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on Big Data |
| By: | Julian Lütticke; Lukas Lautenschlaeger; Wolfgang Schäfers |
| Abstract: | The role of investor sentiment in real estate investment trust (REIT) markets is well-documented. However, traditional sentiment indicators often fail to fully capture real-time market dynamics. This study explores the potential of GenAI-extracted sentiment in forecasting U.S. REIT returns by leveraging large language models (LLMs) to analyze textual data from news media sources. The hypothesis underpinning this study is that LLMs can process textual data in a manner analogous to that of humans. The novel sentiment score is integrated into a machine learning model to predict REIT returns. The analysis differentiates between overall index returns and sector-specific REIT performance, thereby providing a more granular view of sentiment-driven market behavior. In addition to traditional statistical metrics the model performance is assessed by evaluating an active trading strategy based on sentiment signals. This strategy is benchmarked against a buy-and-hold approach to determine whether sentiment-based predictions can systematically outperform the market. The findings contribute to the growing field of AI-driven financial forecasting and offer valuable insights for investors and policymakers in the indirect real estate sector. |
| Keywords: | Generative AI; Large Language Model; News Sentiment; REIT |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_242 |
| By: | Hongyang Yang; Xiao-Yang Liu; Qingwei Wu |
| Abstract: | Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check all S&P 500 stocks (the Standard & Poor's 500). In this paper, we propose a practical scheme that recommends stocks from S&P 500 using machine learning. Our basic idea is to buy and hold the top 20% stocks dynamically. First, we select representative stock indicators with good explanatory power. Secondly, we take five frequently used machine learning methods, including linear regression, ridge regression, stepwise regression, random forest and generalized boosted regression, to model stock indicators and quarterly log-return in a rolling window. Thirdly, we choose the model with the lowest Mean Square Error in each period to rank stocks. Finally, we test the selected stocks by conducting portfolio allocation methods such as equally weighted, mean-variance, and minimum-variance. Our empirical results show that the proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foun dation/Dynamic-Stock-Recommendation-Mach ine_Learning-Published-Paper-IEEE}{GitHu b}. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12129 |
| By: | Jianfei Cao; Michael P. Leung |
| Abstract: | This paper studies double/debiased machine learning (DML) methods applied to weakly dependent data. We allow observations to be situated in a general metric space that accommodates spatial and network data. Existing work implements cross-fitting by excluding from the training fold observations sufficiently close to the evaluation fold. We find in simulations that this can result in exceedingly small training fold sizes, particularly with network data. We therefore seek to establish the validity of DML without cross-fitting, building on recent work by Chen et al. (2022). They study i.i.d. data and require the machine learner to satisfy a natural stability condition requiring insensitivity to data perturbations that resample a single observation. We extend these results to dependent data by strengthening stability to "neighborhood stability, " which requires insensitivity to resampling observations in any slowly growing neighborhood. We show that existing results on the stability of various machine learners can be adapted to verify neighborhood stability. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.10995 |
| By: | Hongyang Yang; Xiao-Yang Liu; Shan Zhong; Anwar Walid |
| Abstract: | Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose an ensemble strategy that employs deep reinforcement schemes to learn a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market situations. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand technique for processing very large data. We test our algorithms on the 30 Dow Jones stocks that have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble strategy is shown to outperform the three individual algorithms and two baselines in terms of the risk-adjusted return measured by the Sharpe ratio. This work is fully open-sourced at \href{https://github.com/AI4Finance-Foun dation/Deep-Reinforcement-Learning-for-A utomated-Stock-Trading-Ensemble-Strategy -ICAIF-2020}{GitHub}. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12120 |
| By: | Qian'ang Mao; Yuxuan Zhang; Jiaman Chen; Wenjun Zhou; Jiaqi Yan |
| Abstract: | As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.15456 |
| By: | Daniel Moulton; Larry Santucci; Robyn Smith |
| Abstract: | This paper examines methodological approaches for extracting structured data from large-scale historical document archives, comparing “hyperspecialized” versus “adaptive modular” strategies. Using 56 years of Philadelphia property deeds as a case study, we show the benefits of the adaptive modular approach leveraging optical character recognition (OCR), full-text search, and frontier large language models (LLMs) to identify deeds containing specific restrictive use language— achieving 98% precision and 90–98% recall. Our adaptive modular methodology enables analysis of historically important economic phenomena including re strictive property covenants, their precise geographic locations, and the localized neighborhood effects of these restrictions. This approach should be easily adapt able to other research involving deeds and similar document. |
