We analyze a market-wide panel dataset on retail option trading from India, which has an 80% share in option contracts traded worldwide. Retail participation is concentrated in and dominates index options trading. Traders exhibit short-term speculative behavior with significant day trading, short-duration directional bets, especially as options converge to 0DTE, and make significant losses. Three natural experiments show that financial constraints and lottery-like preferences shape investor behavior. An exogenous increase in the supply of short-maturity options induces trading. Lot-size increases and delivery margins trying to curb speculation are offset by speculative shifts to small ticket-size, riskier options. While stock market participation increases welfare in canonical household finance models, it can also entrench speculative behavior that is difficult to undo.

Investing in capital markets (stocks, bonds, ETFs, and mutual funds, etc.) helps preserve and grow wealth. Yet participation remains low, particularly among older, less affluent, and less educated individuals. Barriers like costs, investment thresholds, volatility, and limited financial literacy discourage engagement. While digitalization has eased these frictions and allowed for free access to financial education, participation remains low, at 30% in the U.K., 15% in Japan, and under 10% in China and India

We hypothesize that behavioral inertia, passive avoidance of unfamiliar opportunities, contributes to this gap. To test whether digital nudges can mitigate it, we analyze a quasi-natural experiment on a major Chinese platform. Among 560,000 users with no investment records, we randomly selected 64,770 who received prompts promoting low thresholds and diversified products, and free education; a control group, matched via Coarsened Exact Matching, received none.

The initial response rate was 1.5%, rising to 23.6% after six prompts. Of first-time responders, 32% invested an average of CNY 1,989 (~US$277). In the following months, these users, including older, less educated, and lower-income ones, built diversified portfolios and consistently outperformed the control group in risk-adjusted returns. The results show that digital nudges can help overcome inertia and broaden financial inclusion.

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 model the multitasking nature of managerial incentives when ESG metrics are
introduced jointly with standard financial or accounting metrics in executive
compensation. Building on insights from multitasking theory, we predict that
pay-performance sensitivity or dollar delta of standard metrics should optimally
decrease when value-adding but less measurable ESG goals are introduced in executive
pay. Empirical tests support the existence of a significant opportunity cost for the
effort of executives to improve ESG metrics that firms mitigate by decreasing
incentives to achieve standard metrics. Consistently, the downward adjustment in
dollar delta of standard metrics is shown to be larger when the number of ESG metrics
increases, they are less material to the firm, or less measurable. This adjustment is not
offset by a simultaneous increase in the time vesting delta or the executive’s total
compensation. The tests show differential effect of E, S, and G metrics on the dollar
delta of standard metrics. In sharp contrast, there is no variation in the dollar delta of
standard metrics when a new standard metric (instead of an ESG metric) is
introduced. Overall, the evidence is consistent with efficient contracting in the
presence of multitasking when ESG metrics are introduced in executive compensation.

We investigate the second-by-second impact of Fed Chair Jerome Powell's facial and vocal emotional expressions during FOMC press conferences on the market microstructure of SPY and TLT ETFs. Using unique high-frequency multimodal data and robust wild cluster bootstrap inference, we find complex and often counterintuitive effects. Contrary to standard hypotheses, negative emotional cues (facial or vocal) predict significantly lower subsequent volatility, while positive facial expressions predict higher SPY volatility. Positive vocal cues improve TLT liquidity (narrower spreads), but negative vocal cues also predict narrower spreads at longer horizons. Significant interactions between nonverbal cues and textual sentiment reveal further complexity, such as congruent positive signals predicting lower TLT depth. The Treasury market appears more sensitive than the equity market. Our results challenge simple valence-based theories for high-frequency nonverbal cues, highlighting the importance of modality, asset class, microstructure dimension, and interactions with text in shaping immediate market dynamics.

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.

