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on Computational Economics |
| By: | Timoth\'ee Hornek Amir Sartipi; Igor Tchappi; Gilbert Fridgen |
| Abstract: | Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligence (GenAI) and pre-trained large language models (LLMs) have inspired the development of numerous time series foundation models (TSFMs) for time series forecasting, their effectiveness in EPF remains uncertain. To address this gap, we benchmark several state-of-the-art pretrained models--Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT--against established statistical and machine learning (ML) methods for EPF. Using 2024 day-ahead auction (DAA) electricity prices from Germany, France, the Netherlands, Austria, and Belgium, we generate daily forecasts with a one-day horizon. Chronos-Bolt and Time-MoE emerge as the strongest among the TSFMs, performing on par with traditional models. However, the biseasonal MSTL model, which captures daily and weekly seasonality, stands out for its consistent performance across countries and evaluation metrics, with no TSFM statistically outperforming it. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.08113 |
| By: | Liexin Cheng; Xue Cheng; Shuaiqiang Liu |
| Abstract: | This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and unsupervised learning tasks are explored. In supervised learning, ELM is employed to learn parametric option pricing functions, predict intraday stock returns, and complete implied volatility surfaces. Compared with deep neural networks, Gaussian process regression, and logistic regression, ELM achieves higher computational speed, comparable accuracy, and superior generalization. In unsupervised learning, ELM numerically solves Black-Scholes-type PDEs, and outperforms Physics-Informed Neural Networks in training speed without losing precision. The approximation and generalization abilities of ELM are briefly discussed. The findings establish ELM as a practical and efficient tool for various tasks in quantitative finance. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.09551 |
| By: | Francesco Cozzi; Marco Pangallo; Alan Perotti; Andr\'e Panisson; Corrado Monti |
| Abstract: | Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules. However, these rules are, in general, non-differentiable, limiting the use of gradient-based methods for optimization, and thus integration with real-world data. We propose a novel framework to learn a differentiable surrogate of any ABM by observing its generated data. Our method combines diffusion models to capture behavioral stochasticity and graph neural networks to model agent interactions. Distinct from prior surrogate approaches, our method introduces a fundamental shift: rather than approximating system-level outputs, it models individual agent behavior directly, preserving the decentralized, bottom-up dynamics that define ABMs. We validate our approach on two ABMs (Schelling's segregation model and a Predator-Prey ecosystem) showing that it replicates individual-level patterns and accurately forecasts emergent dynamics beyond training. Our results demonstrate the potential of combining diffusion models and graph learning for data-driven ABM simulation. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.21426 |
| By: | Sukru Selim Calik; Andac Akyuz; Zeynep Hilal Kilimci; Kerem Colak |
| Abstract: | Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06345 |
| By: | Mateusz Wilinski; Anubha Goel; Alexandros Iosifidis; Juho Kanniainen |
| Abstract: | The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is finally available. The problem, however, is that we do not have access to ground truth when working with real-world data. This, together with often limited interpretability of modern machine learning methods, makes it difficult to fully utilize the available research potential. In order to deal with this challenge we propose to use a realistic agent-based model as a way to generate synthetic data. This way one has access to ground truth, large replicable data, and limitless research scenarios. Using this approach we show how, even when classifying trading agents in a supervised manner is relatively easy, a more realistic task of unsupervised clustering may give incorrect or even misleading results. We complete the results with investigating the details of how supervised techniques were able to successfully distinguish between different trading behaviors. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.21662 |
| By: | Shu Wang; Zijun Yao; Shuhuai Zhang; Jianuo Gai; Tracy Xiao Liu; Songfa Zhong |
| Abstract: | Advancements in large language models (LLMs) have sparked a growing interest in measuring and understanding their behavior through experimental economics. However, there is still a lack of established guidelines for designing economic experiments for LLMs. By combining principles from experimental economics with insights from LLM research in artificial intelligence, we outline and discuss eight practical tactics for conducting experiments with LLMs. We further perform two sets of experiments to demonstrate the significance of these tactics. Our study enhances the design, replicability, and generalizability of LLM experiments, and broadens the scope of experimental economics in the digital age. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.21371 |
