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on Computational Economics |
| By: | Avidit Acharya; Jens Hainmueller; Yiqing Xu |
| Abstract: | Conjoint experiments randomize multidimensional profiles, offering a powerful design for recovering structural preference parameters -- including marginal rates of substitution, willingness to pay, and the distribution of preferences across a population. Yet the dominant approach in political science has focused on nonparametric causal estimands that do not leverage this potential. We propose a structural approach that embeds a deep neural network within a random utility logit model, allowing preference parameters to vary as a fully flexible function of respondent characteristics. The neural network addresses the concern that a parametric specification may not capture the true data generating process, while double/debiased machine learning provides valid inference on average preference parameters. We apply our method to three prominent conjoint studies and find rich preference heterogeneity masked by reduced-form averages: a near-zero gender effect coexists with 83% preferring female candidates, opposition to undemocratic behavior is near-universal but varies sharply in intensity, and progressive tax preferences cut across every partisan subgroup. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10845 |
| By: | Wei Wei; Jin Zheng; Zining Wang; Weibin Feng |
| Abstract: | The exponential growth of financial research has rendered traditional systematic literature reviews (SLRs) increasingly impractical, as manual screening and narrative synthesis struggle to keep pace with the scale and complexity of modern scholarship. While the existing artificial intelligence (AI) and natural language processing (NLP) approaches often often produce outputs that are efficient but contextually limited, still requiring substantial expert oversight. To address these challenges, we propose LR-Robot, a novel framework in which domain experts define multidimensional classification taxonomies and prompt constraints that encode conceptual boundaries, large language models (LLMs) execute scalable classification across large corpora, and systematic human-in-the-loop evaluation ensures reliability before full-dataset deployment.The framework further leverages retrieval-augmented generation (RAG) to support downstream analyses including temporal evolution tracking and label-enhanced citation networks. We demonstrate the framework on a corpus of 12, 666 option pricing articles spanning 50 years, designing a four-dimensional taxonomy and systematically evaluating up to eleven mainstream LLMs across classification tasks of varying complexity. The results reveal the current capabilities of AI in understanding and synthesizing literature, uncover emerging trends, reveal structural research patterns, and highlight core research directions. By accelerating labor-intensive review stages while preserving interpretive accuracy, LR-Robot provides a practical, customizable, and high-quality approach for AI-assisted SLRs. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14793 |
| By: | Shuze Daniel Liu; Claire Chen; Jiabao Sean Xiao; Lei Lei; Yuheng Zhang; Yisong Yue; David Simchi-Levi |
| Abstract: | The recent advancement of Large Language Models (LLMs) has established their potential as autonomous interactive agents. However, they often struggle in strategic games of incomplete information, such as bilateral price negotiation. In this paper, we investigate if Reinforcement Learning from Verifiable Rewards (RLVR) can effectively teach LLMs to negotiate. Specifically, we explore the strategic behaviors that emerge during the learning process. We introduce a framework that trains a mid-sized buyer agent against a regulated LLM seller across a wide distribution of real-world products. By grounding reward signals directly in the maximization of economic surplus and strict adherence to private budget constraints, we reveal a novel four-phase strategic evolution. The agent progresses from naive bargaining to using aggressive starting prices, moves through a phase of deadlock, and ultimately develops sophisticated persuasive skills. Our results demonstrate that this verifiable training allows a 30B agent to significantly outperform frontier models over ten times its size in extracting surplus. Furthermore, the trained agent generalizes robustly to stronger counterparties unseen during training and remains effective even when facing hostile, adversarial seller personas. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.09855 |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Dhanashree Somani (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA) |
| Abstract: | This study examines the predictive power of multi-scale positive and negative speculative bubbles in equity and energy markets for S&P 500 realized variance across horizons from 1 to 24 months. Using a hierarchical modeling framework and machine learning estimators, the analysis evaluates whether stock and oil bubbles provide incremental information beyond macroeconomic variables and financial uncertainty. Applying Clark and West's (2007) tests for nested model comparisons, the results reveal a hierarchy in predictive content that varies by forecast horizon. At the 1-month horizon, neither stock nor oil bubbles improves forecast accuracy. At the 3-month horizon, oil bubbles emerge as the dominant predictor; the Bayesian Regularized Neural Network (BRNN) estimator achieves a statistically significant improvement when oil bubbles are included with stock bubbles, resulting in a 30.7 percent reduction in mean squared error (MSE). At the 6-month horizon, stock bubbles become more important, with both the Gradient Boosting Machine (GBM) and BRNN estimators showing significant improvements. For longer horizons, oil bubbles remain relevant, but their predictive value depends on the estimator: BRNN captures oil bubble effects at 12 months, while GBM does so at 24 months. These findings highlight the importance of horizonspecific model selection and indicate a complex transmission of speculative shocks across asset classes. |
