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

• 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, Centre for Economic Policy Research, number 19295, Jul.
• Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2024, "Bayesian Neural Networks for Macroeconomic Analysis," CEPR Discussion Papers, Centre for Economic Policy Research, number 19381, Aug.
• Ash, Elliott & Hansen, Stephen & Muvdi, Yabra, 2024, "Large Language Models in Economics," CEPR Discussion Papers, Centre for Economic Policy Research, number 19479, Sep.
• 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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.
• Ouyang, Zisheng & Lu, Min & Ouyang, Zhongzhe & Zhou, Xuewei & Wang, Ren, 2024, "A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention," Energy Economics, Elsevier, volume 138, issue C, DOI: 10.1016/j.eneco.2024.107851.
• Lipiecki, Arkadiusz & Uniejewski, Bartosz & Weron, Rafał, 2024, "Postprocessing of point predictions for probabilistic forecasting of day-ahead electricity prices: The benefits of using isotonic distributional regression," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107934.
• Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024, "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, volume 139, issue C, DOI: 10.1016/j.eneco.2024.107952.
• Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024, "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103094.
• Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024, "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, volume 92, issue C, DOI: 10.1016/j.irfa.2024.103102.
• Wang, Li & Huang, Yiting & Hong, Zhiwu, 2024, "Digitalization as a double-edged sword: A deep learning analysis of risk management in Chinese banks," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103249.
• Heger, Julia & Min, Aleksey & Zagst, Rudi, 2024, "Analyzing credit spread changes using explainable artificial intelligence," International Review of Financial Analysis, Elsevier, volume 94, issue C, DOI: 10.1016/j.irfa.2024.103315.
• Peng, Yaohao & de Moraes Souza, João Gabriel, 2024, "Chaos, overfitting and equilibrium: To what extent can machine learning beat the financial market?," International Review of Financial Analysis, Elsevier, volume 95, issue PB, DOI: 10.1016/j.irfa.2024.103474.
• Liang, Qing & Li, Zhaohua, 2024, "Litigation risk and the cost of debt financing in M&As," International Review of Financial Analysis, Elsevier, volume 96, issue PA, DOI: 10.1016/j.irfa.2024.103586.
• Oehler, Andreas & Horn, Matthias, 2024, "Does ChatGPT provide better advice than robo-advisors?," Finance Research Letters, Elsevier, volume 60, issue C, DOI: 10.1016/j.frl.2023.104898.
• Baruník, Jozef & Hanus, Luboš, 2024, "Fan charts in era of big data and learning," Finance Research Letters, Elsevier, volume 61, issue C, DOI: 10.1016/j.frl.2024.105003.
• Beckmann, Lars & Hark, Paul F., 2024, "ChatGPT and the banking business: Insights from the US stock market on potential implications for banks," Finance Research Letters, Elsevier, volume 63, issue C, DOI: 10.1016/j.frl.2024.105237.
• Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024, "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, volume 65, issue C, DOI: 10.1016/j.frl.2024.105481.
• Göncü, Ahmet & Kuzubaş, Tolga U. & Saltoğlu, Burak, 2024, "Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction," Finance Research Letters, Elsevier, volume 67, issue PB, DOI: 10.1016/j.frl.2024.105874.
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2024, "Deep reinforcement learning for portfolio selection," Global Finance Journal, Elsevier, volume 62, issue C, DOI: 10.1016/j.gfj.2024.101016.
• Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024, "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, volume 116, issue C, pages 114-133, DOI: 10.1016/j.insmatheco.2024.02.007.
• Nguyen, Hang & Sherris, Michael & Villegas, Andrés M. & Ziveyi, Jonathan, 2024, "Scenario selection with LASSO regression for the valuation of variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, volume 116, issue C, pages 27-43, DOI: 10.1016/j.insmatheco.2024.01.006.
• Ballester, Laura & González-Urteaga, Ana & Shen, Long, 2024, "Green bond issuance and credit risk: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 94, issue C, DOI: 10.1016/j.intfin.2024.102013.
