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  • Search: subject:"Financial time-series forecasting"
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Year of publication
Subject
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Financial time series forecasting 13 Forecasting model 13 Prognoseverfahren 13 Time series analysis 12 Zeitreihenanalyse 12 Theorie 11 Theory 11 Börsenkurs 6 Forecast 6 Prognose 6 Share price 6 financial time series forecasting 6 Aktienmarkt 5 Deep learning 5 Neural networks 5 Neuronale Netze 5 Stock market 5 Artificial intelligence 4 Artificial neural network 4 Foreign exchange rates 4 Künstliche Intelligenz 4 Recurrent neural networks 4 Autoregressive integrated moving average 3 Extreme learning machine 3 Genetic algorithm 3 Stock market forecasting 3 ARMA model 2 ARMA-Modell 2 Algorithm 2 Algorithmus 2 Artificial chemical reaction optimization 2 Capital income 2 Chemical reaction optimization 2 Evolutionary algorithm 2 Evolutionärer Algorithmus 2 Exchange rate 2 Financial analysis 2 Finanzanalyse 2 Kapitaleinkommen 2 Mathematical programming 2
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Online availability
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Free 14 Undetermined 6 CC license 5
Type of publication
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Article 18 Book / Working Paper 3
Type of publication (narrower categories)
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Article in journal 13 Aufsatz in Zeitschrift 13 Article 5 Working Paper 2
Language
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English 20 Undetermined 1
Author
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Lessmann, Stefan 4 Mishra, Bijan Bihari 4 Nayak, Sarat 4 Seow, Hsin-Vonn 4 Dautel, Alexander Jakob 3 Härdle, Wolfgang Karl 3 Abadleh, Ahmad 2 Alghamdi, Mansoor 2 Alrashidi, Malek 2 Altarawneh, Ghada A. 2 Hajirahimi, Zahra 2 Hassanat, Ahmad B. 2 Khashei, Mehdi 2 Tarawneh, Ahmad S. 2 Atli, Ayca Hatice 1 Barbu, Adrian G. 1 Cheng, Zhonghou 1 Das, Subhranginee 1 Dautel, Alexander J. 1 Drachal, Krzysztof 1 Huang, Xiaoyong 1 Härdle, Wolfgang 1 Li, Shicheng 1 Lin, Han-Chou 1 Luo, Jian 1 Miśkiewicz-Nawrocka, Monika 1 Nayak, Sarat Chandra 1 Pabuccu, Hakan 1 Pawłowski, Michał 1 Sahoo, Biswajit 1 Sfetsos, A. 1 Siriopoulos, C. 1 Tao, Hong 1 Tas, Engin 1 Tian, Ye 1 Yang, Heng-Li 1 Yi, Yugen 1 Zheng, Junliang 1 Zou, Wei 1
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Institution
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Society for Computational Economics - SCE 1
Published in...
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Financial innovation : FIN 4 Financial Innovation 3 Computational economics 2 IRTG 1792 Discussion Paper 2 Computing in Economics and Finance 2002 1 Digital Finance 1 Digital finance : smart data analytics, investment innovation, and financial technology 1 Economies 1 Economies : open access journal 1 Finance research letters 1 Folia oeconomica Stetinensia : FOS 1 International Journal of Financial Studies : open access journal 1 International journal of computational economics and econometrics : IJCEE 1 Journal of the Operational Research Society 1
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Source
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ECONIS (ZBW) 13 EconStor 7 RePEc 1
Showing 1 - 10 of 21
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Feature selection with annealing for forecasting financial time series
Pabuccu, Hakan; Barbu, Adrian G. - In: Financial innovation : FIN 10 (2024), pp. 1-26
predictions is challenging, noting that accuracy does not equate to reliability, especially when financial time-series forecasting …
Persistent link: https://www.econbiz.de/10015361544
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Forecasting selected commodities' prices with the Bayesian symbolic regression
Drachal, Krzysztof; Pawłowski, Michał - In: International Journal of Financial Studies : open … 12 (2024) 2, pp. 1-56
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities' prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approach to symbolic regression, based on...
Persistent link: https://www.econbiz.de/10014636322
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Stock price forecasting for Jordan insurance companies amid the COVID-19 pandemic utilizing off-the-shelf technical analysis methods
Altarawneh, Ghada A.; Hassanat, Ahmad B.; Tarawneh, Ahmad S. - In: Economies 10 (2022) 2, pp. 1-18
One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical...
Persistent link: https://www.econbiz.de/10013199985
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Stock price forecasting for Jordan insurance companies amid the COVID-19 pandemic utilizing off-the-shelf technical analysis methods
Altarawneh, Ghada A.; Hassanat, Ahmad B.; Tarawneh, Ahmad S. - In: Economies : open access journal 10 (2022) 2, pp. 1-18
One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical...
Persistent link: https://www.econbiz.de/10013164219
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AE-ACG : a novel deep learning-based method for stock price movement prediction
Li, Shicheng; Huang, Xiaoyong; Cheng, Zhonghou; Zou, Wei; … - In: Finance research letters 58 (2023) 1, pp. 1-14
Persistent link: https://www.econbiz.de/10014581306
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A novel hybrid method based on kernel-free support vector regression for stock indices and price forecasting
Zheng, Junliang; Tian, Ye; Luo, Jian; Tao, Hong - In: Journal of the Operational Research Society 74 (2023) 3, pp. 690-702
Persistent link: https://www.econbiz.de/10014331949
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Forex exchange rate forecasting using deep recurrent neural networks
Dautel, Alexander Jakob; Härdle, Wolfgang Karl; … - In: Digital Finance 2 (2020) 1-2, pp. 69-96
Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to...
Persistent link: https://www.econbiz.de/10014504558
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Forex exchange rate forecasting using deep recurrent neural networks
Dautel, Alexander Jakob; Härdle, Wolfgang Karl; … - 2020
Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short- term memory networks and gated recurrent units...
Persistent link: https://www.econbiz.de/10012433250
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Extreme learning with chemical reaction optimization for stock volatility prediction
Nayak, Sarat Chandra; Mishra, Bijan Bihari - In: Financial Innovation 6 (2020) 1, pp. 1-23
Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network...
Persistent link: https://www.econbiz.de/10012602852
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Extreme learning with chemical reaction optimization for stock volatility prediction
Nayak, Sarat; Mishra, Bijan Bihari - In: Financial innovation : FIN 6 (2020) 16, pp. 1-23
Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network...
Persistent link: https://www.econbiz.de/10012268745
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