|
on Financial Markets |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Ankara, Turkiye); Oguzhan Cepni (Department of Economics, Copenhagen Business School, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study extends the literature on the impact of technological shocks on stock market dynamics from a novel perspective in the context of the emerging AI revolution. Utilizing the recently developed AI indexes that capture general public attention towards AI-related developments through the newspaper coverage frequency of artificial intelligence and related topics like machine learning and high-frequency (5-minute interval intraday) data on technology stocks over the period from January 2015 to March 2026, we examine the predictive effect of AI sentiment and uncertainty proxies on crash risks in technology stocks that are directly associated with the emerging AI boom. Employing nonparametric causality-in-quantiles tests, we find that all three AI-related indexes significantly predict future crash risk in technology stocks, primarily during ``normal" and high-risk market states. Sign analysis via average derivatives reveals that general and economic AI shocks positively impact crash risk, while explicit uncertainty exhibits state-dependent characteristics at higher quantiles. These findings suggest that AI uncertainty acts as a behavioural amplifier of market tail risk, driven by investor attention and ``fear of missing out" (FOMO) dynamics. |
| Keywords: | AI Uncertainty, Crash Risk, NASDAQ-100, Quantile Causality, Partial Average Derivatives, FOMO |
| JEL: | C22 C53 G10 O33 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202617 |
| By: | Mahyar Kargar; Benjamin Lester; Semih Üslü; Pierre-Olivier Weill |
| Abstract: | We develop a model of a dealer-intermediated over-the-counter market designed to study three major changes in the structure of the U.S. corporate bond market: the increase in dealers’ balance sheet costs, the emergence of electronic trading platforms, and the growing presence of bond mutual funds and ETFs. Our model provides a unified analysis of these changes, clarifies the economic channels at play, and allows us to quantify their effects on a variety of market outcomes. Our quantitative analysis suggests that, while electronic trading significantly reduced the cost of raising capital in the corporate bond market, these gains were almost completely offset by the combined effects of balance sheet costs and changes in the demand for liquidity. We find that electronic trading also caused a meaningful decline in the bid-ask spread, whereas other changes in the market structure had little effect on transaction costs. |
| Keywords: | Over-the-counter markets; corporate bond market liquidity; dealer intermediation; balance sheet costs; electronic trading platforms |
| JEL: | G11 G12 G14 G24 D83 |
| Date: | 2026–05–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:103286 |
| By: | Nina Boyarchenko; Leonardo Elias |
| Abstract: | The global corporate nonfinancial bond market is both a large investment asset class and a vital source of funding for nonfinancial firms. With $19 trillion outstanding at the end of 2024, a broad portfolio of corporate bonds would be expected to be well diversified. Yet, in 37 percent of months between 1998 and 2024, more than 80 percent of bonds in the ICE Global Bond Indices—a portfolio with over 10, 000 constituents spanning diverse industries, credit ratings, and regions—moved in the same direction, suggesting a large degree of synchronization. In this post, we introduce the global credit factor, which proxies for the global price of risk in international corporate bond markets. The global credit factor creates a global credit cycle in bond risk premia and generates predictable comovement in bond prices. |
| Keywords: | global financial cycle; credit cycles; corporate bond returns |
| JEL: | F30 G15 G12 |
| Date: | 2026–05–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103269 |
| By: | Minxuan Hu; Jiayu Yi; Ziheng Chen; Wenxi Sun; Qishi Zhan |
| Abstract: | Market stress rarely harms investors through one channel alone. Losses, volatility spikes, and deteriorating tradability often arrive together. We examine whether ESG is associated with lower exposure to clustered fragility in equity markets. Using monthly data on S&P 500 constituents from 2014 to 2025, we study downside returns, volatility, illiquidity, and a cofragility state that captures their joint occurrence within the same firm month. The evidence supports a stress-amplified resilience interpretation rather than an unconditional ESG return premium. In the return channel, the ESG association is concentrated in the extreme downside tail during stress months. In the volatility channel, higher ESG is associated with smaller risk spikes when aggregate conditions are weak. In the illiquidity channel, the association is more persistent, suggesting a liquidity-quality component whose relevance increases when market-wide trading conditions deteriorate. The central evidence comes from the joint analysis: a one-standard-deviation increase in ESG lowers the stress-period probability of severe cofragility by 0.92 percentage points, about 9% relative to the baseline. Double Machine Learning shows a similar negative ESG association after flexible adjustment for observable firm characteristics. Pillar evidence suggests stronger baseline resilience for Environmental scores and clearer stress amplification for Social scores. Overall, the findings characterize ESG as a multi-channel fragility signal for tail-risk monitoring, stress analysis, and pillar-level ESG assessment. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.05631 |
| By: | Christelis, Dimitris; Georgarakos, Dimitris; Jappelli, Tullio; Kenny, Geoff; Meyer, Justus |
