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on Computational Economics |
| By: | Maxim Chupilkin |
| Abstract: | How does AI think about economic policy? While the use of large language models (LLMs) in economics is growing exponentially, their assumptions on economic issues remain a black box. This paper uses a conjoint experiment to tease out the main factors influencing LLMs' evaluation of economic policy. It finds that LLMs are most sensitive to unemployment, inequality, financial stability, and environmental harm and less sensitive to traditional macroeconomic concerns such as economic growth, inflation, and government debt. The results are remarkably consistent across scenarios and across models. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.15771 |
| By: | Lin Cai (Department of Statistics, Columbia University, New York, USA); Zhiyang He (Department of Engineering and Informatics, University of Sussex, Brighton, UK); Caiya Zhang (Department of Statistics and Data Science, Hangzhou City University, Hangzhou, China) |
| Abstract: | This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learning algorithms are developed to predict the returns of the stock selection strategy. One is static weighting based on model evaluation metrics, the other is dynamic weighting based on Information Coefficients (IC). Using CSI 300 index data, we empirically evaluate the strategy' s backtested performance and model predictive accuracy. The main results are as follows: (1) The strategy by combined machine learning algorithms significantly outperforms single-model approaches in backtested returns. (2) IC-based weighting (particularly IC_Mean) demonstrates greater competitiveness than evaluation-metric-based weighting in both backtested returns and predictive performance. (3) Factor screening substantially enhances the performance of combined machine learning strategies. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.18592 |
| By: | Patrick Cheridito; Moritz Weiss |
| Abstract: | In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06345 |
| By: | Vîntu, Denis |
| Abstract: | Unemployment is one of the most important macroeconomic indicators for evaluating economic performance and social well-being. Forecasting unemployment is crucial for policymakers, yet traditional econometric models often fail to capture nonlinear and dynamic patterns. This paper presents an experiment applying artificial neural networks (ANNs) to predict the unemployment rate using macroeconomic data. Results show that ANNs outperform traditional ARIMA models, particularly during stable economic conditions. Implications for policy, limitations, and future research are discussed. |
| Keywords: | Simultaneous equations model; Labor market equilibrium; Unemployment rate determination; Wage-setting equation; Price-setting equation; Beveridge curve; Job matching function; Phillips curve; Structural unemployment; Natural rate of unemployment; Labor supply and demand; Endogenous unemployment; Disequilibrium model; Employment dynamics; Wage-unemployment relationship; Aggregate labor market model; Multivariate system estimation; Identification problem; Reduced form equations; Equilibrium unemployment rate |
| JEL: | C30 C31 C32 C33 J64 J68 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125938 |
| By: | Qizhao Chen; Hiroaki Kawashima |
| Abstract: | This paper proposes a reinforcement learning framework that employs Proximal Policy Optimization (PPO) to dynamically optimize the weights of multiple large language model (LLM)-generated formulaic alphas for stock trading strategies. Formulaic alphas are mathematically defined trading signals derived from price, volume, sentiment, and other data. Although recent studies have shown that LLMs can generate diverse and effective alphas, a critical challenge lies in how to adaptively integrate them under varying market conditions. To address this gap, we leverage the deepseek-r1-distill-llama-70b model to generate fifty alphas for five major stocks: Apple, HSBC, Pepsi, Toyota, and Tencent, and then use PPO to adjust their weights in real time. Experimental results demonstrate that the PPO-optimized strategy achieves strong returns and high Sharpe ratios across most stocks, outperforming both an equal-weighted alpha portfolio and traditional benchmarks such as the Nikkei 225, S&P 500, and Hang Seng Index. The findings highlight the importance of reinforcement learning in the allocation of alpha weights and show the potential of combining LLM-generated signals with adaptive optimization for robust financial forecasting and trading. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01393 |
| By: | Mateusz Wilinski; Juho Kanniainen |
| Abstract: | There are multiple explanations for stylized facts in high-frequency trading, including adaptive and informed agents, many of which have been studied through agent-based models. This paper investigates an alternative explanation by examining whether, and under what circumstances, interactions between traders placing limit order book messages can reproduce stylized facts, and what forms of interaction are required. While the agent-based modeling literature has introduced interconnected agents on networks, little attention has been paid to whether specific trading network topologies can generate stylized facts in limit order book markets. In our model, agents are strictly zero-intelligence, with no fundamental knowledge or chartist-like strategies, so that the role of network topology can be isolated. We find that scale-free connectivity between agents reproduces stylized facts observed in markets, whereas no-interaction does not. Our experiments show that regular lattices and Erdos-Renyi networks are not significantly different from the no-interaction baseline. Thus, we provide a completely new, potentially complementary, explanation for the emergence of stylized facts. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20672 |
| By: | Eduardo Abi Jaber; Louis-Amand G\'erard |
| Abstract: | We investigate the use of path signatures in a machine learning context for hedging exotic derivatives under non-Markovian stochastic volatility models. In a deep learning setting, we use signatures as features in feedforward neural networks and show that they outperform LSTMs in most cases, with orders of magnitude less training compute. In a shallow learning setting, we compare two regression approaches: the first directly learns the hedging strategy from the expected signature of the price process; the second models the dynamics of volatility using a signature volatility model, calibrated on the expected signature of the volatility. Solving the hedging problem in the calibrated signature volatility model yields more accurate and stable results across different payoffs and volatility dynamics. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02759 |
| By: | Sophia Kazinnik; Tara M. Sinclair |
| Abstract: | We develop a a multi-agent framework for modeling the Federal Open Market Committee (FOMC) decision making process. The framework combines two approaches: an LLM-based simulation and a Monte Carlo implementation of a generalized Bayesian voting model. Both begin from identical prior beliefs about the appropriate interest rate for each committee member, formed using real-time data and member profiles. In a simulation replicating the July 2025 FOMC meeting, both tracks deliver rates near the 4.25–4.50\% range's upper end (4.42\% LLM, 4.38\% MC). Political pressure scenario increases dissent and dispersion: the LLM track averages 4.38\% and shows dissent in 88\% of meetings; the MC track averages 4.39\% and shows dissent in 61\% of meetings. A negative jobs revision scenario moves outcomes lower: LLM at 4.30\% (dissent in 74\% of meeting), and MC at 4.32\% (dissent in 62\% of meeting), with final decisions remaining inside the 4.25-4.50\% range. The framework isolates small, scenario‑dependent wedges between behavioral and rational baselines, offering an \textit{in silico} environment for counterfactual evaluation in monetary policy. |
| Keywords: | Generative AI; Multi-Agent Systems; Large Language Models, Federal Open Market Committee; Monetary Policy; Simulations |
