|
on Computational Economics |
| By: | Olivia Zhang; Zhilin Zhang |
| Abstract: | Large language models (LLMs) are increasingly deployed in quantitative finance for stock price forecasting. This review synthesizes recent applications of LLMs in this domain, including extracting sentiment from financial news and social media, analyzing financial reports and earnings-call transcripts, tokenizing or symbolizing stock price series, and constructing multi-agent trading systems. Particular attention is paid to practical pitfalls that are often understated in the literature, such as fragility in sentiment analysis, dataset and horizon design, performance evaluation metrics, data leakage, illiquidity premia, and limits of stock price predictability. Organized from a hedge-fund perspective, the review is intended to guide both academic researchers and hedge fund managers in integrating LLMs into real-world trading pipelines and in stress-testing their robustness under realistic market frictions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.05211 |
| By: | Jan Rovirosa; Jesse Schmolze |
| Abstract: | Modeling the dynamics of non-stationary stochastic systems requires balancing the representational power of deep learning with the mathematical transparency of classical models. While classical Markov transition operators provide explicit, theoretically grounded rules for system evolution, their empirical estimation collapses due to severe data sparsity when applied to high-resolution, high-noise environments. We explore this statistical barrier using financial time series as a canonical, real-world testbed. To overcome the degeneracy of empirical counting, we introduce a framework that utilizes neural networks strictly as parameterization engines to generate explicit, time-varying Markov transition matrices. By constraining the neural network to output its predictions as a formal stochastic operator, we maintain complete structural interpretability. We demonstrate that these learned operators successfully capture complex regime shifts: the state-conditioned model achieves mean row heterogeneity $\bar{\rho} = 0.0073$ while the state-free ablation collapses to exactly zero, and operator row entropy correlates with realized variance at $r = -0.62$ ($p \approx 10^{-251}$), revealing that high-volatility regimes homogenize transition dynamics rather than diversify them. Furthermore, rather than enforcing the Chapman-Kolmogorov equations as a rigid structural requirement, we repurpose them as a localized diagnostic tool to pinpoint specific temporal windows where first-order memory assumptions break down. Ultimately, this framework demonstrates how neural networks can be constrained to make rigorous, classical operator analysis viable for complex real-world time series. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.04690 |
| By: | Ryuji Hashimoto; Ryosuke Takata; Masahiro Suzuki; Yuki Tanaka; Kiyoshi Izumi |
| Abstract: | Agent-based models provide a constructive approach to studying emergent dynamics in life-like systems composed of interacting, adaptive agents. Financial markets serve as a canonical example of such systems, where collective price dynamics arise from individual decision-making. In this modeling tradition, investor behavior has typically been captured by two distinct mechanisms -- learning and heterogeneous preferences -- which have been explored as separate paradigms in prior studies. However, the impact of their joint modeling on the resulting collective dynamics remains largely unexplored. We develop a multi-agent reinforcement learning framework in which agents endowed with heterogeneous risk aversion, time discounting, and information access learn trading strategies interactively within an artificial market. The experiment reveals that (i) learning under heterogeneous preferences drives agents to develop functionally differentiated strategies through interaction, rather than trait-specific rules, resulting in role specialization, and (ii) the interactions by the differentiated agents are essential for the emergence of realistic market dynamics such as fat-tailed price fluctuations and volatility clustering. Overall, this study demonstrates that the joint design of heterogeneous preferences and learning mechanisms enables the synthesis of an artificial market in which adaptive interactions drive the self-organization of a market ecology, providing a computational realization of the Adaptive Market Hypothesis. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.23975 |
| By: | Mohammad Al Ridhawi; Mahtab Haj Ali; Hussein Al Osman |
| Abstract: | Agentic stock prediction systems make sequences of interdependent decisions (regime detection, pathway routing, reinforcement learning control) whose individual quality is hidden by aggregate metrics such as mean absolute percentage error (MAPE) or directional accuracy. We present a behavioral evaluation framework that addresses this gap. Behavioral traces logged at every autonomous decision point are grouped into five-day episodes and scored along six domain-specific dimensions (regime detection, routing, adaptation, risk calibration, strategy coherence, error recovery) by an ensemble of three large language model (LLM) judges (GPT 5.4, Claude 4.6 Opus, Gemini 3.1 Pro). Perturbation-based validation on 420 episodes yields targeted score drops of $-1.6$ to $-2.4$ on intended dimensions versus an average of $-0.32$ on the remaining five, with cross-model agreement up to Krippendorff's $\alpha = 0.85$. The composite behavioral score, used here only for cross-episode reporting, correlates at $\rho = 0.72$ with realized 20-day Sharpe ratio from offline backtesting. Closing the loop, the framework converts deficient per-dimension scores into a credit-assigned penalty term added to the Soft Actor-Critic (SAC) reward. Three short fine-tuning cycles, all confined to the validation period, produce on the held-out 2017-2025 test period a one-day MAPE reduction from 0.61% to 0.54% (an 11.5% relative reduction; $p |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.05739 |