| Keywords: | large language models (LLMs); artificial intelligence (AI); machine learning (ML); restrictive covenants; deeds; property; real estate; housing; John Coltrane; digitization |
| JEL: | C81 N32 R31 R38 |
| Date: | 2025–10–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpdp:102114 |
| By: | Ka Man Leung; Yu Cheung Wong; Kin Kwok Lai; Dah Ming Chiu |
| Abstract: | This paper presents a pioneer longitudinal rental analysis of sub-divided units (SDUs) in Hong Kong, employing first-hand data collected from surveys conducted across five time points between 2017 and 2023. Five widely used machine learning algorithms, multiple linear regression, random forest, decision tree, support vector regression and gradient boosting algorithm, are employed. This study aims to identify the key factors influencing the SDU rental values, focusing on variables including physical facilities, locational characteristics, and temporal trends. The longitudinal nature of the data allows for an examination of how rental values have changed over time. As SDU data with detailed internal characteristics are not publicly available, this study provides timely information and insights for the informal housing market and social welfare policy development, contributing to informed decision-making in addressing housing challenges. |
| Keywords: | Informal Housing; rental analyses; sub-divided units; time series analysis |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_137 |
| By: | Phoebe Koundouri; Konstantinos Dellis; Monika Mavragani; Angelos Plataniotis; Chrysilia Pitti; Georgios Feretzakis |
| Abstract: | This paper proposes a novel, data-driven methodology to systematically assess how European Green Deal policy texts address various Human Security Aspects, including newly acknowledged technological vulnerabilities. By analyzing official EU documents using advanced semantic modeling and transformerbased embedding techniques, we demonstrate how machine learning can identify thematic alignments or gaps in addressing human security within policies explicitly connected to the Sustainable Development Goals. Our approach, which employs Sentence-BERT models and cosine similarity measures, reveals that while EU Green Deal policies integrate all eight human security dimensions with relatively balanced coverage, economic, food, and community security receive slightly more emphasis than personal, political, and technological security aspects. These findings illuminate both strengths and opportunities for enhancement in current policy discourse, aiding stakeholders in designing interventions that explicitly integrate comprehensive human security perspectives. |
| Keywords: | Human Security, Machine Learning, EU Green Deal, Sustainability, Climate Policy, Natural Language Processing, Policy Analysis |
| Date: | 2025–11–21 |
| URL: | https://d.repec.org/n?u=RePEc:aue:wpaper:2565 |
| By: | Siqi Huang; Anupam Nanda; Eero Valtonen |
| Abstract: | Traditional ESG ratings, which primarily rely on structured data and qualitative assessments, often fail to capture the nuanced nature of ESG-related communications. Sentiment analysis using computational linguistics approaches offers a valuable complementary approach by systematically capturing the tone of ESG disclosures and media coverage, providing deeper insights into corporate sustainability commitments and stakeholder perceptions. This study adopts a novel approach using textual information set and investigates the relationship between ESG-related sentiment and the financial performance of Real Estate firms. Using a dataset of ESG-related press releases and news articles from 67 U.S. REITs, we employ three sentiment analysis models—TextBlob, Loughran and McDonald, and FinBERT—to extract sentiment scores at multiple levels. A polarity-based ESG Disclosure Sentiment Score (ESGDSS) is then developed and validated through correlation analysis with MSCI ESG Ratings. Furthermore, a regression-based analysis is conducted to evaluate the impact of ESGDSS on key financial and credit performance indicators. The findings add to our understanding of ESG in the real estate sector and its implications for investors. |
| Keywords: | ESG Communication; REITs; Sentiment Analysis |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_241 |
| By: | Zelingher, Rotem |
| Abstract: | Ensuring food security is a global challenge, particularly in low-income countries where food prices affect access to nutritious food. The instability of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Although online platforms exist for monitoring food prices, there is still a need for accessible, detailed forecasts for non-specialists. This paper proposes the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global ACs prices up to one year ahead across different horizons. This innovative integration allows us to model complex interactions while providing clear, interpretable results. We demonstrate the utilization of AGRICAF, applying it to three major commodities and explaining how different factors impact prices across months and forecast horizons. By facilitating access to reliable forecasts of AC prices, AGRICAF can advance a fairer and sustainable food system. |
| Keywords: | Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, International Relations/Trade, Teaching/Communication/Extension/Profession |
| URL: | https://d.repec.org/n?u=RePEc:ags:aes025:356741 |
| By: | Babolmorad, N.; Massoud, N. |
