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on Computational Economics |
| By: | Christian Fieberg; Lars Hornuf; Maximilian Meiler; David J. Streich |
| Abstract: | We study whether large language models (LLMs) can generate suitable financial advice and which LLM features are associated with higher-quality advice. To this end, we elicit portfolio recommendations from 32 LLMs for 64 investor profiles, which differ in their risk preferences, home country, sustainability preferences, gender, and investment experience. Our results suggest that LLMs are generally capable of generating suitable financial advice that takes into account important investor characteristics when determining market and risk exposures. The historical performance of the recommended portfolios is on par with that of professionally managed benchmark portfolios. We also find that foundation models and larger models generate portfolios that are easier to implement and more sensitive to investor characteristics than fine-tuned models and smaller models. Some of our results are consistent with LLMs inheriting human biases such as home bias. We find no evidence of gender-based discrimination, which can be found in human financial advice. |
| Keywords: | generative AI, artificial intelligence, large language models, financial advice portfolio management |
| JEL: | G00 G11 G40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11666 |
| By: | Arnav Grover |
| Abstract: | In response to Task II of the FinRL Challenge at ACM ICAIF 2024, this study proposes a novel prompt framework for fine-tuning large language models (LLM) with Reinforcement Learning from Market Feedback (RLMF). Our framework incorporates market-specific features and short-term price dynamics to generate more precise trading signals. Traditional LLMs, while competent in sentiment analysis, lack contextual alignment for financial market applications. To bridge this gap, we fine-tune the LLaMA-3.2-3B-Instruct model using a custom RLMF prompt design that integrates historical market data and reward-based feedback. Our evaluation shows that this RLMF-tuned framework outperforms baseline methods in signal consistency and achieving tighter trading outcomes; awarded as winner of Task II. You can find the code for this project on GitHub. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01992 |
| By: | Ziyao Zhou; Ronitt Mehra |
| Abstract: | This project introduces an end-to-end trading system that leverages Large Language Models (LLMs) for real-time market sentiment analysis. By synthesizing data from financial news and social media, the system integrates sentiment-driven insights with technical indicators to generate actionable trading signals. FinGPT serves as the primary model for sentiment analysis, ensuring domain-specific accuracy, while Kubernetes is used for scalable and efficient deployment. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01574 |
| By: | Viet Trinh |
| Abstract: | Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00151 |
| By: | Phoebe Koundouri; Conrad Landis; Georgios Feretzakis |
| Abstract: | Machine Learning (ML) and Artificial Intelligence (AI) have become powerful tools for overcoming complex global challenges in harmony with the Sustainable Development Goals (SDGs) of the United Nations. In this article, we illustrate ML and AI technology's contribution to sustainable development through theoretical and practical examples in a variety of sectors. In this article, AI-powered interventions in healthcare, agriculture, greenhouse gas emission reduction, environment tracking, and education have been analyzed. Generative AI technology has changed access to education and personalized learning, and environmental tracking and conservation have been aided through machine learning algorithms. Despite such positive development, considerable obstacles include a lack of data, algorithm bias, ethics, and interpretability of complex AI algorithms. All such impediments remind us of multi-sectoral collaboration and responsible AI intervention for delivering equitable and sustainable development. According to the article, overcoming obstacles necessitates transparent and participatory frameworks and deliberate collaborations between governments, private industries, academe, and civil society groups. With full realization of ML and AI through ethics and participatory policies, we can mobilize effective, evidence-guided interventions and hasten success towards attaining the SDGs. With a demand for ongoing studies in case files for responsible AI interventions with a strong bias for equity, consideration, and humanity, in this article, a clarion call for such studies is placed. |
| Date: | 2025–02–07 |
| URL: | https://d.repec.org/n?u=RePEc:aue:wpaper:2522 |
| By: | Kamlakshya, Tikhnadhi (Citizens Bank); Hota, Ashish |
