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German G Creamer

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Germán G. Creamer & Salvatore J. Stolfo & Mateo Creamer & Shlomo Hershkop & Ryan Rowe & Ning Cai, 2022. "Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case," Complexity, Hindawi, vol. 2022, pages 1-18, February.

    Cited by:

    1. Li, Jiaxu & Yuan, Xiaoqian & Fu, Yude & Li, Jichao & Tan, Wenhui & Lu, Xin, 2025. "Representing significant dependencies with variable orders in networks," Chaos, Solitons & Fractals, Elsevier, vol. 201(P2).

  2. Germán G. Creamer & Tal Ben-Zvi, 2021. "Volatility and Risk in the Energy Market: A Trade Network Approach," Sustainability, MDPI, vol. 13(18), pages 1-17, September.

    Cited by:

    1. Iwona Gorzeń-Mitka & Monika Wieczorek-Kosmala, 2023. "Mapping the Energy Sector from a Risk Management Research Perspective: A Bibliometric and Scientific Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.

  3. Alex Gurvich & Germán G. Creamer, 2021. "Overallocation and Correction of Carbon Emissions in the Evaluation of Carbon Footprint," Sustainability, MDPI, vol. 13(24), pages 1-14, December.

    Cited by:

    1. Zixun Guo & Zhimei Gao & Wenbin Zhang, 2023. "Accounting and Decomposition of Energy Footprint: Evidence from 28 Sectors in China," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    2. Dev, Dhairya & Sharma, Gagan Deep & Gupta, Mansi & Tiwari, Aviral Kumar, 2025. "Sustainable finance in action: A comprehensive framework for policy and practice integration," International Review of Economics & Finance, Elsevier, vol. 103(C).
    3. Wei-Kang Lin & Xiao-Wu Tang & Yuan Zou & Jia-Xin Liang & Ke-Yi Li, 2023. "Research on the Bearing Capacity and Sustainable Construction of a Vacuum Drainage Pipe Pile," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    4. Yong Sun & Xinqi Yang & Runtian Wu & Guangxiang Gong & Tianjie Lei, 2025. "How to address enterprise collusion in falsifying carbon emission data: A game theory analysis," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(1), pages 378-392, January.

  4. Patrick Houlihan & Germán G. Creamer, 2021. "Leveraging Social Media to Predict Continuation and Reversal in Asset Prices," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 433-453, February.

    Cited by:

    1. Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024. "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages," Papers 2404.08665, arXiv.org.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    4. Bhaskar Goswami & Ajim Uddin, 2026. "Significance of predictors: revisiting stock return predictions using explainable AI," Annals of Operations Research, Springer, vol. 357(1), pages 223-257, February.

  5. Patrick Houlihan & Germán G. Creamer, 2019. "Leveraging a call-put ratio as a trading signal," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 763-777, May.

    Cited by:

    1. Li, Yubin & Zhao, Chen & Zhong, Zhaodong (Ken), 2021. "Trading behavior of retail investors in derivatives markets: Evidence from Mini options," Journal of Banking & Finance, Elsevier, vol. 133(C).
    2. Diaz-Rainey, Ivan & Gehricke, Sebastian A. & Roberts, Helen & Zhang, Renzhu, 2021. "Trump vs. Paris: The impact of climate policy on U.S. listed oil and gas firm returns and volatility," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Yue, Tian & Li, Lu-Lu & Ruan, Xinfeng & Zhang, Jin E., 2024. "Smirking in the energy market: Evidence from the Chinese crude oil options market," International Review of Financial Analysis, Elsevier, vol. 96(PA).

  6. Germán G. Creamer & Chihoon Lee, 2019. "A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1531-1542, September.

    Cited by:

    1. Liu, Wei-han & Xu, Xingfu, 2024. "Forecasting crude oil price: A deep forest ensemble approach," Finance Research Letters, Elsevier, vol. 69(PB).
    2. Pavan Kumar Nagula & Christos Alexakis, 2025. "Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1501-1512, July.
    3. Antonis A. Michis, 2023. "Precious Metals Comovements in Turbulent Times: COVID-19 and the Ukrainian Conflict," JRFM, MDPI, vol. 16(5), pages 1-18, May.

  7. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.

