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on Computational Economics |
| By: | Nicolas Baradel |
| Abstract: | In incomplete financial markets, pricing and hedging European options lack a unique no-arbitrage solution due to unhedgeable risks. This paper introduces a constrained deep learning approach to determine option prices and hedging strategies that minimize the Profit and Loss (P&L) distribution around zero. We employ a single neural network to represent the option price function, with its gradient serving as the hedging strategy, optimized via a loss function enforcing the self-financing portfolio condition. A key challenge arises from the non-smooth nature of option payoffs (e.g., vanilla calls are non-differentiable at-the-money, while digital options are discontinuous), which conflicts with the inherent smoothness of standard neural networks. To address this, we compare unconstrained networks against constrained architectures that explicitly embed the terminal payoff condition, drawing inspiration from PDE-solving techniques. Our framework assumes two tradable assets: the underlying and a liquid call option capturing volatility dynamics. Numerical experiments evaluate the method on simple options with varying non-smoothness, the exotic Equinox option, and scenarios with market jumps for robustness. Results demonstrate superior P&L distributions, highlighting the efficacy of constrained networks in handling realistic payoffs. This work advances machine learning applications in quantitative finance by integrating boundary constraints, offering a practical tool for pricing and hedging in incomplete markets. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.20837 |
| By: | Tan, Fuli; Wang, Jingjing; De Steur, Hans; Fan, Shenggen |
| Keywords: | Marketing, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:344020 |
| By: | Zhao, Jing; Cochrane, Mark; Zhang, Xin; Elmore, Andrew; Lee, Janice; Su, Ye |
| Keywords: | Land Economics/Use, Environmental Economics and Policy, Community/Rural/Urban Development |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea24:344016 |
| By: | David Autor; Andrew Caplin; Daniel J. Martin; Philip Marx |
| Abstract: | The cost of error in many high-stakes settings is asymmetric: misdiagnosing pneumonia when absent is an inconvenience, but failing to detect it when present can be life-threatening. Accordingly, artificial intelligence (AI) models used to assist such decisions are frequently trained with asymmetric loss functions that incorporate human decision-makers' trade-offs between false positives and false negatives. In two focal applications, we show that this standard alignment practice can backfire. In both cases, it would be better to train the machine learning model with a loss function that ignores the human’s objective and then adjust predictions ex post according to that objective. We rationalize this result using an economic model of incentive design with endogenous information acquisition. The key insight from our theoretical framework is that machine classifiers perform not one but two incentivized tasks: choosing how to classify and learning how to classify. We show that while the adjustments engineers use correctly incentivize choosing, they can simultaneously reduce the incentives to learn. Our formal treatment of the problem reveals that methods embraced for their intuitive appeal can in fact misalign human and machine objectives in predictable ways. |
| JEL: | C1 D8 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34504 |
| By: | Fermat Leukam; Rock Stephane Koffi; Prudence Djagba |
| Abstract: | This research proposes an enhancement to the innovative portfolio optimization approach using the G-Learning algorithm, combined with parametric optimization via the GIRL algorithm (G-learning approach to the setting of Inverse Reinforcement Learning) as presented by. The goal is to maximize portfolio value by a target date while minimizing the investor's periodic contributions. Our model operates in a highly volatile market with a well-diversified portfolio, ensuring a low-risk level for the investor, and leverages reinforcement learning to dynamically adjust portfolio positions over time. Results show that we improved the Sharpe Ratio from 0.42, as suggested by recent studies using the same approach, to a value of 0.483 a notable achievement in highly volatile markets with diversified portfolios. The comparison between G-Learning and GIRL reveals that while GIRL optimizes the reward function parameters (e.g., lambda = 0.0012 compared to 0.002), its impact on portfolio performance remains marginal. This suggests that reinforcement learning methods, like G-Learning, already enable robust optimization. This research contributes to the growing development of reinforcement learning applications in financial decision-making, demonstrating that probabilistic learning algorithms can effectively align portfolio management strategies with investor needs. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.18076 |
| By: | Thomas R. Cook; Sophia Kazinnik; Zach Modig; Nathan M. Palmer |
| Abstract: | Large language models (LLMs) are now used for economic reasoning, but their implicit "preferences” are poorly understood. We study LLM preferences as revealed by their choices in simple allocation games and a job-search setting. Most models favor equal splits in dictator-style allocation games, consistent with inequality aversion. Structural estimates recover Fehr–Schmidt parameters that indicate inequality aversion is stronger than in similar experiments with human participants. However, we find these preferences are malleable: reframing (e.g., masking social context) and learned control vectors shift choices toward payoff-maximizing behavior, while personas move them less effectively. We then turn to a more complex economic scenario. Extending a McCall job search environment, we also recover effective discounting from accept/reject policies, but observe that model responses may not always be rationalizable, and in some cases suggest inconsistent preferences. Efforts to steer LLM responses in the McCall scenario are also less consistent. Together, our results suggest (i) LLMs exhibit latent preferences that may not perfectly align with typical human preferences and (ii) LLMs can be steered toward desired preferences, though this is more difficult with complex economic tasks. |