This study investigates the impact of Regulation 18 on green innovation within Chinese firms. Introduced in 2013 to sever political ties in corporate governance, Regulation 18 mandated the resignation of government officials from business roles. Studying Chinese listed firms from 2010 to 2016, we utilize a difference-in-differences (DiD) model to evaluate its effects. Our findings reveal a significant influence of Regulation 18 on green innovation. Non-State-Owned Enterprises (non-SOEs) in High Energy Consumption, High Pollution, or Overcapacity (HHO) industries experienced reduced green innovation post-regulation, consistent with the legitimacy theory and social perspective, indicating a positive correlation between political connections and green innovation. Conversely, State-Owned Enterprises (SOEs) in HHO industries exhibited increased green innovation, aligning with the resource curse theory and tunneling arguments, suggesting a negative correlation between political connections and green innovation. Furthermore, the analysis extends to corporate financial performance, revealing a decline for politically connected non-SOEs in HHO industries post-Regulation 18, while improvements are observed for SOEs in the same sector.

Exploiting the adoption of mandatory central clearing by U.S. regulators, we explore the effects of this regulatory reform on banks’ derivative offsetting. Using a triple-difference testing procedure, we find that derivative offsetting increases (decreases) for banks with higher (lower) capital ratios after the adoption of mandatory central clearing, in comparison to the control group. The results are economically significant and robust to a variety of alternative measurements and tests. Our findings suggest banks with different target capital ratios respond to central clearing reform differently.

With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives’ futures. Specifically, as generative models’ power further increases, will they eventually replace all human creatives’ jobs? We argue that the answer is “no,” even if existing generative AI models’ capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox.

We explore the director labor market consequences of social media use. Specifically, we identify directors in S&P 1500 firms who are active on Twitter and examine various director labor market outcomes. We find directors, particularly females and minorities, on Twitter are more likely to gain an additional directorship, a larger directorship and a directorship in a new industry each year than those who are not on Twitter. These results hold when controlling for time invariant unobserved director characteristics and when using an instrumental variable approach to control for endogeneity. They are strongest for directors, primarily females and minorities, who engage more with other directors via social media. Shareholders show more support for social-media-active directors through a greater (lesser) percentage of votes casts “For” (“Against”) their election and through a greater stock price reaction to the announcement of first-time director appointments. These results suggest that social media can play an important role in reducing traditional labor market frictions and facilitating more opportunities for minority and female directors.

Cryptocurrencies exhibit high volatile behavior and observable anomaly effects, yet systematic anomaly detection within these markets remains underexplored. This study introduces a tailored Business Intelligence (BI) system for cryptocurrency anomaly detection. Leveraging advanced analytics and visualization, our novel BI system provides a comprehensive and real-time overview of market behaviors, enabling swift identification of cryptocurrency price anomalies. Our unique system empowers users to navigate the dynamic cryptocurrency trading, enhancing anomaly detection informed decision-making and risk management.

Hash-linked timestamping is the key feature behind blockchain technology. It makes it possible to design financing contracts that are based on reliable and up-to-date records of transactions. For this reason, it is considered to enhance trust. This paper develops a theoretical model that uses dynamic contract theory to derive optimal financing contracts in a blockchain environment. I show that a dynamically adjusting profit sharing rule is optimal and I highlight properties that determine the splitting rule. In contrast to the view that blockchain enhances traditional contracts like debt and equity by bringing efficiency gains, I emphasise that blockchain allows borrowers to learn from data and take effoort decisions more frequently, which make debt and equity contracts costlier.

We use Target Date Funds (TDFs) to study how managers of funds behave when shielded from their investors' short-term needs. We document that asset managers exploit reduced investor attention to deliver lower performance quantifiable in 21% for an average investor holding the fund for 50 years. This underperformance is driven by fund families using TDFs to smooth the flow shocks of affiliated open-end funds and to boost fees by investing in the affiliated expensive share classes. We use the Pension Protection Act of 2006 as an exogenous shock that made TDFs the default investment option within 401(k) retirement plans.

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.

