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on Financial Markets |
| By: | Shuchen Meng; Xupeng Chen |
| Abstract: | We develop a unified model in which AI adoption in financial markets generates systemic risk through three mutually reinforcing channels: performative prediction, algorithmic herding, and cognitive dependency. Within an extended rational expectations framework with endogenous adoption, we derive an equilibrium systemic risk coupling $r(\phi) = \phi\rho\beta/\lambda'(\phi)$, where $\phi$ is the AI adoption share, $\rho$ the algorithmic signal correlation, $\beta$ the performative feedback intensity, and $\lambda'(\phi)$ the endogenous effective price impact. Because $\lambda'(\phi)$ is decreasing in $\phi$, the coupling is convex in adoption, implying that the systemic risk multiplier $M = (1 - r)^{-1}$ grows superlinearly as AI penetration increases. The model is developed in three layers. First, endogenous fragility: market depth is decreasing and convex in AI adoption. Second, embedding the convex coupling within a supermodular adoption game produces a saddle-node bifurcation into an algorithmic monoculture. Third, cognitive dependency as an endogenous state variable yields an impossibility theorem (hysteresis requires dynamics beyond static frameworks) and a channel necessity theorem (each channel is individually necessary). Empirical validation uses the complete universe of SEC Form 13F filings (99.5 million holdings, 10, 957 institutional managers, 2013--2024) with a Bartik shift-share instrument (first-stage $F = 22.7$). The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03272 |
| By: | Daniel Pastorek (Faculty of Business and Economics, Mendel University in Brno, Czech Republic); Peter Albrecht (Faculty of Business and Economics, Mendel University in Brno, Czech Republic) |
| Abstract: | We study how post-trade settlement frictions introduced by spot ETFs reshape cryptocurrency market dynamics. Unlike crypto markets with near-instant delivery, crypto ETF trading is governed by an equity-style clearing and settlement clock, effectively importing a second timing regime into cryptocurrency markets. Using daily ETF failures-to-deliver (FTDs) data, securities-lending conditions, and close-aligned spot prices from ETF inception until 2025, we show that FTDs act as an intertemporal liquidity buffer. Local projections indicate that unexpected increases in FTD intensity do not raise contemporaneous spot volatility on the trade date. Instead, volatility materializes around the regulatory settlement date and spills over into the next session to some extent. In a competing-shocks framework, this response centered around the settlement date remains distinct from standard volatility shocks, which load immediately and mean-revert. Panel regressions further show that FTDs arise systematically when lending constraints bind. Finally, higher FTDs coincide with larger ETF spot tracking errors, consistent with temporary impairments in arbitrage. Overall, spot crypto ETFs import traditional settlement frictions into markets, where these frictions did not occur previously. It reallocates volatility over time and intermittently weakens price parity. |
| Keywords: | Bitcoin, Ethereum, ETFs, Volatility, Market dynamics, FTDs, Settlement frictions |
| JEL: | G11 G12 G14 G23 C58 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:men:wpaper:109_2026 |
| By: | Mykola Babiak; Jozef Barunik; Josef Kurka |
| Abstract: | Cross-sectional dispersion in firm-level realized skewness is significantly and negatively related to future stock market returns. The predictive power of skewness dispersion is robust to in-sample and out-of-sample estimation and is incremental over a broad set of existing predictors, with only a few alternatives retaining independent explanatory ability. Skewness dispersion also delivers substantial economic gains in portfolio allocation. Its forecasting power is concentrated in months with monetary policy announcements, reflecting an information-based mechanism. The empirical evidence suggests that skewness dispersion captures the gradual incorporation of macro news into prices, which is driven by variation in aggregate risk and valuation adjustments. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07870 |
| By: | Nolan Alexander; William Scherer; Jamey Thompson |
| Abstract: | We propose a novel asset allocation model using a Markov process of states defined by clustered efficient frontier coefficients. While most research in Markov models of the market characterize regimes using return and volatility, we instead propose characterizing these states using efficient frontiers, which provide more information on the interactions of underlying assets that comprise the market. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial defined by three coefficients, to provide a dimensionality reduction of the return vector and covariance matrix. Each month, the proposed model hierarchically clusters the monthly coefficients data up to the current month, to characterize the market states, then defines a Markov process on the sequence of states. To incorporate these states into portfolio optimization, for each state, we calculate the tangency portfolio using only return data in that state. We then take the expectation of these weights for each state, weighted by the probability of transitioning from the current state to each state. To empirically validate our proposed model, we employ three sets of assets that span the market, and show that our proposed model significantly outperforms benchmark portfolios. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03946 |
| By: | Wissem Ajili Ben Youssef (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School); Najla Bouebdallah (Excelia Group | La Rochelle Business School); Meriem El Bouhali (ESLSCA Business School - École Supérieure Libre des Sciences Commerciales Appliquées) |
| Abstract: | This study aims to identify factors affecting auditors' intention to use blockchain among the Big Four firms. The research proposes an extended technology acceptance model by integrating the technology acceptance model (TAM) with innovation diffusion theory (IDT). A quantitative approach was employed, utilizing questionnaires to collect data from 130 auditors working at the Big Four. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that auditors' intention to use blockchain is significantly influenced by perceived usefulness (PU) and perceived ease of use (PEOU). The study highlights relative advantage and trialability as the most important attributes of IDT affecting auditors' perceived usefulness and ease of use of blockchain. Observability has a significant positive relationship with perceived ease of use but a nonsignificant correlation with perceived usefulness. However, complexity is statistically insignificant in explaining perceived ease of use. Finally, access to big data significantly enhances auditors' perception of the usefulness of blockchain technology. Therefore, our results recommend communication strategies and training policies to enhance the perceived usefulness of blockchain technology in auditing. Reducing uncertainty about emerging technologies, primarily through standardization, will also improve auditors' intention to use blockchain. |
| Keywords: | Audit, Blockchain, Technology acceptance model, Innovation diffusion theory, Big Four |
| Date: | 2025–06–23 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05568241 |
| By: | Luigi Caputi; Nicholas Meadows |
| Abstract: | In this work we evaluate the performance of three classes of methods for detecting financial anomalies: topological data analysis (TDA), principal component analyis (PCA), and Neural Network-based approaches. We apply these methods to the TSX-60 data to identify major financial stress events in the Canadian stock market. We show how neural network-based methods (such as GlocalKD and One-Shot GIN(E)) and TDA methods achieve the strongest performance. The effectiveness of TDA in detecting financial anomalies suggests that global topological properties are meaningful in distinguishing financial stress events. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02549 |