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on Computational Economics |
| By: | Adam Darmanin; Vince Vella |
| Abstract: | Algorithmic trading requires short-term decisions aligned with long-term financial goals. While reinforcement learning (RL) has been explored for such tactical decisions, its adoption remains limited by myopic behavior and opaque policy rationale. In contrast, large language models (LLMs) have recently demonstrated strategic reasoning and multi-modal financial signal interpretation when guided by well-designed prompts. We propose a hybrid system where LLMs generate high-level trading strategies to guide RL agents in their actions. We evaluate (i) the rationale of LLM-generated strategies via expert review, and (ii) the Sharpe Ratio (SR) and Maximum Drawdown (MDD) of LLM-guided agents versus unguided baselines. Results show improved return and risk metrics over standard RL. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02366 |
| By: | Caio de Souza Barbosa Costa; Anna Helena Reali Costa |
| Abstract: | Recently, reinforcement learning has achieved remarkable results in various domains, including robotics, games, natural language processing, and finance. In the financial domain, this approach has been applied to tasks such as portfolio optimization, where an agent continuously adjusts the allocation of assets within a financial portfolio to maximize profit. Numerous studies have introduced new simulation environments, neural network architectures, and training algorithms for this purpose. Among these, a domain-specific policy gradient algorithm has gained significant attention in the research community for being lightweight, fast, and for outperforming other approaches. However, recent studies have shown that this algorithm can yield inconsistent results and underperform, especially when the portfolio does not consist of cryptocurrencies. One possible explanation for this issue is that the commonly used state normalization method may cause the agent to lose critical information about the true value of the assets being traded. This paper explores this hypothesis by evaluating two of the most widely used normalization methods across three different markets (IBOVESPA, NYSE, and cryptocurrencies) and comparing them with the standard practice of normalizing data before training. The results indicate that, in this specific domain, the state normalization can indeed degrade the agent's performance. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.03910 |
| By: | W\v{e}i Zh\=ang |
| Abstract: | This paper explores neural network-based approaches for algorithmic trading in cryptocurrency markets. Our approach combines multi-timeframe trend analysis with high-frequency direction prediction networks, achieving positive risk-adjusted returns through statistical modeling and systematic market exploitation. The system integrates diverse data sources including market data, on-chain metrics, and orderbook dynamics, translating these into unified buy/sell pressure signals. We demonstrate how machine learning models can effectively capture cross-timeframe relationships, enabling sub-second trading decisions with statistical confidence. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02356 |
| By: | Longfei Lu |
| Abstract: | This work proposes that a vast majority of classical technical indicators in financial analysis are, in essence, special cases of neural networks with fixed and interpretable weights. It is shown that nearly all such indicators, such as moving averages, momentum-based oscillators, volatility bands, and other commonly used technical constructs, can be reconstructed topologically as modular neural network components. Technical Indicator Networks (TINs) are introduced as a general neural architecture that replicates and structurally upgrades traditional indicators by supporting n-dimensional inputs such as price, volume, sentiment, and order book data. By encoding domain-specific knowledge into neural structures, TINs modernize the foundational logic of technical analysis and propel algorithmic trading into a new era, bridging the legacy of proven indicators with the potential of contemporary AI systems. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.20202 |
| By: | Maciej Wysocki; Pawe{\l} Sakowski |
| Abstract: | This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.14999 |
| By: | Orr Shahar; Stefan Lessmann; Daniel Traian Pele |
| Abstract: | Relationships between the energy and the finance markets are increasingly important. Understanding these relationships is vital for policymakers and other stakeholders as the world faces challenges such as satisfying humanity's increasing need for energy and the effects of climate change. In this paper, we investigate the causal effect of electricity market liberalization on the electricity price in the US. By performing this analysis, we aim to provide new insights into the ongoing debate about the benefits of electricity market liberalization. We introduce Causal Machine Learning as a new approach for interventions in the energy-finance field. The development of machine learning in recent years opened the door for a new branch of machine learning models for causality impact, with the ability to extract complex patterns and relationships from the data. We discuss the advantages of causal ML methods and compare the performance of ML-based models to shed light on the applicability of causal ML frameworks to energy policy intervention cases. We find that the DeepProbCP framework outperforms the other frameworks examined. In addition, we find that liberalization of, and individual players' entry to, the electricity market resulted in a 7% decrease in price in the short term. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12331 |
