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

2024

• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16b, Feb, DOI: 10.29338/wp2022-16b.
• Celso Brunetti & Marc Joëts & Valérie Mignon, 2024, "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2024-003, Feb, DOI: 10.17016/FEDS.2024.003.
• Thomas R. Cook & Zach Modig & Nathan M. Palmer, 2024, "Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2024-075, Sep, DOI: 10.17016/FEDS.2024.075.
• Miguel Faria-e-Castro & Fernando Leibovici, 2024, "Artificial Intelligence and Inflation Forecasts," Review, Federal Reserve Bank of St. Louis, volume 106, issue 12, pages 1-14, November, DOI: 10.20955/r.2024.12.
• Konstantinos Kofidis & Cătălina Lucia Cocianu, 2024, "Comparative analysis of RF, SVR with Gaussian kernel and LSTM for predicting loan defaults," Journal of Financial Studies, Institute of Financial Studies, volume 9, issue 17, pages 91-106, November, DOI: 10.55654/JFS.2024.9.17.06.
• Norbert Pfeifer & Miriam Steurer, 2024, "Stabilizing Geo-Spatial Surfaces in Data-Sparse Regions - An Application to Residential Property Prices," Graz Economics Papers, University of Graz, Department of Economics, number 2024-11, Apr.
• Cosimo Magazzino & Marco Mele & Claudiu Tiberiu Albulescu & Nicholas Apergis & Mihai Ioan Mutascu, 2024, "The presence of a latent factor in gasoline and diesel prices co-movements," Post-Print, HAL, number hal-04802059, Mar, DOI: 10.1007/s00181-023-02523-6.
• Tea Šestanović, 2024, "A Comprehensive Approach To Bitcoin Forecasting Using Neural Networks," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), volume 75, issue 1, pages 62-85, DOI: 10.32910/ep.75.1.3.
• Athey, Susan & Simon, Lisa & Skans, Oskar & Johan Vikström, Johan & Yakymovych, Yaroslav, 2024, "The heterogeneous earnings impact of job lossacross workers, establishments, and markets," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy, number 2024:10, May.
• Yaroslav Kouzminov & Ekaterina Kruchinskaia, 2024, "The Evaluation of GenAI Capabilities to Implement Professional Tasks," Foresight and STI Governance, National Research University Higher School of Economics, volume 18, issue 4, pages 67-76.
• Mühlbauer, Sabrina & Weber, Enzo, 2024, "Predicting Job Match Quality: A Machine Learning Approach," IAB-Discussion Paper, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], number 202409, Jul, DOI: 10.48720/IAB.DP.2409.
• Chandan Kumar Roy & Tapati Basak, 2024, "Women in Leadership, Skilled Workforce, and Firm Performance in Bangladesh: A Machine Learning Analysis on Enterprise Survey Data," Croatian Economic Survey, The Institute of Economics, Zagreb, volume 26, issue 1, pages 59-93, June.
• Gunnar Eliasson & Gunnar Eliasson & Dan Johansson & Erol Taymaz, 2024, "Firm Turnover and the Rate of Macro Economic Growth: Simulating the Macroeconomic Effects of Schumpeterian Creative Destruction," International Journal of Microsimulation, International Microsimulation Association, volume 17, issue 2, pages 279-296, DOI: 10.34196/ijm.00299.
• Erwis Melchor Pérez & Moisés Emmanuel Ramírez Guzmán & Araceli Hernández Jiménez & Agustín Santiago Alvarado, 2024, "Predicción del riesgo crediticio a microfinanciera usando aprendizaje computacional," 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 19, issue 4, pages 1-16, Octubre -.
• Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024, "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202402, Feb, revised Feb 2024.
• Johannes Carow & Niklas M. Witzig, 2024, "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, number 2404, Feb.
• Zongwu Cai & Pixiong Chen, 2024, "Online Investor Sentiment via Machine Learning," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202411, Sep, revised Sep 2024.
• Xiaolong Tang & Yuping Song & Xingrui Jiao & Yankun Sun, 2024, "On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model," Computational Economics, Springer;Society for Computational Economics, volume 63, issue 5, pages 2011-2033, May, DOI: 10.1007/s10614-023-10392-5.
• Aparna Gupta & Vipula Rawte & Mohammed J. Zaki, 2024, "Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 1, pages 307-334, July, DOI: 10.1007/s10614-023-10443-x.
