German G Creamer

Personal Details

First Name:	German
Middle Name:	G
Last Name:	Creamer
Suffix:
RePEc Short-ID:	pcr283
	[This author has chosen not to make the email address public]
	http://www.creamer-co.com
Terminal Degree:	1993 Department of Economics; University of Notre Dame (from RePEc Genealogy)

Affiliation

(75%) School of Business
Stevens Institute of Technology

Hoboken, New Jersey (United States)
https://www.stevens.edu/school-business
RePEc:edi:sbsitus (more details at EDIRC)

(25%) Columbia University, Computer Science Department

https://www.cs.columbia.edu/
New York, NY

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 & 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.

2. 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. 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.
   2. 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.

3. 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. 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).
   3. 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.

4. 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).

5. 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. 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.

6. 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. 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.
   2. 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.
   3. 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).
   4. 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).
   5. Ghoddusi, Hamed & Morovati, Mohammad & Rafizadeh, Nima, 2019. "Foreign Exchange Shocks and Gasoline Consumption," Energy Economics, Elsevier, vol. 84(C).
   6. 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.
      • Simon Blöthner & Mario Larch, 2021. "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series 9233, CESifo.
   7. 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.
   8. 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).
   9. Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020. "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers 20-2020, Copenhagen Business School, Department of Economics.
      • 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.
   10. 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.
   11. 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.
   12. 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).
       • Costola, Michele & Nofer, Michael & Hinz, Oliver & Pelizzon, Loriana, 2020. "Machine learning sentiment analysis, Covid-19 news and stock market reactions," SAFE Working Paper Series 288, Leibniz Institute for Financial Research SAFE.
   13. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
   14. 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).
   15. 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.
   16. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
   17. Fraunholz, Christoph & Kraft, Emil & Keles, Dogan & Fichtner, Wolf, 2021. "Advanced price forecasting in agent-based electricity market simulation," Applied Energy, Elsevier, vol. 290(C).
   18. 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.
   19. 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.
   20. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
       • Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
   21. 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.
   22. 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.
       • Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2021. "Artificial Intelligence in the Field of Economics," CREMA Working Paper Series 2021-28, Center for Research in Economics, Management and the Arts (CREMA).
   23. Aggarwal, Sakshi, 2023. "LSTM based Anomaly Detection in Time Series for United States exports and imports," MPRA Paper 117149, University Library of Munich, Germany.
   24. 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.
   25. 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).
   26. 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).
   27. 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).
   28. 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).
   29. 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).
   30. 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.
   31. 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.
   32. 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.
   33. 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.
   34. 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.
   35. 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).
   36. Louis-Gaëtan Giraudet & Antoine Missemer, 2023. "The History of Energy Efficiency in Economics: Breakpoints and Regularities," Post-Print halshs-02301636, HAL.
   37. 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.
   38. 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).
   39. 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.
   40. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
   41. 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).
       • Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021. "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series 8835, CESifo.
   42. 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.
   43. 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.
   44. 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).
   45. 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).
   46. 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.
   47. 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).
   48. 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).
   49. 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.
   50. 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.
   51. Yongtong Shao & Minghao Li & Dermot J. Hayes & Wendong Zhang & Tao Xiong & Wei Xie, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," Center for Agricultural and Rural Development (CARD) Publications 20-wp607, Center for Agricultural and Rural Development (CARD) at Iowa State University.
       • Shao, Yongtong & Xiong, Tao & Li, Minghao & Hayes, Dermot & Zhang, Wendong & Xie, Wei, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," ISU General Staff Papers 202001010800001619, Iowa State University, Department of Economics.
       • 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.
   52. 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).
   53. 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).
   54. 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).
   55. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.
   56. 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.
   57. 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).
   58. 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.
   59. 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.
   60. 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).
   61. 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.
   62. 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.
   63. 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.
   64. 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).
   65. 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.
   66. 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).
   67. 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.
   68. 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).

7. 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. 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.
   2. Anese, Gianluca & Corazza, Marco & Costola, Michele & Pelizzon, Loriana, 2021. "Impact of public news sentiment on stock market index return and volatility," SAFE Working Paper Series 322, Leibniz Institute for Financial Research SAFE.
      • 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.

8. 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.

9. 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. 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.
   2. Junran Wu & Ke Xu & Jichang Zhao, 2019. "Online reviews can predict long-term returns of individual stocks," Papers 1905.03189, arXiv.org.
   3. 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.
   4. 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.
   5. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
   6. 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.
   7. Omid Safarzadeh, 2020. "Generating Trading Signals by ML algorithms or time series ones?," Papers 2007.11098, arXiv.org.
   8. 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.

10. Creamer, Germán, 2004. "Regionalismo abierto en la Comunidad Andina. ¿Creación o desviación de comercio?," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(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, August.

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