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on Computational Economics |
| By: | David Imhof; Emanuel W Viklund; Martin Huber |
| Abstract: | We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive behavior, leveraging predictive features suggested in prior research. We test our approach on a large dataset covering 13 markets across seven countries. Our results show that predictive models based on GATs, trained on a subset of the markets, can be effectively transferred to other markets, achieving accuracy rates between 80% and 90%, depending on the hyperparameter settings. The best-performing configuration, applied to eight markets from Switzerland and the Japanese region of Okinawa, yields an average accuracy of 91% for cross-market prediction. When extended to 12 markets, the method maintains a strong performance with an average accuracy of 84%, surpassing traditional ensemble approaches in machine learning. These results suggest that GAT-based detection methods offer a promising tool for competition authorities to screen markets for potential cartel activity. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12369 |
| By: | Jieyu Chen; Sebastian Lerch; Melanie Schienle; Tomasz Serafin; Rafal Weron |
| Abstract: | The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation. |
| Keywords: | Intraday electricity market; Probabilistic forecast; Path forecast; Prediction bands; Energy score; Machine learning; Generative neural network; Trading recommendations |
| JEL: | C22 C32 C45 C51 C53 Q41 Q47 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ahh:wpaper:worms2505 |
| By: | Daniele Ballinari; Jessica Maly |
| Abstract: | We enhance sentiment analysis in the foreign exchange (FX) market by fine-tuning large language models (LLMs) to better understand and interpret the complex language specific to FX markets. We build on existing methods by using state-of-the-art open source LLMs, fine-tuning them with labelled FX news articles and then comparing their performance against traditional approaches and alternative models. Furthermore, we tested these fine-tuned LLMs by creating investment strategies based on the sentiment they detect in FX analysis articles with the goal of demonstrating how well these strategies perform in real-world trading scenarios. Our findings indicate that the fine-tuned LLMs outperform the existing methods in terms of both the classification accuracy and trading performance, highlighting their potential for improving FX market sentiment analysis and investment decision-making. |
| Keywords: | Large language models, Sentiment analysis, Fine-tuning, Text classification, Natural language processing, Foreign exchange, Financial markets |
| JEL: | F31 G12 G15 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2025-11 |
| 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: | 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: | Gambara, Matteo; Livieri, Giulia; Pallavicini, Andrea |
| Abstract: | Evaluating financial products with early-termination clauses, particularly those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back options, and callable certificates. We will compare regression methods for pricing and computing sensitivities, highlighting modern machine learning techniques against traditional polynomial basis functions. Specifically, we will analyze randomized recurrent and feed-forward neural networks, along with a novel approach using signatures of the underlying price process. For option sensitivities like Delta and Gamma, we will incorporate Chebyshev interpolation. Our findings show that machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates. Furthermore, we apply Chebyshev interpolation for Delta and Gamma calculations for the first time in Asian options and callable certificates. |
| Keywords: | amerasian options; callable certificates; random networks; Chebyshev Greeks; early termination; signature methods |
| JEL: | C63 G13 |
| Date: | 2025–06–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128600 |
| By: | Dimitrios Emmanoulopoulos; Ollie Olby; Justin Lyon; Namid R. Stillman |
| Abstract: | Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have shown promise across multiple domains including in finance, they typically lack a principled model-building step, relying instead on sentiment- or trend-based analysis. We address this gap by developing an agentic system that uses LLMs to iteratively discover stochastic differential equations for financial time series. These models generate risk metrics which inform daily trading decisions. We evaluate our system in both traditional backtests and using a market simulator, which introduces synthetic but causally plausible price paths and news events. We find that model-informed trading strategies outperform standard LLM-based agents, improving Sharpe ratios across multiple equities. Our results show that combining LLMs with agentic model discovery enhances market risk estimation and enables more profitable trading decisions. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08584 |
| 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 |
| 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: | Fabio Baschetti; Giacomo Bormetti; Pietro Rossi |