| JEL: | E52 E58 C63 D83 C73 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:gwc:wpaper:2025-005 |
| By: | Manel Labidi (LEVIATAN); Ying Zhang (LEVIATAN); Matthieu Petit Guillaume (BH - Beyond Horizon - BH - Beyond Horizon); Aurélien Krauth (LEVIATAN) |
| Abstract: | In this article, we present a comparative study of the performance of econometric models (Mundlak model and GEE-Logit model) and artificial intelligence based models, such as stacking model and ensemble model integrating XG-Boost and LightGBM, as well as deep learning models (LSTM, GRU, Transformer-based encoder-decoder, TCN) in a classification task of listed securities into underperfor- ming and outperforming stocks, with a one-year investment horizon. We use annual historical data from 2019 to 2021. The results show that a stacking classification model out-performs the other models and offers a better balance between the true positive rate (70%) and the true negative rate (67%). |
| Abstract: | Dans cet article nous présentons une étude comparative des performances des modèles économétriques (modèle de Mundlak et modèle GEE-Logit) et ceux issus de l'intelligence artificielle comme le modèle en empilement et le modèle ensembliste intégrant XGBoost et LightGBM, ainsi que les modèles d'apprentissage profond (LSTM, GRU, encodeur-décodeur basé sur les Transformers, TCN) dans une tâche de classification de titres cotés en titres sous-performants et titres surperformants, pour un horizon d'investissement à un an. Nous utilisons des données historiques annuelles de 2019 à 2021. Les résultats montrent qu'un modèle de classification en empilement surpasse les autres modèles et offre un meilleur équilibre entre le taux de vrais positifs (70%) et le taux de vrais négatifs (67%). |
| Keywords: | Long Short-Term Memory, Gestion de portefeuilles décision d'investissement eXtreme Gradient Boosting Long Short-Term Memory Light Gradient Boosting Gated Recurrent Unit Temporal Convolutional Network modèle à pile modèle GEE-Logit modèle de Mundlak Portfolio management investment decision eXtreme Gradient Boosting Long Short-Term Memory Light Gradient Boosting Gated Recurrent Unit Temporal Convolutional Network stacking model GEE-Logit model Mundlak model 1. Autoregressive moving-average model 2. Autoregressive Integrated Moving Average model 3. Autoregressive Conditional Heteroskedasticity model 4. Generalized AutoRegressive Conditional Heteroskedasticity model, Gestion de portefeuilles, décision d'investissement, eXtreme Gradient Boosting, Light Gradient Boosting, Mundlak model 1. Autoregressive moving-average model 2. Autoregressive Integrated Moving Average model 3. Autoregressive Conditional Heteroskedasticity model 4. Generalized AutoRegressive Conditional Heteroskedasticity model, GEE-Logit model, stacking model, investment decision, modèle de Mundlak Portfolio management, modèle GEE-Logit, modèle à pile, Temporal Convolutional Network, Gated Recurrent Unit |
| Date: | 2025–07–02 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05168124 |
| By: | Bingyang Wang; Grant Johnson; Maria Hybinette; Tucker Balch |
| Abstract: | This paper investigates whether artificial intelligence can enhance stock clustering compared to traditional methods. We consider this in the context of the semi-strong Efficient Markets Hypothesis (EMH), which posits that prices fully reflect all public information and, accordingly, that clusters based on price information cannot be improved upon. We benchmark three clustering approaches: (i) price-based clusters derived from historical return correlations, (ii) human-informed clusters defined by the Global Industry Classification Standard (GICS), and (iii) AI-driven clusters constructed from large language model (LLM) embeddings of stock-related news headlines. At each date, each method provides a classification in which each stock is assigned to a cluster. To evaluate a clustering, we transform it into a synthetic factor model following the Arbitrage Pricing Theory (APT) framework. This enables consistent evaluation of predictive performance in a roll forward, out-of-sample test. Using S&P 500 constituents from from 2022 through 2024, we find that price-based clustering consistently outperforms both rule-based and AI-based methods, reducing root mean squared error (RMSE) by 15.9% relative to GICS and 14.7% relative to LLM embeddings. Our contributions are threefold: (i) a generalizable methodology that converts any equity grouping: manual, machine, or market-driven, into a real-time factor model for evaluation; (ii) the first direct comparison of price-based, human rule-based, and AI-based clustering under identical conditions; and (iii) empirical evidence reinforcing that short-horizon return information is largely contained in prices. These results support the EMH while offering practitioners a practical diagnostic for monitoring evolving sector structures and provide academics a framework for testing alternative hypotheses about how quickly markets absorb information. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01590 |
| By: | Zehra Usta; Martin Andersson; Katarzyna Kopczewska; Maria Kubara |
| Abstract: | A common proposition is that certain technologies play a catalytic role in regions by paving the way for the emergence of new related technologies, contributing to the development and diversification of technology clusters. This paper employs unsupervised machine learning algorithms with temporally informed association rule mining to identify catalytic patents in clusters in the UK. Using data spanning over 30 years (1980-2015) we show clear asymmetric relationships between patents. Some act as evident catalysts that drive future patent activity in clusters. The results point to a strong empirical relevance of asymmetric relatedness between patents in the development of clusters of technology. They also highlight the usefulness of machine learning algorithms to better understand the long-term evolution of clusters and show how temporally informed association rule mining can be used to analyses asymmetries in relatedness and to identify catalyst technologies. |
| Keywords: | clusters, innovation, cluster dynamics, technological relatedness, asymmetric relatedness, innovation catalysts, patents |
| JEL: | O31 O33 R12 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2528 |
| By: | Shanyan Lai |
| Abstract: | This study investigates the pretrained RNN attention models with the mainstream attention mechanisms such as additive attention, Luong's three attentions, global self-attention (Self-att) and sliding window sparse attention (Sparse-att) for the empirical asset pricing research on top 420 large-cap US stocks. This is the first paper on the large-scale state-of-the-art (SOTA) attention mechanisms applied in the asset pricing context. They overcome the limitations of the traditional machine learning (ML) based asset pricing, such as mis-capturing the temporal dependency and short memory. Moreover, the enforced causal masks in the attention mechanisms address the future data leaking issue ignored by the more advanced attention-based models, such as the classic Transformer. The proposed attention models also consider the temporal sparsity characteristic of asset pricing data and mitigate potential overfitting issues by deploying the simplified model structures. This provides some insights for future empirical economic research. All models are examined in three periods, which cover pre-COVID-19 (mild uptrend), COVID-19 (steep uptrend with a large drawdown) and one year post-COVID-19 (sideways movement with high fluctuations), for testing the stability of these models under extreme market conditions. The study finds that in value-weighted portfolio back testing, Model Self-att and Model Sparse-att exhibit great capabilities in deriving the absolute returns and hedging downside risks, while they achieve an annualized Sortino ratio of 2.0 and 1.80 respectively in the period with COVID-19. And Model Sparse-att performs more stably than Model Self-att from the perspective of absolute portfolio returns with respect to the size of stocks' market capitalization. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19006 |
