Research classified by Journal of Economic Literature (JEL) codes

---

Top JEL
/ C: Mathematical and Quantitative Methods
/ / C4: Econometric and Statistical Methods: Special Topics
/ / / C45: Neural Networks and Related Topics

2025

• Kampouris, Ilias & Mertzanis, Charilaos & Samitas, Aristeidis, 2025, "Natural disaster shocks and commodity market volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102618.
• Chiu, I-Chan & Hung, Mao-Wei, 2025, "Finance-specific large language models: Advancing sentiment analysis and return prediction with LLaMA 2," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102632.
• Cheng, Zijian & Li, Tianze & Liu, Zhangxin (Frank), 2025, "Unveiling the veil: Identifying potential shell firms using machine learning approaches," Pacific-Basin Finance Journal, Elsevier, volume 92, issue C, DOI: 10.1016/j.pacfin.2025.102798.
• Li, Xiao-Xin & Xie, Chi & Wang, Gang-Jin & Zhu, You & Li, Zhao-Chen & Zhang, Zhi-Yu, 2025, "Enhancing stock market return predictability by using a novel autoencoder-based aggregate EPU index," Pacific-Basin Finance Journal, Elsevier, volume 93, issue C, DOI: 10.1016/j.pacfin.2025.102873.
• Morier, Bruno & Valls Pereira, Pedro L., 2025, "Forecasting intraday volatility and densities using deep learning," The Quarterly Review of Economics and Finance, Elsevier, volume 104, issue C, DOI: 10.1016/j.qref.2025.102076.
• Kumar, Satish & Rao, Amar & Dhochak, Monika, 2025, "Hybrid ML models for volatility prediction in financial risk management," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103915.
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2025, "High-dimensional multi-period portfolio allocation using deep reinforcement learning," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103996.
• Thomas Persson, 2025, "Machine Learning Methods," Journal of Economics and Econometrics, Economics and Econometrics Society, volume 68, issue 2, pages 106-129.
• Briola, Antonio & Bartolucci, Silvia & Aste, Tomaso, 2025, "HLOB–Information persistence and structure in limit order books," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 126623, Mar.
• Koukorinis, Andreas & Peters, Gareth W. & Germano, Guido, 2025, "Generative-discriminative machine learning models for high-frequency financial regime classification," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 128016, Jun.
• Martín, Roberto Spacey & Ranger, Nicola & Schimanski, Tobias & Leippold, Markus, 2025, "Empirically assessing corporate adaptation and resilience disclosure using AI," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 130809, Sep.
• Artur Kulpa & Grzegorz Wojarnik, 2025, "Prompt Engineering in Finance: An LLM-Based Multi-Agent Architecture for Decision Support," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 1201-1217.
• Viktor Ivanovich Blanutsa, 2025, "Creating the First Autonomous Systems of Internet in Siberia as a Spatial Diffusion of Innovations," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 1, pages 7-32, DOI: https://dx.doi.org/10.14530/se.2025.
• Monica Bonacina & Mert Demir & Antonio Sileo & Angela Zanoni, 2025, "What Hinders Electric Vehicle Diffusion? Insights from a Neural Network Approach," Working Papers, Fondazione Eni Enrico Mattei, number 2025.16, Aug.
• Monica Bonacina & Romolo Consigna Tokong, 2025, "Is Italy on Track? A Data-Driven Forecast for Road Transport Decarbonisation by 2030," Working Papers, Fondazione Eni Enrico Mattei, number 2025.19, Sep.
• Leland D. Crane & Xiaoyu Ge & Flora Haberkorn & Rithika Iyengar & Seung Jung Lee & Viviana Luccioli & Ryan Panley & Nitish R. Sinha, 2025, "LLM on a Budget: Active Knowledge Distillation for Efficient Classification of Large Text Corpora," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-108, Dec, DOI: 10.17016/FEDS.2025.108.
• Martin Neil Baily & David M. Byrne & Aidan T. Kane & Paul E. Soto, 2025, "Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-053, Jul, DOI: 10.17016/FEDS.2025.053.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," Post-Print, HAL, number halshs-05144088, May, DOI: 10.1257/pandp.20251047.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-05144088, May, DOI: 10.1257/pandp.20251047.
• Carlo Drago & Massimo Arnone & Angelo Leogrande, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," Working Papers, HAL, number hal-04994903, Mar.
• Angelo Leogrande & Nicola Magaletti & Valeria Notarnicola & Mauro Di Molfetta & Stefano Mariani, 2025, "Data-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice," Working Papers, HAL, number hal-05043506, Apr.
• Margareth Antonicelli & Carlo Drago & Alberto Costantiello & Angelo Leogrande, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," Working Papers, HAL, number hal-05091404, May.
• Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025, "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," Working Papers, HAL, number hal-05102974, Jun.
• Onofrio Resta & Emanuela Resta & Alberto Costantiello & Piergiuseppe Liuzzi & Angelo Leogrande, 2025, "Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions," Working Papers, HAL, number hal-05243548, Sep.
• Koji Takahashi & Joon Suk Park, 2025, "Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 25-E-13, Sep.
• Bhaskar Tripathi & Rakesh Kumar Sharma, 2025, "Cryptocurrency Exchanges and Traditional Markets: A Multi-algorithm Liquidity Comparison Using Multi-criteria Decision Analysis," Computational Economics, Springer;Society for Computational Economics, volume 65, issue 5, pages 2649-2677, May, DOI: 10.1007/s10614-024-10655-9.