• Huang, Zih-Chun & Sangiorgi, Ivan & Urquhart, Andrew, 2024, "Forecasting Bitcoin volatility using machine learning techniques," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 97, issue C, DOI: 10.1016/j.intfin.2024.102064.
• Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024, "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, volume 40, issue 4, pages 1521-1538, DOI: 10.1016/j.ijforecast.2024.01.001.
• Kurz, Konstantin & Bock, Carolin & Hanschur, Leonard, 2024, "Flip the tweet – the two-sided coin of entrepreneurial empathy and its ambiguous influence on new product development," Journal of Business Venturing, Elsevier, volume 39, issue 2, DOI: 10.1016/j.jbusvent.2023.106378.
• Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2024, "Social networks and economic transformation: Evidence from a resettled village in Brazil," Journal of Economic Behavior & Organization, Elsevier, volume 221, issue C, pages 17-34, DOI: 10.1016/j.jebo.2024.03.001.
• Moreno-Pérez, Carlos & Minozzo, Marco, 2024, "‘Making text talk’: The minutes of the Central Bank of Brazil and the real economy," Journal of International Money and Finance, Elsevier, volume 147, issue C, DOI: 10.1016/j.jimonfin.2024.103133.
• Christensen, Peter & Francisco, Paul & Myers, Erica & Shao, Hansen & Souza, Mateus, 2024, "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, Elsevier, volume 234, issue C, DOI: 10.1016/j.jpubeco.2024.105098.
• Giraldo, Carlos & Giraldo, Iader & Gomez-Gonzalez, Jose E. & Uribe, Jorge M., 2024, "High frequency monitoring of credit creation: A new tool for central banks in emerging market economies," The Quarterly Review of Economics and Finance, Elsevier, volume 97, issue C, DOI: 10.1016/j.qref.2024.101893.
• Mati, Sagiru & Baita, Abubakar Jamilu & Ismael, Goran Yousif & Abdullahi, Salisu Garba & Samour, Ahmed & Ozsahin, Dilber Uzun, 2024, "Enhancing CO2 emissions prediction in Africa: A novel approach integrating enviroeconomic factors and nature-inspired neural network in the presence of unit root," Renewable Energy, Elsevier, volume 237, issue PA, DOI: 10.1016/j.renene.2024.121561.
• Carbó, José Manuel & Gorjón, Sergio, 2024, "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, volume 92, issue C, pages 123-140, DOI: 10.1016/j.iref.2024.01.070.
• de França Carvalho, João Vinícius & Guimarães, Acássio Silva, 2024, "Systemic risk assessment using complex networks approach: Evidence from the Brazilian (re)insurance market," Research in International Business and Finance, Elsevier, volume 67, issue PA, DOI: 10.1016/j.ribaf.2023.102065.
• Ghosh, Indranil & Jana, Rabin K., 2024, "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, volume 200, issue C, DOI: 10.1016/j.techfore.2023.123148.
• Saâdaoui, Foued & Rabbouch, Hana, 2024, "Financial forecasting improvement with LSTM-ARFIMA hybrid models and non-Gaussian distributions," Technological Forecasting and Social Change, Elsevier, volume 206, issue C, DOI: 10.1016/j.techfore.2024.123539.
• Manuel Hidalgo Pérez, 2024, "Percepción y disrupción: Impacto laboral de la Inteligencia Artificial Generativa en Euskadi," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, volume 105, issue 01, pages 140-171.
• Stiven Agusta & Fuad Rakhman & Jogiyanto Hartono Mustakini & Singgih Wijayana, 2024, "Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron," Asian Journal of Accounting Research, Emerald Group Publishing Limited, volume 9, issue 4, pages 358-377, July, DOI: 10.1108/AJAR-01-2024-0006.
• Luan Thanh Le & Trang Xuan-Thi-Thu, 2024, "Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood," Maritime Business Review, Emerald Group Publishing Limited, volume 9, issue 3, pages 243-262, July, DOI: 10.1108/MABR-10-2023-0074.