| By: | Tobias Schmidt; Kai-Robin Lange; Matthias Reccius; Henrik M\"uller; Michael Roos; Carsten Jentsch |
| Abstract: | As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processing techniques, such as BERT, to tackle this task. While effective on foundational linguistic operations essential for narrative extraction, such models lack the deeper semantic understanding required to distinguish extracting economic narratives from merely conducting classic tasks like Semantic Role Labeling. Instead of relying on complex model pipelines, we evaluate the benefits of Large Language Models (LLMs) by analyzing a corpus of Wall Street Journal and New York Times newspaper articles about inflation. We apply a rigorous narrative definition and compare GPT-4o outputs to gold-standard narratives produced by expert annotators. Our results suggests that GPT-4o is capable of extracting valid economic narratives in a structured format, but still falls short of expert-level performance when handling complex documents and narratives. Given the novelty of LLMs in economic research, we also provide guidance for future work in economics and the social sciences that employs LLMs to pursue similar objectives. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.15041 |
| By: | R. Maria del Rio-Chanona; Marco Pangallo; Cars Hommes |
| Abstract: | We explore the potential of Large Language Models (LLMs) to replicate human behavior in economic market experiments. Compared to previous studies, we focus on dynamic feedback between LLM agents: the decisions of each LLM impact the market price at the current step, and so affect the decisions of the other LLMs at the next step. We compare LLM behavior to market dynamics observed in laboratory settings and assess their alignment with human participants' behavior. Our findings indicate that LLMs do not adhere strictly to rational expectations, displaying instead bounded rationality, similarly to human participants. Providing a minimal context window i.e. memory of three previous time steps, combined with a high variability setting capturing response heterogeneity, allows LLMs to replicate broad trends seen in human experiments, such as the distinction between positive and negative feedback markets. However, differences remain at a granular level--LLMs exhibit less heterogeneity in behavior than humans. These results suggest that LLMs hold promise as tools for simulating realistic human behavior in economic contexts, though further research is needed to refine their accuracy and increase behavioral diversity. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.07457 |
| By: | Zonghan Wu; Junlin Wang; Congyuan Zou; Chenhan Wang; Yilei Shao |
| Abstract: | Generative AI, particularly large language models (LLMs), is beginning to transform the financial industry by automating tasks and helping to make sense of complex financial information. One especially promising use case is the automatic creation of fundamental analysis reports, which are essential for making informed investment decisions, evaluating credit risks, guiding corporate mergers, etc. While LLMs attempt to generate these reports from a single prompt, the risks of inaccuracy are significant. Poor analysis can lead to misguided investments, regulatory issues, and loss of trust. Existing financial benchmarks mainly evaluate how well LLMs answer financial questions but do not reflect performance in real-world tasks like generating financial analysis reports. In this paper, we propose FinAR-Bench, a solid benchmark dataset focusing on financial statement analysis, a core competence of fundamental analysis. To make the evaluation more precise and reliable, we break this task into three measurable steps: extracting key information, calculating financial indicators, and applying logical reasoning. This structured approach allows us to objectively assess how well LLMs perform each step of the process. Our findings offer a clear understanding of LLMs current strengths and limitations in fundamental analysis and provide a more practical way to benchmark their performance in real-world financial settings. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.07315 |
| By: | Qirui Mi; Qipeng Yang; Zijun Fan; Wentian Fan; Heyang Ma; Chengdong Ma; Siyu Xia; Bo An; Jun Wang; Haifeng Zhang |
| Abstract: | Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12110 |
| By: | Fabio Gatti (University of Bern, Switzerland & Baffi Center, Bocconi University, Italy); Joel Huesler (University of Bern, Switzerland) |