• Shun Chen & Lingling Guo & Lei Ge, 2024, "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 5, pages 2853-2878, November, DOI: 10.1007/s10614-024-10547-y.
• Aykut Ekinci & Safa Sen, 2024, "Forecasting Bank Failure in the U.S.: A Cost-Sensitive Approach," Computational Economics, Springer;Society for Computational Economics, volume 64, issue 6, pages 3161-3179, December, DOI: 10.1007/s10614-023-10537-6.
• Jae Hong Kim & Donghwan Ki & Nene Osutei & Sugie Lee & John R. Hipp, 2024, "Beyond visual inspection: capturing neighborhood dynamics with historical Google Street View and deep learning-based semantic segmentation," Journal of Geographical Systems, Springer, volume 26, issue 4, pages 541-564, October, DOI: 10.1007/s10109-023-00420-1.
• Batuhan Kilic & Onur Can Bayrak & Fatih Gülgen & Mert Gurturk & Perihan Abay, 2024, "Unveiling the impact of machine learning algorithms on the quality of online geocoding services: a case study using COVID-19 data," Journal of Geographical Systems, Springer, volume 26, issue 4, pages 601-622, October, DOI: 10.1007/s10109-023-00435-8.
• Kevin Credit, 2024, "Introduction to the special issue on spatial machine learning," Journal of Geographical Systems, Springer, volume 26, issue 4, pages 451-460, October, DOI: 10.1007/s10109-024-00452-1.
• Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024, "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, volume 63, issue 4, pages 1431-1471, November, DOI: 10.1007/s11156-024-01306-z.
• Aike Steentoft & Bu-Sung Lee & Markus Schläpfer, 2024, "Quantifying the uncertainty of mobility flow predictions using Gaussian processes," Transportation, Springer, volume 51, issue 6, pages 2301-2322, December, DOI: 10.1007/s11116-023-10406-z.
• Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024, "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, volume 40, pages 149-176.
• Christoph Engel, 2024, "Experimental comparative law 2.0? Large language models as a novel empirical tool," Discussion Paper Series of the Max Planck Institute for Behavioral Economics, Max Planck Institute for Behavioral Economics, number 2024_12, Jul.
• Johannes Kruse & Christoph Engel, 2024, "Professor GPT: Having a large language model write a commentary on freedom of assembly," Discussion Paper Series of the Max Planck Institute for Behavioral Economics, Max Planck Institute for Behavioral Economics, number 2024_14, Oct, revised Feb 2025.
• Jiti Gao & Fei Liu & Bin Peng & Yanrong Yang, 2024, "Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 14/24.
• Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2024, "Estimation and Inference for a Class of Generalized Hierarchical Models," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 7/24.
• Jiti Gao & Bin Peng & Yayi Yan, 2024, "Robust Inference for High Dimensional Panel Data Models," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 9/24.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "The Unreasonable Effectiveness of Algorithms," NBER Working Papers, National Bureau of Economic Research, Inc, number 32125, Feb.
• Jeff Dominitz & Charles F. Manski, 2024, "Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory," NBER Working Papers, National Bureau of Economic Research, Inc, number 32269, Mar.
• Julian Ashwin & Paul Beaudry & Martin Ellison, 2024, "Neural Network Learning for Nonlinear Economies," NBER Working Papers, National Bureau of Economic Research, Inc, number 32807, Aug.
• Stefania Albanesi & Domonkos F. Vamossy, 2024, "Credit Scores: Performance and Equity," NBER Working Papers, National Bureau of Economic Research, Inc, number 32917, Sep.
• Antoine Didisheim & Shikun (Barry) Ke & Bryan T. Kelly & Semyon Malamud, 2024, "APT or “AIPT”? The Surprising Dominance of Large Factor Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 33012, Sep.
• Ruslan Goyenko & Bryan T. Kelly & Tobias J. Moskowitz & Yinan Su & Chao Zhang, 2024, "Trading Volume Alpha," NBER Working Papers, National Bureau of Economic Research, Inc, number 33037, Oct.
• Sebastian Bell & Ali Kakhbod & Martin Lettau & Abdolreza Nazemi, 2024, "Glass Box Machine Learning and Corporate Bond Returns," NBER Working Papers, National Bureau of Economic Research, Inc, number 33320, Dec.
