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on Financial Markets |
| By: | Orhan Erdem; Ragavi Pobbathi Ashok |
| Abstract: | In this paper, we explore how large language models (LLMs) approach financial decision-making by systematically comparing their responses to those of human participants across the globe. We posed a set of commonly used financial decision-making questions to seven leading LLMs, including five models from the GPT series(GPT-4o, GPT-4.5, o1, o3-mini), Gemini 2.0 Flash, and DeepSeek R1. We then compared their outputs to human responses drawn from a dataset covering 53 nations. Our analysis reveals three main results. First, LLMs generally exhibit a risk-neutral decision-making pattern, favoring choices aligned with expected value calculations when faced with lottery-type questions. Second, when evaluating trade-offs between present and future, LLMs occasionally produce responses that appear inconsistent with normative reasoning. Third, when we examine cross-national similarities, we find that the LLMs' aggregate responses most closely resemble those of participants from Tanzania. These findings contribute to the understanding of how LLMs emulate human-like decision behaviors and highlight potential cultural and training influences embedded within their outputs. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.10933 |
| By: | Paul Glasserman; Kriste Krstovski; Paul Laliberte; Harry Mamaysky |
| Abstract: | Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.4 million news articles. We apply a novel technique for supervised topic analysis that selects news topics based on their ability to explain contemporaneous market returns. We find that time variation in the prevalence of news topics and differences in the responses to news topics both contribute to the difference in intraday and overnight returns. In out-of-sample tests, our approach forecasts which stocks will do particularly well overnight and particularly poorly intraday. Our approach also helps explain patterns of continuation and reversal in intraday and overnight returns. We contrast the effect of news with other mechanisms proposed in the literature to explain overnight returns. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04481 |
| By: | Guilherme V. Moura; Andr\'e P. Santos; Hudson S. Torrent |
| Abstract: | Machine learning (ML) methods have been successfully employed in identifying variables that can predict the equity premium of individual stocks. In this paper, we investigate if ML can also be helpful in selecting variables relevant for optimal portfolio choice. To address this question, we parameterize minimum-variance portfolio weights as a function of a large pool of firm-level characteristics as well as their second-order and cross-product transformations, yielding a total of 4, 610 predictors. We find that the gains from employing ML to select relevant predictors are substantial: minimum-variance portfolios achieve lower risk relative to sparse specifications commonly considered in the literature, especially when non-linear terms are added to the predictor space. Moreover, some of the selected predictors that help decreasing portfolio risk also increase returns, leading to minimum-variance portfolios with good performance in terms of Shape ratios in some situations. Our evidence suggests that ad-hoc sparsity can be detrimental to the performance of minimum-variance characteristics-based portfolios. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.14986 |
| By: | Tianjiao Zhao; Jingrao Lyu; Stokes Jones; Harrison Garber; Stefano Pasquali; Dhagash Mehta |
| Abstract: | The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human-like efficiency and adaptability. In this context, multi-agent collaboration has emerged as a promising approach, enabling multiple AI agents to work together to solve complex challenges. This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management. We present a comprehensive analysis performed by a team of specialized agents and evaluate their stock-picking performance against established benchmarks under varying levels of risk tolerance. Furthermore, we examine the advantages and limitations of employing multi-agent frameworks in equity analysis, offering critical insights into their practical efficacy and implementation challenges. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11152 |
| By: | Can Gao (University of St.Gallen; Swiss Finance Institute; Swisss Institute for Banking and Finance); Brandon Yueyang Han (Robert H. Smith School of Business, University of Maryland) |
| Abstract: | We demonstrate the asset pricing implications of investors’ belief heterogeneity in the frequency of news arrival and its joint impact with heterogeneous beliefs about news content. Investors trade volatility derivatives against each other to speculate on the rate of news arrival: greater disagreement of this kind gives rise to more extreme derivative positions. When disagreement about news arrival frequency is low, volatility exhibits mean reversion because extreme optimists and pessimists incur substantial wealth losses amid intense market swings. In contrast, high disagreement about the news arrival rate leads to volatility persistence. When news is absent in such environments, volatility sellers dominate, and extreme payoffs are underweighted in the formation of market expectations, resulting in lower implied volatility. In this context, “no news” effectively becomes good news for risky asset valuations. |