• Edson Pindza & Jules Clement & Sutene Mwambi & Nneka Umeorah, 2025, "Neural Network for Valuing Bitcoin Options Under Jump-Diffusion and Market Sentiment Model," Computational Economics, Springer;Society for Computational Economics, volume 66, issue 3, pages 2305-2342, September, DOI: 10.1007/s10614-024-10792-1.
• Zareh Asatryan & Carlo Birkholz & Friedrich Heinemann, 2025, "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," International Tax and Public Finance, Springer;International Institute of Public Finance, volume 32, issue 2, pages 625-655, April, DOI: 10.1007/s10797-024-09875-4.
• José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2025, "Networks in Household and Population Socifal Sciences: Encouraging the Need for More Collaborations," Journal of Family and Economic Issues, Springer, volume 46, issue 4, pages 1041-1053, December, DOI: 10.1007/s10834-025-10065-5.
• Hui Dong & Xiao Pan & Xiao Chen & Jing Sun & Shuhai Wang, 2025, "DyAdapTransformer: dynamic adaptive spatial–temporal graph transformer for traffic flow prediction," Journal of Geographical Systems, Springer, volume 27, issue 2, pages 229-255, April, DOI: 10.1007/s10109-025-00464-5.
• Nicholas A. Pairolero & Alexander V. Giczy & Gerard Torres & Tisa Islam Erana & Mark A. Finlayson & Andrew A. Toole, 2025, "The artificial intelligence patent dataset (AIPD) 2023 update," The Journal of Technology Transfer, Springer, volume 50, issue 6, pages 2587-2610, December, DOI: 10.1007/s10961-025-10189-8.
• Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025, "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, volume 65, issue 1, pages 107-148, July, DOI: 10.1007/s11156-023-01192-x.
• Adelaide Baronchelli & Roberto Ricciuti & Mattia Viale, 2025, "Elite persistence in medieval Venice after the Black Death," Rivista di storia economica, Società editrice il Mulino, issue 3, pages 307-329.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2025, "Large Language Models: An Applied Econometric Framework," NBER Working Papers, National Bureau of Economic Research, Inc, number 33344, Jan.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu, 2025, "Artificial Intelligence Asset Pricing Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 33351, Jan.
• Robert Novy-Marx & Mihail Z. Velikov, 2025, "AI-Powered (Finance) Scholarship," NBER Working Papers, National Bureau of Economic Research, Inc, number 33363, Jan.
• Zhouyu Shen & Dacheng Xiu, 2025, "Can Machines Learn Weak Signals?," NBER Working Papers, National Bureau of Economic Research, Inc, number 33421, Jan.
• Clément Gorin & Stephan Heblich & Yanos Zylberberg, 2025, "State of the Art: Economic Development Through the Lens of Paintings," NBER Working Papers, National Bureau of Economic Research, Inc, number 33976, Jun.
• Jesús Fernández-Villaverde, 2025, "Deep Learning for Solving Economic Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 34250, Sep.
• Leonidas Aristodemou & Silvia Appelt & Brigitte van Beuzekom & Fernando Galindo-Rueda, 2025, "Assessing the relevance of R&D funding towards societal goals: Insights from new data sources and AI-assisted methods," OECD Science, Technology and Industry Working Papers, OECD Publishing, number 2025/25, Nov, DOI: 10.1787/bafcdc7b-en.
• Liliana ANGHEL, 2025, "Using Artificial Intelligence In The Financial Planning Mechanism," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, volume 34, issue 2, pages 506-512, December.
• Antonicelli, Margareth & Drago, Carlo & Costantiello, Alberto & Leogrande, Angelo, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," OSF Preprints, Center for Open Science, number tk87m_v1, Jun, DOI: 10.31219/osf.io/tk87m_v1.
• Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," SocArXiv, Center for Open Science, number 4r8ux_v1, Mar, DOI: 10.31219/osf.io/4r8ux_v1.
• Paolo Giordani, 2025, "SMARTboost Learning for Tabular Data," Journal of Financial Econometrics, Oxford University Press, volume 23, issue 3, pages 929-985.
• Manfred Herdt & Hermann Schulte-Mattler, 2025, "Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks," Journal of Banking Regulation, Palgrave Macmillan, volume 26, issue 3, pages 392-407, September, DOI: 10.1057/s41261-025-00269-y.
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2025, "The effect of sport in online dating: evidence from causal machine learning," Humanities and Social Sciences Communications, Palgrave Macmillan, volume 12, issue 1, pages 1-13, December, DOI: 10.1057/s41599-025-04566-9.
• Jesus Fernandez-Villaverde, 2025, "Deep Learning for Solving Economic Models," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 25-017, Sep.
• Nico Knuth & Andreas Nastansky, 2025, "Anwendung von Deep Learning in der Prognose der Volatilität des DAX: Ein Vergleich der Prognosegüte von GARCH und LSTM," Statistische Diskussionsbeiträge, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät, number 59, Mar, DOI: 10.25932/publishup-67486.
• Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," MPRA Paper, University Library of Munich, Germany, number 124006, Mar.
• Magaletti, Nicola & Notarnicola, Valeria & Di Molfetta, Mauro & Mariani, Stefano & Leogrande, Angelo, 2025, "Data-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice," MPRA Paper, University Library of Munich, Germany, number 124548, Apr.
• Antonicelli, Margareth & Drago, Carlo & Costantiello, Alberto & Leogrande, Angelo, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," MPRA Paper, University Library of Munich, Germany, number 124910, May.
• Drago, Carlo & Costantiello, Alberto & Savorgnan, Marco & Leogrande, Angelo, 2025, "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," MPRA Paper, University Library of Munich, Germany, number 124973, Jun.