| Abstract: | This paper introduces a novel framework for multimodal document intelligence, designed to enhance fraud prevention across various sectors. The core innovation lies in the integration of advanced AI and ML techniques, including OCR, deep learning, and NLP, within a purpose-built computer device for multimodal data fusion, as detailed in the author's recently granted patent by www.gov.uk/ [Intellectual Property# 6419907]. This device facilitates the seamless integration of textual, visual, and metadata elements extracted from documents, enabling a holistic understanding of the document's veracity and intent. The escalating sophistication of fraudulent activities across industries necessitates advanced, adaptive security measures. This paper presents a novel framework for multimodal document intelligence, designed to enhance fraud prevention in sectors such as banking and finance, life science and healthcare, government, and the public sector. Grounded in a recently patented AI and ML-enabled computer device for multimodal data fusion, the framework leverages Optical Character Recognition (OCR), deep learning-based image analysis, and natural language processing (NLP). Furthermore, it integrates the capabilities of DeepSeek-R1, a high-performance Mixture-of-Experts (MoE) large language model (LLM), and autonomous AI Agents for advanced reasoning, contextual understanding, and decision-making. This integrated approach facilitates proactive fraud detection, improved risk assessment, and strengthened compliance adherence, while also achieving unprecedented cost-effectiveness in deployment and operation. The efficacy of the framework is demonstrated through illustrative use cases, highlighting its potential to mitigate financial losses and uphold data integrity. Keywords: Salesforce, Salesforce Financial Cloud, RAG, Data Completeness, Finance, Sales, Campaign, Digital Engagement, Customer Data Platform (CDP), Data Cloud, DeepSeek-R1, Optical Character Recognition (OCR), deep learning-based image analysis, and natural language processing (NLP) |
| Date: | 2025–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:g5hw7_v1 |
| By: | Roberto-Rafael Maura-Rivero; Marc Lanctot; Francesco Visin; Kate Larson |
| Abstract: | Reinforcement Learning from Human Feedback (RLHF), the standard for aligning Large Language Models (LLMs) with human values, is known to fail to satisfy properties that are intuitively desirable, such as respecting the preferences of the majority \cite{ge2024axioms}. To overcome these issues, we propose the use of a probabilistic Social Choice rule called \emph{maximal lotteries} as a replacement for RLHF. We show that a family of alignment techniques, namely Nash Learning from Human Feedback (NLHF) \cite{munos2023nash} and variants, approximate maximal lottery outcomes and thus inherit its beneficial properties. We confirm experimentally that our proposed methodology handles situations that arise when working with preferences more robustly than standard RLHF, including supporting the preferences of the majority, providing principled ways of handling non-transitivities in the preference data, and robustness to irrelevant alternatives. This results in systems that better incorporate human values and respect human intentions. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19266 |
| By: | Hipólito, Inês |
| Abstract: | This paper applies complex systems theory to examine generative artificial intelligence (AI) as a contemporary wicked problem. Generative AI technologies, which autonomously create content like images and text, intersect with societal domains such as ethics, economics, and governance, exhibiting complex interdependencies and emergent behaviors. Using methodologies like network analysis and agent-based modeling, the paper maps these interactions and explores potential interventions. A mathematical model is developed to simulate the dynamics between key components of the AI-society system, including AI development, economic concentration, labor markets, regulatory frameworks, public trust, ethical implementation, global competition, and distributed AI ecosystems. The model demonstrates non-linear dynamics, feedback loops, and sensitivity to initial conditions characteristic of complex systems. By simulating various interventions, the study provides insights into strategies for steering AI development towards more positive societal outcomes. These include strengthening regulatory frameworks, enhancing ethical implementation, and promoting distributed AI ecosystems. The paper advocates for using this complex systems framework to inform inclusive policy and regulatory strategies that balance innovation with societal well-being. It concludes that embracing complexity enables stakeholders to better navigate the evolving challenges of generative AI, fostering more sustainable and equitable technological advancements. |
| Date: | 2024–08–29 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:aq4tw_v1 |
| By: | Angelo Mele |
| Abstract: | Exponential random graph models (ERGMs) are very flexible for modeling network formation but pose difficult estimation challenges due to their intractable normalizing constant. Existing methods, such as MCMC-MLE, rely on sequential simulation at every optimization step. We propose a neural network approach that trains on a single, large set of parameter-simulation pairs to learn the mapping from parameters to average network statistics. Once trained, this map can be inverted, yielding a fast and parallelizable estimation method. The procedure also accommodates extra network statistics to mitigate model misspecification. Some simple illustrative examples show that the method performs well in practice. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01810 |
| By: | Yisong Chen; Chuqing Zhao; Yixin Xu; Chuanhao Nie |
| Abstract: | This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00201 |
| By: | Yan Liu |