| Keywords: | large language models; Simulation modeling |
| JEL: | C63 C68 C61 D14 D83 D91 E20 E21 |
| Date: | 2025–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:102166 |
| By: | Zazueta, Jorge |
| Abstract: | Agent-based models (ABMs) are standard tools for modeling social and physical phenomena from the ground up by building detailed simulations in which individual agents interact and study emergent behavior. However, since they are simulations, it is challenging to generalize or even quantitatively interpret the results. A frequent output of an ABM model is a grid with points of one or more classes that represent the agents’ configuration over time. A canonical example is the Schelling segregation model, where agents of two types follow a simple relocation rule based on their tolerance to the proportion of different agents in a given location, resulting in a segregated configuration that is visually revealing but not quantitative. In this work, we propose assigning a quantitative measure of entropy, based on the spatial configuration of the steady state of the Schelling model, to a range of population values in the model using Topological Data Analysis (TDA) techniques. The resulting dataset of quantitative metrics related to the original configuration is analyzed via Sparse Identification of Nonlinear Dynamics (SINDy) methods to obtain a representation of the system dynamics in the form of an ordinary differential equation. |
| Date: | 2025–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:dr857_v1 |
| By: | Aravantinos, Elias; Varoutas, Dimitris |
| Abstract: | This study utilizes machine learning (ML) techniques to identify critical factors affecting broadband diffusion and its impact on economic growth in emerging economies. A K-means clustering algorithm has been applied to classify 29 emerging and developing economies using factors such as Information and Communication Technology (ICT) infrastructure, broadband adoption, and foreign direct investment (FDI). Several predictive ML models, such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), have been employed to assess the economic effects of broadband adoption and the key drivers of digitaldriven growth. The analysis reveals the significant correlations between ICT development, broadband penetration, FDI, and economic growth, highlighting the critical role of digital infrastructure and targeted policy interventions in fostering sustainable economic development in emerging economies. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itse25:331250 |
| By: | Cecchetti, Stephen G.; Lumsdaine, Robin L.; Peltonen, Tuomas; Sánchez Serrano, Antonio |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:srk:srkasc:202516 |
| By: | Ciftci, Muhsin; Wieland, Elisabeth |
| Abstract: | In this paper, we evaluate a set of measures of underlying inflation for Germany using conventional measures, such as core inflation (excluding energy and food items), and alternative measures based on econometric models, machine learning, and micro-price evidence. We compare these measures through detailed in-sample and out-of-sample evaluations. The alternative measures exhibit lower volatility, minimal bias, and superior out-of-sample forecasting accuracy performance. While we find no evidence that any single measure clearly outperforms the others over time, the range of alternatives measures also reflects a somewhat earlier uptick and downturn in light of the recent inflation surge in comparison to traditional ones. In addition, all measures under consideration are highly sensitive to monetary policy shocks. |
| Keywords: | Underlying inflation, monetary policy, local projections, machine learning |
| JEL: | E31 E37 C22 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubtps:333424 |
| By: | Li, Jia PhD; Qi, Yanlin; Zhang, Michael PhD |
| Abstract: | Emerging machine learning capabilities can be leveraged to make transportation infrastructure safer and reduce fatalities by informing decisions about which countermeasures to apply at crash-prone locations. At this time, project prioritization typically involves assessing effectiveness, cost-benefit ratios, and available funding. Crash Modification Factors (CMFs) play an essential role in project assessment by predicting the effectiveness of safety countermeasures. Their applicability has limitations, however. Some of these may be overcome with innovative approaches such as knowledge-mining. |
| Keywords: | Engineering |
| Date: | 2025–08–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:itsdav:qt0x26t67j |
| By: | Giuseppe Matera |
| Abstract: | Economic behavior is shaped not only by quantitative information but also by the narratives through which such information is communicated and interpreted (Shiller, 2017). I show that narratives extracted from earnings calls significantly improve the prediction of both realized earnings and analyst expectations. To uncover the underlying mechanisms, I introduce a novel text-morphing methodology in which large language models generate counterfactual transcripts that systematically vary topical emphasis (the prevailing narrative) while holding quantitative content fixed. This framework allows me to precisely measure how analysts under- and over-react to specific narrative dimensions. The results reveal systematic biases: analysts over-react to sentiment (optimism) and under-react to narratives of risk and uncertainty. Overall, the analysis offers a granular perspective on the mechanisms of expectation formation through the competing narratives embedded in corporate communication. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.15214 |