| By: | Hess, Dieter; Simon, Frederik; Weibels, Sebastian |
| Abstract: | We predict earnings for forecast horizons of up to five years by using the entire set of Compustat financial statement data as input and providing it to state-of-the-art machine learning models capable of approximating arbitrary functional forms. Our approach improves prediction one year ahead by an average of 11% compared to the traditional linear approach that performs best. This superior performance is consistent across a variety of evaluation metrics as well as different firm subsamples and translates into more profitable investment strategies. Extensive model interpretation reveals that income statement variables, especially different definitions of earnings, are by far the most important predictors. Conversely, we find that while income statement variables decline in relevance, balance sheet information becomes more significant as the forecast horizon extends. Lastly, we show that the influence of interactions and non- linearities on the machine learning forecast is modest, but substantial differences between firm subsamples exist. |
| Keywords: | Earnings Forecasts, Cross-Sectional Earnings Models, Machine Learning |
| JEL: | G11 G12 G17 G31 G32 M40 M41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cfrwps:323935 |
| By: | Yu Shi; Zongliang Fu; Shuo Chen; Bohan Zhao; Wei Xu; Changshui Zhang; Jian Li |
| Abstract: | The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis. Our pre-trained model is publicly available at https://github.com/shiyu-coder/Kronos. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02739 |
| By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.); Mounime El Kabbouri (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Research in Finance, Audit and Governance of Organizations (LARFAGO) - National School of Business and Management – ENCG Settat, Hassan The First University, Settat, Morocco.) |
| Abstract: | This study is part of an empirical and quantitative approach aimed at improving stock market fluctuation forecasting through the application of artificial intelligence models. More specifically, it evaluates the performance of two methods based on Long Short-Term Memory (LSTM) neural networks, one of the most powerful algorithms for analyzing financial time series. The first method is grounded in a classic LSTM model, while the second incorporates hyperparameter optimization using the Particle Swarm Optimization (PSO) metaheuristic method, allowing for better convergence and enhanced prediction accuracy. The study is conducted on ten stocks representing the US S&P 500 index, with historical data spanning several decades, collected via the Investing.com and Yahoo Finance platforms. The empirical results demonstrate a clear superiority of the LSTM-PSO model regarding predictive accuracy, with significant reductions in errors (MSE, RMSE, MAE, MSLE, and RMSLE) compared to the traditional model. These findings emphasize the advantages of combining artificial intelligence and algorithmic optimization for handling complex financial data. In the global context of digitization and automation of investment decisions, this research contributes significantly to the development of reliable predictive systems. Finally, the study raises the question of whether this methodological framework could be effectively adapted to emerging markets, such as the Moroccan Stock Market, where financial environments are characterized by lower trading volumes, different volatility patterns, and more limited historical data. This opens up avenues for future research into the challenges and opportunities of applying advanced AI-based forecasting models in less mature financial markets. |
| Keywords: | stock market forecasting, artificial intelligence, LSTM neural networks, Particle Swarm Optimization, financial time series, predictive modeling |
| Date: | 2025–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05177777 |
| By: | Tomu Hirata; Undral Byambadalai; Tatsushi Oka; Shota Yasui; Shingo Uto |
| Abstract: | We propose a novel multi-task neural network approach for estimating distributional treatment effects (DTE) in randomized experiments. While DTE provides more granular insights into the experiment outcomes over conventional methods focusing on the Average Treatment Effect (ATE), estimating it with regression adjustment methods presents significant challenges. Specifically, precision in the distribution tails suffers due to data imbalance, and computational inefficiencies arise from the need to solve numerous regression problems, particularly in large-scale datasets commonly encountered in industry. To address these limitations, our method leverages multi-task neural networks to estimate conditional outcome distributions while incorporating monotonic shape constraints and multi-threshold label learning to enhance accuracy. To demonstrate the practical effectiveness of our proposed method, we apply our method to both simulated and real-world datasets, including a randomized field experiment aimed at reducing water consumption in the US and a large-scale A/B test from a leading streaming platform in Japan. The experimental results consistently demonstrate superior performance across various datasets, establishing our method as a robust and practical solution for modern causal inference applications requiring a detailed understanding of treatment effect heterogeneity. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.07738 |
