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on Financial Markets |
| By: | Keywan Christian Rasekhschaffe |
| Abstract: | We study whether generative AI can automate feature discovery in U.S. equities. Using large language models with retrieval-augmented generation and structured/programmatic prompting, we synthesize economically motivated features from analyst, options, and price-volume data. These features are then used as inputs to a tabular machine-learning model to forecast short-horizon returns. Across multiple datasets, AI-generated features are consistently competitive with baselines, with Sharpe improvements ranging from 14% to 91% depending on dataset and configuration. Retrieval quality is pivotal: better knowledge bases materially improve outcomes. The AI-generated signals are weakly correlated with traditional features, supporting combination. Overall, generative AI can meaningfully augment feature discovery when retrieval quality is controlled, producing interpretable signals while reducing manual engineering effort. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.00196 |
| By: | Keil, Samuel; Martin, Pascal; Schiereck, Dirk |
| Abstract: | Announcements of emerging technologies often lead to notable stock market reactions, with Artificial Intelligence standing out due to its transformative potential and growing regulatory attention. Yet, most research on investor responses to AI disclosures focuses on U.S. firms, leaving the distinct European context unexplored. Using a short-term event study of 526 AI-related announcements by STOXX Europe 600 firms between 2015 and 2024, we report a significantly negative average stock return of -0.176% within a three-day window. However, announcements detailing specific AI technologies, involving collaborations with AI specialists, or made after the release of ChatGPT are associated with less negative reactions. In contrast, references to EU regulatory frameworks like the AI Act show no significant effect. Our findings confirm generally negative investor reactions to AI announcements but show that in Europe, strategic factors such as announcement specificity, collaborations, and timing also significantly mitigate these effects. |
| Date: | 2026–01–06 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:159306 |
| By: | Atilla Aras |
| Abstract: | The aim of this article is to provide the solution to the equity premium puzzle without using calibrated values. Calibrated values of subjective time discount factor were used in the prior derived models because 4 variables were determined from 3 different equations. Furthermore, calculated values and risk behavior determination of prior models were compatible with empirical literature. 4 unknown variables are now calculated from 4 different equations in the new derived model in this article. Subjective time discount factor and coefficient of relative risk aversion are found 0.9581 and 1.0319, respectively from the system of equations which are compatible with empirical studies. Micro and macro studies about CRRA value affirm each other for the first time in the literature. Furthermore, equity and risk-free asset investors are pinned down to be insufficient risk-loving, which can be considered a type of risk-averse behavior. Hence it can be said that calculated values and risk attitude determination align with empirical literature. This shows that derived model is valid and make CCAPM work under the same assumptions with those of prior derived models. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.11687 |
| By: | Willem THORBECKE |
| Abstract: | The correlation between sovereign bond prices and stock prices was positive from the 1970s to 2000 and then turned negative. Researchers have investigated this phenomenon using data from the 1970s to the present. This paper uses data beginning in the 1960s, when there were negative correlations between bond and stock prices, to investigate how positive bond-stock price comovements arose. Evidence from identified vector autoregressions indicates that monetary policy shocks beginning in the late 1960s caused bond and stock prices to covary positively. Evidence from estimating a multi-factor model indicates that news of both monetary policy and inflation contributed to positive bond-stock comovements. The findings imply that rising inflation now that elicits contractionary monetary policy could alter bonds’ risk characteristics, causing them to again covary positively with stocks. To this end, policymakers should be vigilant that large budget deficits do not stoke inflation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:26011 |
| By: | Hangyi Zhao |
