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on Financial Markets |
| By: | Yang Yu; Martin Fleming; Lucy Hampton; Christophe Combemale; Neil Thompson |
| Abstract: | The adoption of artificial intelligence (AI) by large enterprises is an important potential source of aggregate productivity improvement and labor market impact. We study AI adoption of S&P 500 firms over the period 2016 to 2025, estimating adoption at the enterprise level. While generative AI tools are useful for personal and professional applications, our focus is on the deep integration of AI in the business processes of large enterprises which are bellwethers for firm adoption more broadly. We develop a novel measure to assess deep AI adoption (and distinguish it from AI hype) that is based on SEC 10-K filings, where laws and regulations ``prohibit companies from making materially false or misleading statements." In 2025, 11% of S&P 500 enterprises had AI deeply integrated into their business processes, and a further 10% were using AI in the production of goods and delivery of services. AI adoption has more than quadrupled from 5% in 2022 with slowly accelerating adoption among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption. Firm profitability shows a "J-curve" as firms move from no adoption to deep adoption, but we observe no differences in capex or productivity. Among technology firms, but not others, AI adoption is higher for firms with more employees and higher values of Tobin's q. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.08920 |
| By: | Sergio A. Correia; Stephan Luck; Emil Verner |
| Abstract: | We study the causes and consequences of bank runs. By applying large language models to historical newspapers, we create a comprehensive database of bank runs in U.S. history with information on 3, 984 runs on individual banks from 1863 to 1934. Our novel data allow us to establish that runs are considerably more likely in weak banks but also occur in strong banks, especially in response to negative news about the real economy or the broader banking system. However, runs typically only result in failure for banks with poor fundamentals. Strong banks survive runs through various mechanisms, including signaling strength, interbank cooperation, and temporary suspension. At the local level, runs on banks with poor fundamentals translate into substantially larger declines in deposits, lending, and manufacturing activity than runs on strong banks. Our findings imply that poor fundamentals are central to explaining both when runs occur and when they have severe economic effects, tempering the view that small shocks can generate discontinuous jumps to bad equilibria through self-fulfilling run dynamics. |
| Keywords: | bank runs; bank failures; banking crises; financial stability; financial history; artificial intelligence (AI) |
| JEL: | G01 G21 |
| Date: | 2026–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:103544 |
| By: | Niklas Humann; Dimitrios Kanelis; Lars H. Kranzmann; Pierre L. Siklos |
| Abstract: | We study how financial markets respond to the incremental information conveyed by FOMC minutes. Using paragraph-level embeddings, we score each paragraph by its semantic distance to the closest paragraph in previously released public communications. Aggregating these paragraph-level scores yields two indices: overall novelty, which captures the degree of novelty, and novelty tilt, which captures the composition of novelty, i.e., whether new content is concentrated in the staff review or the committee discussion sections. In high-frequency event study regressions around minutes releases, overall novelty is associated primarily with the magnitude, rather than the sign, of asset-price responses, whereas novelty tilt is informative about the direction of repricing. Methodologically, we develop a disclosure-based framework for quantifying novelty in FOMC minutes over time. |
| Keywords: | artificial intelligence, central bank communication, high-frequency event study, FOMC minutes, textual novelty |
| JEL: | E44 E52 E58 G14 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-54 |
| By: | Useong Shin |
| Abstract: | I ask whether a factor model that prices the aggregate market also prices the market's internal components. I construct a CRSP investible market portfolio and split it into size-ranked body and tail legs that exactly recombine to the market. All models pass the aggregate market test. Yet q5 leaves systematic, offsetting alphas: negative in the body and positive in the tail. Random splits remove the rejection. The evidence suggests that the market can appear priced because internal pricing errors cancel. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.23596 |
| By: | Veni Arakelia; Guglielmo Maria Caporale; Mirto M. Gasparinatou; Menelaos Karanasos |
| Abstract: | This paper examines the forecasting of liquidity dynamics in European stock markets by means of traditional econometric models and machine learning techniques. It uses daily data for the DAX, CAC 40, FTSE 100, FTSE MIB, and IBEX 35 over 2010–2026, liquidity being measured by the logarithmic Amihud illiquidity indicator. The empirical framework compares ARIMA models and a dynamic panel specification with Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) within a common rolling one-step-ahead forecasting framework. The results show that liquidity is highly persistent and that the dynamic panel model achieves the lowest forecast errors, although Diebold–Mariano tests indicate no significant predictive advantage over the leading machine learning models. SHAP analysis reveals that trading activity, lagged liquidity, and market uncertainty are the main determinants of liquidity forecasts. The findings highlight the complementary role of explainable machine learning in empirical finance. |
| Keywords: | liquidity dynamics, european stock markets, forecasting, econometric models, machine learning (ML), artificial intelligence (AI) |
| JEL: | C22 C33 C53 G17 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12829 |
| By: | Wen-Ting Wang |
| Abstract: | Trader-facing dynamic fees are increasingly proposed for automated market makers (AMMs), but historical data do not identify how order flow would respond: trader-facing fees do not vary, trader types are latent, and a replayed tape is not a sequential decision environment. We therefore construct a minimal closed-loop simulator in which the missing signal exists by construction: two constant-product pools repriced by an equilibrium-inspired dynamic-fee rule, fee-sensitive noise flow, and closed-form CEX--AMM arbitrage. Equilibrium is used as a closure principle, not as an object the trader learns. Against a tuned benchmark ladder of schedule, planning, lookahead, and tabular policies, a small DQN is the only evaluated valid policy whose paired improvement over tuned one-step routing excludes zero. On a reserved final block of 1{, }000 seeds with completion forced to 1.0 for every policy, it reduces implementation shortfall under every tested intra-step ordering, by $13.3\bps$ of order notional under the pre-specified agent-last ordering, and the edge is concentrated in, and learned from, dynamic-fee environments: under constant fees the paired difference is indistinguishable from zero. The result is model-conditioned counterfactual evidence about execution control in AMMs, not evidence about historical traders, equilibrium play, or deployable profit. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.10960 |