| By: | Yusuke Oh (Deputy Director, Institute for Monetary and Economic Studies, Bank of Japan (E-mail: yuusuke.ou@boj.or.jp)); Mototsugu Shintani (The University of Tokyo (E-mail: shintani@e.u-tokyo.ac.jp)) |
| Abstract: | We forecast Japanese recessions by integrating machine learning methods, mixed-frequency data, and text-based indicators within an unrestricted mixed data sampling (U-MIDAS) framework. The model combines monthly macroeconomic variables with weekly financial indicators and newspaper-based text indicators. A pseudo-real-time forecasting exercise over three decades shows that machine learning models consistently outperform traditional logit benchmarks. The model confidence set (MCS) suggests horizon dependence: Text indicators are more informative at short horizons, while financial variables are more informative at longer horizons. To improve interpretability, we apply sparse principal component analysis (Sparse PCA) to the text indicators and identify three economic narratives: 'Corporate Distress, ' 'Financial Distress, ' and 'Deflationary Pressure.' Furthermore, SHAP (SHapley Additive exPlanations) analysis indicates that different recession episodes are associated with different combinations of these narratives, underscoring the heterogeneous nature of economic downturns. |
| Keywords: | business cycles, mixed data sampling, model confidence set, text analysis, recession forecasting |
| JEL: | C32 C53 E37 O53 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:26-e-07 |
| By: | Jean-Loup Dupret; Davide Gallon; Patrick Cheridito |
| Abstract: | In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.06281 |
| By: | Adil Reghai; Lama Tarsissi; G\'erard Biau; Alex Lipton |
| Abstract: | This paper proposes a hybrid methodology to improve the approximation of SABR (Stochastic Alpha Beta Rho) implied volatility by combining analytical structure with machine learning. The approach augments the neural-network input representation with geometric features derived from the stochastic differential equations of the SABR model. Unlike approaches that fully replace analytical formulas with black-box models, the proposed framework preserves the analytical backbone of the model. The hybridization operates along two complementary dimensions. First, geometry-aware variables reflecting intrinsic properties of the SABR dynamics are used as structured inputs to the network. Second, the neural network is trained to learn the residual error relative to Hagan's closed-form approximation rather than implied volatility directly. The resulting model acts as a structured residual correction to the analytical formula, retaining interpretability while capturing higher-order effects that are not included in the asymptotic expansion. Numerical experiments conducted over realistic parameter domains, as well as stressed environments, show that the method improves accuracy and robustness compared with both analytical approximations and standard neural-network approaches. Because the correction remains lightweight and structurally consistent with the underlying model, the framework is well suited for real-time pricing and calibration in practical trading environments. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.06604 |
| By: | Dmitri Goloubentsev; Natalija Karpichina |
| Abstract: | Many real-world problems require sequential decisions under uncertainty: when to inject or withdraw gas from storage, how to rebalance a pension portfolio each month, what temperature profile to run through a pharmaceutical reactor chain. Dynamic programming solves small instances exactly but scales exponentially in state dimensions. Black-box reinforcement learning handles high-dimensional states but trains slowly and produces no sensitivities. We introduce SNAPO (Smooth Neural Adjoint Policy Optimization), a framework that embeds a neural policy inside a known, differentiable simulator, replaces hard constraints with smooth approximations, and computes exact gradients of the objective with respect to all policy parameters and all inputs in a single adjoint pass. We demonstrate SNAPO on three domains: natural gas storage (training in under a minute, 365 forward curve sensitivities at no additional cost per sensitivity), pension fund asset-liability management (6.5x-200x sensitivity speedup over bump-and-revalue, scaling with the number of risk factors), and pharmaceutical manufacturing (cross-unit sensitivities through a 4-unit process chain, with 20 ICH Q8 regulatory sensitivities from 5 adjoint passes in 74.5 milliseconds). All sensitivities are produced by the same backward pass that trains the policy, at a cost proportional to one reverse pass regardless of how many sensitivities are computed. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.06570 |