• Iva Glišic, 2024, "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin, National Bank of Serbia, number 22, Mar.
• Georgi Hristov, 2024, "Improving the Quality of Financial Information Through Machine Learning," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 529-540, September.
• Fabrice Murtin & Max Salomon-Ermel, 2024, "Nowcasting subjective well-being with Google Trends: A meta-learning approach," OECD Papers on Well-being and Inequalities, OECD Publishing, number 27, Jun, DOI: 10.1787/cbdfb5d9-en.
• Mariapia Mendola & Mengesha Yayo Negasi, 2024, "Nutritional and Schooling Impact of a Social Protection Program in Ethiopia: A Retrospective Analysis of Childhood Exposure," Journal of African Economies, Centre for the Study of African Economies, volume 33, issue 4, pages 390-410.
• Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2024, "Volatility Forecasting with Machine Learning and Intraday Commonality," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 2, pages 492-530.
• Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024, "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, volume 22, issue 3, pages 636-669.
• Yufeng Han & Ai He & David E Rapach & Guofu Zhou, 2024, "Cross-sectional expected returns: new Fama–MacBeth regressions in the era of machine learning," Review of Finance, European Finance Association, volume 28, issue 6, pages 1807-1831.
• Stephen J. Robson & Martin Hensher & Jeffrey C. Looi, 2024, "Can we predict the effects of artificial intelligence and virtual care on the health labour market?," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, volume 27, issue 2, pages 143-160.
• Maria S. Mavillonio, 2024, "Textual Representation of Business Plans and Firm Success," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/308, May.
• Caterina Giannetti & Maria Saveria Mavillonio, 2024, "Crowdfunding Success: Human Insights vs Algorithmic Textual Extraction," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/315, Nov.
• Maria Saveria Mavillonio, 2024, "Natural Language Processing Techniques for Long Financial Document," Discussion Papers, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy, number 2024/317, Nov.
• Mestiri, Sami, 2024, "Financial applications of machine learning using R software," MPRA Paper, University Library of Munich, Germany, number 119998.
• Shakeel, Jovera & Munir, Shehzil & Mirza, Schaff & Abdullah, Khan, 2024, "Impact of Digital Literacy on Financial Outcomes – A Cross-Country Analysis," MPRA Paper, University Library of Munich, Germany, number 123374, Dec.
• Selim Tüzüntürk, 2024, "Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters’ Methods," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 15, issue 4, pages 371-388.
• Taoxiong Liu & Huolan Cheng, 2024, "Can The Classical Economic Model Improve The Performance Of Deep Learning? A GDP Forecasting Example," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 86-110, July.
• Daeyun KANG & Doojin RYU & Alexander WEBB, 2024, "Term Spread Prediction using Lasso in Machine Learning Frameworks," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 31-45, December.
• Alina Cornelia LUCHIAN & Vasile STRAT, 2024, "The Trustworthiness of AI Algorithms and the Simulator Bias in Trading," PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, Bucharest University of Economic Studies, Romania, volume 6, issue 1, pages 211-220, August.
• Andres Christian Admin De la Huerta Avila, 2024, "The Predictive Power of Central Bank Communication: Evidence from Mexico," Sobre México. Revista de Economía, Sobre México. Temas en economía, volume 1, issue 9, pages 83-127.
• Francesco Audrino & Jessica Gentner & Simon Stalder, 2024, "Quantifying uncertainty: a new era of measurement through large language models," Working Papers, Swiss National Bank, number 2024-12.
• Sami Ben Jabeur & Salma Mefteh-Wali & Jean-Laurent Viviani, 2024, "Forecasting gold price with the XGBoost algorithm and SHAP interaction values," Annals of Operations Research, Springer, volume 334, issue 1, pages 679-699, March, DOI: 10.1007/s10479-021-04187-w.
• Nikola Gradojevic & Dragan Kukolj, 2024, "Unlocking the black box: Non-parametric option pricing before and during COVID-19," Annals of Operations Research, Springer, volume 334, issue 1, pages 59-82, March, DOI: 10.1007/s10479-022-04578-7.
• Maria Kubara, 2024, "Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters," The Annals of Regional Science, Springer;Western Regional Science Association, volume 72, issue 3, pages 797-829, March, DOI: 10.1007/s00168-023-01220-7.
