelihood by 14% and 10.5%, respectively, with their predictive power declining by

over 75% for mature companies. Market analysis and business strategy discussions –

comprising over 40% of call content—show minimal predictive power for investment

outcomes. Our findings document both how investors overcome information asymmetries

in early-stage investing and a misalignment between the information they seek and

the information that predicts investment outcomes. Methodologically, we demonstrate

the potential of LLMs to extract nuanced insights from complex qualitative data.

We provide a comprehensive analysis of AI adoption across various aspects of asset management by examining U.S. registered investment advisers. Since 2017, the investment advisory industry has seen significant AI adoption, primarily for investment strategies and IT infrastructure, while generative AI adoption remained minimal until 2023. AI adoption varies substantially across advisers and is related to the size and composition of advisers’ assets under management, client base, and workforce. Advisers with larger assets, a higher share of hedge funds, and fewer employees directly interacting with individual clients exhibit higher levels of AI adoption. Moreover, we estimate that AI adoption leads to increased growth of the investment advisory business and a shift toward private funds, with spillover effects extending beyond hedge funds. Our findings underscore the heterogeneity of AI applications across the diverse funds and services in asset management.

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We document that blocks on the blockchain are rarely filled to capacity, even though
there is excess demand for block space by fee-paying users who want their transactions to
be recorded. In spite of this, higher fee orders are not always prioritized. We show these
patterns are consistent with miners exercising market power: If users believe that only high
fee transactions will be executed expeditiously we show how strategic capacity management
can be used to extract higher fee revenue. Using a novel data set, we provide evidence
consistent with this market power, and estimate that mining pools have extracted least 300
million USD a year in excess fees by making processing capacity artificially scarce.

Liquid staking allows agents to sell ownership of an illiquid claim to satisfy a liquidity need. We develop a model of liquid staking and characterize the effect of the secondary market on protocol stability. We establish that the liquid market has two effects: first, it allows agents to redeem illiquid assets and thus reduces run risk on the protocol, but also conveys information and can act as a coordination mechanism and increase market run risk. Using novel data on the Lido protocol, we present stylized facts on the staking market and relate our results to the design of digital deposits.

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This presentation synthesises evidence from several new survey-based studies (supported by Nasdaq N.F.) that cover more than 2,300 individual investors in Denmark, Finland, Sweden and France, in order to explain why, how and for whom cryptocurrencies are attractive. The combined findings reveal multiple behavioural channels, i.e., demographics, memory accuracy, interpersonal and institutional trust, gendered knowledge gaps in risk perception, promotion-versus-prevention motivation, and values-based beliefs, that jointly determine adoption and price expectations in a market where traditional fundamentals are weak, offering academics new micro-foundations for expectation-formation models.

Public debate about cryptocurrency reveals strong and disparate opinions on potential adoption. The paper argues that this uncertainty is the driving factor of cryptocurrency prices. In the model presented, uncertainty about a cryptocurrency's eventual adoption demand amount leads to a discounted cryptocurrency price. However, over time, investors learn about the likely extent of eventual adoption, and the discount reduces. The model replicates the long-term decline in price growth rates and variance of Bitcoin and other major cryptocurrencies. The model can forecast expected price growth and variance conditional on low interim adoption, providing guidance to cryptocurrency allocation sizing within an investment portfolio. The model also estimates the probability distribution of a cryptocurrency's extent of eventual adoption.

The quality of cryptocurrency market data is critical for academic research and financial applications, yet the topic remains understudied. We analyze data from leading vendors and document pervasive mislabeling, measurement errors, and discrepancies in reported market metrics. To address these issues, we propose a novel aggregation methodology that achieves asymptotic accuracy by identifying unreliable data instances. We also introduce a data quality grading system, offering practical guidance for data consumers. Our findings underscore the risks of relying on a single provider. They highlight a possible need for regulation in the market for crypto data.

How borrowers respond to future changes in the interest rate on their debt is of crucial importance for the transmission of monetary policy and for financial stability. Combining data from a large bank, a letter RCT, and an online survey, we study this question in the context of the German mortgage market, where borrowers face high interest rates since 2022 when their rate fixation period ends. We find that borrowers take various actions to reduce the impact of higher rates on interest payments. Survey responses indicate high awareness of the evolution of interest rates and corroborate a strong propensity to prepare for the rate reset, which we show experimentally is sensitive to the size of the rate increase and to the distance from reset. Our letter intervention does not affect rate beliefs, consistent with high ex-ante knowledge, but increases awareness of available options and the desire to prepare. Ongoing tracking will reveal whether this awareness translates into actual behavior.