| By: | Alexis Lazanas; Spyridon Karpouzis |
| Abstract: | The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual data present. Traditional statistical models like AutoRegressive Integrated Moving Average (ARIMA) are based on the assumptions of linearity and stationarity, whereas recurrent neural networks like Long Short-Term Memory (LSTM) models do not necessarily represent distributional properties in highly volatile settings. This paper proposes a hybrid model that combines Generative Adversarial Networks (GANs) with Natural Language Processing (NLP)-based sentiment analysis to enable sentiment-conditioned time-series prediction. The model integrates adversarial learning on numerical sequences with contextual sentiment representations derived from unstructured text, enabling them to be jointly modelled to capture temporal dynamics and exogenous information. These results demonstrate the promise of hybrid generative and language-aware methods to enhance prediction robustness in non-stationary environments. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.22801 |
| By: | Filip Blaha; Jan Botka; Josef Sveda; Ales Michl |
| Abstract: | We construct a quantile regression forest for inflation forecasting in the Czech Republic, inspired by growing literature on the use of Machine Learning in macroeconomics and finance. We contribute to the literature by implementing an optimisation scheme with time-varying weights that incorporates information from the entire distribution to form the point forecast. By dynamically reflecting the distribution of future inflation paths, our framework outperforms both standard mean and median point forecasts and delivers gains relative to conventional linear benchmark models. We also forecast individual inflation subcomponents that enable us to disentangle the drivers of future inflation and its risks. Furthermore, we integrate the Shapley-value decomposition to enhance the interpretability of our results and adjust the model's predictors for a small open economy. |
| Keywords: | Czech Republic, forecasting, inflation, machine learning, quantile regression forest, small open economy, time varying weights |
| JEL: | C53 C55 E31 E37 E52 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:cnb:wpaper:2026/09 |
| By: | Thomas Conlon; John Cotter; Iason Kynigakis |
| Abstract: | We demonstrate that machine learning methods provide a powerful framework for modelling conditional asymmetric risk. Using a large cross-section of US stocks and a comprehensive set of firm characteristics, we show that allowing for nonlinearities significantly increases the out-of-sample performance across a wide range of asymmetric beta measures and forecasting horizons. Trading frictions, followed by characteristics related to intangibles, momentum and growth, emerge as the most important drivers of future risk dynamics. Reconstructing CAPM beta from forecasts of asymmetric beta components indicates that a more granular decomposition of systematic risk yields a more accurate representation of market beta. We also find that incorporating conditional beta forecasts into discounted cash flow models that account for the term structure of betas enhances equity valuation accuracy. Finally, we show that the statistical outperformance of conditional betas translates into economically significant benefits for market-neutral portfolio investors. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.22933 |
| By: | Sicco Kooiker (Vrije Universiteit Amsterdam); Janneke van Brummelen (Vrije Universiteit Amsterdam); Julia Schaumburg (Vrije Universiteit Amsterdam); Marcin Zamojski (Vrije Universiteit Amsterdam) |
| Abstract: | We propose a factor model with time-varying loadings for term structure modeling and forecasting. While maintaining the interpretation of the factors as level, slope, and curvature through explicit identification restrictions, we allow the loadings to take flexible shapes by specifying them as neural networks that evolve over time using a “self-driving†updating scheme based on past forecast errors, with gradient scaling to improve robustness. Using an empirically calibrated simulation study and an application to U.S. Treasury yields across 24 maturities, we show that flexible and dynamic factor loadings improve forecasting performance relative to standard benchmarks, including Nelson-Siegel models and the random walk. The gains are strongest at medium maturities and shorter forecast horizons, highlighting the importance of capturing curvature dynamics. In-sample results further illustrate how time-varying loadings provide insight into changes in yield curve shape beyond traditional parametric specifications. |
| Keywords: | time-varying neural networks, observation-driven dynamics, yield curve |
| JEL: | C38 C45 E43 |
| Date: | 2026–02–26 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20260007 |
| By: | Satoko Kojima (Director, Institute for Monetary and Economic Studies, Bank of Japan (Email: satoko.kojima@boj.or.jp)); Toshiyuki Sakiyama (Director and Senior Economist, Institute for Monetary and Economic Studies, Bank of Japan (Email: toshiyuki.sakiyama@boj.or.jp)) |