| Abstract: | The correspondence of historical personalities serves as a rich source of psychological, social, and economic information. Letters were indeed used as means of communication within the family circles but also a primary method for exchanging information with colleagues, subordinates, and employers. A quantitative analysis of such material enables scholars to reconstruct both the internal psychology and the relational networks of historical figures, ultimately providing deeper insights into the socio-economic systems in which they were embedded. In this study, we analyze the outgoing correspondence of Michelangelo Buonarroti, a prominent Renaissance artist, using a collection of 523 letters as the basis for a structured text analysis. Our methodological approach compares three distinct Natural Language Processing Methods: an Augmented Dictionary Approach, which relies on static lexicon analysis and Latent Dirichlet Allocation (LDA) for topic modeling, a Supervised Machine Learning Approach that utilizes BERT-generated letter embeddings combined with a Random Forest classifier trained by the authors, and an Unsupervised Machine Learning Method. The comparison of these three methods, benchmarked to biographic knowledge, allows us to construct a robust understanding of Michelangelo’s emotional association to monetary, thematic, and social factors. Furthermore, it highlights how the Supervised Machine Learning method, by incorporating the authors’ domain knowledge and understanding of documents and background, can provide, in the context of Renaissance multi-themed letters, a more nuanced interpretation of contextual meanings, enabling the detection of subtle (positive or negative) sentimental variations due to a variety of factors that other methods can overlook. |
| Keywords: | Text Analysis, Natural Language Processing, Art History, Economic History |
| JEL: | N33 C55 Z11 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:hes:wpaper:0279 |
| By: | Buckmann , Marcus (Bank of England); Hill, Ed (Bank of England) |
| Abstract: | Text classification tasks such as sentiment analysis are common in economics and finance. We demonstrate that smaller, local generative language models can be effectively used for these tasks. Compared to large commercial models, they offer key advantages in privacy, availability, cost, and explainability. We use 17 sentence classification tasks (each with 2 to 4 classes) to show that penalised logistic regression on embeddings from a small language model often matches or exceeds the performance of a large model, even when trained on just dozens of labelled examples per class – the same amount typically needed to validate a large model’s performance. Moreover, this embedding-based approach yields stable and interpretable explanations for classification decisions. |
| Keywords: | Text classification; large language models; machine learning; embeddings; explainability |
| JEL: | C38 C45 C80 |
| Date: | 2025–05–23 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1127 |
| By: | Marcus Buckmann; Quynh Anh Nguyen; Edward Hill |
| Abstract: | We investigate whether the hidden states of large language models (LLMs) can be used to estimate and impute economic and financial statistics. Focusing on county-level (e.g. unemployment) and firm-level (e.g. total assets) variables, we show that a simple linear model trained on the hidden states of open-source LLMs outperforms the models' text outputs. This suggests that hidden states capture richer economic information than the responses of the LLMs reveal directly. A learning curve analysis indicates that only a few dozen labelled examples are sufficient for training. We also propose a transfer learning method that improves estimation accuracy without requiring any labelled data for the target variable. Finally, we demonstrate the practical utility of hidden-state representations in super-resolution and data imputation tasks. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08662 |
| By: | Mihai Cucuringu; Kang Li; Chao Zhang |
| Abstract: | This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading volume is highly predictable, especially with ML and considering commonality. Additionally, we assess the economic benefits of accurate volume forecasting through Volume Weighted Average Price (VWAP) strategies. The results demonstrate that precise intraday forecasting offers substantial advantages, providing valuable insights for traders to optimize their strategies. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08180 |
| By: | Weixian Waylon Li; Hyeonjun Kim; Mihai Cucuringu; Tiejun Ma |
| Abstract: | Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and stock trading applications, enabling AI agents to generate investment decisions from unstructured financial data. However, most evaluations of LLM timing-based investing strategies are conducted on narrow timeframes and limited stock universes, overstating effectiveness due to survivorship and data-snooping biases. We critically assess their generalizability and robustness by proposing FINSABER, a backtesting framework evaluating timing-based strategies across longer periods and a larger universe of symbols. Systematic backtests over two decades and 100+ symbols reveal that previously reported LLM advantages deteriorate significantly under broader cross-section and over a longer-term evaluation. Our market regime analysis further demonstrates that LLM strategies are overly conservative in bull markets, underperforming passive benchmarks, and overly aggressive in bear markets, incurring heavy losses. These findings highlight the need to develop LLM strategies that are able to prioritise trend detection and regime-aware risk controls over mere scaling of framework complexity. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.07078 |
| By: | Yang Qiang |
| Abstract: | This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for positive socioeconomic impacts, its benefits are undermined by the economic disparities among households, resulting in minimal overall improvement. This highlights the complex mechanisms between individual demographics and educational interventions, revealing promising machine learning applications in this field. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.07421 |