| By: | Nobuyuki Hanaki (Institute of Social and Economic Research, Osaka University); Giulia Iori (City, University of London); Pietro Vassallo (Bank of Italy) |
| Abstract: | In this paper we present the results of experiments and computational analyses of trading in decentralized markets with asymmetric information. We consider three trading configurations, namely the ring, the small-world, and the Erdös-Rényi random network, which allow us to introduce heterogeneity in nodes degree, centrality and clustering, while keeping the number of possible trading relationships fixed. We analyze how the prices of a traded risky asset and the profits of differently informed traders are affected by the distribution of the trading links, and by the location of the traders in the network. This allows us to infer key features in the dynamics of learning and information diffusion through the market. Experimental results show that learning is enhanced locally by clustering rather than degree, pointing to a learning dynamic driven by interdependent, successive trading events, rather than independent exposures to informed traders. By calibrating a behavioural agent-based model to the experimental data we are able to estimate the speed at which agents learn and to locate where information accumulates in the market. Interestingly, simulations indicate that proximity to the insiders leads to more information in regular networks but not so in random networks. |
| Keywords: | OTC markets; Asymmetric information; Learning; Information diffusion; Networks; Insider trading |
| JEL: | G1 C6 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:12 |
| By: | Mohammed-Khalil Ghali; Cecil Pang; Oscar Molina; Carlos Gershenson-Garcia; Daehan Won |
| Abstract: | Accurate forecasting of commodity price spikes is vital for countries with limited economic buffers, where sudden increases can strain national budgets, disrupt import-reliant sectors, and undermine food and energy security. This paper introduces a hybrid forecasting framework that combines historical commodity price data with semantic signals derived from global economic news, using an agentic generative AI pipeline. The architecture integrates dual-stream Long Short-Term Memory (LSTM) networks with attention mechanisms to fuse structured time-series inputs with semantically embedded, fact-checked news summaries collected from 1960 to 2023. The model is evaluated on a 64-year dataset comprising normalized commodity price series and temporally aligned news embeddings. Results show that the proposed approach achieves a mean AUC of 0.94 and an overall accuracy of 0.91 substantially outperforming traditional baselines such as logistic regression (AUC = 0.34), random forest (AUC = 0.57), and support vector machines (AUC = 0.47). Additional ablation studies reveal that the removal of attention or dimensionality reduction leads to moderate declines in performance, while eliminating the news component causes a steep drop in AUC to 0.46, underscoring the critical value of incorporating real-world context through unstructured text. These findings demonstrate that integrating agentic generative AI with deep learning can meaningfully improve early detection of commodity price shocks, offering a practical tool for economic planning and risk mitigation in volatile market environments while saving the very high costs of operating a full generative AI agents pipeline. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.06497 |
| By: | Cheuk Hang Leung; Yijun Li; Qi Wu |
| Abstract: | Fintech lending has become a central mechanism through which digital platforms stimulate consumption, offering dynamic, personalized credit limits that directly shape the purchasing power of consumers. Although prior research shows that higher limits increase average spending, scalar-based outcomes obscure the heterogeneous distributional nature of consumer responses. This paper addresses this gap by proposing a new causal inference framework that estimates how continuous changes in the credit limit affect the entire distribution of consumer spending. We formalize distributional causal effects within the Wasserstein space and introduce a robust Distributional Double Machine Learning estimator, supported by asymptotic theory to ensure consistency and validity. To implement this estimator, we design a deep learning architecture comprising two components: a Neural Functional Regression Net to capture complex, nonlinear relationships between treatments, covariates, and distributional outcomes, and a Conditional Normalizing Flow Net to estimate generalized propensity scores under continuous treatment. Numerical experiments demonstrate that the proposed estimator accurately recovers distributional effects in a range of data-generating scenarios. Applying our framework to transaction-level data from a major BigTech platform, we find that increased credit limits primarily shift consumers towards higher-value purchases rather than uniformly increasing spending, offering new insights for personalized marketing strategies and digital consumer finance. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.03063 |
| By: | Vîntu, Denis |
| Abstract: | This study investigates the estimation of the unemployment rate in the Republic of Moldova, focusing on the impact of the COVID-19 pandemic. Two forecasting approaches are compared: the traditional ARIMA model and several machine learning models. The performance of these models is evaluated based on prediction accuracy metrics over pre-pandemic and pandemic periods. Results indicate that while ARIMA captures general trends effectively, machine learning models can better adapt to sudden shocks, such as those induced by the pandemic. |
| Keywords: | Simultaneous equations model; Labor market equilibrium; Unemployment rate determination; Wage-setting equation; Price-setting equation; Beveridge curve; Job matching function; Phillips curve; Structural unemployment; Natural rate of unemployment; Labor supply and demand; Endogenous unemployment; Disequilibrium model; Employment dynamics; Wage-unemployment relationship; Aggregate labor market model; Multivariate system estimation; Identification problem; Reduced form equations; Equilibrium unemployment rate |
| JEL: | C30 C31 C32 C33 C51 J64 J65 J68 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125941 |
| By: | Johannes Kruse (Max Planck Institute for Research on Collective Goods, Bonn); Christoph Engel (Max Planck Institute for Research on Collective Goods, Bonn) |
| Abstract: | In many jurisdictions, academia is at the service of legal practice. Law professors write commentaries that summarize the state of the art of doctrine, chiefly of jurisprudence. In the spirit of a proof of concept, using the guarantee of freedom of assembly in the European Convention on Human Rights, we show that this task can be completely outsourced to large language models. Using standard NLP metrics and an LLM as a judge approach, we develop an evaluation pipeline that works without costly human annotation. The commentaries fully written by GPT 4o, Gemini 2.5 flash or Kimi K2 Instruct are on par with their best human written competitor, the Guide provided by the Court itself. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:mpg:wpaper:2025_14 |
| By: | Chinmay Maheshwari; Chinmay Pimpalkhare; Debasish Chatterjee |