• Bahaa Aly, Tarek, 2025, "Nonlinear Macroeconomic Granger Causality: An ANN Input Occlusion Approach on MSSA-Denoised Data," MPRA Paper, University Library of Munich, Germany, number 125453, Jul.
• Resta, Onofrio & Resta, Emanuela & Costantiello, Alberto & Liuzzi, Piergiuseppe & Leogrande, Angelo, 2025, "Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions," MPRA Paper, University Library of Munich, Germany, number 126073, Sep.
• Boughabi, Houssam, 2025, "Inflation Persistence and Involuntary Unemployment in Pakistan: A Keynesian Econometric Study," MPRA Paper, University Library of Munich, Germany, number 126294, Aug.
• Pinto, Claudio, 2025, "Combining machine learning techniques with NDEA methodology: the use of R.F. and A.N.N," MPRA Paper, University Library of Munich, Germany, number 126539, Sep.
• Slonimczyk, Fabian, 2025, "This Candidate is [MASK]. Prompt-based Sentiment Extraction and Reference Letters," MPRA Paper, University Library of Munich, Germany, number 126675, Oct.
• Kikuchi, Tatsuru, 2025, "Network Contagion Dynamics in European Banking: A Navier-Stokes Framework for Systemic Risk Assessment," MPRA Paper, University Library of Munich, Germany, number 126729.
• Vasilios Plakandaras & Rangan Gupta & Qiang Ji, 2025, "Unraveling Financial Fragility of Global Markets Using Machine Learning," Working Papers, University of Pretoria, Department of Economics, number 202511, Mar.
• Manuel Rosinus, 2025, "Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index," ACTA VSFS, University of Finance and Administration, volume 19, issue 1, pages 32-52.
• Martín Saldias & Sophia Mizinski, 2025, "Sectoral Interconnectedness in Portugal and the Role of Non-Bank Financial Institutions," Working Papers, Banco de Portugal, Economics and Research Department, number w202521.
• Yunus Emre Korkmaz & Serpil Altınırmak & Çağlar Karamaşa, 2025, "Makine Öğrenmesi Yöntemleri ile Kripto Varlık Fiyat Tahmini ve En İyi Yöntemin ÇKKV Teknikleri ile Belirlenmesi
  [Cryptocurrency Price Prediction Using Machine Learning Methods and Determining the Best Method Using MCDM Techniques]," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 16, issue 4, pages 463-492, October, DOI: 10.20409/berj.2025.477.
• Igor A. Kokorev & Vladimir M. Kiselev & Vladimir V. Velikorossov & Andrey L. Poltarykhin & Galiya S. Ukubassova, 2025, "Use of AI technologies in improving the business processes of companies," Economic Consultant, Scientific and Educational Initiative LLC, issue 4, pages 4-16, December, DOI: 10.46224/ecoc.2025.4.1.
• Hanna YAROVENKO & Serhiy LYEONOV & Yuriy BILAN, 2025, "Impact Of Efficiency Of Digital Public Administration Services On Quality Of Life," REVISTA ADMINISTRATIE SI MANAGEMENT PUBLIC, Faculty of Administration and Public Management, Academy of Economic Studies, Bucharest, Romania, volume 2025, issue 45, pages 141-160, DOI: https://doi.org/10.24818/amp/2025.4.
• Weige Huang & Zishan Chen & Hua Wang, 2025, "Does the History of Investment Matter for an IPO? A Machine Learning Approach," SAGE Open, , volume 15, issue 4, pages 21582440251, November, DOI: 10.1177/21582440251383077.
• Carlos Moreno-Pérez & Marco Minozzo, 2025, "Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), volume 19, issue 3, pages 769-793, September, DOI: 10.1007/s11634-024-00596-4.
• Zaheer Anwer & Ashraf Khan & Muhammad Abubakr Naeem & Aviral Kumar Tiwari, 2025, "Modelling systemic risk of energy and non-energy commodity markets during the COVID-19 pandemic," Annals of Operations Research, Springer, volume 345, issue 2, pages 1193-1227, February, DOI: 10.1007/s10479-022-04879-x.
• Sabri Boubaker & Zhenya Liu & Yifan Zhang, 2025, "Forecasting oil commodity spot price in a data-rich environment," Annals of Operations Research, Springer, volume 345, issue 2, pages 685-702, February, DOI: 10.1007/s10479-022-05004-8.
• Ludovic Goudenège & Andrea Molent & Antonino Zanette, 2025, "Backward hedging for American options with transaction costs," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 48, issue 1, pages 541-569, June, DOI: 10.1007/s10203-024-00472-y.
• D. Nam & D. B. Skillicorn, 2025, "Detecting Pump &Dump stock market manipulation from online forums," Digital Finance, Springer, volume 7, issue 1, pages 1-20, March, DOI: 10.1007/s42521-024-00121-4.
• Monia Antar & Tahar Tayachi, 2025, "Partial dependence analysis of financial ratios in predicting company defaults: random forest vs XGBoost models," Digital Finance, Springer, volume 7, issue 4, pages 997-1012, December, DOI: 10.1007/s42521-025-00135-6.
• Ameer Tamoor Khan & Shuai Li & Xinwei Cao, 2025, "Bridging finance and AI: a comprehensive survey of large language models in financial system," Digital Finance, Springer, volume 7, issue 4, pages 679-701, December, DOI: 10.1007/s42521-025-00146-3.
• William Nordansjö & Fredrik Fourong & Muhammad Qasim, 2025, "Financial sentiment analysis with FUNNEL: filtered UNion for NER-based ensemble labeling," Digital Finance, Springer, volume 7, issue 4, pages 725-744, December, DOI: 10.1007/s42521-025-00162-3.
