Research classified by Journal of Economic Literature (JEL) codes

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

2023

• Vitor Joao Pereira Domingues MARTINHO, 2023, "Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches," Regional Science Inquiry, Hellenic Association of Regional Scientists, volume 0, issue 1, pages 29-42, June.
• Alejandro Vargas Sánchez & André Nicolas Monje Prudencio, , "Optimización de Carteras de Renta Variable con Aprendizaje Automático," Investigación & Desarrollo, Universidad Privada Boliviana, number 0223, DOI: 10.23881/idupbo.023.2-2e.
• Oleksandr Lutsii & Oleksandr Helevei, 2023, "Formation of Components of the Marketing Information System for Agricultural Products Using Big Data Methods," Oblik i finansi, Institute of Accounting and Finance, issue 3, pages 145-150, September, DOI: 10.33146/2307-9878-2023-3(101)-145-.
• Saurabh Ghosh & Abhishek Ranjan, 2023, "A Machine Learning Approach To Gdp Nowcasting: An Emerging Market Experience," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 26, issue Special I, pages 33-54, February, DOI: https://doi.org/10.59091/1410-8046..
• Marcus Buckmann & Andreas Joseph, 2023, "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, volume 19, issue 4, pages 449-522, October.
• Naudé, Wim, 2023, "Artificial Intelligence and the Economics of Decision-Making," IZA Discussion Papers, IZA Network @ LISER, number 16000, Mar.
• Osman Semi Ceylan, 2023, "Implementation of Quantum Weyl Transformations on Cirq," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 1-5, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sevdanur GENÇ, 2023, "The Impact of Quantum Technology on the Metaverse: Future Possibilities and Challenges," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 17-27, October, DOI: doi.org/10.5281/zenodo.10102956.
• Ercan ÇaÄŸlar, 2023, "Introduction to Quantum Data Science: Grover Search Algorithm," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 29-34, October, DOI: doi.org/10.5281/zenodo.10102956.
• Bayram Köse, 2023, "Data, Informatics, Artificial Intelligence and Optimization," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 35-40, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sinem Kalkan, 2023, "Magnetized Strange Quark Matter in Lyra Theory," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 41-44, October, DOI: doi.org/10.5281/zenodo.10102956.
• Hüseyin Türker, 2023, "Exploring Quantum Annealing: A Pathway to Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 45-54, October, DOI: doi.org/10.5281/zenodo.10102956.
• Dila BaÅŸpınar & Ela BAÅžPINAR & Umut AldoÄŸan, 2023, "Derivative and partial integral methods and Gauss integral's indefinite integral solution and its use in wave function in quantum mechanics and exact solutions of the wave function depending on position and time," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 55-63, October, DOI: doi.org/10.5281/zenodo.10102956.
• Davut Emre Tasar & Kutan Koruyan & Ceren Öcal CoÅŸar, 2023, "Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 65-72, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sevdanur GENÇ, 2023, "Hamiltonian Analysis of Amino Acid Sequences by Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 7-16, October, DOI: doi.org/10.5281/zenodo.10102956.
• Paolo Massimo Buscema & Francesca Della Torre & Giulia Massini & Guido Ferilli & Pier Luigi Sacco, 2023, "Reconstructing the Emergent Organization of Information Flows in International Stock Markets: A Computational Complex Systems Approach," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 1, pages 49-89, June, DOI: 10.1007/s10614-022-10267-1.
• Ba Chu & Shafiullah Qureshi, 2023, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1567-1609, December, DOI: 10.1007/s10614-022-10312-z.
• Hannes Wallimann & David Imhof & Martin Huber, 2023, "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1669-1720, December, DOI: 10.1007/s10614-022-10315-w.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023, "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1801-1843, December, DOI: 10.1007/s10614-022-10320-z.
• Bhaskar Tripathi & Rakesh Kumar Sharma, 2023, "Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1919-1945, December, DOI: 10.1007/s10614-022-10325-8.
• Indu Khurana & Daniel J. Lee, 2023, "Gender bias in high stakes pitching: an NLP approach," Small Business Economics, Springer, volume 60, issue 2, pages 485-502, February, DOI: 10.1007/s11187-021-00598-y.
