Luca Pacioli, a friend and contemporary of Leonardo da Vinci, is generally considered to be the father of modern accounting. In this paper, Willi Brammertz and Allan I. Mendelowitz of the ACTUS Users Association and ACTUS Financial Research Foundation argue that Pacioli more than 500 years ago proposed a solution for the long-standing data and IT problems that financial institutions are facing today.

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.

We derive an option-implied valuation of impermanent loss for liquidity providers on decentralized exchanges and quantify it based on traded option prices. We propose a model that values impermanent loss through the variance of the tokens' relative price. Since the relative price is not the price of a traded asset, we introduce a model for the distribution of the former and a valuation formula induced by a change of num'{e}raire. We show that impermanent loss arises from the tokens' individual risks and their correlation risk. These risks negatively impact pool sizes and explain the cross-sectional returns of liquidity pools.

Academics and practitioners of finance have focused their work on understanding risk and uncertainty. In the process they have neither recognized nor given attention to the existence of certainty. Certainty exists in finance in the form of the core building blocks of finance – individual financial contracts. Such contracts represent the explicit agreements between counterparties to exchange specific payments (which we refer to as “Cash Flows”). In fact, the consequences of financial risk and uncertainty are only quantifiable to the extent that they alter the promised cash flows defined by a financial contract. This paper: 1) develops the logic behind an explicit recognition of certainty in finance; 2) provides the vehicle for capturing and preserving certainty in finance in the form of an algorithmic financial contract standard, such as the ACTUS Financial Contract Standard; 3) explains how to operationalize the algorithmic financial contract standard in the form of a software implementation that preserves certainty in all operational and analytical activities of a financial institution; and 4) explores the potential benefits in analytical insight, quality, and internal operational efficiency that a financial institution can achieve by acknowledging certainty and leveraging a widely adopted algorithmic financial contract standard; 5) increases the accuracy and reliability of interbank transactions and data sharing. Significant additional benefits can ensue by both reducing the cost and burden of regulatory reporting and enhancing the value of the data and analytics the regulators receive from and provide to the banks.

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.

I provide the first systematic analysis of collateral choices in one of the main short-term funding markets, the repurchase agreement (repo) market. Repos establish a natural connection between short-term and long-term funding markets as long-term bonds serve as collateral in short-term funding trades. In general collateral repos, banks can choose which bond they post as collateral out of a predefined list. In the aggregate, on-the-run bonds are more likely to be delivered than cheapest-to-post securities, which is surprising given that the former are more expensive. I rationalize those findings in a theoretical framework that links the repo to the bond market. My results are relevant for explaining bond market patterns that are different in the United States compared to the euro area.

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.

This paper analyzes the effects of hidden liquidity by studying the introduction of iceberg orders at a large cryptocurrency exchange. Compared to other assets, cryptocurrencies often trade against both fiat currencies and pegged stablecoins. Considering the introduction of iceberg orders for trading pairs against the US dollar but not against a dollar-pegged stablecoin, this study finds that hidden liquidity is associated with increased quoting and trading activity. Larger average trade sizes suggest greater institutional participation. Liquidity improves through tighter spreads and deeper depth, while the price impact of trades declines. Realized spreads increase, indicating improved revenues for market makers while also offering enhanced execution conditions for liquidity takers. Price discovery also shifts significantly toward the markets accepting iceberg orders. Overall, our results suggest that hidden liquidity has positive effects on market quality.

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Abstract:

This study examines the efficiency of Robo-Advisors within the broader context of Fintech in wealth and asset management, aiming to determine their performance relative to traditional asset management strategies and benchmarks. Positioned as a case study, the research explores the performance of a particular leading Robo-Advisor in Japan, decomposing returns into tactical and strategic components, alongside various risk metrics. The work is structured into three key parts: theoretical foundations, methodological development, and empirical analysis.

First a classification framework is proposed to capture the essential features of Robo-Advisory services globally. Then, the experiment is developed and conducted: Empirical results reveal that, across varied market conditions, Robo-Advisors do not consistently outperform a simple fixed-weight strategy, which holds assets at predetermined allocations without frequent adjustments. Furthermore, the study’s comparison with the Japanese Fund Market suggests no statistically significant difference in asset management outcomes between Robo-Advisors and conventional fund strategies.