| Abstract: | Min-max optimization arises in many domains such as game theory, adversarial machine learning, etc., with gradient-based methods as a typical computational tool. Beyond convex-concave min-max optimization, the solutions found by gradient-based methods may be arbitrarily far from global optima. In this work, we present an algorithmic apparatus for computing globally optimal solutions in convex-non-concave and non-convex-concave min-max optimization. For former, we employ a reformulation that transforms it into a non-concave-convex max-min optimization problem with suitably defined feasible sets and objective function. The new form can be viewed as a generalization of Sion's minimax theorem. Next, we introduce EXOTIC-an Exact, Optimistic, Tree-based algorithm for solving the reformulated max-min problem. EXOTIC employs an iterative convex optimization solver to (approximately) solve the inner minimization and a hierarchical tree search for the outer maximization to optimistically select promising regions to search based on the approximate solution returned by convex optimization solver. We establish an upper bound on its optimality gap as a function of the number of calls to the inner solver, the solver's convergence rate, and additional problem-dependent parameters. Both our algorithmic apparatus along with its accompanying theoretical analysis can also be applied for non-convex-concave min-max optimization. In addition, we propose a class of benchmark convex-non-concave min-max problems along with their analytical global solutions, providing a testbed for evaluating algorithms for min-max optimization. Empirically, EXOTIC outperforms gradient-based methods on this benchmark as well as on existing numerical benchmark problems from the literature. Finally, we demonstrate the utility of EXOTIC by computing security strategies in multi-player games with three or more players. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.12479 |
| By: | Maximiliano San Millán |
| Abstract: | We examine the cross-border effects of bank capital requirements using a two-country DSGE model with financial frictions, calibrated to match Euro Area banking flows. Regulation follows a host country principle, applying uniformly to all bank exposures within a country, regardless of the banks' nationality. We find that increasing capital requirements in one country leads to a short run credit contraction in interconnected countries. However, long run credit spillovers are negligible. Instead, we find positive long run welfare spillovers, primarily due to higher bank dividend payouts to foreign bank owners, rather than increased financial stability in the foreign country. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1046 |
| By: | Hui Chen; Antoine Didisheim; Luciano Somoza; Hanqing Tian |
| Abstract: | Emerging techniques in computer science make it possible to "brain scan" large language models (LLMs), identify the plain-English concepts that guide their reasoning, and steer them while holding other factors constant. We show that this approach can map LLM-generated economic forecasts to concepts such as sentiment, technical analysis, and timing, and compute their relative importance without reducing performance. We also show that models can be steered to be more or less risk-averse, optimistic, or pessimistic, which allows researchers to correct or simulate biases. The method is transparent, lightweight, and replicable for empirical research in the social sciences. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.21285 |
| By: | Ko Adachi (Bank of Japan); Naoya Kato (Bank of Japan) |
| Abstract: | This paper empirically examines the second-round effect of raw material price increases using a DSGE model. Specifically, it explores how price increases driven by rising raw material costs spill over into wages, which then feed back into prices. The analysis focuses on Japan and Europe, which share similar structures in terms of raw material inputs. The results show that the first-round effect, which captures the pass-through of rising raw material costs to prices, is slower in Japan than in Europe. On the other hand, the second-round effect through wages is gradual but persistent in both Japan and Europe. Furthermore, during the period of high inflation since 2020, the first-round effect of higher raw material costs was the main driver of inflation in both Japan and Europe, while the second-round effect contributed to the persistence of inflation. The paper also suggests that the recent changes in wage rigidity in Japan may have strengthened the second-round effect. |
| Keywords: | Wages; Prices; Second-Round Effects; DSGE Model |
| JEL: | E17 E31 J30 |
| Date: | 2025–09–04 |
| URL: | https://d.repec.org/n?u=RePEc:boj:bojwps:wp25e10 |
| By: | Patrick Gruning (Latvijas Banka) |
| Abstract: | During the recent monetary policy tightening cycle, the pass-through of monetary policy to interest rates offered by commercial banks and the size of bank profits have attracted substantial attention. In this study, I explore the economic effects of reducing the adjustment speed of monetary policy changes to deposit interest rates, using a suitable New-Keynesian dynamic stochastic general equilibrium model. A lower deposit interest rate adjustment speed increases macroeconomic volatility but decreases the volatility of the credit spread (except in the case of a very low adjustment speed). Bank net interest income and aggregate consumption typically increase relative to a model where the deposit interest rate perfectly tracks the monetary policy rate, while aggregate output and investment dynamics deteriorate. Introducing a tax on the interest income earned by setting deposit interest rates below the monetary policy rate leads to amplified short- and medium-run macroeconomic costs. However, the tax improves long-run economic dynamics. |
| Keywords: | Monetary policy, Financial intermediaries, Deposit interest rates, New Keynesian DSGE model, Excess bank interest income tax |
| JEL: | E31 E32 E44 E52 H25 |
| Date: | 2025–08–18 |
| URL: | https://d.repec.org/n?u=RePEc:ltv:wpaper:202505 |
| By: | Jakub Micha\'nk\'ow; Pawe{\l} Sakowski; Robert \'Slepaczuk |
| Abstract: | The proper design and architecture of testing of machine learning models, especially in their application to quantitative finance problems, is crucial. The most important in this process is selecting an adequate loss function used for training, validation, estimation purposes, and tuning of hyperparameters. Therefore, in this research, through empirical experiments on equity and cryptocurrency assets, we introduce the Mean Absolute Directional Loss (MADL) function which is more adequate for optimizing forecast-generating models used in algorithmic investment strategies. The MADL function results are compared for Transformer and LSTM models and we show that almost in every case Transformer results are significantly better than those obtained with LSTM. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16548 |
| By: | Tianyi Li; Yu Qin; Olivia R. Liu Sheng |
| Abstract: | How much large language models (LLMs) can aid scientific discovery, notably in assisting academic peer review, is in heated debate. Between a literature digest and a human-comparable research assistant lies their practical application potential. We organize individual tasks that computer science studies employ in separate terms into a guided and robust workflow to evaluate LLMs' processing of academic text input. We employ four tasks in the assessment: content reproduction/comparison/scoring/reflection, each demanding a specific role of the LLM (oracle/judgmental arbiter/knowledgeable arbiter/collaborator) in assisting scholarly works, and altogether testing LLMs with questions that increasingly require intellectual capabilities towards a solid understanding of scientific texts to yield desirable solutions. We exemplify a rigorous performance evaluation with detailed instructions on the prompts. Adopting first-rate Information Systems articles at three top journals as the input texts and an abundant set of text metrics, we record a compromised performance of the leading LLM - Google's Gemini: its summary and paraphrase of academic text is acceptably reliable; using it to rank texts through pairwise text comparison is faintly scalable; asking it to grade academic texts is prone to poor discrimination; its qualitative reflection on the text is self-consistent yet hardly insightful to inspire meaningful research. This evidence against an endorsement of LLMs' text-processing capabilities is consistent across metric-based internal (linguistic assessment), external (comparing to the ground truth), and human evaluation, and is robust to the variations of the prompt. Overall, we do not recommend an unchecked use of LLMs in constructing peer reviews. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.11779 |
| By: | Diego Fernando Cuesta-Mora; Camilo Gómez |