• Botond Benedek & Balint Zsolt Nagy, 2023, "Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 22, issue 2, pages 77-98.
• Jonathan Proctor & Tamma Carleton & Sandy Sum, 2023, "Parameter Recovery Using Remotely Sensed Variables," NBER Working Papers, National Bureau of Economic Research, Inc, number 30861, Jan.
• Bryan T. Kelly & Dacheng Xiu, 2023, "Financial Machine Learning," NBER Working Papers, National Bureau of Economic Research, Inc, number 31502, Jul.
• Michael G. Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023, "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," NBER Working Papers, National Bureau of Economic Research, Inc, number 31523, Aug.
• Turan G. Bali & Bryan T. Kelly & Mathis Mörke & Jamil Rahman, 2023, "Machine Forecast Disagreement," NBER Working Papers, National Bureau of Economic Research, Inc, number 31583, Aug.
• D. Babet & Q. Deltour & T. Faria & S. Himpens, 2023, "Les reseaux de neurones appliques a la statistique publique : methodes et cas d’usages," Documents de Travail de l'Insee - INSEE Working Papers, Institut National de la Statistique et des Etudes Economiques, number m2023-01.
• Leonidas Aristodemou & Fernando Galindo-Rueda & Kuniko Matsumoto & Akiyoshi Murakami, 2023, "Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality," OECD Science, Technology and Industry Working Papers, OECD Publishing, number 2023/06, Oct, DOI: 10.1787/4889f5f2-en.
• Dillon Huddleston & Fred Liu & Lars Stentoft, 2023, "Intraday Market Predictability: A Machine Learning Approach," Journal of Financial Econometrics, Oxford University Press, volume 21, issue 2, pages 485-527.
• Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023, "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations, Paderborn University, Faculty of Business Administration and Economics, number 112, Jun.
• Benedek Nagy & Manuela Rozalia Gabor & Ioan Bogdan Baco? & Moaaz Kabil & Kai Zhu & Lóránt Dénes Dávid, 2023, "Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 18, issue 2, pages 419-459, June, DOI: 10.24136/eq.2023.013.
• Vancsura, László & Bareith, Tibor, 2023, "Analysis of the performance of predictive models during Covid-19 and the Russian-Ukrainian war," Public Finance Quarterly, Corvinus University of Budapest, volume 69, issue 2, pages 118-132, DOI: https://doi.org/10.35551/PFQ_2023_2.
• Gutierrez-Lythgoe, Antonio, 2023, "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial
  [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper, University Library of Munich, Germany, number 117103, Apr.
• Gutierrez-Lythgoe, Antonio, 2023, "Autoempleo y Machine Learning: Una aplicación para España
  [Self-employment and Machine Learning: An application for Spain]," MPRA Paper, University Library of Munich, Germany, number 117275, May.
• Temel, Tugrul & Phumpiu, Paul, 2023, "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper, University Library of Munich, Germany, number 118389, Aug.
• Temel, Tugrul & Phumpiu, Paul, 2023, "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper, University Library of Munich, Germany, number 118466, Sep.
• Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria & Fomin, Kiril, 2023, "Forecasting the main economic indicators for industry in the analytical system "Horizon"," MPRA Paper, University Library of Munich, Germany, number 118887, Oct.
• Chen, Ying & Grith, Maria & Lai, Hannah L. H., 2023, "Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach," MPRA Paper, University Library of Munich, Germany, number 119022, Oct.
• Mestiri, Sami, 2023, "How to use machine learning in finance," MPRA Paper, University Library of Munich, Germany, number 120045, Oct.
• Pal, Hemendra, 2023, "The Impact of Russia-Ukraine conflict on Global Commodity Brent Crude Prices," MPRA Paper, University Library of Munich, Germany, number 124770, Aug, revised 02 Oct 2024.
• Jiří Witzany & Milan Fičura, 2023, "Machine Learning Applications to Valuation of Options on Non-liquid Markets," FFA Working Papers, Prague University of Economics and Business, number 5.001, Jan, revised 24 Jan 2023.
