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on Financial Markets |
| By: | Nicola Borri (LUISS University); Yukun Liu (University of Rochester, Simon Business School); Aleh Tsyvinski (Yale University; NBER) |
| Abstract: | Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the AI Premium. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factorÑhigh AI beta firmsÑearn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumptionÑclosed-source models, paying and seasoned users, and long promptsÑbut not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and more negative in analytical, scientific, and operations-control skillsÑan occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI exposure. Additionally, we provide early evidence of the rise of the agentic economy. |
| Date: | 2026–07–07 |
| URL: | https://d.repec.org/n?u=RePEc:cwl:cwldpp:2546 |
| By: | Geofrey Ntale |
| Abstract: | Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets. This paper presents a systematic, comparative evaluation of five prominent LLMs: GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, and the domain-specialized FinGPT, with respect to their capacity for technical market analysis. The evaluation spans four structured tasks: candlestick pattern recognition from OHLCV data, directional signal generation (BUY/SELL/HOLD), backtesting of signal quality through a simulated execution pipeline, and financial report comprehension. Our experimental framework employs rigorous quantitative metrics, including Sharpe ratio, maximum drawdown, Sortino ratio, information coefficient, F1-score, and BLEU score. Findings from simulated backtesting indicate that GPT-4 Turbo achieves the highest annualized return and Sharpe ratio among general-purpose models, while FinGPT demonstrates competitive risk-adjusted performance due to domain-specific fine-tuning. Both models outperform a passive S&P 500 benchmark under the tested conditions. The study identifies persistent failure modes across all evaluated models, including numerical hallucination, context-window limitations, and inconsistent performance in sideways market regimes. We conclude that while LLMs hold genuine promise within AI trading systems, robust deployment requires careful task decomposition, rigorous backtesting protocols, and domain-aware fine-tuning strategies. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.15414 |
| By: | Aleksandra Jandric (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague); Adam Gersl (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague) |
| Abstract: | This paper examines the relationship between private equity investment and industry-level performance in Europe over 2008-2023. We combine Invest Europe and Eurostat data to construct harmonized country-sector-year panels covering 16 countries overall and 10 sectors, with outcome-specific estimation samples. PE intensity is measured relative to sectoral production value and enters the models with a one-year lag. Baseline fixed-effects models are complemented by additional fixed-effects structures, timing tests and robustness checks. Results indicate that higher lagged PE intensity is consistently associated with stronger subsequent nominal growth in output and value added. Personnel-cost growth is also generally positively associated with PE intensity. By contrast, the employment association is weaker: it loses statistical significance under several robustness checks and does not display the temporal ordering observed for the monetary outcomes. The paper updates the limited European industry-level evidence using a novel harmonized dataset and a period covering substantially different economic conditions, offering new insight into the extent to which PE investment intensity is associated with broader sector-level outcomes. |
| Keywords: | Private equity; Investment; Industry growth; Production; Employment; Panel data; Europe |
| JEL: | G24 G32 C23 E44 L25 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2026_22 |
| By: | Afees A. Salisu (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ahamuefula E. Ogbonna (Centre for Econometrics and Applied Research, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | This paper employs the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast monthly and daily stock return volatility in the United States (US), based on a quarterly news-based Price Conflict Index (PCI) that signals “bad macroeconomic news†. An analysis of historical monthly (1860-2023) and daily (1885-2023) data demonstrates that the GARCH-MIDAS model incorporating PCI outperforms both the benchmark GARCH-MIDAS model with realized volatility (GARCH-MIDAS-RV) and models with macroeconomic variables such as output growth, inflation, unemployment, and interest rates. Furthermore, the inclusion of the PCI in modeling stock return volatility provides higher utility gains compared to models that exclude it. These findings have important implications for both investors and policymakers. |
| Keywords: | Price Conflict, Stock Returns Volatility, Forecasting, GARCH-MIDAS |
| JEL: | C32 C53 E31 G12 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202620 |
| By: | Pástor, Luboš; Stambaugh, Robert F.; Taylor, Lucian |