What is an effective signal in crowdfunding? We asked this question to 83 expert researchers who have published the top-notch articles in this field. They stated that, in theory, strong signals include past crowdfunding success, business experience, patent ownership, and the equity share offered. Examining 145 articles published in leading business and economics journals, we find that the empirical evidence from a meta-analysis does not accord with this perception among expert researchers. Signals that expert researchers consider to be theoretically less strong are more often statistically significant predictors of crowdfunding success and have neither larger nor smaller standardized effect sizes than strong signals. A meta-regression suggests that domain-specific signals play the most important role in crowdfunding. The findings of our literature review provide important insights for investors, platform managers, and the academic review process.

This paper analyzes trading patterns and investor behavior in the market for tokenized
financial assets. Even though investors can trade tokenized assets around the clock, trading
activity is substantially higher when the primary listing exchange is open, in particular during
the regular trading session. Likewise, price differences between the tokenized and underlying
asset are wider during the extended trading hours than during the regular trading session.
The majority of trades result from fractional order sizes, but orders still cluster at relatively
round sizes, values, and prices. Additionally, a disproportionate number of trades, especially
buy trades, use the minimum permissible order size, suggesting the presence of retail traders
interested in owning an asset for purposes other than financial gain.

We examine the sustainability of consumption choices using unique data from a FinTech app tracking spending and emissions. Using a randomized encouragement design, we show that carbon calculator services that provide transaction-level information on emissions do not change users’ behavior. Instead, carbon-offsetting services, though less popular, are effective in reducing emissions. A survey of app users suggests the carbon calculator’s ineffectiveness stems from users not prioritizing climate change over other economic issues. Limited attention instead explains the low adoption of carbon offsetting. These findings highlight the challenges and opportunities of sustainability tools that have been increasingly adopted by financial institutions.

Using a novel dataset from Ant Forest, a mini-app within Alibaba’s flagship fintech platform Alipay, we examine how gamified social interactions influence users’ green investment decisions. We find that the users’ green preference, measured by daily low-carbon activities, is enhanced when they engage more in gamified social interactions designed for environmental education, thereby increasing their investment proportion in green mutual funds. We also provide suggestive, but only suggestive, evidence that green education may play a role in shaping users’ green preferences through gamification. Our findings are stronger among male and younger users and those less involved in environmental conservation actions. Our study provides the first mechanism in which gamified social interactions facilitate green investments by enhancing individuals’ green preferences.

We model crowdfunding as a device that commits stakeholders to their ESG preferences. We test the model predictions in the context of crowdfunded microfinance by constructing a novel dataset of partnerships between the Kiva crowdfunding platform and 112 microfinance institutions (MFIs) worldwide. In each partnership, Kiva crowdfunders extend MFI-intermediated loans to necessity entrepreneurs in developing countries. Kiva lenders have strong non-pecuniary preferences: they collectively prefer funding female entrepreneurs and demand zero interest on their loans. In our triple-difference framework, we show that the MFIs that crowdfund a large share of their loan portfolios have more non-performing loans, lower asset utilization rates, and higher labor costs than low take-up counterparts. Importantly, the gender gap in financial inclusion narrows, primarily due to less inclusive MFIs catching up. Our results suggest a costly financial inclusion driven by entrepreneurs left behind by banks but picked up by the Kiva lenders with ESG preferences.

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In this paper, we utilize the public Bitcoin blockchain to track and monitor the movement of Bitcoin from official government sanctioned entities. Specifically, we examine the flow of Bitcoin to and from sanctioned addresses to exchanges and identify the impact that regulations have on those flows. We find that regulations cause a significant decline (increase) in inflows (outflows) to exchanges, suggesting that regulations do have the desired effect. However, we show that the quality of the legal system is a significant, in that the regulation needs to passed in the country of the exchange to have the desired effect. Therefore our analysis shows that regulators wanting to limit the usage of exchanges in their countries by sanctioned entities should regulate the exchanges and have a high level of regulatory quality.

Additionally, the paper scrutinizes the influence of countries' legal system quality and socio-cultural factors on the effectiveness of regulatory interventions. By considering the interplay between regulatory frameworks and societal dynamics, it provides valuable insights into the varying efficacy of regulations across different contexts.

Ultimately, this study underscores the complexity of regulating Bitcoin flows from sanctioned entities and ransomware attackers, emphasizing the importance of tailored regulatory approaches informed by both technical and sociopolitical considerations.