| Abstract: | This paper presents a stress test model used by the Financial Stability Department of the Banco de la República to assess the financial vulnerability of Colombian non financial firms. The model supports the Central Bank’s biannual Financial Stability Report and informs policy decisions by identifying firms that are exposed to credit risk under adverse economic conditions. The proposed model integrates three components: a dynamic balance sheet simulation framework; a suite of machine learning models to estimate credit default probabilities; and a final module that identifies firms at risk of default. This tool strengthens the Central Bank’s capacity to monitor and evaluate risks in the corporate sector with a forward-looking perspective. The paper details each component and illustrates the model’s results using a stress scenario. *****RESUMEN: Este documento presenta un modelo de prueba de estrés empleado por el Departamento de Estabilidad Financiera del Banco de la República para evaluar la vulnerabilidad financiera de las firmas no financieras colombianas. El modelo apoya el Reporte de Estabilidad Financiera semestral del Banco de la República y aporta al diseño de políticas al identificar firmas expuestas al riesgo crediticio en condiciones macroeconómicas adversas. El modelo propuesto integra tres componentes: un marco dinámico de simulación de balances; un conjunto de modelos de machine learning para estimar probabilidades de incumplimiento crediticio; y un módulo final que identifica firmas en riesgo de incumplimiento crediticio. Esta herramienta fortalece la capacidad del Banco de la República para monitorear y evaluar riesgos en el sector empresarial de forma prospectiva. El documento detalla cada componente e ilustra los resultados mediante un escenario de estrés. |
| Keywords: | Stress Testing, Credit Risk, Credit Default, Machine Learning, Prueba de estrés, Riesgo crediticio, Incumplimiento crediticio. |
| JEL: | G3 G21 G01 G17 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:bdr:borrec:1325 |
| By: | Xiangdong Liu; Jiahao Chen |
| Abstract: | In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empirical rules often fail to adapt to dynamic market changes and black swan events due to rigid assumptions and limited generalization. To address these issues, this paper proposes QTMRL (Quantitative Trading Multi-Indicator Reinforcement Learning), an intelligent trading agent combining multi-dimensional technical indicators with reinforcement learning (RL) for adaptive and stable portfolio management. We first construct a comprehensive multi-indicator dataset using 23 years of S&P 500 daily OHLCV data (2000-2022) for 16 representative stocks across 5 sectors, enriching raw data with trend, volatility, and momentum indicators to capture holistic market dynamics. Then we design a lightweight RL framework based on the Advantage Actor-Critic (A2C) algorithm, including data processing, A2C algorithm, and trading agent modules to support policy learning and actionable trading decisions. Extensive experiments compare QTMRL with 9 baselines (e.g., ARIMA, LSTM, moving average strategies) across diverse market regimes, verifying its superiority in profitability, risk adjustment, and downside risk control. The code of QTMRL is publicly available at https://github.com/ChenJiahaoJNU/QTMRL.g it |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20467 |
| By: | Victor-Andrei Carcale (Stefan cel Mare University, Suceava, Romania) |
| Abstract: | Artificial intelligence (AI) is transforming forensic science by enhancing accuracy, efficiency, and investigative precision. Generative AI supports forensic psychiatry, behavioral profiling, and risk assessments, while deep learning models improve forensic odontology and biometric verification. AI-driven tools accelerate crime scene reconstruction, digital forensics, and DNA analysis, reducing processing time and human error. Intelligent systems analyze large datasets, aiding forensic experts in evidence interpretation and criminal profiling. In forensic medicine, AI enhances identification, ballistics analysis, injury assessment, and post-mortem interval estimation. Despite these advancements, ethical concerns persist regarding bias, privacy, and transparency in AI-based forensic decisions. Generative AI raises additional risks, requiring strict regulations and interdisciplinary oversight. The rise of AI-enabled cybercrimes and deepfake content further necessitates advanced security measures. The future of forensic AI relies on responsible governance, ensuring accuracy, fairness, and public trust in criminal investigations. Ethical AI frameworks are essential to balance technological innovation with justice and accountability. |
| Keywords: | Forensic Artificial Intelligence, Crime Scene Reconstruction, Digital Forensics, Generative AI In Forensics, Ethical AI Governance |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0523 |
| By: | Hadi Elzayn; Simon Freyaldenhoven; Minchul Shin |
| Abstract: | We develop a clustering-based algorithm to detect loan applicants who submit multiple applications (“cross-applicants”) in a loan-level dataset without personal identifiers. A key innovation of our approach is a novel evaluation method that does not require labeled training data, allowing us to optimize the tuning parameters of our machine learning algorithm. By applying this methodology to Home Mortgage Disclosure Act (HMDA) data, we create a unique dataset that consolidates mortgage applications to the individual applicant level across the United States. Our preferred specification identifies cross-applicants with 92.3% precision. |
| Date: | 2025–09–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:101559 |
| By: | Kiarash Firouzi |
| Abstract: | Extreme volatility, nonlinear dependencies, and systemic fragility are characteristics of cryptocurrency markets. The assumptions of normality and centralized control in traditional financial risk models frequently cause them to miss these changes. Four components-volatility stress testing, stablecoin hedging, contagion modeling, and Monte Carlo simulation-are integrated into this paper's modular simulation framework for crypto portfolio risk analysis. Every module is based on mathematical finance theory, which includes stochastic price path generation, correlation-based contagion propagation, and mean-variance optimization. The robustness and practical relevance of the framework are demonstrated through empirical validation utilizing 2020-2024 USDT, ETH, and BTC data. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.08915 |
| By: | Cabrera, Carmen (University of Liverpool); Rowe, Francisco (University of Liverpool) |
| Abstract: | Traditional sources of population data, such as censuses and surveys, are costly, infrequent, and often unavailable in crisis-affected regions. Mobile phone application data offer near–realtime, high-resolution insights into population distribution, but their utility is undermined by unequal access to and use of digital technologies, creating biases that threaten representativeness. Despite growing recognition of these issues, there is still no standard framework to measure and explain such biases, limiting the reliability of digital traces for research and policy. We develop and implement a systematic, replicable framework to quantify coverage bias in aggregated mobile phone application data without requiring individual-level demographic attributes. The approach combines a transparent indicator of population coverage with explainable machine learning to identify contextual drivers of spatial bias. Using four datasets for the United Kingdom benchmarked against the 2021 census, we show that mobile phone data consistently achieve higher population coverage than major national surveys, but substantial biases persist across data sources and subnational areas. Coverage bias is strongly associated with demographic, socioeconomic, and geographic features, often in complex nonlinear ways. Contrary to common assumptions, multi-application datasets do not necessarily reduce bias compared to single-app sources. Our findings establish a foundation for bias assessment standards in mobile phone data, offering practical tools for researchers, statistical agencies, and policymakers to harness these datasets responsibly and equitably. |
| Date: | 2025–09–02 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:7temv_v1 |