| Abstract: | This paper presents a multi-sector growth model to elucidate the general equilibrium effects of generative artificial intelligence on economic growth, structural transformation, and international production specialization. Using parameters from the literature, the paper employs simulations to quantify the impacts of artificial intelligence across various scenarios. The paper introduces a crucial distinction between high-skill, highly digitalized, tradable services and low-skill, less digitalized, less-tradable services. The model’s key propositions align with empirical evidence, and the simulations yield novel and sobering predictions. Unless artificial intelligence achieves widespread cross-sector adoption and catalyzes paradigm-shifting innovations that fundamentally reshape consumer preferences, its growth benefits may be limited. Conversely, its disruptive impact on labor markets could be profound. This paper highlights the risk of “premature de-professionalization”, where artificial intelligence likely shrinks the space for countries to generate well-paid jobs in high-skill services. The analysis portends that developing countries failing to adopt artificial intelligence swiftly risk entrapment as commodity exporters, potentially facing massive youth underemployment, diminishing social mobility, and stagnating or even declining living standards. The paper also discusses artificial intelligence’s broader implications on inequality, exploring multiple channels through which it may exacerbate or mitigate economic disparities. |
| Date: | 2024–09–17 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10915 |
| By: | Muhammad Sukri Bin Ramli |
| Abstract: | This study examines the relationship between income inequality, gender, and school completion rates in Malaysia using machine learning techniques. The dataset utilized is from the Malaysia's Public Sector Open Data Portal, covering the period 2016-2022. The analysis employs various machine learning techniques, including K-means clustering, ARIMA modeling, Random Forest regression, and Prophet for time series forecasting. These models are used to identify patterns, trends, and anomalies in the data, and to predict future school completion rates. Key findings reveal significant disparities in school completion rates across states, genders, and income levels. The analysis also identifies clusters of states with similar completion rates, suggesting potential regional factors influencing educational outcomes. Furthermore, time series forecasting models accurately predict future completion rates, highlighting the importance of ongoing monitoring and intervention strategies. The study concludes with recommendations for policymakers and educators to address the observed disparities and improve school completion rates in Malaysia. These recommendations include targeted interventions for specific states and demographic groups, investment in early childhood education, and addressing the impact of income inequality on educational opportunities. The findings of this study contribute to the understanding of the factors influencing school completion in Malaysia and provide valuable insights for policymakers and educators to develop effective strategies to improve educational outcomes. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.18868 |
| By: | Walker, Viviane; Angst, Mario |
| Abstract: | Empirical research in the social sciences is often interested in understanding actor stances; the positions that social actors take regarding normative statements in societal discourse. In automated text analysis applications, the classification task of stance detection remains challenging. Stance detection is especially difficult due to semantic challenges such as implicitness or missing context but also due to the general nature of the task. In this paper, we explore the potential of Large Language Models (LLMs) to enable stance detection in a generalized (non-domain, non-statement specific) form. Specifically, we test a variety of different general prompt chains for zero-shot stance classifications. Our evaluation data consists of textual data from a real-world empirical research project in the domain of sustainable urban transport. For 1710 German newspaper paragraphs, each containing an organizational entity, we annotated the stance of the entity toward one of five normative statements. A comparison of four publicly available LLMs show that they can improve upon existing approaches and achieve adequate performance. However, results heavily depend on the prompt chain method, LLM, and vary by statement. Our findings have implications for computational linguistics methodology and political discourse analysis, as they offer a deeper understanding of the strengths and weaknesses of LLMs in performing the complex semantic task of stance detection. We strongly emphasise the necessity of domain-specific evaluation data for evaluating LLMs and considering trade-offs between model complexity and performance. |
| Date: | 2025–02–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:5a3k8_v1 |
| By: | Kamer Ali Yuksel; Hassan Sawaf |
