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  • Search: subject:"stochastic gradient boosting"
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Year of publication
Subject
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Artificial intelligence 4 Künstliche Intelligenz 4 stochastic gradient boosting 4 Forecasting model 3 Prognoseverfahren 3 Theorie 3 Theory 3 machine learning 3 Machine learning 2 Stochastic gradient boosting 2 bagging 2 forecasting 2 gold and silver prices 2 random forests 2 Corporate Governance 1 Corporate disclosure 1 Corporate governance 1 Credit risk 1 Data Envelopment Analysis 1 Data envelopment analysis 1 Data-Envelopment-Analyse 1 Estimation 1 Feldforschung 1 Field research 1 Firm performance 1 Gewinn 1 Gold 1 Kreditrisiko 1 Narrative disclosure tone 1 Preis 1 Price 1 Production function 1 Produktionsfunktion 1 Profit 1 Profitability 1 Random forest 1 Rentabilität 1 Schätzung 1 Silber 1 Silver 1
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Online availability
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Free 3 Undetermined 3 CC license 1
Type of publication
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Article 6
Type of publication (narrower categories)
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Article in journal 5 Aufsatz in Zeitschrift 5 Article 1
Language
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English 6
Author
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Sadorsky, Perry A. 2 Aparicio, Juan 1 Charles, Vincent 1 Guillen, Maria D. 1 Gupta, Sunil 1 Hasan, Arshad 1 Hussainey, Khaled 1 Jin, Zi 1 Lemmens, Aurélie 1 Sufi, Usman 1 Sun, Han Sheng 1
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Published in...
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European journal of operational research : EJOR 1 Journal of Risk and Financial Management 1 Journal of accounting in emerging economies : JAEE 1 Journal of risk and financial management : JRFM 1 Marketing science 1 The journal of credit risk : published quarterly by Incisive Media 1
Source
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ECONIS (ZBW) 5 EconStor 1
Showing 1 - 6 of 6
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Estimating non-overfitted convex production technologies : a stochastic machine learning approach
Guillen, Maria D.; Charles, Vincent; Aparicio, Juan - In: European journal of operational research : EJOR 323 (2025) 1, pp. 224-240
Persistent link: https://www.econbiz.de/10015415547
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Improving the prediction of firm performance using nonfinancial disclosures : a machine learning approach
Sufi, Usman; Hasan, Arshad; Hussainey, Khaled - In: Journal of accounting in emerging economies : JAEE 14 (2024) 5, pp. 1223-1251
Persistent link: https://www.econbiz.de/10015077695
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Predicting gold and silver price direction using tree-based classifiers
Sadorsky, Perry A. - In: Journal of risk and financial management : JRFM 14 (2021) 5, pp. 1-21
(bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds …. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much … 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for …
Persistent link: https://www.econbiz.de/10012533982
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Cover Image
Predicting gold and silver price direction using tree-based classifiers
Sadorsky, Perry A. - In: Journal of Risk and Financial Management 14 (2021) 5, pp. 1-21
(bagging, stochastic gradient boosting, random forests) to predict the price direction of gold and silver exchange traded funds …. Decision tree bagging, stochastic gradient boosting, and random forests predictions of gold and silver price direction are much … 55% and 60%. Stochastic gradient boosting accuracy is a few percentage points less than that of random forests for …
Persistent link: https://www.econbiz.de/10012611755
Saved in:
Cover Image
Managing churn to maximize profits
Lemmens, Aurélie; Gupta, Sunil - In: Marketing science 39 (2020) 5, pp. 956-973
Persistent link: https://www.econbiz.de/10012305118
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Estimating credit risk parameters using ensemble learning methods : an empirical study on loss given default
Sun, Han Sheng; Jin, Zi - In: The journal of credit risk : published quarterly by … 12 (2016) 3, pp. 43-69
Persistent link: https://www.econbiz.de/10011643773
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