| Abstract: | We build a framework to examine how the training regime-rather than model architecture-drives the performance of financial sentiment models. Using firm-level news and parsimonious classifiers, we compare three supervision regimes: human-only, hybrid, and market-only (fully automated). The framework opens the "black box" of sentiment modeling by tracing how supervision shapes each component of the classifier. Across extensive tests, the hybrid regime consistently outperforms fully automated training in explaining variation in stock returns and trading volume, enhancing interpretability and economic relevance. Human input improves sentiment inference, offering new insights into information processing and price formation in financial markets. |
| Keywords: | Sentiment Analysis, Financial Media News, Investor Sentiment, Stock Markets, Human versus Machine |
| JEL: | G02 G11 G12 G14 |
| Date: | 2025–10–31 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2577 |
| By: | Emmanuel Flachaire; Bertille Picard |
| Abstract: | The Kitagawa-Oaxaca-Blinder decomposition splits the difference in means between two groups into an explained part, due to observable factors, and an unexplained part. In this paper, we reformulate this framework using potential outcomes, highlighting the critical role of the reference outcome. To address limitations like common support and model misspecification, we extend Neumark's (1988) weighted reference approach with a doubly robust estimator. Using Neyman orthogonality and double machine learning, our method avoids trimming and extrapolation. This improves flexibility and robustness, as illustrated by two empirical applications. Nevertheless, we also highlight that the decomposition based on the Neumark reference outcome is particularly sensitive to the inclusion of irrelevant explanatory variables. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.13433 |
| By: | Karen Martinus; Jane Zheng |
| Abstract: | Following a strong period of logistics rental growth in the UK, we are entering a period of more moderate rent growth. It is likely that certain locations and asset types will outperform but in this new environment, gauging future rental growth prospects is increasingly important to investors, developers, and managers. This research aims to provide a granular understanding of the UK logistics market by leveraging geospatial analysis, machine learning, and feature engineering techniques. By integrating high-resolution demographic projections, attributes of existing industrial properties, and rent trends, we develop top-down market analyses and predictive models to estimate rental prices and rent growth potential for individual properties and locations. In doing so, we believe our research has the potential to aid in investment decision making. Additionally, the findings will contribute to a more refined understanding of spatial dependencies and economic drivers within the logistics sector, offering a scalable framework for market forecasting and investment strategy optimization. |
| Keywords: | Forecasting Title change requested on May 15 by email to help; Geospatial; Logistics; Machine Learning |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_259 |
| By: | Martin Regnaud; Julie Le Gallo |
| Abstract: | The paper investigates the dynamics of rental market tightness in Ile-de-France, leveraging user-generated data from the SeLoger platform to provide near-real-time insights.Using an original dataset of over 85, 000 unique rental listings from April 2023 to June 2024, it introduces three novel indicators—email alerts, contact requests, and transformation rates—to capture renters preferences and search intensity. The analysis identifies key market drivers, highlighting intense demand for small, unfurnished apartments, and emphasizes how rising mortgage rates since 2022 have exacerbated competition in the rental market. Employing machine learning models with explainability techniques, the study reveals the interplay of variables related to apartment characteristics, economic conditions, and spatial context in shaping market tightness. The findings highlight the importance of accounting for platform-specific effects, such as listing visibility and listing quality, to achieve an accurate understanding of market dynamics. This research contributes to understanding housing market dynamics and informs policies aimed at mitigating rental market frictions. |
| Keywords: | French Rental market; Housing Market Tightness; Machine Learning; Real Estate Market Platform |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_131 |
| By: | Sofiia Dolgikh; Bodan Potanin |
| Abstract: | We propose plug-in (PI) and double machine learning (DML) estimators of average treatment effect (ATE), average treatment effect on the treated (ATET) and local average treatment effect (LATE) in the multivariate sample selection model with ordinal selection equations. Our DML estimators are doubly-robust and based on the efficient influence functions. Finite sample properties of the proposed estimators are studied and compared on simulated data. Specifically, the results of the analysis suggest that without addressing multivariate sample selection, the estimates of the causal parameters may be highly biased. However, the proposed estimators allow us to avoid these biases. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12640 |
| By: | Luca Corazzini; Elisa Deriu; Marco Guerzoni |
| Abstract: | Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rational agents or whether they reproduce human behavioural tendencies when faced with classic decision problems. Using two canonical experiments in behavioural economics, the ultimatum game and a gambling game, we elicit decisions from two state of the art models, Google Gemma7B and Qwen, under neutral and gender conditioned prompts. We estimate parameters of inequity aversion and loss-aversion and compare them with human benchmarks. The models display attenuated but persistent deviations from rationality, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12319 |