The exponential growth and variety of studies on returns highlights the lack of comprehensive asset pricing theory for explicitly explaining the empirical data. The paper addresses this challenge by proposing a hierarchical state-based asset pricing model based on two interconnected solutions: the state space of assets and explanatory gain decomposition approach. As a result, the states of assets extend the conventional state of nature for bringing fundamental and macroeconomic characteristics into existing asset pricing models. Then, the decomposition approach tackles the complexity and heterogeneity of asset pricing by advancing all-in analyses with hierarchical piecewise finer-grained regressions. The direction is demonstrated with a multi-step analysis subsequently boosting the explanatory power of regressions between price-to-fundamental ratios and asset quality characteristics and resolving the weak correlation between the HML and RMW factors. Furthermore, the proposed model establishes a direct link between theory and empirics encompassing multi-dimensional data and growing stack of data science techniques.

To support a growing financial system, many recent blockchain innovations (e.g., layer-2 scaling solutions, proof-of-stake layer-1 chains, cross-chain interoperability bridges, and data availability layers, etc.) adopt an “optimistic” design principle, that is, the system proceeds as if all participants are well-behaving, presumably sustained by some “stake-and-slash” mechanisms. We formulate the logic behind such optimistic systems in a simple game and characterize its equilibria. We point out that the “optimistic” assumption under current designs is not compatible with all participants being rational. Therefore, to ensure security, protocol designers have to impose trust assumptions regarding the presence of altruistic participants. We explore alternative design changes that may help alleviate the identified problems, including introducing redistribution across participants or adopting succinct proof-based designs.

It is assumed in the news-based model of stock pricing (NBSPM) that stock prices are determined with macroeconomic news (modeled with the total market return in the spirit of CAPM), industry news (modeled with the relevant industry ETF returns), and the company-specific news and momentum that are described using an optimal ARMA-GARCH model. In this work, the NBSPM accuracy for forecasting stock prices is compared with that of the momentum-enhanced five-factor Fama-French model. The results for a representative list of holdings of nine major US equity sector ETFs demonstrates superiority of the NBSPM in most cases.

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.

We propose a new analytical model for the valuation of loan commitments and some of their main features including the MAC (Material Adverse Change) clause. A two-period contingent claims approach in continuous time is developed. The advantage of this approach is that it is based on rational economic considerations that are not based on utility functions.

We offer a new metric, "net-zero alignment strength" (NZAS), which can be used by socially responsible investors for portfolio selection. NZAS is based on the current corporate GHG emission intensity (EI) and its reduction rate (ERR) and, hence, can be used for choosing among the companies with high EI and high ERR versus the companies with low EI and low ERR. We have incorporated NZAS into the mean-variance portfolio (MVP) framework, which yields simultaneous optimization upon high returns and NZAS, and low price volatility. The NZAS contribution to the MVP minimization function is controlled by the net-zero commitment parameter, which is investor’s choice. An example for a portfolio with 29 major constituents of the Energy sector illustrates an interplay between the company Sharpe ratios and NZAS, which determines the effects of the net-zero commitment on the major portfolio holdings. We suggest to use this framework for finding the best-in-class companies within the chosen equity sectors.

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.

When liabilities are deemed improbable or cannot be reliably estimated by management, they are not recorded on the balance sheet. Instead, they are disclosed qualitatively in the company's filings. Examples include obligations related to pending or future lawsuits, product liability, environmental matters, false advertising, or patent and copyright infringements. We refer to these obligations as intangible liabilities (IL). We construct a firm-level, text-based measure of IL. IL is positively related to firm size, volatility, and share turnover, and is negatively correlated with accounting performance, abnormal returns, and Tobin’s Q. IL also predictably varies across industries. Moreover, IL predicts future lawsuits against firms and the future deterioration of their reputations. Companies with higher IL trade at lower valuation ratios and have significantly higher future crash risks. A portfolio that is long on high IL and short on low IL yields an annual abnormal return of 3% after accounting for common factors. Overall, the results suggest that intangible liabilities are a significant determinant of firm value and stock returns.