| By: | Robert Scriba; Yuying Li; Jingbo B Wang |
| Abstract: | Financial derivative pricing is a significant challenge in finance, involving the valuation of instruments like options based on underlying assets. While some cases have simple solutions, many require complex classical computational methods like Monte Carlo simulations and numerical techniques. However, as derivative complexities increase, these methods face limitations in computational power. Cases involving Non-Vanilla Basket pricing, American Options, and derivative portfolio risk analysis need extensive computations in higher-dimensional spaces, posing challenges for classical computers. Quantum computing presents a promising avenue by harnessing quantum superposition and entanglement, allowing the handling of high-dimensional spaces effectively. In this paper, we introduce a self-contained and all-encompassing quantum algorithm that operates without reliance on oracles or presumptions. More specifically, we develop an effective stochastic method for simulating exponentially many potential asset paths in quantum parallel, leading to a highly accurate final distribution of stock prices. Furthermore, we demonstrate how this algorithm can be extended to price more complex options and analyze risk within derivative portfolios. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.09459 |
| By: | Giuseppe Arbia; Luca Morandini; Vincenzo Nardelli |
| Abstract: | This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created original and deliberately altered "counterfactual" summaries from 28 published papers (2005-2024), which were evaluated by a diverse set of LLMs. The LLMs provided qualitative assessments and structured binary classifications on variable choice, coefficient plausibility, and publication suitability. The results indicate that while LLMs can expertly assess the coherence of variable choices (with top models like GPT-4o achieving an overall F1 score of 0.87), their performance varies significantly when evaluating deeper aspects such as coefficient plausibility and overall publication suitability. The results further revealed that the choice of LLM, the specific characteristics of the paper and the interaction between these two factors significantly influence the accuracy of the assessment, particularly for nuanced judgments. These findings highlight LLMs' current strengths in assisting with initial, more surface-level checks and their limitations in performing comprehensive, deep economic reasoning, suggesting a potential assistive role in peer review that still necessitates robust human oversight. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06377 |
| By: | Gabriel Nova; Sander van Cranenburgh; Stephane Hess |
| Abstract: | Discrete choice modelling is a theory-driven modelling framework for understanding and forecasting choice behaviour. To obtain behavioural insights, modellers test several competing model specifications in their attempts to discover the 'true' data generation process. This trial-and-error process requires expertise, is time-consuming, and relies on subjective theoretical assumptions. Although metaheuristics have been proposed to assist choice modellers, they treat model specification as a classic optimisation problem, relying on static strategies, applying predefined rules, and neglecting outcomes from previous estimated models. As a result, current metaheuristics struggle to prioritise promising search regions, adapt exploration dynamically, and transfer knowledge to other modelling tasks. To address these limitations, we introduce a deep reinforcement learning-based framework where an 'agent' specifies models by estimating them and receiving rewards based on goodness-of-fit and parsimony. Results demonstrate the agent dynamically adapts its strategies to identify promising specifications across data generation processes, showing robustness and potential transferability, without prior domain knowledge. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06410 |
| By: | Yu Li; Yuhan Wu; Shuhua Zhang |
| Abstract: | In this paper, we study the continuous-time multi-asset mean-variance (MV) portfolio selection using a reinforcement learning (RL) algorithm, specifically the soft actor-critic (SAC) algorithm, in the time-varying financial market. A family of Gaussian portfolio selections is derived, and a policy iteration process is crafted to learn the optimal exploratory portfolio selection. We prove the convergence of the policy iteration process theoretically, based on which the SAC algorithm is developed. To improve the algorithm's stability and the learning accuracy in the multi-asset scenario, we divide the model parameters that influence the optimal portfolio selection into three parts, and learn each part progressively. Numerical studies in the simulated and real financial markets confirm the superior performance of the proposed SAC algorithm under various criteria. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.07537 |
| By: | Jesse Zhou; Geoffrey T. Wodtke |