• Axel Groß-Klußmann, 2024, "Learning deep news sentiment representations for macro-finance," Digital Finance, Springer, volume 6, issue 3, pages 341-377, September, DOI: 10.1007/s42521-024-00107-2.
• Nacira Agram & Bernt Øksendal & Jan Rems, 2024, "Deep learning for quadratic hedging in incomplete jump market," Digital Finance, Springer, volume 6, issue 3, pages 463-499, September, DOI: 10.1007/s42521-024-00112-5.
• Riu Naito & Toshihiro Yamada, 2024, "Deep high-order splitting method for semilinear degenerate PDEs and application to high-dimensional nonlinear pricing models," Digital Finance, Springer, volume 6, issue 4, pages 693-725, December, DOI: 10.1007/s42521-023-00091-z.
• Cosimo Magazzino & Marco Mele & Claudiu Tiberiu Albulescu & Nicholas Apergis & Mihai Ioan Mutascu, 2024, "The presence of a latent factor in gasoline and diesel prices co-movements," Empirical Economics, Springer, volume 66, issue 5, pages 1921-1939, May, DOI: 10.1007/s00181-023-02523-6.
• Fumitaka Furuoka & Luis A. Gil-Alana & OlaOluwa S. Yaya & Elayaraja Aruchunan & Ahamuefula E. Ogbonna, 2024, "A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis," Empirical Economics, Springer, volume 66, issue 6, pages 2471-2499, June, DOI: 10.1007/s00181-023-02540-5.
• Bassam A. Ibrahim & Ahmed A. Elamer & Thamir H. Alasker & Marwa A. Mohamed & Hussein A. Abdou, 2024, "Volatility contagion between cryptocurrencies, gold and stock markets pre-and-during COVID-19: evidence using DCC-GARCH and cascade-correlation network," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-28, December, DOI: 10.1186/s40854-023-00605-z.
• Mustafa Tevfik Kartal & Serpil Kılıç Depren & Ugur Korkut Pata & Dilvin Taşkın & Tuba Şavlı, 2024, "Modeling the link between environmental, social, and governance disclosures and scores: the case of publicly traded companies in the Borsa Istanbul Sustainability Index," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-20, December, DOI: 10.1186/s40854-024-00619-1.
• Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024, "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 10, issue 1, pages 1-30, December, DOI: 10.1186/s40854-024-00625-3.
• Lukas Gonon, 2024, "Deep neural network expressivity for optimal stopping problems," Finance and Stochastics, Springer, volume 28, issue 3, pages 865-910, July, DOI: 10.1007/s00780-024-00538-0.
• Fuat Sekmen & Isa Demirkol & Haşmet Gökırmak, 2024, "Evaluation of urban transportation preferences with analytical hierarchy process method," Quality & Quantity: International Journal of Methodology, Springer, volume 58, issue 3, pages 2087-2101, June, DOI: 10.1007/s11135-023-01731-7.
• Chris Reimann, 2024, "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, volume 5, issue 1, pages 51-83, June, DOI: 10.1007/s43253-024-00114-4.
• Tharindu P. De Alwis & S. Yaser Samadi, 2024, "Stacking-based neural network for nonlinear time series analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, volume 33, issue 3, pages 901-924, July, DOI: 10.1007/s10260-024-00746-0.
• Gavin Ooft & Monique Thijn-Baank, 2024, "Measuring Financial Stability in Curaçao and Sint Maarten," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 14, issue 4, pages 1-2.
• Joana Katina & Joana Katina & Igor Katin & Igor Katin & Vera Komarova, 2024, "Cryptocurrency price forecasting: a comparative analysis of autoregressive and recurrent neural network models," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 11, issue 4, pages 425-436, June, DOI: 10.9770/jesi.2024.11.4(26).
• Timothy Holt & Mitsuru Igami & Simon Scheidegger, 2024, "Detecting Edgeworth Cycles," Journal of Law and Economics, University of Chicago Press, volume 67, issue 1, pages 67-102, DOI: 10.1086/726224.
• Ubarhande Prashant & Chandani Arti & Pathak Mohit & Agrawal Reena & Bagade Sonali, 2024, "Modelling Financial Variables Using Neural Networking to Access Creditworthiness," Financial Internet Quarterly (formerly e-Finanse), Sciendo, volume 20, issue 2, pages 62-76, DOI: 10.2478/fiqf-2024-0012.