• Jiří Witzany & Milan Fičura, 2023, "A Comparison of Neural Networks and Bayesian MCMC for the Heston Model Estimation (Forget Statistics - Machine Learning is Sufficient!)," FFA Working Papers, Prague University of Economics and Business, number 5.007, Jul, revised 11 Jul 2023.
• Callan Windsor & Max Zang, 2023, "Firms' Price-setting Behaviour: Insights from Earnings Calls," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2023-06, Sep, DOI: 10.47688/rdp2023-06.
• Roy, Sumitra & Gupta, Vishnu & Ray, Samrat, 2023, "Adoption of AI chat bot like Chat GPT in higher education in India: a sem analysis approach," Economic environment, Russian Presidential Academy of National Economy and Public Administration, issue 4, pages 130-149, DOI: 10.36683/2306-1758/2023-4-46/130-14.
• Guodong Guo & Brad R. Humphreys & Qiangchang Wang & Yang Zhou, 2023, "Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance," Journal of Sports Economics, , volume 24, issue 6, pages 737-758, August, DOI: 10.1177/15270025231160769.
• Abdullah Mohammad Ghazi Al khatib & Bayan Mohamad Alshaib & Ali Mohamad Kanaan, 2023, "The Interaction Between Financial Development and Economic Growth: A Novel Application of Transfer Entropy and Nonlinear Approach in Algeria," SAGE Open, , volume 13, issue 4, pages 21582440231, December, DOI: 10.1177/21582440231217871.
• Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023, "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2023_13, Nov.
• Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023, "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, volume 20, issue 1, pages 1-36, December, DOI: 10.1007/s10287-023-00454-2.
• Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023, "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, volume 5, issue 1, pages 29-56, March, DOI: 10.1007/s42521-022-00050-0.
• Huei-Wen Teng & Yu-Hsien Li, 2023, "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, volume 5, issue 1, pages 149-182, March, DOI: 10.1007/s42521-023-00076-y.
• Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023, "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, volume 5, issue 2, pages 295-365, June, DOI: 10.1007/s42521-023-00079-9.
• Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023, "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 13, issue 2, pages 285-305, June, DOI: 10.1007/s40822-023-00232-0.
• Francisco J. Delgado & Elena Fernández-Rodríguez & Roberto García-Fernández & Manuel Landajo & Antonio Martínez-Arias, 2023, "Tax avoidance and earnings management: a neural network approach for the largest European economies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 9, issue 1, pages 1-25, December, DOI: 10.1186/s40854-022-00424-8.
• Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023, "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 9, issue 1, pages 1-34, December, DOI: 10.1186/s40854-022-00446-2.
• Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023, "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, volume 14, issue 1, pages 549-568, March, DOI: 10.1007/s13198-022-01851-7.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2023, "Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market," Journal of Business Economics, Springer, volume 93, issue 9, pages 1629-1660, November, DOI: 10.1007/s11573-023-01138-8.
• K. Kumaraswamy & N. Ch. Bhatracharyulu, 2023, "Statistical brand switching model: an Hidden Markov approach," OPSEARCH, Springer;Operational Research Society of India, volume 60, issue 2, pages 942-950, June, DOI: 10.1007/s12597-022-00613-0.
• Indra Budi & Yaniasih Yaniasih, 2023, "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, volume 128, issue 1, pages 735-759, January, DOI: 10.1007/s11192-022-04567-4.
• Mei-Mei Lin & Fu-Hsiang Kuo, 2023, "A Principal Component Analysis of Digital Banking Development in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 13, issue 4, pages 1-2.
• Yelyzaveta Apanovych & Yelyzaveta Apanovych & Stanislav Prágr, 2023, "Determination of iron procurement strategy for manufacturing companies," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 11, issue 2, pages 331-348, December, DOI: 10.9770/jesi.2023.11.2(23).
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2023, "Can a Machine Correct Option Pricing Models?," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 41, issue 3, pages 995-1009, July, DOI: 10.1080/07350015.2022.2099871.
• Santiago Picasso, 2023, "Crecimiento y convergencia: un análisis desde la teoría de grafos," Documentos de Trabajo (working papers), Instituto de Economía - IECON, number 23-15.
• Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2023, "Machine Learning and Fundraising: Applications of Artificial Neural Networks," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2023: 33.
• Adelaide Baronchelli & Roberto Ricciuti & Mattia Viale, 2023, "Elite persistence in medieval Venice after the Black Death," Working Papers, University of Verona, Department of Economics, number 01/2023, Jan.
• Sabek Amine, 2023, "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 9, issue 1, pages 16-32, July, DOI: 10.2478/crebss-2023-0002.
• Souto Hugo Gobato & Moradi Amir, 2023, "Forecasting realized volatility through financial turbulence and neural networks," Economics and Business Review, Sciendo, volume 9, issue 2, pages 133-159, April, DOI: 10.18559/ebr.2023.2.737.
• Kaczmarek Tomasz & Grobelny Przemysław, 2023, "How to fly to safety without overpaying for the ticket," Economics and Business Review, Sciendo, volume 9, issue 2, pages 160-183, April, DOI: 10.18559/ebr.2023.2.738.
• Węcel Krzysztof & Sawiński Marcin & Stróżyna Milena & Lewoniewski Włodzimierz & Księżniak Ewelina & Stolarski Piotr & Abramowicz Witold, 2023, "Artificial intelligence—friend or foe in fake news campaigns," Economics and Business Review, Sciendo, volume 9, issue 2, pages 41-70, April, DOI: 10.18559/ebr.2023.2.736.
• Manta Eduard Mihai & Bogoevici Flavia, 2023, "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, volume 12, issue 2, pages 88-108, December, DOI: 10.2478/jses-2023-0011.
• Maudud Hassan Uzzal & Robert Ślepaczuk, 2023, "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-05.
• Karol Chojnacki & Robert Ślepaczuk, 2023, "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compa," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-15.
• Damian Ślusarczyk & Robert Ślepaczuk, 2023, "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-17.
• Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023, "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-20.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-23.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-25.
• Sahil Teymurzade & Robert Ślepaczuk, 2023, "Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-27.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023, "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, volume 91, issue 3, pages 869-901, May, DOI: 10.3982/ECTA18180.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2023, "Intelligent stock prediction: A neural network approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 10, issue 01, pages 1-14, March, DOI: 10.1142/S2424786322500165.
• Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025, "Deep parametric portfolio policies," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 23-01, revised 2025.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 188.
• Baumgärtner, Martin & Zahner, Johannes, 2023, "Whatever it takes to understand a central banker: Embedding their words using neural networks," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 194.
• Büchel, Jan & Engler, Jan & Mertens, Armin, 2023, "Gesuchte Datenkompetenzen in Deutschland
  [The demand for data skills in Germany]," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, volume 50, issue 2, pages 3-17, DOI: 10.2373/1864-810X.23-02-01.
• Frank, Johannes, 2023, "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, number 03/2023.
• Singla, Shikhar, 2023, "Regulatory costs and market power," LawFin Working Paper Series, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin), number 47.
• Foltas, Alexander, 2023, "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 44, DOI: 10.18452/27015.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277627.
• Holtemöller, Oliver & Kozyrev, Boris, 2023, "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277688.

2022

• Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022, "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2022-09, May.
• Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2022, "Belief Distortions and Macroeconomic Fluctuations," American Economic Review, American Economic Association, volume 112, issue 7, pages 2269-2315, July, DOI: 10.1257/aer.20201713.
• Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2022, "Peer Effects in Product Adoption," American Economic Journal: Applied Economics, American Economic Association, volume 14, issue 3, pages 488-526, July, DOI: 10.1257/app.20200367.
• Ignacia Mercadal, 2022, "Dynamic Competition and Arbitrage in Electricity Markets: The Role of Financial Players," American Economic Journal: Microeconomics, American Economic Association, volume 14, issue 3, pages 665-699, August, DOI: 10.1257/mic.20190276.
• Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon Hanson & Alex Cloninger & Tajana Rosing & Amit K. Khandelwal, 2022, "Using Neural Networks to Predict Microspatial Economic Growth," American Economic Review: Insights, American Economic Association, volume 4, issue 4, pages 491-506, December, DOI: 10.1257/aeri.20210422.