| Abstract: | Analyses of causal mediation often involve exposure-induced confounders or, relatedly, multiple mediators. In such applications, researchers aim to estimate a variety of different quantities, including interventional direct and indirect effects, multivariate natural direct and indirect effects, and/or path-specific effects. This study introduces a general approach to estimating all these quantities by simulating potential outcomes from a series of distribution models for each mediator and the outcome. Building on similar methods developed for analyses with only a single mediator (Imai et al. 2010), we first outline how to implement this approach with parametric models. The parametric implementation can accommodate linear and nonlinear relationships, both continuous and discrete mediators, and many different types of outcomes. However, it depends on correct specification of each model used to simulate the potential outcomes. To address the risk of misspecification, we also introduce an alternative implementation using a novel class of nonparametric models, which leverage deep neural networks to approximate the relevant distributions without relying on strict assumptions about functional form. We illustrate both methods by reanalyzing the effects of media framing on attitudes toward immigration (Brader et al. 2008) and the effects of prenatal care on preterm birth (VanderWeele et al. 2014). |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.14019 |
| By: | Mukashov, Askar; Robinson, Sherman; Thurlow, James; Arndt, Channing; Thomas, Timothy S. |
| Abstract: | This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities. |
| Keywords: | climate; computable general equilibrium models; machine learning; risk; uncertainty; Kenya; Rwanda; Malawi; Africa; Eastern Africa; Sub-Saharan Africa |
| Date: | 2024–10–25 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:gsspwp:158180 |
| By: | Mindy L. Mallory; Rundong Peng; Meilin Ma; H. Holly Wang |
| Abstract: | Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the "curse of dimensionality" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13967 |
| By: | Stella C. Dong; James R. Finlay |
| Abstract: | This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core research question: Can autonomous, learning-based bidding systems improve risk transfer efficiency and outperform conventional pricing approaches in reinsurance markets? In our model, each reinsurer is represented by an adaptive agent that iteratively refines its bidding strategy within a competitive, partially observable environment. The simulation explicitly incorporates institutional frictions including broker intermediation, incumbent advantages, last-look privileges, and asymmetric access to underwriting information. Empirical analysis demonstrates that MARL agents achieve up to 15% higher underwriting profit, 20% lower tail risk (CVaR), and over 25% improvement in Sharpe ratios relative to actuarial and heuristic baselines. Sensitivity tests confirm robustness across hyperparameter settings, and stress testing reveals strong resilience under simulated catastrophe shocks and capital constraints. These findings suggest that MARL offers a viable path toward more transparent, adaptive, and risk-sensitive reinsurance markets. The proposed framework contributes to emerging literature at the intersection of algorithmic market design, strategic bidding, and AI-enabled financial decision-making. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.13113 |
| By: | Fabian Muny |
| Abstract: | Many programs evaluated in observational studies incorporate a sequential structure, where individuals may be assigned to various programs over time. While this complexity is often simplified by analyzing programs at single points in time, this paper reviews, explains, and applies methods for program evaluation within a sequential framework. It outlines the assumptions required for identification under dynamic confounding and demonstrates how extending sequential estimands to dynamic policies enables the construction of more realistic counterfactuals. Furthermore, the paper explores recently developed methods for estimating effects across multiple treatments and time periods, utilizing Double Machine Learning (DML), a flexible estimator that avoids parametric assumptions while preserving desirable statistical properties. Using Swiss administrative data, the methods are demonstrated through an empirical application assessing the participation of unemployed individuals in active labor market policies, where assignment decisions by caseworkers can be reconsidered between two periods. The analysis identifies a temporary wage subsidy as the most effective intervention, on average, even after adjusting for its extended duration compared to other programs. Overall, DML-based analysis of dynamic policies proves to be a useful approach within the program evaluation toolkit. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.11960 |
| By: | Muhammad Sukri Bin Ramli |
