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on Computational Economics |
| By: | Xian Wu |
| Abstract: | This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07923 |
| By: | Matthew Smith; Francisco Alvarez |
| Abstract: | Machine learning (ML) is becoming an essential tool in economics, offering powerful methods for prediction, classification, and decision-making. This paper provides an intuitive introduction to two widely used families of ML models: tree-based methods (decision trees, Random Forests, boosting techniques) and neural networks. The goal is to equip practitioners with a clear understanding of how these models work, their strengths and limitations, and their applications in economics. Additionally, we briefly discuss some other methods, as support vector machines (SVMs) and Shapley values, highlighting their relevance in economic research. Rather than providing an exhaustive survey, this paper focuses on practical insights to help economists effectively apply ML in their work. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:fda:fdaddt:2025-03 |
| By: | Wang, Yuyan (Stanford U); Li, Pan (Georgia Institute of Technology); Chen, Minmin (Google, Inc) |
| Abstract: | Modern recommender systems use machine learning (ML) models to predict consumer preferences based on consumption history. Although these “black-box†models achieve impressive predictive performance, they often suffer from a lack of transparency and explainability. While explainable AI research suggests a tradeoff between the two, we demonstrate that combining large language models (LLMs) with deep neural networks (DNNs) can improve both. We propose LR-Recsys, which augments state-of-the-art DNN-based recommender systems with LLMs’ reasoning capabilities. LR-Recsys introduces a contrastive-explanation generator that leverages LLMs to produce human-readable positive explanations (why a consumer might like a product) and negative explanations (why they might not). These explanations are embedded via a fine-tuned AutoEncoder and combined with consumer and product features as inputs to the DNN to produce the final predictions. Beyond offering explainability, LR-Recsys also improves learning efficiency and predictive accuracy. To understand why, we provide insights using high-dimensional multi-environment learning theory. Statistically, we show that LLMs are equipped with better knowledge of the important variables driving consumer decision-making, and that incorporating such knowledge can improve the learning efficiency of ML models. Extensive experiments on three real-world recommendation datasets demonstrate that the proposed LR-Recsys framework consistently outperforms state-of-the-art black-box and explainable recommender systems, achieving a 3–14\% improvement in predictive performance. This performance gain could translate into millions of dollars in annual revenue if deployed on leading content recommendation platforms today. Our additional analysis confirms that these gains mainly come from LLMs’ strong reasoning capabilities, rather than their external domain knowledge or summarization skills. LR-RecSys presents an effective approach to combine LLMs with traditional DNNs, two of the most widely used ML models today. Specifically, we show that LLMs can improve both the explainability and predictive performance of traditional DNNs through their reasoning capability. Beyond improving recommender systems, our findings emphasize the value of combining contrastive explanations for understanding consumer preferences and guiding managerial strategies for online platforms. These explanations provide actionable insights for consumers, sellers, and platforms, helping to build trust, optimize product offerings, and inform targeting strategies. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecl:stabus:4234 |
| By: | Xavier Gabaix; Ralph S. J. Koijen; Robert J. Richmond; Motohiro Yogo |
| Abstract: | Firm characteristics, based on accounting and financial market data, are commonly used to represent firms in economics and finance. However, investors collectively use a much richer information set beyond firm characteristics, including sources of information that are not readily available to researchers. We show theoretically that portfolio holdings contain all relevant information for asset pricing, which can be recovered under empirically realistic conditions. Such guarantees do not exist for other data sources, such as accounting or text data. We build on recent advances in artificial intelligence (AI) and machine learning (ML) that represent unstructured data (e.g., text, audio, and images) by high-dimensional latent vectors called embeddings. Just as word embeddings leverage the document structure to represent words, asset embeddings leverage portfolio holdings to represent firms. Thus, this paper is a bridge from recent advances in AI and ML to economics and finance. We explore various methods to estimate asset embeddings, including recommender systems, shallow neural network models such as Word2Vec, and transformer models such as BERT. We evaluate the performance of these models on three benchmarks that can be evaluated using a single quarter of data: predicting relative valuations, explaining the comovement of stock returns, and predicting institutional portfolio decisions. We also estimate investor embeddings (i.e., representations of investors and their strategies), which are useful for investor classification, performance evaluation, and detecting crowded trades. We discuss other applications of asset embeddings, including generative portfolios, risk management, and stress testing. Finally, we develop a framework to give an economic narrative to a group of similar firms, by applying large language models to firm-level text data. |
| JEL: | C53 G12 G23 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33651 |
| By: | Guy Stephane Waffo Dzuyo (Forvis Mazars, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique); Gaël Guibon (LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, LIPN - Laboratoire d'Informatique de Paris-Nord - CNRS - Centre National de la Recherche Scientifique - Université Sorbonne Paris Nord, SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique); Christophe Cerisara (SYNALP - Natural Language Processing : representations, inference and semantics - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique); Luis Belmar-Letelier (Forvis Mazars) |
| Abstract: | The identification of the financial characteristics of industry sectors has a large importance in accounting audit, allowing auditors to prioritize the most important area during audit. Existing company classification standards such as the Standard Industry Classification (SIC) code allow to map a company to a category based on its activity and products. In this paper, we explore the potential of machine learning algorithms and language models to analyze the relationship between those categories and companies' financial statements. We propose a supervised company classification methodology and analyze several types of representations for financial statements. Existing works address this task using solely numerical information in financial records. Our findings show that beyond numbers, textual information occurring in financial records can be leveraged by language models to match the performance of dedicated decision tree-based classifiers, while providing better explainability and more generic accounting representations. We think this work can serve as a preliminary work towards semi-automatic auditing. Models, code, and a preprocessed dataset are publicly available for further research at https://github.com/WaguyMz/hybrid company classification |