| By: | Paul C. Behler (University of Bonn); Laurenz Guenther (Bocconi University, Toulouse School of Economics) |
| Abstract: | While the recent rise of populism has led many scholars to study populism in the modern era, its long-run evolution remains underexplored. This paper analyzes German parliamentary speeches to study populism over the long run, covering the Weimar Republic (1918–1933) and the united Federal Republic (1991–today). We employ a tailored and validated machine learning model to measure populism and dissect it into anti-elitism and people-centrism. We find that in both republics, populism is similarly common, similarly distributed across the ideological spectrum, and increases over time. Moreover, in both states, left-wing parties were initially the most populist group but were eventually overtaken by right-wing parties. However, we find a difference in the form of populism: in the Weimar Republic, the increase in populism is driven by a surge in the anti-elitism of right-wing parties, while in the Federal Republic, it is due to a general rise in people-centrism. |
| Keywords: | Populism, Nazi, Weimar, Radical, Democracy, Right-wing, Far-right, Machine learning, BERT, Text analysis, Rhetoric |
| JEL: | P16 N40 C89 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:381 |
| By: | Fang, Qixiang; Garcia-Bernardo, Javier; van Kesteren, Erik-Jan |
| Abstract: | Large language models (LLMs) have become an essential tool for social scientists and humanities (SSH) researchers who work with textual data. One particularly valuable use case is automating text annotation, traditionally a time-consuming step in preparing data for empirical analysis. Yet, many SSH researchers face two challenges: getting started with LLMs, and understanding how to evaluate and correct for their limitations. The rapid pace of model development can make LLMs appear inaccessible or intimidating, while even experienced users may overlook how annotation errors can bias results from downstream analyses (e.g., regression estimates, $p$-values), even when accuracy appears high. This tutorial provides a step-by-step, hands-on guide to using LLMs for text annotation in SSH research for both Python and R users. We cover (1) how to choose and access LLM APIs, (2) how to design and run annotation tasks programmatically, (3) how to evaluate annotation quality and iterate on prompts, (4) how to integrate annotations into statistical workflows while accounting for uncertainty, and (5) how to manage cost, efficiency, and reproducibility. Throughout, we provide concrete examples, code snippets, and best-practice checklists to help researchers confidently and transparently incorporate LLM-based annotation into their workflows. |
| Date: | 2025–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:v4eq6_v1 |
| By: | Yu, Jiao (Yale University); Cudjoe, Thomas K.M. (Johns Hopkins University); Mathis, Walter S. (Yale University); Chen, Xi (Yale University) |
| Abstract: | This study examined the link between neighborhood disorder trajectories and metabolic and inflammatory biomarkers in U.S. older adults. We analyzed data from community-dwelling Medicare beneficiaries in the National Health and Aging Trends Study. Neighborhood physical disorder was assessed annually through interviewer observations over six years. Latent class analysis was used to identify exposure trajectory subgroups. Machine learning based inverse probability weighted (IPW) regression models were conducted to estimate associations with five biomarkers, including body mass index (BMI), waist circumference, hemoglobin A1C (HbA1c), high-sensitivity C-reactive protein (hsCRP), and interleukin-6 (IL-6). Compared to the stable low exposure group, older adults with increased exposure, decreased exposure, and stable high exposure exhibited higher levels of HbA1c. Only stable high exposure was associated with increased hsCRP. No significant associations were found for other biomarkers. Residential environments play an important role in shaping the biological risk of aging. Incorporating routine screening for neighborhood environmental risks and implementing community-level interventions are pivotal in promoting healthy aging in place. |
| Keywords: | inverse probability weighting, machine learning, metabolic and inflammation biomarkers, neighborhood disorder, latent class analysis |
| JEL: | J14 I12 I14 R20 I18 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18251 |
| By: | Simon Thaler; Felipe Calainho; Marc Francke |
| Abstract: | This study investigates the impact of incorporating building footprint images into Automated Valuation Models (AVMs) for improved property valuation accuracy. Traditional AVMs primarily rely on property characteristics, regional data, and historical transaction records, often overlooking the geometric and spatial features represented in building footprints. This research proposes integrating Convolutional Neural Networks (CNNs) to analyze building footprint images and refine AVM predictions by modeling residuals from a baseline AVM. The dataset includes Austrian residential properties, encompassing transaction prices, property attributes, and building footprint data. By leveraging CNNs, the study aims to capture hidden patterns related to building shape, layout, and surrounding spatial distribution, enhancing the understanding of factors influencing real estate prices. Anticipated results suggest that the inclusion of spatial data in AVMs can lead to more nuanced and accurate valuations, providing valuable insights for financial institutions and the real estate industry. |
| Keywords: | Automated Valuation Methods; Building Footprints; Convolutiona Neural Networks |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_194 |