| Abstract: | Joint calibration to SPX and VIX market data is a delicate task that requires sophisticated modeling and incurs significant computational costs. The latter is especially true when pricing of volatility derivatives hinges on nested Monte Carlo simulation. One such example is the 4-factor Markov Path-Dependent Volatility (PDV) model of Guyon and Lekeufack (2023). Nonetheless, its realism has earned it considerable attention in recent years. Gazzani and Guyon (2025) marked a relevant contribution by learning the VIX as a random variable, i.e., a measurable function of the model parameters and the Markovian factors. A neural network replaces the inner simulation and makes the joint calibration problem accessible. However, the minimization loop remains slow due to expensive outer simulation. The present paper overcomes this limitation by learning SPX implied volatilities, VIX futures, and VIX call option prices. The pricing functions reduce to simple matrix-vector products that can be evaluated on the fly, shrinking calibration times to just a few seconds. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09412 |
| By: | Edmunds, Scott C (GigaScience/BGI Hong Kong); Nogoy, Nicole (GigaScience Press); Lan, Qing; Zhang, Hongfang; Fan, Yannan; Zhou, Hongling; Armit, Chris J |
| Abstract: | The increasing use of AI-based approaches such as machine learning (ML) across diverse scientific fields presents challenges for reproducibly disseminating and assessing research. As ML becomes integral to a growing range of computationally intensive applications (e.g. clinical research), there is a critical need for transparent reporting methods to ensure both comprehensibility and the reproducibility of the supporting studies. There are a growing number of standards, checklists and guidelines enabling more standardized reporting of ML research, but the proliferation and complexity of these make them challenging to use. Particularly in assessment and peer review, which has to date, been an ad hoc process that has struggled to throw light on increasingly complicated computational supporting methods that are otherwise unintelligible to other researchers. Taking the publication process beyond these black boxes, GigaScience Press has experimented with integrating many of these ML-standards into the publication process. Having a broad-scope that necessitated looking at more generalist and automated approaches. Here, we map the current landscape of artificial intelligence (AI) standards, and outline our adoption of the DOME recommendations for Machine Learning in biology. We developed a publishing workflow that integrates the DOME Data Stewardship Wizard and DOME Registry tools into the peer-review and publication process. From this case study we provide journal authors, reviewers and Editors examples of approaches, workflows and strategies to more logically disseminate and review ML research. Demonstrating the need for continued dialogue and collaboration among various ML communities to create unified, comprehensive standards, to enhance the credibility, sustainability and impact of ML-based scientific research. |
| Date: | 2025–08–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:metaar:y6jh2_v1 |
| By: | Yueyi Wang; Qiyao Wei |
| Abstract: | In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use an LLM to automatically assign multi-label event categories to high-sentiment-intensity tweets. We align these labeled sentiment signals with forward returns over 1-to-7-day horizons to evaluate their statistical efficacy and market tradability. Our experiments reveal that certain event labels consistently yield negative alpha, with Sharpe ratios as low as -0.38 and information coefficients exceeding 0.05, all statistically significant at the 95\% confidence level. This study establishes the feasibility of transforming unstructured social media text into structured, multi-label event variables. A key contribution of this work is its commitment to transparency and reproducibility; all code and methodologies are made publicly available. Our results provide compelling evidence that social media sentiment is a valuable, albeit noisy, signal in financial forecasting and underscore the potential of open-source frameworks to democratize algorithmic trading research. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.07408 |
| By: | Jeremy Proz; Martin Huber |
| Abstract: | Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel detection algorithm to two cartel cases in the Italian electricity market and evaluates its out-of-sample performance. Specifically, we consider an ensemble machine learning method that uses statistical screens constructed from the offer price distribution as predictors for the incidence of collusion among electricity providers in specific regions. We propose novel screens related to the capacity-withholding behavior of electricity providers and find that including such screens derived from the day-ahead spot market as predictors can improve cartel detection. We find that, under complete cartels - where collusion in a tender presumably involves all suppliers - the method correctly classifies up to roughly 95% of tenders in our data as collusive or competitive, improving classification accuracy compared to using only previously available screens. However, when trained on larger datasets including non-cartel members and applying algorithms tailored to detect incomplete cartels, the previously existing screens are sufficient to achieve 98% accuracy, and the addition of our newly proposed capacity-withholding screens does not further improve performance. Overall, this study highlights the promising potential of supervised machine learning techniques for detecting and dismantling cartels in electricity markets. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09885 |