| By: | Zhuohang Zhu; Haodong Chen; Qiang Qu; Vera Chung |
| Abstract: | Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-stationarity (distribution changes over time), multi-domain diversity (distinct patterns across financial domains such as stocks, commodities, and futures), and varying temporal resolutions (patterns differing across per-second, hourly, daily, or weekly indicators). While recent deep learning methods attempt to address these complexities, they frequently suffer from overfitting and typically require extensive domain-specific fine-tuning. To overcome these limitations, we introduce FinCast, the first foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets. Remarkably, FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning. Comprehensive empirical and qualitative evaluations demonstrate that FinCast surpasses existing state-of-the-art methods, highlighting its strong generalization capabilities. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19609 |
| By: | Bonacina, Monica; Demir, Mert; Sileo, Antonio; Zanoni, Angela |
| Abstract: | The transition to a zero-emission vehicle fleet represents a pivotal element of Europe’s decarbonization strategy, with Italy’s participation being particularly significant given the size of its automotive market. This study investigates the potential for battery electric cars (BEVs) to drive decarbonization of Italy’s passenger vehicle fleet, focusing on the feasibility of targets set in the National Integrated Plan for Energy and Climate (PNIEC). Leveraging artificial neural networks, we integrate macroeconomic indicators, market-specific variables, and policy instruments to predict fleet dynamics and identify key factors influencing BEV adoption. We forecast that while BEV registrations will continue growing through 2030, the growth rate is projected to decelerate, presenting challenges for meeting ambitious policy targets. Our feature importance analysis demonstrates that BEV adoption is driven by an interconnected set of economic, infrastructural, and behavioral factors. Specifically, our model highlights that hybrid vehicle registrations and the vehicle purchase index exert the strongest influence on BEV registrations, suggesting that policy interventions should prioritize these areas to maximize impact. By offering data-driven insights and methodological innovations, our findings contribute to more effective policy design for accelerating sustainable mobility adoption while accounting for market realities and consumer behavior. |
| Keywords: | Climate Change, Environmental Economics and Policy, Sustainability |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:ags:feemwp:369002 |
| By: | MENG, WEI |
| Abstract: | This study proposes an AI-OSINT framework for transnational religious figures, linking knowledge graphs, graph neural networks and Bayesian updating into a computable evidence chain to reconstruct and quantify the overseas assets and foreign contacts of Shi Yongxin and the Shaolin system (2024-11-2025-06). Combining GCN/GAT with unsupervised anomaly detection (Isolation Forest, LOF) on heterogeneous time-series graphs and Bayesian modelling to form monthly outputs of Religious Diplomatic Risk Index (RDRI, 0-100), assessed by ROC-AUC/AUPRC; link prediction by MRR/Hits@k. The results show that the Vatican meeting (2025-02-01) triggered a short-term peak, with the RDRI rising from c. 24-27 to c. 42, and reaching c. 45 in 2025-03; the subsequent chain of "foundations/cultural centres → out-of-country activities" maintained the index at a medium-term high of c. 40 (±3), with a medium-term high of c. 40 (±3), and a medium-term high of c. 40 (±3). The "Foundation/Cultural Centre → Outbound Activities" chain maintains the index at a medium-term high of ~40 (±3), showing a double-engine rhythm of "event amplification + resource penetration". The reported uncertainty interval is ±3; and the conclusion level is shown to be stable under a priori/likelihood ±10% perturbation. The framework enhances the systematic and verifiable study of transnational religious networks without relying on internal intelligence and is transferable to other religious or transnational NGO contexts; to avoid misuse, the RDRI is defined as an early warning scale rather than a factual or judicial characterisation (all judgements cross-checked against registered and chained anchors). Keywords: knowledge graph; graph neural networks; religious diplomacy; financial anomalies; OSINT |
| Date: | 2025–08–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:2apbf_v1 |
| By: | Rongwei Liu; Jin Zheng; John Cartlidge |
| Abstract: | The optimal asset allocation between risky and risk-free assets is a persistent challenge due to the inherent volatility in financial markets. Conventional methods rely on strict distributional assumptions or non-additive reward ratios, which limit their robustness and applicability to investment goals. To overcome these constraints, this study formulates the optimal two-asset allocation problem as a sequential decision-making task within a Markov Decision Process (MDP). This framework enables the application of reinforcement learning (RL) mechanisms to develop dynamic policies based on simulated financial scenarios, regardless of prerequisites. We use the Kelly criterion to balance immediate reward signals against long-term investment objectives, and we take the novel step of integrating the Time-series Dense Encoder (TiDE) into the Deep Deterministic Policy Gradient (DDPG) RL framework for continuous decision-making. We compare DDPG-TiDE with a simple discrete-action Q-learning RL framework and a passive buy-and-hold investment strategy. Empirical results show that DDPG-TiDE outperforms Q-learning and generates higher risk adjusted returns than buy-and-hold. These findings suggest that tackling the optimal asset allocation problem by integrating TiDE within a DDPG reinforcement learning framework is a fruitful avenue for further exploration. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20103 |
| By: | Carlo Nicolini; Matteo Manzi; Hugo Delatte |
| Abstract: | Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn's fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04176 |
| By: | Abhinav Arun; Fabrizio Dimino; Tejas Prakash Agarwal; Bhaskarjit Sarmah; Stefano Pasquali |
| Abstract: | The financial domain poses unique challenges for knowledge graph (KG) construction at scale due to the complexity and regulatory nature of financial documents. Despite the critical importance of structured financial knowledge, the field lacks large-scale, open-source datasets capturing rich semantic relationships from corporate disclosures. We introduce an open-source, large-scale financial knowledge graph dataset built from the latest annual SEC 10-K filings of all S and P 100 companies - a comprehensive resource designed to catalyze research in financial AI. We propose a robust and generalizable knowledge graph (KG) construction framework that integrates intelligent document parsing, table-aware chunking, and schema-guided iterative extraction with a reflection-driven feedback loop. Our system incorporates a comprehensive evaluation pipeline, combining rule-based checks, statistical validation, and LLM-as-a-Judge assessments to holistically measure extraction quality. We support three extraction modes - single-pass, multi-pass, and reflection-agent-based - allowing flexible trade-offs between efficiency, accuracy, and reliability based on user requirements. Empirical evaluations demonstrate that the reflection-agent-based mode consistently achieves the best balance, attaining a 64.8 percent compliance score against all rule-based policies (CheckRules) and outperforming baseline methods (single-pass and multi-pass) across key metrics such as precision, comprehensiveness, and relevance in LLM-guided evaluations. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.17906 |
| By: | Hung Tran; Tien Mai; Minh Ha Hoang |
