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on Computational Economics |
| By: | Imad Talhartit (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research); Sanae Ait Jillali (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat); Mounime El Kabbouri |
| Abstract: | Capital markets play a fundamental role in the economy by facilitating the flow of funds between investors with capital surpluses and those with financing needs. However, these markets' inherent complexity and high volatility-amplified by economic crises and geopolitical events-make decision-making particularly challenging. In this context, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has become increasingly relevant for modeling complex financial time series such as stock prices. Among various learning approaches, Long Short-Term Memory (LSTM) networks stand out for their ability to capture long-term dependencies in sequential data. This study compares the predictive performance of LSTM and Artificial Neural Networks (ANN) models, on ten stocks comprising the MADEX index of the Casablanca Stock Exchange, across three forecasting horizons (10, 20, and 30 days). Results demonstrate that the LSTM model consistently outperforms the ANN model in terms of accuracy and trend detection. For instance, over a 30-day horizon, the LSTM correctly predicted 8 out of 10 stocks, compared to only 4 for the ANN. This work is part of a broader research effort aimed at identifying the most effective model for stock price forecasting. Building on the results of this and previous studies, particularly those involving LSTM models optimized using genetic algorithms, future research will explore other models such as Gated Recurrent Units (GRU) and Support Vector Machines (SVM) to further enhance prediction accuracy and robustness. |
| Keywords: | Stock price forecasting, Casablanca Stock Exchange, Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), Prediction accuracy |
| Date: | 2025–05–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05063012 |
| By: | Satyadhar Joshi (Bank of America, Touro University, Bar-Ilan University [Israël], Independent Researcher) |
| Abstract: | The rapid advancement of generative artificial intelligence (Gen AI) has revolutionized various domains, including financial analytics. This paper provides a comprehensive review of the applications, challenges, and future directions of Gen Al in financial analytics. We explore its role in risk management, credit scoring, feature engineering, and macroeconomic simulations, while addressing limitations such as data quality, interpretability, and ethical concerns. By synthesizing insights from recent literature, we highlight the transformative potential of Gen AI and propose frameworks for its effective integration into financial workflows. This paper presents a systematic examination of generative artificial intelligence (Gen AI) applications in financial risk management, focusing on architectural frameworks and implementation methodologies. We analyze the integration of large language models (LLMs) with traditional quantitative finance pipelines, addressing key challenges in feature engineering, risk modeling, and regulatory compliance. The study demonstrates how transformer-based architectures enhance financial analytics through automated data processing, risk factor extraction, and scenario generation. Technical implementations leverage hybrid cloud platforms and specialized Python libraries for model deployment, achieving measurable improvements in accuracy and efficiency. Our findings reveal critical considerations for production systems, including computational optimization, model interpretability, and governance protocols. The proposed architecture combines LLM capabilities with domain-specific modules for credit scoring, value-at-risk calculation, and macroeconomic simulation. Empirical results highlight trade-offs between model complexity and operational constraints, providing actionable insights for financial institutions adopting Gen Al solutions. The paper concludes with recommendations for future research directions in financial Al systems. |
| Keywords: | Generative AI financial analytics risk management credit scoring large language models feature engineering, Generative AI, financial analytics, risk management, credit scoring, large language models, feature engineering |
| Date: | 2025–05–29 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05101589 |
| By: | Emmanouil SOFIANOS; Thierry BETTI; Emmanouil Theophilos PAPADIMITRIOU; Amélie BARBIER-GAUCHARD; Periklis GOGAS |
| Abstract: | Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and low-frequency (e.g., quarterly/annual) data availability. This study proposes a novel hybrid framework integrating Dynamic Stochastic General Equilibrium (DSGE) modeling with ML techniques to address these limitations, focusing on the evolution of France’s public debt. We first generate a large synthetic macroeconomic dataset using an estimated DSGE model for France, which allows for efficient training of ML algorithms. These trained models are then applied to actual historical data for directional debt forecasting. The results show that the best machine learning model is an XGBoost achieving 90% accuracy. Our results highlight the viability of combining structural economic models with data-driven techniques to improve macroeconomic forecasting. |
| Keywords: | DSGE, Machine Learning, Public Debt, Forecasting, France. |
| JEL: | C53 E27 E37 H63 H68 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ulp:sbbeta:2025-18 |
| By: | Green, Alicia |
| Abstract: | The integration of artificial intelligence (AI) into financial intelligence systems enables automated risk detection and strategic decision support in African markets. This paper examines the technical architectures and AI methodologies (supervised learning, anomaly detection, natural language processing) employed in real-world African financial applications. We discuss data pipelines combining structured and unstructured data (market transactions, social media, news, macro indicators) and outline algorithmic models for credit risk, market risk, systemic risk, and financial crime detection. Specific cases from Nigeria, Kenya, and South Africa illustrate AI use in fraud/AML detection, credit scoring with alternative data, and portfolio stress-testing. Quantitative indicators (e.g., Nigeria’s NGN1.56 quadrillion digital payments in H1 2024 and 468\% surge in fraud cases) underscore the scale of data and risks. Regulatory contexts (e.g., CBN’s AI‑AML framework, SARB guidelines) and infrastructure constraints (limited data connectivity, power) are highlighted. The paper proposes a system framework comprising data integration, machine learning engines, continuous risk scoring, and visualization dashboards. Key applications include dynamic capital allocation, real-time AML monitoring, and scenario-based stress testing. We conclude by identifying ethical challenges (data privacy, model bias, transparency) and suggesting future directions such as hybrid AI-rule systems, localized language models, and cross-border data sharing platforms. |