• Maen Alaraj & Makoto Nishibe, 2025, "Addressing stagnation in community currencies: enhancing circulation of digital community currencies using neural network-based satisfaction prediction," Evolutionary and Institutional Economics Review, Springer, volume 22, issue 2, pages 285-326, September, DOI: 10.1007/s40844-025-00310-9.
• Valeriia Baklanova, 2025, "The relationships between RedditSI and BTC exchange characteristics: Do Reddit users still control the market?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 15, issue 1, pages 285-306, March, DOI: 10.1007/s40822-024-00304-9.
• Saïd Toumi & Abdussalam Aljadani & Hassen Toumi & Bilel Ammouri & Moez Dhiabi, 2025, "AI for climate change: unveiling pathways to sustainable development through greenhouse gas emission predictions," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 15, issue 4, pages 923-963, December, DOI: 10.1007/s40822-024-00295-7.
• Yang Zhou & Chi Xie & Gang-Jin Wang & Jue Gong & You Zhu, 2025, "Forecasting cryptocurrency volatility: a novel framework based on the evolving multiscale graph neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 11, issue 1, pages 1-52, December, DOI: 10.1186/s40854-025-00768-x.
• Yeonchan Kang & Doojin Ryu & Robert I. Webb, 2025, "How well do machine learning models in finance work?," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 11, issue 1, pages 1-30, December, DOI: 10.1186/s40854-025-00870-0.
• Aleksandar Arandjelović & Thorsten Rheinländer & Pavel V. Shevchenko, 2025, "Importance sampling for option pricing with feedforward neural networks," Finance and Stochastics, Springer, volume 29, issue 1, pages 97-141, January, DOI: 10.1007/s00780-024-00549-x.
• Dina Ait Lahcen & Nour-Eddin Amghar, 2025, "Econometric modeling for proactive risk management of financial failure in Moroccan SMEs: a stepwise logistic regression approach in python," Future Business Journal, Springer, volume 11, issue 1, pages 1-25, December, DOI: 10.1186/s43093-025-00613-8.
• D. Dharani & K. Lavanya Latha, 2025, "Beyond the Click: Unveiling the Role of User-Centric Factors in Public Perception and Engagement with Search Engine Marketing," International Journal of Global Business and Competitiveness, Springer, volume 20, issue 2, pages 143-154, December, DOI: 10.1007/s42943-025-00121-0.
• Aditi Nag, 2025, "Tourism Planning Meets AI: A Fuzzy-Logic and PLS-SEM–ANN Framework for Stakeholder-Centric Destination Competitiveness Forecasting," International Journal of Global Business and Competitiveness, Springer, volume 20, issue 2, pages 117-131, December, DOI: 10.1007/s42943-025-00125-w.
• Joost Bosker & Marc Gürtler & Marvin Zöllner, 2025, "Machine learning-based variable selection for clustered credit risk modeling," Journal of Business Economics, Springer, volume 95, issue 4, pages 617-652, May, DOI: 10.1007/s11573-024-01213-8.
• Manu Sharma & Vinish Kathuria, 2025, "Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 23, issue 2, pages 333-388, June, DOI: 10.1007/s40953-024-00421-x.
• Manuel Muth & Michael Lingenfelder & Gerd Nufer, 2025, "The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review," Management Review Quarterly, Springer, volume 75, issue 3, pages 2759-2802, September, DOI: 10.1007/s11301-024-00447-8.
• Yong-Hyong Kim & Song-Jun Ham & Chong-Sim Ri & Won-Hyok Kim & Wi-Song Ri, 2025, "Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, volume 24, issue 1, pages 151-169, January, DOI: 10.1007/s10258-024-00252-x.
• Ioannis A. Nikas & Athanasios Koutras & Antonopoulou Theodora, 2025, "A Conceptual Framework for Developing and Evaluating Personalized Tourist Recommendation Systems Using Large Language Models," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni & Carlos Costa, "Innovation and Creativity in Tourism, Business and Social Sciences", DOI: 10.1007/978-3-031-78471-2_20.
• Marius Brede & Hannes Gerstel & Arnt Wöhrmann & Andreas Bausch, 2025, "Mind the gap: the effect of cultural distance on mergers and acquisitions—evidence from glassdoor reviews," Review of Managerial Science, Springer, volume 19, issue 8, pages 2279-2326, August, DOI: 10.1007/s11846-024-00811-8.
• Ulysses Araújo Bispo & Mathias Schneid Tessmann, 2025, "Does Deep Learning with Multilayer Perceptron Perform Well in Predicting Credit Risk?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 15, issue 4, pages 1-1.
• Dmitrii Gimmelberg & Alexey Belinskiy & Alexey Belinskiy & Marta Głowacka & Marta Głowacka & Sergei Korotkii & Valentin Artamonov & Iveta Ludviga, 2025, "Market moves predictions using Retrieval-Augmented Generation (RAG) analysis of capital market expert opinions in social media," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 13, issue 1, pages 175-188, September, DOI: 10.9770/w9365778559.
• Jakub Horák & Jakub Horák, 2025, "Capital market behavior and stock forecasting – a neural network approach to Lufthansa’s shares," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 13, issue 2, pages 222-237, December, DOI: 10.9770/e7888298227.
• Yao Luo & Peijun Sang, 2025, "Efficient Estimation of Structural Models via Sieves," Working Papers, University of Toronto, Department of Economics, number tecipa-801, Jun.
• James Alm & Rida Belahouaoui, 2025, "Emerging Trends in Tax Fraud Detection Using Artificial Intelligence-Based Technologies," Working Papers, Tulane University, Department of Economics, number 2511, Nov.