| Abstract: | We quantify the U.S. corporate sector's future carbon damages by computing its "carbon burden"---the present value of social costs of its future carbon emissions. Our baseline estimate of the carbon burden is 131% of total corporate equity value. Even with indirect emissions excluded, 13% of firms have carbon burdens exceeding their market capitalizations. The 30 largest emitters account for all the decarbonization of U.S. corporations predicted by 2050. Predicted emission reductions, and even firms' targets, fall short of the Paris Agreement. Carbon burden is priced: firms with higher burdens have higher costs of capital, even controlling for past emissions. |
| Keywords: | Externality |
| JEL: | D62 G30 G38 Q51 Q54 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19668 |
| By: | Stefan Avdjiev; Bryan Hardy; Maximilian Jager |
| Abstract: | This paper documents that the traditional sovereign-bank nexus has morphed into a broader nexus that now also includes non-bank financial institutions (NBFIs): the sovereign-bank-NBFI nexus. The classical sovereign-bank nexus has been a major financial stability concern following the eurozone crisis. Since then, sovereign debt levels have increased substantially in many major economies, while NBFIs' footprint in sovereign bond markets has grown significantly. This paper examines the transmis sion of risks among banks, sovereigns and NBFIs using European bank-level data and global country-level data. We find that banks' direct sovereign exposures have recently become less important in explaining the co-movement between bank and sovereign risk. By contrast, banks' exposures to NBFIs have become a significant determinant of the bank-sovereign risk co-movement. We also find evidence that NBFIs' sovereign debt holdings have become important drivers of the co-movement between NBFI and sovereign risk. |
| Keywords: | banks, sovereign default, feedback loop, NBFI, nexus, risk |
| JEL: | F34 G01 G21 G23 H63 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1369 |
| By: | Long, S.; Mohaddes, K.; Ul Haq, I. |
| Abstract: | Sustainability-linked bonds (SLBs) embed sustainability commitments directly into debt contracts, but the credibility of these commitments depends on how penalties, reporting obligations, and verification requirements are written and enforced. We construct bond-level measures of contractual enforceability and target precision from SLB frameworks, prospectuses, and performance-target documentation for 915 SLBs, and link them to secondary-market Z-spreads and to matched conventional bonds from the same issuer or corporate parent. Three findings emerge. First, the basic contractual architecture has become nearly universal: 92% of SLBs contain penalty-activation, reporting, and external-assurance provisions. Second, the full-document enforceability score is negatively associated with absolute SLB spreads after controlling for month and currency, although the association is sensitive to issuer controls. The contractual enforceability index is imprecisely related to absolute spreads, while SLB spreads net of matched same-issuer or same-parent conventional-bond spreads are 3.7 to 10.0 basis points lower per standard deviation of this index. These matched estimates are marginally precise and weaken under stricter matching. Third, spread reactions around individual disclosure and non-disclosure events are limited and heterogeneous. The evidence is consistent with credit markets valuing contractual credibility, but it does not establish a causal effect on financing costs. |
| Keywords: | Sustainability-Linked Bonds, Credit Spreads, Contract Design, Enforcement, Credibility, Sustainable Finance |
| JEL: | G12 G32 G38 Q51 Q56 |
| Date: | 2026–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2667 |
| By: | Rohith Surya M; Dr. Arpita Choudhary (Assistant Professor, Madras School of Economics, Chennai, India.) |
| Abstract: | The research investigates how portfolio optimization techniques maintain their effectiveness during different market conditions which affect both emerging and developed equity markets by studying In¬dia and Singapore as case studies. The analysis compares mean–variance, minimum variance, equally weighted, and Conditional Value-at-Risk (CVaR) portfolios under both stable and stress market condi¬tions. The research identifies market regimes through a drawdown-based framework which uses an XGBoost classifier that processes macro-financial data including equity index returns and exchange rate movements and implied volatility indicators. The findings show that diversification strategies achieve better results in emerging markets which experience constant market changes while CVaR-based op-timization delivers better protection against losses and enhanced results in developed markets with extended stressful periods. The results demonstrate that portfolio strength and optimization success depend on the specific market conditions which affect different regimes. |
| Keywords: | Portfolio optimization, CVaR, regime detection, emerging markets, developed markets. |
| JEL: | G11 G17 G32 C58 C63 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2026-302 |