| By: | Sophia Bodensteiner; Lukas Lautenschlaeger; Wolfgang Schäfers; Andrew Mueller |
| Abstract: | The integration of Environmental, Social, and Governance (ESG) factors in Real Estate Investment Trust (REIT) analysis is increasingly recognized as a key element in sustainable investing. It can create long-term value, improve reputation, and help REITs to remain competitive and resilient in the market. This study investigates the influence of ESG-related sentiment on the constituents of the NAREIT Index, addressing the growing need to understand its impact on the public opinion and the real estate market performance. Our analysis examines 10-K reports with an additional focus on ESG-related segments and separate ESG reports of real estate companies in the NAREIT All Equity Index in the period 2016-2023. The sentiment analysis is carried out using a Large Language Model (LLM) and aggregated using different sentiment measures. Finally, an OLS regression is applied to analyze the impact of these sentiment indices on the returns of the companies. Preliminary results indicate a correlation between ESG-related sentiment in the company reports and their price returns. This indicates the recognition of ESG as an investment driver for institutional investors. |
| Keywords: | Corporate Disclosure; Esg; REITs; Sentiment |
| JEL: | R3 |
| Date: | 2025–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2025_151 |
| By: | Emmanuel Lwele; Sabuni Emmanuel; Sitali Gabriel Sitali |
| Abstract: | This paper presents a deep reinforcement learning (DRL) framework for dynamic portfolio optimization under market uncertainty and risk. The proposed model integrates a Sharpe ratio-based reward function with direct risk control mechanisms, including maximum drawdown and volatility constraints. Proximal Policy Optimization (PPO) is employed to learn adaptive asset allocation strategies over historical financial time series. Model performance is benchmarked against mean-variance and equal-weight portfolio strategies using backtesting on high-performing equities. Results indicate that the DRL agent stabilizes volatility successfully but suffers from degraded risk-adjusted returns due to over-conservative policy convergence, highlighting the challenge of balancing exploration, return maximization, and risk mitigation. The study underscores the need for improved reward shaping and hybrid risk-aware strategies to enhance the practical deployment of DRL-based portfolio allocation models. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.11481 |
| By: | Heyang Ma; Qirui Mi; Qipeng Yang; Zijun Fan; Bo Li; Haifeng Zhang |
| Abstract: | Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language-Augmented Multi-Agent Policy), a framework that integrates language into economic decision-making and narrows the gap to real-world settings. LAMP follows a Think-Speak-Decide pipeline: (1) Think interprets numerical observations to extract short-term shocks and long-term trends, caching high-value reasoning trajectories; (2) Speak crafts and exchanges strategic messages based on reasoning, updating beliefs by parsing peer communications; and (3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language-augmented decision-making. Experiments in economic simulation show that LAMP outperforms both MARL and LLM-only baselines in cumulative return (+63.5%, +34.0%), robustness (+18.8%, +59.4%), and interpretability. These results demonstrate the potential of language-augmented policies to deliver more effective and robust economic strategies. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.12876 |
| By: | Benami, Elinor; Carter, Michael R.; Hobbs, Andrew; Jin, Zhenong; Kirchner, Ella |
| Abstract: | Agricultural index insurance seeks to protect producers against negative shocks that are common across a prespecified area, i.e., an index insurance zone. Often, administrative boundaries are used to delineate such index insurance zones. However, administrative boundaries may not reflect relevant variations in yield over space, which can be costly for policyholders as well as the public, especially since agricultural insurance is often heavily subsidized. Increased availability of finely resolved geospatial data on agronomic conditions coupled with machine learning approaches to identify similarities promises the ability to reduce losses associated with index insurance by identifying more homogeneous zones. In this work, we examine the changes in welfare impacts of a hypothetical area-yield index insurance when redrawing zone boundaries on the basis of relevant observed agronomic conditions. Drawing upon crop cut data from over 10, 000 maize fields in Kenya from 2016-2020 combined with satellite-based estimates of agronomic conditions, we examine the changes in expected utility to assess the value of data-driven and administrative insurance zones. When keeping the number of insurance zones equal to the number of administrative zones, we find that data-driven zones may offer only slightly higher risk reduction value than administrative zones. If no set number of zones are prespecified, the data-driven approach offers a flexible approach to identify an optimal number of zones that balances costs and performance. This approach can help inform program design as well as impact evaluations, as it further sheds light on trade-offs between the costs of ground sampling and zone size that can inform how to design and evaluate new programs in resource-constrained environments for maximum impact. |
| Keywords: | Agricultural Finance, International Development, Risk and Uncertainty |
| Date: | 2024–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:ags:iaae24:344685 |