| JEL: | G11 G12 D83 D84 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2561 |
| By: | Rehim Kılıç |
| Abstract: | This paper fills an important gap in the volatility forecasting literature by comparing a broad suite of machine learning (ML) methods with both linear and nonlinear econometric models using high-frequency realized volatility (RV) data for the S&P 500. We evaluate ARFIMA, HAR, regime-switching HAR models (THAR, STHAR, MSHAR), and ML methods including Extreme Gradient Boosting, deep feed-forward neural networks, and recurrent networks (BRNN, LSTM, LSTM-A, GRU). Using rolling forecasts from 2006 onward, we find that regime-switching models—particularly THAR and STHAR—consistently outperform ML and linear models, especially when predictors are limited. These models also deliver more accurate risk forecasts and higher realized utility. While ML models capture some nonlinear patterns, they offer no consistent advantage over simpler, interpretable alternatives. Our findings highlight the importance of modeling regime changes through transparent econometric tools, especially in real-world applications where predictor availability is sparse and model interpretability is critical for risk management and portfolio allocation. |
| Keywords: | Realized volatility; Machine learning; Regime-switching; Nonlinearity; VaR; forecasting |
| JEL: | C10 C50 G11 G15 |
| Date: | 2025–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-61 |
| By: | Qizhao Chen |
| Abstract: | This paper presents a dynamic cryptocurrency portfolio optimization strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. The proposed method employs the 14-day Relative Strength Index (RSI) and 14-day Simple Moving Average (SMA) to capture market momentum, while sentiment scores are extracted from news articles using the VADER (Valence Aware Dictionary and sEntiment Reasoner) model, with compound scores quantifying overall market tone. The large language model Google Gemini is used to further verify the sentiment scores predicted by VADER and give investment decisions. These technical indicator and sentiment signals are incorporated into the expected return estimates before applying mean-variance optimization with constraints on asset weights. The strategy is evaluated through a rolling-window backtest over cryptocurrency market data, with Bitcoin (BTC) and an equal-weighted portfolio of selected cryptocurrencies serving as benchmarks. Experimental results show that the proposed approach achieves a cumulative return of 38.72, substantially exceeding Bitcoin's 8.85 and the equal-weighted portfolio's 21.65 over the same period, and delivers a higher Sharpe ratio (1.1093 vs. 0.8853 and 1.0194, respectively). However, the strategy exhibits a larger maximum drawdown (-18.52%) compared to Bitcoin (-4.48%) and the equal-weighted portfolio (-11.02%), indicating higher short-term downside risk. These results highlight the potential of combining sentiment and technical signals to improve cryptocurrency portfolio performance, while also emphasizing the need to address risk exposure in volatile markets. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.16378 |
| By: | Theodore Kapopoulos (Athens University of Economics and Business - Department of Accounting and Finance); Dimitrios Anastasiou (Athens University of Economics and Business - Department of Business Administration); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Athanasios Sakkas (Athens University of Economics and Business - Department of Accounting and Finance) |
| Abstract: | We investigate the relationship between global geopolitical risk and bank deposit flows across a wide panel of European countries. Motivated by the pivotal role of deposit stability for financial intermediation and systemic resilience, we explore whether geopolitical shocks alter depositors' portfolio choices. Using quarterly country-level data and employing the Geopolitical Risk Index (GPR) of Caldara and Iacoviello (2022) along with its sub-indices (GPR Acts and GPR Threats), we document that rising global geopolitical risk significantly increases aggregate bank deposits. Specifically, a one-standard-deviation increase in geopolitical risk is associated with an average rise of €13.3 billion in household deposits and €5.6 billion in corporate deposits, highlighting the sizable financial reallocation triggered by global uncertainty. This positive effect is channelled through a reallocation from riskier assets to deposits, with a stronger reaction observed among households compared to firms. Our findings suggest that bank deposits act as a safe-haven asset in periods of heightened global tensions, complementing the flight-to-safety phenomenon documented in sovereign bond markets. The results have important implications for financial stability analysis, monetary policy transmission and banks' liquidity risk management under geopolitical stress. |