• Dahani Zouhair & Dehhaoui Mohammed & Bousselhami Ahmed & Maatala Nassreddine, 2024, "Analysis of the Determinants of Technological Innovations Within Agri-Food Companies in Morocco," Folia Oeconomica Stetinensia, Sciendo, volume 24, issue 1, pages 1-21, DOI: 10.2478/foli-2024-0001.
• Renigier-Biłozor Małgorzata & Janowski Artur, 2024, "Human-Machine Synergy in Real Estate Similarity Concept," Real Estate Management and Valuation, Sciendo, volume 32, issue 2, pages 13-30, DOI: 10.2478/remav-2024-0010.
• Wagner Marco, 2024, "Künstliche Intelligenz: ChatGPT bei EZB-Prognosen," Wirtschaftsdienst, Sciendo, volume 104, issue 9, pages 592-592, DOI: 10.2478/wd-2024-0154.
• Bartosz Bieganowski & Robert Ślepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-03.
• Kamil Kashif & Robert Ślepaczuk, 2024, "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-07.
• Adam Korniejczuk & Robert Ślepaczuk, 2024, "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-09.
• Sugarbayar Enkhbayar & Robert Ślepaczuk, 2024, "Predictive modeling of foreign exchange trading signals using machine learning techniques," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-10.
• Zuzanna Kostecka & Robert Ślepaczuk, 2024, "Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-12.
• Natalia Roszyk & Robert Ślepaczuk, 2024, "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-13.
• Maciej Wysocki & Robert Ślepaczuk, 2024, "Construction and Hedging of Equity Index Options Portfolios," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-14.
• Stanisław Łaniewski & Robert Ślepaczuk, 2024, "Enhancing literature review with NLP methods Algorithmic investment strategies case," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-16.
• Filip Stefaniuk & Robert Ślepaczuk, 2024, "The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Me," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-27.
• Zeeshan Nezami Ansari & Md Mustafa & Rajendra Narayan Paramanik, 2024, "Linkages of International Business Cycle: An Euclidean Distance-Based Network Approach," Economic Research Guardian, Mutascu Publishing, volume 14, issue 2, pages 163-175, December.
• Jiawen Luo & Tony Klein & Thomas Walther & Qiang Ji, 2024, "Forecasting realized volatility of crude oil futures prices based on machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., volume 43, issue 5, pages 1422-1446, August, DOI: 10.1002/for.3077.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2024, "Estimating Nonlinear Heterogeneous Agent Models with Neural Networks," The Warwick Economics Research Paper Series (TWERPS), University of Warwick, Department of Economics, number 1499.
• Shang, Linmei & Wang, Jifeng & Schäfer, David & Heckelei, Thomas & Gall, Juergen & Appel, Franziska & Storm, Hugo, 2024, "Surrogate modelling of a detailed farm‐level model using deep learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 75, issue 1, pages 235-260, DOI: 10.1111/1477-9552.12543.
• Tänzer, Alina, 2024, "The effectiveness of central bank purchases of long-term treasury securities: A neural network approach," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 204.
• Holtemöller, Oliver & Kozyrev, Boris, 2024, "Forecasting economic activity using a neural network in uncertain times: Monte Carlo evidence and application to the German GDP," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 6/2024.
• Kozyrev, Boris, 2024, "Forecast combination and interpretability using random subspace," IWH Discussion Papers, Halle Institute for Economic Research (IWH), number 21/2024.
• Büchel, Jan & Engler, Jan, 2024, "Generative KI in Deutschland: Künstliche Intelligenz in Gesellschaft und Unternehmen," IW-Reports, Institut der deutschen Wirtschaft (IW) / German Economic Institute, number 23/2024.
• Büchel, Jan & Monsef, Roschan, 2024, "Künstliche Intelligenz: Bessere Entlohnung durch Produktivitätsbooster?
  [Artificial Intelligence: Will boosted productivity lead to better pay?]," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, volume 51, issue 2, pages 45-63, DOI: 10.2373/1864-810X.24-02-03.
• Gschnaidtner, Christoph & Dehghan, Robert & Hottenrott, Hanna & Schwierzy, Julian, 2024, "Adoption and diffusion of blockchain technology," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 24-018.
• Asatryan, Zareh & Birkholz, Carlo & Heinemann, Friedrich, 2024, "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 24-037.