Tradefeedr applies the principles of open banking APIs to financial markets, reducing the friction of data sharing between market participants. Through a unified, secure analytics API connected to over 20 major liquidity providers (LPs) and 10 trading platforms, we provide a single data agreement that covers all participants. Our standardized data, metrics, and analysis empower trading teams, while independent client reporting for LPs and bilateral client–LP data dialogue foster transparency and collaboration. Access to this unique dataset enables market participants to unlock the full value of their data and leverage AI and machine learning to make smarter trading decisions. Everything we do is driven by the needs of the trading community.

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.

We introduce an out-of-sample neural-network-based measure of firm-level operating leverage, which outperforms existing ones in capturing the elasticity of operating profits to gross profits. Strikingly, our analysis uncovers a non-monotonic—and potentially negative—relationship between operating leverage and the risk premium. This challenges conventional wisdom and contradicts explanations that link operating leverage to the value premium. A production-based asset pricing model incorporating both variable and fixed costs provides a possible rationale for these empirical findings. Furthermore, our analysis offers a fresh perspective on the idiosyncratic volatility premium by emphasizing the interplay between the operating hedge effect induced by variable costs and the operating leverage effect induced by fixed costs.

Japan’s FinTech sector has undergone a rapid transformation in the past five years, driven by progressive open-banking rules, a vibrant Web3 start-up scene, and a public-private push toward cashless payments ahead of Expo 2025 Osaka-Kansai. Representing 500+ member firms, the Fintech Association of Japan (FAJ) will share fresh data on investment flows, consumer adoption, and regulatory sandboxes that enable speedy experimentation without compromising consumer protection.

Building on this evidence, the session explores how Japanese and European innovators can co-create next-generation payment rails, tokenized capital-market pilots, and inclusive digital-finance programmes. By highlighting flexible regulatory pathways, reciprocal sandbox access, and practical partnership models—rather than focusing on any single legislative regime—the talk offers a future-oriented roadmap for policymakers, founders, and investors eager to scale solutions across Asia-Europe corridors.

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.

We investigate open-source innovation by public firms and the private value it generates for these firms. Unlike patents, which grant inventors exclusive rights to their inventions, open-source innovations can be used by anyone. Nevertheless, using an extensive dataset of public-firm activity on GitHub, we find that firms with open-source projects represent 68% of the U.S.~stock market across 86% of industries. We estimate the private value of all projects in our sample to be nearly $25 billion, with the average project generating $842,000. We find that projects with fully permissive licenses are generally less valuable and firms facing higher competition tend to generate less private value from their projects. We also find that the degree to which a project complements commercial products is not a primary driver of private value. Finally, open-source value is associated with a firm's substantial growth in terms of sales, profits, employment, and patenting, yet it also induces creative destruction. These results contribute to our understanding of the private value generated by innovation in the absence of legal excludability.

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.

This paper considers introducing asymmetric privacy in the design of central bank digital currencies (CBDC) and digital currencies more generally, to preserve the privacy of money spent while keeping the benefits of digital records for money received. It is shown that this feature would help minimize real distortions between consumers, firms, and financiers, while enabling tax optimization and better access to external financing. Protecting the privacy of consumers is desirable from a welfare and efficiency standpoint as long as there exist noticeable privacy concerns. Implementing asymmetric privacy is technologically feasible, using for instance Zero-Knowledge proofs or other privacy tools.

The interconnectedness of firms through various networks, such as production, credit, and competition, plays a critical role in determining firm-level and aggregate outcomes. However, data on these connections are often limited. This paper introduces a novel artificial intelligence methodology that extracts explicit firm relationship networks from financial news articles, providing comprehensive and interpretable data across multiple dimensions. Applying this methodology to New York Times articles since 1981, we generate extensive networks that predict key macroeconomic indicators. Our publicly accessible dataset offers valuable insights for future research on firm networks and aggregate fluctuations.

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.

This paper analyzes investor search behavior across financial information providers by modeling demand within a multi-class classification framework. Examining searches on Bloomberg Terminals and EDGAR, I explore how subscription prices, expected stock returns, and investor income influence the selection of financial data sources. The findings offer insights into information-seeking behavior, retrieval patterns, and access dynamics, highlighting the economic factors that drive the use of proprietary and open-access financial platforms.