• Eoin T. Flaherty & Sean O'Boyle & Giselle Myles, 2025, "Sectorally Concentrated? The Irish Economy in European Context," Working Papers, Geary Institute, University College Dublin, number 202503, Jan.
• Capilla, Javier & Alcaraz, Alba & Valarezo, à ngel & García-Hiernaux, Alfredo & Pérez Amaral, Teodosio, 2025, "Eco-RETINA: a green flexible algorithm for model building," Documentos de Trabajo del ICAE, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, number 2025-01.
• Mario Alberto Morales Sánchez & Héctor Eduardo Díaz Rodríguez, 2025, "Level of development as a determinant of innovation capability. Evidence for 132 economies using artificial neural networks," Estudios de Economia, University of Chile, Department of Economics, volume 52, issue 1 Year 20, pages 59-95, June.
• Diana Barro & Antonella Basso & Marco Corazza & Guglielmo Alessandro Visentin, 2025, "A Neural Network-VAR for Long-Term Forecasting: An Application to Monetary Policy Effects in the Euro Area," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2025: 24.
• Ardelia L. Amardana & Diana Barro & Marco Corazza, 2025, "Sustainability in LSTM Price Prediction for Portfolio Optimization in the European Market," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2025: 25.
• ANGHEL, Bogdan Ionut, 2025, "Forecasting Stock Market Liquidity With Machine Learning: An Empirical Evaluation In The German Market," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", volume 29, issue 2, pages 34-47, June.
• Michalak Joanna, 2025, "Affect Indicators for Stock Market Forecasting," Central European Economic Journal, Sciendo, volume 12, issue 59, pages 412-432, DOI: 10.2478/ceej-2025-0024.
• Perez Katarzyna & Bartkowiak Marcin, 2025, "Chasing Returns of Open-End Investment Funds Using Recurrent Neural Networks. A Long-Term Study," Central European Economic Journal, Sciendo, volume 12, issue 59, pages 49-65, DOI: 10.2478/ceej-2025-0004.
• Cellmer Radosław & Kobylińska Katarzyna, 2025, "Housing Price Prediction - Machine Learning and Geostatistical Methods," Real Estate Management and Valuation, Sciendo, volume 33, issue 1, pages 1-10, DOI: 10.2478/remav-2025-0001.
• Ergenç Cansu & Aktaş Rafet, 2025, "A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index," Review of Economic Perspectives, Sciendo, volume 25, issue 1, pages 66-90, DOI: 10.2478/revecp-2025-0005.
• Neagu Dan Claudiu, 2025, "Bertweetro: Pre-Trained Language Models for Romanian Social Media Content," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, volume 70, issue 1, pages 83-111, DOI: 10.2478/subboec-2025-0005.
• Ungar Kevin & Oprean-Stan Camelia, 2025, "Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, volume 35, issue 1, pages 49-82, DOI: 10.2478/sues-2025-0004.
• Jędrzej Maskiewicz & Paweł Sakowski, 2025, "Can Artificial Intelligence Trade the Stock Market?," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-14.
• Yufei Sun, 2025, "A survey of statistical arbitrage pair trading with machine learning, deep learning, and reinforcement learning methods," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-22.
• Zofia Bracha & Jakub Michańków & Paweł Sakowski, 2025, "Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2025-25.
• Muhammad Farooq & Ayesha Ayesha & Ahtasham Gul, 2025, "Forecasting the Future: Neural Networks vs. Time Series in Pakistan’s CPI," Economic Research Guardian, Mutascu Publishing, volume 15, issue 2, pages 219-231, December.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2025, "Corrigendum: Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, volume 93, issue 4, pages 1491-1496, July, DOI: 10.3982/ECTA22259.
• Paola Bongini & Monica Rossolini & Andrea Maurino & Francesco Osborne, 2025, "The Information Power Of Social Media For Investment Decisions: An Ai-Driven Analysis Of Reddit Posts," Journal of Financial Management, Markets and Institutions (JFMMI), World Scientific Publishing Co. Pte. Ltd., volume 13, issue 02, pages 1-35, December, DOI: 10.1142/S2282717X25500082.
• Saira Yamin & Saqib Gulzar, 2025, "Multiples And Stock Price, New Approach For Relative Valuation Through Neural Network," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 70, issue 04, pages 953-971, June, DOI: 10.1142/S0217590820480045.
• Kian Guan Lim, 2025, "Machine Learning in Business Finance using Python," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 14271, ISBN: ARRAY(0x6d46e288).
• Horst Simon (ed.), 2025, "Transactions of ADIA Lab:Interdisciplinary Advances in Data and Computational Science," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 14310, ISBN: ARRAY(0x6e3b7508).