| Abstract: | The recursive logit (RL) model has become a widely used framework for route choice modeling, but it suffers from a key limitation: it assigns nonzero probabilities to all paths in the network, including those that are unrealistic, such as routes exceeding travel time deadlines or violating energy constraints. To address this gap, we propose a novel Constrained Recursive Logit (CRL) model that explicitly incorporates feasibility constraints into the RL framework. CRL retains the main advantages of RL-no path sampling and ease of prediction-but systematically excludes infeasible paths from the universal choice set. The model is inherently non-Markovian; to address this, we develop a tractable estimation approach based on extending the state space, which restores the Markov property and enables estimation using standard value iteration methods. We prove that our estimation method admits a unique solution under positive discrete costs and establish its equivalence to a multinomial logit model defined over restricted universal path choice sets. Empirical experiments on synthetic and real networks demonstrate that CRL improves behavioral realism and estimation stability, particularly in cyclic networks. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01595 |
| By: | Nicola Jean; Giacomo Le Pera; Lorenzo Giada; Claudio Nordio |
| Abstract: | We present a novel framework for pricing waterfall structures by simulating the uncertainty of the cashflow generated by the underlying assets in terms of value, time, and confidence levels. Our approach incorporates various probability distributions calibrated on the market price of the tranches at inception. The framework is fully implemented in PyTorch, leveraging its computational efficiency and automatic differentiation capabilities through Adjoint Algorithmic Differentiation (AAD). This enables efficient gradient computation for risk sensitivity analysis and optimization. The proposed methodology provides a flexible and scalable solution for pricing complex structured finance instruments under uncertainty |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.13324 |
| By: | Nicholas Gray (Reserve Bank of Australia); Finn Lattimore (Reserve Bank of Australia); Kate McLoughlin (Reserve Bank of Australia); Callan Windsor (Reserve Bank of Australia) |
| Abstract: | In a world of high policy uncertainty, central banks are relying more on soft information sources to complement traditional economic statistics and model-based forecasts. One valuable source of soft information comes from intelligence gathered through central bank liaison programs – structured programs in which central bank staff regularly talk with firms to gather insights. This paper introduces a new text analytics and retrieval tool that efficiently processes, organises, and analyses liaison intelligence gathered from firms using modern natural language processing techniques. The textual dataset spans around 25 years, integrates new information as soon as it becomes available, and covers a wide range of business sizes and industries. The tool uses both traditional text analysis techniques and powerful language models to provide analysts and researchers with three key capabilities: (1) quickly querying the entire history of business liaison meeting notes; (2) zooming in on particular topics to examine their frequency (topic exposure) and analysing the associated tone and uncertainty of the discussion; and (3) extracting precise numerical values from the text, such as firms' reported figures for wages and prices growth. We demonstrate how these capabilities are useful for assessing economic conditions by generating text-based indicators of wages growth and incorporating them into a nowcasting model. We find that adding these text-based features to current best-in-class predictive models, combined with the use of machine learning methods designed to handle many predictors, significantly improves the performance of nowcasts for wages growth. Predictive gains are driven by a small number of features, indicating a sparse signal in contrast to other predictive problems in macroeconomics, where the signal is typically dense. |
| Keywords: | central banking; macroeconomic policy; wages and labour costs; machine learning; econometric modelling; information retrieval systems; firm behaviour |
| JEL: | C5 C8 D2 E5 E6 J3 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:rba:rbardp:rdp2025-06 |
| By: | Annie Liang |
| Abstract: | Machine learning algorithms can now outperform classic economic models in predicting quantities ranging from bargaining outcomes, to choice under uncertainty, to an individual's future jobs and wages. Yet this predictive accuracy comes at a cost: most machine learning algorithms function as black boxes, offering little insight into \emph{why} outcomes occur. This article asks whether machine learning can guide the development of new economic theories. Economic models serve an important purpose beyond prediction -- they uncover the general mechanisms behind observed behaviors. A model that identifies the causal pathways of economic development is more valuable than one that merely predicts which countries will escape poverty, because it enables policymakers to encourage that development in countries where it might not have happened otherwise. Similarly, a model that predicts imperfectly across many domains can be more valuable than one that is highly accurate in a specific domain, since the former allows insights and data obtained from one setting to inform decisions and policy in another. Applying machine learning algorithms off-the-shelf is unlikely to yield such models. But recent work shows that, when reconceived with the aims of an economic modeler in mind, machine learning methods can improve both prediction and understanding. These approaches range from adversarially training algorithms to expose the limits of existing models, to imposing economic theory as a constraint on algorithmic search. Advances in large language models complement these strategies and open new research directions. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19136 |
| By: | Benjamin Moll |
| Abstract: | The thesis of this essay is that, in heterogeneous agent macroeconomics, the assumption of rational expectations about equilibrium prices is unrealistic and should be replaced. Rational expectations imply that decision makers forecast equilibrium prices like interest rates by forecasting cross-sectional distributions. This leads to an extreme version of the curse of dimensionality: dynamic programming problems in which the entire distribution is a state variable ("Master equation" a.k.a. "Monster equation"). Frontier computational methods struggle with these infinite-dimensional Bellman equations, making it implausible that real-world agents solve the associated decision problems. These difficulties also limit the applicability of the heterogeneous-agent approach to central questions in macroeconomics -- those involving aggregate risk and non-linearities such as financial crises. This troublesome feature of the rational expectations assumption poses a challenge: what should replace it? I outline three criteria for alternative approaches: (1) computational tractability, (2) consistency with empirical evidence, and (3) (some) immunity to the Lucas critique. I then discuss several promising directions, including temporary equilibrium approaches, incorporating survey expectations, least-squares learning, and reinforcement learning. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20571 |
| By: | Mrinalini Choudhary (Shenandoah University, Virginia, USA) |
| Abstract: | Artificial intelligence has substantially transformed the digital marketing landscape. Marketers continue to rely on advanced algorithms and data-driven insights to create effective marketing campaigns. Future trends indicate that marketers will rely increasingly on Generative AI and AI integration to enhance customer experience through targeted marketing and highly personalized content. As AI progresses, it becomes increasingly important to consider the ethical concerns of using it in marketing. Researchers have highlighted significant ethical concerns about consumer manipulation, discrimination, and data privacy. This research investigates the ethical consequences, particularly analyzing how AI-driven techniques such as predictive modeling and digital nudging might influence customer decisions. It explores the way in which AI algorithms can exploit consumer vulnerabilities and potentially override rational decision-making processes. Based on the findings from current research and industry trends, the paper proposes an ethical framework for AI in digital marketing, emphasizing transparency, consumer autonomy, and the preservation of human agency. Furthermore, this research emphasizes the need to ensure that AI technologies are as a constructive force in marketing that not only protects consumer rights but also retains societal value. |