| Abstract: | This paper examines whether SEC Form 4 insider purchase filings predict abnormal returns in U.S. microcap stocks. The analysis covers 17, 237 open-market purchases across 1, 343 issuers from 2018 through 2024, restricted to market capitalizations between \$30M and \$500M. A gradient boosting classifier trained on insider identity, transaction history, and market conditions at disclosure achieves AUC of 0.70 on out-of-sample 2024 data. At an optimized threshold of 0.20, precision is 0.38 and recall is 0.69. The distance from the 52-week high dominates feature importance, accounting for 36% of predictive signal. A momentum pattern emerges in the data: transactions disclosed after price appreciation exceeding 10% yield the highest mean cumulative abnormal return (6.3%) and the highest probability of outperformance (36.7%). This contrasts with the simple mean-reversion intuition often applied to post-run-up entries. The result is robust to winsorization and holds across subsamples. These patterns are consistent with slower information incorporation in illiquid markets, where trend confirmation may filter for higher-conviction insider signals. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.06198 |
| By: | Walid Siala (SnT, University of Luxembourg, Luxembourg); Ahmed Khanfir (RIADI, ENSI, University of Manouba, Tunisia; SnT, University of Luxembourg, Luxembourg); Mike Papadakis (SnT, University of Luxembourg, Luxembourg) |
| Abstract: | This paper addresses stock price movement prediction by leveraging LLM-based news sentiment analysis. Earlier works have largely focused on proposing and assessing sentiment analysis models and stock movement prediction methods, however, separately. Although promising results have been achieved, a clear and in-depth understanding of the benefit of the news sentiment to this task, as well as a comprehensive assessment of different architecture types in this context, is still lacking. Herein, we conduct an evaluation study that compares 3 different LLMs, namely, DeBERTa, RoBERTa and FinBERT, for sentiment-driven stock prediction. Our results suggest that DeBERTa outperforms the other two models with an accuracy of 75% and that an ensemble model that combines the three models can increase the accuracy to about 80%. Also, we see that sentiment news features can benefit (slightly) some stock market prediction models, i.e., LSTM-, PatchTST- and tPatchGNN-based classifiers and PatchTST- and TimesNet-based regression tasks models. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.00086 |
| By: | Pavel Ciaian; d'Artis Kancs; Miroslava Rajcaniova |
| Abstract: | Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag. |
| Keywords: | Bitcoin, price, on-chain, off-chain, blockchain, supply, demand, LocalBitcoins. |
| JEL: | E31 E42 G12 |
| Date: | 2026–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2026_01 |
| By: | Marcin Dec (Group for Research in Applied Economics (GRAPE)) |
| Abstract: | This paper shows that in a less liquid government bond market, filtering term premia through a regression-based Adrian, Crump & Moench (ACM) framework yields risk neutral short rate expectations that match, and often rival, the accuracy of Survey of Professional Forecasters (SPF). Using monthly zero-coupon yields, we extract a model consistent risk free yield curve whose implied forward rates exhibit forecasting performance comparable to SPF paths across horizons up to three years. Crucially, these expectations can be generated daily, providing far higher frequency information than SPF’s quarterly releases. We find that term premia are negligible at the short end but rise with maturity, and that the level factor—despite capturing most yield variance-does not command a price of risk. Cointegration tests indicate that SPF forecasts contain no incremental information beyond the filtered curve. The results highlight a practical advantage: once premia are removed, the yield curve becomes a reliable, high frequency source of monetary policy expectations suitable for policy analysis and market surveillance. |
| Keywords: | Term Premia Extraction, Risk Neutral Interest Rate Expectations, Yield Curve Decomposition, Survey of Professional Forecasters |
| JEL: | E43 G12 G17 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:fme:wpaper:113 |
| By: | Giovanni Bonaccolto (Department of Economics and Law, ``Kore" University of Enna, Piazza dell'Universita, 94100 Enna, Italy); Massimiliano Caporin (Department of Statistical Sciences, University of Padova, Via Cesare Battisti 241/243, Padova, Italy); Oguzhan Cepni (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We investigate whether sentiment innovations help forecast realized volatility in U.S. state-level stock markets. We combine 5-minute intraday data for 50 U.S. states with a daily state-level Twitter-based sentiment index over the period August 2011 to August 2024. Realized variance, skewness, and kurtosis are constructed using intermittency-adjusted estimators that account for sparse trading and zero returns. We adopt a Heterogeneous Autoregressive framework and enrich it