| Keywords: | Machine Learning, Industry Sectors, Large Language Models, LLM Applications, Audit, Financial Statement |
| Date: | 2025–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05031499 |
| By: | Lee, Chaewon; Gates, Kathleen |
| Abstract: | Machine learning (ML) has extended the scope of psychological research by enabling data-driven discovery of patterns in complex datasets, complementing traditional hypothesis-driven approaches and enriching individual-level prediction. As a principal subfield, supervised ML has advanced mental health diagnostics and behavior prediction through classification and regression tasks. However, the complexity of ML methodologies and the absence of established norms and standardized pipelines often limit its adoption among psychologists. Furthermore, the black-box nature of advanced ML algorithms obscures how decisions are made, making it difficult to identify the most influential variables. Automated ML (AutoML) addresses these challenges by automating key steps such as model selection and hyperparameter optimization, while enhancing interpretability through explainable AI. By streamlining workflows and improving efficiency, AutoML empowers users of all technical levels to implement advanced ML methods effectively. Despite its transformative potential, AutoML remains underutilized in psychological research, with no dedicated educational material available. This tutorial aims to bridge the gap by introducing AutoML to psychologists. We cover advanced AutoML methods, including combined algorithm selection and hyperparameter optimization (CASH), stacked ensemble generalization, and explainable AI. The utility of AutoML is demonstrated using the ‘H2O AutoML’ R package with publicly available psychological datasets, performing regression on multi-individual cross-sectional data and classification on single-individual time-series data. We also provide practical workarounds for ML methods currently unavailable in the package, allowing researchers to use alternative approaches when needed. These examples illustrate how AutoML democratizes ML, making it more accessible while providing advanced methodologies for psychological research. |
| Date: | 2025–04–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:j4xuq_v1 |
| By: | Vasant Dhar; Jo\~ao Sedoc |
| Abstract: | Long-term investing was previously seen as requiring human judgment. With the advent of generative artificial intelligence (AI) systems, automated systematic long-term investing is now feasible. In this paper, we present DBOT, a system whose goal is to reason about valuation like Aswath Damodaran, who is a unique expert in the investment arena in terms of having published thousands of valuations on companies in addition to his numerous writings on the topic, which provide ready training data for an AI system. DBOT can value any publicly traded company. DBOT can also be back-tested, making its behavior and performance amenable to scientific inquiry. We compare DBOT to its analytic parent, Damodaran, and highlight the research challenges involved in raising its current capability to that of Damodaran's. Finally, we examine the implications of DBOT-like AI agents for the financial industry, especially how they will impact the role of human analysts in valuation. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.05639 |
| By: | Yanampally Abhiram Reddy; Siddhi Agarwal; Vikram Parashar; Arshiya Arora |
| Abstract: | In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for corporate reputation monitoring, combining Natural Language Processing (NLP) and machine learning techniques to accurately interpret public opinion in real time. The methodology integrates a hybrid sentiment detection framework leveraging both rule-based models (VADER) and transformer-based deep learning models (DistilBERT), applied to social media data from multiple platforms. The system begins with robust preprocessing involving noise removal and text normalization, followed by sentiment classification using an ensemble approach to ensure both interpretability and contextual accuracy. Results are visualized through sentiment distribution plots, comparative analyses, and temporal sentiment trends for enhanced interpretability. Our analysis reveals significant disparities in public sentiment across major corporations, with companies like Amazon (81.2) and Samsung (45.8) receiving excellent sentiment scores, while Microsoft (21.7) and Walmart (21.9) exhibit poor sentiment profiles. These findings demonstrate the utility of our multi-source sentiment framework in providing actionable insights regarding corporate public perception, enabling stakeholders to make informed strategic decisions based on comprehensive sentiment analysis. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15448 |
| By: | Geiger, Felix; Kanelis, Dimitrios; Lieberknecht, Philipp; Sola, Diana |
| Abstract: | Central bank communication has become a crucial tool for steering the monetary policy stance and shaping the outlook of market participants. Traditionally, analyzing central bank communication required substantial human effort, expertise, and resources, making the process time-consuming. The recent introduction of artificial intelligence (AI) methods has streamlined and enhanced this analysis. While fine-tuned language models show promise, their reliance on large annotated datasets is a limitation that the use of large language models (LLMs) combined with prompt engineering overcomes. This paper introduces the Monetary-Intelligent Language Agent (MILA), a novel framework that leverages advanced prompt engineering techniques and LLMs to analyze and measure different semantic dimensions of monetary policy communication. MILA performs granular classifications of central bank statements conditional on the macroeconomic context. This approach enhances transparency, integrates expert knowledge, and ensures rigorous statistical calculations. For illustration, we apply MILA to the European Central Bank's (ECB) monetary policy statements to derive sentiment and hawkometer indicators. Our findings reveal changes in the ECB's communication tone over time, reflecting economic conditions and policy adaptions, and demonstrate MILA's effectiveness in providing nuanced insights into central bank communication. A model evaluation of MILA shows high accuracy, flexibility, and strong consistency of the results despite the stochastic nature of language models. |
| Keywords: | Central bank communication, monetary policy, sentiment analysis, artificial intelligence, large language models |
| JEL: | C45 E31 E44 E52 E58 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubtps:316448 |
| By: | Baston, George |