Desing/Approach:

A case study methodology is employed, focusing on a leading Japanese Robo-Advisor. Theoretical foundation of the Robo-Advisor methodology and data from the Japanese Fund Association are utilized to construct a composite benchmark and evaluate returns, which are decomposed into tactical and strategic components. Statistical tests and risk metrics are applied to data to determine significant performance differences between the Robo-Advisor and traditional asset management strategies.

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.

Most human decisions are taken intuitively, with a mix of reflection and emotions that is often impossible to disentangle. Economists model decisions explicitly as a mixture of objective and subjective elements: economic agents objectively (mathematically) optimize a subjective value function. Using this model, one can create situations where choice is independent of subjective value and demonstrate that humans often fail at the objective part of decision making. Algorithmic advisers can thus help humans, as they never fail at objective optimization.

However, since decision optimality depends both on correct optimization and on knowledge of the right subjective value function, machines who disregard the taste or “preferences” of the human on whose behalf they act, will make poor decisions. Thus, the performance of algorithmic advisers is crucially affected by the machine’s ability to learn about a particular human’s preferences. But will a human do better at communicating their preferences to a machine than at making their decisions themselves? We know humans fail at common tasks of deciding what to consume or invest in, but will they be less faulty at the even less natural task of communicating their preferences?

In the controlled environment of the economic laboratory – taken online via a platform to recruit a diverse set of participants (Prolific) – we induce a specific type of risk preferences and ask participants to create investment portfolios of a risky and a risk-free asset to maximize this preference, either directly or through a robotic adviser. To induce preferences, participant payoff is a fixed transformation of the probability distribution of risky asset payoffs, the payoff of the risk-free asset, and the participants’ chosen holdings of these two assets. Thus, participants do not face true risk: their payoff depends on the entire distribution of payoffs, not on realized payoff only. By controlling participant “risk preferences”, we can assess if the human-algorithm interaction leads to a correct treatment of the subjective part of decision making. In all experimental treatments, we vary the risk preferences we induce over time, so to see if participants react to and attempt to communicate these changes.

We have one treatment where participants choose portfolios on their own and three treatments where participants are advised by algorithms who elicit their human boss’s risk preference via a test (lottery choice). We ask if portfolio choices with or without the algorithmic adviser are better for the preferences we induce. To refine our question, we vary the frequency at which the algorithm elicits risk preferences from humans. This gives us three treatments with an algorithmic adviser, depending on whether the frequency of elicitation is equal, higher, or lower than the frequency at which we change participants’ risk preferences. We ask whether frequent communication allows for better fine-tuning of communicated preferences or, instead, adds noise due to, for example, a biased perception of past algorithm outcomes by the human.

The experiment, coded in oTree, will be preregistered on the platform AsPredicted and approved by the internal review board (IRB) of the University of Utah.

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.

Authorised Participants (APs), primarily market makers, create and redeem ETF shares in response to investor demand, making their behaviour crucial for ETF liquidity and price alignment. We formulate a dynamic equilibrium model of APs' trading decisions, explicitly capturing their inventory management incentives and arbitrage motives, and derive predictions linking ETF mispricing to inventory risk and aggregate demand shocks. Using a novel regulatory dataset covering primary and secondary market trades for 128 ETFs between 2018 and 2022, we empirically validate our model's predictions. Results confirm that APs' real-time inventory positions and investor demand significantly explain ETF price deviations from net asset values (NAVs), offering insights beyond traditional economic and fundamental factors. Our model further clarifies APs' incentives and sheds light on mechanisms underlying the severe mispricing episodes observed in March 2020 across various ETF classes.

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.

Higher ETF ownership lowers the volatility of corporate bonds returns, particularly small and less liquid bonds. The distinguishing features of ETF ownership– exchange trading and in-kind creation and redemption – have differential impacts. Secondary market trading, concentrated in just a few funds, serves as a liquidity buffer. The negative effect of ownership is heightened for bonds held by ETFs with greater trading volume and institutional ownership. In contrast, greater in-kind creation and redemption activity process mitigates the negative effect of ownership on volatility. Thus, ETF ownership serves as both a buffer and transmitter in corporate bond markets.

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.