• Petar Cisar & Sanja Maravic Cisar & Attila Pásztor, 2024, "Improving Synchronous Motor Modelling with Artificial Intelligence," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, volume 22, issue 3, pages 329-340.

2023

• Selim Tüzüntürk & Fatma Sert Eteman, 2023, "Forecasting National Cement Demand in the Turkish Domestic Market with Artificial Neural Networks," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., volume 38, issue 120, pages 131-154, October, DOI: https://doi.org/10.33203/mfy.129736.
• John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2023, "Supervised Machine Learning for Eliciting Individual Demand," American Economic Journal: Microeconomics, American Economic Association, volume 15, issue 4, pages 146-182, November, DOI: 10.1257/mic.20210069.
• Alistair Macaulay & Wenting Song, 2023, "News Media, Inflation, and Sentiment," AEA Papers and Proceedings, American Economic Association, volume 113, pages 172-176, May, DOI: 10.1257/pandp.20231117.
• Anton Korinek, 2023, "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, volume 61, issue 4, pages 1281-1317, December, DOI: 10.1257/jel.20231736.
• Berta Marcos Ceron & Manuel Monge, 2023, "Financial Technologies (FINTECH) Revolution and Covid-19: Time Trends and Persistence," Review of Development Finance Journal, Chartered Institute of Development Finance, volume 13, issue 1, pages 58-64.
• Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2023, "Trading on short-term path forecasts of intraday electricity prices. Part II -- Distributional Deep Neural Networks," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/23/01.
• Elliott Ash & Stephen Hansen, 2023, "Text Algorithms in Economics," Annual Review of Economics, Annual Reviews, volume 15, issue 1, pages 659-688, September, DOI: 10.1146/annurev-economics-082222-07.
• Дәулетханұлы Е. // Dauletkhanuly Ye. & Ойшынова Г.А. // Oishynova G.А., 2023, "Применение машинного обучения и искусственного интеллекта монетарным регулятором // Using Machine Learning and Artificial Intelligence by a Monetary Regulator," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 4, pages 4-19.
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• Ajit Desai, 2023, "Machine learning for economics research: when, what and how," Staff Analytical Notes, Bank of Canada, number 2023-16, Oct, DOI: 10.34989/san-2023-16.
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• Karina Acosta & Yuri Reina-Aranza, 2023, "Categorías municipales en Colombia: Avanzando hacia un modelo de descentralización asimétrica," Documentos de trabajo sobre Economía Regional y Urbana, Banco de la Republica de Colombia, number 321, Dec, DOI: 10.32468/dtseru.321.
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• Anastasios Petropoulos & Evangelos Stavroulakis & Panagiotis Lazaris & Vasilis Siakoulis & Nikolaos Vlachogiannakis, 2023, "Is COVID-19 reflected in AnaCredit dataset? A big data - machine learning approach for analysing behavioural patterns using loan level granular information," Working Papers, Bank of Greece, number 315, Mar, DOI: 10.52903/wp2023315.
• Ollech Daniel & Webel Karsten, 2023, "A Random Forest-based Approach to Combining and Ranking Seasonality Tests," Journal of Econometric Methods, De Gruyter, volume 12, issue 1, pages 117-130, January, DOI: 10.1515/jem-2020-0020.
• Michael Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023, "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," Working Papers, Center for Economic Studies, U.S. Census Bureau, number 23-01, Jan.
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• Ahrens, Maximilian & Erdemlioglu, Deniz & Mcmahon, Michael & Neely, Christopher J & Yang, Xiye, 2023, "Mind Your Language: Market Responses to Central Bank Speeches," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 18191, Jun.
• Valérie Mignon & Celso Brunetti & Marc Joëts, 2023, "Reasons Behind Words: OPEC Narratives and the Oil Market," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2023-24.
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• Christian Manuel Moreno Rocha & Jorge D. Pertuz Ortiz & Neyder A. Rodriguez Ibanez, 2023, "A Diffuse Analysis Based on Analytical Processes to Prioritize Barriers in the Development of Renewable Energy Technologies in Alignment with the United Nations Sustainable Development Goals: Evidence," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 4, pages 481-195, July.
• Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023, "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, volume 13, issue 6, pages 679-689, November.