| Abstract: | Financial metrics like the Sharpe ratio are pivotal in evaluating investment performance by balancing risk and return. However, traditional metrics often struggle with robustness and generalization, particularly in dynamic and volatile market conditions. This paper introduces AlphaSharpe, a novel framework leveraging large language models (LLMs) to iteratively evolve and optimize financial metrics. AlphaSharpe generates enhanced risk-return metrics that outperform traditional approaches in robustness and correlation with future performance metrics by employing iterative crossover, mutation, and evaluation. Key contributions of this work include: (1) an innovative use of LLMs for generating and refining financial metrics inspired by domain-specific knowledge, (2) a scoring mechanism to ensure the evolved metrics generalize effectively to unseen data, and (3) an empirical demonstration of 3x predictive power for future risk-return forecasting. Experimental results on a real-world dataset highlight the superiority of AlphaSharpe metrics, making them highly relevant for portfolio managers and financial decision-makers. This framework not only addresses the limitations of existing metrics but also showcases the potential of LLMs in advancing financial analytics, paving the way for informed and robust investment strategies. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00029 |
| By: | Emanuele Dri; Achille Yomi; Muthumanimaran Vetrivelan; Cedric Kuassivi; Iv\`an Diego Exposito |
| Abstract: | In this paper, we present an approach for estimating significant financial metrics within risk management by utilizing quantum phenomena for random number generation. We explore Quantum-Enhanced Monte Carlo, a method that combines traditional and quantum techniques for enhanced precision through Quantum Random Numbers Generation (QRNG). The proposed methods can be based on the use of photonic phenomena or quantum processing units to generate random numbers. The results are promising, hinting at improved accuracy with the proposed methods and slightly lower estimates (both for VaR and CVaR estimation) using the quantum-based methodology. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02125 |
| By: | Ismael Yacoubou Djima; Marco Tiberti; Talip Kilic |
| Abstract: | This paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting, the most accurate method for yield measurement, is often limited due to cost constraints. Multiple imputation techniques, supported by machine learning models are used to predict missing yield data. This method is validated using survey data from Mali, which includes both crop-cut and self-reported yield information. The analysis covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial variables, and the conditions under which the approach is valid. The findings show that machine learning-based imputations can provide accurate yield estimates, especially for crops with low intercropping rates and higher commercialization. However, survey-to-survey imputations are less accurate than within-survey imputations, suggesting limitations in extrapolating data across different survey rounds. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods. |
| Date: | 2024–11–04 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10964 |
| By: | Dylan C. Terry-Doyle; Adam B. Barrett |
| Abstract: | The ever-approaching limits of the Earth's biosphere and the potentially catastrophic consequences caused by climate change have begun to call into question the endless growth of the economy. There is increasing interest in the prospects of zero economic growth from the degrowth and post-growth literature. In particular, the question arises as to whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable. There have been several answers to this question using macroeconomic models; some find a zero-growth trajectory is stable, while other models show an economic breakdown. However, the capitalist system in a period of growth is not guaranteed to be stable. Hence, a more appropriate methodology is to compare the relative stability between a growth and zero-growth scenario on the same model. Such a question has not yet been answered at any disaggregated level. It's important to investigate the consequences of zero-growth on market share instability and concentration, bankruptcy rates, income distribution, and credit network risk. To answer such questions, we develop a macroeconomic agent-based model incorporating Minskyan financial dynamics. The growth and zero-growth scenarios are accomplished by changing an average productivity growth parameter for the firms in the model. The model results showed that real GDP growth rates were more stable in the zero-growth scenario, there were fewer economic crises, lower unemployment rates, a higher wage share of output for workers, and capital firm and bank market shares were relatively more stable. Some of the consequences of zero-growth were a higher rate of inflation than in the growth scenario, increased market concentration for both firms and banks, and a higher level of financial risk in the credit network. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19168 |
| By: | Moisio, Pasi; Mesiäislehto, Merita; Peltoniemi, Johanna; Pihlajamäki, Mika; Hiilamo, Heikki |