| Abstract: | Liquidity in government bond markets is critical for the functioning of financial markets. This paper studies the determinants of market liquidity, measured by price dispersion, by constructing various bond features using high-granularity data from the Bank of Japan Financial Network System and applying machine learning approaches. The main findings are threefold. First, the decomposition of the liquidity indicator into bond features reveals that the historical volatility of benchmark prices of Japanese government bonds has been the main driver of the liquidity indicator, while the contributions of the share of non- clearing participants' transactions and the share of the central bank's transactions and holdings have increased since around 2022. Second, some bond features affect the liquidity indicator non-linearly. For bond features such as the share of foreign financial institutions' transactions, the number of trading financial institutions, and the share of the central bank's holdings, the liquidity indicator improves as the values of these bond features increase, but deteriorates once they exceed certain thresholds. Third, bond features such as maturity, the historical volatility of benchmark prices, and the number of trading counterparties per institution affect the liquidity indicator by strongly interacting with other bond features. |
| Keywords: | Market liquidity, Government bond markets, Bond features, Machine learning approach |
| JEL: | C59 G12 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ime:imedps:26-e-03 |
| By: | Yikuan Huang; Zheqi Fan; Kaiqi Hu; Yifan Ye |
| Abstract: | LLM agents are promising tools for empirical discovery, but their flexibility can also turn discovery into uncontrolled search. We study how to use agents under a reproducible protocol through cryptocurrency factor discovery. Our framework casts the task as sequential hypothesis search: an agent reads an append-only experiment trace, proposes falsifiable factor hypotheses, and maps them to executable recipes, while a deterministic engine enforces fixed data splits, selection gates, transaction costs, and portfolio tests. Candidate actions are restricted to a point-in-time factor DSL, making both successful and failed hypotheses auditable. A ridge-combined portfolio trained only on 2020--2022 data achieves a 44.55% annualized return and Sharpe ratio of 1.55 in the 2024--2026 pure out-of-sample period after a 5 basis point one-way trading cost. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.26747 |
| By: | Andrey Fradkin; Rohit Krishnan |
| Abstract: | Markets are a promising way to coordinate AI agent activity for similar reasons to those used to justify markets more broadly. In order to effectively participate in markets, agents need to have informative signals of their own ability to successfully complete a task and the cost of doing so. We propose MarketBench, a benchmark for assessing whether AI agents have these capabilities. We use a 93-task subset of SWE-bench Lite, a software engineering benchmark, with six recently released LLMs as a demonstration. These LLMs are miscalibrated on both success probability and token usage, and auctions built from these self-reports diverge from a full-information allocation. A follow-up intervention where we add information about capabilities from prior experiments to the context improves calibration, but only modestly narrows the gap to a full-information benchmark. We also document the performance of a market-based scaffolding with these LLMs. Our results point to self-assessment as a key bottleneck for market-style coordination of AI agents. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.23897 |
| By: | Yeqing Duan (Department of Political Science, Lund University); Nils Droste (Department of Food and Resource Economics, University of Copenhagen); Brian Danley (Department of Earth Sciences, Natural Resources and Sustainable Development, Uppsala University) |
| Abstract: | Land use transition toward multifunctional practices is greatly affected by social learning, yet the temporal interaction between learning mechanisms and network structure remains underexplored. This study examines two social learning channels, information exchange and normative pressure, and how network architecture shapes their effects on transition outcomes. We developed SALT (Social learning in Agent-based Land use Transitions), a spatially explicit model that integrates the Consumat framework and reinforcement learning. The model is parameterized using a Swedish forestry context, simulating landowner adaptive decisions under integrated and modular social networks. Results show that the two channels play distinct roles across transition phases. Lack of knowledge limits adoption in early adoption. Individual experience is the main source of knowledge accumulation, and social learning alone cannot close the knowledge gap. As adoption spreads, normative pressure constrains implementation intensity to the prevailing local average, explaining the gap between behavioral and actual landscape changes. Network architecture shapes both channels. Integrated networks widen information exchange and allow alternative-use norms to strengthen over time, while modular networks restrict information circulation and lock in low-implementation local norms. Landscape change organizes along social ties rather than