Cryptocurrencies have made their mark on financial markets, becoming a fast-growing segment with more than 20,000 assets since Bitcoin's introduction in 2009. As the most popular cryptocurrencies are often considered diversifiers in traditional portfolios, understanding their dependence structure and risk spillover mechanisms becomes crucial.
This article explores connectedness in the cryptocurrency market through the lens of two critical dimensions: volatility and liquidity. We examine spillovers across 55 of the most capitalized cryptocurrencies continuously traded between January 2018 and December 2024, a period encompassing several major market disruptions. Using network science tools and the frequency connectedness approach based on variance decomposition, we investigate how much of the future volatility or liquidity of a crypto asset relies on shocks observed in other cryptocurrencies.

For both volatility and liquidity connectedness, we build networks and identify the most central cryptocurrencies—those that function as primary contagion sources within the system. We apply several centrality measures and utilize rankings based on the TOPSIS method to determine the most influential players. Our analysis employs both static approaches for the full sample and dynamic approaches within moving windows, with LASSO methods applied in estimating the complex multivariate system.

Our findings reveal that liquidity networks and volatility networks exhibit distinct patterns. The dynamic approach shows that over time, spillovers in liquidity are stronger than in volatility, with both increasing during turbulent periods. When considering market capitalization, the strongest transmission of volatility and liquidity flows from highly capitalized cryptos to smaller ones, but the transmission power of the biggest cryptos is decreasing over time.
We contribute to the literature by jointly examining volatility and liquidity risk dimensions, identifying central nodes in both networks, and providing a temporal analysis spanning multiple market regimes. Our findings carry important implications for international investors and portfolio managers who consider incorporating cryptocurrencies into their portfolios, enabling better-fitted investment strategies and enhanced understanding of the cryptocurrency market's resilience and contagion mechanisms.

The discourse surrounding Central Bank Digital Currencies (CBDCs) involves a diverse range of stakeholders from traditional and non-traditional financial sectors. In this study, we analyzed interviews conducted with four distinct groups of financial experts shaping the global financial system: Federal Reserve representatives (FED), global central bankers represented by the Basel Committee for International Settlements (BIS), the decentralized finance (DeFi) community and the financial regulation experts (Regtech). The research question aims to identify the underlying connections and differences in the CBDC adoption discourse among the stakeholders. We used mixed methods approach and conducted comprehensive content analysis. Factor analysis was performed using WordStat software and the results were extended with natural language processing (NLP) techniques using NVivo 14 software by employing automatic themes detection on aggregated textual data, manual coding and pattern-based auto-coding to map underlying topics and identify underlaying connections within the groups. The common topics identified were payments, settlement efficiency, implications for commercial banks, market infrastructure and cash. Differences in perspectives include DeFi envisioning the future financial system as open source smart contracts-based system, while BIS or FED seeing CBDC as better payment tools which may bring some functionality to wholesale market or Regtech community mostly focuses on payments and risks simulations. By shedding light on differing perspectives regarding CBDCs, this study provides insights for policymakers, industry professionals, and researchers and will lead to more inform decision making bringing together the diverse expertise from various stakeholder groups, helping to agree on the key disagreements lying in smaller details while fostering alignment on shared long-term goals to foster global financial system with innovations and achieve better financial inclusion and market efficiency.

Everyday Banking is a sub-sector of financial services that comprises institutions and processes enabling the servicing of current accounts and mobile wallets. Current accounts are the simplest financial products—abstracted as places where individuals and SMEs store their money in the most liquid form to receive, deposit, or withdraw funds and pay for regular expenses and bills.

Everyday banking is part of the finance sector, formally recognised as a critical national infrastructure of the UK. Despite its CNI status, finance remains largely a self-regulated sector, with little to no standardized resilience modelling for its industries. Recent regulation in the EU and the UK addressing the operational resilience of the financial institutions is prescriptive and at the same time narrow, avoiding a systemic view. This paper will not focus on the regulation in place but rather points towards a few mechanisms which change significantly for everyday banking industry operates.

I will discuss three types of unbundling: 1) the unbundling of banking, payments, and money 2) technology driven unbundling or modularisation and 3) spatial unbundling. These phenomena are by no means unique to the UK. They are all quite poorly understood especially in their concurrent effects in terms of industry resilience.