| Abstract: | Tariff exemptions are fundamental to attracting Foreign Direct Investment (FDI) into the manufacturing sector, though the associated administrative processes present areas for optimization for both investing entities and the national tax authority. This paper proposes a conceptual framework to empower tax administration by leveraging a synergistic integration of Optical Character Recognition (OCR) and Large Language Model (LLM) technologies. The proposed system is designed to first utilize OCR for intelligent digitization, precisely extracting data from diverse application documents and key regulatory texts such as tariff orders. Subsequently, the LLM would enhance the capabilities of administrative officers by automating the critical and time-intensive task of verifying submitted HS Tariff Codes for machinery, equipment, and raw materials against official exemption lists. By enhancing the speed and precision of these initial assessments, this AI-driven approach systematically reduces potential for non-alignment and non-optimized exemption utilization, thereby streamlining the investment journey for FDI companies. For the national administration, the benefits include a significant boost in operational capacity, reduced administrative load, and a strengthened control environment, ultimately improving the ease of doing business and solidifying the nation's appeal as a premier destination for high-value manufacturing FDI. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12093 |
| By: | Jan Hurt; Stefan Thurner; Peter Klimek |
| Abstract: | Dynamic input-output models are standard tools for understanding inter-industry dependencies and how economies respond to shocks like disasters and pandemics. However, traditional approaches often assume fixed prices, limiting their ability to capture realistic economic behavior. Here, we introduce an adaptive extension to dynamic input-output recovery models where producers respond to shocks through simultaneous price and quantity adjustments. Our framework preserves the economic constraints of the Leontief input-output model while converging towards equilibrium configurations based on sector-specific behavioral parameters. When applied to input-output data, the model allows us to compute behavioral metrics indicating whether specific sectors predominantly favor price or quantity adjustments. Using the World Input-Output Database, we identify strong, consistent regional and sector-specific behavioral patterns. These findings provide insights into how different regions employ distinct strategies to manage shocks, thereby influencing economic resilience and recovery dynamics. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.10146 |
| By: | James Cussens; Julia Hatamyar; Vishalie Shah; Noemi Kreif |
| Abstract: | We develop and implement a version of the popular "policytree" method (Athey and Wager, 2021) using discrete optimisation techniques. We test the performance of our algorithm in finite samples and find an improvement in the runtime of optimal policy tree learning by a factor of nearly 50 compared to the original version. We provide an R package, "fastpolicytree", for public use. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.15435 |
| By: | Ningzi Li; Shiyang Lai; James Evans |
| Abstract: | As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate 'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08706 |
| By: | Xueying Ding; Aakriti Mittal; Achintya Gopal |
| Abstract: | Time-series data is a vital modality within data science communities. This is particularly valuable in financial applications, where it helps in detecting patterns, understanding market behavior, and making informed decisions based on historical data. Recent advances in language modeling have led to the rise of time-series pre-trained models that are trained on vast collections of datasets and applied to diverse tasks across financial domains. However, across financial applications, existing time-series pre-trained models have not shown boosts in performance over simple finance benchmarks in both zero-shot and fine-tuning settings. This phenomenon occurs because of a i) lack of financial data within the pre-training stage, and ii) the negative transfer effect due to inherently different time-series patterns across domains. Furthermore, time-series data is continuous, noisy, and can be collected at varying frequencies and with varying lags across different variables, making this data more challenging to model than languages. To address the above problems, we introduce a Pre-trained MoDEL for FINance TimE-series (Delphyne). Delphyne achieves competitive performance to existing foundation and full-shot models with few fine-tuning steps on publicly available datasets, and also shows superior performances on various financial tasks. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.06288 |
| By: | Thiago Christiano Silva; Kei Moriya; Mr. Romain M Veyrune |