| By: | Vidya Sagar G; Shifat Ali; Siddhartha P. Chakrabarty |
| Abstract: | This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing through dimensionality reduction and latent factor modeling via Principal Component Analysis and Autoencoders. Building on this, we extend the methodology using Variational Autoencoders, which introduces a probabilistic structure to the latent space. This enables Monte Carlo-based scenario generation, allowing for more nuanced, distribution-aware simulation of stressed market conditions. The proposed framework captures complex non-linear dependencies and supports risk estimation through Value-at-Risk and Expected Shortfall. Together, these pipelines demonstrate the potential of Machine Learning approaches to improve the flexibility, robustness, and realism of financial stress testing. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02011 |
| By: | Aryan Varshney; Venkat Ram Reddy Ganuthula |
| Abstract: | This study investigates whether large language models (LLMs) exhibit consistent behavior (signal) or random variation (noise) when screening resumes against job descriptions, and how their performance compares to human experts. Using controlled datasets, we tested three LLMs (Claude, GPT, and Gemini) across contexts (No Company, Firm1 [MNC], Firm2 [Startup], Reduced Context) with identical and randomized resumes, benchmarked against three human recruitment experts. Analysis of variance revealed significant mean differences in four of eight LLM-only conditions and consistently significant differences between LLM and human evaluations (p 0.1), while all LLMs differed significantly from human experts across contexts. Meta-cognition analysis highlighted adaptive weighting patterns that differ markedly from human evaluation approaches. Findings suggest LLMs offer interpretable patterns with detailed prompts but diverge substantially from human judgment, informing their deployment in automated hiring systems. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08019 |
| By: | Igor Halperin |
| Abstract: | The proliferation of Large Language Models (LLMs) is challenged by hallucinations, critical failure modes where models generate non-factual, nonsensical or unfaithful text. This paper introduces Semantic Divergence Metrics (SDM), a novel lightweight framework for detecting Faithfulness Hallucinations -- events of severe deviations of LLMs responses from input contexts. We focus on a specific implementation of these LLM errors, {confabulations, defined as responses that are arbitrary and semantically misaligned with the user's query. Existing methods like Semantic Entropy test for arbitrariness by measuring the diversity of answers to a single, fixed prompt. Our SDM framework improves upon this by being more prompt-aware: we test for a deeper form of arbitrariness by measuring response consistency not only across multiple answers but also across multiple, semantically-equivalent paraphrases of the original prompt. Methodologically, our approach uses joint clustering on sentence embeddings to create a shared topic space for prompts and answers. A heatmap of topic co-occurances between prompts and responses can be viewed as a quantified two-dimensional visualization of the user-machine dialogue. We then compute a suite of information-theoretic metrics to measure the semantic divergence between prompts and responses. Our practical score, $\mathcal{S}_H$, combines the Jensen-Shannon divergence and Wasserstein distance to quantify this divergence, with a high score indicating a Faithfulness hallucination. Furthermore, we identify the KL divergence KL(Answer $||$ Prompt) as a powerful indicator of \textbf{Semantic Exploration}, a key signal for distinguishing different generative behaviors. These metrics are further combined into the Semantic Box, a diagnostic framework for classifying LLM response types, including the dangerous, confident confabulation. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10192 |
| By: | Nishan Ranabhat; Behnam Javanparast; David Goerz; Estelle Inack |
| Abstract: | Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using autoregressive neural networks. We demonstrate that VNA can identify near-optimal solutions for portfolios comprising more than 2, 000 assets and yields performance comparable to that of state-of-the-art optimizers, such as Mosek, while exhibiting faster convergence on hard instances. Finally, we present a dynamical finite-size scaling analysis applied to the S&P 500, Russell 1000, and Russell 3000 indices, revealing universal behavior and polynomial annealing time scaling of the VNA algorithm on portfolio optimization problems. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07159 |