| Abstract: | Utilizing Large Language Models (LLM), this study investigates the evolution of an innovative social security policy idea, the General Benefit concept into a policy reform proposal in Fin-land from 2007 to 2023. Drawing from the ideational analysis we hypothesize that political parties struggled over social security conditionality during the 2010s and that social security simplification was manipulated differently in relation to conditionality. Our primary data is elec-tion manifestos and governmental programs from 2007-2023. We employed LLMs, mainly a customized ChatGPT, for the text analysis of policy documents. Additionally, we conduct a critical human evaluation of the LLMs analysis and publish our model in the GPT store for the open replication of analyses. Findings indicate that the weakening of the tripartite industrial relations system and the break-ing of “status quo of three big parties” allowed new parties to influence social policy in 2010s. The General Benefit emerged as a response to calls for social security simplification and for countering (unconditional) basic income proposals. Adopted in 2023, the General Benefit concept aims to merge Finnish universal / residence-based social insurance benefits for the working-aged while preserving core principles like social risk categories and conditionality. Despite increased nativism from the rising True Finns party, and the adoption of universal / unconditional basic income by several parties, Finnish social policy trends from 2007 to 2023 continued to emphasize employment and public finance sustainability. Our study also contributes to methodological discussions on using LLMs in policy analysis. The “human evaluation”, performed by the authors, confirms that the LLM analysis accurately summarises the main features of the policy evolution. However, we also found that the LLM lacks ability to recognise the nuances of “multidimensional” political language and is not very helpful in cross-sectional evaluation, which leaves the analysis partly shallow. Thus, we con-clude that in qualitative policy analysis, LLMs in their current form are suitable for comple-menting rather than substituting human evaluation. |
| Date: | 2024–10–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:ab8mr_v1 |
| By: | Pat Pataranutaporn; Nattavudh Powdthavee; Pattie Maes |
| Abstract: | Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases. This study investigates whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Drawing on 600 surnames from the United States and Thailand, countries with differing sociohistorical dynamics and surname conventions, we categorize names into Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings reveal that elite surnames consistently predict AI-generated perceptions of power, intelligence, and wealth, leading to significant consequences for decisions in high-stakes situations. Mediation analysis highlights perceived intelligence as a crucial pathway through which surname biases operate. Providing objective qualifications alongside the surnames reduces, but does not eliminate, these biases, especially in contexts with uniformly low credentials. These results call for fairness-aware algorithms and robust policy interventions to mitigate the reinforcement of inherited inequalities by AI systems. Our work also urges a reexamination of algorithmic accountability and its societal impact, particularly in systems designed for meritocratic outcomes. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.19407 |
| By: | Luca Delle Foglie (DEF, University of Rome "Tor Vergata"); Stefano Papa (DEF, University of Rome "Tor Vergata"); Giancarlo Spagnolo (CEIS & DEF, University of Rome "Tor Vergata") |
| Abstract: | We examine how betrayal aversion and ambiguity attitudes influence trust. To disentangle these effects, we use a Trust game and manipulate trustors’ perception of being the intentional recipients of trustees’ betrayal by varying the nature of the latter: a human or a machine that replicates human choices in probability. After confirming that this manipulation does not affect ambiguity attitudes or beliefs about others’ behavior, we find that both factors significantly influence trust. Nonetheless, even when controlling for these attitudes and beliefs, participants exhibit lower trust in humans than in machine. Furthermore, using Noldus’ FaceReader technology to measure emotions during trustors’ decision-making process, we find that participants express greater anger toward human trustees. Our results indicate that both betrayal aversion and ambiguity attitudes play important roles in shaping trust decisions. |
| Keywords: | Ambiguity attitudes, Anger, Betrayal cost, Emotions, FaceReader, Trust game |
| JEL: | A13 C91 D03 D64 D90 |
| Date: | 2025–02–21 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:593 |
| By: | Palinski, Michal; Asik, Günes; Gajderowicz, Tomasz; Jakubowski, Maciej; Efsan Nas Ozen; Dhushyanth Raju |
| Abstract: | This study expands the inventory of green job titles by incorporating a global perspective and using contemporary sources. It leverages natural language processing, specifically a retrieval-augmented generation model, to identify green job titles. The process began with a search of academic literature published after 2008 using the official APIs of Scopus and Web of Science. The search yielded 1, 067 articles, from which 695 unique potential green job titles were identified. The retrieval-augmented generation model used the advanced text analysis capabilities of Generative Pre-trained Transformer 4, providing a reproducible method to categorize jobs within various green economy sectors. The research clustered these job titles into 25 distinct sectors. This categorization aligns closely with established frameworks, such as the U.S. Department of Labor’s Occupational Information Network, and suggests potential new categories like green human resources. The findings demonstrate the efficacy of advanced natural language processing models in identifying emerging green job roles, contributing significantly to the ongoing discourse on the green economy transition. |
| Date: | 2024–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10908 |