My research focuses on the modeling of temporal and structural dynamics in economics and finance, with an emphasis on convergence trajectories, institutional transformations, and systemic risk propagation. I develop and apply advanced machine learning methodologies—including Causal GNN, Mixture-of-Experts architectures, survival models, and deep time-series models—to address core problems in predictive modeling, credit and market risk assessment, and the detection of anomalous financial behavior.
A key objective of my work is to advance the theoretical and empirical understanding of algorithmic decision-making in complex environments, contributing to the literature at the intersection of statistical learning, financial econometrics, and applied artificial intelligence.
Since 2024, I have been actively publishing in peer-reviewed journals, consolidating a research agenda that bridges methodological innovation with policy-relevant applications in finance and economics.
Vallarino, D. (2025) “Augmenting Trade Complexity Analysis with Deep Learning: An AI-Based Framework for Small Open Economies.” Applied Economics Letters
—— (2025). GNN–GAN Structural Explorer: Interactive Companion to “Augmenting Trade Complexity Analysis with Deep Learning.” Shiny App (v1.0). [Available here]
Vallarino, D. (2025) "Causal-GNN for Ethical AI in Financial Services: Ensuring Fairness, Compliance, and Transparency in Automated Decision-Making" Artificial Intelligence and Law
Vallarino, D. (2025) “Bridging the Credit Gap: How Causal AI Can Enhance Fairness and Profitability in Emerging Markets.” Global Business and Economics Review
Vallarino, D. (2025) “Advancing Fraud Detection with Hybrid AI: A MoE, RNN, and Transformer-Based Approach for Financial Risk Assessment” Journal of Information Economics
Vallarino, D. (2025) “When Preferences Diverge: Rethinking Rational Choice with AI-Based Economic Modeling.” Journal of Economic Analysis
Vallarino, D. (2025) “Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas.” Journal of Regional Economics 4(1), 17.
Vallarino, D. (2025). “An AI-Enhanced Forecasting Framework: Integrating LSTM and Transformer-Based Sentiment for Stock Price Prediction”. Journal of Economic Analysis 4(3), 109
Vallarino, D. (2025). “Understanding the Market Trends: A Hybrid Approach to Stock Price Prediction Using RNNs and Transformer-Based Sentiment Analysis" Journal of Applied Economic Sciences, Volume XX, Spring 2025, Issue 1(87)
Vallarino, D. (2024). “Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to Gross Domestic Product Per capita Trajectories”. Journal of Applied Economic Sciences, Volume XIX, Summer, 2(84), 131–145.
Vallarino, D. (2024) “Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study” Review of Economic Assessment 3(1), 1-19
Vallarino, D. (2024). “A Comparative Machine Learning Survival Models Analysis for Predicting Bank Failure in the US (2001-2023)”. Journal of Economic Analysis. 3(1), 129-144
Vallarino, D. (2024). “Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and examples”. International Journal of Artificial Intelligence and Machine Learning. 4(1), 10-21
Vallarino, D. (2023). “Predicting Machine Learning Survival Models comparison: the case of startups time to failed with collinearity-related issues”. Journal of Economics Statistics. 1(3), 1-15.
Vallarino, D. (2023). “When should they buy? Surviving machine learning models for purchase timing”. International Journal of Data Mining & Knowledge Management Process vol 13, no 4/5
Navarro, D. V. (2007). “El comportamiento innovador como fuente del cambio: el ejemplo en las empresas de servicios”. Revista da FAE 10(1)
Vallarino, D. (2005). “Enterprise Innovation Model: cómo las empresas de Latinoamérica enfrentan la nueva competencia”. Revista de Antiguos Alumnos del IEEM, 8(2), 100-108
Vallarino, D. (2025). "Invited to Develop: Institutional Belonging and the Counterfactual Architecture of Developments" Available on arXiv.