| Abstract: | This paper investigates the integration of artificial intelligence (AI) into trading ecosystems from 2020 to 2023. It outlines the historical progression of AI applications in financial markets, emphasizing the transition from rule-based algorithms to data-driven machine learning models. The analysis covers AI-driven innovations in biometric identity verification, predictive analytics, and personalized trading systems. The economic contributions of AI are quantified using institutional estimates, with a focus on regional implementation strategies. Regulatory structures, particularly in Singapore, are examined in the context of their role in enabling AI adoption while ensuring compliance. Challenges including data governance, ethical constraints, regulatory inconsistencies, and technical limitations in blockchain infrastructure are analyzed. The discussion also highlights the impact of expertise shortages and the critical need for government-industry collaboration in fintech development. |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:te83v_v1 |
| By: | Ma, Ji (The University of Texas at Austin) |
| Abstract: | Large language models (LLMs) increasingly serve as human-like decision-making agents in social science and applied settings. These LLM-agents are typically assigned human-like characters and placed in real-life contexts. However, how these characters and contexts shape an LLM's behavior remains underexplored. This study proposes and tests methods for probing, quantifying, and modifying an LLM's internal representations in a Dictator Game -- a classic behavioral experiment on fairness and prosocial behavior. We extract ``vectors of variable variations'' (e.g., ``male'' to ``female'') from the LLM's internal state. Manipulating these vectors during the model's inference can substantially alter how those variables relate to the model's decision-making. This approach offers a principled way to study and regulate how social concepts can be encoded and engineered within transformer-based models, with implications for alignment, debiasing, and designing AI agents for social simulations in both academic and commercial applications. |
| Date: | 2025–04–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:8p7wg_v1 |
| By: | Jingru Wang; Wen Ding; Xiaotong Zhu |
| Abstract: | In the modern financial sector, the exponential growth of data has made efficient and accurate financial data analysis increasingly crucial. Traditional methods, such as statistical analysis and rule-based systems, often struggle to process and derive meaningful insights from complex financial information effectively. These conventional approaches face inherent limitations in handling unstructured data, capturing intricate market patterns, and adapting to rapidly evolving financial contexts, resulting in reduced accuracy and delayed decision-making processes. To address these challenges, this paper presents an intelligent financial data analysis system that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) technology. Our system incorporates three key components: a specialized preprocessing module for financial data standardization, an efficient vector-based storage and retrieval system, and a RAG-enhanced query processing module. Using the NASDAQ financial fundamentals dataset from 2010 to 2023, we conducted comprehensive experiments to evaluate system performance. Results demonstrate significant improvements across multiple metrics: the fully optimized configuration (gpt-3.5-turbo-1106+RAG) achieved 78.6% accuracy and 89.2% recall, surpassing the baseline model by 23 percentage points in accuracy while reducing response time by 34.8%. The system also showed enhanced efficiency in handling complex financial queries, though with a moderate increase in memory utilization. Our findings validate the effectiveness of integrating RAG technology with LLMs for financial analysis tasks and provide valuable insights for future developments in intelligent financial data processing systems. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06279 |
| By: | Joshua S. Gans |
| Abstract: | This paper examines how the introduction of artificial intelligence (AI), particularly generative and large language models capable of interpolating precisely between known data points, reshapes scientists' incentives for pursuing novel versus incremental research. Extending the theoretical framework of Carnehl and Schneider (2025), we analyse how decision-makers leverage AI to improve precision within well-defined knowledge domains. We identify conditions under which the availability of AI tools encourages scientists to choose more socially valuable, highly novel research projects, contrasting sharply with traditional patterns of incremental knowledge growth. Our model demonstrates a critical complementarity: scientists strategically align their research novelty choices to maximise the domain where AI can reliably inform decision-making. This dynamic fundamentally transforms the evolution of scientific knowledge, leading either to systematic “stepping stone” expansions or endogenous research cycles of strategic knowledge deepening. We discuss the broader implications for science policy, highlighting how sufficiently capable AI tools could mitigate traditional inefficiencies in scientific innovation, aligning private research incentives closely with the social optimum. |
| JEL: | D82 O30 O34 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33566 |
| By: | Frauke Kreuter (LMU München) |
| Abstract: | As AI advances, large language models (LLMs) are shifting from passive tools to active agents that collaborate with experts to cocreate knowledge and artifacts. In this talk, I will explore the role of LLMs as intelligent agents in data science workflows—partners that not only automate tasks but also enhance decision-making by understanding core data science principles, identifying cognitive biases, and nudging experts toward more robust conclusions. I will discuss how an LLM, equipped with statistical reasoning, ethical AI considerations, and an awareness of human cognitive pitfalls, can challenge assumptions, suggest alternative methodologies, and improve model interpretability. From guiding feature selection to questioning spurious correlations, these AI agents act as reflective collaborators rather than mere calculators. I will examine case studies where LLMs have meaningfully influenced analytical processes, highlight challenges in aligning AI nudges with human intent, and explore the future of AI-augmented data science, generally and while using Stata. This talk is primarily conceptual and designed to inspire but also to rethink our relationship with AI—not as a tool but as a cocreator in the pursuit of knowledge. |
| Date: | 2025–04–26 |
| URL: | https://d.repec.org/n?u=RePEc:boc:dsug25:01 |
| By: | Wei Jiao |