| By: | Akash Deep; Chris Monico; W. Brent Lindquist; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume frictionless markets, overlooking empirical features such as bid-ask spreads, discrete price movements, and serial return correlations. Our framework augments the binomial tree with path-dependent transition probabilities estimated via Random Forest classifiers trained on high-frequency market data. This approach preserves no-arbitrage conditions while embedding real-world trading dynamics into the pricing model. Using 46, 655 minute-level observations of SPY from January to June 2025, we achieve an AUC of 88.25% in forecasting one-step price movements. Order flow imbalance is identified as the most influential predictor, contributing 43.2% to feature importance. After resolving time-scaling inconsistencies in tree construction, our model yields option prices that deviate by 13.79% from Black-Scholes benchmarks, highlighting the impact of microstructure on fair value estimation. While computational limitations restrict the model to short-term derivatives, our results offer a robust, data-driven alternative to classical pricing methods grounded in empirical market behavior. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16701 |
| By: | Craig S Wright |
| Abstract: | This paper presents a praxeological analysis of artificial intelligence and algorithmic governance, challenging assumptions about the capacity of machine systems to sustain economic and epistemic order. Drawing on Misesian a priori reasoning and Austrian theories of entrepreneurship, we argue that AI systems are incapable of performing the core functions of economic coordination: interpreting ends, discovering means, and communicating subjective value through prices. Where neoclassical and behavioural models treat decisions as optimisation under constraint, we frame them as purposive actions under uncertainty. We critique dominant ethical AI frameworks such as Fairness, Accountability, and Transparency (FAT) as extensions of constructivist rationalism, which conflict with a liberal order grounded in voluntary action and property rights. Attempts to encode moral reasoning in algorithms reflect a misunderstanding of ethics and economics. However complex, AI systems cannot originate norms, interpret institutions, or bear responsibility. They remain opaque, misaligned, and inert. Using the concept of epistemic scarcity, we explore how information abundance degrades truth discernment, enabling both entrepreneurial insight and soft totalitarianism. Our analysis ends with a civilisational claim: the debate over AI concerns the future of human autonomy, institutional evolution, and reasoned choice. The Austrian tradition, focused on action, subjectivity, and spontaneous order, offers the only coherent alternative to rising computational social control. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01483 |
| By: | Del Frari, Elisa; Aassve, Arnstein; Melegaro, Alessia (Bocconi University) |
| Abstract: | Ongoing demographic changes driven by increased life expectancy and declining fertility rates are starting to exert pressure on Pay-As-You-Go pension schemes, which depend on the transfer of resources from the employed population to the retired one. Existing research presents mixed conclusions on the effectiveness of various policy measures designed to ensure the long-term fiscal sustainability of pension systems. This paper contributes to the literature by employing a tailored Agent-Based Model (ABM) for Italy, which integrates demographic and pension dynamics. The model evaluates the impact of policies aimed at increasing labor market participation, specifically reducing the number of NEETs, boosting female labor force participation, introducing more flexible retirement options, increasing immigration and raising fertility rates. Projections extending to 2070 indicate that the aging process will persist, leading to a continued deterioration in the fiscal balance of the Italian pension system, despite the automatic adjustments to the retirement age linked to variations in life expectancy. The results indicate that promoting labour participation significantly enhances the sustainability of the pension system. In particular, policies aimed at increasing female participation emerge as the most effective individual intervention. However, no single measure, nor any combination of the simulated policies, is sufficient to place the Italian pension system to a fully sustainable trajectory. |
| Date: | 2025–08–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:g2xqt_v1 |
| By: | Ivan Letteri |
