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on Financial Markets |
| By: | Cohen, Lior |
| Abstract: | I examine whether investors perceive higher-priced stocks as riskier despite identical percentage price changes. Using survey evidence, the study investigates whether nominal stock prices influence decisions to sell during market downturns. The findings suggest that investors exhibit non-proportional reasoning, attributing significance to nominal price movements rather than proportional changes. |
| Keywords: | Non-proportional reasoning, Behavioral Finance, stock price illusion, market inefficiency, investor behavior |
| JEL: | G41 G11 G14 D91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:342228 |
| By: | Andrew Y. Chen; Ivo Welch |
| Abstract: | This paper examines about 200 published long-short anomaly equity portfolios (Chen and Zimmermann, 2022). Over the period through 2005 (December 2005 and earlier) and across all stocks, their median zero-investment return was an impressive 48 bp per month. Using only post-2005 years (January 2006 onward) reduces this to 19 bp. Using only "non-micro" top-3, 000 stocks in the top 90% of market capitalization reduces this to 26 bp. Using only post-2005 and non-micro stocks reduces this to 7 bp. Even modest allowances for luck or transaction costs would have eliminated even these 7 bp. The evidence strongly suggests that published academic anomalies have been useless to non-micro-cap portfolio managers in the 21st century. Public stock markets were very efficient. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.06502 |
| By: | Husnu C. Dalgic; Galip Kemal Ozhan |
| Abstract: | This paper studies how dominant-currency pricing affects currency risk premia. Empirically, we extract common risk factors from excess currency returns using principal components and relate countries’ factor exposures to observable macroeconomic characteristics, with export dollar invoicing emerging as a predictor of carry trade exposure. A small open-economy model with dominant-currency pricing and dollar-denominated liabilities explains why. Dollar export invoicing weakens the exchange rate's stabilizing effect on external demand, while dollar debt makes depreciation costly for leveraged intermediaries. When the two frictions interact, depreciations occur in bad states, local-currency assets become risky, the currency premium rises, and the risk-adjusted neutral rate increases. Under a standard Taylor rule, this mechanism generates persistently higher inflation. |
| Keywords: | Currency returns; dominant currency pricing; uncovered interest parity; inflation; dollar debt |
| Date: | 2026–07–31 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/158 |
| By: | Maria Saveria Mavillonio; Stefano Borgioli; Caterina Giannetti; Chiara Ongari; Giampiero M. Gallo |
| Abstract: | Measuring sentiment from financial news is a central task in economics and finance, yet most existing indicators rely on dictionary-based approaches that infer sentiment from word counts and only partially capture context, negation, and semantic structure. This paper proposes a framework for constructing daily news mood indices using transformer-based language models and evaluates whether they better represent sentiment than dictionary-based alternatives. Using 143, 755 financial news articles from Factiva, we classify sentiment at the sentence level with FinBERT and aggregate these predictions into article-level and daily sentiment measures through alternative normalization schemes. We compare the resulting indices with benchmark measures based on Shapiro et al., 2022 and Barbaglia et al., 2025. A central contribution is the validation of alternative sentiment measures against human judgments. We conducted an incentivized annotation exercise in which 444 participants evaluated a validation subsample of 588 financial news articles. Consensus ratings from independent human evaluations serve as an external benchmark for assessing the quality of automated sentiment measures. Across correlation, regression, and classification exercises, transformer-based measures show stronger agreement with human judgments than vocabulary-based alternatives and perform substantially better in distinguishing positive, neutral, and negative articles. Overall, the results suggest that incorporating contextual information through transformer-based language models produces sentiment measures that more closely reflect human assessments of financial news. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.13968 |
| By: | Igor Halperin; Andrey Itkin |