Most large economies especially in the Global North do not have yet an uncoupling of money, payments, and banking of the type of Alipay and WeChat in China or PIX in Brazil (Awrey, 2021). However, signs of uncoupling exist even in the UK, and the paper will highlight how these manifests in the UK. Technology-driven unbundling goes far beyond digital unbundling, much praised in fintech conferences as a phenomenon which allows new entrants to offer an increased, hyper-personalised choice of financial services coupled with a re-bundling of selected services on one’s phone. The technology driven unbundling refers to a restructuring and modularisation of the deeper layers of banking technology leading to new possible business models and legal structures for delivering financial services (Sonea, 2017).

Last but not least, spatial unbundling is new as a concept (Sonea, 2024) and it refer to a process of extreme spatial separation of basic banking services viewed from the point of the customer. In such a case, example a person who cannot do digital banking has to go to a post office mobile van on Tuesday to get their pension in cash, look for a bank mobile van if somehow they missed the post office van stopping for half an hour every other week in their area, drive or be driven for 40km to town to pay their credit card in a physical bank branch. This is not an exception or some rare occurrence but a phenomenon which I found in the UK through computational spatial analysis of access to banking triangulated with field observation. The UK the branch network shrinked in the past five years to an extreme; the typology of branches communicates little to the user about their real functionality. As such, this process forces an increased monetary, physical, and cognitive cost on the most vulnerable individuals in society. In contrast to digital unbundling, the spatial unbundling of the basic banking services is exactly the opposite - it is the removal of control and choice.

All types of unbundling are usually followed by re-bundling of components and services in new forms in terms of firms, business models, and ways of consuming the resulting services. It is not yet fully clear how the system reconfigures. Equally important, when CNIs operations are disrupted or compromised, they could have a significant impact on people and economy. With this in mind, the monitoring of unbundling and re-bundling of basic banking services is not a theoretical exercise but something that should be at the top of the regulators’ agenda. The paper will present a couple of unbundling scenarios which ask for a rethink of everyday banking infrastructure resilience modelling and monitoring.

Generative Artificial Intelligence (GenAI) has taken society by storm in recent years, with comparisons drawn to past industrial revolutions in the literature in terms of disruptive potential. Innovations like GenAI force businesses to transform their business models to remain competitive. Literature connecting AI and business model innovation (BMI) is limited but emerging; specifically, GenAI's ability to shape future business models has not been studied at an industry level. Banking warrants focused attention given it has been at the forefront of digital innovation, its diverse service types across multiple customer segments, and the significant information asymmetries that GenAI could address. This paper explores BMI in banking through a GenAI lens, conducting a structured literature review of GenAI applications, followed by a hybrid thematic analysis. We contribute to theory by combining value chain analysis with BMI frameworks, creating an integrated analytical approach for studying technological disruption. We identify 28 distinct applications across 40 articles, mapping them to the banking value chain and outlining six levers through which BMI could be delivered in banking. Our study presents methodologies replicable for other industries and identifies avenues for future research.

This study evaluates the suitability of various event study methodologies for cryptocurrency markets, focusing on identifying the most effective statistical tests for event-induced returns and volatility across different cryptocurrency sub-samples. Through extensive analysis, we find that non-parametric tests provide more robust and reliable results, particularly in environments characterized by high volatility and non-normal return distributions. Our findings also show that larger sample sizes improve the accuracy of test results, reinforcing the effectiveness of value-weighted indices as benchmarks for large-cap cryptocurrencies. However, these indices demonstrate limitations when applied to smaller or highly volatile cryptocurrencies. This research enhances the adaptability of event study methodologies to the dynamic nature of cryptocurrency markets, offering broader implications for emerging and volatile financial markets.

We explore how stock price reactions to Twitter (now known as X) posts are associated with the perceived credibility of social media users making the posts. We introduce new credibility metrics based on the sender and the content of Twitter posts. Less credible tweets influence prices through a transient liquidity effect, while more credible tweets lead to a persistent information effect. Our results support the Elaboration Likelihood Model by demonstrating that the direct route of persuasion (represented by post credibility) is larger in magnitude and more persistent over time than the peripheral route of persuasion (represented by sender credibility).

This study examines the effects of bot-generated social media content on stock market behavior.

We examine whether bots amplify or suppress the impact of social media on trading

activity and returns. Using data from Twitter (now X), covering 2019 to 2022, we construct

measures capturing the likelihood of bot activity in posts mentioning well-known stocks

using cashtags. Our findings show that bot activity reduces the impact of social media on

both trading volume and returns. Our results imply that investors have the ability to distinguish

between genuine and automated activity on social media and that genuine activity is

perceived as a more reliable source of financial information.