• Alexandre V. Antonov & Koushik Balasubramanian & Alexander Lipton & Marcos Lopez de Prado, 2025, "A Geometric Approach to Asset Allocation with Investor Views," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 1, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Álvaro F. Macías & Jorge P. Zubelli, 2025, "Static Liquidation and Risk Management," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 2, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Alexandre Antonov & Alexander Lipton & Marcos Lopez de Prado, 2025, "Overcoming Markowitz’s Instability with the Help of the Hierarchical Risk Parity (HRP): Theoretical Evidence," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 3, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Vinicius V. L. Albani & Leonardo Sarmanho & Jorge P. Zubelli, 2025, "A Statistical Learning Approach to Local Volatility Calibration and Option Pricing," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 4, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Laura Sanz Martín & Javier Parra Domínguez & Guillermo Rivas & Alexander Lipton & Juan Manuel Corchado, 2025, "Challenges of Artificial Intelligence and Quantum Potential in the Digital Economy: A Literature Review," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 5, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Manuel J. Cobo & Nadia Karina Gamboa-Rosales & José Ricardo López-Robles & Enrique Herrera-Viedma, 2025, "Exploring the Digital Economy: Current Research Trends, Challenges, and Opportunities," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 6, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Thomas Hardjono & Alexander Lipton & Alex Pentland, 2025, "Interoperability Challenges in Tokenized Asset Networks," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 7, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• David Garvin & Oleksiy Kondratyev & Alexander Lipton & Marco Paini, 2025, "Symmetric Encryption on a Quantum Computer," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 8, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Joshua Chung & Marcos Lopez de Prado & Horst D. Simon & Kesheng Wu, 2025, "Performance-Driven Dimensionality Reduction: A Data-Centric Approach to Feature Engineering in Machine Learning," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 9, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Torsten Hoefler & Alexandru Calotoiu & Anurag Dipankar & Thomas Schulthess & Xavier Lapillonne & Oliver Fuhrer, 2025, "Toward Specialized Supercomputers for Climate Sciences: Computational Requirements of the Icosahedral Nonhydrostatic Weather and Climate Model," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 10, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Ana Paula Peron & Emilio Porcu, 2025, "Dimension Walks on Generalized Spaces," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 11, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Francisco Herrera & Andres Herrera & Javier Del Ser & Enrique Herrera-Viedma & Marcos López de Prado, 2025, "Trustworthy Artificial Intelligence: Nature, Requirements, Regulation, and Emerging Discussions," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 12, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Kevin Webster & Nicholas Westray, 2025, "Getting More for Less: Better A/B Testing via Causal Regularization," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 13, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Alik Sokolov & Fabrizzio Sabelli & Behzad Azadie Faraz & Wuding Li & Luis Seco, 2025, "Toward Automating Causal Discovery in Financial Markets and Beyond," World Scientific Book Chapters, World Scientific Publishing Co. Pte. Ltd., chapter 14, in: Horst Simon, "Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science".
• Geiger, Felix & Kanelis, Dimitrios & Lieberknecht, Philipp & Sola, Diana, 2025, "Monetary-Intelligent Language Agent (MILA)," Technical Papers, Deutsche Bundesbank, number 01/2025.
• Janda, Karel & Rozsahegyi, Marketa & Quang Van Tran & Zhang, Binyi, 2025, "The Impact of Machine Learning Derived Green Bonds Sentiment on Performance of Green Bond Portfolio," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335550.
• Ficura, Milan & Ibragimov, Rustam & Janda, Karel, 2025, "Artificial Intelligence–Based Forecasting of Oil Prices: Evidence from Neural Network Models," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335571.
• Janda, Karel & Rozsahegyi, Marketa & Quang Van Tran & Zhang, Binyi, 2025, "Using Natural Language Processing to Identify Sentiment of Green Investors," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 335572.
• Capilla, Javier & Alcaráz, Alba & Valarezo, Angel & Garcia-Hiernaux, Alfredo & Pérez-Amaral, Teodosio, 2025, "Eco-RETINA: a green flexible algorithm for model building," 33rd European Regional ITS Conference, Edinburgh, 2025: Digital innovation and transformation in uncertain times, International Telecommunications Society (ITS), number 331255.
• Dahlke, Johannes & Schmidt, Sebastian & Lenz, David & Kinne, Jan & Dehghan, Robert & Abbasiharofteh, Milad & Schütz, Moritz & Kriesch, Lukas & Hottenrott, Hanna & Kanilmaz, Umut Nefta & Grashof, Nils , 2025, "The WebAI paradigm of innovation research: Extracting insight from organizational web data through AI," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 25-019.

2024

• Gharad Bryan & Dean Karlan & Adam Osman, 2024, "Big Loans to Small Businesses: Predicting Winners and Losers in an Entrepreneurial Lending Experiment," American Economic Review, American Economic Association, volume 114, issue 9, pages 2825-2860, September, DOI: 10.1257/aer.20220616.
• Anil Kumar & Che-Yuan Liang, 2024, "Labor Market Effects of Credit Constraints: Evidence from a Natural Experiment," American Economic Journal: Economic Policy, American Economic Association, volume 16, issue 3, pages 1-26, August, DOI: 10.1257/pol.20200683.
• Nikhil Agarwal & Ray Huang & Alex Moehring & Pranav Rajpurkar & Tobias Salz & Feiyang Yu, 2024, "Comparative Advantage of Humans versus AI in the Long Tail," AEA Papers and Proceedings, American Economic Association, volume 114, pages 618-622, May, DOI: 10.1257/pandp.20241071.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "The Unreasonable Effectiveness of Algorithms," AEA Papers and Proceedings, American Economic Association, volume 114, pages 623-627, May, DOI: 10.1257/pandp.20241072.
• Annie Liang & Jay Lu, 2024, "Algorithmic Fairness and Social Welfare," AEA Papers and Proceedings, American Economic Association, volume 114, pages 628-632, May, DOI: 10.1257/pandp.20241073.
• Victoria Angelova & Will Dobbie & Crystal S. Yang, 2024, "Algorithmic Recommendations When the Stakes Are High: Evidence from Judicial Elections," AEA Papers and Proceedings, American Economic Association, volume 114, pages 633-637, May, DOI: 10.1257/pandp.20241074.
• Abbate Nicolás Francisco & Gasparini Leonardo & Ronchetti Franco & Quiroga Facundo, 2024, "High-Resolution Income Estimates Using Satellite Imagery: A Deep Learning Approach applied in Buenos Aires," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4701, Nov.