| By: | Jinbo Cai; Wenze Li; Wenjie Wang |
| Abstract: | With stakeholder-level in-market data, we conduct a comparative analysis of machine learning (ML) for forecasting electricity prices in Singapore, spanning 15 individual models and 4 ensemble approaches. Our empirical findings justify the three virtues of ML models: (1) the virtue of capturing non-linearity, (2) the complexity (Kelly et al., 2024) and (3) the l2-norm and bagging techniques in a weak factor environment (Shen and Xiu, 2024). Simulation also supports the first virtue. Penalizing prediction correlation improves ensemble performance when individual models are highly correlated. The predictability can be translated into sizable economic gains under the mean-variance framework. We also reveal significant patterns of time-series heterogeneous predictability across macro regimes: predictability is clustered in expansion, volatile market and extreme geopolitical risk periods. Our feature importance results agree with the complex dynamics of Singapore's electricity market after de regulation, yet highlight its relatively supply-driven nature with the continued presence of strong regulatory influences. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07477 |
| By: | Arkadiusz Lipiecki; Kaja Bilinska; Nikolaos Kourentzes; Rafal Weron |
| Abstract: | We introduce the concept of Temporal Hierarchy Forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a state-of-the-art transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice. |
| Keywords: | Electricity price; Temporal Hierarchy Forecasting (THieF); Forecast reconciliation; Regression; Machine learning |
| JEL: | C22 C45 C51 C53 Q41 Q47 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ahh:wpaper:worms2506 |
| By: | John Rust (Department of Economics, Georgetown University); Tianshi Mu (Tsinghua University); Pranjal Rawat (Georgetown University); Chengjun Zhang (Morgan Stanley); Qixuan Zhong (Department of Economics, Georgetown University) |
| Abstract: | We compare human and artificially intelligent (AI) subjects in classification tasks where the optimal decision rule is given by Bayes’ Rule. Experimental studies reach mixed conclusions about whether human beliefs and decisions accord with Bayes’ Rule. We reanalyze land- mark experiments using a new model of decision making and show that decisions can be nearly optimal even when beliefs are not Bayesian. Using an objective measure of “decision efficiency, ” we find that humans are 96% efficient despite the fact that only a minority have Bayesian beliefs. We replicate these same experiments using three generations of ChatGPT as subjects. Using the reasoning provided by GPT responses to understand its “thought process, ” we find that GPT-3.5 ignores the prior and is only 75% efficient, whereas GPT-4 and GPT-4o use Bayes’ Rule and are 93% and 99% efficient, respectively. Most errors by GPT-4 and GPT-4o are algebraic mistakes in computing the posterior, but GPT-4o is far less error-prone. GPT performance increased from sub-human to super-human in just 3 years. By version 4o, its beliefs and decision making had become nearly perfectly Bayesian. |
| Keywords: | Bayes’ Rule, decision making, statistical decision theory, win and loss func- tions, learning, Bayes’ compatible beliefs, noisy Bayesians, classification, machine learning, artificial intelligence, large language models, ChatGPT, maximum likelihood, heterogeneity, mixture models, Estimation-Classification (EC) algorithm, binary logit model, structural models |
| JEL: | C91 D91 |
| Date: | 2025–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:geo:guwopa:gueconwpa~25-25-02 |
| 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: | Öztürk Kösenciğ, Kamile; Okuyucu, Elif Bahar; Balaban, Özgün (Tilburg University, School of Economics and Management) |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:011d684a-ee40-426c-a8f2-01b4f117dc79 |
| By: | Dixon Domfeh; Saeid Safarveisi |
| Abstract: | Traditional models for pricing catastrophe (CAT) bonds struggle to capture the complex, relational data inherent in these instruments. This paper introduces CATNet, a novel framework that applies a geometric deep learning architecture, the Relational Graph Convolutional Network (R-GCN), to model the CAT bond primary market as a graph, leveraging its underlying network structure for spread prediction. Our analysis reveals that the CAT bond market exhibits the characteristics of a scale-free network, a structure dominated by a few highly connected and influential hubs. CATNet demonstrates high predictive performance, significantly outperforming a strong Random Forest benchmark. The inclusion of topological centrality measures as features provides a further, significant boost in accuracy. Interpretability analysis confirms that these network features are not mere statistical artifacts; they are quantitative proxies for long-held industry intuition regarding issuer reputation, underwriter influence, and peril concentration. This research provides evidence that network connectivity is a key determinant of price, offering a new paradigm for risk assessment and proving that graph-based models can deliver both state-of-the-art accuracy and deeper, quantifiable market insights. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10208 |