• Rossella Calvi & Jacob Penglase & Denni Tommasi, 2022, "Measuring Women's Empowerment in Collective Households," AEA Papers and Proceedings, American Economic Association, volume 112, pages 556-560, May, DOI: 10.1257/pandp.20221054.
• Laurence Kotlikoff, 2022, "Does Prediction Machines Predict Our AI Future? A Review," Journal of Economic Literature, American Economic Association, volume 60, issue 3, pages 1052-1057, September, DOI: 10.1257/jel.20191519.
• Mariam Dundua & Otar Gorgodze, 2022, "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers, National Bank of Georgia, number 02/2022, Nov.
• ȘTefan Bolotä‚ & Mircea Asandului, 2022, "Using Machine Learning In Detecting Fake News," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 30, pages 53-66, December, DOI: 10.47743/rebs-2022-2-0004.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022, "“An application of deep learning for exchange rate forecasting”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202201, Jan, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202207, Jul, revised Jul 2022.
• Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022, "Distributional neural networks for electricity price forecasting," Papers, arXiv.org, number 2207.02832, Jul, revised Dec 2022.
• Ramis Khabibullin & Sergei Seleznev, 2022, "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers, arXiv.org, number 2210.07154, Oct.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022, "Bayesian Neural Networks for Macroeconomic Analysis," Papers, arXiv.org, number 2211.04752, Nov, revised Apr 2024.
• Anton A. Gerunov, 2022, "Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 15-43.
• Andrés Alonso-Robisco & José Manuel Carbó, 2022, "Inteligencia artificial y finanzas: una alianza estratégica," Occasional Papers, Banco de España, number 2222, Oct.
• José Manuel Carbó & Sergio Gorjón, 2022, "Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin," Working Papers, Banco de España, number 2215, Apr.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "Natural Language Processing and Financial Markets: Semi-supervised Modelling of Coronavirus and Economic News," Working Papers, Banco de España, number 2228, Aug.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers, Banco de España, number 2229, Aug.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers, Banco de España, number 2240, Nov, DOI: https://doi.org/10.53479/23646.
• Valerio Astuti & Alessio Ciarlone & Alberto Coco, 2022, "The role of central bank communication in inflation-targeting Eastern European emerging economies," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1381, Oct.
• Torre Leonardo & González Eva & Casillas Ramón & Alvarado Jorge, 2022, "Sentiment Indexes and Economic Activity Indicators in Mexico 2016-2021," Working Papers, Banco de México, number 2022-18, Dec.
• Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022, "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Machine learning in central banking".
• Jošić Hrvoje & Žmuk Berislav, 2022, "A Machine Learning Approach to Forecast International Trade: The Case of Croatia," Business Systems Research, Sciendo, volume 13, issue 3, pages 144-160, October, DOI: 10.2478/bsrj-2022-0030.
• Ramis Khabibullin & Sergei Seleznev, 2022, "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series, Bank of Russia, number wps104, Dec.
• Martin Huber & David Imhof & Rieko Ishii, 2022, "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, volume 185, issue 3, pages 1074-1114, July, DOI: 10.1111/rssa.12811.
• Marcus Buckmann & Andreas Joseph, 2022, "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers, Bank of England, number 984, Jun.
• Elliott Ash & Ruben Durante & Maria Grebenshchikova & Carlo Schwarz, 2022, "Visual Representation and Stereotypes in News Media," CESifo Working Paper Series, CESifo, number 9686.
• David Anderson & Urban Ulrych, 2022, "Accelerated American Option Pricing with Deep Neural Networks," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-03, Jan.
• Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022, "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-41, May.
• Oksana Bashchenko, 2022, "Bitcoin Price Factors: Natural Language Processing Approach," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-48, May.
• Fredy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos Leon-Rinc�n & Hern�n Rincon-Castro, 2022, "Colombian Liberalization and Integration into World Trade Markets: Much Ado about Nothing," Revista de Economía del Rosario, Universidad del Rosario, volume 25, issue 2, pages 1-44.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022, "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17391, Jun.
• Katumullage, Duwani & Yang, Chenyu & Barth, Jackson & Cao, Jing, 2022, "Using Neural Network Models for Wine Review Classification," Journal of Wine Economics, Cambridge University Press, volume 17, issue 1, pages 27-41, February.