| Keywords: | Artificial Intelligence, Algorithm, Ethics, Consumer Autonomy, Digital Marketing |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:smo:raiswp:0495 |
| By: | Jing Wang (Numerical Analysis, Delft University of Technology, Delft, the Netherlands); Shuaiqiang Liu (Numerical Analysis, Delft University of Technology, Delft, the Netherlands; ING Bank, Amsterdam, the Netherlands); Cornelis Vuik (Numerical Analysis, Delft University of Technology, Delft, the Netherlands) |
| Abstract: | This paper presents a deep generative modeling framework for controllably synthesizing implied volatility surfaces (IVSs) using a variational autoencoder (VAE). Unlike conventional data-driven models, our approach provides explicit control over meaningful shape features (e.g., volatility level, slope, curvature, term-structure) to generate IVSs with desired characteristics. In our framework, financially interpretable shape features are disentangled from residual latent factors. The target features are embedded into the VAE architecture as controllable latent variables, while the residual latent variables capture additional structure to preserve IVS shape diversity. To enable this control, IVS feature values are quantified via regression at an anchor point and incorporated into the decoder to steer generation. Numerical experiments demonstrate that the generative model enables rapid generation of realistic IVSs with desired features rather than arbitrary patterns, and achieves high accuracy across both single- and multi-feature control settings. For market validity, an optional post-generation latent-space repair algorithm adjusts only the residual latent variables to remove occasional violations of static no-arbitrage conditions without altering the specified features. Compared with black-box generators, the framework combines interpretability, controllability, and flexibility for synthetic IVS generation and scenario design. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01743 |
| By: | Tianyu Fan |
| Abstract: | This paper establishes geopolitical relations as a first-order determinant of economic growth. We construct a novel event-based measure of bilateral geopolitical alignment by employing large language models with web search capabilities to analyze over 440, 000 political events across 196 countries from 1960--2019. This comprehensive measure enables us to identify the precise timing and magnitude of geopolitical shifts within countries over time. Using local projections with country fixed effects, we find that a one-standard-deviation improvement in geopolitical relations increases GDP per capita by 9.6 log points over 25 years. These persistent effects operate through multiple reinforcing channels -- enhanced political stability, increased investment, expanded trade, and productivity gains. Across our sample, geopolitical factors generate GDP variations ranging from -35% to +30%, with developing nations facing particularly severe penalties from international isolation. Our findings reveal how geopolitical alignment shapes economic prosperity in an increasingly fragmented global economy. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.04833 |
| By: | Hongjun Ding; Binqi Chen; Jinsheng Huang; Taian Guo; Zhengyang Mao; Guoyi Shao; Lutong Zou; Luchen Liu; Ming Zhang |
| Abstract: | Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures. Backtesting is computationally intensive, inherently sequential, and sensitive to specific strategy parameters. Correlation-based metrics, though efficient, assess only predictive ability and overlook other crucial properties such as temporal stability, robustness, diversity, and interpretability. Additionally, the closed-source nature of most existing alpha mining models hinders reproducibility and slows progress in this field. To address these issues, we propose AlphaEval, a unified, parallelizable, and backtest-free evaluation framework for automated alpha mining models. AlphaEval assesses the overall quality of generated alphas along five complementary dimensions: predictive power, stability, robustness to market perturbations, financial logic, and diversity. Extensive experiments across representative alpha mining algorithms demonstrate that AlphaEval achieves evaluation consistency comparable to comprehensive backtesting, while providing more comprehensive insights and higher efficiency. Furthermore, AlphaEval effectively identifies superior alphas compared to traditional single-metric screening approaches. All implementations and evaluation tools are open-sourced to promote reproducibility and community engagement. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.13174 |
| By: | Sudheer Chava; Wendi Du; Indrajit Mitra; Agam Shah; Linghang Zeng |
| Abstract: | We develop firm-level measures of input and output price changes using textual analysis of earnings calls. We establish five facts: (1) Input prices increase (decrease) at the median firm once every seven (30) months. (2) Input price changes contain an equal blend of aggregate and firm-specific components. (3) A firm's stock price experiences a –1.15 percent return when our input price change measure is in the top tercile of price increases. (4) Our input price change measure predicts future changes in the cost of goods sold. (5) Firms pass through input price changes to output prices in the same quarter with a magnitude of 0.7. |
| Keywords: | deep learning; input price; cost pass-through |
| JEL: | D24 E12 E44 L11 |
| Date: | 2025–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedawp:101518 |
| By: | Yulin Liu; Mocca Schweitzer |
| Abstract: | The Decentralized Physical Infrastructure (DePIN) market is revolutionizing the sharing economy through token-based economics and smart contracts that govern decentralized operations. By 2024, DePIN projects have exceeded \$10 billion in market capitalization, underscoring their rapid growth. However, the unregulated nature of these markets, coupled with the autonomous deployment of AI agents in smart contracts, introduces risks such as inefficiencies and potential misalignment with human values. To address these concerns, we introduce EconAgentic, a Large Language Model (LLM)-powered framework designed to mitigate these challenges. Our research focuses on three key areas: 1) modeling the dynamic evolution of DePIN markets, 2) evaluating stakeholders' actions and their economic impacts, and 3) analyzing macroeconomic indicators to align market outcomes with societal goals. Through EconAgentic, we simulate how AI agents respond to token incentives, invest in infrastructure, and adapt to market conditions, comparing AI-driven decisions with human heuristic benchmarks. Our results show that EconAgentic provides valuable insights into the efficiency, inclusion, and stability of DePIN markets, contributing to both academic understanding and practical improvements in the design and governance of decentralized, tokenized economies. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.21368 |
| By: | Mr. Sakai Ando; Shuvam Das; Sultan Orazbayev |
| Abstract: | In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining smoothness are important but challenging. Ando (2024) proposes a systematic approach, but a user-friendly package to implement it has not been developed. This paper addresses this gap by introducing a Python package, macroframe-forecast, that allows users to generate forecasts that are both smooth over time and consistent with user-specified constraints. We demonstrate the package’s functionality with two examples about forecasting US GDP and fiscal variables. |
| Keywords: | Forecast Reconciliation; Python Package; Macroframework |
| Date: | 2025–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/172 |