    Cited by:

    1. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    2. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    3. Andres Alonso-Robisco & Jose Carbo & Emily Kormanyos & Elena Triebskorn, 2025. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.
    4. Aggarwal, Sakshi, 2023. "LSTM based Anomaly Detection in Time Series for United States exports and imports," MPRA Paper 117149, University Library of Munich, Germany.
    5. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    6. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    7. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    8. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    9. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
    10. Zenelabden, Nouran & Oyenubi, Adeola & Dikgang, Johane, 2025. "Fuel stacking, housing quality, and health disparities in rural South Africa: A double machine learning approach," Energy Economics, Elsevier, vol. 151(C).
    11. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
    12. ErLe Du & Meng Ji, 2021. "Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-18, June.
    13. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    14. Jinbo Cai & Wenze Li & Wenjie Wang, 2025. "Electricity Market Predictability: Virtues of Machine Learning and Links to the Macroeconomy," Papers 2507.07477, arXiv.org.
    15. Serge Nyawa & Christian Gnekpe & Dieudonné Tchuente, 2025. "Transparent machine learning models for predicting decisions to undertake energy retrofits in residential buildings," Annals of Operations Research, Springer, vol. 354(1), pages 459-487, November.
    16. Krzysztof Drachal & Michał Pawłowski, 2021. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," Economies, MDPI, vol. 9(1), pages 1-22, January.
    17. Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.
    18. Sinem Guler Kangalli Uyar & Umut Uyar & Emrah Balkan, 2024. "Fundamental predictors of price bubbles in precious metals: a machine learning analysis," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(1), pages 65-87, March.
    19. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    20. Farwah Ali Syed & Kwo-Ting Fang & Adiqa Kausar Kiani & Muhammad Shoaib & Muhammad Asif Zahoor Raja, 2025. "Design of Neuro-Stochastic Bayesian Networks for Nonlinear Chaotic Differential Systems in Financial Mathematics," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 241-270, January.
    21. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
    22. Gao, Daquan & Li, Songsong & Tian, Zhihong, 2025. "Geopolitical risk, energy market volatility, and corporate energy dependence: The role of green Total factor productivity and decentralized top management team network," Energy Economics, Elsevier, vol. 148(C).
    23. Carlo Mari & Tiziana Laureti & Cristiano Baldassari, 2025. "Group detection in energy commodity markets through manifold-informed Wasserstein barycenter," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(3), pages 2197-2227, June.
    24. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    25. Mario Figueiredo & Yuri F. Saporito, 2023. "Forecasting the term structure of commodities future prices using machine learning," Digital Finance, Springer, vol. 5(1), pages 57-90, March.
    26. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
    27. Zou, Wei & He, Xing & Zhao, You, 2025. "Hierarchical optimal scheduling of electricity-hydrogen integrated energy systems via collective neurodynamic optimization," Applied Energy, Elsevier, vol. 398(C).
    28. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    29. Opeoluwa Seun Ojekemi & Mehmet Ağa & Cosimo Magazzino, 2023. "Towards Achieving Sustainability in the BRICS Economies: The Role of Renewable Energy Consumption and Economic Risk," Energies, MDPI, vol. 16(14), pages 1-18, July.
    30. Kelk, Rainer & Podofillini, Luca & Dang, Vinh N. & Panos, Evangelos, 2025. "Explorative application of discrete Bayesian networks as surrogate models for energy systems analysis," Applied Energy, Elsevier, vol. 394(C).
    31. Ti-Ching Peng, 2021. "The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(1), pages 1-32, February.
    32. Fugang LI & Guangwen MA & Shijun CHEN & Weibin HUANG, 2021. "An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2941-2963, July.
    33. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
    34. Fang Qu & Wensen She, 2025. "Artificial Intelligence Technology and Regional Carbon Emission Performance: Does Energy Transition or Industrial Transformation Matter?," Sustainability, MDPI, vol. 17(5), pages 1-31, February.
    35. Tian, Yingjie & Wen, Haonan & Guo, Kun, 2025. "Machine learning applications in climate finance: An overview," Research in International Business and Finance, Elsevier, vol. 79(C).
    36. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
    37. Manuel Muth & Michael Lingenfelder & Gerd Nufer, 2025. "The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review," Management Review Quarterly, Springer, vol. 75(3), pages 2759-2802, September.
    38. Shuaiwei Shi & Meiyi Hou & Zifan Gu & Ce Jiang & Weiqiang Zhang & Mengyang Hou & Chenxi Li & Zenglei Xi, 2022. "Estimation of Heavy Metal Content in Soil Based on Machine Learning Models," Land, MDPI, vol. 11(7), pages 1-19, July.
    39. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    40. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    41. Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    42. Md Mohsan Khudri & Kang Keun Rhee & Mohammad Shabbir Hasan & Karar Zunaid Ahsan, 2023. "Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-31, May.