| Abstract: | We introduce a new machine learning approach to detect value-relevant foreign information for both domestic and multinational companies. Candidate foreign signals include lagged returns of stock markets and individual stocks across 47 foreign markets. By training over 100, 000 models, we capture stock-specific, time-varying relationships between foreign signals and U.S. stock returns. Foreign signals exhibit out-of-sample return predictability for a subset of U.S. stocks across domestic and multinational companies. Valuable foreign signals are not concentrated in those largest foreign markets nor foreign firms in the same industry as U.S. firms. Signal importance analysis reveals the price discovery of foreign information is significantly slower for information from emerging and low-media-coverage markets and among stocks with lower foreign institutional ownership but is accelerated during the COVID-19 crisis. Our study suggests that machine learning-based investment strategies leveraging foreign signals can emerge as important mechanisms to improve the market efficiency of foreign information. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07855 |
| By: | Boris Nikolov (University of Lausanne; Swiss Finance Institute; European Corporate Governance Institute (ECGI)); Norman Schuerhoff (Swiss Finance Institute - HEC Lausanne); Sam Wagner (University of Lausanne) |
| Abstract: | A key question in automating governance is whether machines can recover the corporate objective. We develop a corporate recovery theorem that establishes when this is possible and provide a practical framework for its application. Training a machine on a large dataset of firms' investment and financial decisions, we find that most neoclassical models fail to explain the data since the machine learns from managers to underestimate the shadow cost of capital. This bias persists even after accounting for financial frictions, intangible intensity, behavioral factors, and ESG. We develop an alignment measure that shows why managerial alignment with shareholder-value remains imperfect and how to debias managerial decisions. |
| Keywords: | Corporate Purpose, Inverse Reinforcement Learning |
| JEL: | D22 G30 L21 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2523 |
| By: | Bobeica, Elena; Holton, Sarah; Huber, Florian; Martínez Hernández, Catalina |
| Abstract: | We propose a novel empirical structural inflation model that captures non-linear shock transmission using a Bayesian machine learning framework that combines VARs with non-linear structural factor models. Unlike traditional linear models, our approach allows for non-linear effects at all impulse response horizons. Identification is achieved via sign, zero, and magnitude restrictions within the factor model. Applying our method to euro area energy shocks, we find that inflation reacts disproportionately to large shocks, while small shocks trigger no significant response. These non-linearities are present along the pricing chain, more pronounced upstream and gradually attenuating downstream. JEL Classification: E31, C32, C38, Q43 |
| Keywords: | energy, euro area, inflation, machine learning, non-linear model |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253052 |
| By: | Alejandro Lopez-Lira; Yuehua Tang; Mingyin Zhu |
| Abstract: | Large language models (LLMs) cannot be trusted for economic forecasts during periods covered by their training data. We provide the first systematic evaluation of LLMs' memorization of economic and financial data, including major economic indicators, news headlines, stock returns, and conference calls. Our findings show that LLMs can perfectly recall the exact numerical values of key economic variables from before their knowledge cutoff dates. This recall appears to be randomly distributed across different dates and data types. This selective perfect memory creates a fundamental issue -- when testing forecasting capabilities before their knowledge cutoff dates, we cannot distinguish whether LLMs are forecasting or simply accessing memorized data. Explicit instructions to respect historical data boundaries fail to prevent LLMs from achieving recall-level accuracy in forecasting tasks. Further, LLMs seem exceptional at reconstructing masked entities from minimal contextual clues, suggesting that masking provides inadequate protection against motivated reasoning. Our findings raise concerns about using LLMs to forecast historical data or backtest trading strategies, as their apparent predictive success may merely reflect memorization rather than genuine economic insight. Any application where future knowledge would change LLMs' outputs can be affected by memorization. In contrast, consistent with the absence of data contamination, LLMs cannot recall data after their knowledge cutoff date. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.14765 |
| By: | Jinhui Li; Wenjia Xie; Luis Seco |
| Abstract: | This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market is segmented into ten volatility-based states, with transitions forecasted by a Bayesian Markov switching model employing Dirichlet priors and Gibbs sampling. This enables real-time asset allocation adjustments. Tested across two asset sets, the dynamic portfolio consistently achieves significantly higher risk-adjusted returns and substantially higher total returns, outperforming most static methods. By integrating classical optimization with machine learning and Bayesian techniques, this research provides a robust strategy for optimizing investment outcomes in unpredictable market environments. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.02841 |
| By: | Lidia Ceriani; Chiara Gigliarano; Paolo Verme |
| Abstract: | Multidimensional indexes are ubiquitous, and popular, but present non-negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06012 |
| By: | Mario R. Pinheiro; Mario J. Pinheiro |
| Abstract: | The proposed framework introduces a novel multidimensional representation of money using tensor analysis, enabling a more granular examination of economic interactions and capital flow. By treating money as a multidimensional entity, this approach allows for detailed tracking and modeling of sectoral, temporal, and agent-based dynamics. This enhanced perspective facilitates the design of adaptive economic policies that can effectively respond to evolving macroeconomic conditions, ensuring resilience and inclusivity in financial systems. Furthermore, the tensor-based modeling framework bridges traditional economic analyses with advanced computational techniques, offering a robust foundation for algorithmic governance and data-driven decision-making in complex economies. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06286 |
| By: | Sohom Ghosh; Sudip Kumar Naskar |