| By: | Askitas, Nikos (IZA) |
| Abstract: | We examine the uptake of GPT-assisted writing in economics working paper abstracts. Using data from the IZA DP series, we detect a clear stylistic shift after the release of ChatGPT-3.5 in March 2023. This shift is evident in core textual metrics—mean word length, type-token ratio, and readability—and reflects growing convergence with machine-generated writing. While the ChatGPT launch was an exogenous shock, adoption is endogenous: authors choose whether to use AI. To capture this behavioral response, we combine stylometric analysis, machine learning classification, and prompt-based similarity testing. Event-study regressions with fixed effects and placebo checks confirm that the change is abrupt, persistent, and not explained by pre-existing trends. A similarity experiment using OpenAI’s API shows that post-ChatGPT abstracts resemble their GPT-optimized versions more closely than pre-ChatGPT resemble theirs. A classifier, trained on these variants, flags a growing share of post-March 2023 texts as GPT-like. Rather than suggesting full automation, our findings indicate selective human–AI augmentation. Our framework generalizes to other contexts such as e.g. resumes, job ads, legal briefs, research proposals, or programming code. |
| Keywords: | AI-assisted writing, linguistic metrics, event study, machine learning, natural language processing (NLP), text analysis, academic writing, GPT adoption, diffusion of technology |
| JEL: | C55 C88 O33 C81 L86 J24 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18062 |
| By: | Askitas, Nikos (IZA) |
| Abstract: | Generative AI (GenAI) and Large Language Models (LLMs) are moving into domains once seen as uniquely human: reasoning, synthesis, abstraction, and rhetoric. Addressed to labor economists and informed readers, this paper clarifies what is truly new about LLMs, what is not, and why it matters. Using an analogy to auto-regressive models from economics, we explain their stochastic nature, whose fluency is often mistaken for agency. We place LLMs in the longer history of human–machine outsourcing, from digestion to cognition, and examine disruptive effects on white-collar labor, institutions, and epistemic norms. Risks emerge when synthetic content becomes both product and input, creating feedback loops that erode originality and reliability. Grounding the discussion in conceptual clarity over hype, we argue that while GenAI may substitute for some labor, statistical limits will, probably but not without major disruption, preserve a key role for human judgment. The question is not only how these tools are used, but which tasks we relinquish and how we reallocate expertise in a new division of cognitive labor. |
| Keywords: | automation and outsourcing, technological change, labor economics, autoregressive models, Large Language Models, Generative Artificial Intelligence, human-machine collaboration knowledge work, epistemic norms, digital transformation |
| JEL: | J24 O33 O31 J22 D83 L86 J44 O38 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18070 |
| 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: | Harold Gu\'eneau (SAMM); Alain Celisse (LPP, MODAL); Pascal Delange |
| Abstract: | The present work tackles the money laundering detection problem. A new procedure is introduced which exploits structured time series of both qualitative and quantitative data by means of a transformer neural network. The first step of this procedure aims at learning representations of time series through contrastive learning (without any labels). The second step leverages these representations to generate a money laundering scoring of all observations. A two-thresholds approach is then introduced, which ensures a controlled false-positive rate by means of the Benjamini-Hochberg (BH) procedure. Experiments confirm that the transformer is able to produce general representations that succeed in exploiting money laundering patterns with minimal supervision from domain experts. It also illustrates the higher ability of the new procedure for detecting nonfraudsters as well as fraudsters, while keeping the false positive rate under control. This greatly contrasts with rule-based procedures or the ones based on LSTM architectures. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08835 |
| By: | Arkadiusz Lipiecki; Kaja Bilinska; Nicolaos Kourentzes; Rafal Weron |
| Abstract: | We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a state-of-the-art transformer. Given that (i) trading of block products is becoming more common and (ii) the computational cost of reconciliation is comparable to that of predicting hourly prices alone, we recommend using it in daily forecasting practice. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11372 |
| 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: | Ryuji Hashimoto; Kiyoshi Izumi |