• Acedo Colli Luis Abel, 2024, "IED, capital humano y términos de intercambio: un enfoque de efectos de umbral," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4702, Nov.
• Khulood Mohammed BaLashwar & Yuosuf Khalid Al-Hamar & Seyed-Ali Sadegh-Zadeh, 2024, "Optimizing Bank Stability Through MSME Loan Securitization: A Predictive and Prescriptive Analytics Approach," The African Finance Journal, Africagrowth Institute, volume 26, issue 2, pages 58-79.
• Hauke Licht & Ronja Sczepanski & Moritz Laurer & Ayjeren Bekmuratovna, 2024, "No More Cost in Translation: Validating Open-Source Machine Translation for Quantitative Text Analysis," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 276, Feb.
• Hauke Licht & Ronja Sczepanski, 2024, "Who are They Talking About? Detecting Mentions of Social Groups in Political Texts with Supervised Learning," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 277, Feb.
• Simon D Angus, 2024, "Tracking Policy-relevant Narratives of Democratic Resilience at Scale: from experts and machines, to AI & the transformer revolution," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2024-07, Dec.
• Michal Mec & Mikulas Zeman & Klara Cermakova, 2024, "Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market," International Journal of Economic Sciences, European Research Center, volume 13, issue 2, pages 87-103, December.
• Anna A. Maigur, 2024, "Machine learning algorithms for predicting unemployment duration in Russia," Russian Journal of Economics, ARPHA Platform, volume 10, issue 4, pages 365-384, December, DOI: 10.32609/j.ruje.10.128611.
• Jeff Dominitz & Charles F. Manski, 2024, "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," Papers, arXiv.org, number 2403.11016, Mar, revised Apr 2025.
• Bartosz Bieganowski & Robert Slepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers, arXiv.org, number 2404.01866, Apr, revised Jun 2024.
• Arkadiusz Lipiecki & Bartosz Uniejewski & Rafa{l} Weron, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Papers, arXiv.org, number 2404.02270, Apr, revised Oct 2024.
• Annie Liang & Jay Lu, 2024, "Algorithmic Fairness and Social Welfare," Papers, arXiv.org, number 2404.04424, Apr.
• Adam Korniejczuk & Robert 'Slepaczuk, 2024, "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Papers, arXiv.org, number 2406.10695, Jun.
• Zuzanna Kostecka & Robert 'Slepaczuk, 2024, "Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data," Papers, arXiv.org, number 2406.17308, Jun.
• Kamil Kashif & Robert 'Slepaczuk, 2024, "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers, arXiv.org, number 2406.18206, Jun.
• Maciej Wysocki & Robert 'Slepaczuk, 2024, "Construction and Hedging of Equity Index Options Portfolios," Papers, arXiv.org, number 2407.13908, Jul.
• Melissa Dell, 2024, "Deep Learning for Economists," Papers, arXiv.org, number 2407.15339, Jul, revised Nov 2024.
• Natalia Roszyk & Robert 'Slepaczuk, 2024, "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers, arXiv.org, number 2407.16780, Jul.
• Stefania Albanesi & Domonkos F. Vamossy, 2024, "Credit Scores: Performance and Equity," Papers, arXiv.org, number 2409.00296, Aug.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "Large Language Models: An Applied Econometric Framework," Papers, arXiv.org, number 2412.07031, Dec, revised Dec 2025.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2024, "Machine Learning the Macroeconomic Effects of Financial Shocks," Papers, arXiv.org, number 2412.07649, Dec.
• Manuela Pedio & Massimo Guidolin & Giulia Panzeri, 2024, "Machine Learning in Portfolio Decisions," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 24233.
• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers, Bank of Canada, number 24-15, May, DOI: 10.34989/swp-2024-15.
• Pierre Beck & Pablo Garcia Sanchez & Alban Moura & Julien Pascal & Olivier Pierrard, 2024, "Deep learning solutions of DSGE models: A technical report," BCL working papers, Central Bank of Luxembourg, number 184, May.
• Piotr Gorzelańczyk, 2024, "Forecasting the number of road accidents caused by pedestrians in Poland using neural networks," Cognitive Sustainability, Cognitive Sustainability Ltd., volume 3, issue 4, pages 5-14, December, DOI: 10.55343/CogSust.102.
• Furkan TURKOGLU & Eda GOCECEK & Yavuz YUMRUKUZ, 2024, "Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 18, issue 2, pages 186-210.
• Francesco Braggiotti & Nicola Chiarini & Giulio Dondi & Luciano Lavecchia & Valeria Lionetti & Juri Marcucci & Riccardo Russo, 2024, "Predicting buildings' EPC in Italy: a machine learning based-approach," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 850, Jun.
• José Manuel Carbó Martinez & Sergio Gorjón Rivas, 2024, "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Granular data: new horizons and challenges".
• Ajit Desai & Jacob Sharples & Anneke Kosse, 2024, "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Granular data: new horizons and challenges".
• Ajit Desai & Anneke Kosse & Jacob Sharples, 2024, "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," BIS Working Papers, Bank for International Settlements, number 1188, May.
• Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024, "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, volume 75, issue 1, pages 235-260, February, DOI: 10.1111/1477-9552.12543.
• SIDOROV Andrei, 2024, "The Impact Of Announcements On Cryptocurrency Prices," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, volume 76, issue 4, pages 69-94, December, DOI: 10.56043/reveco-2024-0035.
• Burda Martin & Schroeder Adrian K., 2024, "Recurrent Neural Network GO-GARCH Model for Portfolio Selection," Journal of Time Series Econometrics, De Gruyter, volume 16, issue 2, pages 67-81, DOI: 10.1515/jtse-2023-0012.