This study exploits the non-fungible token (NFT) setting to estimate the value of non-pecuniary benefits, a long- standing empirical challenge in private-value markets such as art, antiques, and other collectibles. After developing and validating our emotional dividend proxy (LOVE), we apply deep learning algorithms and discover that contemporaneous price fluctuations, certain collection features, and ownership wealth significantly contribute to its formation. Leveraging these parameters, we employ AI-powered models to estimate NFT prices with high accuracy, but find their predictive ability is decreasing in LOVE. Additionally, we demonstrate that LOVE-driven trading results in long-term financial losses for the average investor, highlighting a trade-off between wealth and emotional utility. Our study pro- vides novel economic insights into the factors shaping emotional dividends and their role in the pricing of private-value assets. It also surfaces the limitations AI faces in emotionally charged markets, revealing new challenges for algorithmic trading when assets carry significant emotional utility.

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.

The problem - Despite dynamic demographics and urgent infrastructure needs, Africa receives less than 5% of global VC funding. In South Africa, early-stage ventures and property developers face a paradox: they have the ideas, markets, and social but not the capital.

The solution - frntr‑X is a South African blockchain platform that enables anyone—from institutions to retail investors—to invest in fractionalized, asset-backed tokens linked to real projects such as housing estates, student residences, ambitious young companies, and mixed-use infrastructure.

The opportunity - Africa is not just an “impact story”—it’s one of the most promising investment frontiers in the world:

- Demographic tailwinds: Young, urbanizing populations fueling housing, education, and SME demand.

- $1 trillion+ funding gap in infrastructure, student housing, and green energy.

- Unmet demand from diaspora and institutional investors seeking exposure to high-growth, high-impact assets.

frntr‑X makes this investable—offering real-world yield, secondary market liquidity, and transparent governance.

Each tokenized investment supports UN Sustainable Development Goals (e.g. SDG 8, 9, 11). Investors don’t have to choose between return and responsibility—they get both.

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.

Open source remains controversial in finance due to security, compliance, and competitive concerns; I can talk about how Interledger navigated these barriers to widespread adoption.

Explore the strategic, technical, and community-building decisions Interledger made to foster trust and collaboration across the industry.

Understand why embracing transparency through open source can enhance innovation, interoperability, and ultimately, market positioning in the financial sector.

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.

As AI and algorithmic systems increasingly mediate financial decision-making, the need to measure and optimize human cognitive states in real time is growing. Direct brain-computer communication is poised to transform financial decision-making by combining human intuition with machine intelligence and relentless work ethic and speed. This talk will showcase how cognitive data can optimize financial user interfaces, support adaptive trading environments, and will reveal new neural markers of decision quality, fatigue, and confidence. From System 1 and System 2 research, to quant trading to financial education, understanding how people think, not just what they choose, opens new frontiers in personalized finance, AI alignment, and risk modeling.

We examine how real‑time decoding of human cognitive states (enabled by a non‑invasive BCI platform) reconfigures financial decision making by uniting human and AI capabilities in a symbiotic system that reinforces desired financial outcomes.

Our platform, Arctop, makes neuroscience laboratory‑grade metrics, such as visual and auditory attention, cognitive workload, focus, enjoyment, and Brain ID, accessible to AI agents and other humans working in financial environments through consumer headsets that seamlessly fit into existing workflows without altering behavior. This innovation not only preserves existing best practices, but amplifies them by integrating neural markers into trading interfaces, adaptive environments, and risk assessments. By shifting the lens from “what decisions individuals make” to “how individuals and groups think,” our approach offers a transformative frontier in optimizing financial outcomes and enriching economic models.

While Bitcoin mining consumes a huge amount of electricity, does it necessarily translate into increased carbon emission? In an analytical model featuring endogenous renewable energy adoption decisions, we show that with appropriate electricity price policies, the high electricity demand from Bitcoin mining may actually subsidize the capacity building of renewable energy plants and thus lower total carbon emission. A key intuition is that unlike other electricity uses, Bitcoin mining intensity can be elastically dialed up or down without disrupting operations, and thus can replace fuel-based electricity generation as an effective shock absorber for the volatile supply of renewable electricity generation. We characterize conditions when this carbon emission reduction effect stands.