| Abstract: | This paper introduces a classification framework to analyze central bank communications across four dimensions: topic, communication stance, sentiment, and audience. Using a fine-tuned large language model trained on central bank documents, we classify individual sentences to transform policy language into systematic and quantifiable metrics on how central banks convey information to diverse stakeholders. Applied to a multilingual dataset of 74, 882 documents from 169 central banks spanning 1884 to 2025, this study delivers the most comprehensive empirical analysis of central bank communication to date. Monetary policy communication changes significantly with inflation targeting, as backward-looking exchange rate discussions give way to forward-looking statements on inflation, interest rates, and economic conditions. We develop a directional communication index that captures signals about future policy rate changes and unconventional measures, including forward guidance and balance sheet operations. This unified signal helps explain future movements in market rates. While tailoring messages to audiences is often asserted, we offer the first systematic quantification of this practice. Audience-specific risk communication has remained stable for decades, suggesting a structural and deliberate tone. Central banks adopt neutral, fact-based language with financial markets, build confidence with the public, and highlight risks to governments. During crises, however, this pattern shifts remarkably: confidence-building rises in communication to the financial sector and government, while risk signaling increases for other audiences. Forward-looking risk communication also predicts future market volatility, demonstrating that central bank language plays a dual role across monetary and financial stability channels. Together, these findings provide novel evidence that communication is an active policy tool for steering expectations and shaping economic and financial conditions. |
| Keywords: | Central bank communication; large language models; forward guidance; monetary policy; sentiment analysis |
| Date: | 2025–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/109 |
| By: | Magaletti, Nicola; Notarnicola, Valeria; Di Molfetta, Mauro; Mariani, Stefano; Leogrande, Angelo |
| Abstract: | This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance drawing upon the multi-methodological framework of combining econometric with state-of-the-art machine learning approaches. Employing IV panel data regressions, viz. 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz. Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz. Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model Based, and Random Forest—were applied to uncover unknown structures and predict the behaviour of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), the social (S), and the governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labour, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust. |
| Keywords: | Logistics Performance Index (LPI), Environmental Social and Governance (ESG) Indicators, Panel Data Analysis, Instrumental Variables (IV) Approach, Sustainable Economic Development. |
| JEL: | C33 F14 M14 O18 Q56 |
| Date: | 2025–05–14 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124746 |
| By: | Qingkai Zhang; L. Jeff Hong; Houmin Yan |
| Abstract: | The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small and medium-sized enterprises (SMEs), yet financing remains a critical challenge due to SMEs' limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study represents a pioneering application of generative AI in CBEC SCF risk management, offering a solid foundation for enhanced credit practices and improved SME access to capital. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.15305 |
| By: | Falck-Zepeda, José B.; Zambrano, Patricia; Sanders, Arie; Trabanino, Carlos Rogelio |
| Abstract: | Robust impact assessment methods need credible yield, costs, and other production performance parameter estimates. Sample data issues and the realities of producer heterogeneity and markets, including endogeneity, simultaneity, and outliers can affect such parameters. Methods have continued to evolve that may address data issues identified in the earlier literature examining genetically modified (GM) crops impacts especially those of conventional field level surveys. These methods may themselves have limitations, introduce trade-offs, and may not always be successful in addressing such issues. Experimental methods such as randomized control trials have been proposed to address several control treatment data issues, but these may not be suitable for every situation and issue and may be more expensive and complex than conventional field surveys. Furthermore, experimental methods may induce the unfortunate outcome of crowding-out impact assessors from low- and middle-income countries. The continued search for alternatives that help address conventional survey shortcomings remains critical. Previously, existing assessment methods were applied to the impact assessment of insect resistant and herbicide tolerant maize adoption in Honduras in 2008 and 2012. Results from assessments identified endogeneity issues such as self-selection and simultaneity concurrently with influential outliers. Procedures used to address these issues independently showed trade-offs between addressing endogeneity and outliers. Thus, the need to identify methods that address both issues simultaneously, minimizing as much as possible the impact of method trade-offs, continues. We structured this paper as follows. First, we review the literature to delineate data and assessment issues potentially affecting robust performance indicators such as yields and costs differentials. Second, we discuss and apply four types of approaches that can be used to obtain robust performance estimates for yield and cost differentials including: 1) Robust Instrumental Variables, 2) Instrumental Variable Regressions, and 3) Control/Treatment, and 4) Machine Learning methods that are amenable to robust strategies to deal with outliers including Random Forest and a Stacking regression approach that allows for a number of “base learners” in order to examine the pooled 2008 and 2012 Honduras field surveys. Third, we discuss implications for impact assessment results and implementation limitations especially in low- and middle-income countries. We further discuss and draw some conclusions regarding methodological issues for consideration by impact assessors and stakeholders. |