• Leonardo E. Torre & Eva E. González & Luis R. Casillas & Jorge A. Alvarado, 2024, "Índices de sentimiento regionales y su asociación con indicadores oportunos de actividad económica en México, 2016-2021/Regional sentiment indexes and their association with timely indicators of economic activity in Mexico, 2016-2021," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, volume 39, issue 2, pages 349-419.
• Sleeman, Cath, 2024, "Extracting Dimensions of Job Quality from Online Employee Reviews," Discussion Papers, Economic Statistics Centre of Excellence, number escoe-dp-2024-01, Jan.
• Daria Wotzka & Pawel Fracz & Jolanta Staszewska & Joachim Foltys & Malgorzata Smolarek & Krzysztof Orzechowski, 2024, "The Application of Selected Supervised Machine Learning Methods in the Classification of Family Businesses in the Context of Cluster Formation," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 248-272.
• Sebastian Zupok, 2024, "Contemporary Tools for Creating Customer Value," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 545-559.
• Jacek Rudewicz, 2024, "Theoretical Insights into Artificial Intelligence Applications in Human Geography and Spatial Management (GeoAI)," European Research Studies Journal, European Research Studies Journal, volume 0, issue 4, pages 973-984.
• Pawel Rymarczyk & Tomasz Smutek & Daria Stefanczak & Wiktor Cwynar & Sebastian Zupok, 2024, "Self-learning Recommendation System Using Reinforcement Learning," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 137-149.
• Mariusz Mazurek & Konrad Gauda & Sylwia Skrzypek-Ahmed & Artem Artiukov & Piotr Stalinski, 2024, "Collection Life Cycle Analysis: Enhancing Market Strategies through Advanced Data Analytics," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 14-24.
• Bartosz Przysucha & Pawel Kaleta & Artur Dmowski & Jacek Piwkowski & Piotr Czarnecki & Tomasz Cieplak, 2024, "Product Knowledge Graphs - Creating a Knowledge System for Customer Support," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 150-159.
• Tomasz Smutek & Marcin Marczuk & Michal Jarmul & Ewelina Jurczak & Damian Pliszczuk, 2024, "Chatbot to Support the Customer Service Process," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 160-168.
• Michal Maj & Artur Dmowski & Ryszard Nowak & Tadeusz Medzelowski & Tomasz Cieplak, 2024, "Enhancing Customer Service in Shopping Malls with an Advanced Chatbot-Integrated Concierge Device," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 169-178.
• Pawel Olszewski & Leszek Gil & Natalia Rak & Tomasz Wolowiec & Michal Jasienski, 2024, "Construction of Regression Models Predicting Lead Times and Classification Models," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 179-189.
• Michal Maj & Jacek Korzeniak & Jozef Stoklosa & Pawel Barwiak & Bartlomiej Bartnik & Lukasz Maciura, 2024, "A Real-Time Autonomous Machine Learning System for Face Recognition Using Pre-Trained Convolutional Neural Networks," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 3-13.
• Krzysztof Krol & Michal Oleszek & Grzegorz Bartnik & Olena Ivashko & Marek Rutkowski & Adam Hernas, 2024, "Developing an IoT and Machine Learning-Based Monitoring System for Discrete Production Processes," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 38-48.
• Mariusz Mazurek & Magdalena Halas & Jan Sikora & Anna Wisniewska - Vistula & Diana Wroblewska & Sebastian Zupok, 2024, "Analysis of Consumer Behavior Using an Intelligent Multi-Source System," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 49-58.
• Tomasz Smutek & Jan Sikora & Sylwester Bogacki & Marek Rutkowski & Dariusz Wozniak, 2024, "Use of Autoencoder and One-Hot Encoding for Customer Segmentation," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 72-82.
• Tomasz Smutek & Marcin Kowalski & Olena Ivashko & Robert Chmura & Justyna Sokolowska-Wozniak, 2024, "A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 83-93.
• Bartosz Przysucha & Magdalena Halas & Cezary Figura & Natalia Rak & Pawel Barwiak & Adam Hernas, 2024, "Exploring and Analyzing YouTube Communities through Data Mining and Knowledge Graphs," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special A, pages 94-102.
