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on Financial Markets |
| By: | Bong-Gyu Jang; Younwoo Jeong; Changeun Kim |
| Abstract: | We introduce the \textit{Consensus-Bottleneck Asset Pricing Model} (CB-APM), a partially interpretable neural network that replicates the reasoning processes of sell-side analysts by capturing how dispersed investor beliefs are compressed into asset prices through a consensus formation process. By modeling this ``bottleneck'' to summarize firm- and macro-level information, CB-APM not only predicts future risk premiums of U.S. equities but also links belief aggregation to expected returns in a structurally interpretable manner. The model improves long-horizon return forecasts and outperforms standard deep learning approaches in both predictive accuracy and explanatory power. Comprehensive portfolio analyses show that CB-APM's out-of-sample predictions translate into economically meaningful payoffs, with monotonic return differentials and stable long-short performance across regularization settings. Empirically, CB-APM leverages consensus as a regularizer to amplify long-horizon predictability and yields interpretable consensus-based components that clarify how information is priced in returns. Moreover, regression and GRS-based pricing diagnostics reveal that the learned consensus representations capture priced variation only partially spanned by traditional factor models, demonstrating that CB-APM uncovers belief-driven structure in expected returns beyond the canonical factor space. Overall, CB-APM provides an interpretable and empirically grounded framework for understanding belief-driven return dynamics. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.16251 |
| By: | Miguel Acosta; Andrea Ajello; Michael D. Bauer; Francesca Loria; Silvia Miranda-Agrippino |
| Abstract: | This paper introduces the U.S. Monetary Policy Event-Study Database (USMPD), a novel, public, and regularly updated dataset of financial market data around Federal Open Market Committee (FOMC) policy announcements, press conferences, and minutes releases. Using the rich high-frequency data in the USMPD, we document several new empirical findings. Large monetary policy surprises have made a comeback in recent years, and post-meeting press conferences have become the most important source of policy news. Monetary policy surprises have pronounced negative effects on breakeven inflation based on Treasury yields. Risk assets, including dividend derivatives, also respond strongly and negatively to monetary policy surprises, consistent with conventional channels of monetary transmission. Press conferences have stronger effects than FOMC statements on most asset prices. Finally, the term structure evidence shows peak effects on market-based inflation and dividend expectations at horizons of several years. |
| Keywords: | Federal Reserve; monetary policy surprises; high-frequency event studies |
| JEL: | E43 E52 E58 |
| Date: | 2025–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedfwp:102219 |
| By: | Benjamin Born; Nora Lamersdorf; Jana-Lynn Schuster; Sascha Steffen |
| Abstract: | Using modern natural language processing, we construct a high-frequency inflation expectations index from German-language tweets. This index closely tracks realized inflation and aligns even more closely with household survey expectations. It also improves short-run forecasts relative to standard benchmarks. In response to monetary policy tightening, the index declines within about a week, with the effects concentrated in tweets by private individuals and during the recent period of elevated inflation. Using 117 million online transactions from German retailers, we show that higher inflation expectations are followed by lower household spending on discretionary goods. By linking these shifts in demand to stock returns, we find that, during periods of elevated inflation, firms operating in discretionary sectors experience significantly lower stock returns when inflation expectations rise. Thus, our Twitter-based index provides market participants and policymakers with a timely tool to monitor inflation sentiment and its economic consequences. |
| Keywords: | inflation expectations, social media (Twitter/X), large language models (LLMs), NLP, household consumption, stock returns, monetary policy |
| JEL: | E31 D84 E58 C45 C81 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12361 |
| By: | Fekria Belhouichet; Guglielmo Maria Caporale; Luis Alberiko Gil-Alana |
| Abstract: | This study examines the contemporaneous and lagged connectedness between the daily returns of AI and robotics-related assets, a global stock market index, commodity prices (gold and Brent crude oil), cryptocurrencies, and a carbon index over the period from 3 January 2023 to 30 September 2025, against a backdrop of persistent geopolitical tensions, using the innovative R² connectedness method developed by Balli et al. (2023). The results reveal that contemporaneous effects predominate over lagged ones. Furthermore, AI and robotics-related assets behave primarily as net emitters of shocks, as does the MSCI World Index, which exerts positive contagion effects and plays a central role in risk transmission. By constrast, gold and Brent crude oil act as net receivers of shocks, which in the case of the former reflects its role as a safe-haven asset. Cryptocurrencies instead exhibit heterogeneous dynamics : Cardano (ADA) acts as a net transmitter of shocks, while Bitcoin (BTC) and Stellar (XLM) behave more as receivers, contributing to market stability. Finally, the CO₂ index displays net negative connectedness, which confirm its role as a receiver of shocks. These findings provide useful information to investors and portfolio managers for risk diversification purposes and to policy-makers for ensuring financial stabilily, especially during periods of market turbulence. |
| Keywords: | assets returns, CO2 emissions, contemporaneous and lagged R2 connectedness |
| JEL: | C32 G11 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12333 |
| By: | Julia Ko\'nczal; Micha{\l} Balcerek; Krzysztof Burnecki |
| Abstract: | In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{\~n}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.22660 |
| By: | Stefano Caselli, Marta Zava |
| Abstract: | The study examines the structure, functioning, and strategic implications of financial ecosystems across four European countries—France, Sweden, the United Kingdom, and Italy—to identify institutional best practices relevant to the ongoing transformation of Italy’s financial system. Building on a comparative analysis of legislation and regulation, taxation, investor bases, and financial intermediation, the report highlights how distinct historical and institutional trajectories have shaped divergent models: the French dirigiste system anchored by powerful state-backed institutions and deep asset management pools; the Swedish social-democratic ecosystem driven by broad household equity participation, tax efficient savings vehicles, and equity-oriented pension funds; and the British liberal model, characterized by deep capital markets, strong institutional investor engagement, and globally competitive listing infrastructure. In contrast, Italy remains predominantly bank-centric, with fragmented institutional investment, limited retail equity participation, underdeveloped public markets, and a structural reliance on domestic banking channels for corporate finance. |
| Keywords: | financial ecosystems; capital markets; institutional investors; household savings; taxation; IPO markets; SME finance; European financial integration; Savings and Investments Union. |
| JEL: | G10 G18 G23 G28 O16 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:baf:cbafwp:cbafwp25261 |