with higher-order realized moments and changes in state-level sentiment, estimating the models via weighted least squares to mitigate heteroskedasticity effects. Out-of-sample performance is assessed in a rolling-window forecasting design for daily, weekly, and monthly horizons, and formal forecast comparisons are conducted using Diebold-Mariano and Clark-West tests. Our results confirm that the Heterogeneous Autoregressive components remain the dominant drivers of realized volatility dynamics across all horizons. Importantly, tail-risk information, proxied by realized kurtosis, delivers the most systematic and economically meaningful improvements in predictive accuracy, particularly at short horizons. Sentiment changes exhibit an episodic but non-negligible predictive foot-print: while their average in-sample contribution is limited, they enhance forecast performance for a subset of states, especially when combined with higher-moment information in richer specifications. Overall, our findings highlight that integrating in-traday distributional characteristics and sentiment innovations can improve volatility forecasting at the regional level, albeit in a state- and horizon-dependent manner. |
| Keywords: | State-level stock markets, Sentiment, HAR-RV, Realized moments, Forecast evaluation |
| JEL: | C53 C58 G11 G17 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202603 |
| By: | Fernando Broner; Juan Cortina; Sergio Schmukler; Tomas Williams |
| Abstract: | This paper examines how shifts in investor demand influence firm financing and investment decisions. For identification, the paper exploits a large-scale MSCI methodological reform that mechanically redefined the stock weights in major international equity benchmark indexes, changing the portfolio allocation of 2, 508 firms across 49 countries. Because benchmark-tracking investors closely follow these indexes, the rebalancing constituted a clean shock to equity demand. The results show that portfolio rebalancing by benchmark-tracking investors generated significant capital inflows and outflows at the firm level. Firms experiencing larger inflows increased equity issuance, even more so debt financing, and real investment. The paper complements the empirical analysis with a simple model of firm financing in which a decline in the cost of equity increases the value of equity and relaxes borrowing constraints. Higher equity valuations allow firms to expand borrowing even without issuing substantial new equity, so debt financing responds more strongly than equity issuance. |
| Keywords: | asset managers; benchmark indexes; corporate debt; equity; investment; institutional investors; issuance activity. |
| JEL: | F33 G00 G01 G15 G21 G23 G31 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2026-002 |
| By: | Sriya Anbil; Alyssa G. Anderson; Benjamin Eyal |
| Abstract: | The Global Financial Crisis (GFC) and the Federal Reserve's (Fed) large-scale asset purchases fundamentally reshaped the U.S. monetary policy implementation framework. Before 2008, the Fed operated under a scarce-reserves regime, steering the federal funds rate through daily open market operations. |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:102397 |
| By: | Ilias Aarab; Thomas Gottron; Andrea Colombo; J\"org Reddig; Annalauro Ianiro |
| Abstract: | Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10960 |
| By: | Jose J. Canals-Cerda |
| Abstract: | We examine the challenges of economic forecasting and model misspecification errors confronted by financial institutions implementing the novel current expected credit loss (CECL) allowance methodology and its impact on model risk and bias in CECL projections. We document the increased sensitivity to model and macroeconomic forecasting error of the CECL framework with respect to the incurred loss framework that it replaces. An empirical application illustrates how to leverage simple machine learning (ML) strategies and statistical principles in the design of a nimble and flexible CECL modeling framework. We show that, even in consumer loan portfolios with tens of millions of loans, like mortgage, auto, or credit card portfolios, one can develop, estimate, and deploy an array of models quickly and efficiently, and without a forecasting performance penalty. Drawing on more than 20 years of auto loans data and the experience from the Great Recession and the COVID-19 pandemic, we leverage basic econometric principles to identify strategies to deal with biased model projections in times of high economic uncertainty. We advocate for a focus on resiliency and adaptability of models and model infrastructures to novel shocks and uncertain economic conditions. |
| Keywords: | CECL; Allowance for Loan and Lease Losses; Accounting Regulations; Model Risk |
| JEL: | G01 G21 G28 G50 M41 |
| Date: | 2026–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:102431 |