| Keywords: | maize; yields; impact assessment; agriculture; data; capacity building; machine learning; parametric programming; herbicide resistance; Honduras; Latin America and the Caribbean; Central America |
| Date: | 2025–04–24 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:gsspwp:174327 |
| By: | Falck-Zepeda, José B.; Zambrano, Patricia; Sanders, Arie; Trabanino, Carlos Rogelio |
| Abstract: | Robust impact assessment methods need credible yield, costs, and other production performance parameter estimates. Sample data issues and the realities of producer heterogeneity and markets, including endogeneity, simultaneity, and outliers can affect such parameters. Methods have continued to evolve that may address data issues identified in the earlier literature examining genetically modified (GM) crops impacts especially those of conventional field level surveys. These methods may themselves have limitations, introduce trade-offs, and may not always be successful in addressing such issues. Experimental methods such as randomized control trials have been proposed to address several control treatment data issues, but these may not be suitable for every situation and issue and may be more expensive and complex than conventional field surveys. Furthermore, experimental methods may induce the unfortunate outcome of crowding-out impact assessors from low- and middle-income countries. The continued search for alternatives that help address conventional survey shortcomings remains critical. Previously, existing assessment methods were applied to the impact assessment of insect resistant and herbicide tolerant maize adoption in Honduras in 2008 and 2012. Results from assessments identified endogeneity issues such as self-selection and simultaneity concurrently with influential outliers. Procedures used to address these issues independently showed trade-offs between addressing endogeneity and outliers. Thus, the need to identify methods that address both issues simultaneously, minimizing as much as possible the impact of method trade-offs, continues. We structured this paper as follows. First, we review the literature to delineate data and assessment issues potentially affecting robust performance indicators such as yields and costs differentials. Second, we discuss and apply four types of approaches that can be used to obtain robust performance estimates for yield and cost differentials including: 1) Robust Instrumental Variables, 2) Instrumental Variable Regressions, and 3) Control/Treatment, and 4) Machine Learning methods that are amenable to robust strategies to deal with outliers including Random Forest and a Stacking regression approach that allows for a number of “base learners” in order to examine the pooled 2008 and 2012 Honduras field surveys. Third, we discuss implications for impact assessment results and implementation limitations especially in low- and middle-income countries. We further discuss and draw some conclusions regarding methodological issues for consideration by impact assessors and stakeholders. |
| Keywords: | maize; yields; impact assessment; agriculture; data; capacity building; machine learning; parametric programming; herbicide resistance; Honduras; Latin America and the Caribbean; Central America |
| Date: | 2025–04–24 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:ifprid:174327 |
| By: | Glyn-Davies, Alex; Vadeboncoeur, Arnaud; Akyildiz, O. Deniz; Kazlauskaite, Ieva; Girolami, Mark |
| Abstract: | Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantification and practical tractability. It excels at generative modelling and inversion tasks due to its built-in Bayesian regularization and flexibility, essential qualities for physics-related problems. For such problems, the underlying physical model determines the dependence between variables of interest, which in turn will require a tailored derivation for the central VI learning objective. Furthermore, in many physical inference applications, this structure has rich meaning and is essential for accurately capturing the dynamics of interest. In this paper, we provide an accessible and thorough technical introduction to VI for forward and inverse problems, guiding the reader through standard derivations of the VI framework and how it can best be realized through deep learning. We then review and unify recent literature exemplifying the flexibility allowed by VI. This paper is designed for a general scientific audience looking to solve physics-based problems with an emphasis on uncertainty quantification. This article is part of the theme issue ‘Generative modelling meets Bayesian inference: a new paradigm for inverse problems’. |
| Keywords: | PDE; physics-informed; generative model; deep learning; variational inference |
| JEL: | C1 |
| Date: | 2025–06–19 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128504 |