| Abstract: | This paper introduces a dynamic portfolio optimization framework for large institutional investors using Scientific Physics-Informed Reinforcement Learning (SciPhyRL). Formulated in continuous time over an extended state space that includes explicit cumulative costs, the approach leverages offline historical data to learn optimal, distribution-aware strategies. A core innovation reduces the optimization challenge to solving an HJB equation by projecting it onto observed trajectories as a pathwise Hamilton-Jacobi equation. This is solved directly from data using PINN in a single offline sweep, eliminating the need for traditional value or policy iteration. To make the method effective at practical short horizons, the control variable is recast from a continuous trading rate to a discrete target holding. This ensures signal-implied positions are reached immediately, while execution costs are evaluated against a microstructure-grounded quadratic price impact model. Evaluated on a $14$-asset ETF universe using an engineered oracle signal, the learned Gibbs policy yields substantial out-of-sample Sharpe ratio improvements over static and myopic baselines. The results demonstrate that the proposed framework successfully translates known signal quality into a robust, multi-period, and cost-aware allocation mechanism with strictly controlled volatility and turnover. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.15195 |
| By: | Møller, Niels Framroze; Pöschl, Johannes |
| Abstract: | This study investigates the effect of the large shock to energy prices following the Russian invasion of Ukraine on bank credit to firms. To isolate the causal effect of the shock, it compares bank lending to high-energy-intensive firms to that of similar low-energy-intensive firms. Following the shock, bank credit to high-energy-intensive firms persistently declined, while their interest rates on new loans rose and other loan terms tightened. Across the distribution, safer firms reduced outstanding credit lines and paid unchanged interest rates on new bank loans, while riskier firms borrowed at higher interest rates. JEL Classification: G21, G32, Q43 |
| Keywords: | bank credit, credit register, energy price shock, firm credit, firm heterogeneity |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263260 |
| By: | Federico M. Bandi; Yinan Su |
| Abstract: | Compute (computing power) is a scarce, capital-intensive input at the center of the AI economy. Compute capital expenditure and service flow already exceed 1% of U.S. GDP and are growing rapidly. The price of compute reflects uncertainty over AI adoption. The announced launch of compute futures turns this uncertainty into a tradable risk, raising questions on the pricing of a new asset class. We provide an early asset-pricing framework for compute. We begin by discussing the underlying compute rental market and its indexation. We then turn to pricing: 1) direct no-arbitrage links between futures prices and current spot prices fail due to the non-storable nature of compute, 2) synthetic futures prices from existing term rental contracts are likely upper bounds on true futures prices and, 3) upon financialization, futures prices will be investors' expectations of spot prices at expiration net of a risk premium. Using synthetic futures as stand-ins before the compute futures market launches, we construct the first compute futures return panel sorted by GPU generation and maturity. Our preliminary evidence is consistent with a positive compute risk premium, suggesting hedging pressure on the part of compute providers. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.12156 |
| By: | Berg, K.; Danyu-Zhang, J.; Gaviano, L. G.; Yannelis, C. |
| Abstract: | For most households, human capital is the largest asset they own, and rapid advances in artificial intelligence (AI) may change its value. This paper studies whether workers whose occupations are more exposed to AI use financial and labor markets to hedge this risk, by investing in firms that gain from the new technology. We develop a portfolio-choice model with nontradable human capital in which AI-related equity pays off in states where exposed workers’ labor income falls through technological unemployment. The model predicts that more exposed workers should hold more equity, especially when human capital is large relative to financial wealth. We test these predictions using linked Norwegian administrative data on workers’ occupations, employers, income, wealth, and equity holdings. Workers in more AI-exposed occupations are more likely to participate in equity markets and, conditional on participation, hold more equity, especially from firms located in countries with firms more exposed to the AI boom. The exposure–equity relationship is stronger for younger workers, consistent with life-cycle hedging. Following the release of ChatGPT, workers with greater AI exposure also become more likely to move into lower-exposure industries and senior management roles. Our results highlight a channel through which financial markets may partially insure workers against technological unemployment. |
| Keywords: | Artificial Intelligence, Portfolio Allocation, Income Risk, Stock Market Participation |
| JEL: | G11 G51 J32 |
| Date: | 2026–07–27 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2660 |