    43. Lawchak Fadhil Khalid & Adnan Mohsin Abdulazeez, 2021. "Identifying Speakers Using Deep Learning: A review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 15-26.
    44. Wang, Donghua & Fang, Tianhui, 2025. "Study on influencing factors and forecast of global crude oil prices based on the hybrid model," Energy, Elsevier, vol. 328(C).
    45. Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    46. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    47. Kushawaha, Deepak, 2025. "Understanding the role of greenfield and mergers & acquisitions foreign direct investments in renewable energy expansion in developing countries," Energy Economics, Elsevier, vol. 145(C).
    48. Chenglong Chen & Yikun Liu & Decai Lin & Guohui Qu & Jiqiang Zhi & Shuang Liang & Fengjiao Wang & Dukui Zheng & Anqi Shen & Lifeng Bo & Shiwei Zhu, 2021. "Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks," Energies, MDPI, vol. 14(18), pages 1-25, September.
    49. Wang, Jiqian & Guo, Xiaozhu & Tan, Xueping & Chevallier, Julien & Ma, Feng, 2023. "Which exogenous driver is informative in forecasting European carbon volatility: Bond, commodity, stock or uncertainty?," Energy Economics, Elsevier, vol. 117(C).
    50. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    51. Shaoze Cui & Dujuan Wang & Yunqiang Yin & Xin Fan & Lalitha Dhamotharan & Ajay Kumar, 2025. "Carbon trading price prediction based on a two-stage heterogeneous ensemble method," Annals of Operations Research, Springer, vol. 345(2), pages 953-977, February.
    52. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    53. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    54. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.
    55. Ritesh Patel, 2025. "Analyzing the energy markets and financial markets linkage: A bibliometric analysis and future research agenda," Review of Financial Economics, John Wiley & Sons, vol. 43(1), pages 23-61, January.
    56. De Blauwe, Jilles & Zhang, Xiaobing & Keles, Dogan, 2025. "Investigating empirical bidding curves in the electricity spot market: Expected patterns vs anomalies?," Energy Economics, Elsevier, vol. 152(C).
    57. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    58. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    59. Cheng, Zishu & Li, Mingchen & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," Energy Economics, Elsevier, vol. 134(C).
    60. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    61. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    62. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    63. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    64. Nguyen, Bich Ngoc, 2025. "A sentiment-based approach to predict energy price volatility using distilRoBERTa and GARCH models," Energy Economics, Elsevier, vol. 149(C).
    65. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    66. Seulki Chung, 2024. "Modelling and Forecasting Energy Market Volatility Using GARCH and Machine Learning Approach," Papers 2405.19849, arXiv.org.
    67. Li, Ranran & Hu, Yucai & Heng, Jiani & Chen, Xueli, 2021. "A novel multiscale forecasting model for crude oil price time series," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    68. Yueqiang Xu & Petri Ahokangas & Jean-Nicolas Louis & Eva Pongrácz, 2019. "Electricity Market Empowered by Artificial Intelligence: A Platform Approach," Energies, MDPI, vol. 12(21), pages 1-21, October.
    69. Feng Ma & Xinjie Lu & Bo Zhu, 2025. "Uncertainty and fluctuation in crude oil price: evidence from machine learning models," Annals of Operations Research, Springer, vol. 345(2), pages 725-755, February.
    70. Iwona Gorzeń-Mitka & Monika Wieczorek-Kosmala, 2023. "Mapping the Energy Sector from a Risk Management Research Perspective: A Bibliometric and Scientific Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.
    71. Jin Zeng & Jingwen Wu, 2025. "Cross-market volatility spillovers between China and the United States: A DCC-EGARCH-t-Copula framework with out-of-sample forecasting," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-23, October.
    72. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    73. Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
    74. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2020. "Predictive Trading Strategy for Physical Electricity Futures," Energies, MDPI, vol. 13(14), pages 1-24, July.
    75. Dong, Zequn & Tan, Chaodan & Ma, Biao & Ning, Zhaoshuo, 2024. "The impact of artificial intelligence on the energy transition: The role of regulatory quality as a guardrail, not a wall," Energy Economics, Elsevier, vol. 140(C).
    76. Burke, M. & Agarwala, M. & Klusak, P. & Mohaddes, K., 2024. "Climate Policy and Sovereign Debt: The Impact of Transition Scenarios on Sovereign Creditworthiness," Cambridge Working Papers in Economics 2470, Faculty of Economics, University of Cambridge.
    77. Fabra, Natalia & Lacuesta, Aitor & Souza, Mateus, 2022. "The implicit cost of carbon abatement during the COVID-19 pandemic," European Economic Review, Elsevier, vol. 147(C).
    78. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.
    79. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    80. Li, Lanbing & Zhao, Jiawei & Yang, Yuhan & Ma, Dan, 2025. "Artificial intelligence and green development well-being: Effects and mechanisms in China," Energy Economics, Elsevier, vol. 141(C).