• Tomasz Strak, 2024, "Generative AI as Tax Attorneys: Exploring Legal Understanding Through Experiments," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 1057-1094.
• Damian Pliszczuk & Michal Maj & Patryk Marek & Weronika Wilczewska & Tomasz Cieplak & Tomasz Rymarczyk, 2024, "The Effect of the Context Length of Large Language Models on the Quality of Extracted Information in Polish in the Process of Project Management," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 414-425.
• Michal Maj & Damian Pliszczuk & Patryk Marek & Weronika Wilczewska & Bartosz Przysucha & Tomasz Rymarczyk, 2024, "Optimizing Customer Support Using Text2SQL to Query Natural Language Databases," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special B, pages 426-438.
• Mikhail Vyacheslavovich Shatunov & Natalia Anatolyevna Navrotskaya, 2024, "Spatial Organization of Global Value Chains in the Optical and Electronic Equipment Industry," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 4, pages 108-134, DOI: https://dx.doi.org/10.14530/se.2024.
• Karel Janda & Mathieu Petit, 2024, "Analyzing Decision-Making in Deep-Q Reinforcement Learning for Trading: A Case Study on Tesla Company and its Supply Chain," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2024/40, Nov, revised Nov 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/wp2022-16.
• 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.
• Yalçıner YALÇIN & Selcen ÖZTÃœRK, 2024, "Detecting Algorithmic Collusion: Insights from Moment Screening Methods," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3, DOI: 10.25295/fsecon.1477143.
• Abdullah BALLI, 2024, "The Effect of Product Personalization on Consumer Purchasing Intention, Customer Satisfaction, Brand Loyalty and Artificial Intelligence Applications with Machine Learning," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3, DOI: 10.25295/fsecon.1449755.
• 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.
• Mehmet Cengiz Calikoglu, 2024, "Kolektif Siber Guvenligin Onemi ve Turk Devletler Teskilati/The Importance of Collective Cybersecurity and the Organization of Turkic States," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 2, issue 1, pages 1-13, February, DOI: doi.org/10.5281/zenodo.10102956.
• Sevdanur GENC, 2024, "Implementation of Quantum Weyl Transformations on Cirq," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 2, issue 1, pages 15-29, February, DOI: doi.org/10.5281/zenodo.10102956.
• Ayetullah Ercel & Emin ULUGERGERLI, 2024, "Hamiltonian Analysis of Amino Acid Sequences by Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 2, issue 1, pages 31-43, February, DOI: doi.org/10.5281/zenodo.10102956.
• Osman Semi Ceylan & Cumali Yasar, 2024, "Hamiltonian Analysis of Amino Acid Sequences by Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 2, issue 1, pages 45-49, February, DOI: doi.org/10.5281/zenodo.10102956.
• 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, DOI: 10.26180/28757252.
• 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, DOI: 10.26180/28757234.
• 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, DOI: 10.26180/28757240.
• 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.
• Jane Binner & Huw Dixon & Barry Jones & Jon Tepper, 2024, "Box A: A Neural Network Approach to Forecasting Inflation," National Institute UK Economic Outlook, National Institute of Economic and Social Research, issue 14, pages 8-11.
• 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.
• Marcel PRODAN & Gabriel DOROBANTU & Narcis IONITA & Mihai-Lucian VONCILA & Nicolae TARBA & Costin-Anton BOIANGIU & Nicolae GOGA, 2024, "Traffic-Sign Recognition," Journal of Information Systems & Operations Management, Romanian-American University, volume 18, issue 1, pages 186-205, May, DOI: 10.5281/zenodo.21825588.
• 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), Paradigm, 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, Paradigm, 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, Paradigm, volume 32, issue 2, pages 13-30, DOI: 10.2478/remav-2024-0010.
• Wagner Marco, 2024, "Künstliche Intelligenz: ChatGPT bei EZB-Prognosen," Wirtschaftsdienst, Paradigm, 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.
• 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.