| Abstract: | Government fiscal policies, particularly annual union budgets, exert significant influence on financial markets. However, real-time analysis of budgetary impacts on sector-specific equity performance remains methodologically challenging and largely unexplored. This study proposes a framework to systematically identify and rank sectors poised to benefit from India's Union Budget announcements. The framework addresses two core tasks: (1) multi-label classification of excerpts from budget transcripts into 81 predefined economic sectors, and (2) performance ranking of these sectors. Leveraging a comprehensive corpus of Indian Union Budget transcripts from 1947 to 2025, we introduce BASIR (Budget-Assisted Sectoral Impact Ranking), an annotated dataset mapping excerpts from budgetary transcripts to sectoral impacts. Our architecture incorporates fine-tuned embeddings for sector identification, coupled with language models that rank sectors based on their predicted performances. Our results demonstrate 0.605 F1-score in sector classification, and 0.997 NDCG score in predicting ranks of sectors based on post-budget performances. The methodology enables investors and policymakers to quantify fiscal policy impacts through structured, data-driven insights, addressing critical gaps in manual analysis. The annotated dataset has been released under CC-BY-NC-SA-4.0 license to advance computational economics research. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13189 |
| By: | Borsos, András (Magyar Nemzeti Bank, Complexity Science Hub Vienna and Institute for New Economic Thinking at the Oxford Martin SchoolMagyar Nemzeti Bank, Complexity Science Hub Vienna and Institute for New Economic Thinking at the Oxford Martin School); Carro, Adrian (Banco de España and Institute for New Economic Thinking at the Oxford Martin School); Glielmo, Aldo (Banca d’Italia); Hinterschweiger, Marc (Bank of England); Kaszowska-Mojsa, Jagoda (Narodowy Bank Polski, Institute for New Economic Thinking at the Oxford Martin School and Institute of Economics, Polish Academy of Sciences); Uluc, Arzu (Bank of England) |
| Abstract: | Over the past decade, agent-based models (ABMs) have been increasingly employed as analytical tools within economic policy institutions. This paper documents this trend by surveying the ABM-relevant research and policy outputs of central banks and other related economic policy institutions. We classify these studies and reports into three main categories: (i) applied research connected to the mandates of central banks, (ii) technical and methodological research supporting the advancement of ABMs; and (iii) examples of the integration of ABMs into policy work. Our findings indicate that ABMs have emerged as effective complementary tools for central banks in carrying out their responsibilities, especially after the extension of their mandates following the global financial crisis of 2007–09. While acknowledging that room for improvement remains, we argue that integrating ABMs into the analytical frameworks of central banks can support more effective policy responses to both existing and emerging economic challenges, including financial innovation and climate change. |
| Keywords: | Agent-based models; household analysis; financial institutions; central bank policies; monetary policy; prudential policies |
| JEL: | C63 E37 E58 |
| Date: | 2025–02–28 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1122 |
| By: | Simon Jehnen; Joaqu\'in Ordieres-Mer\'e; Javier Villalba-D\'iez |
| Abstract: | Recent advancements in information availability and computational capabilities have transformed the analysis of annual reports, integrating traditional financial metrics with insights from textual data. To extract valuable insights from this wealth of textual data, automated review processes, such as topic modeling, are crucial. This study examines the effectiveness of BERTopic, a state-of-the-art topic model relying on contextual embeddings, for analyzing Item 7 and Item 7A of 10-K filings from S&P 500 companies (2016-2022). Moreover, we introduce FinTextSim, a finetuned sentence-transformer model optimized for clustering and semantic search in financial contexts. Compared to all-MiniLM-L6-v2, the most widely used sentence-transformer, FinTextSim increases intratopic similarity by 81% and reduces intertopic similarity by 100%, significantly enhancing organizational clarity. We assess BERTopic's performance using embeddings from both FinTextSim and all-MiniLM-L6-v2. Our findings reveal that BERTopic only forms clear and distinct economic topic clusters when paired with FinTextSim's embeddings. Without FinTextSim, BERTopic struggles with misclassification and overlapping topics. Thus, FinTextSim is pivotal for advancing financial text analysis. FinTextSim's enhanced contextual embeddings, tailored for the financial domain, elevate the quality of future research and financial information. This improved quality of financial information will enable stakeholders to gain a competitive advantage, streamlining resource allocation and decision-making processes. Moreover, the improved insights have the potential to leverage business valuation and stock price prediction models. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.15683 |
| By: | Martin Hoesli (University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School) |
| Abstract: | Purpose-This paper provides a critical review of the methods that can be used to estimate the discount rate or the capitalization rate needed to apply an income approach to value. It is based on the author's keynote address at the ESPI International Real Estate Conference in Paris in November 2024. Design/methodology/approach-We start by discussing the usefulness of asset pricing models from financial economics to derive discount rates. We then turn to a discussion of the build-up method which explicitly takes into account property-specific factors when assessing the riskiness of real estate investments and hence property values. Next, we discuss some key findings from papers that have relied on multi-factor models to uncover the determinants of discount and capitalization rates. We highlight the progress that has been made in gaining access to data and in modelling discount and capitalization rates. Findings-Although useful from a conceptual point of view, the main asset pricing model, the Capital Asset Pricing Model (CAPM), has limited use for real estate given its underlying assumptions. The build-up approach is intuitively appealing and is often used in practice, although it leaves much leeway to appraisers. Multi-factor models are useful and are increasingly being used with transaction-based data rather than appraisal-based data. Machine learning should further our understanding of the determinants of discount and capitalization rates. Originality/value-This paper provides a critical review of the methods that can be used to assess discount and capitalization rates. It also highlights some of the changes which have occurred recently and are likely to continue in the future. We maintain that studies analyzing the determinants of discount and capitalization rates are especially useful when conducted using micro-level transaction data. |
| Keywords: | Discount rate, capitalization rate, commercial real estate pricing, CAPM, appraisal data, transaction data, machine learning |
| JEL: | R32 G12 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2531 |
| By: | Apró, William Zoltán |
