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  • Search: subject:"computational science"
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Year of publication
Subject
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computational science 10 big data 6 Computational science 5 Theorie 5 Theory 5 Big Data 4 economics 4 finance 4 management 4 Big data 3 Computer science 3 Economics 3 Informatik 3 applications 3 Applications 2 Econometric and statistical models 2 Finance 2 Management 2 Theoretical models 2 Wirtschaftswissenschaft 2 adaptive market efficiency 2 bank regulatory capital requirements 2 country equity returns 2 covariance matrix estimation 2 econometric and statistical models 2 efficient market hypothesis 2 excess returns 2 international financial markets 2 investor sentiment 2 marketing 2 portfolio risk measurement 2 price-volume relationship 2 psychology 2 social media 2 stock investment 2 supply chain finance 2 theoretical models 2 Agent-based model 1 Agent-based modeling 1 Agentenbasierte Modellierung 1
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Online availability
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Free 11 Undetermined 5 CC license 1
Type of publication
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Article 10 Book / Working Paper 6
Type of publication (narrower categories)
All
Article in journal 4 Aufsatz in Zeitschrift 4 Working Paper 4 Arbeitspapier 2 Article 2 Graue Literatur 2 Non-commercial literature 2 Aufsatz im Buch 1 Book section 1 Statistics 1 Statistik 1
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Language
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English 11 Undetermined 5
Author
All
McAleer, Michael 6 Chang, Chia-Lin 4 Wong, Wing Keung 4 Bollen, Johan 2 Counts, Scott 2 Mao, Huina 2 Bertsekas, Dimitri P. 1 Castillo, Elizabeth A. 1 Cheng, Mark 1 Fox, Geoffrey C. 1 Garabedian, Paul R. 1 Gentleman, Robert 1 Hurlin, Christophe 1 Kim, Sohee 1 Li, Wenyin 1 Liu, Menghan 1 Malcangi, Mario 1 McFadden, Geoffrey B. 1 Pedrielli, Giulia 1 Pham, Anh 1 Poppleton, Erik 1 Pérignon, Christophe 1 Stodden, Victoria 1 Sulc, Petr 1 Trinh, Mai P. 1 Woodward, Darren 1 Ziebart, David A. 1
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Institution
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HAL 1 International Institute of Social and Economic Sciences 1
Published in...
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Journal of Risk and Financial Management 2 Journal of risk and financial management : JRFM 2 Discussion paper / Tinbergen Institute 1 ECB Statistics Paper 1 Energies 1 Handbook of investment analysis, portfolio management, and financial derivatives ; Volume 3 1 INFORMS journal on computing : JOC ; charting new directions in operations research and computer science ; a journal of the Institute for Operations Research and the Management Sciences 1 Mathematics and Computers in Simulation (MATCOM) 1 Proceedings of International Academic Conferences 1 Statistic paper series / European Central Bank : SPS 1 Statistical Applications in Genetics and Molecular Biology 1 The leadership quarterly : LQ ; an international journal of political, social and behavioral science 1 Tinbergen Institute Discussion Paper 1 Working Papers / HAL 1
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Source
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ECONIS (ZBW) 7 RePEc 5 EconStor 4
Showing 1 - 10 of 16
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Using computational science methods in accounting and finance research
Ziebart, David A.; Cheng, Mark; Kim, Sohee; Li, Wenyin; … - 2024
Persistent link: https://www.econbiz.de/10015047565
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Review papers for journal of risk and financial management (JRFM)
McAleer, Michael - In: Journal of Risk and Financial Management 13 (2020) 8, pp. 1-18
global guidelines to their United States (US) implementation, connections among big data, computational science, economics …
Persistent link: https://www.econbiz.de/10012611387
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Review papers for journal of risk and financial management (JRFM)
McAleer, Michael - In: Journal of risk and financial management : JRFM 13 (2020) 8/185, pp. 1-18
global guidelines to their United States (US) implementation, connections among big data, computational science, economics …
Persistent link: https://www.econbiz.de/10012321338
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ExpertRNA : a new framework for RNA secondary structure prediction
Liu, Menghan; Poppleton, Erik; Pedrielli, Giulia; Sulc, Petr - In: INFORMS journal on computing : JOC ; charting new … 34 (2022) 5, pp. 2464-2484
Persistent link: https://www.econbiz.de/10014325451
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Big data, computational science, economics, finance, marketing, management, and psychology: Connections
Chang, Chia-Lin; McAleer, Michael; Wong, Wing Keung - In: Journal of Risk and Financial Management 11 (2018) 1, pp. 1-29
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance …
Persistent link: https://www.econbiz.de/10012611003
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Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections
Chang, Chia-Lin; McAleer, Michael; Wong, Wing Keung - 2018
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance …
Persistent link: https://www.econbiz.de/10011819526
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Big Data, computational science, economics, finance, marketing, management, and psychology : connections
Chang, Chia-Lin; McAleer, Michael; Wong, Wing Keung - 2018
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance …
Persistent link: https://www.econbiz.de/10011794391
Saved in:
Cover Image
Big data, computational science, economics, finance, marketing, management, and psychology : connections
Chang, Chia-Lin; McAleer, Michael; Wong, Wing Keung - In: Journal of risk and financial management : JRFM 11 (2018) 1, pp. 1-29
The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance …
Persistent link: https://www.econbiz.de/10011855163
Saved in:
Cover Image
Quantifying the effects of online bullishness on international financial markets
Mao, Huina; Counts, Scott; Bollen, Johan - 2015
Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous...
Persistent link: https://www.econbiz.de/10011606343
Saved in:
Cover Image
Quantifying the effects of online bullishness on international financial markets
Mao, Huina; Counts, Scott; Bollen, Johan - 2015
Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous...
Persistent link: https://www.econbiz.de/10011664041
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