| Keywords: | Stock Market Realized Variance, Stock and Oil Bubbles, Machine Learning, Forecasting |
| JEL: | C22 C53 G10 Q51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202611 |
| By: | Cory Baird; Jonathan Benchimol; Wook Sohn; Vira Vyshnevska; Iegor Vyshnevskyi |
| Abstract: | This study introduces the Monetary Policy Statement Database (MPSD), comprising 6, 693 statements from 51 central banks worldwide (1990-2024). We develop a reproducible pipeline combining standard natural language preprocessing with large language model (LLM) tools for cross-country analysis. Four key findings emerge. First, statements lengthened substantially after the Global Financial Crisis while readability improved modestly. Second, inflation references comove across countries during global inflation episodes. Third, LLM-based question answering and aspect-based sentiment reveal that central banks attribute global financial conditions primarily to broad U.S. macroeconomic developments rather than to Federal Reserve policy actions specifically. Fourth, using a benchmark dictionary-based sentiment index and LLM-derived aspect-based sentiment indicators, Granger causality tests suggest that statement sentiment predicts the Global Financial Cycle rather than merely responding to it. The MPSD and accompanying codebase support reproducible research on monetary policy communication and international transmission. |
| Keywords: | central bank communication, large language models, text analysis, generative database, machine learning |
| JEL: | C55 C63 E52 E58 G15 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-25 |
| By: | Xiang Ao; Jingxuan Zhang; Xinyu Zhao |
| Abstract: | Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a dynamic early warning system integrating economic theory with deep temporal networks. Using Chinese A-share panel data (2014-2024), we employ a hybrid Temporal Convolutional Network (TCN) and Attention-based LSTM to capture long- and short-term financial evolutionary patterns. Rolling-window cross-validation demonstrates our model significantly outperforms static baselines like Logistic Regression and XGBoost. Furthermore, utilizing Explainable AI (XAI), we reveal the temporal dynamics of repurchase decisions: prolonged "undervaluation" serves as the long-term underlying motive, while a sharp increase in "cash flow" acts as the decisive short-term trigger. This study provides a robust deep learning paradigm for financial forecasting and offers dynamic empirical support for classic corporate finance hypotheses. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.09650 |
| By: | Pu Cheng; Juncheng Liu; Yunshen Long |
| Abstract: | Predicting real-world events from live market signals demands systems that fuse qualitative news with quantitative order-book dynamics under strict temporal discipline -- a challenge existing benchmarks fail to capture. We present \textbf{PolyBench}, a multimodal benchmark derived from Polymarket that records point-in-time cross-sections of 38, 666 binary prediction markets spanning 4, 997 events, synchronously coupling each snapshot with a Central Limit Order Book (CLOB) state and a real-time news stream. Using PolyBench, we evaluate seven state-of-the-art Large Language Models -- spanning open- and closed-source families -- generating 36, 165 predictions under identical, timestamp-locked market states collected between February 6 and 12, 2026. Our multidimensional framework assesses directional accuracy, our proposed Confidence-Weighted Return (CWR), Annualized Percentage Yield (APY), and Sharpe ratio via realistic order-book execution simulation. The results reveal a pronounced performance divergence: only two of seven models achieve positive financial returns -- MiMo-V2-Flash at \textbf{17.6%} CWR and Gemini-3-Flash at 6.2% CWR -- while the remaining five incur losses despite uniformly high stated confidence. These findings highlight the gap between surface-level language fluency and genuine probabilistic reasoning under live market uncertainty, and establish PolyBench as a contamination-proof, financially-grounded evaluation standard for future LLM research. Our dataset and code available at \underline{\href{https://github.com/Poly Bench/PolyBench}{https://github.com/Poly Bench/PolyBench}}. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14199 |
| By: | Kun Liu; Liqun Chen |
| Abstract: | The alignment of Multi-Agent Systems (MAS) for autonomous software engineering is constrained by evaluator epistemic uncertainty. Current paradigms, such as Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF), frequently induce model sycophancy, while execution-based environments suffer from adversarial "Test Evasion" by unconstrained agents. In this paper, we introduce an objective alignment paradigm: \textbf{Out-of-Money Reinforcement Learning (OOM-RL)}. By deploying agents into the non-stationary, high-friction reality of live financial markets, we utilize critical capital depletion as an un-hackable negative gradient. Our longitudinal 20-month empirical study (July 2024 -- February 2026) chronicles the system's evolution from a high-turnover, sycophantic baseline to a robust, liquidity-aware architecture. We demonstrate that the undeniable ontological consequences of financial loss forced the MAS to abandon overfitted hallucinations in favor of the \textbf{Strict Test-Driven Agentic Workflow (STDAW)}, which enforces a Byzantine-inspired uni-directional state lock (RO-Lock) anchored to a deterministically verified $\geq 95\%$ code coverage constraint matrix. Our results show that while early iterations suffered severe execution decay, the final OOM-RL-aligned system achieved a stable equilibrium with an annualized Sharpe ratio of 2.06 in its mature phase. We conclude that substituting subjective human preference with rigorous economic penalties provides a robust methodology for aligning autonomous agents in high-stakes, real-world environments, laying the groundwork for generalized paradigms where computational billing acts as an objective physical constraint |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.11477 |