• Yang, Chenyu & Barth, Jackson & Katumullage, Duwani & Cao, Jing, 2022, "Wine Review Descriptors as Quality Predictors: Evidence from Language Processing Techniques," Journal of Wine Economics, Cambridge University Press, volume 17, issue 1, pages 64-80, February.
• Festus Victor Bekun, 2022, "Mitigating Emissions in India: Accounting for the Role of Real Income, Renewable Energy Consumption and Investment in Energy," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 1, pages 188-192.
• Christian Manuel Moreno Rocha & Jose Ricardo Nunez Alvarez & Daniel A. Diaz Castillo & Esnaider D. Florian Domingue & Juan Camilo Barrera Hernandez, 2022, "Implementation of the Hierarchical Analytical Process in the Selection of the Best Source of Renewable Energy in the Colombian Caribbean Region," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 111-119, March.
• Adrian-Nicolae Buturache & Stelian Stancu, 2022, "Building Energy Consumption Prediction Using Neural-Based Models," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 30-38, March.
• Christian Manuel Moreno Rocha & Esnaider D. Florian Domingue & Daniel A. Diaz Castillo & Kevin Logreira Vargas & Andres Alfredo Medina Guzman, 2022, "Evaluation of Energy Alternatives through FAHP for the Energization of Colombian Insular Areas," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 4, pages 87-98, July.
• Christian Manuel Moreno Rocha & David Fernandez Perez & Jesus Rodriguez Retamoza & Jorge Silva Ortega & Denis Brieva Bohorquez & Luis Taborda Catalan, 2022, "Evaluation, Hierarchy and Selection of the best Source of Energy by using AHP, as a Proposed Solution to an Energy and Socio-economic Problem, in the case of Colombia s Pacific Zone," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 5, pages 409-419, September.
• Davidescu, Adriana AnaMaria & Petcu, Monica Aureliana & Curea, Stefania Cristina & Manta, Eduard Mihai, 2022, "Two faces of the same coin: Exploring the multilateral perspective of informality in relation to Sustainable Development Goals based on bibliometric analysis," Economic Analysis and Policy, Elsevier, volume 73, issue C, pages 683-705, DOI: 10.1016/j.eap.2021.12.016.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022, "On modeling IPO failure risk," Economic Modelling, Elsevier, volume 109, issue C, DOI: 10.1016/j.econmod.2022.105790.
• Sadorsky, Perry, 2022, "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.najef.2022.101705.
• Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022, "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, volume 105, issue C, DOI: 10.1016/j.eneco.2021.105711.
• Lei, Heng & Xue, Minggao & Liu, Huiling, 2022, "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, volume 113, issue C, DOI: 10.1016/j.eneco.2022.106189.
• Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022, "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106285.
• Ghabri, Yosra & Ben Rhouma, Oussama & Gana, Marjène & Guesmi, Khaled & Benkraiem, Ramzi, 2022, "Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions," International Review of Financial Analysis, Elsevier, volume 82, issue C, DOI: 10.1016/j.irfa.2022.102197.
• Alonso-Robisco, Andrés & Carbó, José Manuel, 2022, "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, volume 84, issue C, DOI: 10.1016/j.irfa.2022.102372.
• Kutuk, Yasin & Barokas, Lina, 2022, "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102198.
• García-Céspedes, Rubén & Moreno, Manuel, 2022, "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2021.102669.
• Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022, "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102856.
• Barua, Ronil & Sharma, Anil K., 2022, "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103111.
• Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022, "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 144-174, DOI: 10.1016/j.insmatheco.2022.03.010.
• Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022, "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 57-67, DOI: 10.1016/j.insmatheco.2022.07.013.
• Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022, "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106339.
• Plakandaras, Vasilios & Ji, Qiang, 2022, "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2022.100245.
• Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022, "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102778.
• Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022, "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, volume 76, issue C, DOI: 10.1016/j.pacfin.2022.101886.