• Kwon, Yujin & Park, Sung Y., 2023, "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, volume 84, issue C, DOI: 10.1016/j.asieco.2022.101574.
• Chuliá, Helena & Estévez, Marc & Uribe, Jorge M., 2023, "Systemic political risk," Economic Modelling, Elsevier, volume 125, issue C, DOI: 10.1016/j.econmod.2023.106375.
• Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023, "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.najef.2023.101895.
• Amendolagine, Vito & von Jacobi, Nadia, 2023, "Symbiotic relationships among formal and informal institutions: Comparing five Brazilian cultural ecosystems," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101092.
• Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023, "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, volume 151, issue C, DOI: 10.1016/j.euroecorev.2022.104254.
• Saâdaoui, Foued & Ben Jabeur, Sami, 2023, "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106793.
• Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023, "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106843.
• Zhao, Yinglan & Feng, Chen & Xu, Nuo & Peng, Song & Liu, Chang, 2023, "Early warning of exchange rate risk based on structural shocks in international oil prices using the LSTM neural network model," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106921.
• Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023, "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106985.
• Wongsinhirun, Nopparat & Chatjuthamard, Pattanaporn & Jiraporn, Pornsit, 2023, "Corporate culture and board gender diversity: Evidence from textual analysis," International Review of Financial Analysis, Elsevier, volume 86, issue C, DOI: 10.1016/j.irfa.2023.102534.
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• Barua, Ronil & Sharma, Anil K., 2023, "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104515.
• Coita, Ioana-Florina & Belbe, Stefana (Ștefana) & Mare, Codruta (Codruța) & Osterrieder, Joerg & Hopp, Christian, 2023, "Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104549.
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• Huber, Martin & Imhof, David, 2023, "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, volume 89, issue C, DOI: 10.1016/j.ijindorg.2023.102946.
• Decarolis, Francesco & Li, Muxin, 2023, "Regulating online search in the EU: From the android case to the digital markets act and digital services act," International Journal of Industrial Organization, Elsevier, volume 90, issue C, DOI: 10.1016/j.ijindorg.2023.102983.
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• Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 884-900, DOI: 10.1016/j.ijforecast.2022.03.001.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023, "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, volume 150, issue 1, pages 94-138, DOI: 10.1016/j.jfineco.2023.07.004.
• Schade, Philipp & Schuhmacher, Monika C., 2023, "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, volume 19, issue C, DOI: 10.1016/j.jbvi.2022.e00357.
• Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023, "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104060.
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• Cesar Ramos, 2023, "Machine Learning Insights into Bolivia’s Economic Downturns," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 6, issue 2, pages 5-33, December.
• Driss El Kadiri Boutchich, 2023, "Painting art and sustainability: relationship from composite indices and a neural network," International Journal of Social Economics, Emerald Group Publishing Limited, volume 51, issue 1, pages 46-61, July, DOI: 10.1108/IJSE-01-2023-0006.
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• Miguel Faria-e-Castro & Fernando Leibovici, 2023, "Artificial Intelligence and Inflation Forecasts," Working Papers, Federal Reserve Bank of St. Louis, number 2023-015, Jul, revised 26 Feb 2024, DOI: 10.20955/wp.2023.015.
• Anastasia D. Petaykina, 2023, "Predicting Changes in Household Consumption Using Neural Networks
  [Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Yury A. Pleskachyev & Yury Yu. Ponomarev & Matvey A. Saprykin, 2023, "Territorial Planning and Forecasting of Economic Indicators by Machine Learning Methods
  [Территориальное Планирование И Прогнозирование Экономических Показателей Методами Машинного Обучения]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 9, pages 46-57, September.
• Anastasia D. Petaykina, 2023, "Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Yury A. Pleskachyev & Yury Yu. Ponomarev & Matvey A. Saprykin, 2023, "Территориальное Планирование И Прогнозирование Экономических Показателей Методами Машинного Обучения," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 9, pages 46-57, September.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print, HAL, number emse-04624940, Jul, DOI: 10.1016/j.ijforecast.2022.04.009.
• Sahed Abdelkader & Kahoui Hacene, 2023, "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print, HAL, number hal-04183403, Jun.
• Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023, "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print, HAL, number hal-04223161, DOI: 10.1007/s11156-023-01192-x.
• Celso Brunetti & Marc Joëts & Valérie Mignon, 2023, "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers, HAL, number hal-04196053.