| By: | Bin Ramli, Muhammad Sukri |
| Abstract: | The global copper market is experiencing a period of fundamental structural volatility, guided by supply chain realignments, geopolitical friend-shoring, and an increasing reliance on the circular economy. To accurately diagnose the current state of this critical mineral, this paper presents a strictly empirical, data-driven algorithmic pipeline, the Apex Empirical Model, applied to recent UN Comtrade transaction ledgers (2020-2025). By utilizing robust machine learning architectures, this research systematically identifies a phenomenon we term Stage-Specific Starvation (SSS) across the upstream, midstream, circular, and downstream stages of the value chain. Integrating Deep Autoencoders, Network Graph Analysis, Holt-Winters Time-Series Forecasting, and Risk-Parity Optimization, the model successfully isolates targeted capital flight via transfer mispricing and maps the exact flow-through volumes of global transshipment hubs. Furthermore, the framework applies network topology to assess systemic vulnerabilities, empirically confirming the existence of a geopolitical price premium, and engineers a continuous mass-balance metric to predict projected smelter capacity adjustments six months into the future. Finally, our resilience metrics mathematically prove the financial arbitrage and stability advantages of secondary scrap integration. Ultimately, this research leverages Causal Inference to introduce Circular Risk Parity (CRP), providing a prescriptive, optimized portfolio allocation that balances risk equally across the supply chain, allowing stakeholders to navigate exogenous supply shocks in the modern copper market. |
| Date: | 2026–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:v53nw_v1 |
| By: | Gotherwal, Deepesh; Ranjan, Pritam; Lekivetz, Ryan |
| Abstract: | Verification and validation (V&V) are integral parts of any simulation study. Validation assesses how accurately conceptual models represent the real system, while verification ensures correct implementation in software. V&V plays a critical role in business and manufacturing, where simulation models imitate complex real-world systems. However, comprehensive statistically grounded literature reviews on V&V of simulation models, particularly from a business management and manufacturing domain standpoint, are scarce. This study addresses that gap by performing topic modeling to identify prominent research themes, then reviewing all important research articles to outline the evolution of quantitative methodologies and algorithms on V&V. We also highlight various research gaps and potential directions for future work. For this study, we reviewed the abstracts of more than 6, 000 articles indexed in Scopus and Web of Science, along with a comprehensive analysis of 300 research articles. |
| Keywords: | Latent Dirichlet allocation, Discrete event simulation, system dynamics model, statistical metamodeling |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:339750 |
| By: | J\'er\^ome Lelong (LJK); V\'eronique Maume-Deschamps (ICJ, PSPM); William Thevenot (ICJ, PSPM) |
| Abstract: | We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14439 |
| By: | Ecenur Oguz; Robert L. Bray |
| Abstract: | We develop the first general-purpose estimator for infinite-horizon dynamic discrete choice models whose estimation problem, after pre-computation, is unencumbered by large systems of linear equations -- either imposed as constraints, or embedded in the objective function. Our unnested fixed point (UFXP) and optimal unnested fixed point (OUFXP) estimators exploit a dual representation of Bellman's equation to separate the utility parameters from the dynamic programming fixed point. We establish the consistency and asymptotic normality of UFXP and OUFXP, as well as the efficiency of the latter. Our estimators enable researchers to model utility functions non-parametrically via flexible neural-network approximations. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.09736 |
| By: | Mansur M. Arief |
| Abstract: | The bullwhip effect remains operationally persistent despite decades of analytical research. Two computational deficiencies hinder progress: the absence of modular open-source simulation tools for multi-echelon inventory dynamics with asymmetric costs, and the lack of a standardized benchmarking protocol for comparing mitigation strategies across shared metrics and datasets. This paper introduces deepbullwhip, an open-source Python package that integrates a simulation engine for serial supply chains (with pluggable demand generators, ordering policies, and cost functions via abstract base classes, and a vectorized Monte Carlo engine achieving 50 to 90 times speedup) with a registry-based benchmarking framework shipping a curated catalog of ordering policies, forecasting methods, six bullwhip metrics, and demand datasets including WSTS semiconductor billings. Five sets of experiments on a four-echelon semiconductor chain demonstrate cumulative amplification of 427x (Monte Carlo mean across 1, 000 paths), a stochastic filtering phenomenon at upstream tiers (CV = 0.01), super-exponential lead time sensitivity, and scalability to 20.8 million simulation cells in under 7 seconds. Benchmark experiments reveal a 155x disparity between synthetic AR(1) and real WSTS bullwhip severity under the Order-Up-To policy, and quantify the BWR-NSAmp tradeoff across ordering policies, demonstrating that no single metric captures policy quality. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.13478 |