| Abstract: | Purpose: This study introduces a novel framework for identifying and exploiting predictive lead-lag relationships in financial markets. We propose an integrated approach that combines advanced statistical methodologies with machine learning models to enhance the identification and exploitation of predictive relationships between equities. Methods: We employed a Gaussian Mixture Model (GMM) to cluster nine prominent stocks based on their mid-range historical volatility profiles over a three-year period. From the resulting clusters, we constructed a multi-stage causal inference pipeline, incorporating the Granger Causality Test (GCT), a customised Peter-Clark Momentary Conditional Independence (PCMCI) test, and Effective Transfer Entropy (ETE) to identify robust, predictive linkages. Subsequently, Dynamic Time Warping (DTW) and a K-Nearest Neighbours (KNN) classifier were utilised to determine the optimal time lag for trade execution. The resulting strategy was rigorously backtested. Results: The proposed volatility-based trading strategy, tested from 8 June 2023 to 12 August 2023, demonstrated substantial efficacy. The portfolio yielded a total return of 15.38%, significantly outperforming the 10.39% return of a comparative Buy-and-Hold strategy. Key performance metrics, including a Sharpe Ratio up to 2.17 and a win rate up to 100% for certain pairs, confirmed the strategy's viability. Conclusion: This research contributes a systematic and robust methodology for identifying profitable trading opportunities derived from volatility-based causal relationships. The findings have significant implications for both academic research in financial modelling and the practical application of algorithmic trading, offering a structured approach to developing resilient, data-driven strategies. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09347 |
| By: | Facundo Arga\~naraz; Juan Carlos Escanciano |
| Abstract: | Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedures for valid inference on functionals of UH, allowing for partial identification of multivariate target and high-dimensional nuisance parameters. Our main contribution is a full characterization of all relevant Neyman-orthogonal moments in models with nonparametric UH, where relevance means informativeness about the parameter of interest. Under additional support conditions, orthogonal moments are globally robust to the distribution of the UH. They may still involve other high-dimensional nuisance parameters, but their local robustness reduces regularization bias and enables valid DML inference. We apply these results to: (i) common parameters, average marginal effects, and variances of UH in panel data models with high-dimensional controls; (ii) moments of the common factor in the Kotlarski model with a factor loading; and (iii) smooth functionals of teacher value-added. Monte Carlo simulations show substantial efficiency gains from using efficient orthogonal moments relative to ad-hoc choices. We illustrate the practical value of our approach by showing that existing estimates of the average and variance effects of maternal smoking on child birth weight are robust. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.13788 |
| By: | Joshua Aslett; Thomas Cantens; François Chastel; Emmanuel A Crown; Stuart Hamilton |
| Abstract: | This technical note provides an introduction to generative artificial intelligence (GenAI) and its potential to support compliance risk analysis in tax and customs administration. Written primarily for a technical audience, it seeks to raise awareness of GenAI by explaining and demonstrating its capabilities. The note opens with a brief conceptual overview of GenAI technology. It then describes four generalized use cases where GenAI can augment the work of risk analysts. As experimental proofs of concept, a selection of worked examples is presented. Having demonstrated GenAI’s potential, the note then provides basic guidelines to help administrations that may be considering implementing the technology in an operational setting. It concludes with forward-looking statements on likely developments. |
| Keywords: | Tax administration; customs administration; artificial intelligence |
| Date: | 2025–08–08 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imftnm:2025/013 |
| By: | Davide Veronelli; Francesca Cibrario; Emanuele Dri; Valeria Zaffaroni; Giacomo Ranieri; Davide Corbelletto; Bartolomeo Montrucchio |
| Abstract: | The analysis of credit risk is crucial for the efficient operation of financial institutions. Quantum Amplitude Estimation (QAE) offers the potential for a quadratic speed-up over classical methods used to estimate metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). However, numerous limitations remain in efficiently scaling the implementation of quantum circuits that solve these estimation problems. One of the main challenges is the use of costly and restrictive arithmetic that must be implemented within the quantum circuit. In this paper, we propose using Quantum Singular Value Transformation (QSVT) to significantly reduce the cost of implementing the state preparation operator, which underlies QAE for credit risk analysis. We also present an end-to-end code implementation and the results of a simulation study to validate the proposed approach and demonstrate its benefits. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.19206 |
| By: | Anastasis Kratsios; Ariel Neufeld; Philipp Schmocker |