| Keywords: | bank deposit flows, geopolitical risk, financial instability |
| JEL: | G4 G21 F51 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2564 |
| By: | Ivan Letteri |
| Abstract: | The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26, 204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14960 |
| By: | Ca' Zorzi, Michele; Manu, Ana-Simona; Lopardo, Gianluigi |
| Abstract: | This paper investigates the economic impact of technological innovation, focusing on generative AI (GenAI) following ChatGPT’s release in November 2022. We propose a novel framework leveraging large language models to analyze earnings call transcripts. Our method quantifies firms’ GenAI exposure and classifies sentiment as opportunity, adoption, or risk. Using panel econometric techniques, we assess GenAI exposure’s impact on S&P 500 firms’ financial performance over 2014-2023. We find two main results. First, GenAI exposure rose sharply after ChatGPT’s release, particularly in IT, Consumer Services, and Consumer Discretionary sectors, coinciding with sentiment shifts toward adoption. Second, GenAI exposure significantly influenced stock market performance. Firms with early and high GenAI exposure saw stronger returns, though earnings expectations improved modestly. Panel regressions show a 1 percentage point increase in GenAI exposure led to 0.26% rise in quarterly excess returns. Difference-in-Difference estimates indicate 2.4% average quarterly stock price increases following ChatGPT’s release. JEL Classification: C80, G14, G30, L25, O33 |
| Keywords: | artificial intelligence, ChatGPT, earnings call, equity returns, generative AI |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253093 |
| By: | Biswarup Chakraborty |
| Abstract: | Portfolio optimization has long been dominated by covariance-based strategies, such as the Markowitz Mean-Variance framework. However, these approaches often fail to ensure a balanced risk structure across assets, leading to concentration in a few securities. In this paper, we introduce novel risk measures grounded in the equal-correlation portfolio strategy, aiming to construct portfolios where each asset maintains an equal correlation with the overall portfolio return. We formulate a mathematical optimization framework that explicitly controls portfolio-wide correlation while preserving desirable risk-return trade-offs. The proposed models are empirically validated using historical stock market data. Our findings show that portfolios constructed via this approach demonstrate superior risk diversification and more stable returns under diverse market conditions. This methodology offers a compelling alternative to conventional diversification techniques and holds practical relevance for institutional investors, asset managers, and quantitative trading strategies. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.03704 |
| By: | Philippe Bacchetta (University of Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); J. Scott Davis (Federal Reserve Banks - Federal Reserve Bank of Dallas); Eric van Wincoop (University of Virginia - Department of Economics; National Bureau of Economic Research (NBER)) |
| Abstract: | Global non-US banks have significant dollar exposure both on and off their balance sheet. We develop a model to analyze their adjustment to dollar funding shocks, whether from reduced direct lending or external dollar shortages. The model provides insight into banks' responses through borrowing, lending, and FX swap positions, as well as the impact on their net worth, their probability of default and CIP deviations. Implications of the model are confronted with data on the response of non-US global banks to major dollar funding shocks. We examine the benefits from buffering these shocks through central bank dollar swap lines or local currency lending by the central bank. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2565 |
| By: | Diego Vallarino |
| Abstract: | This study develops and empirically validates a Mixture of Experts (MoE) framework for stock price prediction across heterogeneous volatility regimes using real market data. The proposed model combines a Recurrent Neural Network (RNN) optimized for high-volatility stocks with a linear regression model tailored to stable equities. A volatility-aware gating mechanism dynamically weights the contributions of each expert based on asset classification. Using a dataset of 30 publicly traded U.S. stocks spanning diverse sectors, the MoE approach consistently outperforms both standalone models. Specifically, it achieves up to 33% improvement in MSE for volatile assets and 28% for stable assets relative to their respective baselines. Stratified evaluation across volatility classes demonstrates the model's ability to adapt complexity to underlying market dynamics. These results confirm that no single model suffices across market regimes and highlight the advantage of adaptive architectures in financial prediction. Future work should explore real-time gate learning, dynamic volatility segmentation, and applications to portfolio optimization. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02686 |