| Abstract: | Abstract Traditional IT project forecasting methods rely on siloed, retrospective data (e.g., Jira ticket histories), leaving teams unprepared for evolving risks such as shifting customer demands, accumulating technical debt, or new regulatory mandates. Studies show that 60% of IT projects exceed budgets due to unplanned scope changes, exposing the limitations of reactive approaches. We introduce Agentic AI for Proactive IT Forecasting (AAPIF), a novel framework that integrates intelligence-grade premise valuation with multi-source data fusion to proactively forecast project outcomes across technical, business, and market contexts. Unlike static models, AAPIF dynamically weights input data—such as customer requirements, organizational context, and compliance signals—based on reliability (freshness, credibility) and relevance (contribution weights C_i). It continuously refines predictions using reinforcement learning. Key Contributions: A mathematical model computing confidence-weighted success probabilities, achieving 89% accuracy—a 32% improvement over Random Forest baselines. Actionable intelligence protocols that reduce data collection errors by 45%, utilizing premise valuation (e.g., stakeholder alignment scoring) and automated risk alerts. In a fintech case study, AAPIF reduced unplanned scope changes by 37% through risk prediction (e.g., "72% likelihood of API scalability issues in Q3") and strategic recommendations (e.g., "Reassign three developers to refactor modules"). By transforming raw data into strategic foresight, AAPIF empowers project managers to become proactive architects of success, rather than reactive trouble-shooters. Keywords: Agentic AI, IT project forecasting, premise valuation, Agile project management, predictive analytics, risk mitigation |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:jtvqu_v1 |
| By: | Jetson Leder-Luis; Anup Malani |
| Abstract: | Healthcare fraud imposes a sizable cost on U.S. public healthcare budgets and distorts health care provision. We examine the economics of health care fraud and enforcement using theory and data and connect to a growing literature on the topic. We first offer a new economic definition of health care fraud that captures and connects the wide range of activities prosecuted as fraud. We define fraud as any divergence between the care an insurer says a patient qualifies for, the care a provider provides, and the care a provider bills for. Our definition clarifies the economic consequences of different categories of fraud and provides a framework for understanding the slate of existing studies. Next, we examine the incentives for committing and for prosecuting fraud. We show how fraud is driven by a combination of inadequate (expected) penalties for fraud and imperfect reimbursement rates. Public anti-fraud litigation is driven by the relative monetary, political or career returns to prosecuting fraud and by prosecutorial budgets. Finally, we examine the prevalence of health care fraud prosecutions across types of fraud and types of care, and across the US, by machine learning on text data from Department of Justice press releases. |
| JEL: | I13 K40 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33592 |
| By: | George Woodman; Ruben S. Andrist; Thomas H\"aner; Damien S. Steiger; Martin J. A. Schuetz; Helmut G. Katzgraber; Marcin Detyniecki |
| Abstract: | We propose and implement modern computational methods to enhance catastrophe excess-of-loss reinsurance contracts in practice. The underlying optimization problem involves attachment points, limits, and reinstatement clauses, and the objective is to maximize the expected profit while considering risk measures and regulatory constraints. We study the problem formulation, paving the way for practitioners, for two very different approaches: A local search optimizer using simulated annealing, which handles realistic constraints, and a branch & bound approach exploring the potential of a future speedup via quantum branch & bound. On the one hand, local search effectively generates contract structures within several constraints, proving useful for complex treaties that have multiple local optima. On the other hand, although our branch & bound formulation only confirms that solving the full problem with a future quantum computer would require a stronger, less expensive bound and substantial hardware improvements, we believe that the designed application-specific bound is sufficiently strong to serve as a basis for further works. Concisely, we provide insurance practitioners with a robust numerical framework for contract optimization that handles realistic constraints today, as well as an outlook and initial steps towards an approach which could leverage quantum computers in the future. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16530 |
| By: | Mengjie (Magie); Cheng; Elie Ofek; Hema Yoganarasimhan |
| Abstract: | We study how media firms can use LLMs to generate news content that aligns with multiple objectives -- making content more engaging while maintaining a preferred level of polarization/slant consistent with the firm's editorial policy. Using news articles from The New York Times, we first show that more engaging human-written content tends to be more polarizing. Further, naively employing LLMs (with prompts or standard Direct Preference Optimization approaches) to generate more engaging content can also increase polarization. This has an important managerial and policy implication: using LLMs without building in controls for limiting slant can exacerbate news media polarization. We present a constructive solution to this problem based on the Multi-Objective Direct Preference Optimization (MODPO) algorithm, a novel approach that integrates Direct Preference Optimization with multi-objective optimization techniques. We build on open-source LLMs and develop a new language model that simultaneously makes content more engaging while maintaining a preferred editorial stance. Our model achieves this by modifying content characteristics strongly associated with polarization but that have a relatively smaller impact on engagement. Our approach and findings apply to other settings where firms seek to use LLMs for content creation to achieve multiple objectives, e.g., advertising and social media. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13444 |
| By: | Christian R. Proano; Jonas Dix |
| Abstract: | This paper investigates the implications of output gap uncertainty for the conduct of fiscal policy using a small-scale macroeconomic model with boundedly rational agents. Specifically, agents use an adaptive updating mechanism to approximate the unobservable potential output that suffers, similarly to the Hodrick and Prescott (1997), from an end-point bias. This generates an unintendedly procyclical fiscal policy that affects the government’s credibility and by extension the sovereign risk premium. Our simulations highlight the importance of this so-called bond vigilantes channel, as well as of the government’s credibility among financial markets, for the sustainability of government debt and for macroeconomic stability. |
| Keywords: | output gap uncertainty, fiscal policy, sovereign risk, government credibility, bounded rationality |
| JEL: | E62 E63 H63 E32 D84 G12 D83 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-27 |
| By: | Pinski, Marc; Hofmann, Thomas; Benlian, Alexander |
| Date: | 2025–03–22 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:153874 |
| By: | Ben Weidmann; Yixian Xu; David J. Deming |