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.

The European mortgage market remains highly fragmented, relying on heterogeneous national legislations, paper-based workflows, and localised assessment methodologies. These inefficiencies hinder cross-border lending and complicate the creation of a truly unified mortgage market within the EU. Digitalisation has the potential to overcome these barriers, but requires rigorous methodological and technological advances.

This presentation examines the technical and regulatory challenges of mortgage digitalisation across Europe, with a focus on DigFin’s research-driven approach. We will discuss three key areas where academic collaboration is critical:

1. EU-wide creditworthiness methodology: developing robust, transparent models capable of assessing the creditworthiness of borrowers across different income types, employment structures, and tax regimes, while ensuring fairness and explainability.

2. Multilingual mortgage robo-advisory: designing AI-driven advisory systems capable of delivering accurate, compliant, and user-trusted guidance in all European languages, drawing on advances in natural language processing and human-computer interaction.

3. Automated document information extraction: applying document AI and legal informatics to parse unstructured financial and legal documents (e.g., contracts, payslips, deeds) across diverse formats and jurisdictions.

By framing these challenges as research questions, the talk highlights how collaboration between industry and academia can accelerate the development of digital mortgage solutions that are scalable, compliant, and equitable across the EU.

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.

We demonstrate that prevalent empirical implementations of asset pricing are inconsistent with dynamic equilibria [multiperiod equilibria with no single-period (static) representations]. Specifically, empirical implementations are misspecified with respect to three essential asset pricing questions (TEQ) on: dependency on higher moments, complexity of risk premia, and mean-variance efficiency of the “market portfolio” (the pricing kernel/SDF). While we already know that “Merton models” and their derivatives differ from static models in all TEQ, we show that this is the case for all dynamic equilibria, even for the minimal dynamic equilibria (dynamic equilibria with the simplest structure).

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.

GNU Taler is a payment system that offers privacy-preserving payments for buyers while ensuring income-transparency for sellers. Our payment system does not use a blockchain and can process tens of thousands of payments on a single computer with negligible costs per transaction. Modern cryptography is used to implement digital cash held decentralized in self-custody.

Because spending digital cash does not require authentication, GNU Taler offers superior usability compared to other payment solutions. Building on ideas from Oral Information Management (OIM), we created a version of the Taler wallet that does not require users to be able to read, and have conducted usability studies with illiterate and innumerate people that were quickly able to correctly use the GNU Taler wallet for payments despite being unable to read 2-digit numbers.

As Free/Libre Open Source software we are able to develop the solution in collaboration with a growing group of academics, volunteers, non-profits and small businesses. This allows our small team to drive an outsized number of innovations in a field where proprietary systems have long stifled innovation. As a result, secure payments to offline merchants, programmable money, business contracts using secure multiparty computation, subscriptions, coupons, privacy-preserving tax deductable donations and even post-quantum cryptography are other present or upcoming features.

The payment system is operational today in Swizerland (eCHF), and is expected to be operated by GLS for Euros starting in Q3'2025.

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.

We develop an Overlapping Generations (OLG) model with a nested CES production function and endogenous fertility to analyze the dual impact of two mega-trends: the emergence of Artificial General Intelligence (AGI) and demographic decline. Our analysis identifies two paths to economic instability: an "Edge of Stability" driven by strong human-AI complementarity, and a "Wage-Fertility Spiral" where high wages suppress population growth. AGI-Labor complementarity in aging economies leads to a paradox of soaring per-capita GDP amidst stagnating aggregate output, while substitutability leads to “High-Tech Feudalism”, where a few own most of the wealth.

Extending the model to an open economy reveals that global capital flows and migration act as powerful stabilizers, transforming domestic challenges into questions of international policy. We evaluate a portfolio of fiscal responses and conclude that an optimal strategy combines internationally coordinated AGI taxes with domestic policies focused on migration management, human capital investment, and social cohesion.