Vallarino. D. (2025) "Data for Inclusion: The Redistributive Power of Data Economics” Available at ArXiv
Vallarino, D. (2025) “Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting” Available at ArXiv
Vallarino, D. (2025) “Causal GNNs and the Anthropology of Data: An Ethical Approach to Fairness and Transparency in Financial AI” Available at SSRN
Vallarino, D. (2025) “AI-Powered Fraud Detection in Financial Services: GNN, Compliance Challenges, and Risk Mitigation” Available at SSRN
Vallarino, D. (2025) “How Do Consumers Really Choose? Exposing Hidden Preferences with the Mixture of Experts Model.” Available at ArXiv
Vallarino, D. (2024) “Modeling Adaptive Fraud Patterns: An Agent-Centric Hybrid Framework with MoE and Deep Learning” Available at SSRN
Vallarino, D. (2024) “A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profiles” Available at ArXiv
Vallarino, D. (2024) “Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency” Available at ArXiv
Vallarino, D. (2024) “Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)” Available at ArXiv
Vallarino, D. (2024) “Understanding the Market Trends: A Hybrid Approach to Stock Price Prediction Using RNNs and Transformer-Based Sentiment Analysis” Available at SSRN
Vallarino, D. (2024) “Decoding the Puzzle of Joblessness: Machine Learning Predicts Unemployment Trends in the Americas” Available at SSRN
Vallarino, D. (2024) “Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories” Available at arXiv
Vallarino, D. (2023) “An historical perspective of Structural Economic Changes evidence from the Industrial and Investment Promotion in Uruguay (1974-2010)” Available at SSRN
Vallarino, D. (2023) “Incentives for Private Industrial Investment in historical perspective: the case of industrial promotion and investment promotion in Uruguay” (1974-2010) Available at SSRN
Vallarino, D (2023). “A relevant Weakness in Survival Machine Learning Models: non-Ergodicity”. Working Paper
“Taxing the Invisible: Graph-Based Information Structures for Redistributive Design under Uncertainty.” Under review.
“Causal Network Analysis in the long run: Modeling Collapse, Exposure, and Spillovers in Historical Polities” Under review.
“Open Finance, Complexity, and Institutional Survival: A Graph-Based Approach to Sustaining Financial Inclusion in Latin America" Under review
“Institutional Persistence and Rupture in Latin America 1900-2024: A Historical-Structural Approach with Network-Based Evidence” Presented papers.
“Graph-Embedded Hazard Models: Integrating Network Dependence into Survival Econometrics.” Presented papers.
Vallarino, D. (2025) “Breaking the Product Space: Rethinking Economic Complexity with GNNs and Synthetic Trade Networks in Emerging Economies.” Paper accepted for presentation at the Conference on Economic Complexity 2025 (July 9–11, Toulouse, France). The author could not attend in person due to funding constraints.
Vallarino, D. (2025). “Market Power and Tax Incentives for Sustainable Growth: A Network-Based Approach in Latin America”. BALAS 2025 Conference, (San José, Costa Rica, from April 7 to 11, 2025)
Vallarino, D. (2024). “Stock Behavior Using RNN Models and Transformer-Based Text Analysis”. LatinR 2024 Conference, Montevideo, Uruguay
Vallarino, D. (2023). “Comparative Analysis of Machine Learning Models for Survival Analysis: Empirical Study and Performance Assessment” LatinR 2023 Conference, Montevideo, Uruguay
Vallarino, D. (2023). “Buy when? Survival machine learning model comparison for purchase timing.” 4th International Conference on Big Data, Machine Learning and IoT (BMLI 2023) Dubai, UAE.
Vallarino, D., Azúa C, Úbeda, R. (2023). “Differentiated Roles and Perceptions of the Controller Function: An Exploratory Study”. Business Association of Latin America Studies (BALAS) Annual Conference, Mexico City, Mexico.
Note: Since July 2024 I have been on a one-year research sabbatical (July 2024 – June 2025), fully dedicated to independent research. Even outside formal appointments I have consistently advanced projects at the intersection of machine learning, econometrics, and finance, reflecting my commitment to the field. This independent trajectory has enabled me to push forward innovative methods such as causal graph neural networks and survival models for economic applications.