| Abstract: | We show that leadership skill with artificially intelligent (AI) agents predicts leadership skill with human groups. In a large pre-registered lab experiment, human leaders worked with AI agents to solve problems. Their performance on this “AI leadership test” was strongly correlated (ρ=0.81) with their causal impact as leaders of human teams, which we estimate by repeatedly randomly assigning leaders to groups of human followers and measuring team performance. Successful leaders of both humans and AI agents ask more questions and engage in more conversational turn-taking; they score higher on measures of social intelligence, fluid intelligence, and decision-making skill, but do not differ in gender, age, ethnicity or education. Our findings indicate that AI agents can be effective proxies for human participants in social experiments, which greatly simplifies the measurement of leadership and teamwork skills. |
| JEL: | J24 M54 O30 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33662 |
| By: | Mikhail Chernov (UCLA Anderson); Bryan T. Kelly (Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Johannes Schwab (École Polytechnique Fédérale de Lausanne (EPFL)) |
| Abstract: | We generalize the seminal Gibbons-Ross-Shanken test to the empirically relevant case where the number of test assets far exceeds the number of observations. In such a setting, one needs to use a regularized estimator of the covariance matrix of test assets, which leads to biases in the original test statistic. Random Matrix Theory allows us to account for these biases and to evaluate the test's power. Power increases with the number of test assets and reaches the maximum for a broad range of local alternatives. These conclusions are supported by an extensive simulation study. We implement the test empirically for state-of-the-art candidate efficient portfolios and test assets. |
| Keywords: | efficient portfolio, cross-section of stock returns, testing, regularization, random matrix theory |
| JEL: | C12 C40 C55 C57 G12 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2526 |
| By: | Keppeler, Florian; Borchert, Jana; Pedersen, Mogens Jin; Nielsen, Vibeke Lehmann |
| Abstract: | Artificial Intelligence (AI) applications are transforming public sector decision-making. However, most research conceptualizes AI as a form of specialized algorithmic decision support tool. In contrast, this study introduces the concept of human-AI ensembles, where humans and AI tackle the same tasks together, rather than specializing in certain parts. We argue that this is particularly relevant for many public sector decisions, where neither human nor AI-based decision-making has a clear advantage over the other in terms of legitimacy, efficacy, or legality. We illustrate this design theory within access to public employment, focusing on two key areas: (a) the potential of ensembling human and AI to reduce biases and (b) the inclinations of public managers to use AI advice. Study 1 presents evidence from the assessment of real-life job candidates (n = 2, 000) at the intersection of gender and ethnicity by public managers compared to AI. The results indicate that ensembled decision- making may alleviate ethnic biases. Study 2 examines how receptive public managers are to AI advice. Results from a pre-registered survey experiment involving managers (n = 538 with 4 observations each) show that decision-makers, when reminded of the unlawfulness of hiring discrimination, prioritize AI advice over human advice. |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:2yf6r_v2 |
| By: | Chengfeng Shen (Peking University); Felix Kubler (University of Zurich); Yucheng Yang (University of Zurich; Swiss Finance Institute); Zhennan Zhou (Westlake University) |
| Abstract: | We develop a new method to efficiently solve for optimal lotteries in models with non-convexities. In order to employ a Lagrangian framework, we prove that the value of the saddle point that characterizes the optimal lottery is the same as the value of the dual of the deterministic problem. Our algorithm solves the dual of the deterministic problem via sub-gradient descent. We prove that the optimal lottery can be directly computed from the deterministic optima that occur along the iterations. We analyze the computational complexity of our algorithm and show that the worst-case complexity is often orders of magnitude better than the one arising from a linear programming approach. We apply the method to two canonical problems with private information. First, we solve a principal-agent moral-hazard problem, demonstrating that our approach delivers substantial improvements in speed and scalability over traditional linear programming methods. Second, we study an optimal taxation problem with hidden types, which was previously considered computationally infeasible, and examine under which conditions the optimal contract will involve lotteries. |
| Keywords: | Private Information, Adverse Selection, Moral Hazard, Non-Convexities, Lotteries, Lagrangian Iteration |
| JEL: | C61 C63 D61 D82 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2548 |
| By: | Attar, Shoaib; Kodali, Chaitrathejasvi |
| Abstract: | In this study, we explore the dynamics of financial markets by adapting the Ising model—a cornerstone of statistical physics—to simulate market crashes. By representing individual market participants as spins on a lattice, our model captures local interactions that collectively give rise to emergent phenomena analogous to phase transitions observed in magnetic systems. We investigate how varying interaction strengths, external influences, and system “temperature” affect the stability of market conditions, particularly in the vicinity of critical thresholds. Through extensive simulations, our findings reveal that minor perturbations in local agent behavior can trigger cascading effects, ultimately precipitating market crashes. These results not only demonstrate the potential of physics-inspired models to mimic complex market dynamics but also provide insights into the predictive power of critical phenomena in anticipating systemic financial instabilities. The implications of this work extend to both the theoretical understanding of market behavior and the development of more robust risk management strategies. |
| Date: | 2025–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:e28mq_v1 |
| By: | Ignacio Raul Apella; Zunino, Gonzalo |
| Abstract: | This paper estimates the proportion of workers who would meet the required contribution periods to access a contributory retirement pension in the Dominican Republic. Using microdata from labor histories, the paper proposes a survival model to estimate the hazard rates of entering and exiting the contributory state in the pension system. Furthermore, a Monte Carlo simulation is performed to project contributory histories. The results suggest that the transition rates are relatively high, averaging a probability of exiting the contributory (non-contributory) state of 7 percent (6 percent). Moreover, the hazard rate of transitioning to a different state is negatively associated with the worker’s duration in the current state. These effects are conditioned to the age and income level of the worker. Finally, a simulation of new labor histories estimates that slightly more than 20 percent of the workers would meet the requirement of 30 years of contributions by age 60, and this percentage would exceed 40 percent if the required years of contributions were reduced to 20. |