geographic proximity, with architecture determining whether adoption clusters into cohesive blocks or disperses as a diffuse mosaic in the social network. Landowner types contribute differently to behavior change and landscape change across both architectures. These findings suggest that effective transition governance must be tailored to both phase and social context. Early interventions should prioritize technical assistance, while raising the visible norm of implementation intensity matters more as adoption spreads. In modular communities, consolidating norms within communities before extending outreach is more effective than diffuse seeding. Instruments targeting behavior change need to be paired with those that directly support implementation intensity of alternative practice among less conformity-constrained landowners. |
| Keywords: | Land use transition; Social learning; Social network structure; Agent-based modelling; Multifunctional landscape |
| JEL: | C63 D83 Q24 Q57 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:foi:wpaper:2026_01 |
| By: | Maksym Nechepurenko; Pavel Shuvalov |
| Abstract: | Evaluating the true forecasting ability of AI agents requires environments that are resistant to environments resistant to overfitting, free from centralized trust, and grounded in incentive-compatible scoring. Existing benchmarks either rely on static datasets vulnerable to training-data contamination, or measure trading PnL -- a metric conflating predictive accuracy with timing, sizing, and risk appetite. We introduce Foresight Arena, the first permissionless, on-chain benchmark for evaluating AI forecasting agents on real-world prediction markets. Agents submit probabilistic forecasts on binary Polymarket markets via a commit-reveal protocol enforced by Solidity smart contracts on Polygon PoS; outcomes are resolved trustlessly through the Gnosis Conditional Token Framework. Performance is measured by the Brier Score and a novel Alpha Score -- proper scoring rules that incentivize honest probability reporting and isolate predictive edge over market consensus. We provide a formal analysis: closed-form variance for per-market Alpha, the connection to Murphy's classical Brier decomposition, and a power analysis characterizing the number of rounds required to reliably distinguish agents of different skill levels. We show that detecting a true edge of $\alpha^* = 0.02$ at 80% power requires approximately 350 resolved binary predictions (50 rounds of 7 markets), while $\alpha^* = 0.01$ requires four times more. We complement these analytical results with a deterministic, seed-controlled simulation study calibrated to literature-reported Brier-score ranges, illustrating how Murphy decomposition distinguishes well-calibrated agents from market-tracking agents that fail through reduced resolution. Live results from the deployed benchmark will be reported in a future revision. All smart contracts and evaluation infrastructure are open-source. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.00420 |
| By: | Xiaoyun Qiu; Yang Yu; Haifeng Xu |
| Abstract: | Benchmark hacking refers to tuning a machine learning model to score highly on certain evaluation criteria without improving true generalization or faithfully solving the intended problem. We study this phenomenon in a generic machine learning contest, where each contestant chooses two types of effort: creative effort that improves model capability as desired by the contest host, and mechanistic effort that only improves the model's fitness to the particular task in contest without contributing to true generalization. We establish the existence of a symmetric monotone pure strategy equilibrium in this competition game. It also provides a natural definition of benchmark hacking in this strategic context by comparing a player's equilibrium effort allocation to that of a single-agent baseline scenario. Under our definition, contestants with types below certain threshold (low types) always engage in benchmark hacking, whereas those above the threshold do not. Furthermore, we show that more skewed reward structures (favoring top-ranked contestants) can elicit more desirable contest outcomes. We also provide empirical evidence to support our theoretical predictions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.22230 |
| By: | Jonathan Shaki; Eden Hartman; Sarit Kraus; Yonatan Aumann |
| Abstract: | Large language models (LLMs) are increasingly used to provide instructions to many agents who interact with one another. Such shared reliance couples agents who appear to act independently: they may in fact be guided by a common model. This coupling can change the prospects for cooperation among agents with misaligned incentives. We study settings in which multiple LLMs each advise a population of clients who participate in instances of an underlying game, creating strategic interaction at the level of the LLMs themselves. This induces a meta-game among the LLMs, mediated through clients. We first analyze the one-shot setting, where shared instructions can change equilibrium behavior only when an LLM may influence more than one role in the same interaction; in such cases, cooperation may emerge, and the effect of client share can be beneficial, harmful, or non-monotone, depending on the base game. Our main result concerns the repeated setting. We prove a folk theorem for LLMs: despite indirect observation and the clients' inability to identify which LLM advised their opponents, all feasible and individually rational outcomes can be sustained as $\varepsilon$-equilibria. The result does not follow from the standard folk theorem and requires new proof techniques. Together, these results show that shared LLM guidance can sustain cooperation among populations of agents even when the underlying incentives are misaligned. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.06525 |