| Date: | 2025–06–18 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:ynph2_v1 |
| By: | Ozili, Peterson K |
| Abstract: | Artificial intelligence is a branch of computer science that develop intelligent machines to perform human tasks. Recently, there is growing interest in AI applications in professions that have many processes that can be easily automated. There is widespread optimism that AI systems can lead to new innovations or improve existing processes. This study focuses on some applications of artificial intelligence in the accounting, finance, economics, business, and management profession. The study provides a basic understanding of how AI will be useful in the accounting, finance, economics, business and management professions. The study also offered some insights into the risks posed by the use of artificial intelligence. |
| Keywords: | Artificial intelligence, AI, machine learning, accounting, finance, economics, business, management. |
| JEL: | M1 M2 M5 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:125036 |
| By: | Winder, Philipp (University of St.Gallen) |
| Abstract: | This paper presents a novel capability-based framework for assessing organizational readiness in deploying large language models (LLMs) in the banking sector. While LLMs offer significant potential across domains such as customer service, compliance, and risk assessment, banks face unique deployment challenges due to regulatory constraints, legacy systems, and data sensitivity. Building on the dynamic capability view and adapting maturity levels from the Capability Maturity Model Integration (CMMI), the framework identifies and structures the organizational, contextual, and technical capabilities necessary for effective LLM deployment. It introduces a maturity-scaled self-assessment tool that enables banks to evaluate their current LLM readiness, diagnose capability gaps, and guide strategic investment decisions. Although developed for banking, the framework offers conceptual relevance to other high-stakes, highly regulated sectors. |
| Date: | 2025–06–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:zqsa3_v1 |
| By: | Walenta, Danilo C.; Sturm, Timo; Scholz, Yven; Buxmann, Peter |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:155372 |
| By: | Lukas Janasek (Institute of Economic Studies, Charles University, Prague, Czech Republic) |
| Abstract: | This paper develops a novel gradient-based reinforcement learning algorithm for solving dynamic quantile models with uncertainty. Unlike traditional approaches that rely on expected utility maximization, we focus on agents who evaluate outcomes based on specific quantiles of the utility distribution, capturing intratemporal risk attitudes via a quantile level ? ? (0, 1). We formulate a recursive quantile value function associated with time consistent dynamic quantile preferences in Markov decision process. At each period, the agent aims to maximize the quantile of a distribution composed of instantaneous utility combined with the discounted future value, conditioned on the current state. Next, we adapt the Actor-Critic framework to learn ?-quantile of the distribution and policy maximizing the ?-quantile. We demonstrate the accuracy and robustness of the proposed algorithm using an quantile intertemporal consumption model with known analytical solutions. The results confirm the effectiveness of our algorithm in capturing optimal quantile-based behavior and stability of the algorithm. |
| Keywords: | Dynamic programming, Quantile preferences, Reinforcement learning |
| JEL: | C61 C63 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:fau:wpaper:wp2025_12 |
| By: | Sophia Kazinnik; Erik Brynjolfsson |
| Abstract: | This paper examines how central banks can strategically integrate artificial intelligence (AI) to enhance their operations. Using a dual-framework approach, we demonstrate how AI can transform both strategic decision-making and daily operations within central banks, taking the Federal Reserve System (FRS) as a representative example. We first consider a top-down view, showing how AI can modernize key central banking functions. We then adopt a bottom-up approach focusing on the impact of generative AI on specific tasks and occupations within the Federal Reserve and find a significant potential for workforce augmentation and efficiency gains. We also address critical challenges associated with AI adoption, such as the need to upgrade data infrastructure and manage workforce transitions. |
| JEL: | C8 C9 G4 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33998 |
| By: | Kleijnen, Jack (Tilburg University, School of Economics and Management); Angun, Ebru; Nieuwenhuyse, Inneke Van; van Beers, Wim |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:77a5dd43-1a28-40d1-ac5c-b41fb4d3e9f5 |
| By: | Karim Moussa (Vrije Universiteit Amsterdam and Tinbergen Institute) |
| Abstract: | This paper introduces a novel approach to simulation smoothing for nonlinear and non-Gaussian state space models. It allows for computing smoothed estimates of the states and nonlinear functions of the states, as well as visualizing the joint smoothing distribution. The approach combines extremum estimation with simulated data from the model to estimate the conditional distributions in the backward smoothing decomposition. The method is generally applicable and can be paired with various estimators of conditional distributions. Several applications to nonlinear models are presented for illustration. An empirical application based on a stochastic volatility model with stable errors highlights the flexibility of the approach. |
| Date: | 2025–05–16 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250034 |
| By: | Sun, Yiqiao; de Bondt, Gabe |
| Abstract: | This study involves tasking ChatGPT with classifying an “activity sentiment score” based on PMI news releases. It explores the predictive power of this score for euro area GDP nowcasting. We find that the PMI text scores enhance GDP nowcasts beyond what is embedded in ECB/Eurosystem Staff projections and Eurostat’s first GDP estimate. The ChatGPT-derived activity score retains its significance in regressions that also include the composite output PMI diffusion index. GDP nowcasts are significantly enhanced with PMI text scores even when accounting for methodological variations, excluding extraordinary economic events like the pandemic, and for different GDP growth quantiles. However, the forecast gains from the enhancement of GDP nowcasts with ChatGPT scores are time dependent, varying by calendar years. Sizeable forecast gains of on average about 20% were obtained apart from the two most recent years due to exceptionally low forecast errors of the two benchmarks, especially the first GDP estimate. JEL Classification: C8, E32, C22 |
| Keywords: | chat generative pre-training transformer, nowcasting GDP, purchasing managers’ index (PMI), text analysis, zero-shot sentiment analysis |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253063 |