• Ouyang, Zisheng & Chen, Shili & Lai, Yongzeng & Yang, Xite, 2022, "The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 600, issue C, DOI: 10.1016/j.physa.2022.127518.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022, "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, volume 94, issue C, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022, "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, volume 60, issue C, DOI: 10.1016/j.ribaf.2021.101610.
• Kristóf, Tamás & Virág, Miklós, 2022, "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.ribaf.2022.101644.
• Bernt P. Stigum, 2022, "Consumer Choice under Certainty and Uncertainty in Applied Econometrics," Journal of Economics and Econometrics, Economics and Econometrics Society, volume 65, issue 1, pages 1-27.
• Adriana Anamaria Davidescu & Răzvan Gabriel Hapau & Eduard Mihai Manta, 2022, "Impact of Crises on Capital Market Volatility: A Bibliometric Analysis," Contemporary Studies in Economic and Financial Analysis, Emerald Group Publishing Limited, "The New Digital Era: Other Emerging Risks and Opportunities", DOI: 10.1108/S1569-37592022000109B003.
• Lukas Janasek, 2022, "Acquisition of Costly Information in Data-Driven Decision Making," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2022/10, May, revised May 2022.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16, Nov, DOI: 10.29338/wp2022-16.
• Nelson P. Rayl & Nitish R. Sinha, 2022, "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2022-042, Jul, DOI: 10.17016/FEDS.2022.042.
• Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022, "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, volume 15, issue 2, pages 1-30, February.
• Susan Ariel Aaronson, 2022, "Wicked Problems Might Inspire Greater Data Sharing," Working Papers, The George Washington University, Institute for International Economic Policy, number 2022-09, Sep.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2022, "Intelligent Stock Prediction: A Neural Network Approach," Post-Print, HAL, number hal-03884171, Jun, DOI: 10.1142/S2424786322500165.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print, HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Sciences Po Economics Publications (main), HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Südbeck, Insa & Mindlina, Julia & Schnabel, André & Helber, Stefan, 2022, "Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-703, Nov.
• Tea Baldigara, 2022, "Primjena Umjetnih Neuronskih Mreža U Modeliranju Domaće Turističke Potražnje," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), volume 73, issue 3, pages 349-370, DOI: 10.32910/ep.73.3.1.
• Mühlbauer, Sabrina & Weber, Enzo, 2022, "Machine Learning for Labour Market Matching," IAB-Discussion Paper, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], number 202203, Feb, DOI: 10.48720/IAB.DP.2203.
• Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022, "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 17, issue 1, pages 1-23, Enero - M.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022, ""An application of deep learning for exchange rate forecasting"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202201, Jan, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202210, Jul, revised Jul 2022.
• Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022, ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202215, Oct, revised Oct 2022.
• Cochrane, William & Poot, Jacques & Roskruge, Matthew, 2022, "Urban Resilience and Social Security Uptake: New Zealand Evidence from the Global Financial Crisis and the COVID-19 Pandemic," IZA Discussion Papers, IZA Network @ LISER, number 15510, Aug.
• William Barnett & Kun He & Jingtong He, 2022, "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202217, Nov.
• Giovanni Villani, 2022, "A Neural Network Approach to Value R&D Compound American Exchange Option," Computational Economics, Springer;Society for Computational Economics, volume 60, issue 1, pages 305-324, June, DOI: 10.1007/s10614-021-10150-5.
• Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022, "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, volume 36, issue 1, pages 1-28, March, DOI: 10.1007/s11408-021-00391-7.
• Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022, "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, volume 47, issue 2, pages 476-505, April, DOI: 10.1007/s10961-021-09900-2.
• Paolo Savona, 2022, "Prospects for Reforming the Money and Financial System," Open Economies Review, Springer, volume 33, issue 1, pages 187-195, February, DOI: 10.1007/s11079-021-09628-4.
• Chris Charalambous & Spiros H. Martzoukos & Zenon Taoushianis, 2022, "Estimating corporate bankruptcy forecasting models by maximizing discriminatory power," Review of Quantitative Finance and Accounting, Springer, volume 58, issue 1, pages 297-328, January, DOI: 10.1007/s11156-021-00995-0.