    81. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    82. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    83. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
    84. Lukasz Mach & Dariusz Zmarzly & Ireneusz Dabrowski & Pawel Fracz, 2020. "Comparison on Subannual Seasonality of Building Construction in European Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 241-257.
    85. Ha, Le Thanh, 2024. "Dynamic spill-over influences of FinTech innovation development on renewable energy volatility during the time of war in pandemic: A novel insight from a wavelet model," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 515-529.
    86. László Vancsura & Tibor Tatay & Tibor Bareith, 2025. "Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps," Forecasting, MDPI, vol. 7(3), pages 1-49, July.
    87. Ghoddusi, Hamed & Morovati, Mohammad & Rafizadeh, Nima, 2019. "Foreign Exchange Shocks and Gasoline Consumption," Energy Economics, Elsevier, vol. 84(C).
    88. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
    89. Mustafa Raza Rabbani & M. Kabir Hassan & Austin Dejan & Abu Bashar & Md. Bokhtiar Hasan, 2024. "A bibliometric analysis of the review papers in finance: Evidence from the last two decades," Review of Financial Economics, John Wiley & Sons, vol. 42(3), pages 241-257, July.
    90. Chowdhury, Mohammad Ashraful Ferdous & Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2025. "Geopolitical risk and energy market tail risk forecasting: An explainable machine learning approach," Journal of Commodity Markets, Elsevier, vol. 39(C).
    91. Zhang, Ditian & Tang, Pan & Tang, Chun & Lai, Xiaobing, 2025. "Interpretable machine learning unveils nonlinear drivers of global energy risk spillovers: A TVP-VAR approach," Economic Modelling, Elsevier, vol. 151(C).
    92. Khaled Yousef & Baris Yuce & Allen He, 2025. "A Hybrid Deep Learning Framework for Wind Speed Prediction with Snake Optimizer and Feature Explainability," Sustainability, MDPI, vol. 17(12), pages 1-25, June.
    93. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    94. Linya Huang & Xite Yang & Yongzeng Lai & Ankang Zou & Jilin Zhang, 2024. "Crude Oil Futures Price Forecasting Based on Variational and Empirical Mode Decompositions and Transformer Model," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
    95. Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024. "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, vol. 137(C).
    96. Louis-Gaëtan Giraudet & Antoine Missemer, 2023. "The History of Energy Efficiency in Economics: Breakpoints and Regularities," Post-Print halshs-02301636, HAL.
    97. Yang, Fan & Lee, Hyoungsuk, 2022. "An innovative provincial CO2 emission quota allocation scheme for Chinese low-carbon transition," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    98. David Alaminos & María Belén Salas & Manuel A. Fernández-Gámez, 2024. "High-Frequency Trading in Bond Returns: A Comparison Across Alternative Methods and Fixed-Income Markets," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2263-2354, October.
    99. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    100. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).
    101. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2022. "l 1 -Regularization in Portfolio Selection with Machine Learning," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    102. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
    103. Zhang, Hongwei & Wang, Wentao & Niu, Zibo, 2024. "Geopolitical risks and crude oil futures volatility: Evidence from machine learning," Resources Policy, Elsevier, vol. 98(C).
    104. Mohammed Alruqimi & Luca Di Persio, 2025. "Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process," Mathematics, MDPI, vol. 13(2), pages 1-16, January.
    105. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
    106. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    107. Claudia Condemi & Loretta Mastroeni & Pierluigi Vellucci, 2021. "The impact of Clean Spark Spread expectations on storage hydropower generation," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1111-1146, December.
    108. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).
    109. Paulo Roberto de Melo Barros Junior & Monica Alexandra Vilar Ribeiro De Meireles & Jose Luis Lima de Jesus Silva, 2025. "Optimizing Information Asset Investment Strategies in the Exploratory Phase of the Oil and Gas Industry: A Reinforcement Learning Approach," Papers 2512.00243, arXiv.org.
    110. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    111. Muneer M. Alshater & Ilias Kampouris & Hazem Marashdeh & Osama F. Atayah & Hasanul Banna, 2025. "Early warning system to predict energy prices: the role of artificial intelligence and machine learning," Annals of Operations Research, Springer, vol. 345(2), pages 1297-1333, February.
    112. Gnekpe, Christian & Tchuente, Dieudonné & Nyawa, Serge & Dey, Prasanta Kumar, 2024. "Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches," Journal of Business Research, Elsevier, vol. 183(C).
    113. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    114. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.
    115. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.
    116. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
    117. David Mhlanga, 2023. "Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review," Energies, MDPI, vol. 16(2), pages 1-17, January.
    118. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).