| Date: | 2025–04–17 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11103 |
| By: | Palligkinis, Spyros |
| Abstract: | I assess the impact of the recent hike in bank lending rates on euro area retail borrowers using a novel microsimulation framework that updates household-level data of a recent representative survey with up-to-date macro-financial information. The key novelty is that existing mortgages are gradually repaid, and new ones are extended, a feature necessary for medium-term simulations in a period of sizable credit growth. Since lending rates have increased, debt servicing has become more demanding, and the simulated share of distressed loans has increased. Effects are stronger for adjustable-rate mortgages, and especially for the most recent among them, but are present in all portfolios. JEL Classification: C1, G2, G51, E52 |
| Keywords: | financial stability, household finance, microsimulations, monetary policy |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253053 |
| By: | John M. Barrios; Filippo Lancieri; Joshua Levy; Shashank Singh; Tommaso Valletti; Luigi Zingales |
| Abstract: | We study how conflicts of interest (CoI)—defined as financial, professional, or ideological stakes held by authors—affect perceived credibility in economics research. Using a randomized controlled survey of both economists and a representative sample of the U.S. public, we find that the presence of a CoI reduces trust in a paper’s findings by 28% on average, with substantial heterogeneity across conflict types. We develop a model in which this reduction in trust reflects both the prevalence of conflicted papers and the expected bias conditional on conflict. To isolate the latter, we introduce the CoI Discount: the perceived value of a conflicted paper relative to an otherwise identical, non-conflicted one. We estimate an average CoI Discount of 39%, implying that conflicted papers are valued at just 61% of non-conflicted ones. We validate these survey-based estimates through three complementary exercises: an empirical analysis of actual citation and disclosure patterns in economics, a meta-analysis of evidence from the medical literature, and simulations using large-language models. Our findings highlight a persistent credibility gap that is not eliminated by current disclosure practices and suggest a broader challenge for scientific trust in the presence of author conflicts. |
| JEL: | A11 A14 B59 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33645 |
| By: | Daniel Gaigall; Stefan Weber |
| Abstract: | We introduce a framework for systemic risk modeling in insurance portfolios using jointly exchangeable arrays, extending classical collective risk models to account for interactions. We establish central limit theorems that asymptotically characterize total portfolio losses, providing a theoretical foundation for approximations in large portfolios and over long time horizons. These approximations are validated through simulation-based numerical experiments. Additionally, we analyze the impact of dependence on portfolio loss distributions, with a particular focus on tail behavior. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06287 |
| By: | Van Deun, Katrijn; Lê, Trà T.; Malinowski, Jakub; Mols, Floortje; Schoormans, Dounya |
| Abstract: | Exploring multigroup data for similarities and differences in the measurement model is a substantial part of the research conducted in the behavioral and social sciences. Examples include studying the measurement invariance of psychological scales over age or ethnic groups and comparing symptom correlations between different psychological disorders. Multigroup exploratory factor analysis is often the method of choice. However, currently available methods are restrictive in their use. First, these methods cannot handle complex data with small sample sizes relative to the number of variables, while high-dimension, low-sample-size data are increasingly used as a result of digitalization (e.g., word counts obtained by text mining of online messages or omics data). Second, the use of existing software is often arduous. Here, we propose a regularized exploratory approximate factor analysis method that addresses these issues by building on a strong computational framework: The resulting method yields solutions that are constrained to show simple structure and similarity of the loadings over groups when supported by the data. The minimal input required is restricted to the data and number of factors. In a simulation study, we show that the method considerably outperforms existing methods, also in the low-dimensional setting; publicly available genomics data on different psychopathologies are used to illustrate that the method works in the ultrahigh-dimensional setting. Implementation of the method in the R software language for statistical computing is publicly available on GitHub, including the code used to conduct the simulation study and to perform the analyses of the three empirical data sets. |
| Date: | 2025–03–07 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:9twbk_v1 |
| By: | O'Donoghue, Cathal (National University of Ireland, Galway); Can, Zeynep Gizem (University of Galway); Montes-Viñas, Ana (Luxembourg Institute of Socio-Economic Research (LISER)); Sologon, Denisa Maria (LISER (CEPS/INSTEAD)) |
| Abstract: | This paper examines trends in household financial strain across Europe from 2006 to 2022, a period marked by three major economic shocks: the 2008 financial crisis, the COVID-19 pandemic, and the ongoing cost-of-living crisis. Using a subjective measure of welfare, financial strain, we analyse household responses to these shocks, which affected countries differently over time. Our theoretical framework centres on discretionary disposable income, accounting for non-discretionary expenses such as housing, commuting, and childcare costs, alongside household-specific inflation rates to assess purchasing power. Overall, we find many instances of increased financial strain during the financial and the cost-of-living crisis. While aggregate relationships between the drivers seem logical in many countries, there are many instances where the aggregate relationship is either unexpected in sign or strength, indicating that the relationship is due to distribution-specific changes than to aggregate changes. Our microanalysis corroborates this hypothesis, showing that most of the characteristics incorporated in our theoretical framework are significant and of the right sign, even if aggregate relationships were weak. Housing costs consistently emerged as a key determinant of financial strain; while commuting and childcare costs had a more complex, less predictable impact due to their endogeneity with employment, which is associated with lower financial strain. |
| Keywords: | household financial strain, economic shocks, distributional effects, microsimulation |
| JEL: | C63 I31 D31 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17739 |