• Guy Aridor & Rava Azeredo da Silveira & Michael Woodford, 2024, "Information-Constrained Coordination of Economic Behavior," CESifo Working Paper Series, CESifo, number 10935.
• Julian Ashwin & Paul Beaudry & Martin Ellison, 2024, "Neural Network Learning for Nonlinear Economies," Discussion Papers, Centre for Macroeconomics (CFM), number 2432, Jul.
• Aysun Can Turetken & Markus Leippold, 2024, "Battle of Transformers: Adversarial Attacks on Financial Sentiment Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 24-59, Nov.
• Francesco Audrino & Jessica Gentner & Simon Stalder, 2024, "Quantifying Uncertainty: A New Era of Measurement through Large Language Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 24-68, Aug.
• Pavel Vidal & Lya Paola Sierra-Suárez & Julieth Cerón, 2024, "Indicator for the Regional Labor Market Using Machine Learning Techniques: Application to Colombian Cities," Revista de Economía del Rosario, Universidad del Rosario, volume 27, issue 1, pages 1-31, DOI: 10.12804/revistas.urosario.edu.co/e.
• Carlos Giraldo & Iader Giraldo & Jose E. Gomez-Gonzalez & Jorge M. Uribe, 2024, "High Frequency Monitoring of Credit Creation: A New Tool for Central Banks in Emerging Market Economies," Documentos de trabajo, FLAR, number 21077, Mar.
• Peter B. Dixon & Maureen T. Rimmer & Florian Schiffmann, 2024, "Neural-Network approximation of reduced forms for CGE models explained by elementary examples," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-348, Nov.
• Peter Dixon & Michael Jerie & Dean Mustakinov & Maureen T. Rimmer & Nicholas Sheard & Florian Schiffmann & Glyn Wittwer, 2024, "Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-349, Dec.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2024, "Neural Network Learning for Nonlinear Economies," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 19295, Jul.
• Hauzenberger , Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024, "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 19381, Aug.
• Julen Iglesias Tejedor, 2024, "Creación de una cartera de inversión que venza la inflación atendiendo a criterios ESG gestionada mediante machine learning," Revista de Economía y Finanzas (REyF), Asociación Cuadernos de Economía, volume 2, issue 5, pages 79-100, Mayo.
• Iskren Tairov & Nadezhda Stefanova & Aleksandrina Aleksandrova, 2024, "Artificial Intelligence Application in Human Resources Management," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 3 Year 20, pages 72-88.
• Christian Manuel Moreno Rocha & Melidey Díaz Ospino & Israel Blanco Ramos & Andres Medina Guzman, 2024, "Enhancing Sustainable Mobility: Multi-Criteria Analysis for Electric Vehicle Integration and Policy Implementation," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 1, pages 205-218, January.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024, "Electrical Load Forecasting to Plan the Increase in Renewable Energy Sources and Electricity Demand: a CNN-QR-RTCF and Deep Learning Approach," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 4, pages 186-194, July.
• Christian Manuel Moreno Rocha & Daina Arenas Buelvas & Sandra De la Hoz Escorcia, 2024, "Evaluation and Ranking of Energy Alternatives for Implementation in Different Geographic Scenarios using Decision Methods: Case Study of Colombia," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 5, pages 191-202, September.
• Wellcome Peujio Jiotsop-Foze & Adrián Hernández-del-Valle & Francisco Venegas-Martínez, 2024, "Transforming Mexico’s Electric Load Infrastructure: A Quantile Transformer Network Deep Learning Approach, 2019-2020," International Journal of Energy Economics and Policy, Econjournals, volume 14, issue 5, pages 527-533, September.
• Nguyen, Jeremy K., 2024, "Human bias in AI models? Anchoring effects and mitigation strategies in large language models," Journal of Behavioral and Experimental Finance, Elsevier, volume 43, issue C, DOI: 10.1016/j.jbef.2024.100971.
• Pascal, Julien, 2024, "Artificial neural networks to solve dynamic programming problems: A bias-corrected Monte Carlo operator," Journal of Economic Dynamics and Control, Elsevier, volume 162, issue C, DOI: 10.1016/j.jedc.2024.104853.
• Wang, Jia & Wang, Xinyi & Wang, Xu, 2024, "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, volume 70, issue C, DOI: 10.1016/j.najef.2023.102065.
• Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024, "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, volume 74, issue C, DOI: 10.1016/j.najef.2024.102252.
• Zhang, Wenyu, 2024, "Dynamic monitoring of financial security risks: A novel China financial risk index and an early warning system," Economics Letters, Elsevier, volume 234, issue C, DOI: 10.1016/j.econlet.2023.111445.
• Baumgärtner, Martin & Zahner, Johannes, 2024, "Talking fragmentation away – Decoding the ’whatever it takes’ effect," Economics Letters, Elsevier, volume 234, issue C, DOI: 10.1016/j.econlet.2023.111484.
• Abdou, Hussein A. & Elamer, Ahmed A. & Abedin, Mohammad Zoynul & Ibrahim, Bassam A., 2024, "The impact of oil and global markets on Saudi stock market predictability: A machine learning approach," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107416.
• Zadeh, Omid Razavi & Romagnoli, Silvia, 2024, "Financing sustainable energy transition with algorithmic energy tokens," Energy Economics, Elsevier, volume 132, issue C, DOI: 10.1016/j.eneco.2024.107420.
• Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024, "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, volume 133, issue C, DOI: 10.1016/j.eneco.2024.107466.
• Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024, "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, volume 136, issue C, DOI: 10.1016/j.eneco.2024.107733.
• Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024, "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, volume 137, issue C, DOI: 10.1016/j.eneco.2024.107770.