  8. Patrick Houlihan & Germán G. Creamer, 2017. "Can Sentiment Analysis and Options Volume Anticipate Future Returns?," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 669-685, December.

    Cited by:

    1. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    2. Huynh, Nhan & De Mello, Lurion & Li, Kai, 2025. "Evolution of investor sentiment: A systematic literature review and bibliometric analysis," International Review of Economics & Finance, Elsevier, vol. 100(C).
    3. Meng‐Feng Yen & Yu‐Pei Huang & Liang‐Chih Yu & Yueh‐Ling Chen, 2022. "A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1677-1698, April.

  9. Germán Creamer, 2012. "Model calibration and automated trading agent for Euro futures," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 531-545, December.

    Cited by:

    1. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    2. 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, vol. 95(PB).
    3. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    4. 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, vol. 64(5), pages 2853-2878, November.
    5. Javier Falces Marin & David Díaz Pardo de Vera & Eduardo Lopez Gonzalo, 2022. "A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-32, December.

  10. German Creamer & Yoav Freund, 2010. "Automated trading with boosting and expert weighting," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 401-420.

    Cited by:

    1. Junran Wu & Ke Xu & Jichang Zhao, 2019. "Online reviews can predict long-term returns of individual stocks," Papers 1905.03189, arXiv.org.
    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Ash Booth & Enrico Gerding & Frank McGroarty, 2015. "Performance-weighted ensembles of random forests for predicting price impact," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1823-1835, November.
    4. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).
    5. Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).
    6. Massoud Metghalchi & Linda A. Hayes & Farhang Niroomand, 2019. "A technical approach to equity investing in emerging markets," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 389-403, July.
    7. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    8. Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2016. "The profitability of pairs trading strategies: distance, cointegration and copula methods," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1541-1558, October.
    9. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    10. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
    11. Omid Safarzadeh, 2020. "Generating Trading Signals by ML algorithms or time series ones?," Papers 2007.11098, arXiv.org.
    12. Yang ZHANG & Ziang QIU Ziang & Donghyun PARK & Shu TIAN, 2026. "Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability," Working Papers wp61, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Feb 2026.

  11. Germán Creamer & Yoav Freund, 2010. "Using Boosting for Financial Analysis and Performance Prediction: Application to S&P 500 Companies, Latin American ADRs and Banks," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 133-151, August.

    Cited by:

    1. Ionuț Nica & Daniela Blană Alexandru & Simona Liliana Paramon Crăciunescu & Ștefan Ionescu, 2021. "Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques," Sustainability, MDPI, vol. 13(9), pages 1-27, May.
    2. Murphy, Brid & Feeney, Orla & Rosati, Pierangelo & Lynn, Theo, 2024. "Exploring accounting and AI using topic modelling," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).

  12. Creamer, Germán, 2004. "Regionalismo abierto en la Comunidad Andina. ¿Creación o desviación de comercio?," El Trimestre Económico, Fondo de Cultura Económica, vol. 71(281), pages 45-71, enero-mar.

    Cited by:

    1. Eric Tremolada Álvarez (editor), 2013. "Repensando la integración y las integraciones," Books, Universidad Externado de Colombia, Facultad de Finanzas, Gobierno y Relaciones Internacionales, edition 1, volume 1, number 85.

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