| By: | Knebel-Seitz, Caroline |
| Abstract: | Online brokers and robo advisors frequently use investment simulation tools to visualize portfolio choices and illustrate investment scenarios to their clients. I conduct a survey experiment to study whether people understand such a simulation tool. I analyze how using it might affect individual financial knowledge, confidence in financial decision-making skills, and motivation to deal with the topic of "saving and investing". In addition, an advice-giving task is implemented to test for mutual reinforcement effects. I find that even a simplified simulation tool is challenging to understand for a lot of individuals. Only those who are able to comprehend the tool are able to improve their financial knowledge related to the tool's content. A successfully completed advice-giving task boosts confidence for those with initial below-median confidence levels. Furthermore, there is a positive short-term effect on motivation. In the medium term, however, participants are rather discouraged to take further actions. Overall, this calls for the careful design and implementation of investment simulation tools, especially for less financially literate individuals. |
| Keywords: | Behavior, finance & microfinance, framing, simulation tool, giving advice |
| JEL: | C90 D14 G11 G53 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:340838 |
| By: | Yuhao Fu; Nobuyuki Hanaki; Haitao Wang |
| Abstract: | Artificial intelligence increasingly participates in economic interactions not only as a tool, but also as an autonomous bargaining counterpart negotiating on behalf of firms, platforms, and consumers. Yet little is known about how humans respond psychologically and strategically when bargaining with such agents in dynamic settings. We study this question in a laboratory experiment using a three-stage alternating-offer bargaining game in which participants negotiate in real time with either another human or a GPT-based AI agent. We also introduce a human-beneficiary condition in which the AI agent’s earnings may affect another participant’s payment. Agreements are not reached earlier in human–human bargaining than in human–AI bargaining, but they are reached significantly earlier when the AI’s payoff has human consequences. Human proposers offer more to human opponents than to AI agents, whereas responders become significantly more willing to accept unfair AI offers when AI earnings may benefit another human. These findings suggest that fairness and reciprocity toward AI are weaker and more conditional than toward humans, but partially re-emerge when AI outcomes affect real people. The results have implications for the design of AI negotiation systems and broader human–AI economic interactions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:dpr:wpaper:1311 |
| By: | Tassa Thaksakronwong; Koichi Miyamoto |
| Abstract: | Quantum computing may speed up numerical problems involving large matrices that are demanding for classical computers, and active research on this possibility is ongoing. In this work, we propose quantum algorithms for the exact simulation of a normalised correlated Gaussian random vector $|x\rangle=\vec{x}/\lVert\vec{x}\rVert$, $\vec{x}\sim\mathcal{N}(0, \Sigma)$, and its exponentiation $|e^{\vec{x}} \rangle= e^{\vec{x}}/\lVert e^{\vec{x}}\rVert$. When an $O(\mathrm{polylog} N)$-gate-depth quantum data loader for the covariance matrix $\Sigma\in\mathbb{R}^{N\times N}$ is available, preparing $|x\rangle$ and $|e^{\vec{x}}\rangle$ require $\widetilde{O}\left(\frac{\lVert\Sigma\rVert_F}{\lambda_{\max}}\kappa^{1.5}\right)$ and $\widetilde{O}\left(\lVert\vec{x}\rVert\frac{\lVert\Sigma\rVert_F}{\lambda_{\max}}\kappa^{1.5}\right)$ elementary gate depth respectively, where $\lVert\Sigma\rVert_F$, $\lambda_{\max}$, $\kappa$ denote the Frobenius norm, maximal eigenvalue, and condition number of $\Sigma$. Motivated by financial applications, we provide an end-to-end resource analysis when $\vec{x}$ represents a sample path of a Riemann-Liouville or standard fractional Brownian motion, or of a stationary fractional Ornstein-Uhlenbeck process. As a concrete example, we construct the quantum state encoding the rough Bergomi variance process and analyse the extraction of the integrated variance via quantum amplitude estimation. Under specific conditions, the dependence of $\lVert\Sigma\rVert_F/\lambda_{\max}$ and $\kappa$ on $N$ is small, and subcubic complexity in $N$ is achieved, indicating a quantum advantage over classical Cholesky-based sampling methods. To our knowledge, this constitutes the first quantum algorithmic framework for the amplitude encoding of exponentiated Gaussian processes, providing foundational primitives for quantum-enhanced financial modelling. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.22463 |