| Abstract: | We investigate the mechanisms behind the power-law distribution of stock returns using artificial market simulations. While traditional financial theory assumes Gaussian price fluctuations, empirical studies consistently show that the tails of return distributions follow a power law. Previous research has proposed hypotheses for this phenomenon -- some attributing it to investor behavior, others to institutional demand imbalances. However, these factors have rarely been modeled together to assess their individual and joint contributions. The complexity of real financial markets complicates the isolation of the contribution of a single component using existing data. To address this, we construct artificial markets and conduct controlled experiments using optimal transport (OT) as a quantitative similarity measure. Our proposed framework incrementally introduces behavioral components into the agent models, allowing us to compare each simulation output with empirical data via OT distances. The results highlight that informational effect of prices plays a dominant role in reproducing power-law behavior and that multiple components interact synergistically to amplify this effect. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09863 |
| By: | Juchan Kim; Inwoo Tae; Yongjae Lee |
| Abstract: | Portfolio optimization constitutes a cornerstone of risk management by quantifying the risk-return trade-off. Since it inherently depends on accurate parameter estimation under conditions of future uncertainty, the selection of appropriate input parameters is critical for effective portfolio construction. However, most conventional statistical estimators and machine learning algorithms determine these parameters by minimizing mean-squared error (MSE), a criterion that can yield suboptimal investment decisions. In this paper, we adopt decision-focused learning (DFL) - an approach that directly optimizes decision quality rather than prediction error such as MSE - to derive the global minimum-variance portfolio (GMVP). Specifically, we theoretically derive the gradient of decision loss using the analytic solution of GMVP and its properties regarding the principal components of itself. Through extensive empirical evaluation, we show that prediction-focused estimation methods may fail to produce optimal allocations in practice, whereas DFL-based methods consistently deliver superior decision performance. Furthermore, we provide a comprehensive analysis of DFL's mechanism in GMVP construction, focusing on its volatility reduction capability, decision-driving features, and estimation characteristics. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.10776 |
| By: | Jackson, Emerson Abraham |
| Abstract: | Artificial Intelligence (AI) is transforming the way individuals engage with information, especially in educational environments where there is an increasing need for tailored, scalable, and effective learning models. This study offers a thorough evaluation of the changing impact of AI on knowledge acquisition, emphasising learners’ adaptability, engagement, and performance. This paper employs a mixed-methods approach with a carefully selected sample size of 150 participants from various academic institutions and learning environments to assess the effectiveness, challenges, and equity dimensions of AI-enabled educational tools. The findings indicate significant enhancements in understanding and memory retention among users of AI platforms, while also highlighting inequalities in access and the necessity for responsible implementation. The research provides practical policy recommendations to facilitate the sustainable integration of AI in knowledge delivery systems. |
| Keywords: | Artificial Intelligence, Knowledge Acquisition, Digital Pedagogy, Personalised Learning, Cognitive Enhancement |
| JEL: | C38 D83 I21 O33 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125529 |
| By: | Sipeng Zeng; Xiaoning Wang; Tianshu Sun |
| Abstract: | Although Artificial Intelligence (AI) holds great promise for enhancing innovation and productivity, many firms struggle to realize its benefits. We investigate why some firms and industries succeed with AI while others do not, focusing on the degree to which an industrial domain is technologically integrated with AI, which we term "domain AI readiness". Using panel data on Chinese listed firms from 2016 to 2022, we examine how the interaction between firm-level AI capabilities and domain AI readiness affects firm performance. We create novel constructs from patent data and measure the domain AI readiness of a specific domain by analyzing the co-occurrence of four-digit International Patent Classification (IPC4) codes related to AI with the specific domain across all patents in that domain. Our findings reveal a strong complementarity: AI capabilities yield greater productivity and innovation gains when deployed in domains with higher AI readiness, whereas benefits are limited in domains that are technologically unprepared or already obsolete. These results remain robust when using local AI policy initiatives as instrumental variables. Further analysis shows that this complementarity is driven by external advances in domain-AI integration, rather than firms' own strategic pivots. Time-series analysis of IPC4 co-occurrence patterns further suggests that improvements in domain AI readiness stem primarily from the academic advancements of AI in specific domains. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.09634 |