| Abstract: | We examine recent changes in stock market participation using newly available survey data from eleven euro area countries over the period 2020–2024. The evidence points to substantial turnover, with around 10% of non-stockholders entering the market each year, and more than 20% of stockholders exiting. New entrants tend to have lower education, income, financial literacy, and risk tolerance than established investors, indicating a shift in the composition of market participants. We also highlight the growing importance of cryptocurrency investments among retail investors. Overall, these findings shed new light on evolving household financial behavior and its implications for market participation and financial stability. JEL Classification: D14, E21, G51 |
| Keywords: | consumer expectations survey, crypto assets, household finance, mutual funds, stocks |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263239 |
| By: | Konrad Och\k{e}dzan; Nino Antulov-Fantulin |
| Abstract: | This paper examines the impact of market informedness on the profitability of market makers. In contrast to the existing literature, the analysis is conducted in a complex market environment featuring heterogeneous market-making agents that differ in terms of information sets and aversion to inventory risk, endogenous price formation, exogenous fundamental value dynamics, and self-exciting market order flow. The paper also establishes finite-horizon stability guarantees for the resulting state-dependent Hawkes market-taker process, including non-explosion, exponential mispricing integrability, occupation-time bounds, and a pathwise mispricing tail estimate. To address the market-making problem, the study employs a reinforcement learning framework based on the multi-agent proximal policy optimization (MAPPO) algorithm in a centralized training with decentralized execution (CTDE) setting. The study shows that informed market order flow is particularly dangerous in poorly informed markets, leading to severe adverse-selection risk. Although the complex market dynamics together with stochastic training give rise to locally non-monotonic outcomes, the results nevertheless reveal an overall upward trend in market makers' profitability as market informedness increases, suggesting that price discovery resulting from higher market informedness offsets the negative impact of adverse selection. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.05882 |
| By: | Maurice Bun; Eric Cuijpers |
| Abstract: | We study the heterogeneous relationship between bank capital ratios, capital requirements, bank lending and loan pricing using data on portfolios and bank characteristics for a sample of large European banks in the period 2014-2025. Exploiting dynamic panel data models with parameter heterogeneity, we relate time-varying bank capital ratios and bank capital requirements to portfolio exposures and loan rates. We establish a pattern of differentiated deleveraging whereby higher capital ratios are associated with smaller portfolio sizes, but only for high-risk portfolios and banks with low leverage ratios. On the pricing side, higher capital requirements are associated with only a small increase in portfolio loan rates. The empirical evidence suggests that, once banks are adequately capitalized, capital requirements can be varied without causing substantial changes in bank loan supply and loan pricing. |
| Keywords: | Bank lending; capital ratio; capital requirement; loan pricing; panel data model |
| JEL: | C23 C54 G21 G28 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:862 |
| By: | Raza, Hassan; Siddiqui, Danish Ahmed |
| Abstract: | he primary objective of this study is to develop an optimal framework for a Retail Central Bank Digital Currency (CBDC) by determining how its design features should be structured to achieve financial inclusion and monetary policy enhancement in a developing economy like Pakistan, while explicitly mitigating risks to financial stability and public adoption. This research employed a Systematic Literature Review (SLR) following the PRISMA 2020 Guidelines. A total of 224 academic papers were assessed to synthesize global findings and identify critical consensus and conflicts regarding CBDC design, particularly in contexts relevant to developing nations. The analysis reveals a strong consensus supporting a two-tiered, accessible, and interoperable model as the most pragmatic design. Literature frequently points to the use of Distributed Ledger Technology (DLT) for smart contract functionality and enhanced security, regardless of whether the system is account- or token-based. The review establishes two central tensions in design. The first highlighted is the core conflict between mitigating systemic risk and expanding access. The Second seem to arise from the perspective of Privacy vs. AML/CFT concerns. The viable path for this trade-off is indicated as a tiered system offering high privacy for low-value transactions while mandating stringent AML/CFT checks for large transfers. These findings provide a robust, evidence-based design blueprint for the State Bank of Pakistan and other developing countries considering the issuance of a retail CBDC. By explicitly defining the necessary trade-offs (stability vs. inclusion; privacy vs. compliance), this framework allows policymakers to prioritize design features that maximize public trust and regulatory compliance, thereby significantly accelerating the successful adoption and effective use of a CBDC as a tool for economic modernization and financial deepening. |
| Keywords: | Central Bank Digital Currency, Distributed Ledger Technology, PRISMA2020 Guidelines, Digital Pakistani Rupees, Retail CBDC, Financial Inclusion, Financial Stablitiy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341059 |