• Miriam Sosa & Christian Bucio & Edgar Ortiz Calisto, 2022, "Dynamic Stock Dependence and Monetary Variables in the United States (2000- 2016) - A Copula and Neural Network Approach," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 96, pages 201-234, January-J, DOI: 10.17533/udea.le.n96a345321.
• Daniel Stempel & Johannes Zahner, 2022, "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202232.
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022, "The Virtue of Complexity in Return Prediction," NBER Working Papers, National Bureau of Economic Research, Inc, number 30217, Jul.
• Yacine Aït-Sahalia & Jianqing Fan & Lirong Xue & Yifeng Zhou, 2022, "How and When are High-Frequency Stock Returns Predictable?," NBER Working Papers, National Bureau of Economic Research, Inc, number 30366, Aug.
• Anton A. Gerunov, 2022, "Forecasting Customer Support Resolution Times Through Automated Machine Learning," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, volume 19, issue 2, pages 1-11, DOI: 10.37708/em.swu.v19i2.1.
• Dimitris Korobilis & Kenichi Shimizu, 2022, "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, volume 11, issue 4, pages 230-354, June, DOI: 10.1561/0800000041.
• Rodríguez Guevara, David Esteban & Rendón García, Juan Fernando & Trespalacios Carrasquilla, Alfredo & Jiménez Echeverri, Edwin Andrés, 2022, "Modelación de riesgo de crédito de personas naturales. Un caso aplicado a una caja de compensación familiar colombiana
  [Natural People Credit Risk Modeling. An applied case in a Colombian Family Benefit Fund]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 33, issue 1, pages 29-48, June, DOI: https://doi.org/10.46661/revmetodos.
• Díez, José Ignacio & Giannasi, Nadia & Scudelati, Mariela, 2022, "Redes, conocimientos y capacidades en el contexto de la industria del software. Análisis del caso Bahía Blanca
  [Network, knowledge and capabilities in the context of the software industry. Analysis of Bahía Blanca case]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 34, issue 1, pages 137-154, December, DOI: https://doi.org/10.46661/revmetodos.
• Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022, "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, volume 13, issue 4, pages 1215-1251, December, DOI: 10.24136/oc.2022.035.
• Prisznyák, Alexandra, 2022, "Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management — Prevention of Money Laundering and Terrorist Financing," Public Finance Quarterly, Corvinus University of Budapest, volume 67, issue 2, pages 288-303, DOI: https://doi.org/10.35551/PFQ_2022_2.
• Liu, Mengxiao & Wang, Luhang & Yi, Yimin, 2022, "Quality Innovation, Cost Innovation, Export, and Firm Productivity Evolution: Evidence from the Chinese Electronics Industry," MPRA Paper, University Library of Munich, Germany, number 113270, Jul.
• Yang, Bill Huajian, 2022, "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper, University Library of Munich, Germany, number 114188, Aug, revised 18 Jul 2022.
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2022, "Can a Machine Correct Option Pricing Models?," Working Papers, Princeton University. Economics Department., number 2022-9, Jul.
• Damian Przekop, 2022, "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, volume 14, issue 2, pages 199-223, June.
• Aike Steentoft, Bu-Sung Lee, Markus Schl pfer, 2022, "Interpretable Prediction of Urban Mobility Flows with Deep Neural Networks as Gaussian Processes," Diskussionsschriften, Universitaet Bern, Departement Volkswirtschaft - CRED, number credresearchpaper36, May.
• Radu LUPU & Iulia LUPU & Tanase STAMULE & Mihai ROMAN, 2022, "Entropy as Leading Indicator for Extreme Systemic Risk Events," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 58-73, December.
• Riccardo Di Francesco, 2022, "Aggregation Trees," CEIS Research Paper, Tor Vergata University, CEIS, number 546, Dec, revised 20 Nov 2023.
• Soham Banerjee & Diganta Mukherjee, 2022, "Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective," Studies in Microeconomics, , volume 10, issue 1, pages 23-49, June, DOI: 10.1177/2321022220980537.
• Elda Xhumari & Suela Maxhelaku & Endrit Xhina, 2022, "A review of Knowledge Graph and Graph Neural Network application," Proceedings of Economics and Finance Conferences, International Institute of Social and Economic Sciences, number 13615626, Jul.