We use TRACE transactions data to assess trading activity and measure dealer markups on riskless principal trades in structured products. Median markups on such transactions with market values in the $5-$10 million range for MBS and ABS are just 0.03%, comparable to the 0.02% observed for Corporate bonds. Corresponding median markups are 0.10% for Agency CMO and 0.20% for Non-Agency CMO. Skewed markup distributions exist in all products, suggesting that customers are short-changed in a significant number of trades by opportunistic (“bandit”) dealers. The top quartile of both Agency and Non-Agency CMO riskless principal trades cross at markups above 1.0%, more than quadruple their median values. The top eighth of these paired trades cross at markups above 2.0%, more than nine times their median values.

The incidence of dealer banditry increased during the Pandemic crisis week beginning March 23, 2020. One bandit dealer made $54.5 million in excessive markups by buying 238 Non Agency CMO worth $1.732 billion from a single seller, while simultaneously splitting sales of these same positions among five counterparty accounts during a 12-minute “fire sale” on March 25, 2020. Benchmarks suggest this dealer also facilitated at least a 20% suppression of the fair value of these trades, benefiting the buying group while disadvantaging the seller by an extra $346 million. One of the buyers realized a $139.4 million capital gain (39% return on investment) after unwinding 35 days later in highly unusual “after hours” trades that also netted the dealer an extra $22.9 million in markup profits.

In sharp contrast to its near immediate dissemination of prices from Corporate bond, MBS, and ABS transactions, FINRA waits more than 18 months after the trade date to release data for CMO trades with transaction quantities equal to or greater than $1 million. We show that the 3/20/2017 rollout of reporting for CMO trades with transaction quantities less than $1 million appears to have reduced both the level and variability of markups in that segment. However, incidences of banditry appear to have increased for Non-Agency CMO trades with sizes of $1 million or more. The March 25, 2020, “crime scene” makes the costs of continuing to withhold reliable and timely information from customers all too real. We recommend that FINRA commence near real-time dissemination of TRACE transactions reports on all riskless principal trades in all structured products, including not only CMO but also Commercial Mortgage Backed Securities (CMBS), Collateralized Loan Obligations (CLO), and Collateralized Debt Obligations (CDO).

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.

This paper investigates the puzzling negative empirical relationship between tail risk and expected return. Using Expected Shortfall as a measure of tail risk, this study decomposes it into elemental systematic and idiosyncratic components which allow for a deep probing of the relationship. The evidence suggests that while Expected Shortfall is an important determinant of expected returns, it earns a negative risk premium in stark contradiction of theory. After verifying empirically the negative tail risk premium anomaly, the paper investigates how the systematic and idiosyncratic components of tail risk influence expected returns and challenges prevailing explanations of tail risk premia. The negative tail risk premium anomaly is driven mainly by idiosyncratic Expected Shortfall. Moreover, contradicting recent findings in the literature which document that systematic tail risk has a positive impact on expected returns, the systematic Expected Shortfall is either negative or at times insignificantly positive. This is a new and puzzling finding. These findings contribute to a deeper understanding of the drivers behind tail risk anomalies and hold implications for both investment strategies and the interpretation of tail risk-return relationships.

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).

Firms are increasingly exposed to cybersecurity risks. We examine whether lenders recognize and accordingly price firms’ cybersecurity risks. Our findings indicate that lenders on average charge a 4 to 12 basis points higher loan rate when a firm exhibits greater cybersecurity risk over time. Commercial banks tend to adopt a more stringent approach to pricing cybersecurity risks compared to non-bank lenders. Finally, the purchase of cybersecurity insurance by a firm does not mitigate the higher loan spreads associated with elevated cybersecurity risks.

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.

Despite numerous contributions on the topic, the study of the dynamics that influences the risk of SME insolvency still finds remarkable interest. In recent years, the use of machine learning algorithms in this field has increased the predictive accuracy of credit risk models. Using a set of fourteen indicators derived from a proprietary dataset, our study compare the predictive effectiveness of different machine learning models, currently widely used in the literature and in credit risk applications, through the calculation of specific evaluation indicators (e.g., accuracy, precision, F1 score, ROC curve (AUC), precision/recall curve). In addition, we provide useful information on the role of financial and accounting indicators in providing warning lights to entrepreneurs and managers to anticipate and manage potential default risks through the implementation of a Feature Importance Analysis.