| Abstract: | Neural operators (NOs) are a class of deep learning models designed to simultaneously solve infinitely many related problems by casting them into an infinite-dimensional space, whereon these NOs operate. A significant gap remains between theory and practice: worst-case parameter bounds from universal approximation theorems suggest that NOs may require an unrealistically large number of parameters to solve most operator learning problems, which stands in direct opposition to a slew of experimental evidence. This paper closes that gap for a specific class of {NOs}, generative {equilibrium operators} (GEOs), using (realistic) finite-dimensional deep equilibrium layers, when solving families of convex optimization problems over a separable Hilbert space $X$. Here, the inputs are smooth, convex loss functions on $X$, and outputs are the associated (approximate) solutions to the optimization problem defined by each input loss. We show that when the input losses lie in suitable infinite-dimensional compact sets, our GEO can uniformly approximate the corresponding solutions to arbitrary precision, with rank, depth, and width growing only logarithmically in the reciprocal of the approximation error. We then validate both our theoretical results and the trainability of GEOs on three applications: (1) nonlinear PDEs, (2) stochastic optimal control problems, and (3) hedging problems in mathematical finance under liquidity constraints. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.14995 |
| By: | Jakub Karnowski; Przemys{\l}aw Szufel |
| Abstract: | Oligarchic control exerts significant distortions on economic efficiency. Ukraine exemplifies this phenomenon, where oligarchs dominate key sectors and achieve economies of scale through vertical integration of coal mines, steel mills, and power plants while controlling critical infrastructure (e.g. access to transportation networks) to stifle competition. Their Soviet-era production chain monopolization strategies, coupled with political patronage networks (including both local and national governments), reinforce systemic inefficiencies and barriers to market entry. Although existing studies highlight the developmental benefits of de-oligarchization, this work advances the literature through computational modeling. We develop an agent-based model of a partially oligarch-controlled economy, where firms with heterogeneous production functions interact within a value-added network. Through numerical simulations, we quantify how different de-oligarchization policies affect aggregate GDP growth. The results indicate that the optimal de-oligarchization strategies are determined by the position of the oligarch in the production chain. Depending on the oligarch's position, dismantling oligarchic structures should either focus on removing oligarchs' access to raw materials or on breaking oligarchs' influence on other transactions in the production chain. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02949 |
| By: | Thomas Hazenberg; Yao Ma; Seyed Sahand Mohammadi Ziabari; Marijn van Rijswijk |
| Abstract: | This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied reinforcement learning to pricing, most implementations remain single-agent and fail to model the interdependent nature of real-world supply chains. This study addresses that gap by evaluating the performance of three MARL algorithms: MADDPG, MADQN, and QMIX against static rule-based baselines, within a simulated environment informed by real e-commerce transaction data and a LightGBM demand prediction model. Results show that rule-based agents achieve near-perfect fairness (Jain's Index: 0.9896) and the highest price stability (volatility: 0.024), but they fully lack competitive dynamics. Among MARL agents, MADQN exhibits the most aggressive pricing behaviour, with the highest volatility and the lowest fairness (0.5844). MADDPG provides a more balanced approach, supporting market competition (share volatility: 9.5 pp) while maintaining relatively high fairness (0.8819) and stable pricing. These findings suggest that MARL introduces emergent strategic behaviour not captured by static pricing rules and may inform future developments in dynamic pricing. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.02698 |
| By: | Easton, Peter; Kapons, Martin (Tilburg University, School of Economics and Management); Monahan, S.; Schütt, Harm (Tilburg University, School of Economics and Management); Weisbrod, Eric H. |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:47b3dffb-5015-44a5-8519-f1c5c94a835c |
| By: | Egil Diau |
| Abstract: | A central challenge in economics and artificial intelligence is explaining how financial behaviors-such as credit, insurance, and trade-emerge without formal institutions. We argue that these functions are not products of institutional design, but structured extensions of a single behavioral substrate: reciprocity. Far from being a derived strategy, reciprocity served as the foundational logic of early human societies-governing the circulation of goods, regulation of obligation, and maintenance of long-term cooperation well before markets, money, or formal rules. Trade, commonly regarded as the origin of financial systems, is reframed here as the canonical form of reciprocity: simultaneous, symmetric, and partner-contingent. Building on this logic, we reconstruct four core financial functions-credit, insurance, token exchange, and investment-as expressions of the same underlying principle under varying conditions. By grounding financial behavior in minimal, simulateable dynamics of reciprocal interaction, this framework shifts the focus from institutional engineering to behavioral computation-offering a new foundation for modeling decentralized financial behavior in both human and artificial agents. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.00099 |